JanSchTech/starcoderdata-python-edu-lang-score

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benchmarks/SimResults/combinations_spec_mylocality/oldstuff/cmp_soplexmcfcalculixgcc/power.py	TugberkArkose/MLScheduler	0	10200	<filename>benchmarks/SimResults/combinations_spec_mylocality/oldstuff/cmp_soplexmcfcalculixgcc/power.py power = {'BUSES': {'Area': 1.33155, 'Bus/Area': 1.33155, 'Bus/Gate Leakage': 0.00662954, 'Bus/Peak Dynamic': 0.0, 'Bus/Runtime Dynamic': 0.0, 'Bus/Subthreshold Leakage': 0.0691322, 'Bus/Subthreshold Leakage with power gating': 0.0259246, 'Gate Leakage': 0.00662954, 'Peak Dynamic': 0.0, 'Runtime Dynamic': 0.0, 'Subthreshold Leakage': 0.0691322, 'Subthreshold Leakage with power gating': 0.0259246}, 'Core': [{'Area': 32.6082, 'Execution Unit/Area': 8.2042, 'Execution Unit/Complex ALUs/Area': 0.235435, 'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646, 'Execution Unit/Complex ALUs/Peak Dynamic': 0.181181, 'Execution Unit/Complex ALUs/Runtime Dynamic': 0.344996, 'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111, 'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163, 'Execution Unit/Floating Point Units/Area': 4.6585, 'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156, 'Execution Unit/Floating Point Units/Peak Dynamic': 0.977935, 'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033, 'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829, 'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061, 'Execution Unit/Gate Leakage': 0.122718, 'Execution Unit/Instruction Scheduler/Area': 2.17927, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.486054, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066, 'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101, 'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996, 'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112, 'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911, 'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.841669, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351, 'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781, 'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232, 'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399, 'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892, 'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.482721, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339, 'Execution Unit/Instruction Scheduler/Runtime Dynamic': 1.81044, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291, 'Execution Unit/Integer ALUs/Area': 0.47087, 'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291, 'Execution Unit/Integer ALUs/Peak Dynamic': 0.330514, 'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344, 'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222, 'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833, 'Execution Unit/Peak Dynamic': 7.28395, 'Execution Unit/Register Files/Area': 0.570804, 'Execution Unit/Register Files/Floating Point RF/Area': 0.208131, 'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788, 'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.184753, 'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.0176198, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968, 'Execution Unit/Register Files/Gate Leakage': 0.000622708, 'Execution Unit/Register Files/Integer RF/Area': 0.362673, 'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992, 'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.195265, 'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.130309, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675, 'Execution Unit/Register Files/Peak Dynamic': 0.380018, 'Execution Unit/Register Files/Runtime Dynamic': 0.147929, 'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387, 'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643, 'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0442632, 'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00607074, 'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.521478, 'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 1.08927, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.0920413, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0345155, 'Execution Unit/Runtime Dynamic': 3.79801, 'Execution Unit/Subthreshold Leakage': 1.83518, 'Execution Unit/Subthreshold Leakage with power gating': 0.709678, 'Gate Leakage': 0.372997, 'Instruction Fetch Unit/Area': 5.86007, 'Instruction Fetch Unit/Branch Predictor/Area': 0.138516, 'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00272158, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00272158, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.0023766, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000923356, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045, 'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838, 'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732, 'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05, 'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602, 'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00187191, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733, 'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00969166, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282, 'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954, 'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758, 'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867, 'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0258763, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357, 'Instruction Fetch Unit/Gate Leakage': 0.0590479, 'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323, 'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05, 'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827, 'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.12527, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682, 'Instruction Fetch Unit/Instruction Cache/Area': 3.14635, 'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931, 'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 6.43323, 'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.372767, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386, 'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799, 'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493, 'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404, 'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.425473, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104, 'Instruction Fetch Unit/Peak Dynamic': 8.96874, 'Instruction Fetch Unit/Runtime Dynamic': 0.959077, 'Instruction Fetch Unit/Subthreshold Leakage': 0.932587, 'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.408542, 'L2/Area': 4.53318, 'L2/Gate Leakage': 0.015464, 'L2/Peak Dynamic': 0.090727, 'L2/Runtime Dynamic': 0.0127692, 'L2/Subthreshold Leakage': 0.834142, 'L2/Subthreshold Leakage with power gating': 0.401066, 'Load Store Unit/Area': 8.80969, 'Load Store Unit/Data Cache/Area': 6.84535, 'Load Store Unit/Data Cache/Gate Leakage': 0.0279261, 'Load Store Unit/Data Cache/Peak Dynamic': 4.08122, 'Load Store Unit/Data Cache/Runtime Dynamic': 1.38167, 'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675, 'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085, 'Load Store Unit/Gate Leakage': 0.0351387, 'Load Store Unit/LoadQ/Area': 0.0836782, 'Load Store Unit/LoadQ/Gate Leakage': 0.00059896, 'Load Store Unit/LoadQ/Peak Dynamic': 0.0920133, 'Load Store Unit/LoadQ/Runtime Dynamic': 0.0920133, 'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961, 'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918, 'Load Store Unit/Peak Dynamic': 4.51749, 'Load Store Unit/Runtime Dynamic': 1.92746, 'Load Store Unit/StoreQ/Area': 0.322079, 'Load Store Unit/StoreQ/Gate Leakage': 0.00329971, 'Load Store Unit/StoreQ/Peak Dynamic': 0.226889, 'Load Store Unit/StoreQ/Runtime Dynamic': 0.453778, 'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621, 'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004, 'Load Store Unit/Subthreshold Leakage': 0.591622, 'Load Store Unit/Subthreshold Leakage with power gating': 0.283406, 'Memory Management Unit/Area': 0.434579, 'Memory Management Unit/Dtlb/Area': 0.0879726, 'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729, 'Memory Management Unit/Dtlb/Peak Dynamic': 0.0805237, 'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0817258, 'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699, 'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485, 'Memory Management Unit/Gate Leakage': 0.00813591, 'Memory Management Unit/Itlb/Area': 0.301552, 'Memory Management Unit/Itlb/Gate Leakage': 0.00393464, 'Memory Management Unit/Itlb/Peak Dynamic': 0.399995, 'Memory Management Unit/Itlb/Runtime Dynamic': 0.061585, 'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758, 'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842, 'Memory Management Unit/Peak Dynamic': 0.697703, 'Memory Management Unit/Runtime Dynamic': 0.143311, 'Memory Management Unit/Subthreshold Leakage': 0.0769113, 'Memory Management Unit/Subthreshold Leakage with power gating': 0.0399462, 'Peak Dynamic': 26.1203, 'Renaming Unit/Area': 0.369768, 'Renaming Unit/FP Front End RAT/Area': 0.168486, 'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00489731, 'Renaming Unit/FP Front End RAT/Peak Dynamic': 3.33511, 'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.644561, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0437281, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.024925, 'Renaming Unit/Free List/Area': 0.0414755, 'Renaming Unit/Free List/Gate Leakage': 4.15911e-05, 'Renaming Unit/Free List/Peak Dynamic': 0.0401324, 'Renaming Unit/Free List/Runtime Dynamic': 0.0326103, 'Renaming Unit/Free List/Subthreshold Leakage': 0.000670426, 'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000377987, 'Renaming Unit/Gate Leakage': 0.00863632, 'Renaming Unit/Int Front End RAT/Area': 0.114751, 'Renaming Unit/Int Front End RAT/Gate Leakage': 0.00038343, 'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.86945, 'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.237087, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00611897, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00348781, 'Renaming Unit/Peak Dynamic': 4.56169, 'Renaming Unit/Runtime Dynamic': 0.914258, 'Renaming Unit/Subthreshold Leakage': 0.070483, 'Renaming Unit/Subthreshold Leakage with power gating': 0.0362779, 'Runtime Dynamic': 7.75489, 'Subthreshold Leakage': 6.21877, 'Subthreshold Leakage with power gating': 2.58311}, {'Area': 32.0201, 'Execution Unit/Area': 7.68434, 'Execution Unit/Complex ALUs/Area': 0.235435, 'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646, 'Execution Unit/Complex ALUs/Peak Dynamic': 0.11996, 'Execution Unit/Complex ALUs/Runtime Dynamic': 0.29691, 'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111, 'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163, 'Execution Unit/Floating Point Units/Area': 4.6585, 'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156, 'Execution Unit/Floating Point Units/Peak Dynamic': 0.64733, 'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033, 'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829, 'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061, 'Execution Unit/Gate Leakage': 0.120359, 'Execution Unit/Instruction Scheduler/Area': 1.66526, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.04181, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.234954, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.0143453, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519, 'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913, 'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223, 'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00414562, 'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 1.6763, 'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.378972, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0625755, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0355964, 'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262, 'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388, 'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.00056608, 'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451, 'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.191292, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.00906853, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446, 'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.805218, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.0859892, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.047346, 'Execution Unit/Integer ALUs/Area': 0.47087, 'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291, 'Execution Unit/Integer ALUs/Peak Dynamic': 0.169475, 'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344, 'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222, 'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833, 'Execution Unit/Peak Dynamic': 5.2954, 'Execution Unit/Register Files/Area': 0.570804, 'Execution Unit/Register Files/Floating Point RF/Area': 0.208131, 'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788, 'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.122295, 'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.00985502, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968, 'Execution Unit/Register Files/Gate Leakage': 0.000622708, 'Execution Unit/Register Files/Integer RF/Area': 0.362673, 'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992, 'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.116195, 'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0728839, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675, 'Execution Unit/Register Files/Peak Dynamic': 0.23849, 'Execution Unit/Register Files/Runtime Dynamic': 0.0827389, 'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387, 'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643, 'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912, 'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00537402, 'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.274787, 'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.565173, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.081478, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0305543, 'Execution Unit/Runtime Dynamic': 2.15542, 'Execution Unit/Subthreshold Leakage': 1.79543, 'Execution Unit/Subthreshold Leakage with power gating': 0.688821, 'Gate Leakage': 0.368936, 'Instruction Fetch Unit/Area': 5.85939, 'Instruction Fetch Unit/Branch Predictor/Area': 0.138516, 'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00133282, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00133282, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.00118494, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000471861, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045, 'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838, 'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732, 'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05, 'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602, 'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00104698, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733, 'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00489756, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282, 'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954, 'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758, 'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867, 'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0119197, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357, 'Instruction Fetch Unit/Gate Leakage': 0.0589979, 'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323, 'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05, 'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827, 'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0700652, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682, 'Instruction Fetch Unit/Instruction Cache/Area': 3.14635, 'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931, 'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 4.45674, 'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.197355, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386, 'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799, 'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493, 'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404, 'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.237973, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104, 'Instruction Fetch Unit/Peak Dynamic': 6.89155, 'Instruction Fetch Unit/Runtime Dynamic': 0.522211, 'Instruction Fetch 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'Memory Management Unit/Gate Leakage': 0.00808595, 'Memory Management Unit/Itlb/Area': 0.301552, 'Memory Management Unit/Itlb/Gate Leakage': 0.00393464, 'Memory Management Unit/Itlb/Peak Dynamic': 0.277104, 'Memory Management Unit/Itlb/Runtime Dynamic': 0.0325171, 'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758, 'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842, 'Memory Management Unit/Peak Dynamic': 0.504457, 'Memory Management Unit/Runtime Dynamic': 0.0746925, 'Memory Management Unit/Subthreshold Leakage': 0.0766103, 'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333, 'Peak Dynamic': 19.2571, 'Renaming Unit/Area': 0.303608, 'Renaming Unit/FP Front End RAT/Area': 0.131045, 'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00351123, 'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468, 'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.321701, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0308571, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885, 'Renaming Unit/Free List/Area': 0.0340654, 'Renaming Unit/Free List/Gate Leakage': 2.5481e-05, 'Renaming Unit/Free List/Peak Dynamic': 0.0306032, 'Renaming Unit/Free List/Runtime Dynamic': 0.0145155, 'Renaming Unit/Free List/Subthreshold Leakage': 0.000370144, 'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000201064, 'Renaming Unit/Gate Leakage': 0.00708398, 'Renaming Unit/Int Front End RAT/Area': 0.0941223, 'Renaming Unit/Int Front End RAT/Gate Leakage': 0.000283242, 'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.731965, 'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.111753, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00435488, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00248228, 'Renaming Unit/Peak Dynamic': 3.58947, 'Renaming Unit/Runtime Dynamic': 0.44797, 'Renaming Unit/Subthreshold Leakage': 0.0552466, 'Renaming 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0.165386, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0625755, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0355964, 'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262, 'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388, 'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.00056608, 'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451, 'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.0834813, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.00906853, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446, 'Execution Unit/Instruction Scheduler/Runtime Dynamic': 0.351403, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.0859892, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.047346, 'Execution Unit/Integer ALUs/Area': 0.47087, 'Execution 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Unit/Register Files/Integer RF/Gate Leakage': 0.00038992, 'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.0336025, 'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0318071, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675, 'Execution Unit/Register Files/Peak Dynamic': 0.0399443, 'Execution Unit/Register Files/Runtime Dynamic': 0.0361079, 'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387, 'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643, 'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912, 'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00537402, 'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.0724192, 'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.179703, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.081478, 'Execution Unit/Results Broadcast 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Predictor/Global Predictor/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00112696, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.000995662, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000393137, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045, 'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838, 'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732, 'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05, 'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602, 'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000456911, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733, 'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.0037065, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282, 'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954, 'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758, 'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867, 'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0103022, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357, 'Instruction Fetch Unit/Gate Leakage': 0.0589979, 'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323, 'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05, 'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827, 'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0305769, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682, 'Instruction Fetch Unit/Instruction Cache/Area': 3.14635, 'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931, 'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 1.94496, 'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.0958958, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386, 'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799, 'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493, 'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404, 'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.103853, 'Instruction Fetch Unit/Instruction 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Store Unit/LoadQ/Area': 0.0836782, 'Load Store Unit/LoadQ/Gate Leakage': 0.00059896, 'Load Store Unit/LoadQ/Peak Dynamic': 0.0256105, 'Load Store Unit/LoadQ/Runtime Dynamic': 0.0256104, 'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961, 'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918, 'Load Store Unit/Peak Dynamic': 2.14967, 'Load Store Unit/Runtime Dynamic': 0.554282, 'Load Store Unit/StoreQ/Area': 0.322079, 'Load Store Unit/StoreQ/Gate Leakage': 0.00329971, 'Load Store Unit/StoreQ/Peak Dynamic': 0.063151, 'Load Store Unit/StoreQ/Runtime Dynamic': 0.126302, 'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621, 'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004, 'Load Store Unit/Subthreshold Leakage': 0.591321, 'Load Store Unit/Subthreshold Leakage with power gating': 0.283293, 'Memory Management Unit/Area': 0.4339, 'Memory Management Unit/Dtlb/Area': 0.0879726, 'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729, 'Memory 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'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00351123, 'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468, 'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.0166828, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0308571, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885, 'Renaming Unit/Free List/Area': 0.0340654, 'Renaming Unit/Free List/Gate Leakage': 2.5481e-05, 'Renaming Unit/Free List/Peak Dynamic': 0.0306032, 'Renaming Unit/Free List/Runtime Dynamic': 0.00482915, 'Renaming Unit/Free List/Subthreshold Leakage': 0.000370144, 'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000201064, 'Renaming Unit/Gate Leakage': 0.00708398, 'Renaming Unit/Int Front End RAT/Area': 0.0941223, 'Renaming Unit/Int Front End RAT/Gate Leakage': 0.000283242, 'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.731965, 'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.0520126, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 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'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033, 'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829, 'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061, 'Execution Unit/Gate Leakage': 0.120359, 'Execution Unit/Instruction Scheduler/Area': 1.66526, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.04181, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.106185, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.0143453, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519, 'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913, 'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223, 'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00414562, 'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 1.6763, 'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.171272, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0625755, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0355964, 'Execution Unit/Instruction Scheduler/Peak Dynamic': 3.82262, 'Execution Unit/Instruction Scheduler/ROB/Area': 0.584388, 'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.00056608, 'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.10451, 'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.0864526, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.00906853, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00364446, 'Execution Unit/Instruction Scheduler/Runtime 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0.00399698, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968, 'Execution Unit/Register Files/Gate Leakage': 0.000622708, 'Execution Unit/Register Files/Integer RF/Area': 0.362673, 'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992, 'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.0347798, 'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.0329391, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675, 'Execution Unit/Register Files/Peak Dynamic': 0.0416718, 'Execution Unit/Register Files/Runtime Dynamic': 0.037393, 'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387, 'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643, 'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0390912, 'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00537402, 'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.0749788, 'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.202833, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.081478, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0305543, 'Execution Unit/Runtime Dynamic': 1.21756, 'Execution Unit/Subthreshold Leakage': 1.79543, 'Execution Unit/Subthreshold Leakage with power gating': 0.688821, 'Gate Leakage': 0.368936, 'Instruction Fetch Unit/Area': 5.85939, 'Instruction Fetch Unit/Branch Predictor/Area': 0.138516, 'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.000625326, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.000625326, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.000550159, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000215984, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045, 'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838, 'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732, 'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05, 'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602, 'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000473173, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733, 'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00227399, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282, 'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954, 'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758, 'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867, 'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.00579905, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357, 'Instruction Fetch Unit/Gate Leakage': 0.0589979, 'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323, 'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05, 'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827, 'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0316652, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682, 'Instruction Fetch Unit/Instruction Cache/Area': 3.14635, 'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931, 'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 2.01418, 'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.0689457, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386, 'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799, 'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493, 'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404, 'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.107549, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104, 'Instruction Fetch Unit/Peak Dynamic': 4.33045, 'Instruction Fetch Unit/Runtime Dynamic': 0.216233, 'Instruction Fetch Unit/Subthreshold Leakage': 0.932286, 'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843, 'L2/Area': 4.53318, 'L2/Gate Leakage': 0.015464, 'L2/Peak Dynamic': 0.0418086, 'L2/Runtime Dynamic': 0.00989266, 'L2/Subthreshold Leakage': 0.834142, 'L2/Subthreshold Leakage with power gating': 0.401066, 'Load Store Unit/Area': 8.80901, 'Load Store Unit/Data Cache/Area': 6.84535, 'Load Store Unit/Data Cache/Gate Leakage': 0.0279261, 'Load Store Unit/Data Cache/Peak Dynamic': 2.36015, 'Load Store Unit/Data Cache/Runtime Dynamic': 0.554162, 'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675, 'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085, 'Load Store Unit/Gate Leakage': 0.0350888, 'Load Store Unit/LoadQ/Area': 0.0836782, 'Load Store Unit/LoadQ/Gate Leakage': 0.00059896, 'Load Store Unit/LoadQ/Peak Dynamic': 0.0363327, 'Load Store Unit/LoadQ/Runtime Dynamic': 0.0363327, 'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961, 'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918, 'Load Store Unit/Peak Dynamic': 2.53172, 'Load Store Unit/Runtime Dynamic': 0.769675, 'Load Store Unit/StoreQ/Area': 0.322079, 'Load Store Unit/StoreQ/Gate Leakage': 0.00329971, 'Load Store Unit/StoreQ/Peak Dynamic': 0.0895903, 'Load Store Unit/StoreQ/Runtime Dynamic': 0.17918, 'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621, 'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004, 'Load Store Unit/Subthreshold Leakage': 0.591321, 'Load Store Unit/Subthreshold Leakage with power gating': 0.283293, 'Memory Management Unit/Area': 0.4339, 'Memory Management Unit/Dtlb/Area': 0.0879726, 'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729, 'Memory Management Unit/Dtlb/Peak Dynamic': 0.0317959, 'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0324228, 'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699, 'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485, 'Memory Management Unit/Gate Leakage': 0.00808595, 'Memory Management Unit/Itlb/Area': 0.301552, 'Memory Management Unit/Itlb/Gate Leakage': 0.00393464, 'Memory Management Unit/Itlb/Peak Dynamic': 0.125234, 'Memory Management Unit/Itlb/Runtime Dynamic': 0.0113054, 'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758, 'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842, 'Memory Management Unit/Peak Dynamic': 0.335963, 'Memory Management Unit/Runtime Dynamic': 0.0437282, 'Memory Management Unit/Subthreshold Leakage': 0.0766103, 'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333, 'Peak Dynamic': 14.9434, 'Renaming Unit/Area': 0.303608, 'Renaming Unit/FP Front End RAT/Area': 0.131045, 'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00351123, 'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468, 'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.0181291, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0308571, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885, 'Renaming Unit/Free List/Area': 0.0340654, 'Renaming Unit/Free List/Gate Leakage': 2.5481e-05, 'Renaming Unit/Free List/Peak Dynamic': 0.0306032, 'Renaming Unit/Free List/Runtime Dynamic': 0.0050114, 'Renaming Unit/Free List/Subthreshold Leakage': 0.000370144, 'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000201064, 'Renaming Unit/Gate Leakage': 0.00708398, 'Renaming Unit/Int Front End RAT/Area': 0.0941223, 'Renaming Unit/Int Front End RAT/Gate Leakage': 0.000283242, 'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.731965, 'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.0551057, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00435488, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00248228, 'Renaming Unit/Peak Dynamic': 3.58947, 'Renaming Unit/Runtime Dynamic': 0.0782462, 'Renaming Unit/Subthreshold Leakage': 0.0552466, 'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461, 'Runtime Dynamic': 2.33534, 'Subthreshold Leakage': 6.16288, 'Subthreshold Leakage with power gating': 2.55328}], 'DRAM': {'Area': 0, 'Gate Leakage': 0, 'Peak Dynamic': 3.868411224021876, 'Runtime Dynamic': 3.868411224021876, 'Subthreshold Leakage': 4.252, 'Subthreshold Leakage with power gating': 4.252}, 'L3': [{'Area': 61.9075, 'Gate Leakage': 0.0484137, 'Peak Dynamic': 0.371973, 'Runtime Dynamic': 0.183113, 'Subthreshold Leakage': 6.80085, 'Subthreshold Leakage with power gating': 3.32364}], 'Processor': {'Area': 191.908, 'Gate Leakage': 1.53485, 'Peak Dynamic': 75.1614, 'Peak Power': 108.274, 'Runtime Dynamic': 16.5813, 'Subthreshold Leakage': 31.5774, 'Subthreshold Leakage with power gating': 13.9484, 'Total Cores/Area': 128.669, 'Total Cores/Gate Leakage': 1.4798, 'Total Cores/Peak Dynamic': 74.7894, 'Total Cores/Runtime Dynamic': 16.3982, 'Total Cores/Subthreshold Leakage': 24.7074, 'Total Cores/Subthreshold Leakage with power gating': 10.2429, 'Total L3s/Area': 61.9075, 'Total L3s/Gate Leakage': 0.0484137, 'Total L3s/Peak Dynamic': 0.371973, 'Total L3s/Runtime Dynamic': 0.183113, 'Total L3s/Subthreshold Leakage': 6.80085, 'Total L3s/Subthreshold Leakage with power gating': 3.32364, 'Total Leakage': 33.1122, 'Total NoCs/Area': 1.33155, 'Total NoCs/Gate Leakage': 0.00662954, 'Total NoCs/Peak Dynamic': 0.0, 'Total NoCs/Runtime Dynamic': 0.0, 'Total NoCs/Subthreshold Leakage': 0.0691322, 'Total NoCs/Subthreshold Leakage with power gating': 0.0259246}}	<filename>benchmarks/SimResults/combinations_spec_mylocality/oldstuff/cmp_soplexmcfcalculixgcc/power.py power = {'BUSES': {'Area': 1.33155, 'Bus/Area': 1.33155, 'Bus/Gate Leakage': 0.00662954, 'Bus/Peak Dynamic': 0.0, 'Bus/Runtime Dynamic': 0.0, 'Bus/Subthreshold Leakage': 0.0691322, 'Bus/Subthreshold Leakage with power gating': 0.0259246, 'Gate Leakage': 0.00662954, 'Peak Dynamic': 0.0, 'Runtime Dynamic': 0.0, 'Subthreshold Leakage': 0.0691322, 'Subthreshold Leakage with power gating': 0.0259246}, 'Core': [{'Area': 32.6082, 'Execution Unit/Area': 8.2042, 'Execution Unit/Complex ALUs/Area': 0.235435, 'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646, 'Execution Unit/Complex ALUs/Peak Dynamic': 0.181181, 'Execution Unit/Complex ALUs/Runtime Dynamic': 0.344996, 'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111, 'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163, 'Execution Unit/Floating Point Units/Area': 4.6585, 'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156, 'Execution Unit/Floating Point Units/Peak Dynamic': 0.977935, 'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033, 'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829, 'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061, 'Execution Unit/Gate Leakage': 0.122718, 'Execution Unit/Instruction Scheduler/Area': 2.17927, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.486054, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066, 'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101, 'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996, 'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112, 'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911, 'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.841669, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351, 'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781, 'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232, 'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399, 'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892, 'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.482721, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339, 'Execution Unit/Instruction Scheduler/Runtime Dynamic': 1.81044, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291, 'Execution Unit/Integer ALUs/Area': 0.47087, 'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291, 'Execution Unit/Integer ALUs/Peak Dynamic': 0.330514, 'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344, 'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222, 'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833, 'Execution Unit/Peak Dynamic': 7.28395, 'Execution Unit/Register Files/Area': 0.570804, 'Execution Unit/Register Files/Floating Point RF/Area': 0.208131, 'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788, 'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.184753, 'Execution Unit/Register Files/Floating Point RF/Runtime Dynamic': 0.0176198, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage': 0.00399698, 'Execution Unit/Register Files/Floating Point RF/Subthreshold Leakage with power gating': 0.00176968, 'Execution Unit/Register Files/Gate Leakage': 0.000622708, 'Execution Unit/Register Files/Integer RF/Area': 0.362673, 'Execution Unit/Register Files/Integer RF/Gate Leakage': 0.00038992, 'Execution Unit/Register Files/Integer RF/Peak Dynamic': 0.195265, 'Execution Unit/Register Files/Integer RF/Runtime Dynamic': 0.130309, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage': 0.00614175, 'Execution Unit/Register Files/Integer RF/Subthreshold Leakage with power gating': 0.00246675, 'Execution Unit/Register Files/Peak Dynamic': 0.380018, 'Execution Unit/Register Files/Runtime Dynamic': 0.147929, 'Execution Unit/Register Files/Subthreshold Leakage': 0.0101387, 'Execution Unit/Register Files/Subthreshold Leakage with power gating': 0.00423643, 'Execution Unit/Results Broadcast Bus/Area Overhead': 0.0442632, 'Execution Unit/Results Broadcast Bus/Gate Leakage': 0.00607074, 'Execution Unit/Results Broadcast Bus/Peak Dynamic': 0.521478, 'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 1.08927, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.0920413, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0345155, 'Execution Unit/Runtime Dynamic': 3.79801, 'Execution Unit/Subthreshold Leakage': 1.83518, 'Execution Unit/Subthreshold Leakage with power gating': 0.709678, 'Gate Leakage': 0.372997, 'Instruction Fetch Unit/Area': 5.86007, 'Instruction Fetch Unit/Branch Predictor/Area': 0.138516, 'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.00272158, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.00272158, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.0023766, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000923356, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045, 'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838, 'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732, 'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05, 'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602, 'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.00187191, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733, 'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00969166, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282, 'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954, 'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758, 'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867, 'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.0258763, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357, 'Instruction Fetch Unit/Gate Leakage': 0.0590479, 'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323, 'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05, 'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827, 'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.12527, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682, 'Instruction Fetch Unit/Instruction Cache/Area': 3.14635, 'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931, 'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 6.43323, 'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.372767, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386, 'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799, 'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493, 'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404, 'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.425473, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104, 'Instruction Fetch Unit/Peak Dynamic': 8.96874, 'Instruction Fetch Unit/Runtime Dynamic': 0.959077, 'Instruction Fetch Unit/Subthreshold Leakage': 0.932587, 'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.408542, 'L2/Area': 4.53318, 'L2/Gate Leakage': 0.015464, 'L2/Peak Dynamic': 0.090727, 'L2/Runtime Dynamic': 0.0127692, 'L2/Subthreshold Leakage': 0.834142, 'L2/Subthreshold Leakage with power gating': 0.401066, 'Load Store Unit/Area': 8.80969, 'Load Store Unit/Data Cache/Area': 6.84535, 'Load Store Unit/Data Cache/Gate Leakage': 0.0279261, 'Load Store Unit/Data Cache/Peak Dynamic': 4.08122, 'Load Store Unit/Data Cache/Runtime Dynamic': 1.38167, 'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675, 'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085, 'Load Store Unit/Gate Leakage': 0.0351387, 'Load Store Unit/LoadQ/Area': 0.0836782, 'Load Store Unit/LoadQ/Gate Leakage': 0.00059896, 'Load Store Unit/LoadQ/Peak Dynamic': 0.0920133, 'Load Store Unit/LoadQ/Runtime Dynamic': 0.0920133, 'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961, 'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918, 'Load Store Unit/Peak Dynamic': 4.51749, 'Load Store Unit/Runtime Dynamic': 1.92746, 'Load Store Unit/StoreQ/Area': 0.322079, 'Load Store Unit/StoreQ/Gate Leakage': 0.00329971, 'Load Store Unit/StoreQ/Peak Dynamic': 0.226889, 'Load Store Unit/StoreQ/Runtime Dynamic': 0.453778, 'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621, 'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004, 'Load Store Unit/Subthreshold Leakage': 0.591622, 'Load Store Unit/Subthreshold Leakage with power gating': 0.283406, 'Memory Management Unit/Area': 0.434579, 'Memory Management Unit/Dtlb/Area': 0.0879726, 'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729, 'Memory Management Unit/Dtlb/Peak Dynamic': 0.0805237, 'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0817258, 'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699, 'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485, 'Memory Management Unit/Gate Leakage': 0.00813591, 'Memory Management Unit/Itlb/Area': 0.301552, 'Memory Management Unit/Itlb/Gate Leakage': 0.00393464, 'Memory Management Unit/Itlb/Peak Dynamic': 0.399995, 'Memory Management Unit/Itlb/Runtime Dynamic': 0.061585, 'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758, 'Memory Management 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'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682, 'Instruction Fetch Unit/Instruction Cache/Area': 3.14635, 'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931, 'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 1.94496, 'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.0958958, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386, 'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799, 'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493, 'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404, 'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.103853, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943, 'Instruction Fetch Unit/Instruction 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'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829, 'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061, 'Execution Unit/Gate Leakage': 0.120359, 'Execution Unit/Instruction Scheduler/Area': 1.66526, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.04181, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.106185, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.0143453, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519, 'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913, 'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223, 'Execution Unit/Instruction Scheduler/Instruction Window/Gate 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gating': 3.32364}], 'Processor': {'Area': 191.908, 'Gate Leakage': 1.53485, 'Peak Dynamic': 75.1614, 'Peak Power': 108.274, 'Runtime Dynamic': 16.5813, 'Subthreshold Leakage': 31.5774, 'Subthreshold Leakage with power gating': 13.9484, 'Total Cores/Area': 128.669, 'Total Cores/Gate Leakage': 1.4798, 'Total Cores/Peak Dynamic': 74.7894, 'Total Cores/Runtime Dynamic': 16.3982, 'Total Cores/Subthreshold Leakage': 24.7074, 'Total Cores/Subthreshold Leakage with power gating': 10.2429, 'Total L3s/Area': 61.9075, 'Total L3s/Gate Leakage': 0.0484137, 'Total L3s/Peak Dynamic': 0.371973, 'Total L3s/Runtime Dynamic': 0.183113, 'Total L3s/Subthreshold Leakage': 6.80085, 'Total L3s/Subthreshold Leakage with power gating': 3.32364, 'Total Leakage': 33.1122, 'Total NoCs/Area': 1.33155, 'Total NoCs/Gate Leakage': 0.00662954, 'Total NoCs/Peak Dynamic': 0.0, 'Total NoCs/Runtime Dynamic': 0.0, 'Total NoCs/Subthreshold Leakage': 0.0691322, 'Total NoCs/Subthreshold Leakage with power gating': 0.0259246}}	none	1		1.286766	1
packages/gtmcore/gtmcore/environment/conda.py	gigabackup/gigantum-client	60	10201	from typing import List, Dict import json from gtmcore.http import ConcurrentRequestManager, ConcurrentRequest from gtmcore.environment.packagemanager import PackageManager, PackageResult, PackageMetadata from gtmcore.container import container_for_context from gtmcore.labbook import LabBook from gtmcore.logging import LMLogger logger = LMLogger.get_logger() class CondaPackageManagerBase(PackageManager): """Class to implement the conda package manager """ def __init__(self): # String to be set in child classes indicating which python version you are checking. Typically should be either # python 3.6* or python 2.7* self.python_depends_str = None # String of the name of the conda environment (e.g. py36 or py27, as created via container build) self.python_env = None # Note, currently we hard code channel config. Future changes to support the user specifying channels # will modify this behavior self.channel_priority = ['conda-forge', 'anaconda'] self.request_mgr = ConcurrentRequestManager() def list_versions(self, package_name: str, labbook: LabBook, username: str) -> List[str]: """Method to list all available versions of a package based on the package name Args: package_name: Name of the package to query labbook: Subject LabBook username: username of current user Returns: list(str): Version strings """ # Check for package in channels, picking out version by priority request_list = list() for channel in self.channel_priority: request_list.append(ConcurrentRequest(f"https://api.anaconda.org/package/{channel}/{package_name}", headers={'Accept': 'application/json'})) responses = self.request_mgr.resolve_many(request_list) versions = None for response in responses: if response.status_code != 200: continue versions = response.json.get('versions') break if not versions: raise ValueError(f"Package {package_name} not found in channels {' ,'.join(self.channel_priority)}.") versions.reverse() return versions def list_installed_packages(self, labbook: LabBook, username: str) -> List[Dict[str, str]]: """Method to get a list of all packages that are currently installed Note, this will return results for the computer/container in which it is executed. To get the properties of a LabBook container, a docker exec command would be needed from the Gigantum application container. return format is a list of dicts with the format (name: <package name>, version: <version string>) Returns: list """ project_container = container_for_context(username, labbook=labbook) result = project_container.run_container("conda list --no-pip --json", wait_for_output=True) if result: data = json.loads(result) if data: return [{"name": x['name'], 'version': x['version']} for x in data] else: return [] def validate_packages(self, package_list: List[Dict[str, str]], labbook: LabBook, username: str) \ -> List[PackageResult]: """Method to validate a list of packages, and if needed fill in any missing versions Should check both the provided package name and version. If the version is omitted, it should be generated from the latest version. Args: package_list(list): A list of dictionaries of packages to validate labbook(str): The labbook instance username(str): The username for the logged in user Returns: namedtuple: namedtuple indicating if the package and version are valid """ result = list() # Check for package in channels, picking out version by priority request_list = list() for pkg in package_list: for channel in self.channel_priority: request_list.append(ConcurrentRequest(f"https://api.anaconda.org/package/{channel}/{pkg['package']}", headers={'Accept': 'application/json'})) responses = self.request_mgr.resolve_many(request_list) # Repack into groups by package responses_per_package = list(zip((iter(responses),) len(self.channel_priority))) for package, responses in zip(package_list, responses_per_package): versions = None latest_version = None for response in responses: if response.status_code != 200: continue versions = response.json.get('versions') latest_version = response.json.get('latest_version') break if not versions: # Package is not found result.append(PackageResult(package=package['package'], version=package.get('version'), error=True)) continue if package.get('version'): # Package has been set, so validate it if package.get('version') in versions: # Both package name and version are valid result.append(PackageResult(package=package['package'], version=package.get('version'), error=False)) else: # The package version is not in the list, so invalid result.append(PackageResult(package=package['package'], version=package.get('version'), error=True)) else: # You need to look up the latest version since not included result.append(PackageResult(package=package['package'], version=str(latest_version), error=False)) return result def get_packages_metadata(self, package_list: List[str], labbook: LabBook, username: str) -> List[PackageMetadata]: """Method to get package metadata Args: package_list: List of package names labbook(str): The labbook instance username(str): The username for the logged in user Returns: list """ def _extract_metadata(data): """Extraction method to pull out the docs URL and description""" latest_val = data.get('latest_version') description_val = data.get('summary').strip() docs_val = data.get('doc_url') if not docs_val: docs_val = data.get('html_url') return latest_val, description_val, docs_val # Check for package in channels, picking out version by priority request_list = list() for pkg in package_list: for channel in self.channel_priority: request_list.append(ConcurrentRequest(f"https://api.anaconda.org/package/{channel}/{pkg}", headers={'Accept': 'application/json'}, extraction_function=_extract_metadata)) responses = self.request_mgr.resolve_many(request_list) # Repack into groups by package responses_per_package = list(zip((iter(responses),) len(self.channel_priority))) result = list() for package, responses in zip(package_list, responses_per_package): data = None for response in responses: if response.status_code == 200: data = response.extracted_json break if data: latest_version, description, docs_url = data result.append(PackageMetadata(package_manager="conda", package=package, latest_version=latest_version, description=description, docs_url=docs_url)) else: result.append(PackageMetadata(package_manager="conda", package=package, latest_version=None, description=None, docs_url=None)) return result def generate_docker_install_snippet(self, packages: List[Dict[str, str]], single_line: bool = False) -> List[str]: """Method to generate a docker snippet to install 1 or more packages Note: Because conda be so slow to solve environments with conda-forge included, always single line it. Args: packages(list(dict)): A list of package names and versions to install single_line(bool): If true, collapse Returns: list """ package_strings = [f"{x['name']}={x['version']}" for x in packages] if single_line: return [f"RUN conda install -yq {' '.join(package_strings)}"] else: return [f"RUN conda install -yq {' '.join(package_strings)}"] class Conda3PackageManager(CondaPackageManagerBase): """Class to implement the conda3 package manager """ def __init__(self): super().__init__() self.python_depends_str = 'python 3.6' self.python_env = 'py36' class Conda2PackageManager(CondaPackageManagerBase): """Class to implement the conda2 package manager """ def __init__(self): super().__init__() self.python_depends_str = 'python 2.7' self.python_env = 'py27'	from typing import List, Dict import json from gtmcore.http import ConcurrentRequestManager, ConcurrentRequest from gtmcore.environment.packagemanager import PackageManager, PackageResult, PackageMetadata from gtmcore.container import container_for_context from gtmcore.labbook import LabBook from gtmcore.logging import LMLogger logger = LMLogger.get_logger() class CondaPackageManagerBase(PackageManager): """Class to implement the conda package manager """ def __init__(self): # String to be set in child classes indicating which python version you are checking. Typically should be either # python 3.6* or python 2.7* self.python_depends_str = None # String of the name of the conda environment (e.g. py36 or py27, as created via container build) self.python_env = None # Note, currently we hard code channel config. Future changes to support the user specifying channels # will modify this behavior self.channel_priority = ['conda-forge', 'anaconda'] self.request_mgr = ConcurrentRequestManager() def list_versions(self, package_name: str, labbook: LabBook, username: str) -> List[str]: """Method to list all available versions of a package based on the package name Args: package_name: Name of the package to query labbook: Subject LabBook username: username of current user Returns: list(str): Version strings """ # Check for package in channels, picking out version by priority request_list = list() for channel in self.channel_priority: request_list.append(ConcurrentRequest(f"https://api.anaconda.org/package/{channel}/{package_name}", headers={'Accept': 'application/json'})) responses = self.request_mgr.resolve_many(request_list) versions = None for response in responses: if response.status_code != 200: continue versions = response.json.get('versions') break if not versions: raise ValueError(f"Package {package_name} not found in channels {' ,'.join(self.channel_priority)}.") versions.reverse() return versions def list_installed_packages(self, labbook: LabBook, username: str) -> List[Dict[str, str]]: """Method to get a list of all packages that are currently installed Note, this will return results for the computer/container in which it is executed. To get the properties of a LabBook container, a docker exec command would be needed from the Gigantum application container. return format is a list of dicts with the format (name: <package name>, version: <version string>) Returns: list """ project_container = container_for_context(username, labbook=labbook) result = project_container.run_container("conda list --no-pip --json", wait_for_output=True) if result: data = json.loads(result) if data: return [{"name": x['name'], 'version': x['version']} for x in data] else: return [] def validate_packages(self, package_list: List[Dict[str, str]], labbook: LabBook, username: str) \ -> List[PackageResult]: """Method to validate a list of packages, and if needed fill in any missing versions Should check both the provided package name and version. If the version is omitted, it should be generated from the latest version. Args: package_list(list): A list of dictionaries of packages to validate labbook(str): The labbook instance username(str): The username for the logged in user Returns: namedtuple: namedtuple indicating if the package and version are valid """ result = list() # Check for package in channels, picking out version by priority request_list = list() for pkg in package_list: for channel in self.channel_priority: request_list.append(ConcurrentRequest(f"https://api.anaconda.org/package/{channel}/{pkg['package']}", headers={'Accept': 'application/json'})) responses = self.request_mgr.resolve_many(request_list) # Repack into groups by package responses_per_package = list(zip((iter(responses),) len(self.channel_priority))) for package, responses in zip(package_list, responses_per_package): versions = None latest_version = None for response in responses: if response.status_code != 200: continue versions = response.json.get('versions') latest_version = response.json.get('latest_version') break if not versions: # Package is not found result.append(PackageResult(package=package['package'], version=package.get('version'), error=True)) continue if package.get('version'): # Package has been set, so validate it if package.get('version') in versions: # Both package name and version are valid result.append(PackageResult(package=package['package'], version=package.get('version'), error=False)) else: # The package version is not in the list, so invalid result.append(PackageResult(package=package['package'], version=package.get('version'), error=True)) else: # You need to look up the latest version since not included result.append(PackageResult(package=package['package'], version=str(latest_version), error=False)) return result def get_packages_metadata(self, package_list: List[str], labbook: LabBook, username: str) -> List[PackageMetadata]: """Method to get package metadata Args: package_list: List of package names labbook(str): The labbook instance username(str): The username for the logged in user Returns: list """ def _extract_metadata(data): """Extraction method to pull out the docs URL and description""" latest_val = data.get('latest_version') description_val = data.get('summary').strip() docs_val = data.get('doc_url') if not docs_val: docs_val = data.get('html_url') return latest_val, description_val, docs_val # Check for package in channels, picking out version by priority request_list = list() for pkg in package_list: for channel in self.channel_priority: request_list.append(ConcurrentRequest(f"https://api.anaconda.org/package/{channel}/{pkg}", headers={'Accept': 'application/json'}, extraction_function=_extract_metadata)) responses = self.request_mgr.resolve_many(request_list) # Repack into groups by package responses_per_package = list(zip((iter(responses),) len(self.channel_priority))) result = list() for package, responses in zip(package_list, responses_per_package): data = None for response in responses: if response.status_code == 200: data = response.extracted_json break if data: latest_version, description, docs_url = data result.append(PackageMetadata(package_manager="conda", package=package, latest_version=latest_version, description=description, docs_url=docs_url)) else: result.append(PackageMetadata(package_manager="conda", package=package, latest_version=None, description=None, docs_url=None)) return result def generate_docker_install_snippet(self, packages: List[Dict[str, str]], single_line: bool = False) -> List[str]: """Method to generate a docker snippet to install 1 or more packages Note: Because conda be so slow to solve environments with conda-forge included, always single line it. Args: packages(list(dict)): A list of package names and versions to install single_line(bool): If true, collapse Returns: list """ package_strings = [f"{x['name']}={x['version']}" for x in packages] if single_line: return [f"RUN conda install -yq {' '.join(package_strings)}"] else: return [f"RUN conda install -yq {' '.join(package_strings)}"] class Conda3PackageManager(CondaPackageManagerBase): """Class to implement the conda3 package manager """ def __init__(self): super().__init__() self.python_depends_str = 'python 3.6' self.python_env = 'py36' class Conda2PackageManager(CondaPackageManagerBase): """Class to implement the conda2 package manager """ def __init__(self): super().__init__() self.python_depends_str = 'python 2.7' self.python_env = 'py27'	en	0.821075	Class to implement the conda package manager # String to be set in child classes indicating which python version you are checking. Typically should be either # python 3.6* or python 2.7* # String of the name of the conda environment (e.g. py36 or py27, as created via container build) # Note, currently we hard code channel config. Future changes to support the user specifying channels # will modify this behavior Method to list all available versions of a package based on the package name Args: package_name: Name of the package to query labbook: Subject LabBook username: username of current user Returns: list(str): Version strings # Check for package in channels, picking out version by priority Method to get a list of all packages that are currently installed Note, this will return results for the computer/container in which it is executed. To get the properties of a LabBook container, a docker exec command would be needed from the Gigantum application container. return format is a list of dicts with the format (name: <package name>, version: <version string>) Returns: list Method to validate a list of packages, and if needed fill in any missing versions Should check both the provided package name and version. If the version is omitted, it should be generated from the latest version. Args: package_list(list): A list of dictionaries of packages to validate labbook(str): The labbook instance username(str): The username for the logged in user Returns: namedtuple: namedtuple indicating if the package and version are valid # Check for package in channels, picking out version by priority # Repack into groups by package # Package is not found # Package has been set, so validate it # Both package name and version are valid # The package version is not in the list, so invalid # You need to look up the latest version since not included Method to get package metadata Args: package_list: List of package names labbook(str): The labbook instance username(str): The username for the logged in user Returns: list Extraction method to pull out the docs URL and description # Check for package in channels, picking out version by priority # Repack into groups by package Method to generate a docker snippet to install 1 or more packages Note: Because conda be so slow to solve environments with conda-forge included, always single line it. Args: packages(list(dict)): A list of package names and versions to install single_line(bool): If true, collapse Returns: list Class to implement the conda3 package manager Class to implement the conda2 package manager	2.353314	2
netchos/io/io_mpl_to_px.py	brainets/netchos	11	10202	<gh_stars>10-100 """Conversion of Matplotlib / Seaborn inputs to plotly.""" import os.path as op from pkg_resources import resource_filename import json def mpl_to_px_inputs(inputs, plt_types=None): """Convert typical matplotlib inputs to plotly to simplify API. Parameters ---------- inputs : dict Dictionary of inputs plt_types : string or list or None Sub select some plotting types (e.g heatmap, line etc.). If None, all types are used Returns ------- outputs : dict Dictionary of converted inputs """ # load reference table file = op.join(op.dirname(__file__), "io_mpl_to_px.json") with open(file, 'r') as f: table = json.load(f) # go through the desired plotting types for conversion if plt_types is None: plt_types = list(table.keys()) if isinstance(plt_types, str): plt_types = [plt_types] ref = {} for plt_type in plt_types: ref.update(table[plt_type]) # convert inputs outputs = {} for k, v in inputs.items(): if k in ref.keys(): k = ref[k] outputs[k] = v return outputs	"""Conversion of Matplotlib / Seaborn inputs to plotly.""" import os.path as op from pkg_resources import resource_filename import json def mpl_to_px_inputs(inputs, plt_types=None): """Convert typical matplotlib inputs to plotly to simplify API. Parameters ---------- inputs : dict Dictionary of inputs plt_types : string or list or None Sub select some plotting types (e.g heatmap, line etc.). If None, all types are used Returns ------- outputs : dict Dictionary of converted inputs """ # load reference table file = op.join(op.dirname(__file__), "io_mpl_to_px.json") with open(file, 'r') as f: table = json.load(f) # go through the desired plotting types for conversion if plt_types is None: plt_types = list(table.keys()) if isinstance(plt_types, str): plt_types = [plt_types] ref = {} for plt_type in plt_types: ref.update(table[plt_type]) # convert inputs outputs = {} for k, v in inputs.items(): if k in ref.keys(): k = ref[k] outputs[k] = v return outputs	en	0.415941	Conversion of Matplotlib / Seaborn inputs to plotly. Convert typical matplotlib inputs to plotly to simplify API. Parameters ---------- inputs : dict Dictionary of inputs plt_types : string or list or None Sub select some plotting types (e.g heatmap, line etc.). If None, all types are used Returns ------- outputs : dict Dictionary of converted inputs # load reference table # go through the desired plotting types for conversion # convert inputs	3.279682	3
fizzbuzz_for_02.py	toastyxen/FizzBuzz	0	10203	"""Fizzbuzz for loop variant 3""" for x in range(1, 101): OUTPUT = "" if x % 3 == 0: OUTPUT += "Fizz" if x % 5 == 0: OUTPUT += "Buzz" print(OUTPUT or x)	"""Fizzbuzz for loop variant 3""" for x in range(1, 101): OUTPUT = "" if x % 3 == 0: OUTPUT += "Fizz" if x % 5 == 0: OUTPUT += "Buzz" print(OUTPUT or x)	en	0.564812	Fizzbuzz for loop variant 3	3.897786	4
cnn/struct/layer/parse_tensor_module.py	hslee1539/GIS_GANs	0	10204	from tensor.main_module import Tensor import numpy as np def getTensor(value): if type(value) is np.ndarray: return Tensor.numpy2Tensor(value) elif type(value) is Tensor: return value else: raise Exception	from tensor.main_module import Tensor import numpy as np def getTensor(value): if type(value) is np.ndarray: return Tensor.numpy2Tensor(value) elif type(value) is Tensor: return value else: raise Exception	none	1		2.842778	3
openstack_dashboard/dashboards/admin/volume_types/qos_specs/forms.py	hemantsonawane95/horizon-apelby	0	10205	<reponame>hemantsonawane95/horizon-apelby<filename>openstack_dashboard/dashboards/admin/volume_types/qos_specs/forms.py # 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 re from django.urls import reverse from django.utils.translation import gettext_lazy as _ from horizon import exceptions from horizon import forms from horizon import messages from openstack_dashboard import api KEY_NAME_REGEX = re.compile(r"^[a-zA-Z0-9-_:. /]+$", re.UNICODE) KEY_ERROR_MESSAGES = { 'invalid': _("The key must match the following the regex: " "'^[a-zA-Z0-9-_:. /]'")} class CreateKeyValuePair(forms.SelfHandlingForm): # this if for creating a spec key-value pair for an existing QOS Spec key = forms.RegexField(max_length=255, label=_("Key"), regex=KEY_NAME_REGEX, error_messages=KEY_ERROR_MESSAGES) value = forms.CharField(max_length=255, label=_("Value")) def handle(self, request, data): qos_spec_id = self.initial['qos_spec_id'] try: # first retrieve current value of specs specs = api.cinder.qos_spec_get(request, qos_spec_id) # now add new key-value pair to list of specs specs.specs[data['key']] = data['value'] api.cinder.qos_spec_set_keys(request, qos_spec_id, specs.specs) msg = _('Created spec "%s".') % data['key'] messages.success(request, msg) return True except Exception: redirect = reverse("horizon:admin:volume_types:index") exceptions.handle(request, _("Unable to create spec."), redirect=redirect) class EditKeyValuePair(forms.SelfHandlingForm): value = forms.CharField(max_length=255, label=_("Value")) # update the backend with the new qos spec value def handle(self, request, data): key = self.initial['key'] qos_spec_id = self.initial['qos_spec_id'] # build up new 'specs' object with all previous values plus new value try: # first retrieve current value of specs specs = api.cinder.qos_spec_get_keys(request, qos_spec_id, raw=True) specs.specs[key] = data['value'] api.cinder.qos_spec_set_keys(request, qos_spec_id, specs.specs) msg = _('Saved spec "%s".') % key messages.success(request, msg) return True except Exception: redirect = reverse("horizon:admin:volume_types:index") exceptions.handle(request, _("Unable to edit spec."), redirect=redirect)	# 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 re from django.urls import reverse from django.utils.translation import gettext_lazy as _ from horizon import exceptions from horizon import forms from horizon import messages from openstack_dashboard import api KEY_NAME_REGEX = re.compile(r"^[a-zA-Z0-9-_:. /]+$", re.UNICODE) KEY_ERROR_MESSAGES = { 'invalid': _("The key must match the following the regex: " "'^[a-zA-Z0-9-_:. /]'")} class CreateKeyValuePair(forms.SelfHandlingForm): # this if for creating a spec key-value pair for an existing QOS Spec key = forms.RegexField(max_length=255, label=_("Key"), regex=KEY_NAME_REGEX, error_messages=KEY_ERROR_MESSAGES) value = forms.CharField(max_length=255, label=_("Value")) def handle(self, request, data): qos_spec_id = self.initial['qos_spec_id'] try: # first retrieve current value of specs specs = api.cinder.qos_spec_get(request, qos_spec_id) # now add new key-value pair to list of specs specs.specs[data['key']] = data['value'] api.cinder.qos_spec_set_keys(request, qos_spec_id, specs.specs) msg = _('Created spec "%s".') % data['key'] messages.success(request, msg) return True except Exception: redirect = reverse("horizon:admin:volume_types:index") exceptions.handle(request, _("Unable to create spec."), redirect=redirect) class EditKeyValuePair(forms.SelfHandlingForm): value = forms.CharField(max_length=255, label=_("Value")) # update the backend with the new qos spec value def handle(self, request, data): key = self.initial['key'] qos_spec_id = self.initial['qos_spec_id'] # build up new 'specs' object with all previous values plus new value try: # first retrieve current value of specs specs = api.cinder.qos_spec_get_keys(request, qos_spec_id, raw=True) specs.specs[key] = data['value'] api.cinder.qos_spec_set_keys(request, qos_spec_id, specs.specs) msg = _('Saved spec "%s".') % key messages.success(request, msg) return True except Exception: redirect = reverse("horizon:admin:volume_types:index") exceptions.handle(request, _("Unable to edit spec."), redirect=redirect)	en	0.789195	# 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. # this if for creating a spec key-value pair for an existing QOS Spec # first retrieve current value of specs # now add new key-value pair to list of specs # update the backend with the new qos spec value # build up new 'specs' object with all previous values plus new value # first retrieve current value of specs	1.806878	2
data_structure/const_tree.py	alipay/StructuredLM_RTDT	42	10206	<reponame>alipay/StructuredLM_RTDT # coding=utf-8 # Copyright (c) 2021 <NAME> import sys LABEL_SEP = '@' INDENT_STRING1 = '│ ' INDENT_STRING2 = '├──' EMPTY_TOKEN = '___EMPTY___' def print_tree(const_tree, indent=0, out=sys.stdout): for i in range(indent - 1): out.write(INDENT_STRING1) if indent > 0: out.write(INDENT_STRING2) out.write(const_tree.tag) if not isinstance(const_tree.children[0], ConstTree): out.write(f' {const_tree.children[0].string}\n') else: out.write('\n') for child in const_tree.children: print_tree(child, indent + 1, out) def _make_tree(string, make_leaf_fn, make_internal_fn): tokens = string.replace('(', ' ( ').replace(')', ' ) ').split() index, stack = 0, [] lexicons = [] root = None while index < len(tokens): token = tokens[index] index += 1 if token == ')': if not stack: raise ConstTreeParserError('redundant ")" at token ' + str(index)) node = stack.pop() if not stack: root = node else: stack[-1].children.append(node) elif token == '(': tag = tokens[index] index += 1 stack.append(make_internal_fn(tag)) else: if not stack: raise ConnectionError('??? at pos ' + str(index)) new_token = [] while token != ')': if not token != '(': raise Exception('bracket error') new_token.append(token) token = tokens[index] index += 1 # is lexicon leaf_node = make_leaf_fn('_'.join(new_token)) lexicons.append(leaf_node) postag_node = stack.pop() postag_node.children.append(leaf_node) if not stack: root = postag_node else: stack[-1].children.append(postag_node) if not root or stack: raise ConstTreeParserError('missing ")".') return root, lexicons class ConstTreeParserError(Exception): pass class Lexicon: __slots__ = ('string', 'span', 'parent') def __init__(self, string, span=None): self.string = string self.span = span def __str__(self): return f'<Lexicon {self.string}>' def __repr__(self): return str(self) def __eq__(self, other): return self.string == other.string def __hash__(self): return hash(self.string) + 2 @property def tag(self): return self.string def to_string(self, quote_lexicon): if quote_lexicon: return f'"{self.string}"' return self.string class ConstTree: __slots__ = ('children', 'tag', 'span', 'index', 'parent', 'attrs') ROOT_LABEL = 'ROOT' def __init__(self, tag, children=None, span=None): self.tag = tag self.children = children if children is not None else [] self.span = span self.index = None def __str__(self): child_string = ' + '.join(child.tag for child in self.children) return f'{self.span} {self.tag} => {child_string}' def __repr__(self): return str(self) def __getitem__(self, index): if isinstance(index, int): return self.children[index] if isinstance(index, str): for child in self.children: if isinstance(child, ConstTree) and child.tag == index.upper(): return child raise KeyError def to_string(self, quote_lexicon=False): child_string = ' '.join(child.to_string(quote_lexicon) for child in self.children) return f'({self.tag} {child_string})' @staticmethod def from_string(string): """ Construct ConstTree from parenthesis representation. :param string: string of parenthesis representation :return: ConstTree root and all leaf Lexicons """ tree, lexicons = _make_tree(string, Lexicon, ConstTree) for index, lexicon in enumerate(lexicons): lexicon.span = index, index + 1 tree.populate_spans_internal() return tree, lexicons def traverse_postorder(self): for child in self.children: if isinstance(child, ConstTree): yield from child.traverse_postorder() yield self def traverse_postorder_with_lexicons(self): for child in self.children: if isinstance(child, ConstTree): yield from child.traverse_postorder_with_lexicons() else: yield child yield self def generate_preterminals(self): for child in self.children: if isinstance(child, ConstTree): yield from child.generate_preterminals() for child in self.children: if isinstance(child, Lexicon): yield self def generate_lexicons(self): for child in self.children: if isinstance(child, ConstTree): yield from child.generate_lexicons() for child in self.children: if isinstance(child, Lexicon): yield child def is_binary_tree(self): if isinstance(self.children[0], Lexicon): return True return len(self.children <= 2) and all(child.is_binary_tree() for child in self.children) def condensed_unary_chain(self, include_preterminal=True, remove_root=None): if self.tag == remove_root: assert len(self.children) == 1 return self.children[0].condensed_unary_chain(include_preterminal=include_preterminal) if len(self.children) > 1: return ConstTree(self.tag, children=list(child.condensed_unary_chain() for child in self.children), span=self.span) if isinstance(self.children[0], Lexicon): return ConstTree((self.tag if include_preterminal else EMPTY_TOKEN), children=list(self.children), span=self.span) assert isinstance(self.children[0], ConstTree) node = self new_tag = self.tag while len(node.children) == 1 and isinstance(node.children[0], ConstTree): node = node.children[0] if include_preterminal or isinstance(node.children[0], ConstTree): new_tag += LABEL_SEP + node.tag if len(node.children) == 1: children = list(node.children) else: children = list(child.condensed_unary_chain() for child in node.children) return ConstTree(new_tag, children=children, span=self.span) def expanded_unary_chain(self, add_root=None): if isinstance(self.children[0], Lexicon): children = list(self.children) else: children = list(child.expanded_unary_chain() for child in self.children) tags = self.tag.split(LABEL_SEP) for tag in reversed(tags): children = [ConstTree(tag, children=children, span=self.span)] root = children[0] if add_root: root = ConstTree(add_root, children=[root]) return root def calculate_span(self): self.span = self.children[0].span[0], self.children[-1].span[1] def populate_spans_internal(self): for child in self.children: if isinstance(child, ConstTree): child.populate_spans_internal() self.calculate_span() def add_postorder_index(self): for index, node in enumerate(self.traverse_postorder()): node.index = index def add_parents(self, parent=None): self.parent = parent for child in self.children: if isinstance(child, ConstTree): child.add_parents(self) def is_ancestor_of(self, other): other = other.parent while other is not None and other is not self: other = other.parent return other is not None def generate_path_to_root(self, include_self=False): node = self if not include_self: node = self.parent while node is not None: yield node node = node.parent def lowest_common_ancestor(self, other): path = list(other.generate_path_to_root()) for node in self.generate_path_to_root(): try: return path[path.index(node)] except ValueError: pass def remove_nodes(self, filter): _children = [] for c in self.children: if isinstance(c, ConstTree): if filter(c): pass else: filtered_node = c.remove_nodes(filter) _children.append(filtered_node) else: _children.append(c) return ConstTree(self.tag, _children)	# coding=utf-8 # Copyright (c) 2021 <NAME> import sys LABEL_SEP = '@' INDENT_STRING1 = '│ ' INDENT_STRING2 = '├──' EMPTY_TOKEN = '___EMPTY___' def print_tree(const_tree, indent=0, out=sys.stdout): for i in range(indent - 1): out.write(INDENT_STRING1) if indent > 0: out.write(INDENT_STRING2) out.write(const_tree.tag) if not isinstance(const_tree.children[0], ConstTree): out.write(f' {const_tree.children[0].string}\n') else: out.write('\n') for child in const_tree.children: print_tree(child, indent + 1, out) def _make_tree(string, make_leaf_fn, make_internal_fn): tokens = string.replace('(', ' ( ').replace(')', ' ) ').split() index, stack = 0, [] lexicons = [] root = None while index < len(tokens): token = tokens[index] index += 1 if token == ')': if not stack: raise ConstTreeParserError('redundant ")" at token ' + str(index)) node = stack.pop() if not stack: root = node else: stack[-1].children.append(node) elif token == '(': tag = tokens[index] index += 1 stack.append(make_internal_fn(tag)) else: if not stack: raise ConnectionError('??? at pos ' + str(index)) new_token = [] while token != ')': if not token != '(': raise Exception('bracket error') new_token.append(token) token = tokens[index] index += 1 # is lexicon leaf_node = make_leaf_fn('_'.join(new_token)) lexicons.append(leaf_node) postag_node = stack.pop() postag_node.children.append(leaf_node) if not stack: root = postag_node else: stack[-1].children.append(postag_node) if not root or stack: raise ConstTreeParserError('missing ")".') return root, lexicons class ConstTreeParserError(Exception): pass class Lexicon: __slots__ = ('string', 'span', 'parent') def __init__(self, string, span=None): self.string = string self.span = span def __str__(self): return f'<Lexicon {self.string}>' def __repr__(self): return str(self) def __eq__(self, other): return self.string == other.string def __hash__(self): return hash(self.string) + 2 @property def tag(self): return self.string def to_string(self, quote_lexicon): if quote_lexicon: return f'"{self.string}"' return self.string class ConstTree: __slots__ = ('children', 'tag', 'span', 'index', 'parent', 'attrs') ROOT_LABEL = 'ROOT' def __init__(self, tag, children=None, span=None): self.tag = tag self.children = children if children is not None else [] self.span = span self.index = None def __str__(self): child_string = ' + '.join(child.tag for child in self.children) return f'{self.span} {self.tag} => {child_string}' def __repr__(self): return str(self) def __getitem__(self, index): if isinstance(index, int): return self.children[index] if isinstance(index, str): for child in self.children: if isinstance(child, ConstTree) and child.tag == index.upper(): return child raise KeyError def to_string(self, quote_lexicon=False): child_string = ' '.join(child.to_string(quote_lexicon) for child in self.children) return f'({self.tag} {child_string})' @staticmethod def from_string(string): """ Construct ConstTree from parenthesis representation. :param string: string of parenthesis representation :return: ConstTree root and all leaf Lexicons """ tree, lexicons = _make_tree(string, Lexicon, ConstTree) for index, lexicon in enumerate(lexicons): lexicon.span = index, index + 1 tree.populate_spans_internal() return tree, lexicons def traverse_postorder(self): for child in self.children: if isinstance(child, ConstTree): yield from child.traverse_postorder() yield self def traverse_postorder_with_lexicons(self): for child in self.children: if isinstance(child, ConstTree): yield from child.traverse_postorder_with_lexicons() else: yield child yield self def generate_preterminals(self): for child in self.children: if isinstance(child, ConstTree): yield from child.generate_preterminals() for child in self.children: if isinstance(child, Lexicon): yield self def generate_lexicons(self): for child in self.children: if isinstance(child, ConstTree): yield from child.generate_lexicons() for child in self.children: if isinstance(child, Lexicon): yield child def is_binary_tree(self): if isinstance(self.children[0], Lexicon): return True return len(self.children <= 2) and all(child.is_binary_tree() for child in self.children) def condensed_unary_chain(self, include_preterminal=True, remove_root=None): if self.tag == remove_root: assert len(self.children) == 1 return self.children[0].condensed_unary_chain(include_preterminal=include_preterminal) if len(self.children) > 1: return ConstTree(self.tag, children=list(child.condensed_unary_chain() for child in self.children), span=self.span) if isinstance(self.children[0], Lexicon): return ConstTree((self.tag if include_preterminal else EMPTY_TOKEN), children=list(self.children), span=self.span) assert isinstance(self.children[0], ConstTree) node = self new_tag = self.tag while len(node.children) == 1 and isinstance(node.children[0], ConstTree): node = node.children[0] if include_preterminal or isinstance(node.children[0], ConstTree): new_tag += LABEL_SEP + node.tag if len(node.children) == 1: children = list(node.children) else: children = list(child.condensed_unary_chain() for child in node.children) return ConstTree(new_tag, children=children, span=self.span) def expanded_unary_chain(self, add_root=None): if isinstance(self.children[0], Lexicon): children = list(self.children) else: children = list(child.expanded_unary_chain() for child in self.children) tags = self.tag.split(LABEL_SEP) for tag in reversed(tags): children = [ConstTree(tag, children=children, span=self.span)] root = children[0] if add_root: root = ConstTree(add_root, children=[root]) return root def calculate_span(self): self.span = self.children[0].span[0], self.children[-1].span[1] def populate_spans_internal(self): for child in self.children: if isinstance(child, ConstTree): child.populate_spans_internal() self.calculate_span() def add_postorder_index(self): for index, node in enumerate(self.traverse_postorder()): node.index = index def add_parents(self, parent=None): self.parent = parent for child in self.children: if isinstance(child, ConstTree): child.add_parents(self) def is_ancestor_of(self, other): other = other.parent while other is not None and other is not self: other = other.parent return other is not None def generate_path_to_root(self, include_self=False): node = self if not include_self: node = self.parent while node is not None: yield node node = node.parent def lowest_common_ancestor(self, other): path = list(other.generate_path_to_root()) for node in self.generate_path_to_root(): try: return path[path.index(node)] except ValueError: pass def remove_nodes(self, filter): _children = [] for c in self.children: if isinstance(c, ConstTree): if filter(c): pass else: filtered_node = c.remove_nodes(filter) _children.append(filtered_node) else: _children.append(c) return ConstTree(self.tag, _children)	en	0.743059	# coding=utf-8 # Copyright (c) 2021 <NAME> # is lexicon Construct ConstTree from parenthesis representation. :param string: string of parenthesis representation :return: ConstTree root and all leaf Lexicons	3.503178	4
tests/test_minimize.py	The-Ludwig/iminuit	0	10207	<filename>tests/test_minimize.py import pytest from iminuit import minimize import numpy as np from numpy.testing import assert_allclose, assert_equal opt = pytest.importorskip("scipy.optimize") def func(x, args): c = args[0] if args else 1 return c + x[0] * 2 + (x[1] - 1) 2 + (x[2] - 2) 2 def grad(x, args): return 2 (x - (0, 1, 2)) def test_simple(): result = minimize(func, (1, 1, 1)) assert_allclose(result.x, (0, 1, 2), atol=1e-8) assert_allclose(result.fun, 1) assert result.nfev > 0 assert result.njev == 0 def test_gradient(): result = minimize(func, (1, 1, 1), jac=grad) assert_allclose(result.x, (0, 1, 2), atol=1e-8) assert_allclose(result.fun, 1) assert result.nfev > 0 assert result.njev > 0 def test_args(): result = minimize(func, np.ones(3), args=(5,)) assert_allclose(result.x, (0, 1, 2), atol=1e-8) assert_allclose(result.fun, 5) assert result.nfev > 0 assert result.njev == 0 def test_callback(): trace = [] result = minimize(func, np.ones(3), callback=lambda x: trace.append(x.copy())) assert_allclose(result.x, (0, 1, 2), atol=1e-8) assert_allclose(result.fun, 1) assert result.nfev == len(trace) assert_allclose(trace[0], np.ones(3), atol=1e-2) assert_allclose(trace[-1], result.x, atol=1e-2) def test_tol(): ref = np.ones(2) def rosen(par): x, y = par return (1 - x) ** 2 + 100 * (y - x 2) 2 r1 = minimize(rosen, (0, 0), tol=1) r2 = minimize(rosen, (0, 0), tol=1e-6) assert max(np.abs(r2.x - ref)) < max(np.abs(r1.x - ref)) def test_disp(capsys): minimize(lambda x: x 2, 0) assert capsys.readouterr()[0] == "" minimize(lambda x: x 2, 0, options={"disp": True}) assert capsys.readouterr()[0] != "" def test_hessinv(): r = minimize(func, (1, 1, 1)) href = np.zeros((3, 3)) for i in range(3): href[i, i] = 0.5 assert_allclose(r.hess_inv, href, atol=1e-8) def test_unsupported(): with pytest.raises(ValueError): minimize(func, (1, 1, 1), constraints=[]) with pytest.raises(ValueError): minimize(func, (1, 1, 1), jac=True) def test_call_limit(): ref = minimize(func, (1, 1, 1)) with pytest.warns(UserWarning): r1 = minimize(func, (1, 1, 1), options={"maxiter": 1}) assert r1.nfev < ref.nfev assert not r1.success assert "Call limit" in r1.message with pytest.warns(DeprecationWarning): r2 = minimize(func, (1, 1, 1), options={"maxfev": 1}) assert not r2.success assert r2.nfev == r1.nfev r3 = minimize(func, (1, 1, 1), options={"maxfun": 1}) assert not r3.success assert r3.nfev == r1.nfev def test_eps(): ref = minimize(func, (1, 1, 1)) r = minimize(func, (1, 1, 1), options={"eps": 1e-10}) assert np.any(ref.x != r.x) assert_allclose(r.x, ref.x, atol=1e-9) def test_bad_function(): class Fcn: n = 0 def __call__(self, x): self.n += 1 return x ** 2 + 1e-4 * (self.n % 3) r = minimize(Fcn(), [1], options={"maxfun": 100000000}) assert not r.success assert "Estimated distance to minimum too large" in r.message def test_bounds(): r1 = minimize(func, (1.5, 1.7, 1.5), bounds=opt.Bounds((1, 1.5, 1), (2, 2, 2))) assert r1.success assert_allclose(r1.x, (1, 1.5, 2), atol=1e-2) r2 = minimize(func, (1.5, 1.7, 1.5), bounds=((1, 2), (1.5, 2), (1, 2))) assert r2.success assert_equal(r1.x, r2.x) def test_method_warn(): with pytest.raises(ValueError): minimize(func, (1.5, 1.7, 1.5), method="foo") def test_hess_warn(): with pytest.warns(UserWarning): minimize(func, (1.5, 1.7, 1.5), hess=True) def test_unreliable_uncertainties(): r = minimize(func, (1.5, 1.7, 1.5), options={"stra": 0}) assert ( r.message == "Optimization terminated successfully, but uncertainties are unrealiable." ) def test_simplex(): r = minimize(func, (1.5, 1.7, 1.5), method="simplex", tol=1e-4) assert r.success assert_allclose(r.x, (0, 1, 2), atol=2e-3)	<filename>tests/test_minimize.py import pytest from iminuit import minimize import numpy as np from numpy.testing import assert_allclose, assert_equal opt = pytest.importorskip("scipy.optimize") def func(x, args): c = args[0] if args else 1 return c + x[0] * 2 + (x[1] - 1) 2 + (x[2] - 2) 2 def grad(x, args): return 2 (x - (0, 1, 2)) def test_simple(): result = minimize(func, (1, 1, 1)) assert_allclose(result.x, (0, 1, 2), atol=1e-8) assert_allclose(result.fun, 1) assert result.nfev > 0 assert result.njev == 0 def test_gradient(): result = minimize(func, (1, 1, 1), jac=grad) assert_allclose(result.x, (0, 1, 2), atol=1e-8) assert_allclose(result.fun, 1) assert result.nfev > 0 assert result.njev > 0 def test_args(): result = minimize(func, np.ones(3), args=(5,)) assert_allclose(result.x, (0, 1, 2), atol=1e-8) assert_allclose(result.fun, 5) assert result.nfev > 0 assert result.njev == 0 def test_callback(): trace = [] result = minimize(func, np.ones(3), callback=lambda x: trace.append(x.copy())) assert_allclose(result.x, (0, 1, 2), atol=1e-8) assert_allclose(result.fun, 1) assert result.nfev == len(trace) assert_allclose(trace[0], np.ones(3), atol=1e-2) assert_allclose(trace[-1], result.x, atol=1e-2) def test_tol(): ref = np.ones(2) def rosen(par): x, y = par return (1 - x) ** 2 + 100 * (y - x 2) 2 r1 = minimize(rosen, (0, 0), tol=1) r2 = minimize(rosen, (0, 0), tol=1e-6) assert max(np.abs(r2.x - ref)) < max(np.abs(r1.x - ref)) def test_disp(capsys): minimize(lambda x: x 2, 0) assert capsys.readouterr()[0] == "" minimize(lambda x: x 2, 0, options={"disp": True}) assert capsys.readouterr()[0] != "" def test_hessinv(): r = minimize(func, (1, 1, 1)) href = np.zeros((3, 3)) for i in range(3): href[i, i] = 0.5 assert_allclose(r.hess_inv, href, atol=1e-8) def test_unsupported(): with pytest.raises(ValueError): minimize(func, (1, 1, 1), constraints=[]) with pytest.raises(ValueError): minimize(func, (1, 1, 1), jac=True) def test_call_limit(): ref = minimize(func, (1, 1, 1)) with pytest.warns(UserWarning): r1 = minimize(func, (1, 1, 1), options={"maxiter": 1}) assert r1.nfev < ref.nfev assert not r1.success assert "Call limit" in r1.message with pytest.warns(DeprecationWarning): r2 = minimize(func, (1, 1, 1), options={"maxfev": 1}) assert not r2.success assert r2.nfev == r1.nfev r3 = minimize(func, (1, 1, 1), options={"maxfun": 1}) assert not r3.success assert r3.nfev == r1.nfev def test_eps(): ref = minimize(func, (1, 1, 1)) r = minimize(func, (1, 1, 1), options={"eps": 1e-10}) assert np.any(ref.x != r.x) assert_allclose(r.x, ref.x, atol=1e-9) def test_bad_function(): class Fcn: n = 0 def __call__(self, x): self.n += 1 return x ** 2 + 1e-4 * (self.n % 3) r = minimize(Fcn(), [1], options={"maxfun": 100000000}) assert not r.success assert "Estimated distance to minimum too large" in r.message def test_bounds(): r1 = minimize(func, (1.5, 1.7, 1.5), bounds=opt.Bounds((1, 1.5, 1), (2, 2, 2))) assert r1.success assert_allclose(r1.x, (1, 1.5, 2), atol=1e-2) r2 = minimize(func, (1.5, 1.7, 1.5), bounds=((1, 2), (1.5, 2), (1, 2))) assert r2.success assert_equal(r1.x, r2.x) def test_method_warn(): with pytest.raises(ValueError): minimize(func, (1.5, 1.7, 1.5), method="foo") def test_hess_warn(): with pytest.warns(UserWarning): minimize(func, (1.5, 1.7, 1.5), hess=True) def test_unreliable_uncertainties(): r = minimize(func, (1.5, 1.7, 1.5), options={"stra": 0}) assert ( r.message == "Optimization terminated successfully, but uncertainties are unrealiable." ) def test_simplex(): r = minimize(func, (1.5, 1.7, 1.5), method="simplex", tol=1e-4) assert r.success assert_allclose(r.x, (0, 1, 2), atol=2e-3)	none	1		2.427253	2
murtanto/parsing.py	amandatv20/botfb	1	10208	<filename>murtanto/parsing.py # coded by: salism3 # 23 - 05 - 2020 23:18 (<NAME>) from bs4 import BeautifulSoup as parser from . import sorting import re def to_bs4(html): return parser(html, "html.parser") def refsrc(html): return True if re.search(r'http.+\Wrefsrc', html) else False def parsing_href(html, href, one = False, bs4_class = False): data = to_bs4(html) if one: data = data.find("a", href = lambda x: x and href in x) if not bs4_class and data != None: data = sorting.to_mbasic(data["href"]) else: data = data.find_all("a", href = lambda x: x and href in x) if not bs4_class: data = [sorting.to_mbasic(x["href"]) for x in data] return data def parsing_href_regex(html, pattern, one = False, bs4_class = False): data = to_bs4(html) if one: data = data.find("a", href = lambda x: x and re.search(pattern, x)) if not bs4_class and data != None: data = sorting.to_mbasic(data["href"]) else: data = data.find_all("a", href = lambda x: x and re.search(pattern, x)) if not bs4_class: data = [sorting.to_mbasic(x["href"]) for x in data] return data def getMyName(html): data = to_bs4(html).find("title").text return data def getName(html): data = to_bs4(html).find("title").text return data def getMyId(html): data = to_bs4(html).find("a", href = lambda x:"/allactivity" in x)["href"] data = re.search(r"/\d+/?", data).group().replace("/", "") return data def getHiddenInput(html, post_action): rv = {} data = to_bs4(html).find("form", action = lambda x: post_action in x) data = data.find_all("input", {"type":"hidden", "name":True, "value":True}) for x in data: rv[x["name"]] = x["value"] return rv def friendRequestParser(html): confirm = parsing_href(html, "?confirm=") reject = parsing_href(html, "?delete=") rv = list(zip(confirm, reject)) next = parsing_href(html, "?ppk=", one = True) return {"items":rv, "next":next} def listFriendParser(html): data = parsing_href(html, "fref=fr_tab", bs4_class = True) nama = [x.text for x in data] id_ = [re.search(r"\w[\w.]+", x["href"].replace("/", "").replace("profile.php?id=", "")).group() for x in data] img = [x["src"] for x in to_bs4(html).find_all("img", alt = lambda x: x and "profile picture" in x)] if "/allactivity?" in html: del img[0] next = parsing_href(html, "unit_cursor=", one = True) return {"items":list(zip(nama, id_, img)), "next":next, "html":html}	<filename>murtanto/parsing.py # coded by: salism3 # 23 - 05 - 2020 23:18 (<NAME>) from bs4 import BeautifulSoup as parser from . import sorting import re def to_bs4(html): return parser(html, "html.parser") def refsrc(html): return True if re.search(r'http.+\Wrefsrc', html) else False def parsing_href(html, href, one = False, bs4_class = False): data = to_bs4(html) if one: data = data.find("a", href = lambda x: x and href in x) if not bs4_class and data != None: data = sorting.to_mbasic(data["href"]) else: data = data.find_all("a", href = lambda x: x and href in x) if not bs4_class: data = [sorting.to_mbasic(x["href"]) for x in data] return data def parsing_href_regex(html, pattern, one = False, bs4_class = False): data = to_bs4(html) if one: data = data.find("a", href = lambda x: x and re.search(pattern, x)) if not bs4_class and data != None: data = sorting.to_mbasic(data["href"]) else: data = data.find_all("a", href = lambda x: x and re.search(pattern, x)) if not bs4_class: data = [sorting.to_mbasic(x["href"]) for x in data] return data def getMyName(html): data = to_bs4(html).find("title").text return data def getName(html): data = to_bs4(html).find("title").text return data def getMyId(html): data = to_bs4(html).find("a", href = lambda x:"/allactivity" in x)["href"] data = re.search(r"/\d+/?", data).group().replace("/", "") return data def getHiddenInput(html, post_action): rv = {} data = to_bs4(html).find("form", action = lambda x: post_action in x) data = data.find_all("input", {"type":"hidden", "name":True, "value":True}) for x in data: rv[x["name"]] = x["value"] return rv def friendRequestParser(html): confirm = parsing_href(html, "?confirm=") reject = parsing_href(html, "?delete=") rv = list(zip(confirm, reject)) next = parsing_href(html, "?ppk=", one = True) return {"items":rv, "next":next} def listFriendParser(html): data = parsing_href(html, "fref=fr_tab", bs4_class = True) nama = [x.text for x in data] id_ = [re.search(r"\w[\w.]+", x["href"].replace("/", "").replace("profile.php?id=", "")).group() for x in data] img = [x["src"] for x in to_bs4(html).find_all("img", alt = lambda x: x and "profile picture" in x)] if "/allactivity?" in html: del img[0] next = parsing_href(html, "unit_cursor=", one = True) return {"items":list(zip(nama, id_, img)), "next":next, "html":html}	en	0.44789	# coded by: salism3 # 23 - 05 - 2020 23:18 (<NAME>)	2.850233	3
test/test_watchdog_status.py	ike709/tgs4-api-pyclient	0	10209	# coding: utf-8 """ TGS API A production scale tool for BYOND server management # noqa: E501 OpenAPI spec version: 9.0.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import swagger_client from swagger_client.models.watchdog_status import WatchdogStatus # noqa: E501 from swagger_client.rest import ApiException class TestWatchdogStatus(unittest.TestCase): """WatchdogStatus unit test stubs""" def setUp(self): pass def tearDown(self): pass def testWatchdogStatus(self): """Test WatchdogStatus""" # FIXME: construct object with mandatory attributes with example values # model = swagger_client.models.watchdog_status.WatchdogStatus() # noqa: E501 pass if __name__ == '__main__': unittest.main()	# coding: utf-8 """ TGS API A production scale tool for BYOND server management # noqa: E501 OpenAPI spec version: 9.0.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import swagger_client from swagger_client.models.watchdog_status import WatchdogStatus # noqa: E501 from swagger_client.rest import ApiException class TestWatchdogStatus(unittest.TestCase): """WatchdogStatus unit test stubs""" def setUp(self): pass def tearDown(self): pass def testWatchdogStatus(self): """Test WatchdogStatus""" # FIXME: construct object with mandatory attributes with example values # model = swagger_client.models.watchdog_status.WatchdogStatus() # noqa: E501 pass if __name__ == '__main__': unittest.main()	en	0.55272	# coding: utf-8 TGS API A production scale tool for BYOND server management # noqa: E501 OpenAPI spec version: 9.0.0 Generated by: https://github.com/swagger-api/swagger-codegen.git # noqa: E501 WatchdogStatus unit test stubs Test WatchdogStatus # FIXME: construct object with mandatory attributes with example values # model = swagger_client.models.watchdog_status.WatchdogStatus() # noqa: E501	1.742767	2
setup.py	joesan/housing-classification-example	0	10210	from setuptools import find_packages, setup setup( name='src', packages=find_packages(), version='0.1.0', description='Python codebase for the housing classification ML problem', author='Joesan', license='', )	from setuptools import find_packages, setup setup( name='src', packages=find_packages(), version='0.1.0', description='Python codebase for the housing classification ML problem', author='Joesan', license='', )	none	1		1.049739	1
tests/test_models/test_backbones/test_sr_backbones/test_edvr_net.py	wangruohui/mmediting	45	10211	# Copyright (c) OpenMMLab. All rights reserved. import pytest import torch from mmedit.models.backbones.sr_backbones.edvr_net import (EDVRNet, PCDAlignment, TSAFusion) def test_pcd_alignment(): """Test PCDAlignment.""" # cpu pcd_alignment = PCDAlignment(mid_channels=4, deform_groups=2) input_list = [] for i in range(3, 0, -1): input_list.append(torch.rand(1, 4, 2i, 2i)) pcd_alignment = pcd_alignment input_list = [v for v in input_list] output = pcd_alignment(input_list, input_list) assert output.shape == (1, 4, 8, 8) with pytest.raises(AssertionError): pcd_alignment(input_list[0:2], input_list) # gpu if torch.cuda.is_available(): pcd_alignment = PCDAlignment(mid_channels=4, deform_groups=2) input_list = [] for i in range(3, 0, -1): input_list.append(torch.rand(1, 4, 2i, 2i)) pcd_alignment = pcd_alignment.cuda() input_list = [v.cuda() for v in input_list] output = pcd_alignment(input_list, input_list) assert output.shape == (1, 4, 8, 8) with pytest.raises(AssertionError): pcd_alignment(input_list[0:2], input_list) def test_tsa_fusion(): """Test TSAFusion.""" # cpu tsa_fusion = TSAFusion(mid_channels=4, num_frames=5, center_frame_idx=2) input_tensor = torch.rand(1, 5, 4, 8, 8) output = tsa_fusion(input_tensor) assert output.shape == (1, 4, 8, 8) # gpu if torch.cuda.is_available(): tsa_fusion = tsa_fusion.cuda() input_tensor = input_tensor.cuda() output = tsa_fusion(input_tensor) assert output.shape == (1, 4, 8, 8) def test_edvrnet(): """Test EDVRNet.""" # cpu # with tsa edvrnet = EDVRNet( 3, 3, mid_channels=8, num_frames=5, deform_groups=2, num_blocks_extraction=1, num_blocks_reconstruction=1, center_frame_idx=2, with_tsa=True) input_tensor = torch.rand(1, 5, 3, 8, 8) edvrnet.init_weights(pretrained=None) output = edvrnet(input_tensor) assert output.shape == (1, 3, 32, 32) # without tsa edvrnet = EDVRNet( 3, 3, mid_channels=8, num_frames=5, deform_groups=2, num_blocks_extraction=1, num_blocks_reconstruction=1, center_frame_idx=2, with_tsa=False) output = edvrnet(input_tensor) assert output.shape == (1, 3, 32, 32) with pytest.raises(AssertionError): # The height and width of inputs should be a multiple of 4 input_tensor = torch.rand(1, 5, 3, 3, 3) edvrnet(input_tensor) with pytest.raises(TypeError): # pretrained should be str or None edvrnet.init_weights(pretrained=[1]) # gpu if torch.cuda.is_available(): # with tsa edvrnet = EDVRNet( 3, 3, mid_channels=8, num_frames=5, deform_groups=2, num_blocks_extraction=1, num_blocks_reconstruction=1, center_frame_idx=2, with_tsa=True).cuda() input_tensor = torch.rand(1, 5, 3, 8, 8).cuda() edvrnet.init_weights(pretrained=None) output = edvrnet(input_tensor) assert output.shape == (1, 3, 32, 32) # without tsa edvrnet = EDVRNet( 3, 3, mid_channels=8, num_frames=5, deform_groups=2, num_blocks_extraction=1, num_blocks_reconstruction=1, center_frame_idx=2, with_tsa=False).cuda() output = edvrnet(input_tensor) assert output.shape == (1, 3, 32, 32) with pytest.raises(AssertionError): # The height and width of inputs should be a multiple of 4 input_tensor = torch.rand(1, 5, 3, 3, 3).cuda() edvrnet(input_tensor) with pytest.raises(TypeError): # pretrained should be str or None edvrnet.init_weights(pretrained=[1])	# Copyright (c) OpenMMLab. All rights reserved. import pytest import torch from mmedit.models.backbones.sr_backbones.edvr_net import (EDVRNet, PCDAlignment, TSAFusion) def test_pcd_alignment(): """Test PCDAlignment.""" # cpu pcd_alignment = PCDAlignment(mid_channels=4, deform_groups=2) input_list = [] for i in range(3, 0, -1): input_list.append(torch.rand(1, 4, 2i, 2i)) pcd_alignment = pcd_alignment input_list = [v for v in input_list] output = pcd_alignment(input_list, input_list) assert output.shape == (1, 4, 8, 8) with pytest.raises(AssertionError): pcd_alignment(input_list[0:2], input_list) # gpu if torch.cuda.is_available(): pcd_alignment = PCDAlignment(mid_channels=4, deform_groups=2) input_list = [] for i in range(3, 0, -1): input_list.append(torch.rand(1, 4, 2i, 2i)) pcd_alignment = pcd_alignment.cuda() input_list = [v.cuda() for v in input_list] output = pcd_alignment(input_list, input_list) assert output.shape == (1, 4, 8, 8) with pytest.raises(AssertionError): pcd_alignment(input_list[0:2], input_list) def test_tsa_fusion(): """Test TSAFusion.""" # cpu tsa_fusion = TSAFusion(mid_channels=4, num_frames=5, center_frame_idx=2) input_tensor = torch.rand(1, 5, 4, 8, 8) output = tsa_fusion(input_tensor) assert output.shape == (1, 4, 8, 8) # gpu if torch.cuda.is_available(): tsa_fusion = tsa_fusion.cuda() input_tensor = input_tensor.cuda() output = tsa_fusion(input_tensor) assert output.shape == (1, 4, 8, 8) def test_edvrnet(): """Test EDVRNet.""" # cpu # with tsa edvrnet = EDVRNet( 3, 3, mid_channels=8, num_frames=5, deform_groups=2, num_blocks_extraction=1, num_blocks_reconstruction=1, center_frame_idx=2, with_tsa=True) input_tensor = torch.rand(1, 5, 3, 8, 8) edvrnet.init_weights(pretrained=None) output = edvrnet(input_tensor) assert output.shape == (1, 3, 32, 32) # without tsa edvrnet = EDVRNet( 3, 3, mid_channels=8, num_frames=5, deform_groups=2, num_blocks_extraction=1, num_blocks_reconstruction=1, center_frame_idx=2, with_tsa=False) output = edvrnet(input_tensor) assert output.shape == (1, 3, 32, 32) with pytest.raises(AssertionError): # The height and width of inputs should be a multiple of 4 input_tensor = torch.rand(1, 5, 3, 3, 3) edvrnet(input_tensor) with pytest.raises(TypeError): # pretrained should be str or None edvrnet.init_weights(pretrained=[1]) # gpu if torch.cuda.is_available(): # with tsa edvrnet = EDVRNet( 3, 3, mid_channels=8, num_frames=5, deform_groups=2, num_blocks_extraction=1, num_blocks_reconstruction=1, center_frame_idx=2, with_tsa=True).cuda() input_tensor = torch.rand(1, 5, 3, 8, 8).cuda() edvrnet.init_weights(pretrained=None) output = edvrnet(input_tensor) assert output.shape == (1, 3, 32, 32) # without tsa edvrnet = EDVRNet( 3, 3, mid_channels=8, num_frames=5, deform_groups=2, num_blocks_extraction=1, num_blocks_reconstruction=1, center_frame_idx=2, with_tsa=False).cuda() output = edvrnet(input_tensor) assert output.shape == (1, 3, 32, 32) with pytest.raises(AssertionError): # The height and width of inputs should be a multiple of 4 input_tensor = torch.rand(1, 5, 3, 3, 3).cuda() edvrnet(input_tensor) with pytest.raises(TypeError): # pretrained should be str or None edvrnet.init_weights(pretrained=[1])	en	0.78364	# Copyright (c) OpenMMLab. All rights reserved. Test PCDAlignment. # cpu # gpu Test TSAFusion. # cpu # gpu Test EDVRNet. # cpu # with tsa # without tsa # The height and width of inputs should be a multiple of 4 # pretrained should be str or None # gpu # with tsa # without tsa # The height and width of inputs should be a multiple of 4 # pretrained should be str or None	2.108957	2
mat2py/core/datastoreio.py	mat2py/mat2py	0	10212	# type: ignore __all__ = [ "readDatastoreImage", "datastore", ] def readDatastoreImage(args): raise NotImplementedError("readDatastoreImage") def datastore(args): raise NotImplementedError("datastore")	# type: ignore __all__ = [ "readDatastoreImage", "datastore", ] def readDatastoreImage(args): raise NotImplementedError("readDatastoreImage") def datastore(args): raise NotImplementedError("datastore")	it	0.190853	# type: ignore	1.800924	2
enjoliver-api/tests/test_generate_groups.py	netturpin/enjoliver	11	10213	import os from shutil import rmtree from tempfile import mkdtemp from unittest import TestCase from enjoliver import generator class GenerateGroupTestCase(TestCase): api_uri = None test_matchbox_path = None test_resources_path = None tests_path = None @classmethod def setUpClass(cls): cls.tests_path = mkdtemp(dir='/tmp') cls.test_matchbox_path = os.path.join(cls.tests_path, 'test_matchbox') cls.test_resources_path = os.path.join(cls.tests_path, 'test_resources') os.mkdir(cls.test_matchbox_path) os.mkdir(cls.test_resources_path) os.mkdir(os.path.join(cls.test_matchbox_path, 'groups')) cls.api_uri = "http://127.0.0.1:5000" @classmethod def tearDownClass(cls): rmtree(cls.tests_path) class TestGenerateGroups(GenerateGroupTestCase): @classmethod def setUpClass(cls): super().setUpClass() cls.gen = generator.GenerateGroup( api_uri=cls.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="TestGenerateProfiles", matchbox_path=cls.test_matchbox_path ) cls.gen.profiles_path = cls.test_resources_path def test_instantiate_generate_group_with_incorrect_parameters(self): with self.assertRaises(TypeError): generator.GenerateGroup() def test_instantiate_generate_group_with_non_existing_matchbox_path(self): with self.assertRaises(OSError): generator.GenerateGroup( api_uri='foobar', _id='foo', name='foo-bar', profile='foo-bar-baz', matchbox_path='/foo/bar' ) def test_instantiate_generate_group(self): sandbox = mkdtemp(dir='/tmp') os.mkdir(os.path.join(sandbox, 'groups')) generator.GenerateGroup( api_uri='foobar', _id='foo', name='foo-bar', profile='foo-bar-baz', matchbox_path=sandbox ) rmtree(sandbox) def test_00_uri(self): ip = self.gen.api_uri self.assertIsNotNone(ip) def test_01_metadata(self): expect = {'etcd_initial_cluster': '', 'api_uri': '%s' % self.gen.api_uri, 'ssh_authorized_keys': []} self.gen._metadata() self.assertEqual(expect['api_uri'], self.gen._target_data["metadata"]["api_uri"]) def test_990_generate(self): expect = { 'profile': 'etcd-proxy.yaml', 'metadata': { 'api_uri': '%s' % self.gen.api_uri, 'ssh_authorized_keys': [] }, 'id': 'etcd-proxy', 'name': 'etcd-proxy' } new = generator.GenerateGroup( api_uri=self.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="etcd-proxy.yaml", matchbox_path=self.test_matchbox_path ) result = new.generate() self.assertEqual(expect["profile"], result["profile"]) self.assertEqual(expect["id"], result["id"]) self.assertEqual(expect["name"], result["name"]) self.assertEqual(expect["metadata"]["api_uri"], result["metadata"]["api_uri"]) def test_991_dump(self): _id = "etcd-test-%s" % self.test_991_dump.__name__ new = generator.GenerateGroup( api_uri=self.api_uri, _id=_id, name="etcd-test", profile="etcd-test.yaml", matchbox_path=self.test_matchbox_path ) self.assertTrue(new.dump()) self.assertTrue(os.path.isfile("%s/groups/%s.json" % (self.test_matchbox_path, _id))) self.assertFalse(new.dump()) self.assertTrue(os.path.isfile("%s/groups/%s.json" % (self.test_matchbox_path, _id))) new = generator.GenerateGroup( api_uri=self.api_uri, _id=_id, name="etcd-test", profile="etcd-test.yaml", matchbox_path=self.test_matchbox_path, selector={"one": "selector"} ) self.assertTrue(new.dump()) self.assertTrue(os.path.isfile("%s/groups/%s.json" % (self.test_matchbox_path, _id))) os.remove("%s/groups/%s.json" % (self.test_matchbox_path, _id)) class TestGenerateGroupsSelectorLower(GenerateGroupTestCase): @classmethod def setUpClass(cls): super().setUpClass() os.environ["MATCHBOX_URI"] = "http://127.0.0.1:8080" os.environ["API_URI"] = "http://127.0.0.1:5000" cls.gen = generator.GenerateGroup( api_uri=cls.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="TestGenerateProfiles", selector={"mac": "08:00:27:37:28:2e"}, matchbox_path=cls.test_matchbox_path ) def test_00_api_uri(self): ip = self.gen.api_uri self.assertIsNotNone(ip) def test_01_metadata(self): expect = { 'api_uri': "%s" % self.gen.api_uri, 'ssh_authorized_keys': [] } self.gen._metadata() self.gen._target_data["metadata"]['ssh_authorized_keys'] = [] self.assertEqual(expect, self.gen._target_data["metadata"]) def test_02_selector(self): expect = {'mac': '08:00:27:37:28:2e'} self.gen._selector() self.assertEqual(expect, self.gen._target_data["selector"]) def test_990_generate(self): expect = { 'profile': 'etcd-proxy.yaml', 'metadata': { 'api_uri': self.gen.api_uri, 'selector': {'mac': '08:00:27:37:28:2e'}, 'ssh_authorized_keys': [] }, 'id': 'etcd-proxy', 'name': 'etcd-proxy', 'selector': {'mac': '08:00:27:37:28:2e'} } new = generator.GenerateGroup( api_uri=self.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="etcd-proxy.yaml", selector={"mac": "08:00:27:37:28:2e"}, matchbox_path=self.test_matchbox_path) result = new.generate() result["metadata"]['ssh_authorized_keys'] = [] self.assertEqual(expect, result) def test_991_dump(self): _id = "etcd-test-%s" % self.test_991_dump.__name__ new = generator.GenerateGroup( api_uri=self.api_uri, _id="%s" % _id, name="etcd-test", profile="etcd-test.yaml", matchbox_path=self.test_matchbox_path, selector={"mac": "08:00:27:37:28:2e"} ) self.assertTrue(new.dump()) self.assertTrue(os.path.isfile("%s/groups/%s.json" % (self.test_matchbox_path, _id))) os.remove("%s/groups/%s.json" % (self.test_matchbox_path, _id)) class TestGenerateGroupsSelectorUpper(GenerateGroupTestCase): @classmethod def setUpClass(cls): super().setUpClass() os.environ["MATCHBOX_URI"] = "http://127.0.0.1:8080" os.environ["API_URI"] = "http://127.0.0.1:5000" cls.gen = generator.GenerateGroup( api_uri=cls.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="TestGenerateProfiles", selector={"mac": "08:00:27:37:28:2E"}, matchbox_path=cls.test_matchbox_path ) def test_00_ip_address(self): ip = self.gen.api_uri self.assertIsNotNone(ip) def test_01_metadata(self): expect = { 'api_uri': "%s" % self.gen.api_uri, 'ssh_authorized_keys': [] } self.gen._metadata() self.gen._target_data["metadata"]['ssh_authorized_keys'] = [] self.assertEqual(expect, self.gen._target_data["metadata"]) def test_02_selector(self): expect = {'mac': '08:00:27:37:28:2e'} self.gen._selector() self.assertEqual(expect, self.gen._target_data["selector"]) def test_990_generate(self): expect = { 'profile': 'etcd-proxy.yaml', 'metadata': { 'api_uri': "%s" % self.gen.api_uri, 'selector': {'mac': '08:00:27:37:28:2e'}, 'ssh_authorized_keys': [] }, 'id': 'etcd-proxy', 'name': 'etcd-proxy', 'selector': {'mac': '08:00:27:37:28:2e'} } new = generator.GenerateGroup( api_uri=self.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="etcd-proxy.yaml", selector={"mac": "08:00:27:37:28:2e"}, matchbox_path=self.test_matchbox_path ) result = new.generate() result["metadata"]['ssh_authorized_keys'] = [] self.assertEqual(expect, result) def test_991_dump(self): _id = "etcd-test-%s" % self.test_991_dump.__name__ new = generator.GenerateGroup( api_uri=self.api_uri, _id="%s" % _id, name="etcd-test", profile="etcd-test.yaml", matchbox_path=self.test_matchbox_path, selector={"mac": "08:00:27:37:28:2e"} ) new.dump() self.assertTrue(os.path.isfile("%s/groups/%s.json" % (self.test_matchbox_path, _id))) os.remove("%s/groups/%s.json" % (self.test_matchbox_path, _id)) class TestGenerateGroupsExtraMetadata(GenerateGroupTestCase): @classmethod def setUpClass(cls): super().setUpClass() os.environ["MATCHBOX_URI"] = "http://127.0.0.1:8080" os.environ["API_URI"] = "http://127.0.0.1:5000" cls.gen = generator.GenerateGroup( api_uri=cls.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="TestGenerateProfiles", selector={"mac": "08:00:27:37:28:2E"}, metadata={"etcd_initial_cluster": "static0=http://192.168.1.1:2379", "api_seed": "http://192.168.1.2:5000"}, matchbox_path=cls.test_matchbox_path ) def test_00_api_uri(self): ip = self.gen.api_uri self.assertIsNotNone(ip) def test_01_metadata(self): expect = {'etcd_initial_cluster': 'static0=http://192.168.1.1:2379', 'api_uri': "%s" % self.gen.api_uri, 'api_seed': 'http://192.168.1.2:5000', 'ssh_authorized_keys': []} self.gen._metadata() self.gen._target_data["metadata"]['ssh_authorized_keys'] = [] self.assertEqual(expect, self.gen._target_data["metadata"]) def test_02_selector(self): expect = {'mac': '08:00:27:37:28:2e'} self.gen._selector() self.assertEqual(expect, self.gen._target_data["selector"]) def test_990_generate(self): expect = { 'profile': 'etcd-proxy.yaml', 'metadata': { 'api_uri': "%s" % self.gen.api_uri, 'selector': {'mac': '08:00:27:37:28:2e'}, 'ssh_authorized_keys': [] }, 'id': 'etcd-proxy', 'name': 'etcd-proxy', 'selector': {'mac': '08:00:27:37:28:2e'} } new = generator.GenerateGroup( api_uri=self.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="etcd-proxy.yaml", selector={"mac": "08:00:27:37:28:2e"}, matchbox_path=self.test_matchbox_path ) result = new.generate() result["metadata"]["ssh_authorized_keys"] = [] self.assertEqual(expect, result) def test_991_dump(self): _id = "etcd-test-%s" % self.test_991_dump.__name__ new = generator.GenerateGroup( api_uri=self.api_uri, _id="%s" % _id, name="etcd-test", profile="etcd-test.yaml", matchbox_path=self.test_matchbox_path, selector={"mac": "08:00:27:37:28:2e"} ) self.assertTrue(new.dump()) self.assertTrue(os.path.isfile("%s/groups/%s.json" % (self.test_matchbox_path, _id))) os.remove("%s/groups/%s.json" % (self.test_matchbox_path, _id)) self.assertTrue(new.dump()) for i in range(10): self.assertFalse(new.dump()) new.api_uri = "http://google.com" self.assertTrue(new.dump()) self.assertFalse(new.dump())	import os from shutil import rmtree from tempfile import mkdtemp from unittest import TestCase from enjoliver import generator class GenerateGroupTestCase(TestCase): api_uri = None test_matchbox_path = None test_resources_path = None tests_path = None @classmethod def setUpClass(cls): cls.tests_path = mkdtemp(dir='/tmp') cls.test_matchbox_path = os.path.join(cls.tests_path, 'test_matchbox') cls.test_resources_path = os.path.join(cls.tests_path, 'test_resources') os.mkdir(cls.test_matchbox_path) os.mkdir(cls.test_resources_path) os.mkdir(os.path.join(cls.test_matchbox_path, 'groups')) cls.api_uri = "http://127.0.0.1:5000" @classmethod def tearDownClass(cls): rmtree(cls.tests_path) class TestGenerateGroups(GenerateGroupTestCase): @classmethod def setUpClass(cls): super().setUpClass() cls.gen = generator.GenerateGroup( api_uri=cls.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="TestGenerateProfiles", matchbox_path=cls.test_matchbox_path ) cls.gen.profiles_path = cls.test_resources_path def test_instantiate_generate_group_with_incorrect_parameters(self): with self.assertRaises(TypeError): generator.GenerateGroup() def test_instantiate_generate_group_with_non_existing_matchbox_path(self): with self.assertRaises(OSError): generator.GenerateGroup( api_uri='foobar', _id='foo', name='foo-bar', profile='foo-bar-baz', matchbox_path='/foo/bar' ) def test_instantiate_generate_group(self): sandbox = mkdtemp(dir='/tmp') os.mkdir(os.path.join(sandbox, 'groups')) generator.GenerateGroup( api_uri='foobar', _id='foo', name='foo-bar', profile='foo-bar-baz', matchbox_path=sandbox ) rmtree(sandbox) def test_00_uri(self): ip = self.gen.api_uri self.assertIsNotNone(ip) def test_01_metadata(self): expect = {'etcd_initial_cluster': '', 'api_uri': '%s' % self.gen.api_uri, 'ssh_authorized_keys': []} self.gen._metadata() self.assertEqual(expect['api_uri'], self.gen._target_data["metadata"]["api_uri"]) def test_990_generate(self): expect = { 'profile': 'etcd-proxy.yaml', 'metadata': { 'api_uri': '%s' % self.gen.api_uri, 'ssh_authorized_keys': [] }, 'id': 'etcd-proxy', 'name': 'etcd-proxy' } new = generator.GenerateGroup( api_uri=self.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="etcd-proxy.yaml", matchbox_path=self.test_matchbox_path ) result = new.generate() self.assertEqual(expect["profile"], result["profile"]) self.assertEqual(expect["id"], result["id"]) self.assertEqual(expect["name"], result["name"]) self.assertEqual(expect["metadata"]["api_uri"], result["metadata"]["api_uri"]) def test_991_dump(self): _id = "etcd-test-%s" % self.test_991_dump.__name__ new = generator.GenerateGroup( api_uri=self.api_uri, _id=_id, name="etcd-test", profile="etcd-test.yaml", matchbox_path=self.test_matchbox_path ) self.assertTrue(new.dump()) self.assertTrue(os.path.isfile("%s/groups/%s.json" % (self.test_matchbox_path, _id))) self.assertFalse(new.dump()) self.assertTrue(os.path.isfile("%s/groups/%s.json" % (self.test_matchbox_path, _id))) new = generator.GenerateGroup( api_uri=self.api_uri, _id=_id, name="etcd-test", profile="etcd-test.yaml", matchbox_path=self.test_matchbox_path, selector={"one": "selector"} ) self.assertTrue(new.dump()) self.assertTrue(os.path.isfile("%s/groups/%s.json" % (self.test_matchbox_path, _id))) os.remove("%s/groups/%s.json" % (self.test_matchbox_path, _id)) class TestGenerateGroupsSelectorLower(GenerateGroupTestCase): @classmethod def setUpClass(cls): super().setUpClass() os.environ["MATCHBOX_URI"] = "http://127.0.0.1:8080" os.environ["API_URI"] = "http://127.0.0.1:5000" cls.gen = generator.GenerateGroup( api_uri=cls.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="TestGenerateProfiles", selector={"mac": "08:00:27:37:28:2e"}, matchbox_path=cls.test_matchbox_path ) def test_00_api_uri(self): ip = self.gen.api_uri self.assertIsNotNone(ip) def test_01_metadata(self): expect = { 'api_uri': "%s" % self.gen.api_uri, 'ssh_authorized_keys': [] } self.gen._metadata() self.gen._target_data["metadata"]['ssh_authorized_keys'] = [] self.assertEqual(expect, self.gen._target_data["metadata"]) def test_02_selector(self): expect = {'mac': '08:00:27:37:28:2e'} self.gen._selector() self.assertEqual(expect, self.gen._target_data["selector"]) def test_990_generate(self): expect = { 'profile': 'etcd-proxy.yaml', 'metadata': { 'api_uri': self.gen.api_uri, 'selector': {'mac': '08:00:27:37:28:2e'}, 'ssh_authorized_keys': [] }, 'id': 'etcd-proxy', 'name': 'etcd-proxy', 'selector': {'mac': '08:00:27:37:28:2e'} } new = generator.GenerateGroup( api_uri=self.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="etcd-proxy.yaml", selector={"mac": "08:00:27:37:28:2e"}, matchbox_path=self.test_matchbox_path) result = new.generate() result["metadata"]['ssh_authorized_keys'] = [] self.assertEqual(expect, result) def test_991_dump(self): _id = "etcd-test-%s" % self.test_991_dump.__name__ new = generator.GenerateGroup( api_uri=self.api_uri, _id="%s" % _id, name="etcd-test", profile="etcd-test.yaml", matchbox_path=self.test_matchbox_path, selector={"mac": "08:00:27:37:28:2e"} ) self.assertTrue(new.dump()) self.assertTrue(os.path.isfile("%s/groups/%s.json" % (self.test_matchbox_path, _id))) os.remove("%s/groups/%s.json" % (self.test_matchbox_path, _id)) class TestGenerateGroupsSelectorUpper(GenerateGroupTestCase): @classmethod def setUpClass(cls): super().setUpClass() os.environ["MATCHBOX_URI"] = "http://127.0.0.1:8080" os.environ["API_URI"] = "http://127.0.0.1:5000" cls.gen = generator.GenerateGroup( api_uri=cls.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="TestGenerateProfiles", selector={"mac": "08:00:27:37:28:2E"}, matchbox_path=cls.test_matchbox_path ) def test_00_ip_address(self): ip = self.gen.api_uri self.assertIsNotNone(ip) def test_01_metadata(self): expect = { 'api_uri': "%s" % self.gen.api_uri, 'ssh_authorized_keys': [] } self.gen._metadata() self.gen._target_data["metadata"]['ssh_authorized_keys'] = [] self.assertEqual(expect, self.gen._target_data["metadata"]) def test_02_selector(self): expect = {'mac': '08:00:27:37:28:2e'} self.gen._selector() self.assertEqual(expect, self.gen._target_data["selector"]) def test_990_generate(self): expect = { 'profile': 'etcd-proxy.yaml', 'metadata': { 'api_uri': "%s" % self.gen.api_uri, 'selector': {'mac': '08:00:27:37:28:2e'}, 'ssh_authorized_keys': [] }, 'id': 'etcd-proxy', 'name': 'etcd-proxy', 'selector': {'mac': '08:00:27:37:28:2e'} } new = generator.GenerateGroup( api_uri=self.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="etcd-proxy.yaml", selector={"mac": "08:00:27:37:28:2e"}, matchbox_path=self.test_matchbox_path ) result = new.generate() result["metadata"]['ssh_authorized_keys'] = [] self.assertEqual(expect, result) def test_991_dump(self): _id = "etcd-test-%s" % self.test_991_dump.__name__ new = generator.GenerateGroup( api_uri=self.api_uri, _id="%s" % _id, name="etcd-test", profile="etcd-test.yaml", matchbox_path=self.test_matchbox_path, selector={"mac": "08:00:27:37:28:2e"} ) new.dump() self.assertTrue(os.path.isfile("%s/groups/%s.json" % (self.test_matchbox_path, _id))) os.remove("%s/groups/%s.json" % (self.test_matchbox_path, _id)) class TestGenerateGroupsExtraMetadata(GenerateGroupTestCase): @classmethod def setUpClass(cls): super().setUpClass() os.environ["MATCHBOX_URI"] = "http://127.0.0.1:8080" os.environ["API_URI"] = "http://127.0.0.1:5000" cls.gen = generator.GenerateGroup( api_uri=cls.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="TestGenerateProfiles", selector={"mac": "08:00:27:37:28:2E"}, metadata={"etcd_initial_cluster": "static0=http://192.168.1.1:2379", "api_seed": "http://192.168.1.2:5000"}, matchbox_path=cls.test_matchbox_path ) def test_00_api_uri(self): ip = self.gen.api_uri self.assertIsNotNone(ip) def test_01_metadata(self): expect = {'etcd_initial_cluster': 'static0=http://192.168.1.1:2379', 'api_uri': "%s" % self.gen.api_uri, 'api_seed': 'http://192.168.1.2:5000', 'ssh_authorized_keys': []} self.gen._metadata() self.gen._target_data["metadata"]['ssh_authorized_keys'] = [] self.assertEqual(expect, self.gen._target_data["metadata"]) def test_02_selector(self): expect = {'mac': '08:00:27:37:28:2e'} self.gen._selector() self.assertEqual(expect, self.gen._target_data["selector"]) def test_990_generate(self): expect = { 'profile': 'etcd-proxy.yaml', 'metadata': { 'api_uri': "%s" % self.gen.api_uri, 'selector': {'mac': '08:00:27:37:28:2e'}, 'ssh_authorized_keys': [] }, 'id': 'etcd-proxy', 'name': 'etcd-proxy', 'selector': {'mac': '08:00:27:37:28:2e'} } new = generator.GenerateGroup( api_uri=self.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="etcd-proxy.yaml", selector={"mac": "08:00:27:37:28:2e"}, matchbox_path=self.test_matchbox_path ) result = new.generate() result["metadata"]["ssh_authorized_keys"] = [] self.assertEqual(expect, result) def test_991_dump(self): _id = "etcd-test-%s" % self.test_991_dump.__name__ new = generator.GenerateGroup( api_uri=self.api_uri, _id="%s" % _id, name="etcd-test", profile="etcd-test.yaml", matchbox_path=self.test_matchbox_path, selector={"mac": "08:00:27:37:28:2e"} ) self.assertTrue(new.dump()) self.assertTrue(os.path.isfile("%s/groups/%s.json" % (self.test_matchbox_path, _id))) os.remove("%s/groups/%s.json" % (self.test_matchbox_path, _id)) self.assertTrue(new.dump()) for i in range(10): self.assertFalse(new.dump()) new.api_uri = "http://google.com" self.assertTrue(new.dump()) self.assertFalse(new.dump())	none	1		2.376044	2
HackerRank/Calendar Module/solution.py	nikku1234/Code-Practise	9	10214	# Enter your code here. Read input from STDIN. Print output to STDOUT import calendar mm,dd,yyyy = map(int,input().split()) day = ["MONDAY","TUESDAY","WEDNESDAY","THURSDAY","FRIDAY","SATURDAY","SUNDAY"] val = int (calendar.weekday(yyyy,mm,dd)) print(day[val])	# Enter your code here. Read input from STDIN. Print output to STDOUT import calendar mm,dd,yyyy = map(int,input().split()) day = ["MONDAY","TUESDAY","WEDNESDAY","THURSDAY","FRIDAY","SATURDAY","SUNDAY"] val = int (calendar.weekday(yyyy,mm,dd)) print(day[val])	en	0.824269	# Enter your code here. Read input from STDIN. Print output to STDOUT	3.817077	4
scale/trigger/models.py	stevevarner/scale	0	10215	"""Defines the models for trigger rules and events""" from __future__ import unicode_literals import django.contrib.postgres.fields from django.db import models, transaction from django.utils.timezone import now class TriggerEventManager(models.Manager): """Provides additional methods for handling trigger events """ def create_trigger_event(self, trigger_type, rule, description, occurred): """Creates a new trigger event and returns the event model. The given rule model, if not None, must have already been saved in the database (it must have an ID). The returned trigger event model will be saved in the database. :param trigger_type: The type of the trigger that occurred :type trigger_type: str :param rule: The rule that triggered the event, possibly None :type rule: :class:`trigger.models.TriggerRule` :param description: The JSON description of the event as a dict :type description: dict :param occurred: When the event occurred :type occurred: :class:`datetime.datetime` :returns: The new trigger event :rtype: :class:`trigger.models.TriggerEvent` """ if trigger_type is None: raise Exception('Trigger event must have a type') if description is None: raise Exception('Trigger event must have a JSON description') if occurred is None: raise Exception('Trigger event must have a timestamp') event = TriggerEvent() event.type = trigger_type event.rule = rule event.description = description event.occurred = occurred event.save() return event class TriggerEvent(models.Model): """Represents an event where a trigger occurred :keyword type: The type of the trigger that occurred :type type: :class:`django.db.models.CharField` :keyword rule: The rule that triggered this event, possibly None (some events are not triggered by rules) :type rule: :class:`django.db.models.ForeignKey` :keyword description: JSON description of the event. This will contain fields specific to the type of the trigger that occurred. :type description: :class:`django.contrib.postgres.fields.JSONField` :keyword occurred: When the event occurred :type occurred: :class:`django.db.models.DateTimeField` """ type = models.CharField(db_index=True, max_length=50) rule = models.ForeignKey('trigger.TriggerRule', blank=True, null=True, on_delete=models.PROTECT) description = django.contrib.postgres.fields.JSONField(default=dict) occurred = models.DateTimeField(db_index=True) objects = TriggerEventManager() class Meta(object): """meta information for the db""" db_table = 'trigger_event' class TriggerRuleManager(models.Manager): """Provides additional methods for handling trigger rules """ @transaction.atomic def archive_trigger_rule(self, trigger_rule_id): """Archives the trigger rule (will no longer be active) with the given ID :param trigger_rule_id: The ID of the trigger rule to archive :type trigger_rule_id: int """ rule = TriggerRule.objects.select_for_update().get(pk=trigger_rule_id) rule.is_active = False rule.archived = now() rule.save() def create_trigger_rule(self, trigger_type, configuration, name='', is_active=True): """Creates a new trigger rule and returns the rule model. The returned trigger rule model will be saved in the database. :param trigger_type: The type of this trigger rule :type trigger_type: str :param configuration: The rule configuration :type configuration: :class:`trigger.configuration.TriggerRuleConfiguration` :param name: An optional name for the trigger :type name: str :param is_active: Whether or not the trigger should be active :type is_active: bool :returns: The new trigger rule :rtype: :class:`trigger.models.TriggerRule` :raises trigger.configuration.exceptions.InvalidTriggerRule: If the configuration is invalid """ if not trigger_type: raise Exception('Trigger rule must have a type') if not configuration: raise Exception('Trigger rule must have a configuration') configuration.validate() rule = TriggerRule() rule.type = trigger_type rule.name = name rule.is_active = is_active rule.configuration = configuration.get_dict() rule.save() return rule def get_by_natural_key(self, name): """Django method to retrieve a trigger rule for the given natural key. NOTE: All trigger rule names are NOT unique. This is implemented to allow the loading of defined system trigger rules which do have unique names. :param name: The name of the trigger rule :type name: str :returns: The trigger rule defined by the natural key :rtype: :class:`error.models.Error` """ return self.get(name=name) class TriggerRule(models.Model): """Represents a rule that, when triggered, creates a trigger event :keyword type: The type of the trigger for the rule :type type: :class:`django.db.models.CharField` :keyword name: The identifying name of the trigger rule used by clients for queries :type name: :class:`django.db.models.CharField` :keyword configuration: JSON configuration for the rule. This will contain fields specific to the type of the trigger. :type configuration: :class:`django.contrib.postgres.fields.JSONField` :keyword is_active: Whether the rule is still active (false once rule is archived) :type is_active: :class:`django.db.models.BooleanField` :keyword created: When the rule was created :type created: :class:`django.db.models.DateTimeField` :keyword archived: When the rule was archived (no longer active) :type archived: :class:`django.db.models.DateTimeField` :keyword last_modified: When the rule was last modified :type last_modified: :class:`django.db.models.DateTimeField` """ type = models.CharField(max_length=50, db_index=True) name = models.CharField(blank=True, max_length=50) configuration = django.contrib.postgres.fields.JSONField(default=dict) is_active = models.BooleanField(default=True, db_index=True) created = models.DateTimeField(auto_now_add=True) archived = models.DateTimeField(blank=True, null=True) last_modified = models.DateTimeField(auto_now=True) objects = TriggerRuleManager() def get_configuration(self): """Returns the configuration for this trigger rule :returns: The configuration for this trigger rule :rtype: :class:`trigger.configuration.trigger_rule.TriggerRuleConfiguration` :raises :class:`trigger.configuration.exceptions.InvalidTriggerType`: If the trigger type is invalid """ from trigger.handler import get_trigger_rule_handler handler = get_trigger_rule_handler(self.type) return handler.create_configuration(self.configuration) def natural_key(self): """Django method to define the natural key for a trigger rule as the name :returns: A tuple representing the natural key :rtype: tuple(str,) """ return (self.name,) class Meta(object): """meta information for the db""" db_table = 'trigger_rule'	"""Defines the models for trigger rules and events""" from __future__ import unicode_literals import django.contrib.postgres.fields from django.db import models, transaction from django.utils.timezone import now class TriggerEventManager(models.Manager): """Provides additional methods for handling trigger events """ def create_trigger_event(self, trigger_type, rule, description, occurred): """Creates a new trigger event and returns the event model. The given rule model, if not None, must have already been saved in the database (it must have an ID). The returned trigger event model will be saved in the database. :param trigger_type: The type of the trigger that occurred :type trigger_type: str :param rule: The rule that triggered the event, possibly None :type rule: :class:`trigger.models.TriggerRule` :param description: The JSON description of the event as a dict :type description: dict :param occurred: When the event occurred :type occurred: :class:`datetime.datetime` :returns: The new trigger event :rtype: :class:`trigger.models.TriggerEvent` """ if trigger_type is None: raise Exception('Trigger event must have a type') if description is None: raise Exception('Trigger event must have a JSON description') if occurred is None: raise Exception('Trigger event must have a timestamp') event = TriggerEvent() event.type = trigger_type event.rule = rule event.description = description event.occurred = occurred event.save() return event class TriggerEvent(models.Model): """Represents an event where a trigger occurred :keyword type: The type of the trigger that occurred :type type: :class:`django.db.models.CharField` :keyword rule: The rule that triggered this event, possibly None (some events are not triggered by rules) :type rule: :class:`django.db.models.ForeignKey` :keyword description: JSON description of the event. This will contain fields specific to the type of the trigger that occurred. :type description: :class:`django.contrib.postgres.fields.JSONField` :keyword occurred: When the event occurred :type occurred: :class:`django.db.models.DateTimeField` """ type = models.CharField(db_index=True, max_length=50) rule = models.ForeignKey('trigger.TriggerRule', blank=True, null=True, on_delete=models.PROTECT) description = django.contrib.postgres.fields.JSONField(default=dict) occurred = models.DateTimeField(db_index=True) objects = TriggerEventManager() class Meta(object): """meta information for the db""" db_table = 'trigger_event' class TriggerRuleManager(models.Manager): """Provides additional methods for handling trigger rules """ @transaction.atomic def archive_trigger_rule(self, trigger_rule_id): """Archives the trigger rule (will no longer be active) with the given ID :param trigger_rule_id: The ID of the trigger rule to archive :type trigger_rule_id: int """ rule = TriggerRule.objects.select_for_update().get(pk=trigger_rule_id) rule.is_active = False rule.archived = now() rule.save() def create_trigger_rule(self, trigger_type, configuration, name='', is_active=True): """Creates a new trigger rule and returns the rule model. The returned trigger rule model will be saved in the database. :param trigger_type: The type of this trigger rule :type trigger_type: str :param configuration: The rule configuration :type configuration: :class:`trigger.configuration.TriggerRuleConfiguration` :param name: An optional name for the trigger :type name: str :param is_active: Whether or not the trigger should be active :type is_active: bool :returns: The new trigger rule :rtype: :class:`trigger.models.TriggerRule` :raises trigger.configuration.exceptions.InvalidTriggerRule: If the configuration is invalid """ if not trigger_type: raise Exception('Trigger rule must have a type') if not configuration: raise Exception('Trigger rule must have a configuration') configuration.validate() rule = TriggerRule() rule.type = trigger_type rule.name = name rule.is_active = is_active rule.configuration = configuration.get_dict() rule.save() return rule def get_by_natural_key(self, name): """Django method to retrieve a trigger rule for the given natural key. NOTE: All trigger rule names are NOT unique. This is implemented to allow the loading of defined system trigger rules which do have unique names. :param name: The name of the trigger rule :type name: str :returns: The trigger rule defined by the natural key :rtype: :class:`error.models.Error` """ return self.get(name=name) class TriggerRule(models.Model): """Represents a rule that, when triggered, creates a trigger event :keyword type: The type of the trigger for the rule :type type: :class:`django.db.models.CharField` :keyword name: The identifying name of the trigger rule used by clients for queries :type name: :class:`django.db.models.CharField` :keyword configuration: JSON configuration for the rule. This will contain fields specific to the type of the trigger. :type configuration: :class:`django.contrib.postgres.fields.JSONField` :keyword is_active: Whether the rule is still active (false once rule is archived) :type is_active: :class:`django.db.models.BooleanField` :keyword created: When the rule was created :type created: :class:`django.db.models.DateTimeField` :keyword archived: When the rule was archived (no longer active) :type archived: :class:`django.db.models.DateTimeField` :keyword last_modified: When the rule was last modified :type last_modified: :class:`django.db.models.DateTimeField` """ type = models.CharField(max_length=50, db_index=True) name = models.CharField(blank=True, max_length=50) configuration = django.contrib.postgres.fields.JSONField(default=dict) is_active = models.BooleanField(default=True, db_index=True) created = models.DateTimeField(auto_now_add=True) archived = models.DateTimeField(blank=True, null=True) last_modified = models.DateTimeField(auto_now=True) objects = TriggerRuleManager() def get_configuration(self): """Returns the configuration for this trigger rule :returns: The configuration for this trigger rule :rtype: :class:`trigger.configuration.trigger_rule.TriggerRuleConfiguration` :raises :class:`trigger.configuration.exceptions.InvalidTriggerType`: If the trigger type is invalid """ from trigger.handler import get_trigger_rule_handler handler = get_trigger_rule_handler(self.type) return handler.create_configuration(self.configuration) def natural_key(self): """Django method to define the natural key for a trigger rule as the name :returns: A tuple representing the natural key :rtype: tuple(str,) """ return (self.name,) class Meta(object): """meta information for the db""" db_table = 'trigger_rule'	en	0.757686	Defines the models for trigger rules and events Provides additional methods for handling trigger events Creates a new trigger event and returns the event model. The given rule model, if not None, must have already been saved in the database (it must have an ID). The returned trigger event model will be saved in the database. :param trigger_type: The type of the trigger that occurred :type trigger_type: str :param rule: The rule that triggered the event, possibly None :type rule: :class:`trigger.models.TriggerRule` :param description: The JSON description of the event as a dict :type description: dict :param occurred: When the event occurred :type occurred: :class:`datetime.datetime` :returns: The new trigger event :rtype: :class:`trigger.models.TriggerEvent` Represents an event where a trigger occurred :keyword type: The type of the trigger that occurred :type type: :class:`django.db.models.CharField` :keyword rule: The rule that triggered this event, possibly None (some events are not triggered by rules) :type rule: :class:`django.db.models.ForeignKey` :keyword description: JSON description of the event. This will contain fields specific to the type of the trigger that occurred. :type description: :class:`django.contrib.postgres.fields.JSONField` :keyword occurred: When the event occurred :type occurred: :class:`django.db.models.DateTimeField` meta information for the db Provides additional methods for handling trigger rules Archives the trigger rule (will no longer be active) with the given ID :param trigger_rule_id: The ID of the trigger rule to archive :type trigger_rule_id: int Creates a new trigger rule and returns the rule model. The returned trigger rule model will be saved in the database. :param trigger_type: The type of this trigger rule :type trigger_type: str :param configuration: The rule configuration :type configuration: :class:`trigger.configuration.TriggerRuleConfiguration` :param name: An optional name for the trigger :type name: str :param is_active: Whether or not the trigger should be active :type is_active: bool :returns: The new trigger rule :rtype: :class:`trigger.models.TriggerRule` :raises trigger.configuration.exceptions.InvalidTriggerRule: If the configuration is invalid Django method to retrieve a trigger rule for the given natural key. NOTE: All trigger rule names are NOT unique. This is implemented to allow the loading of defined system trigger rules which do have unique names. :param name: The name of the trigger rule :type name: str :returns: The trigger rule defined by the natural key :rtype: :class:`error.models.Error` Represents a rule that, when triggered, creates a trigger event :keyword type: The type of the trigger for the rule :type type: :class:`django.db.models.CharField` :keyword name: The identifying name of the trigger rule used by clients for queries :type name: :class:`django.db.models.CharField` :keyword configuration: JSON configuration for the rule. This will contain fields specific to the type of the trigger. :type configuration: :class:`django.contrib.postgres.fields.JSONField` :keyword is_active: Whether the rule is still active (false once rule is archived) :type is_active: :class:`django.db.models.BooleanField` :keyword created: When the rule was created :type created: :class:`django.db.models.DateTimeField` :keyword archived: When the rule was archived (no longer active) :type archived: :class:`django.db.models.DateTimeField` :keyword last_modified: When the rule was last modified :type last_modified: :class:`django.db.models.DateTimeField` Returns the configuration for this trigger rule :returns: The configuration for this trigger rule :rtype: :class:`trigger.configuration.trigger_rule.TriggerRuleConfiguration` :raises :class:`trigger.configuration.exceptions.InvalidTriggerType`: If the trigger type is invalid Django method to define the natural key for a trigger rule as the name :returns: A tuple representing the natural key :rtype: tuple(str,) meta information for the db	2.763077	3
leetcode/0506_relative_ranks.py	chaosWsF/Python-Practice	0	10216	""" Given scores of N athletes, find their relative ranks and the people with the top three highest scores, who will be awarded medals: "Gold Medal", "Silver Medal" and "Bronze Medal". Example 1: Input: [5, 4, 3, 2, 1] Output: ["Gold Medal", "Silver Medal", "Bronze Medal", "4", "5"] Explanation: The first three athletes got the top three highest scores, so they got "Gold Medal", "Silver Medal" and "Bronze Medal". For the left two athletes, you just need to output their relative ranks according to their scores. Note: N is a positive integer and won't exceed 10,000. All the scores of athletes are guaranteed to be unique. """ class Solution: def findRelativeRanks(self, nums): scores_rank = sorted(nums, reverse=True) d = {} for i, score in enumerate(scores_rank): if i == 0: d[score] = 'Gold Medal' elif i == 1: d[score] = 'Silver Medal' elif i == 2: d[score] = 'Bronze Medal' else: d[score] = str(i + 1) return [d[x] for x in nums]	""" Given scores of N athletes, find their relative ranks and the people with the top three highest scores, who will be awarded medals: "Gold Medal", "Silver Medal" and "Bronze Medal". Example 1: Input: [5, 4, 3, 2, 1] Output: ["Gold Medal", "Silver Medal", "Bronze Medal", "4", "5"] Explanation: The first three athletes got the top three highest scores, so they got "Gold Medal", "Silver Medal" and "Bronze Medal". For the left two athletes, you just need to output their relative ranks according to their scores. Note: N is a positive integer and won't exceed 10,000. All the scores of athletes are guaranteed to be unique. """ class Solution: def findRelativeRanks(self, nums): scores_rank = sorted(nums, reverse=True) d = {} for i, score in enumerate(scores_rank): if i == 0: d[score] = 'Gold Medal' elif i == 1: d[score] = 'Silver Medal' elif i == 2: d[score] = 'Bronze Medal' else: d[score] = str(i + 1) return [d[x] for x in nums]	en	0.931818	Given scores of N athletes, find their relative ranks and the people with the top three highest scores, who will be awarded medals: "Gold Medal", "Silver Medal" and "Bronze Medal". Example 1: Input: [5, 4, 3, 2, 1] Output: ["Gold Medal", "Silver Medal", "Bronze Medal", "4", "5"] Explanation: The first three athletes got the top three highest scores, so they got "Gold Medal", "Silver Medal" and "Bronze Medal". For the left two athletes, you just need to output their relative ranks according to their scores. Note: N is a positive integer and won't exceed 10,000. All the scores of athletes are guaranteed to be unique.	4.113877	4
barriers/models/history/assessments/economic_impact.py	felix781/market-access-python-frontend	1	10217	from ..base import BaseHistoryItem, GenericHistoryItem from ..utils import PolymorphicBase class ArchivedHistoryItem(BaseHistoryItem): field = "archived" field_name = "Valuation assessment: Archived" def get_value(self, value): if value is True: return "Archived" elif value is False: return "Unarchived" class ExplanationHistoryItem(BaseHistoryItem): field = "explanation" field_name = "Valuation assessment: Explanation" class ImpactHistoryItem(BaseHistoryItem): field = "impact" field_name = "Valuation assessment: Impact" def get_value(self, value): if value: return value.get("name") class EconomicImpactAssessmentHistoryItem(PolymorphicBase): model = "economic_impact_assessment" key = "field" subclasses = ( ArchivedHistoryItem, ExplanationHistoryItem, ImpactHistoryItem, ) default_subclass = GenericHistoryItem class_lookup = {}	from ..base import BaseHistoryItem, GenericHistoryItem from ..utils import PolymorphicBase class ArchivedHistoryItem(BaseHistoryItem): field = "archived" field_name = "Valuation assessment: Archived" def get_value(self, value): if value is True: return "Archived" elif value is False: return "Unarchived" class ExplanationHistoryItem(BaseHistoryItem): field = "explanation" field_name = "Valuation assessment: Explanation" class ImpactHistoryItem(BaseHistoryItem): field = "impact" field_name = "Valuation assessment: Impact" def get_value(self, value): if value: return value.get("name") class EconomicImpactAssessmentHistoryItem(PolymorphicBase): model = "economic_impact_assessment" key = "field" subclasses = ( ArchivedHistoryItem, ExplanationHistoryItem, ImpactHistoryItem, ) default_subclass = GenericHistoryItem class_lookup = {}	none	1		2.689965	3
link_prob_show.py	Rheinwalt/spatial-effects-networks	3	10218	<filename>link_prob_show.py import sys import numpy as np from sern import * ids, lon, lat = np.loadtxt('nodes', unpack = True) links = np.loadtxt('links', dtype = 'int') A, b = AdjacencyMatrix(ids, links) lon, lat = lon[b], lat[b] n = A.shape[0] # LinkProbability expects A as triu A = A[np.triu_indices(n, 1)] # play around with the scale, maybe you don't need log binning? D, x = IntegerDistances(lat, lon, scale = 50) p = LinkProbability(A, D) from matplotlib import pyplot as pl pl.plot(p, 'bo') pl.ylabel('Link probability given distance') pl.xlabel('Bin number') pl.savefig('link_prob_bin.png') pl.close('all') pl.semilogx(x, p, 'bo') pl.ylabel('Link probability given distance') pl.xlabel('Distance [km]') pl.savefig('link_prob_distance.png')	<filename>link_prob_show.py import sys import numpy as np from sern import * ids, lon, lat = np.loadtxt('nodes', unpack = True) links = np.loadtxt('links', dtype = 'int') A, b = AdjacencyMatrix(ids, links) lon, lat = lon[b], lat[b] n = A.shape[0] # LinkProbability expects A as triu A = A[np.triu_indices(n, 1)] # play around with the scale, maybe you don't need log binning? D, x = IntegerDistances(lat, lon, scale = 50) p = LinkProbability(A, D) from matplotlib import pyplot as pl pl.plot(p, 'bo') pl.ylabel('Link probability given distance') pl.xlabel('Bin number') pl.savefig('link_prob_bin.png') pl.close('all') pl.semilogx(x, p, 'bo') pl.ylabel('Link probability given distance') pl.xlabel('Distance [km]') pl.savefig('link_prob_distance.png')	en	0.989835	# LinkProbability expects A as triu # play around with the scale, maybe you don't need log binning?	2.420012	2
controller/components/app.py	isabella232/flight-lab	15	10219	# Copyright 2018 Flight Lab authors. # # 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 # # https://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. """Library for components related to running apps.""" import subprocess import threading from components import base from protos import controller_pb2 from utils import app class AppComponent(base.Component): """Component to run command-line based app on any platform. This component can start app, restart app upon crash, and stop app. Events: "status_changed": when status of the app is changed. Args: app_component: instance of this class. """ def __init__(self, proto, args, kwargs): """Initializes the component. Args: proto: flightlab.App proto defining app details and options. """ super(AppComponent, self).__init__(proto, args, **kwargs) self._app = app.Application( name=self.name, bin_path=self.settings.executable_path, arguments=(list(self.settings.arguments) if self.settings.arguments else []), working_dir=self.settings.working_dir, restart_on_crash=(self.settings.restart_on_crash if self.settings.restart_on_crash else False), env=(self.settings.env if self.settings.env else None)) self._app.on('started', self._on_app_started) self._app.on('stopped', self._on_app_stopped) self._monitor = threading.Timer(1, self._check_status) self._monitor.start() def close(self): if self._monitor: self._monitor.cancel() self._monitor = None self._app.stop() def _check_status(self): if self._app.has_running_instance(): component_status = controller_pb2.Component.ON app_status = controller_pb2.App.RUNNING else: component_status = controller_pb2.Component.OFF app_status = controller_pb2.App.NOT_RUNNING if (self.proto.status != component_status or self.settings.status != app_status): self.proto.status = component_status self.settings.status = app_status self.emit('status_changed', self) def _start(self): self.logger.info('[App - {0}] Starting...'.format(self.name)) self._app.start() def _stop(self): self.logger.info('[App - {0}] Stopping...'.format(self.name)) self._app.stop() def _restart(self): self._stop() self._start() def _on_app_started(self, app): self.logger.info('[App - {0}] Started.'.format(self.name)) self.settings.status = controller_pb2.App.RUNNING self.proto.status = controller_pb2.Component.ON self.emit('status_changed', self) def _on_app_stopped(self, app): self.logger.info('[App - {0}] Stopped.'.format(self.name)) self.settings.status = controller_pb2.App.NOT_RUNNING self.proto.status = controller_pb2.Component.OFF self.emit('status_changed', self) class CommandLineComponent(base.Component): """Component to run command-line based apps on any platform.""" def _start(self): for cmd in self.settings.when_on: self.logger.info('[{0}] Running: {1}'.format(self.name, cmd)) ret = subprocess.call(cmd) self.logger.info('[{0}] Done (return code={1})'.format(self.name, ret)) def _stop(self): for cmd in self.settings.when_off: self.logger.info('[{0}] Running: {1}'.format(self.name, cmd)) ret = subprocess.call(cmd) self.logger.info('[{0}] Done (return code={1})'.format(self.name, ret))	# Copyright 2018 Flight Lab authors. # # 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 # # https://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. """Library for components related to running apps.""" import subprocess import threading from components import base from protos import controller_pb2 from utils import app class AppComponent(base.Component): """Component to run command-line based app on any platform. This component can start app, restart app upon crash, and stop app. Events: "status_changed": when status of the app is changed. Args: app_component: instance of this class. """ def __init__(self, proto, args, kwargs): """Initializes the component. Args: proto: flightlab.App proto defining app details and options. """ super(AppComponent, self).__init__(proto, args, **kwargs) self._app = app.Application( name=self.name, bin_path=self.settings.executable_path, arguments=(list(self.settings.arguments) if self.settings.arguments else []), working_dir=self.settings.working_dir, restart_on_crash=(self.settings.restart_on_crash if self.settings.restart_on_crash else False), env=(self.settings.env if self.settings.env else None)) self._app.on('started', self._on_app_started) self._app.on('stopped', self._on_app_stopped) self._monitor = threading.Timer(1, self._check_status) self._monitor.start() def close(self): if self._monitor: self._monitor.cancel() self._monitor = None self._app.stop() def _check_status(self): if self._app.has_running_instance(): component_status = controller_pb2.Component.ON app_status = controller_pb2.App.RUNNING else: component_status = controller_pb2.Component.OFF app_status = controller_pb2.App.NOT_RUNNING if (self.proto.status != component_status or self.settings.status != app_status): self.proto.status = component_status self.settings.status = app_status self.emit('status_changed', self) def _start(self): self.logger.info('[App - {0}] Starting...'.format(self.name)) self._app.start() def _stop(self): self.logger.info('[App - {0}] Stopping...'.format(self.name)) self._app.stop() def _restart(self): self._stop() self._start() def _on_app_started(self, app): self.logger.info('[App - {0}] Started.'.format(self.name)) self.settings.status = controller_pb2.App.RUNNING self.proto.status = controller_pb2.Component.ON self.emit('status_changed', self) def _on_app_stopped(self, app): self.logger.info('[App - {0}] Stopped.'.format(self.name)) self.settings.status = controller_pb2.App.NOT_RUNNING self.proto.status = controller_pb2.Component.OFF self.emit('status_changed', self) class CommandLineComponent(base.Component): """Component to run command-line based apps on any platform.""" def _start(self): for cmd in self.settings.when_on: self.logger.info('[{0}] Running: {1}'.format(self.name, cmd)) ret = subprocess.call(cmd) self.logger.info('[{0}] Done (return code={1})'.format(self.name, ret)) def _stop(self): for cmd in self.settings.when_off: self.logger.info('[{0}] Running: {1}'.format(self.name, cmd)) ret = subprocess.call(cmd) self.logger.info('[{0}] Done (return code={1})'.format(self.name, ret))	en	0.859134	# Copyright 2018 Flight Lab authors. # # 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 # # https://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. Library for components related to running apps. Component to run command-line based app on any platform. This component can start app, restart app upon crash, and stop app. Events: "status_changed": when status of the app is changed. Args: app_component: instance of this class. Initializes the component. Args: proto: flightlab.App proto defining app details and options. Component to run command-line based apps on any platform.	2.215003	2
botorch/acquisition/__init__.py	jmren168/botorch	1	10220	#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved from .acquisition import AcquisitionFunction from .analytic import ( AnalyticAcquisitionFunction, ConstrainedExpectedImprovement, ExpectedImprovement, NoisyExpectedImprovement, PosteriorMean, ProbabilityOfImprovement, UpperConfidenceBound, ) from .fixed_feature import FixedFeatureAcquisitionFunction from .monte_carlo import ( MCAcquisitionFunction, qExpectedImprovement, qNoisyExpectedImprovement, qProbabilityOfImprovement, qSimpleRegret, qUpperConfidenceBound, ) from .objective import ( ConstrainedMCObjective, GenericMCObjective, IdentityMCObjective, LinearMCObjective, MCAcquisitionObjective, ScalarizedObjective, ) from .utils import get_acquisition_function __all__ = [ "AcquisitionFunction", "AnalyticAcquisitionFunction", "ConstrainedExpectedImprovement", "ExpectedImprovement", "FixedFeatureAcquisitionFunction", "NoisyExpectedImprovement", "PosteriorMean", "ProbabilityOfImprovement", "UpperConfidenceBound", "qExpectedImprovement", "qNoisyExpectedImprovement", "qProbabilityOfImprovement", "qSimpleRegret", "qUpperConfidenceBound", "ConstrainedMCObjective", "GenericMCObjective", "IdentityMCObjective", "LinearMCObjective", "MCAcquisitionFunction", "MCAcquisitionObjective", "ScalarizedObjective", "get_acquisition_function", ]	#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved from .acquisition import AcquisitionFunction from .analytic import ( AnalyticAcquisitionFunction, ConstrainedExpectedImprovement, ExpectedImprovement, NoisyExpectedImprovement, PosteriorMean, ProbabilityOfImprovement, UpperConfidenceBound, ) from .fixed_feature import FixedFeatureAcquisitionFunction from .monte_carlo import ( MCAcquisitionFunction, qExpectedImprovement, qNoisyExpectedImprovement, qProbabilityOfImprovement, qSimpleRegret, qUpperConfidenceBound, ) from .objective import ( ConstrainedMCObjective, GenericMCObjective, IdentityMCObjective, LinearMCObjective, MCAcquisitionObjective, ScalarizedObjective, ) from .utils import get_acquisition_function __all__ = [ "AcquisitionFunction", "AnalyticAcquisitionFunction", "ConstrainedExpectedImprovement", "ExpectedImprovement", "FixedFeatureAcquisitionFunction", "NoisyExpectedImprovement", "PosteriorMean", "ProbabilityOfImprovement", "UpperConfidenceBound", "qExpectedImprovement", "qNoisyExpectedImprovement", "qProbabilityOfImprovement", "qSimpleRegret", "qUpperConfidenceBound", "ConstrainedMCObjective", "GenericMCObjective", "IdentityMCObjective", "LinearMCObjective", "MCAcquisitionFunction", "MCAcquisitionObjective", "ScalarizedObjective", "get_acquisition_function", ]	en	0.797894	#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved	1.340029	1
examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_alternating_legs_env_randomizer.py	felipeek/bullet3	9,136	10221	"""Randomize the minitaur_gym_alternating_leg_env when reset() is called. The randomization include swing_offset, extension_offset of all legs that mimics bent legs, desired_pitch from user input, battery voltage and motor damping. """ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) parentdir = os.path.dirname(os.path.dirname(parentdir)) os.sys.path.insert(0, parentdir) import numpy as np import tf.compat.v1 as tf from pybullet_envs.minitaur.envs import env_randomizer_base # Absolute range. NUM_LEGS = 4 BATTERY_VOLTAGE_RANGE = (14.8, 16.8) MOTOR_VISCOUS_DAMPING_RANGE = (0, 0.01) class MinitaurAlternatingLegsEnvRandomizer(env_randomizer_base.EnvRandomizerBase): """A randomizer that changes the minitaur_gym_alternating_leg_env.""" def __init__(self, perturb_swing_bound=0.1, perturb_extension_bound=0.1, perturb_desired_pitch_bound=0.01): super(MinitaurAlternatingLegsEnvRandomizer, self).__init__() self.perturb_swing_bound = perturb_swing_bound self.perturb_extension_bound = perturb_extension_bound self.perturb_desired_pitch_bound = perturb_desired_pitch_bound def randomize_env(self, env): perturb_magnitude = np.random.uniform(low=-self.perturb_swing_bound, high=self.perturb_swing_bound, size=NUM_LEGS) env.set_swing_offset(perturb_magnitude) tf.logging.info("swing_offset: {}".format(perturb_magnitude)) perturb_magnitude = np.random.uniform(low=-self.perturb_extension_bound, high=self.perturb_extension_bound, size=NUM_LEGS) env.set_extension_offset(perturb_magnitude) tf.logging.info("extension_offset: {}".format(perturb_magnitude)) perturb_magnitude = np.random.uniform(low=-self.perturb_desired_pitch_bound, high=self.perturb_desired_pitch_bound) env.set_desired_pitch(perturb_magnitude) tf.logging.info("desired_pitch: {}".format(perturb_magnitude)) randomized_battery_voltage = np.random.uniform(BATTERY_VOLTAGE_RANGE[0], BATTERY_VOLTAGE_RANGE[1]) env.minitaur.SetBatteryVoltage(randomized_battery_voltage) tf.logging.info("battery_voltage: {}".format(randomized_battery_voltage)) randomized_motor_damping = np.random.uniform(MOTOR_VISCOUS_DAMPING_RANGE[0], MOTOR_VISCOUS_DAMPING_RANGE[1]) env.minitaur.SetMotorViscousDamping(randomized_motor_damping) tf.logging.info("motor_damping: {}".format(randomized_motor_damping))	"""Randomize the minitaur_gym_alternating_leg_env when reset() is called. The randomization include swing_offset, extension_offset of all legs that mimics bent legs, desired_pitch from user input, battery voltage and motor damping. """ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) parentdir = os.path.dirname(os.path.dirname(parentdir)) os.sys.path.insert(0, parentdir) import numpy as np import tf.compat.v1 as tf from pybullet_envs.minitaur.envs import env_randomizer_base # Absolute range. NUM_LEGS = 4 BATTERY_VOLTAGE_RANGE = (14.8, 16.8) MOTOR_VISCOUS_DAMPING_RANGE = (0, 0.01) class MinitaurAlternatingLegsEnvRandomizer(env_randomizer_base.EnvRandomizerBase): """A randomizer that changes the minitaur_gym_alternating_leg_env.""" def __init__(self, perturb_swing_bound=0.1, perturb_extension_bound=0.1, perturb_desired_pitch_bound=0.01): super(MinitaurAlternatingLegsEnvRandomizer, self).__init__() self.perturb_swing_bound = perturb_swing_bound self.perturb_extension_bound = perturb_extension_bound self.perturb_desired_pitch_bound = perturb_desired_pitch_bound def randomize_env(self, env): perturb_magnitude = np.random.uniform(low=-self.perturb_swing_bound, high=self.perturb_swing_bound, size=NUM_LEGS) env.set_swing_offset(perturb_magnitude) tf.logging.info("swing_offset: {}".format(perturb_magnitude)) perturb_magnitude = np.random.uniform(low=-self.perturb_extension_bound, high=self.perturb_extension_bound, size=NUM_LEGS) env.set_extension_offset(perturb_magnitude) tf.logging.info("extension_offset: {}".format(perturb_magnitude)) perturb_magnitude = np.random.uniform(low=-self.perturb_desired_pitch_bound, high=self.perturb_desired_pitch_bound) env.set_desired_pitch(perturb_magnitude) tf.logging.info("desired_pitch: {}".format(perturb_magnitude)) randomized_battery_voltage = np.random.uniform(BATTERY_VOLTAGE_RANGE[0], BATTERY_VOLTAGE_RANGE[1]) env.minitaur.SetBatteryVoltage(randomized_battery_voltage) tf.logging.info("battery_voltage: {}".format(randomized_battery_voltage)) randomized_motor_damping = np.random.uniform(MOTOR_VISCOUS_DAMPING_RANGE[0], MOTOR_VISCOUS_DAMPING_RANGE[1]) env.minitaur.SetMotorViscousDamping(randomized_motor_damping) tf.logging.info("motor_damping: {}".format(randomized_motor_damping))	en	0.691932	Randomize the minitaur_gym_alternating_leg_env when reset() is called. The randomization include swing_offset, extension_offset of all legs that mimics bent legs, desired_pitch from user input, battery voltage and motor damping. # Absolute range. A randomizer that changes the minitaur_gym_alternating_leg_env.	2.704479	3
pygsti/modelmembers/states/tensorprodstate.py	pyGSTi-Developers/pyGSTi	73	10222	""" The TensorProductState class and supporting functionality. """ #************************************************************************************************* # Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS). # Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights # in this software. # 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 or in the LICENSE file in the root pyGSTi directory. #************************************************************************************************* import functools as _functools import itertools as _itertools import numpy as _np from pygsti.modelmembers.states.state import State as _State from pygsti.modelmembers import modelmember as _modelmember, term as _term from pygsti.baseobjs import statespace as _statespace from pygsti.tools import listtools as _lt from pygsti.tools import matrixtools as _mt class TensorProductState(_State): """ A state vector that is a tensor-product of other state vectors. Parameters ---------- factors : list of States a list of the component states to take the tensor product of. state_space : StateSpace, optional The state space for this operation. """ def __init__(self, factors, state_space): assert(len(factors) > 0), "Must have at least one factor!" self.factors = factors # do not copy - needs to reference common objects evotype = self.factors[0]._evotype rep = evotype.create_tensorproduct_state_rep([f._rep for f in factors], state_space) _State.__init__(self, rep, evotype) self.init_gpindices() # initialize our gpindices based on sub-members self._update_rep() # initializes rep data #Note: no to_memoized_dict needed, as ModelMember version does all we need. @classmethod def _from_memoized_dict(cls, mm_dict, serial_memo): state_space = _statespace.StateSpace.from_nice_serialization(mm_dict['state_space']) factors = [serial_memo[i] for i in mm_dict['submembers']] return cls(factors, state_space) def submembers(self): """ Get the ModelMember-derived objects contained in this one. Returns ------- list """ return self.factors # factor POVM object def _update_rep(self): self._rep.reps_have_changed() @property def parameter_labels(self): """ An array of labels (usually strings) describing this model member's parameters. """ vl = _np.empty(self.num_params, dtype=object) for factor_state, factor_local_inds in zip(self.factors, self._submember_rpindices): vl[factor_local_inds] = factor_state.parameter_labels return vl def to_dense(self, on_space='minimal', scratch=None): """ Return this state vector as a (dense) numpy array. The memory in `scratch` maybe used when it is not-None. Parameters ---------- on_space : {'minimal', 'Hilbert', 'HilbertSchmidt'} The space that the returned dense operation acts upon. For unitary matrices and bra/ket vectors, use `'Hilbert'`. For superoperator matrices and super-bra/super-ket vectors use `'HilbertSchmidt'`. `'minimal'` means that `'Hilbert'` is used if possible given this operator's evolution type, and otherwise `'HilbertSchmidt'` is used. scratch : numpy.ndarray, optional scratch space available for use. Returns ------- numpy.ndarray """ return self._rep.to_dense(on_space) def taylor_order_terms(self, order, max_polynomial_vars=100, return_coeff_polys=False): """ Get the `order`-th order Taylor-expansion terms of this state vector. This function either constructs or returns a cached list of the terms at the given order. Each term is "rank-1", meaning that it is a state preparation followed by or POVM effect preceded by actions on a density matrix `rho` of the form: `rho -> A rho B` The coefficients of these terms are typically polynomials of the State's parameters, where the polynomial's variable indices index the global parameters of the State's parent (usually a :class:`Model`) , not the State's local parameter array (i.e. that returned from `to_vector`). Parameters ---------- order : int The order of terms to get. max_polynomial_vars : int, optional maximum number of variables the created polynomials can have. return_coeff_polys : bool Whether a parallel list of locally-indexed (using variable indices corresponding to this object's parameters rather than its parent's) polynomial coefficients should be returned as well. Returns ------- terms : list A list of :class:`RankOneTerm` objects. coefficients : list Only present when `return_coeff_polys == True`. A list of compact polynomial objects, meaning that each element is a `(vtape,ctape)` 2-tuple formed by concatenating together the output of :method:`Polynomial.compact`. """ terms = [] fnq = [int(round(_np.log2(f.dim))) // 2 for f in self.factors] # num of qubits per factor # assumes density matrix evolution total_nQ = sum(fnq) # total number of qubits for p in _lt.partition_into(order, len(self.factors)): factor_lists = [self.factors[i].taylor_order_terms(pi, max_polynomial_vars) for i, pi in enumerate(p)] # When possible, create COLLAPSED factor_lists so each factor has just a single # (State) pre & post op, which can be formed into the new terms' # TensorProdState ops. # - DON'T collapse stabilizer states & clifford ops - can't for POVMs collapsible = False # bool(self._evotype =="svterm") # need to use reps for collapsing now... TODO? if collapsible: factor_lists = [[t.collapse_vec() for t in fterms] for fterms in factor_lists] for factors in _itertools.product(factor_lists): # create a term with a TensorProdState - Note we always create # "prep"-mode vectors, since even when self._prep_or_effect == "effect" these # vectors are created with factor (prep- or effect-type) States not factor POVMs # we workaround this by still allowing such "prep"-mode # TensorProdStates to be represented as effects (i.e. in torep('effect'...) works) coeff = _functools.reduce(lambda x, y: x.mult(y), [f.coeff for f in factors]) pre_rep = self._evotype.create_tensorproduct_state_rep( [f.pre_state for f in factors if (f.pre_state is not None)], self.state_space) post_rep = self._evotype.create_tensorproduct_state_rep( [f.post_state for f in factors if (f.post_state is not None)], self.state_space) term = _term.RankOnePolynomialPrepTerm.create_from(coeff, pre_rep, post_rep, self._evotype, self.state_space) if not collapsible: # then may need to add more ops. Assume factor ops are clifford gates # Embed each factors ops according to their target qubit(s) and just daisy chain them ss = _statespace.QubitSpace(total_nQ); curQ = 0 for f, nq in zip(factors, fnq): targetLabels = tuple(range(curQ, curQ + nq)); curQ += nq term._rep.pre_ops.extend([self._evotype.create_embedded_rep(ss, targetLabels, op) for op in f.pre_ops]) # embed and add ops term._rep.post_ops.extend([self._evotype.create_embedded_rep(ss, targetLabels, op) for op in f.post_ops]) # embed and add ops terms.append(term) if return_coeff_polys: def _decompose_indices(x): return tuple(_modelmember._decompose_gpindices( self.gpindices, _np.array(x, _np.int64))) poly_coeffs = [t.coeff.map_indices(_decompose_indices) for t in terms] # with local* indices tapes = [poly.compact(complex_coeff_tape=True) for poly in poly_coeffs] if len(tapes) > 0: vtape = _np.concatenate([t[0] for t in tapes]) ctape = _np.concatenate([t[1] for t in tapes]) else: vtape = _np.empty(0, _np.int64) ctape = _np.empty(0, complex) coeffs_as_compact_polys = (vtape, ctape) #self.local_term_poly_coeffs[order] = coeffs_as_compact_polys #FUTURE? return terms, coeffs_as_compact_polys else: return terms # Cache terms in FUTURE? @property def num_params(self): """ Get the number of independent parameters which specify this state vector. Returns ------- int the number of independent parameters. """ return len(self.gpindices_as_array()) def to_vector(self): """ Get the state vector parameters as an array of values. Returns ------- numpy array The parameters as a 1D array with length num_params(). """ v = _np.empty(self.num_params, 'd') for factor_state, factor_local_inds in zip(self.factors, self._submember_rpindices): v[factor_local_inds] = factor_state.to_vector() return v def from_vector(self, v, close=False, dirty_value=True): """ Initialize the state vector using a 1D array of parameters. Parameters ---------- v : numpy array The 1D vector of state vector parameters. Length must == num_params() close : bool, optional Whether `v` is close to this state vector's current set of parameters. Under some circumstances, when this is true this call can be completed more quickly. dirty_value : bool, optional The value to set this object's "dirty flag" to before exiting this call. This is passed as an argument so it can be updated recursively. Leave this set to `True` unless you know what you're doing. Returns ------- None """ for factor_state, factor_local_inds in zip(self.factors, self._submember_rpindices): factor_state.from_vector(v[factor_local_inds], close, dirty_value) #Update representation, which may be a dense matrix or # just fast-kron arrays or a stabilizer state. self._update_rep() # TODO - how does this apply to state reps?? def deriv_wrt_params(self, wrt_filter=None): """ The element-wise derivative this state vector. Construct a matrix whose columns are the derivatives of the state vector with respect to a single param. Thus, each column is of length dimension and there is one column per state vector parameter. An empty 2D array in the StaticState case (num_params == 0). Parameters ---------- wrt_filter : list or numpy.ndarray List of parameter indices to take derivative with respect to. (None means to use all the this operation's parameters.) Returns ------- numpy array Array of derivatives, shape == (dimension, num_params) """ typ = self.factors[0].to_dense(on_space='minimal').dtype if len(self.factors) > 0 else 'd' #HACK to deal with fact that output of to_dense is really what is differentiated # but this may not match self.dim == self.state_space.dim, e.g. for pure state vecs. dims = [len(fct.to_dense(on_space='minimal')) for fct in self.factors] dim = int(_np.product(dims)) derivMx = _np.zeros((dim, self.num_params), typ) #Product rule to compute jacobian # loop over the spamvec/povm we differentiate wrt: for i, (fct, fct_local_inds, fct_dim) in enumerate(zip(self.factors, self._submember_rpindices, dims)): vec = fct if vec.num_params == 0: continue # no contribution deriv = vec.deriv_wrt_params(None) # TODO: use filter?? / make relative to this gate... deriv.shape = (fct_dim, vec.num_params) if i > 0: # factors before ith pre = self.factors[0].to_dense(on_space='minimal') for vecA in self.factors[1:i]: pre = _np.kron(pre, vecA.to_dense(on_space='minimal')) deriv = _np.kron(pre[:, None], deriv) # add a dummy 1-dim to 'pre' and do kron properly... if i + 1 < len(self.factors): # factors after ith post = self.factors[i + 1].to_dense(on_space='minimal') for vecA in self.factors[i + 2:]: post = _np.kron(post, vecA.to_dense(on_space='minimal')) deriv = _np.kron(deriv, post[:, None]) # add a dummy 1-dim to 'post' and do kron properly... assert(fct_local_inds is not None), \ "Error: gpindices has not been initialized for factor %d - cannot compute derivative!" % i derivMx[:, fct_local_inds] += deriv derivMx.shape = (dim, self.num_params) # necessary? if wrt_filter is None: return derivMx else: return _np.take(derivMx, wrt_filter, axis=1) def has_nonzero_hessian(self): """ Whether this state vector has a non-zero Hessian with respect to its parameters. Returns ------- bool """ return False def __str__(self): s = "Tensor product %s vector with length %d\n" % (self._prep_or_effect, self.dim) #ar = self.to_dense() #s += _mt.mx_to_string(ar, width=4, prec=2) # factors are just other States s += " x ".join([_mt.mx_to_string(fct.to_dense(on_space='minimal'), width=4, prec=2) for fct in self.factors]) return s	""" The TensorProductState class and supporting functionality. """ #************************************************************************************************* # Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS). # Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights # in this software. # 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 or in the LICENSE file in the root pyGSTi directory. #************************************************************************************************* import functools as _functools import itertools as _itertools import numpy as _np from pygsti.modelmembers.states.state import State as _State from pygsti.modelmembers import modelmember as _modelmember, term as _term from pygsti.baseobjs import statespace as _statespace from pygsti.tools import listtools as _lt from pygsti.tools import matrixtools as _mt class TensorProductState(_State): """ A state vector that is a tensor-product of other state vectors. Parameters ---------- factors : list of States a list of the component states to take the tensor product of. state_space : StateSpace, optional The state space for this operation. """ def __init__(self, factors, state_space): assert(len(factors) > 0), "Must have at least one factor!" self.factors = factors # do not copy - needs to reference common objects evotype = self.factors[0]._evotype rep = evotype.create_tensorproduct_state_rep([f._rep for f in factors], state_space) _State.__init__(self, rep, evotype) self.init_gpindices() # initialize our gpindices based on sub-members self._update_rep() # initializes rep data #Note: no to_memoized_dict needed, as ModelMember version does all we need. @classmethod def _from_memoized_dict(cls, mm_dict, serial_memo): state_space = _statespace.StateSpace.from_nice_serialization(mm_dict['state_space']) factors = [serial_memo[i] for i in mm_dict['submembers']] return cls(factors, state_space) def submembers(self): """ Get the ModelMember-derived objects contained in this one. Returns ------- list """ return self.factors # factor POVM object def _update_rep(self): self._rep.reps_have_changed() @property def parameter_labels(self): """ An array of labels (usually strings) describing this model member's parameters. """ vl = _np.empty(self.num_params, dtype=object) for factor_state, factor_local_inds in zip(self.factors, self._submember_rpindices): vl[factor_local_inds] = factor_state.parameter_labels return vl def to_dense(self, on_space='minimal', scratch=None): """ Return this state vector as a (dense) numpy array. The memory in `scratch` maybe used when it is not-None. Parameters ---------- on_space : {'minimal', 'Hilbert', 'HilbertSchmidt'} The space that the returned dense operation acts upon. For unitary matrices and bra/ket vectors, use `'Hilbert'`. For superoperator matrices and super-bra/super-ket vectors use `'HilbertSchmidt'`. `'minimal'` means that `'Hilbert'` is used if possible given this operator's evolution type, and otherwise `'HilbertSchmidt'` is used. scratch : numpy.ndarray, optional scratch space available for use. Returns ------- numpy.ndarray """ return self._rep.to_dense(on_space) def taylor_order_terms(self, order, max_polynomial_vars=100, return_coeff_polys=False): """ Get the `order`-th order Taylor-expansion terms of this state vector. This function either constructs or returns a cached list of the terms at the given order. Each term is "rank-1", meaning that it is a state preparation followed by or POVM effect preceded by actions on a density matrix `rho` of the form: `rho -> A rho B` The coefficients of these terms are typically polynomials of the State's parameters, where the polynomial's variable indices index the global parameters of the State's parent (usually a :class:`Model`) , not the State's local parameter array (i.e. that returned from `to_vector`). Parameters ---------- order : int The order of terms to get. max_polynomial_vars : int, optional maximum number of variables the created polynomials can have. return_coeff_polys : bool Whether a parallel list of locally-indexed (using variable indices corresponding to this object's parameters rather than its parent's) polynomial coefficients should be returned as well. Returns ------- terms : list A list of :class:`RankOneTerm` objects. coefficients : list Only present when `return_coeff_polys == True`. A list of compact polynomial objects, meaning that each element is a `(vtape,ctape)` 2-tuple formed by concatenating together the output of :method:`Polynomial.compact`. """ terms = [] fnq = [int(round(_np.log2(f.dim))) // 2 for f in self.factors] # num of qubits per factor # assumes density matrix evolution total_nQ = sum(fnq) # total number of qubits for p in _lt.partition_into(order, len(self.factors)): factor_lists = [self.factors[i].taylor_order_terms(pi, max_polynomial_vars) for i, pi in enumerate(p)] # When possible, create COLLAPSED factor_lists so each factor has just a single # (State) pre & post op, which can be formed into the new terms' # TensorProdState ops. # - DON'T collapse stabilizer states & clifford ops - can't for POVMs collapsible = False # bool(self._evotype =="svterm") # need to use reps for collapsing now... TODO? if collapsible: factor_lists = [[t.collapse_vec() for t in fterms] for fterms in factor_lists] for factors in _itertools.product(factor_lists): # create a term with a TensorProdState - Note we always create # "prep"-mode vectors, since even when self._prep_or_effect == "effect" these # vectors are created with factor (prep- or effect-type) States not factor POVMs # we workaround this by still allowing such "prep"-mode # TensorProdStates to be represented as effects (i.e. in torep('effect'...) works) coeff = _functools.reduce(lambda x, y: x.mult(y), [f.coeff for f in factors]) pre_rep = self._evotype.create_tensorproduct_state_rep( [f.pre_state for f in factors if (f.pre_state is not None)], self.state_space) post_rep = self._evotype.create_tensorproduct_state_rep( [f.post_state for f in factors if (f.post_state is not None)], self.state_space) term = _term.RankOnePolynomialPrepTerm.create_from(coeff, pre_rep, post_rep, self._evotype, self.state_space) if not collapsible: # then may need to add more ops. Assume factor ops are clifford gates # Embed each factors ops according to their target qubit(s) and just daisy chain them ss = _statespace.QubitSpace(total_nQ); curQ = 0 for f, nq in zip(factors, fnq): targetLabels = tuple(range(curQ, curQ + nq)); curQ += nq term._rep.pre_ops.extend([self._evotype.create_embedded_rep(ss, targetLabels, op) for op in f.pre_ops]) # embed and add ops term._rep.post_ops.extend([self._evotype.create_embedded_rep(ss, targetLabels, op) for op in f.post_ops]) # embed and add ops terms.append(term) if return_coeff_polys: def _decompose_indices(x): return tuple(_modelmember._decompose_gpindices( self.gpindices, _np.array(x, _np.int64))) poly_coeffs = [t.coeff.map_indices(_decompose_indices) for t in terms] # with local* indices tapes = [poly.compact(complex_coeff_tape=True) for poly in poly_coeffs] if len(tapes) > 0: vtape = _np.concatenate([t[0] for t in tapes]) ctape = _np.concatenate([t[1] for t in tapes]) else: vtape = _np.empty(0, _np.int64) ctape = _np.empty(0, complex) coeffs_as_compact_polys = (vtape, ctape) #self.local_term_poly_coeffs[order] = coeffs_as_compact_polys #FUTURE? return terms, coeffs_as_compact_polys else: return terms # Cache terms in FUTURE? @property def num_params(self): """ Get the number of independent parameters which specify this state vector. Returns ------- int the number of independent parameters. """ return len(self.gpindices_as_array()) def to_vector(self): """ Get the state vector parameters as an array of values. Returns ------- numpy array The parameters as a 1D array with length num_params(). """ v = _np.empty(self.num_params, 'd') for factor_state, factor_local_inds in zip(self.factors, self._submember_rpindices): v[factor_local_inds] = factor_state.to_vector() return v def from_vector(self, v, close=False, dirty_value=True): """ Initialize the state vector using a 1D array of parameters. Parameters ---------- v : numpy array The 1D vector of state vector parameters. Length must == num_params() close : bool, optional Whether `v` is close to this state vector's current set of parameters. Under some circumstances, when this is true this call can be completed more quickly. dirty_value : bool, optional The value to set this object's "dirty flag" to before exiting this call. This is passed as an argument so it can be updated recursively. Leave this set to `True` unless you know what you're doing. Returns ------- None """ for factor_state, factor_local_inds in zip(self.factors, self._submember_rpindices): factor_state.from_vector(v[factor_local_inds], close, dirty_value) #Update representation, which may be a dense matrix or # just fast-kron arrays or a stabilizer state. self._update_rep() # TODO - how does this apply to state reps?? def deriv_wrt_params(self, wrt_filter=None): """ The element-wise derivative this state vector. Construct a matrix whose columns are the derivatives of the state vector with respect to a single param. Thus, each column is of length dimension and there is one column per state vector parameter. An empty 2D array in the StaticState case (num_params == 0). Parameters ---------- wrt_filter : list or numpy.ndarray List of parameter indices to take derivative with respect to. (None means to use all the this operation's parameters.) Returns ------- numpy array Array of derivatives, shape == (dimension, num_params) """ typ = self.factors[0].to_dense(on_space='minimal').dtype if len(self.factors) > 0 else 'd' #HACK to deal with fact that output of to_dense is really what is differentiated # but this may not match self.dim == self.state_space.dim, e.g. for pure state vecs. dims = [len(fct.to_dense(on_space='minimal')) for fct in self.factors] dim = int(_np.product(dims)) derivMx = _np.zeros((dim, self.num_params), typ) #Product rule to compute jacobian # loop over the spamvec/povm we differentiate wrt: for i, (fct, fct_local_inds, fct_dim) in enumerate(zip(self.factors, self._submember_rpindices, dims)): vec = fct if vec.num_params == 0: continue # no contribution deriv = vec.deriv_wrt_params(None) # TODO: use filter?? / make relative to this gate... deriv.shape = (fct_dim, vec.num_params) if i > 0: # factors before ith pre = self.factors[0].to_dense(on_space='minimal') for vecA in self.factors[1:i]: pre = _np.kron(pre, vecA.to_dense(on_space='minimal')) deriv = _np.kron(pre[:, None], deriv) # add a dummy 1-dim to 'pre' and do kron properly... if i + 1 < len(self.factors): # factors after ith post = self.factors[i + 1].to_dense(on_space='minimal') for vecA in self.factors[i + 2:]: post = _np.kron(post, vecA.to_dense(on_space='minimal')) deriv = _np.kron(deriv, post[:, None]) # add a dummy 1-dim to 'post' and do kron properly... assert(fct_local_inds is not None), \ "Error: gpindices has not been initialized for factor %d - cannot compute derivative!" % i derivMx[:, fct_local_inds] += deriv derivMx.shape = (dim, self.num_params) # necessary? if wrt_filter is None: return derivMx else: return _np.take(derivMx, wrt_filter, axis=1) def has_nonzero_hessian(self): """ Whether this state vector has a non-zero Hessian with respect to its parameters. Returns ------- bool """ return False def __str__(self): s = "Tensor product %s vector with length %d\n" % (self._prep_or_effect, self.dim) #ar = self.to_dense() #s += _mt.mx_to_string(ar, width=4, prec=2) # factors are just other States s += " x ".join([_mt.mx_to_string(fct.to_dense(on_space='minimal'), width=4, prec=2) for fct in self.factors]) return s	en	0.753729	The TensorProductState class and supporting functionality. #************************************************************************************************* # Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS). # Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights # in this software. # 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 or in the LICENSE file in the root pyGSTi directory. #************************************************************************************************* A state vector that is a tensor-product of other state vectors. Parameters ---------- factors : list of States a list of the component states to take the tensor product of. state_space : StateSpace, optional The state space for this operation. # do not copy - needs to reference common objects # initialize our gpindices based on sub-members # initializes rep data #Note: no to_memoized_dict needed, as ModelMember version does all we need. Get the ModelMember-derived objects contained in this one. Returns ------- list # factor POVM object An array of labels (usually strings) describing this model member's parameters. Return this state vector as a (dense) numpy array. The memory in `scratch` maybe used when it is not-None. Parameters ---------- on_space : {'minimal', 'Hilbert', 'HilbertSchmidt'} The space that the returned dense operation acts upon. For unitary matrices and bra/ket vectors, use `'Hilbert'`. For superoperator matrices and super-bra/super-ket vectors use `'HilbertSchmidt'`. `'minimal'` means that `'Hilbert'` is used if possible given this operator's evolution type, and otherwise `'HilbertSchmidt'` is used. scratch : numpy.ndarray, optional scratch space available for use. Returns ------- numpy.ndarray Get the `order`-th order Taylor-expansion terms of this state vector. This function either constructs or returns a cached list of the terms at the given order. Each term is "rank-1", meaning that it is a state preparation followed by or POVM effect preceded by actions on a density matrix `rho` of the form: `rho -> A rho B` The coefficients of these terms are typically polynomials of the State's parameters, where the polynomial's variable indices index the global parameters of the State's parent (usually a :class:`Model`) , not the State's local parameter array (i.e. that returned from `to_vector`). Parameters ---------- order : int The order of terms to get. max_polynomial_vars : int, optional maximum number of variables the created polynomials can have. return_coeff_polys : bool Whether a parallel list of locally-indexed (using variable indices corresponding to this object's parameters rather than its parent's) polynomial coefficients should be returned as well. Returns ------- terms : list A list of :class:`RankOneTerm` objects. coefficients : list Only present when `return_coeff_polys == True`. A list of compact polynomial objects, meaning that each element is a `(vtape,ctape)` 2-tuple formed by concatenating together the output of :method:`Polynomial.compact`. # num of qubits per factor # assumes density matrix evolution # total number of qubits # When possible, create COLLAPSED factor_lists so each factor has just a single # (State) pre & post op, which can be formed into the new terms' # TensorProdState ops. # - DON'T collapse stabilizer states & clifford ops - can't for POVMs # bool(self._evotype =="svterm") # need to use reps for collapsing now... TODO? # create a term with a TensorProdState - Note we always create # "prep"-mode vectors, since even when self._prep_or_effect == "effect" these # vectors are created with factor (prep- or effect-type) States not factor POVMs # we workaround this by still allowing such "prep"-mode # TensorProdStates to be represented as effects (i.e. in torep('effect'...) works) # then may need to add more ops. Assume factor ops are clifford gates # Embed each factors ops according to their target qubit(s) and just daisy chain them # embed and add ops # embed and add ops # with local indices #self.local_term_poly_coeffs[order] = coeffs_as_compact_polys #FUTURE? # Cache terms in FUTURE? Get the number of independent parameters which specify this state vector. Returns ------- int the number of independent parameters. Get the state vector parameters as an array of values. Returns ------- numpy array The parameters as a 1D array with length num_params(). Initialize the state vector using a 1D array of parameters. Parameters ---------- v : numpy array The 1D vector of state vector parameters. Length must == num_params() close : bool, optional Whether `v` is close to this state vector's current set of parameters. Under some circumstances, when this is true this call can be completed more quickly. dirty_value : bool, optional The value to set this object's "dirty flag" to before exiting this call. This is passed as an argument so it can be updated recursively. Leave this set to `True` unless you know what you're doing. Returns ------- None #Update representation, which may be a dense matrix or # just fast-kron arrays or a stabilizer state. # TODO - how does this apply to state reps?? The element-wise derivative this state vector. Construct a matrix whose columns are the derivatives of the state vector with respect to a single param. Thus, each column is of length dimension and there is one column per state vector parameter. An empty 2D array in the StaticState case (num_params == 0). Parameters ---------- wrt_filter : list or numpy.ndarray List of parameter indices to take derivative with respect to. (None means to use all the this operation's parameters.) Returns ------- numpy array Array of derivatives, shape == (dimension, num_params) #HACK to deal with fact that output of to_dense is really what is differentiated # but this may not match self.dim == self.state_space.dim, e.g. for pure state vecs. #Product rule to compute jacobian # loop over the spamvec/povm we differentiate wrt: # no contribution # TODO: use filter?? / make relative to this gate... # factors before ith # add a dummy 1-dim to 'pre' and do kron properly... # factors after ith # add a dummy 1-dim to 'post' and do kron properly... # necessary? Whether this state vector has a non-zero Hessian with respect to its parameters. Returns ------- bool #ar = self.to_dense() #s += _mt.mx_to_string(ar, width=4, prec=2) # factors are just other States	1.80285	2
edivorce/apps/core/views/graphql.py	gerritvdm/eDivorce	6	10223	<filename>edivorce/apps/core/views/graphql.py import graphene import graphene_django from django.http import HttpResponseForbidden from graphene_django.views import GraphQLView from graphql import GraphQLError from edivorce.apps.core.models import Document class PrivateGraphQLView(GraphQLView): def dispatch(self, request, args, kwargs): if not request.user.is_authenticated: return HttpResponseForbidden() return super().dispatch(request, args, kwargs) class DocumentType(graphene_django.DjangoObjectType): file_url = graphene.String(source='get_file_url') content_type = graphene.String(source='get_content_type') class Meta: model = Document exclude = ('id', 'file') class Query(graphene.ObjectType): documents = graphene.List(DocumentType, doc_type=graphene.String(required=True), party_code=graphene.Int(required=True)) def resolve_documents(self, info, kwargs): if info.context.user.is_anonymous: raise GraphQLError('Unauthorized') q = Document.objects.filter(bceid_user=info.context.user, kwargs) for doc in q: if not doc.file_exists(): q.delete() return Document.objects.none() return q class DocumentInput(graphene.InputObjectType): filename = graphene.String(required=True) size = graphene.Int(required=True) width = graphene.Int() height = graphene.Int() rotation = graphene.Int() class DocumentMetaDataInput(graphene.InputObjectType): files = graphene.List(DocumentInput, required=True) doc_type = graphene.String(required=True) party_code = graphene.Int(required=True) class UpdateMetadata(graphene.Mutation): class Arguments: input = DocumentMetaDataInput(required=True) documents = graphene.List(DocumentType) def mutate(self, info, kwargs): input_ = kwargs['input'] documents = Document.objects.filter(bceid_user=info.context.user, doc_type=input_['doc_type'], party_code=input_['party_code']) unique_files = [dict(s) for s in set(frozenset(d.items()) for d in input_['files'])] if documents.count() != len(input_['files']) or documents.count() != len(unique_files): raise GraphQLError("Invalid input: there must be the same number of files") for i, file in enumerate(input_['files']): try: doc = documents.get(filename=file['filename'], size=file['size']) doc.sort_order = i + 1 doc.width = file.get('width', doc.width) doc.height = file.get('height', doc.height) doc.rotation = file.get('rotation', doc.rotation) if doc.rotation not in [0, 90, 180, 270]: raise GraphQLError(f"Invalid rotation {doc.rotation}, must be 0, 90, 180, 270") doc.save() except Document.DoesNotExist: raise GraphQLError(f"Couldn't find document '{file['filename']}' with size '{file['size']}'") return UpdateMetadata(documents=documents.all()) class Mutations(graphene.ObjectType): update_metadata = UpdateMetadata.Field() graphql_schema = graphene.Schema(query=Query, mutation=Mutations)	<filename>edivorce/apps/core/views/graphql.py import graphene import graphene_django from django.http import HttpResponseForbidden from graphene_django.views import GraphQLView from graphql import GraphQLError from edivorce.apps.core.models import Document class PrivateGraphQLView(GraphQLView): def dispatch(self, request, args, kwargs): if not request.user.is_authenticated: return HttpResponseForbidden() return super().dispatch(request, args, kwargs) class DocumentType(graphene_django.DjangoObjectType): file_url = graphene.String(source='get_file_url') content_type = graphene.String(source='get_content_type') class Meta: model = Document exclude = ('id', 'file') class Query(graphene.ObjectType): documents = graphene.List(DocumentType, doc_type=graphene.String(required=True), party_code=graphene.Int(required=True)) def resolve_documents(self, info, kwargs): if info.context.user.is_anonymous: raise GraphQLError('Unauthorized') q = Document.objects.filter(bceid_user=info.context.user, kwargs) for doc in q: if not doc.file_exists(): q.delete() return Document.objects.none() return q class DocumentInput(graphene.InputObjectType): filename = graphene.String(required=True) size = graphene.Int(required=True) width = graphene.Int() height = graphene.Int() rotation = graphene.Int() class DocumentMetaDataInput(graphene.InputObjectType): files = graphene.List(DocumentInput, required=True) doc_type = graphene.String(required=True) party_code = graphene.Int(required=True) class UpdateMetadata(graphene.Mutation): class Arguments: input = DocumentMetaDataInput(required=True) documents = graphene.List(DocumentType) def mutate(self, info, kwargs): input_ = kwargs['input'] documents = Document.objects.filter(bceid_user=info.context.user, doc_type=input_['doc_type'], party_code=input_['party_code']) unique_files = [dict(s) for s in set(frozenset(d.items()) for d in input_['files'])] if documents.count() != len(input_['files']) or documents.count() != len(unique_files): raise GraphQLError("Invalid input: there must be the same number of files") for i, file in enumerate(input_['files']): try: doc = documents.get(filename=file['filename'], size=file['size']) doc.sort_order = i + 1 doc.width = file.get('width', doc.width) doc.height = file.get('height', doc.height) doc.rotation = file.get('rotation', doc.rotation) if doc.rotation not in [0, 90, 180, 270]: raise GraphQLError(f"Invalid rotation {doc.rotation}, must be 0, 90, 180, 270") doc.save() except Document.DoesNotExist: raise GraphQLError(f"Couldn't find document '{file['filename']}' with size '{file['size']}'") return UpdateMetadata(documents=documents.all()) class Mutations(graphene.ObjectType): update_metadata = UpdateMetadata.Field() graphql_schema = graphene.Schema(query=Query, mutation=Mutations)	none	1		2.255126	2
amazing/maze.py	danieloconell/maze-solver	0	10224	from .exceptions import MazeNotSolved, AlgorithmNotFound from .dijkstra import Dijkstra from .astar import Astar from functools import wraps import warnings from daedalus import Maze as _maze from PIL import Image warnings.simplefilter("once", UserWarning) class Maze: """ Create a maze and solve it. Available algorithms: dijkstra astar (WIP) Steps: 1. Create maze using the daedalus library. 2. Convert maze to graph. 3. Solve maze with algorithm. """ WHITE = (0, 0, 0) BLACK = (255, 255, 255) RED = (255, 0, 0) def __init__(self, width, height, algorithm="dijkstra"): """Set algorithm to be used when solving. Args: algorithm (str) to be used when solving maze width (int) of maze in pixels height (int) of maze in pixels """ self.algorithm = algorithm if not width % 2 or not height % 2: warnings.warn( "Using even width or height, use even numbers for optimal images" ) self._create_maze(width, height) self._create_graph() self.width = width self.height = height def _create_maze(self, width, height): """Make maze to be solved and add border to maze. Args: width (int) of maze height (int) of maze """ # create maze self.maze = _maze(width, height) self.maze.create_perfect() # define maze variables self.entrance = self.maze.entrance self.exit = self.maze.exit # add index to maze self.maze = { row_i: {item_i: item for item_i, item in enumerate(row)} for row_i, row in enumerate(self.maze) } def _create_graph(self): """Remove unnecessary states from maze and convert maze to graph to be solved.""" self.graph = {} # convert to graph for column in self.maze.keys(): for row in self.maze[column].keys(): item = self.maze[column][row] if item != 1: neighbours = [] try: if self.maze[column][row - 1] != 1: neighbours.append(["left", (column, row - 1)]) except KeyError: None try: if self.maze[column][row + 1] != 1: neighbours.append(["right", (column, row + 1)]) except KeyError: None try: if self.maze[column - 1][row] != 1: neighbours.append(["above", (column - 1, row)]) except KeyError: None try: if self.maze[column + 1][row] != 1: neighbours.append(["below", (column + 1, row)]) except KeyError: None self.graph[(column, row)] = {x[:][1]: 1 for x in neighbours} # TODO: remove unnecessary states def _maze_maker(file_name): def real_decorator(func): @wraps(func) def wrapper(self, args, *kwargs): data = [] for row_i, row in enumerate(list(self.maze)): for item_i, item in enumerate(self.maze[row].values()): func(self, data, item, row_i=row_i, item_i=item_i) # save maze image = Image.new("RGB", (self.width, self.height)) image.putdata(data) image.save(file_name) return wrapper return real_decorator @_maze_maker("maze.png") def save(self, data, item, row_i=None, item_i=None): """Save maze locally as an image.""" # invert maze because maze is incorrect if item: data.append(self.WHITE) else: data.append(self.BLACK) def solve(self): """ Solve maze using specified algorithm. Returns: shortest path as a queue from start to finish of maze """ if self.algorithm == "astar": algorithm = Astar() elif self.algorithm == "dijkstra": algorithm = Dijkstra() else: raise AlgorithmNotFound( f"Invalid algorithm: {self.algorithm}. See help({type(self).__name__}) for available algorithms." ) # add nodes to graph for node in self.graph: algorithm.add_node(node, self.graph[node]) # pydaedalus stores y then x value which need to be reversed self.entrance = tuple(reversed(self.entrance)) self.exit = tuple(reversed(self.exit)) self.path = algorithm.shortest_path(self.entrance, self.exit) @_maze_maker("solution.png") def save_solution(self, data, item, row_i=None, item_i=None): """Save maze image and the shortest path.""" if not hasattr(self, "path"): raise MazeNotSolved( f"Maze must be solved to save solution. Run {type(self).__name__}.solve() first." ) if (row_i, item_i) in self.path: data.append(self.RED) elif item: data.append(self.WHITE) else: data.append(self.BLACK) def __str__(self): """Just cause it looks nice.""" string = [] for row in self.maze: string.append(["█" if item else " " for item in self.maze[row].values()]) return "\n".join(["".join(line) for line in string]) def __repr__(self): """Easier on the eyes.""" return f"Maze(algorithm='{self.algorithm}', width={self.width}, height={self.height})"	from .exceptions import MazeNotSolved, AlgorithmNotFound from .dijkstra import Dijkstra from .astar import Astar from functools import wraps import warnings from daedalus import Maze as _maze from PIL import Image warnings.simplefilter("once", UserWarning) class Maze: """ Create a maze and solve it. Available algorithms: dijkstra astar (WIP) Steps: 1. Create maze using the daedalus library. 2. Convert maze to graph. 3. Solve maze with algorithm. """ WHITE = (0, 0, 0) BLACK = (255, 255, 255) RED = (255, 0, 0) def __init__(self, width, height, algorithm="dijkstra"): """Set algorithm to be used when solving. Args: algorithm (str) to be used when solving maze width (int) of maze in pixels height (int) of maze in pixels """ self.algorithm = algorithm if not width % 2 or not height % 2: warnings.warn( "Using even width or height, use even numbers for optimal images" ) self._create_maze(width, height) self._create_graph() self.width = width self.height = height def _create_maze(self, width, height): """Make maze to be solved and add border to maze. Args: width (int) of maze height (int) of maze """ # create maze self.maze = _maze(width, height) self.maze.create_perfect() # define maze variables self.entrance = self.maze.entrance self.exit = self.maze.exit # add index to maze self.maze = { row_i: {item_i: item for item_i, item in enumerate(row)} for row_i, row in enumerate(self.maze) } def _create_graph(self): """Remove unnecessary states from maze and convert maze to graph to be solved.""" self.graph = {} # convert to graph for column in self.maze.keys(): for row in self.maze[column].keys(): item = self.maze[column][row] if item != 1: neighbours = [] try: if self.maze[column][row - 1] != 1: neighbours.append(["left", (column, row - 1)]) except KeyError: None try: if self.maze[column][row + 1] != 1: neighbours.append(["right", (column, row + 1)]) except KeyError: None try: if self.maze[column - 1][row] != 1: neighbours.append(["above", (column - 1, row)]) except KeyError: None try: if self.maze[column + 1][row] != 1: neighbours.append(["below", (column + 1, row)]) except KeyError: None self.graph[(column, row)] = {x[:][1]: 1 for x in neighbours} # TODO: remove unnecessary states def _maze_maker(file_name): def real_decorator(func): @wraps(func) def wrapper(self, args, *kwargs): data = [] for row_i, row in enumerate(list(self.maze)): for item_i, item in enumerate(self.maze[row].values()): func(self, data, item, row_i=row_i, item_i=item_i) # save maze image = Image.new("RGB", (self.width, self.height)) image.putdata(data) image.save(file_name) return wrapper return real_decorator @_maze_maker("maze.png") def save(self, data, item, row_i=None, item_i=None): """Save maze locally as an image.""" # invert maze because maze is incorrect if item: data.append(self.WHITE) else: data.append(self.BLACK) def solve(self): """ Solve maze using specified algorithm. Returns: shortest path as a queue from start to finish of maze """ if self.algorithm == "astar": algorithm = Astar() elif self.algorithm == "dijkstra": algorithm = Dijkstra() else: raise AlgorithmNotFound( f"Invalid algorithm: {self.algorithm}. See help({type(self).__name__}) for available algorithms." ) # add nodes to graph for node in self.graph: algorithm.add_node(node, self.graph[node]) # pydaedalus stores y then x value which need to be reversed self.entrance = tuple(reversed(self.entrance)) self.exit = tuple(reversed(self.exit)) self.path = algorithm.shortest_path(self.entrance, self.exit) @_maze_maker("solution.png") def save_solution(self, data, item, row_i=None, item_i=None): """Save maze image and the shortest path.""" if not hasattr(self, "path"): raise MazeNotSolved( f"Maze must be solved to save solution. Run {type(self).__name__}.solve() first." ) if (row_i, item_i) in self.path: data.append(self.RED) elif item: data.append(self.WHITE) else: data.append(self.BLACK) def __str__(self): """Just cause it looks nice.""" string = [] for row in self.maze: string.append(["█" if item else " " for item in self.maze[row].values()]) return "\n".join(["".join(line) for line in string]) def __repr__(self): """Easier on the eyes.""" return f"Maze(algorithm='{self.algorithm}', width={self.width}, height={self.height})"	en	0.809515	Create a maze and solve it. Available algorithms: dijkstra astar (WIP) Steps: 1. Create maze using the daedalus library. 2. Convert maze to graph. 3. Solve maze with algorithm. Set algorithm to be used when solving. Args: algorithm (str) to be used when solving maze width (int) of maze in pixels height (int) of maze in pixels Make maze to be solved and add border to maze. Args: width (int) of maze height (int) of maze # create maze # define maze variables # add index to maze Remove unnecessary states from maze and convert maze to graph to be solved. # convert to graph # TODO: remove unnecessary states # save maze Save maze locally as an image. # invert maze because maze is incorrect Solve maze using specified algorithm. Returns: shortest path as a queue from start to finish of maze # add nodes to graph # pydaedalus stores y then x value which need to be reversed Save maze image and the shortest path. Just cause it looks nice. Easier on the eyes.	3.357956	3
config.py	FarbodFarhangfar/midi_player_python	0	10225	<reponame>FarbodFarhangfar/midi_player_python import os def get_note_dic(): _note_dic = {'C': 0, 'C#': 1, 'Db': 1, 'D': 2, 'D#': 3, 'Eb': 3, 'E': 4, 'F': 5, 'F#': 6, 'Gb': 6, 'G': 7, 'G#': 8, 'Ab': 8, 'A': 9, 'A#': 10, 'Bb': 10, 'B': 11} return _note_dic def get_value_list(): values = {"16": 16, "8": 8, "4": 4, "2": 2, "1": 1, "0.5": 0.5, "1/2": 0.5, "0.25": 0.25, "1/4": 0.25, "0.125": 0.125, "1/8": 0.125, "0.0625": 0.0625, "1/16": 0.0625, "0.03125": 0.03125, "1/32": 0.03125} return values def instruments(inst): instruments_dict = { # Piano 'Acoustic Grand Piano': '1', 'Bright Acoustic Piano': '2', 'Electric Grand Piano': '3', 'Honky-tonk Piano': '4', 'Electric Piano 1': '5', 'Electric Piano 2': '6', 'Harpsichord': '7', 'Clavi': '8', # Chromatic Percussion 'Celesta': '9', 'Glockenspiel': '10', 'Music Box': '11', 'Vibraphone': '12', 'Marimba': '13', 'Xylophone': '14', 'Tubular Bells': '15', 'Dulcimer': '16', # Organ 'Drawbar Organ': '17', 'Percussive Organ': '18', 'Rock Organ': '19', 'Church Organ': '20', 'Reed Organ': '21', 'Accordion': '22', 'Harmonica': '23', 'Tango Accordion': '24', # Guitar 'Acoustic Guitar (nylon)': '25', 'Acoustic Guitar (steel)': '26', 'Electric Guitar (jazz)': '27', 'Electric Guitar (clean)': '28', 'Electric Guitar (muted)': '29', 'Overdriven Guitar': '30', 'Distortion Guitar': '31', 'Guitar Harmonics': '32', # Bass 'Acoustic Bass': '33', 'Electric Bass (finger)': '34', 'Electric Bass (pick)': '35', 'Fretless Bass': '36', 'Slap Bass 1': '37', 'Slap Bass 2': '38', 'Synth Bass 1': '39', 'Synth Bass 2': '40', # Strings 'Violin': '41', 'Viola': '42', 'Cello': '43', 'Contrabass': '44', 'Tremolo Strings': '45', 'Pizzicato Strings': '46', 'Orchestral Harp': '47', 'Timpani': '48', # Ensemble 'String Ensemble 1': '49', 'String Ensemble 2': '50', 'Synth Strings 1': '51', 'Synth Strings 2': '52', 'Choir Aahs': '53', 'Voice Oohs': '54', 'Synth Choir': '55', 'Orchestra Hit': '56', # Brass 'Trumpet': '57', 'Trombone': '58', 'Tuba': '59', 'Muted Trumpet': '60', 'French Horn': '61', 'Brass Section': '62', 'Synth Brass 1': '63', 'Synth Brass 2': '64', # Reed 'Soprano Sax': '65', 'Alto Sax': '66', 'Tenor Sax': '67', 'Baritone Sax': '68', 'Oboe': '69', 'English Horn': '70', 'Bassoon': '71', 'Clarinet': '72', # Pipe 'Piccolo': '73', 'Flute': '74', 'Recorder': '75', 'Pan Flute': '76', 'Blown bottle': '77', 'Shakuhachi': '78', 'Whistle': '79', 'Ocarina': '80', # Synth Lead 'Lead 1 (square)': '81', 'Lead 2 (sawtooth)': '82', 'Lead 3 (calliope)': '83', 'Lead 4 (chiff)': '84', 'Lead 5 (charang)': '85', 'Lead 6 (voice)': '86', 'Lead 7 (fifths)': '87', 'Lead 8 (bass + lead)': '88', # Synth Pad 'Pad 1 (new age)': '89', 'Pad 2 (warm)': '90', 'Pad 3 (polysynth)': '91', 'Pad 4 (choir)': '92', 'Pad 5 (bowed)': '93', 'Pad 6 (metallic)': '94', 'Pad 7 (halo)': '95', 'Pad 8 (sweep)': '96', # Synth Effects 'FX 1 (rain)': '97', 'FX 2 (soundtrack)': '98', 'FX 3 (crystal)': '99', 'FX 4 (atmosphere)': '100', 'FX 5 (brightness)': '101', 'FX 6 (goblins)': '102', 'FX 7 (echoes)': '103', 'FX 8 (sci-fi)': '104', # Ethnic 'Sitar': '105', 'Banjo': '106', 'Shamisen': '107', 'Koto': '108', 'Kalimba': '109', 'Bagpipe': '110', 'Fiddle': '111', 'Shanai': '112', # Percussive 'Tinkle Bell': '113', 'Agogo': '114', 'Steel Drums': '115', 'Woodblock': '116', 'Taiko Drum': '117', 'Melodic Tom': '118', 'Synth Drum': '119', 'Reverse Cymbal': '120', # Sound effects 'Guitar Fret Noise': '121', 'Breath Noise': '122', 'Seashore': '123', 'Bird Tweet': '124', 'Telephone Ring': '125', 'Helicopter': '126', 'Applause': '127'} return instruments_dict	import os def get_note_dic(): _note_dic = {'C': 0, 'C#': 1, 'Db': 1, 'D': 2, 'D#': 3, 'Eb': 3, 'E': 4, 'F': 5, 'F#': 6, 'Gb': 6, 'G': 7, 'G#': 8, 'Ab': 8, 'A': 9, 'A#': 10, 'Bb': 10, 'B': 11} return _note_dic def get_value_list(): values = {"16": 16, "8": 8, "4": 4, "2": 2, "1": 1, "0.5": 0.5, "1/2": 0.5, "0.25": 0.25, "1/4": 0.25, "0.125": 0.125, "1/8": 0.125, "0.0625": 0.0625, "1/16": 0.0625, "0.03125": 0.03125, "1/32": 0.03125} return values def instruments(inst): instruments_dict = { # Piano 'Acoustic Grand Piano': '1', 'Bright Acoustic Piano': '2', 'Electric Grand Piano': '3', 'Honky-tonk Piano': '4', 'Electric Piano 1': '5', 'Electric Piano 2': '6', 'Harpsichord': '7', 'Clavi': '8', # Chromatic Percussion 'Celesta': '9', 'Glockenspiel': '10', 'Music Box': '11', 'Vibraphone': '12', 'Marimba': '13', 'Xylophone': '14', 'Tubular Bells': '15', 'Dulcimer': '16', # Organ 'Drawbar Organ': '17', 'Percussive Organ': '18', 'Rock Organ': '19', 'Church Organ': '20', 'Reed Organ': '21', 'Accordion': '22', 'Harmonica': '23', 'Tango Accordion': '24', # Guitar 'Acoustic Guitar (nylon)': '25', 'Acoustic Guitar (steel)': '26', 'Electric Guitar (jazz)': '27', 'Electric Guitar (clean)': '28', 'Electric Guitar (muted)': '29', 'Overdriven Guitar': '30', 'Distortion Guitar': '31', 'Guitar Harmonics': '32', # Bass 'Acoustic Bass': '33', 'Electric Bass (finger)': '34', 'Electric Bass (pick)': '35', 'Fretless Bass': '36', 'Slap Bass 1': '37', 'Slap Bass 2': '38', 'Synth Bass 1': '39', 'Synth Bass 2': '40', # Strings 'Violin': '41', 'Viola': '42', 'Cello': '43', 'Contrabass': '44', 'Tremolo Strings': '45', 'Pizzicato Strings': '46', 'Orchestral Harp': '47', 'Timpani': '48', # Ensemble 'String Ensemble 1': '49', 'String Ensemble 2': '50', 'Synth Strings 1': '51', 'Synth Strings 2': '52', 'Choir Aahs': '53', 'Voice Oohs': '54', 'Synth Choir': '55', 'Orchestra Hit': '56', # Brass 'Trumpet': '57', 'Trombone': '58', 'Tuba': '59', 'Muted Trumpet': '60', 'French Horn': '61', 'Brass Section': '62', 'Synth Brass 1': '63', 'Synth Brass 2': '64', # Reed 'Soprano Sax': '65', 'Alto Sax': '66', 'Tenor Sax': '67', 'Baritone Sax': '68', 'Oboe': '69', 'English Horn': '70', 'Bassoon': '71', 'Clarinet': '72', # Pipe 'Piccolo': '73', 'Flute': '74', 'Recorder': '75', 'Pan Flute': '76', 'Blown bottle': '77', 'Shakuhachi': '78', 'Whistle': '79', 'Ocarina': '80', # Synth Lead 'Lead 1 (square)': '81', 'Lead 2 (sawtooth)': '82', 'Lead 3 (calliope)': '83', 'Lead 4 (chiff)': '84', 'Lead 5 (charang)': '85', 'Lead 6 (voice)': '86', 'Lead 7 (fifths)': '87', 'Lead 8 (bass + lead)': '88', # Synth Pad 'Pad 1 (new age)': '89', 'Pad 2 (warm)': '90', 'Pad 3 (polysynth)': '91', 'Pad 4 (choir)': '92', 'Pad 5 (bowed)': '93', 'Pad 6 (metallic)': '94', 'Pad 7 (halo)': '95', 'Pad 8 (sweep)': '96', # Synth Effects 'FX 1 (rain)': '97', 'FX 2 (soundtrack)': '98', 'FX 3 (crystal)': '99', 'FX 4 (atmosphere)': '100', 'FX 5 (brightness)': '101', 'FX 6 (goblins)': '102', 'FX 7 (echoes)': '103', 'FX 8 (sci-fi)': '104', # Ethnic 'Sitar': '105', 'Banjo': '106', 'Shamisen': '107', 'Koto': '108', 'Kalimba': '109', 'Bagpipe': '110', 'Fiddle': '111', 'Shanai': '112', # Percussive 'Tinkle Bell': '113', 'Agogo': '114', 'Steel Drums': '115', 'Woodblock': '116', 'Taiko Drum': '117', 'Melodic Tom': '118', 'Synth Drum': '119', 'Reverse Cymbal': '120', # Sound effects 'Guitar Fret Noise': '121', 'Breath Noise': '122', 'Seashore': '123', 'Bird Tweet': '124', 'Telephone Ring': '125', 'Helicopter': '126', 'Applause': '127'} return instruments_dict	en	0.497871	#': 1, 'Db': 1, 'D': 2, 'D#': 3, 'Eb': 3, 'E': 4, 'F': 5, 'F#': 6, #': 8, 'Ab': 8, 'A': 9, 'A#': 10, 'Bb': 10, 'B': 11} # Piano # Chromatic Percussion # Organ # Guitar # Bass # Strings # Ensemble # Brass # Reed # Pipe # Synth Lead # Synth Pad # Synth Effects # Ethnic # Percussive # Sound effects	2.8247	3
roles/openshift_health_checker/library/ocutil.py	shgriffi/openshift-ansible	164	10226	#!/usr/bin/python """Interface to OpenShift oc command""" import os import shlex import shutil import subprocess from ansible.module_utils.basic import AnsibleModule ADDITIONAL_PATH_LOOKUPS = ['/usr/local/bin', os.path.expanduser('~/bin')] def locate_oc_binary(): """Find and return oc binary file""" # https://github.com/openshift/openshift-ansible/issues/3410 # oc can be in /usr/local/bin in some cases, but that may not # be in $PATH due to ansible/sudo paths = os.environ.get("PATH", os.defpath).split(os.pathsep) + ADDITIONAL_PATH_LOOKUPS oc_binary = 'oc' # Use shutil.which if it is available, otherwise fallback to a naive path search try: which_result = shutil.which(oc_binary, path=os.pathsep.join(paths)) if which_result is not None: oc_binary = which_result except AttributeError: for path in paths: if os.path.exists(os.path.join(path, oc_binary)): oc_binary = os.path.join(path, oc_binary) break return oc_binary def main(): """Module that executes commands on a remote OpenShift cluster""" module = AnsibleModule( argument_spec=dict( namespace=dict(type="str", required=False), config_file=dict(type="str", required=True), cmd=dict(type="str", required=True), extra_args=dict(type="list", default=[]), ), ) cmd = [locate_oc_binary(), '--config', module.params["config_file"]] if module.params["namespace"]: cmd += ['-n', module.params["namespace"]] cmd += shlex.split(module.params["cmd"]) + module.params["extra_args"] failed = True try: cmd_result = subprocess.check_output(list(cmd), stderr=subprocess.STDOUT) failed = False except subprocess.CalledProcessError as exc: cmd_result = '[rc {}] {}\n{}'.format(exc.returncode, ' '.join(exc.cmd), exc.output) except OSError as exc: # we get this when 'oc' is not there cmd_result = str(exc) module.exit_json( changed=False, failed=failed, result=cmd_result, ) if __name__ == '__main__': main()	#!/usr/bin/python """Interface to OpenShift oc command""" import os import shlex import shutil import subprocess from ansible.module_utils.basic import AnsibleModule ADDITIONAL_PATH_LOOKUPS = ['/usr/local/bin', os.path.expanduser('~/bin')] def locate_oc_binary(): """Find and return oc binary file""" # https://github.com/openshift/openshift-ansible/issues/3410 # oc can be in /usr/local/bin in some cases, but that may not # be in $PATH due to ansible/sudo paths = os.environ.get("PATH", os.defpath).split(os.pathsep) + ADDITIONAL_PATH_LOOKUPS oc_binary = 'oc' # Use shutil.which if it is available, otherwise fallback to a naive path search try: which_result = shutil.which(oc_binary, path=os.pathsep.join(paths)) if which_result is not None: oc_binary = which_result except AttributeError: for path in paths: if os.path.exists(os.path.join(path, oc_binary)): oc_binary = os.path.join(path, oc_binary) break return oc_binary def main(): """Module that executes commands on a remote OpenShift cluster""" module = AnsibleModule( argument_spec=dict( namespace=dict(type="str", required=False), config_file=dict(type="str", required=True), cmd=dict(type="str", required=True), extra_args=dict(type="list", default=[]), ), ) cmd = [locate_oc_binary(), '--config', module.params["config_file"]] if module.params["namespace"]: cmd += ['-n', module.params["namespace"]] cmd += shlex.split(module.params["cmd"]) + module.params["extra_args"] failed = True try: cmd_result = subprocess.check_output(list(cmd), stderr=subprocess.STDOUT) failed = False except subprocess.CalledProcessError as exc: cmd_result = '[rc {}] {}\n{}'.format(exc.returncode, ' '.join(exc.cmd), exc.output) except OSError as exc: # we get this when 'oc' is not there cmd_result = str(exc) module.exit_json( changed=False, failed=failed, result=cmd_result, ) if __name__ == '__main__': main()	en	0.793541	#!/usr/bin/python Interface to OpenShift oc command Find and return oc binary file # https://github.com/openshift/openshift-ansible/issues/3410 # oc can be in /usr/local/bin in some cases, but that may not # be in $PATH due to ansible/sudo # Use shutil.which if it is available, otherwise fallback to a naive path search Module that executes commands on a remote OpenShift cluster # we get this when 'oc' is not there	2.471472	2
code/network/__init__.py	michalochman/complex-networks	0	10227	import fractions class Network(object): def __init__(self, network): self.network = network def degree(self, link_type, key): return len(self.network.get(link_type).get(key)) def average_degree(self, link_type): degree = 0 for link in self.network.get(link_type).itervalues(): degree += len(link) return float(degree) / float(len(self.network.get(link_type))) def nn_degree(self, link_type, link_n_type, key): degree = self.degree(link_type, key) nn_degree = 0 for n_key in self.network.get(link_type, key): nn_degree += self.degree(link_n_type, n_key) return '%d/%d' % (nn_degree, degree) def jaccard_index(self, set_a, set_b): n = len(set_a & set_b) return float(n)/float(len(set_a) + len(set_b) - n) def jaccard_similarity(self, link_type, key_a, key_b, return_string=False): key_a = int(key_a) key_b = int(key_b) set_a = set(self.network.get(link_type).get(key_a).values()) set_b = set(self.network.get(link_type).get(key_b).values()) if return_string: intersection = len(set_a & set_b) union = len(set_a \| set_b) gcd = fractions.gcd(intersection, union) return '%d/%d' % (intersection/gcd, union/gcd) return self.jaccard_index(set_a, set_b) def collaborative_similarity(self, link_type, link_n_type, key, return_string=False): degree = self.degree(link_type, key) if degree <= 1: return 0 similarity_sum = 0 for n_key_1 in self.network.get(link_type).get(key).itervalues(): for n_key_2 in self.network.get(link_type).get(key).itervalues(): if n_key_1 == n_key_2: continue similarity_sum += self.jaccard_similarity(link_n_type, n_key_1, n_key_2) if return_string: precision = 1e3 new_similarity_sum = round(similarity_sum * degree(degree-1) precision) gcd = fractions.gcd(new_similarity_sum, degree(degree-1) precision) new_similarity_sum /= gcd return '%d/%d' % (new_similarity_sum, degree(degree-1)round(new_similarity_sum/similarity_sum)) return similarity_sum / (degree(degree-1)) def average_jaccard_similarity(self, link_type, link_n_type, return_string=False): nodes = 0 similarity_sum = 0 for key_links in self.network.get(link_type).itervalues(): for n_key_1 in key_links.itervalues(): for n_key_2 in key_links.itervalues(): if n_key_1 == n_key_2: continue nodes += 1 similarity_sum += self.jaccard_similarity(link_n_type, n_key_1, n_key_2) if nodes == 0: return 0 if return_string: precision = 1e3 new_similarity_sum = round(similarity_sum nodes * precision) gcd = fractions.gcd(new_similarity_sum, nodes * precision) new_similarity_sum /= gcd return '%d/%d' % (new_similarity_sum, nodesround(new_similarity_sum/similarity_sum)) return similarity_sum / nodes def network_collaborative_similarity(self, link_type, link_n_type, return_string=False): nodes = 0 similarity_sum = 0 for key, key_links in self.network.get(link_type).iteritems(): if self.degree(link_type, key) <= 1: continue nodes += 1 collaborative_similarity = self.collaborative_similarity(link_type, link_n_type, key) similarity_sum += collaborative_similarity if nodes == 0: return 0 if return_string: precision = 1e3 new_similarity_sum = round(similarity_sum nodes * precision) gcd = fractions.gcd(new_similarity_sum, nodes * precision) new_similarity_sum /= gcd return '%d/%d' % (new_similarity_sum, nodes*(new_similarity_sum/similarity_sum)) return similarity_sum/nodes	import fractions class Network(object): def __init__(self, network): self.network = network def degree(self, link_type, key): return len(self.network.get(link_type).get(key)) def average_degree(self, link_type): degree = 0 for link in self.network.get(link_type).itervalues(): degree += len(link) return float(degree) / float(len(self.network.get(link_type))) def nn_degree(self, link_type, link_n_type, key): degree = self.degree(link_type, key) nn_degree = 0 for n_key in self.network.get(link_type, key): nn_degree += self.degree(link_n_type, n_key) return '%d/%d' % (nn_degree, degree) def jaccard_index(self, set_a, set_b): n = len(set_a & set_b) return float(n)/float(len(set_a) + len(set_b) - n) def jaccard_similarity(self, link_type, key_a, key_b, return_string=False): key_a = int(key_a) key_b = int(key_b) set_a = set(self.network.get(link_type).get(key_a).values()) set_b = set(self.network.get(link_type).get(key_b).values()) if return_string: intersection = len(set_a & set_b) union = len(set_a \| set_b) gcd = fractions.gcd(intersection, union) return '%d/%d' % (intersection/gcd, union/gcd) return self.jaccard_index(set_a, set_b) def collaborative_similarity(self, link_type, link_n_type, key, return_string=False): degree = self.degree(link_type, key) if degree <= 1: return 0 similarity_sum = 0 for n_key_1 in self.network.get(link_type).get(key).itervalues(): for n_key_2 in self.network.get(link_type).get(key).itervalues(): if n_key_1 == n_key_2: continue similarity_sum += self.jaccard_similarity(link_n_type, n_key_1, n_key_2) if return_string: precision = 1e3 new_similarity_sum = round(similarity_sum * degree(degree-1) precision) gcd = fractions.gcd(new_similarity_sum, degree(degree-1) precision) new_similarity_sum /= gcd return '%d/%d' % (new_similarity_sum, degree(degree-1)round(new_similarity_sum/similarity_sum)) return similarity_sum / (degree(degree-1)) def average_jaccard_similarity(self, link_type, link_n_type, return_string=False): nodes = 0 similarity_sum = 0 for key_links in self.network.get(link_type).itervalues(): for n_key_1 in key_links.itervalues(): for n_key_2 in key_links.itervalues(): if n_key_1 == n_key_2: continue nodes += 1 similarity_sum += self.jaccard_similarity(link_n_type, n_key_1, n_key_2) if nodes == 0: return 0 if return_string: precision = 1e3 new_similarity_sum = round(similarity_sum nodes * precision) gcd = fractions.gcd(new_similarity_sum, nodes * precision) new_similarity_sum /= gcd return '%d/%d' % (new_similarity_sum, nodesround(new_similarity_sum/similarity_sum)) return similarity_sum / nodes def network_collaborative_similarity(self, link_type, link_n_type, return_string=False): nodes = 0 similarity_sum = 0 for key, key_links in self.network.get(link_type).iteritems(): if self.degree(link_type, key) <= 1: continue nodes += 1 collaborative_similarity = self.collaborative_similarity(link_type, link_n_type, key) similarity_sum += collaborative_similarity if nodes == 0: return 0 if return_string: precision = 1e3 new_similarity_sum = round(similarity_sum nodes * precision) gcd = fractions.gcd(new_similarity_sum, nodes * precision) new_similarity_sum /= gcd return '%d/%d' % (new_similarity_sum, nodes*(new_similarity_sum/similarity_sum)) return similarity_sum/nodes	none	1		3.337132	3
invoke_ansible.py	samvarankashyap/ansible_api_usage	0	10228	import ansible import pprint from ansible import utils from jinja2 import Environment, PackageLoader from collections import namedtuple from ansible import utils from ansible.parsing.dataloader import DataLoader from ansible.vars import VariableManager from ansible.inventory import Inventory from ansible.executor.playbook_executor import PlaybookExecutor from ansible.plugins.callback import CallbackBase from callbacks import PlaybookCallback def invoke_ansible_playbook(module_path, e_vars, playbook_path="site.yml", console=True): """ Invokes playbook """ loader = DataLoader() variable_manager = VariableManager() variable_manager.extra_vars = e_vars inventory = Inventory(loader=loader, variable_manager=variable_manager, host_list=['localhost']) passwords = {} utils.VERBOSITY = 4 Options = namedtuple('Options', ['listtags', 'listtasks', 'listhosts', 'syntax', 'connection', 'module_path', 'forks', 'remote_user', 'private_key_file', 'ssh_common_args', 'ssh_extra_args', 'sftp_extra_args', 'scp_extra_args', 'become', 'become_method', 'become_user', 'verbosity', 'check']) options = Options(listtags=False, listtasks=False, listhosts=False, syntax=False, connection='ssh', module_path=module_path, forks=100, remote_user='root', private_key_file=None, ssh_common_args=None, ssh_extra_args=None, sftp_extra_args=None, scp_extra_args=None, become=False, become_method=None, become_user='root', verbosity=utils.VERBOSITY, check=False) pbex = PlaybookExecutor(playbooks=[playbook_path], inventory=inventory, variable_manager=variable_manager, loader=loader, options=options, passwords=passwords) if not console: cb = PlaybookCallback() pbex._tqm._stdout_callback = cb return_code = pbex.run() results = cb.results else: results = pbex.run() return results	import ansible import pprint from ansible import utils from jinja2 import Environment, PackageLoader from collections import namedtuple from ansible import utils from ansible.parsing.dataloader import DataLoader from ansible.vars import VariableManager from ansible.inventory import Inventory from ansible.executor.playbook_executor import PlaybookExecutor from ansible.plugins.callback import CallbackBase from callbacks import PlaybookCallback def invoke_ansible_playbook(module_path, e_vars, playbook_path="site.yml", console=True): """ Invokes playbook """ loader = DataLoader() variable_manager = VariableManager() variable_manager.extra_vars = e_vars inventory = Inventory(loader=loader, variable_manager=variable_manager, host_list=['localhost']) passwords = {} utils.VERBOSITY = 4 Options = namedtuple('Options', ['listtags', 'listtasks', 'listhosts', 'syntax', 'connection', 'module_path', 'forks', 'remote_user', 'private_key_file', 'ssh_common_args', 'ssh_extra_args', 'sftp_extra_args', 'scp_extra_args', 'become', 'become_method', 'become_user', 'verbosity', 'check']) options = Options(listtags=False, listtasks=False, listhosts=False, syntax=False, connection='ssh', module_path=module_path, forks=100, remote_user='root', private_key_file=None, ssh_common_args=None, ssh_extra_args=None, sftp_extra_args=None, scp_extra_args=None, become=False, become_method=None, become_user='root', verbosity=utils.VERBOSITY, check=False) pbex = PlaybookExecutor(playbooks=[playbook_path], inventory=inventory, variable_manager=variable_manager, loader=loader, options=options, passwords=passwords) if not console: cb = PlaybookCallback() pbex._tqm._stdout_callback = cb return_code = pbex.run() results = cb.results else: results = pbex.run() return results	en	0.212026	Invokes playbook	2.031261	2
bin/python/csv2es.py	reid-wagner/proteomics-pipelines	2	10229	#!/usr/bin/env python3 import itertools import string from elasticsearch import Elasticsearch,helpers import sys import os from glob import glob import pandas as pd import json host = sys.argv[1] port = int(sys.argv[2]) alias = sys.argv[3] print(host) print(port) print(alias) es = Elasticsearch([{'host': host, 'port': port}]) # create our test index # Get all csv files in /root/data files = [y for x in os.walk('/root/data') for y in glob(os.path.join(x[0], '.csv'))] count = 0 def clean_field(val): val = val.split('.') val = [i for i in val if i != ''] val = '_'.join(val) val = val.split() val = [i for i in val if i != ''] val = '_'.join(val) val = val.split('/') val = [i for i in val if i != ''] val = '_'.join(val) return val es.indices.delete(index=alias + '', ignore=[400, 404]) indices = [] for file in files: data = pd.read_csv(file, sep=None, engine='python') index = alias + '_'.join(file.split('/')) index = clean_field(index).lower().split('_csv')[0] indices.append(index) es.indices.create(index) for col in data.columns: if col.startswith('Unnamed'): del data[col] else: data.rename(columns= { col : clean_field(col) },inplace=True ) data = data.reset_index() # Make sure there is no duplicate indexing data.rename(columns={'index':'row'},inplace =True) data['File'] = file data['_id'] = data['File'] + '.{}.'.format(str(count)) + data.reset_index()['index'].apply(str) data['_type'] = "document" data['_index'] = index records = data.to_json(orient='records') records = json.loads(records) helpers.bulk(es, records, chunk_size=100) count += 1 print(es.count(index=index)) # Create an index table in elasticsearch to locate the files indices_table = pd.DataFrame() indices_table['Index'] = pd.Series(indices) indices_table['File'] = pd.Series(files) indices_table['Alias'] = alias indices_table['_id'] = indices_table['Alias'] + '.' + indices_table['File'] indices_table['_type'] = "document" indices_table['_index'] = alias + '_indices' es.indices.create(alias + '_indices') records = indices_table.to_json(orient='records') records = json.loads(records) helpers.bulk(es, records, chunk_size=100) print(es.count(index=alias + '_indices'))	#!/usr/bin/env python3 import itertools import string from elasticsearch import Elasticsearch,helpers import sys import os from glob import glob import pandas as pd import json host = sys.argv[1] port = int(sys.argv[2]) alias = sys.argv[3] print(host) print(port) print(alias) es = Elasticsearch([{'host': host, 'port': port}]) # create our test index # Get all csv files in /root/data files = [y for x in os.walk('/root/data') for y in glob(os.path.join(x[0], '.csv'))] count = 0 def clean_field(val): val = val.split('.') val = [i for i in val if i != ''] val = '_'.join(val) val = val.split() val = [i for i in val if i != ''] val = '_'.join(val) val = val.split('/') val = [i for i in val if i != ''] val = '_'.join(val) return val es.indices.delete(index=alias + '', ignore=[400, 404]) indices = [] for file in files: data = pd.read_csv(file, sep=None, engine='python') index = alias + '_'.join(file.split('/')) index = clean_field(index).lower().split('_csv')[0] indices.append(index) es.indices.create(index) for col in data.columns: if col.startswith('Unnamed'): del data[col] else: data.rename(columns= { col : clean_field(col) },inplace=True ) data = data.reset_index() # Make sure there is no duplicate indexing data.rename(columns={'index':'row'},inplace =True) data['File'] = file data['_id'] = data['File'] + '.{}.'.format(str(count)) + data.reset_index()['index'].apply(str) data['_type'] = "document" data['_index'] = index records = data.to_json(orient='records') records = json.loads(records) helpers.bulk(es, records, chunk_size=100) count += 1 print(es.count(index=index)) # Create an index table in elasticsearch to locate the files indices_table = pd.DataFrame() indices_table['Index'] = pd.Series(indices) indices_table['File'] = pd.Series(files) indices_table['Alias'] = alias indices_table['_id'] = indices_table['Alias'] + '.' + indices_table['File'] indices_table['_type'] = "document" indices_table['_index'] = alias + '_indices' es.indices.create(alias + '_indices') records = indices_table.to_json(orient='records') records = json.loads(records) helpers.bulk(es, records, chunk_size=100) print(es.count(index=alias + '_indices'))	en	0.575823	#!/usr/bin/env python3 # create our test index # Get all csv files in /root/data # Make sure there is no duplicate indexing # Create an index table in elasticsearch to locate the files	2.789907	3
main/src/preparation/parsers/tree-sitter-python/examples/crlf-line-endings.py	jason424217/Artificial-Code-Gen	0	10230	<filename>main/src/preparation/parsers/tree-sitter-python/examples/crlf-line-endings.py<gh_stars>0 print a if b: if c: d e	<filename>main/src/preparation/parsers/tree-sitter-python/examples/crlf-line-endings.py<gh_stars>0 print a if b: if c: d e	none	1		1.835831	2
Src/main.py	DukeA/DAT02X-19-03-MachineLearning-Starcraft2	0	10231	from absl import app from mainLoop import main if __name__ == '__main__': app.run(main)	from absl import app from mainLoop import main if __name__ == '__main__': app.run(main)	none	1		1.250541	1
bos_sarcat_scraper/__main__.py	hysds/bos_sarcat_scraper	1	10232	<gh_stars>1-10 from __future__ import absolute_import from builtins import str from builtins import input import sys import argparse from . import bosart_scrape import datetime import json def valid_date(s): try: try: date = datetime.datetime.strptime(s, "%Y-%m-%dT%H:%M:%S.%fZ") except: date = datetime.datetime.strptime(s, "%Y-%m-%dT%H:%M:%SZ") return date except ValueError: msg = "Not a valid date: '{0}'.".format(s) raise argparse.ArgumentTypeError(msg) def geojson(spatial_extent): if type(json.loads(spatial_extent)) is dict: return spatial_extent def sort_field(s_f): if s_f == "start_time" or s_f == "stop_time" or s_f == "bos_ingest": return s_f else: raise argparse.ArgumentError("The value for sortBy should be either start_time, stop_time or bos_ingest not %s."%s_f) def sort_order(order): if order == "asc" or order == "des": return order else: raise argparse.ArgumentError("The value for sort should be either asc or des not %s,"%order) def check_inputs(args): yes = "y" no = "n" if not args.fromTime and not args.fromBosIngestTime: print ("You have NOT specified any start time using --fromTime, -from or --fromBosIngestTime. \nYou are asking to find all acquisitions from the beginning of time! \nThis query will take a very long time.\nTHIS IS NOT RECOMMENDED.") resp = str(eval(input('Are you sure you want to proceed? (y/n):'))) if resp.lower() == yes.lower(): print("Okay! Please wait...") return True elif resp.lower() == no.lower(): print("Please try again with the start time specified using --fromTime, -from or --fromBosIngestTime.") exit() else: print("Please specify y/n\n") return False return True def main(): parser = argparse.ArgumentParser(description='Query BOS SarCat for acquisitions.') parser.add_argument("-from","--fromTime", help='specify the temporal start point in format , to get acquisitions starting after the given timestamp in the format yyyy-mm-ddThh:mm:ss.sssZ', type=valid_date) parser.add_argument("--fromBosIngestTime", help='provide date and time in format , to get acquisitions acquired by BOS after the given timestamp in the format yyyy-mm-ddThh:mm:ss.sssZ', type=valid_date) parser.add_argument("-to","--toTime", help='specify the temporal end point in format , to get acquisitions ending before the given timestamp in the format yyyy-mm-ddThh:mm:ss.sssZ', type=valid_date) parser.add_argument("--spatialExtent", help='specify the area of interest in GeoJSON format', type = geojson) parser.add_argument("--sortBy", help='type "start_time" , "stop_time" or "bos_ingest" to sort results by field', type = sort_field) parser.add_argument("--sort", help='type "asc" or "des" to get results in ascending or descending order of time respectively. If sortBy is specified but sort is not, then defaults to ascending', type = sort_order) args = parser.parse_args() checked = False while not checked: checked = check_inputs(args) # construct the parameter list based on user specified restrictions params = {} if args.fromTime: params["fromTime"] = args.fromTime if args.fromBosIngestTime: params["fromBosIngestTime"] = args.fromBosIngestTime if args.toTime: params["toTime"] = args.toTime if args.spatialExtent: params["spatialExtent"] = json.dumps(args.spatialExtent) if args.sortBy: params["sortBy"] = args.sortBy if args.sort: params["sort"] = args.sort print(bosart_scrape.make_api_call(parameters=params)) if __name__ == '__main__': main()	from __future__ import absolute_import from builtins import str from builtins import input import sys import argparse from . import bosart_scrape import datetime import json def valid_date(s): try: try: date = datetime.datetime.strptime(s, "%Y-%m-%dT%H:%M:%S.%fZ") except: date = datetime.datetime.strptime(s, "%Y-%m-%dT%H:%M:%SZ") return date except ValueError: msg = "Not a valid date: '{0}'.".format(s) raise argparse.ArgumentTypeError(msg) def geojson(spatial_extent): if type(json.loads(spatial_extent)) is dict: return spatial_extent def sort_field(s_f): if s_f == "start_time" or s_f == "stop_time" or s_f == "bos_ingest": return s_f else: raise argparse.ArgumentError("The value for sortBy should be either start_time, stop_time or bos_ingest not %s."%s_f) def sort_order(order): if order == "asc" or order == "des": return order else: raise argparse.ArgumentError("The value for sort should be either asc or des not %s,"%order) def check_inputs(args): yes = "y" no = "n" if not args.fromTime and not args.fromBosIngestTime: print ("You have NOT specified any start time using --fromTime, -from or --fromBosIngestTime. \nYou are asking to find all acquisitions from the beginning of time! \nThis query will take a very long time.\nTHIS IS NOT RECOMMENDED.") resp = str(eval(input('Are you sure you want to proceed? (y/n):'))) if resp.lower() == yes.lower(): print("Okay! Please wait...") return True elif resp.lower() == no.lower(): print("Please try again with the start time specified using --fromTime, -from or --fromBosIngestTime.") exit() else: print("Please specify y/n\n") return False return True def main(): parser = argparse.ArgumentParser(description='Query BOS SarCat for acquisitions.') parser.add_argument("-from","--fromTime", help='specify the temporal start point in format , to get acquisitions starting after the given timestamp in the format yyyy-mm-ddThh:mm:ss.sssZ', type=valid_date) parser.add_argument("--fromBosIngestTime", help='provide date and time in format , to get acquisitions acquired by BOS after the given timestamp in the format yyyy-mm-ddThh:mm:ss.sssZ', type=valid_date) parser.add_argument("-to","--toTime", help='specify the temporal end point in format , to get acquisitions ending before the given timestamp in the format yyyy-mm-ddThh:mm:ss.sssZ', type=valid_date) parser.add_argument("--spatialExtent", help='specify the area of interest in GeoJSON format', type = geojson) parser.add_argument("--sortBy", help='type "start_time" , "stop_time" or "bos_ingest" to sort results by field', type = sort_field) parser.add_argument("--sort", help='type "asc" or "des" to get results in ascending or descending order of time respectively. If sortBy is specified but sort is not, then defaults to ascending', type = sort_order) args = parser.parse_args() checked = False while not checked: checked = check_inputs(args) # construct the parameter list based on user specified restrictions params = {} if args.fromTime: params["fromTime"] = args.fromTime if args.fromBosIngestTime: params["fromBosIngestTime"] = args.fromBosIngestTime if args.toTime: params["toTime"] = args.toTime if args.spatialExtent: params["spatialExtent"] = json.dumps(args.spatialExtent) if args.sortBy: params["sortBy"] = args.sortBy if args.sort: params["sort"] = args.sort print(bosart_scrape.make_api_call(parameters=params)) if __name__ == '__main__': main()	en	0.404797	# construct the parameter list based on user specified restrictions	2.897735	3
vgm2electron.py	simondotm/vgm2electron	2	10233	#!/usr/bin/env python # vgm2electron.py # Tool for converting SN76489-based PSG VGM data to Acorn Electron # By <NAME> (https://github.com/simondotm/) # See https://github.com/simondotm/vgm-packer # # Copyright (c) 2019 <NAME>. All rights reserved. # # "MIT License": # 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. import functools import itertools import struct import sys import time import binascii import math import operator import os from modules.vgmparser import VgmStream class VgmElectron: OUTPUT_RAWDATA = False # output raw dumps of the data that was compressed by LZ4/Huffman VERBOSE = True # 0-3 represents approx the loudest 50% of volumes (=ON), 4-15 are the quietest 50% (=OFF) ATTENTUATION_THRESHOLD1 = 10 ATTENTUATION_THRESHOLD2 = 10 ATTENTUATION_THRESHOLD3 = 10 # define the number of octaves to transpose whole song by, in case too much bass getting lost TRANSPOSE_OCTAVES1 = 0 TRANSPOSE_OCTAVES2 = 0 TRANSPOSE_OCTAVES3 = 0 #-1 ENABLE_CHANNEL1 = True ENABLE_CHANNEL2 = True ENABLE_CHANNEL3 = True USE_TECHNIQUE = 2 def __init__(self): print("init") #---------------------------------------------------------- # Utilities #---------------------------------------------------------- # split the packed raw data into 11 separate streams # returns array of 11 bytearrays def split_raw(self, rawData, stripCommands = True): registers = [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] registers_opt = [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] latched_channel = -1 output_block = bytearray() output_blocks = [] for o in range(11): output_blocks.append( bytearray() ) if stripCommands: register_mask = 15 else: register_mask = 255 # unpack the raw binary data in 11 arrays of register data without any deltas between them # eg. the raw chip writes to all 11 registers every frame n = 0 Packet = True verbose = False while (Packet): packet_size = rawData[n] if verbose: print("packet_size=" + str(packet_size)) n += 1 if packet_size == 255: Packet = False else: for x in range(packet_size): d = rawData[n+x] #if verbose: # print " frame byte number=" +str(x) # print " frame byte=" +str(d) if d & 128: # latch c = (d>>5)&3 latched_channel = c if d & 16: # volume if verbose: print(" volume on channel " + str(c)) registers[c+7] = d & register_mask else: # tone if verbose: print(" tone on channel " + str(c)) registers[c2+0] = d & register_mask else: if verbose: print(" tone data on latched channel " + str(latched_channel)) registers[latched_channel2+1] = d # we no longer do any masking here # d & 63 # tone data only contains 6 bits of info anyway, so no need for mask if latched_channel == 3: print("ERROR CHANNEL") # emit current state of each of the 11 registers to 11 different bytearrays for x in range(11): output_blocks[x].append( registers[x] ) # next packet n += packet_size #print(output_blocks[6]) #IGNORE we no longer do this - let the decoder do it instead. if False: # make sure we only emit tone3 when it changes, or 15 for no-change # this prevents the LFSR from being reset lastTone3 = 255 for x in range(len(output_blocks[6])): t = output_blocks[6][x] if t == lastTone3: output_blocks[6][x] = 15 lastTone3 = t # print(output_blocks[6]) # Add EOF marker (0x08) to tone3 byte stream output_blocks[6].append(0x08) # 0x08 is an invalid noise tone. # return the split blocks return output_blocks # given an array of data points, serialize it to a bytearray # size is the number of bytes to be used to represent each element in the source array. def toByteArray(self, array, size = 1): r = bytearray() for v in array: if size < 2: r.append(v & 255) else: r.append(v & 255) r.append(v >> 8) return r #---------------------------------------------------------- # Process(filename) # Convert the given VGM file to an electron VGM file #---------------------------------------------------------- def process(self, src_filename, dst_filename): # load the VGM file, or alternatively interpret as a binary if src_filename.lower()[-4:] != ".vgm": print("ERROR: Not a VGM source") return vgm = VgmStream(src_filename) data_block = vgm.as_binary() data_offset = 0 # parse the header header_size = data_block[0] # header size play_rate = data_block[1] # play rate if header_size == 5 and play_rate == 50: packet_count = data_block[2] + data_block[3]256 # packet count LO duration_mm = data_block[4] # duration mm duration_ss = data_block[5] # duration ss data_offset = header_size+1 data_offset += data_block[data_offset]+1 data_offset += data_block[data_offset]+1 print("header_size=" +str(header_size)) print("play_rate="+str(play_rate)) print("packet_count="+str(packet_count)) print("duration_mm="+str(duration_mm)) print("duration_ss="+str(duration_ss)) print("data_offset="+str(data_offset)) else: print("No header.") print("") # Trim off the header data. The rest is raw data. data_block = data_block[data_offset:] #---------------------------------------------------------- # Unpack the register data into 11 separate data streams #---------------------------------------------------------- registers = self.split_raw(data_block, True) #---------------------------------------------------------- # Begin VGM conversion to Electron #---------------------------------------------------------- # Filter out channels we do not need # Modify all volumes to full or none # Interleave sound to a single channel # output final VGM vgm_stream = bytearray() vgm_time = 0 electron_data = bytearray() # given an SN76489 tone register value, return the equivalent Electron ULA register setting def sn_to_electron(tone_value): # hack to protect against divbyzero if (tone_value == 0): tone_value = 1 hz = float(vgm.vgm_source_clock) / ( 2.0 float(tone_value) * 16.0) print(" sn_to_electron freq " + str(hz) + "hz") # electron # Sound frequency = 1 MHz / [32 * (S + 1)] # f * 32(S+1) = 1Mhz # 32(S+1) = 1Mhz / f # (S+1) = 1Mhz / f32 #print ("SN freq is " + str(hz)) ula6 = int( 1000000.0 / (hz 32.0) ) - 1 # check we are within range if ula6 < 0: print(" WARNING: Electron freqency '" + str(ula6) + "' too high (" + str(hz) + ")") ula6 = 0 if ula6 > 255: print(" WARNING: Electron frequency '" + str(ula6) + "' too low (" + str(hz) + ")") ula6 = 255 return ula6 #-------------------------------------------------------------- # conversion settings #-------------------------------------------------------------- # convert the register data to a vgm stream sample_interval = int(44100 / vgm.metadata['rate']) # 882 # 50hz - TODO: use frame rate print("sample_interval=" + str(sample_interval)) USE_TONE3 = VgmElectron.ENABLE_CHANNEL3 # True # TODO: make these all parameters # Add channel filter option # Add mix type options # --attentuation 468 --filter 123 --transpose 00F --mix 123 --arpeggio 2 --rate 50 # Add option to clamp or transpose out of range frequencies # Make the .ula output file filename.electron.ula # Add 0x01 as a terminating byte in the output ULA MIX_RATE = 2 # modulo 2 for interleaving channels # other options # bias for channels # transpose or silence out of range notes channel_mix = 0 #-------------------------------------------------------------- # pre-process music to suit Electron capabilities #-------------------------------------------------------------- for i in range(len(registers[0])): print("Frame " + str(i)) #-------------------------------------------------------------- # step 1- map volumes to 1-bit precision #-------------------------------------------------------------- # 11 registers per frame # Tone 0 HL Tone 1 HL Tone 2 HL Tone 3 Vol 0123 for r in range(11): if r > 6: register_data = registers[r][i] # apply the threshold for each channel threshold = VgmElectron.ATTENTUATION_THRESHOLD1 if r == 8: threshold = VgmElectron.ATTENTUATION_THRESHOLD2 if r == 9: threshold = VgmElectron.ATTENTUATION_THRESHOLD3 # if its a volume, map to loudest volume or no volume (using logarithmic scale) if register_data < threshold: register_data = 0 # full volume else: register_data = 15 # zero volume if r == 7 and VgmElectron.ENABLE_CHANNEL1 == False: register_data = 15 # zero volume if r == 8 and VgmElectron.ENABLE_CHANNEL2 == False: register_data = 15 # zero volume if r == 9 and VgmElectron.ENABLE_CHANNEL3 == False: register_data = 15 # zero volume registers[r][i] = register_data #-------------------------------------------------------------- # step 2 - transpose to fit frequency range #-------------------------------------------------------------- # final step - bring tone1 into the frequency range of the electron # if the frequency goes below the range of the ULA capabilities, add an octave def retune(octaves, l,h,v): #if (octaves == 0): # print(" No transpose performed, octaves set to 0") # return print( " tonehi=" + str(registers[h][i]) + ", tonelo=" + str(registers[l][i])) tone_value = (registers[h][i] << 4) + registers[l][i] if tone_value > 0: tone_freq = float(vgm.vgm_source_clock) / ( 2.0 * float(tone_value) * 16.0) print(" Retune, Channel " + str(int(l/2)) + " tone=" + str(tone_value) + ", freq=" + str(tone_freq)) # electron baseline is 122Hz not 244Hz as the AUG states. baseline_freq = 1000000.0 / (32.0256.0) target_freq = tone_freq retuned = 0 transpose = abs(octaves) while retuned != transpose: # target_freq < baseline_freq: if (octaves < 0): target_freq /= 2.0 else: target_freq = 2.0 retuned += 1 # if cant reach baseline freq, transpose once, then silence if still too low :( if target_freq < baseline_freq: print(" WARNING: Freq too low - Added " + str(1) + " octave(s) - from " + str(target_freq) + " to " + str(target_freq2.0) + "Hz") # better to just clamp low frequencies at the bottom, and risk tuning issues rather than transposition jumps target_freq = baseline_freq #= 2.0 retuned = 1 if target_freq < baseline_freq: registers[v][i] = 15 print(" Tone " + str(i) + " silenced because frequency too low - " + str(target_freq)) #target_freq = 2.0 #retuned += 1 if retuned: #print(" WARNING: Freq too low - Added " + str(retuned) + " octave(s) - from " + str(tone_freq) + " to " + str(target_freq) + "Hz") tone_value = int( round( float(vgm.vgm_source_clock) / (2.0 target_freq * 16.0 ) ) ) registers[h][i] = tone_value >> 4 registers[l][i] = tone_value & 15 # transpose #if TRANSPOSE_OCTAVES > 0: print(" Transposing ") retune(VgmElectron.TRANSPOSE_OCTAVES1, 0,1,7) retune(VgmElectron.TRANSPOSE_OCTAVES2, 2,3,8) retune(VgmElectron.TRANSPOSE_OCTAVES3, 4,5,9) #-------------------------------------------------------------- # Step 3 - mix the 2 primary channels down to 1 channel #-------------------------------------------------------------- # map channel 2 to channel 1 # noise channel is completely ignored ENABLE_DOWNMIX = True if ENABLE_DOWNMIX: print(" Downmix channels ") #print("Frame " + str(i)) vol1 = registers[7][i] vol2 = registers[8][i] vol3 = registers[9][i] tone1_active = vol1 != 15 tone2_active = vol2 != 15 tone3_active = vol3 != 15 tone_active = tone1_active or tone2_active or tone3_active if tone_active: print(" Tone active, mixing") output_tone = 1 if self.USE_TECHNIQUE == 2: c1f = (registers[1][i] << 4) + registers[0][i] c2f = (registers[3][i] << 4) + registers[2][i] c3f = (registers[5][i] << 4) + registers[4][i] active_channels = [ False, False, False ] if tone1_active: active_channels[0] = True print("Channel 1 is active volume") if tone2_active: active_channels[1] = True print("Channel 2 is active volume") if tone3_active: active_channels[2] = True print("Channel 3 is active volume") # any channels playing the same frequency are filtered out if tone1_active and tone2_active and c2f == c1f: active_channels[1] = False print("Channel 2 is same freq as Channel 1, filtered") if tone1_active and tone3_active and c3f == c1f: active_channels[2] = False print("Channel 3 is same freq as Channel 1, filtered") if tone2_active and tone3_active and c2f == c3f: active_channels[2] = False print("Channel 3 is same freq as Channel 2, filtered") channel_count = 0 if active_channels[0]: channel_count += 1 if active_channels[1]: channel_count += 1 if active_channels[2]: channel_count += 1 print("channel_count=" + str(channel_count)) output_mix = [] if active_channels[0]: output_mix.append(1) if active_channels[1]: output_mix.append(2) if active_channels[2]: output_mix.append(3) mix = (i % channel_count) output_tone = output_mix[mix] if self.USE_TECHNIQUE == 1: # interleaving of channels 1+2 is done on odd/even frames for a consistent effect mix = (i % MIX_RATE) == 0 #(i & 1) == 0 # random is no good, thought it might average out but it sounds , well random #mix = random.random() < 0.5 # test code to see if modulo 3 any good, it wasn't if False: if channel_mix == 0 and vol1 != 0: channel_mix = (channel_mix + 1) % 3 if channel_mix == 1 and vol2 != 0: channel_mix = (channel_mix + 1) % 3 if channel_mix == 1 and vol3 != 0: channel_mix = (channel_mix + 1) % 3 output_tone = (channel_mix % 3) + 1 print("output tone=" + str(output_tone)) channel_mix = (channel_mix + 1) % 3 if True: # detect if channel 1 needs priority this frame # - its volume is on, and the alternative frame mix flag is good c1p = vol1 == 0 and mix # don't give channel 2 priority if tone is the same and channel1 is playing c1f = (registers[1][i] << 4) + registers[0][i] c2f = (registers[3][i] << 4) + registers[2][i] sametone = (c1f == c2f/2) or (c1f == c2f * 2) or (c1f == c2f) sametone = sametone and (vol1 == vol2) and (vol1 == 0) if vol1 == 0 and sametone: #diff < 100: #registers[0][i] == registers[2][i] and registers[1][i] == registers[2][i] and vol1 == 0: c1p = True print(" NOTE: channel 1 & channel 2 have same tone") # replace channel 1 data with channel 2 data # if, channel2 is active, but c1 doesn't have priority this frame if vol2 == 0 and not c1p:# and vol1 != 0: output_tone = 2 # if no volume on tone1, we can look at channel 3 too if USE_TONE3: #if registers[7][i] == 15: if vol1 == 15 and vol2 == 15 and vol3 == 0 and not mix:# and not c1p and output_tone != 2: print("tone3 active") output_tone = 3 # pick which tone to output if output_tone == 1: # do nothing, because tone1 register frequency already setup output_tone = 1 elif output_tone == 2: # replace tone 1 frequency with tone 2 frequency registers[0][i] = registers[2][i] registers[1][i] = registers[3][i] registers[7][i] = registers[8][i] elif output_tone == 3: # replace tone 1 frequency with tone 3 frequency registers[0][i] = registers[4][i] registers[1][i] = registers[5][i] registers[7][i] = registers[9][i] else: print("UNHANDLED CASE - output_tone not set") # output ULA data final_volume = registers[7][i] ula_tone = 0 # zero is highest freq. so inaudible, so thats how we handle volume if final_volume == 0: final_tone1 = (registers[1][i] << 4) + registers[0][i] ula_tone = sn_to_electron(final_tone1) electron_data.append( ula_tone ) # write to output ULA file ula_file = open(dst_filename + ".ula.bin", 'wb') ula_file.write(electron_data) ula_file.close() #-------------------------------------------------------------- # Final stage - output to vgm #-------------------------------------------------------------- # Tone1----- Tone2----- Tone3----- Tone4 Vol1 Vol2 Vol3 Vol4 control = [ 0x80, 0x00, 0xa0, 0x00, 0xc0, 0x00, 0xe0, 0x90, 0xb0, 0xd0, 0xf0 ] #filter = [ 0,1,2,3,7,8 ] #filter = [ 2,3,8 ] #filter = [ 0,1,2,3,4,5,6,7,8,9,10 ] filter = [ 0,1,2,3,4,5,7,8,9 ] if ENABLE_DOWNMIX: filter = [ 0,1,7 ] last_tone3 = 255 for i in range(len(registers[0])): # 11 registers per frame # Tone 0 HL Tone 1 HL Tone 2 HL Tone 3 Vol 0123 for r in range(11): register_data = registers[r][i] # dont update noise register unless different update = True if r == 6: if register_data == last_tone3: update = False else: last_tone3 = register_data if not r in filter: update = False if update: register_data \|= control[r] vgm_stream.extend( struct.pack('B', 0x50) ) # COMMAND vgm_stream.extend( struct.pack('B', register_data) ) # DATA # next frame if sample_interval == 882: # wait 50 vgm_stream.extend( struct.pack('B', 0x63) ) elif sample_interval == 735: # wait 60 vgm_stream.extend( struct.pack('B', 0x62) ) else: vgm_stream.extend( struct.pack('B', 0x61) ) vgm_stream.extend( struct.pack('B', int(sample_interval % 256)) ) vgm_stream.extend( struct.pack('B', int(sample_interval / 256)) ) # END command vgm_stream.extend( struct.pack('B', 0x66) ) vgm.write_vgm(vgm_stream, dst_filename) #output = bytearray() # write the electron vgm file #open(dst_filename, "wb").write( output ) #------------------------------------------------------------------------ # Main() #------------------------------------------------------------------------ import argparse # Determine if running as a script if __name__ == '__main__': print("Vgm2Electron.py : VGM music converter for Acorn Electron") print("Written in 2019 by <NAME>, https://github.com/simondotm/vgm-packer") print("") epilog_string = "" parser = argparse.ArgumentParser( formatter_class=argparse.RawDescriptionHelpFormatter, epilog=epilog_string) parser.add_argument("input", help="VGM source file (must be single SN76489 PSG format) [input]") parser.add_argument("-o", "--output", metavar="<output>", help="write VGC file <output> (default is '[input].vgc')") parser.add_argument("-v", "--verbose", help="Enable verbose mode", action="store_true") parser.add_argument("-a", "--attenuation", default="444", metavar="<nnn>", help="Set attenuation threshold for each channel, 3 character string where each character is 0-F and 0 is loudest, 4 is 50%, F is quietest, default: 444") parser.add_argument("-t", "--transpose", default="000", metavar="<nnn>", help="Set octaves to transpose for each channel, where 1 is +1 octave and F is -1 octave.") parser.add_argument("-c", "--channels", default="123", metavar="[1][2][3]", help="Set which channels will be included in the conversion, default 123, which means all 3 channels") parser.add_argument("-q", "--technique", default=2, metavar="<n>", help="Set which downmix technique to use 1 or 2.") args = parser.parse_args() src = args.input dst = args.output if dst == None: dst = os.path.splitext(src)[0] + ".electron.vgm" # attenuation options attenuation = args.attenuation if (len(attenuation) != 3): print("ERROR: attenuation must be 3 values eg. '444'") sys.exit() #print("attenuation=" + attenuation) VgmElectron.ATTENTUATION_THRESHOLD1 = int(attenuation[0],16) VgmElectron.ATTENTUATION_THRESHOLD2 = int(attenuation[1],16) VgmElectron.ATTENTUATION_THRESHOLD3 = int(attenuation[2],16) # transpose options transpose = args.transpose if (len(transpose) != 3): print("ERROR: transpose must be 3 values eg. '000'") sys.exit() #print("transpose=" + transpose) # 0 1 2 3 4 5 6 7 8 9 a b c d e f ttable = [0,1,2,3,4,5,6,7,-8,-7,-6,-5,-4,-3,-2,-1] VgmElectron.TRANSPOSE_OCTAVES1 = ttable[ int(transpose[0],16) ] VgmElectron.TRANSPOSE_OCTAVES2 = ttable[ int(transpose[1],16) ] VgmElectron.TRANSPOSE_OCTAVES3 = ttable[ int(transpose[2],16) ] # channel options print(args.channels) VgmElectron.ENABLE_CHANNEL1 = args.channels.find("1") >= 0 VgmElectron.ENABLE_CHANNEL2 = args.channels.find("2") >= 0 VgmElectron.ENABLE_CHANNEL3 = args.channels.find("3") >= 0 print("Channel 1: Enabled=" + str(VgmElectron.ENABLE_CHANNEL1) + ", Transpose=" + str(VgmElectron.TRANSPOSE_OCTAVES1) + ", Attenuation="+str(VgmElectron.ATTENTUATION_THRESHOLD1)) print("Channel 2: Enabled=" + str(VgmElectron.ENABLE_CHANNEL2) + ", Transpose=" + str(VgmElectron.TRANSPOSE_OCTAVES2) + ", Attenuation="+str(VgmElectron.ATTENTUATION_THRESHOLD2)) print("Channel 3: Enabled=" + str(VgmElectron.ENABLE_CHANNEL3) + ", Transpose=" + str(VgmElectron.TRANSPOSE_OCTAVES3) + ", Attenuation="+str(VgmElectron.ATTENTUATION_THRESHOLD3)) # technique VgmElectron.USE_TECHNIQUE = int(args.technique) print("Using technique " + str(VgmElectron.USE_TECHNIQUE)) # check for missing files if not os.path.isfile(src): print("ERROR: File '" + src + "' not found") sys.exit() packer = VgmElectron() packer.VERBOSE = args.verbose packer.process(src, dst)	#!/usr/bin/env python # vgm2electron.py # Tool for converting SN76489-based PSG VGM data to Acorn Electron # By <NAME> (https://github.com/simondotm/) # See https://github.com/simondotm/vgm-packer # # Copyright (c) 2019 <NAME>. All rights reserved. # # "MIT License": # 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. import functools import itertools import struct import sys import time import binascii import math import operator import os from modules.vgmparser import VgmStream class VgmElectron: OUTPUT_RAWDATA = False # output raw dumps of the data that was compressed by LZ4/Huffman VERBOSE = True # 0-3 represents approx the loudest 50% of volumes (=ON), 4-15 are the quietest 50% (=OFF) ATTENTUATION_THRESHOLD1 = 10 ATTENTUATION_THRESHOLD2 = 10 ATTENTUATION_THRESHOLD3 = 10 # define the number of octaves to transpose whole song by, in case too much bass getting lost TRANSPOSE_OCTAVES1 = 0 TRANSPOSE_OCTAVES2 = 0 TRANSPOSE_OCTAVES3 = 0 #-1 ENABLE_CHANNEL1 = True ENABLE_CHANNEL2 = True ENABLE_CHANNEL3 = True USE_TECHNIQUE = 2 def __init__(self): print("init") #---------------------------------------------------------- # Utilities #---------------------------------------------------------- # split the packed raw data into 11 separate streams # returns array of 11 bytearrays def split_raw(self, rawData, stripCommands = True): registers = [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] registers_opt = [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] latched_channel = -1 output_block = bytearray() output_blocks = [] for o in range(11): output_blocks.append( bytearray() ) if stripCommands: register_mask = 15 else: register_mask = 255 # unpack the raw binary data in 11 arrays of register data without any deltas between them # eg. the raw chip writes to all 11 registers every frame n = 0 Packet = True verbose = False while (Packet): packet_size = rawData[n] if verbose: print("packet_size=" + str(packet_size)) n += 1 if packet_size == 255: Packet = False else: for x in range(packet_size): d = rawData[n+x] #if verbose: # print " frame byte number=" +str(x) # print " frame byte=" +str(d) if d & 128: # latch c = (d>>5)&3 latched_channel = c if d & 16: # volume if verbose: print(" volume on channel " + str(c)) registers[c+7] = d & register_mask else: # tone if verbose: print(" tone on channel " + str(c)) registers[c2+0] = d & register_mask else: if verbose: print(" tone data on latched channel " + str(latched_channel)) registers[latched_channel2+1] = d # we no longer do any masking here # d & 63 # tone data only contains 6 bits of info anyway, so no need for mask if latched_channel == 3: print("ERROR CHANNEL") # emit current state of each of the 11 registers to 11 different bytearrays for x in range(11): output_blocks[x].append( registers[x] ) # next packet n += packet_size #print(output_blocks[6]) #IGNORE we no longer do this - let the decoder do it instead. if False: # make sure we only emit tone3 when it changes, or 15 for no-change # this prevents the LFSR from being reset lastTone3 = 255 for x in range(len(output_blocks[6])): t = output_blocks[6][x] if t == lastTone3: output_blocks[6][x] = 15 lastTone3 = t # print(output_blocks[6]) # Add EOF marker (0x08) to tone3 byte stream output_blocks[6].append(0x08) # 0x08 is an invalid noise tone. # return the split blocks return output_blocks # given an array of data points, serialize it to a bytearray # size is the number of bytes to be used to represent each element in the source array. def toByteArray(self, array, size = 1): r = bytearray() for v in array: if size < 2: r.append(v & 255) else: r.append(v & 255) r.append(v >> 8) return r #---------------------------------------------------------- # Process(filename) # Convert the given VGM file to an electron VGM file #---------------------------------------------------------- def process(self, src_filename, dst_filename): # load the VGM file, or alternatively interpret as a binary if src_filename.lower()[-4:] != ".vgm": print("ERROR: Not a VGM source") return vgm = VgmStream(src_filename) data_block = vgm.as_binary() data_offset = 0 # parse the header header_size = data_block[0] # header size play_rate = data_block[1] # play rate if header_size == 5 and play_rate == 50: packet_count = data_block[2] + data_block[3]256 # packet count LO duration_mm = data_block[4] # duration mm duration_ss = data_block[5] # duration ss data_offset = header_size+1 data_offset += data_block[data_offset]+1 data_offset += data_block[data_offset]+1 print("header_size=" +str(header_size)) print("play_rate="+str(play_rate)) print("packet_count="+str(packet_count)) print("duration_mm="+str(duration_mm)) print("duration_ss="+str(duration_ss)) print("data_offset="+str(data_offset)) else: print("No header.") print("") # Trim off the header data. The rest is raw data. data_block = data_block[data_offset:] #---------------------------------------------------------- # Unpack the register data into 11 separate data streams #---------------------------------------------------------- registers = self.split_raw(data_block, True) #---------------------------------------------------------- # Begin VGM conversion to Electron #---------------------------------------------------------- # Filter out channels we do not need # Modify all volumes to full or none # Interleave sound to a single channel # output final VGM vgm_stream = bytearray() vgm_time = 0 electron_data = bytearray() # given an SN76489 tone register value, return the equivalent Electron ULA register setting def sn_to_electron(tone_value): # hack to protect against divbyzero if (tone_value == 0): tone_value = 1 hz = float(vgm.vgm_source_clock) / ( 2.0 float(tone_value) * 16.0) print(" sn_to_electron freq " + str(hz) + "hz") # electron # Sound frequency = 1 MHz / [32 * (S + 1)] # f * 32(S+1) = 1Mhz # 32(S+1) = 1Mhz / f # (S+1) = 1Mhz / f32 #print ("SN freq is " + str(hz)) ula6 = int( 1000000.0 / (hz 32.0) ) - 1 # check we are within range if ula6 < 0: print(" WARNING: Electron freqency '" + str(ula6) + "' too high (" + str(hz) + ")") ula6 = 0 if ula6 > 255: print(" WARNING: Electron frequency '" + str(ula6) + "' too low (" + str(hz) + ")") ula6 = 255 return ula6 #-------------------------------------------------------------- # conversion settings #-------------------------------------------------------------- # convert the register data to a vgm stream sample_interval = int(44100 / vgm.metadata['rate']) # 882 # 50hz - TODO: use frame rate print("sample_interval=" + str(sample_interval)) USE_TONE3 = VgmElectron.ENABLE_CHANNEL3 # True # TODO: make these all parameters # Add channel filter option # Add mix type options # --attentuation 468 --filter 123 --transpose 00F --mix 123 --arpeggio 2 --rate 50 # Add option to clamp or transpose out of range frequencies # Make the .ula output file filename.electron.ula # Add 0x01 as a terminating byte in the output ULA MIX_RATE = 2 # modulo 2 for interleaving channels # other options # bias for channels # transpose or silence out of range notes channel_mix = 0 #-------------------------------------------------------------- # pre-process music to suit Electron capabilities #-------------------------------------------------------------- for i in range(len(registers[0])): print("Frame " + str(i)) #-------------------------------------------------------------- # step 1- map volumes to 1-bit precision #-------------------------------------------------------------- # 11 registers per frame # Tone 0 HL Tone 1 HL Tone 2 HL Tone 3 Vol 0123 for r in range(11): if r > 6: register_data = registers[r][i] # apply the threshold for each channel threshold = VgmElectron.ATTENTUATION_THRESHOLD1 if r == 8: threshold = VgmElectron.ATTENTUATION_THRESHOLD2 if r == 9: threshold = VgmElectron.ATTENTUATION_THRESHOLD3 # if its a volume, map to loudest volume or no volume (using logarithmic scale) if register_data < threshold: register_data = 0 # full volume else: register_data = 15 # zero volume if r == 7 and VgmElectron.ENABLE_CHANNEL1 == False: register_data = 15 # zero volume if r == 8 and VgmElectron.ENABLE_CHANNEL2 == False: register_data = 15 # zero volume if r == 9 and VgmElectron.ENABLE_CHANNEL3 == False: register_data = 15 # zero volume registers[r][i] = register_data #-------------------------------------------------------------- # step 2 - transpose to fit frequency range #-------------------------------------------------------------- # final step - bring tone1 into the frequency range of the electron # if the frequency goes below the range of the ULA capabilities, add an octave def retune(octaves, l,h,v): #if (octaves == 0): # print(" No transpose performed, octaves set to 0") # return print( " tonehi=" + str(registers[h][i]) + ", tonelo=" + str(registers[l][i])) tone_value = (registers[h][i] << 4) + registers[l][i] if tone_value > 0: tone_freq = float(vgm.vgm_source_clock) / ( 2.0 * float(tone_value) * 16.0) print(" Retune, Channel " + str(int(l/2)) + " tone=" + str(tone_value) + ", freq=" + str(tone_freq)) # electron baseline is 122Hz not 244Hz as the AUG states. baseline_freq = 1000000.0 / (32.0256.0) target_freq = tone_freq retuned = 0 transpose = abs(octaves) while retuned != transpose: # target_freq < baseline_freq: if (octaves < 0): target_freq /= 2.0 else: target_freq = 2.0 retuned += 1 # if cant reach baseline freq, transpose once, then silence if still too low :( if target_freq < baseline_freq: print(" WARNING: Freq too low - Added " + str(1) + " octave(s) - from " + str(target_freq) + " to " + str(target_freq2.0) + "Hz") # better to just clamp low frequencies at the bottom, and risk tuning issues rather than transposition jumps target_freq = baseline_freq #= 2.0 retuned = 1 if target_freq < baseline_freq: registers[v][i] = 15 print(" Tone " + str(i) + " silenced because frequency too low - " + str(target_freq)) #target_freq = 2.0 #retuned += 1 if retuned: #print(" WARNING: Freq too low - Added " + str(retuned) + " octave(s) - from " + str(tone_freq) + " to " + str(target_freq) + "Hz") tone_value = int( round( float(vgm.vgm_source_clock) / (2.0 target_freq * 16.0 ) ) ) registers[h][i] = tone_value >> 4 registers[l][i] = tone_value & 15 # transpose #if TRANSPOSE_OCTAVES > 0: print(" Transposing ") retune(VgmElectron.TRANSPOSE_OCTAVES1, 0,1,7) retune(VgmElectron.TRANSPOSE_OCTAVES2, 2,3,8) retune(VgmElectron.TRANSPOSE_OCTAVES3, 4,5,9) #-------------------------------------------------------------- # Step 3 - mix the 2 primary channels down to 1 channel #-------------------------------------------------------------- # map channel 2 to channel 1 # noise channel is completely ignored ENABLE_DOWNMIX = True if ENABLE_DOWNMIX: print(" Downmix channels ") #print("Frame " + str(i)) vol1 = registers[7][i] vol2 = registers[8][i] vol3 = registers[9][i] tone1_active = vol1 != 15 tone2_active = vol2 != 15 tone3_active = vol3 != 15 tone_active = tone1_active or tone2_active or tone3_active if tone_active: print(" Tone active, mixing") output_tone = 1 if self.USE_TECHNIQUE == 2: c1f = (registers[1][i] << 4) + registers[0][i] c2f = (registers[3][i] << 4) + registers[2][i] c3f = (registers[5][i] << 4) + registers[4][i] active_channels = [ False, False, False ] if tone1_active: active_channels[0] = True print("Channel 1 is active volume") if tone2_active: active_channels[1] = True print("Channel 2 is active volume") if tone3_active: active_channels[2] = True print("Channel 3 is active volume") # any channels playing the same frequency are filtered out if tone1_active and tone2_active and c2f == c1f: active_channels[1] = False print("Channel 2 is same freq as Channel 1, filtered") if tone1_active and tone3_active and c3f == c1f: active_channels[2] = False print("Channel 3 is same freq as Channel 1, filtered") if tone2_active and tone3_active and c2f == c3f: active_channels[2] = False print("Channel 3 is same freq as Channel 2, filtered") channel_count = 0 if active_channels[0]: channel_count += 1 if active_channels[1]: channel_count += 1 if active_channels[2]: channel_count += 1 print("channel_count=" + str(channel_count)) output_mix = [] if active_channels[0]: output_mix.append(1) if active_channels[1]: output_mix.append(2) if active_channels[2]: output_mix.append(3) mix = (i % channel_count) output_tone = output_mix[mix] if self.USE_TECHNIQUE == 1: # interleaving of channels 1+2 is done on odd/even frames for a consistent effect mix = (i % MIX_RATE) == 0 #(i & 1) == 0 # random is no good, thought it might average out but it sounds , well random #mix = random.random() < 0.5 # test code to see if modulo 3 any good, it wasn't if False: if channel_mix == 0 and vol1 != 0: channel_mix = (channel_mix + 1) % 3 if channel_mix == 1 and vol2 != 0: channel_mix = (channel_mix + 1) % 3 if channel_mix == 1 and vol3 != 0: channel_mix = (channel_mix + 1) % 3 output_tone = (channel_mix % 3) + 1 print("output tone=" + str(output_tone)) channel_mix = (channel_mix + 1) % 3 if True: # detect if channel 1 needs priority this frame # - its volume is on, and the alternative frame mix flag is good c1p = vol1 == 0 and mix # don't give channel 2 priority if tone is the same and channel1 is playing c1f = (registers[1][i] << 4) + registers[0][i] c2f = (registers[3][i] << 4) + registers[2][i] sametone = (c1f == c2f/2) or (c1f == c2f * 2) or (c1f == c2f) sametone = sametone and (vol1 == vol2) and (vol1 == 0) if vol1 == 0 and sametone: #diff < 100: #registers[0][i] == registers[2][i] and registers[1][i] == registers[2][i] and vol1 == 0: c1p = True print(" NOTE: channel 1 & channel 2 have same tone") # replace channel 1 data with channel 2 data # if, channel2 is active, but c1 doesn't have priority this frame if vol2 == 0 and not c1p:# and vol1 != 0: output_tone = 2 # if no volume on tone1, we can look at channel 3 too if USE_TONE3: #if registers[7][i] == 15: if vol1 == 15 and vol2 == 15 and vol3 == 0 and not mix:# and not c1p and output_tone != 2: print("tone3 active") output_tone = 3 # pick which tone to output if output_tone == 1: # do nothing, because tone1 register frequency already setup output_tone = 1 elif output_tone == 2: # replace tone 1 frequency with tone 2 frequency registers[0][i] = registers[2][i] registers[1][i] = registers[3][i] registers[7][i] = registers[8][i] elif output_tone == 3: # replace tone 1 frequency with tone 3 frequency registers[0][i] = registers[4][i] registers[1][i] = registers[5][i] registers[7][i] = registers[9][i] else: print("UNHANDLED CASE - output_tone not set") # output ULA data final_volume = registers[7][i] ula_tone = 0 # zero is highest freq. so inaudible, so thats how we handle volume if final_volume == 0: final_tone1 = (registers[1][i] << 4) + registers[0][i] ula_tone = sn_to_electron(final_tone1) electron_data.append( ula_tone ) # write to output ULA file ula_file = open(dst_filename + ".ula.bin", 'wb') ula_file.write(electron_data) ula_file.close() #-------------------------------------------------------------- # Final stage - output to vgm #-------------------------------------------------------------- # Tone1----- Tone2----- Tone3----- Tone4 Vol1 Vol2 Vol3 Vol4 control = [ 0x80, 0x00, 0xa0, 0x00, 0xc0, 0x00, 0xe0, 0x90, 0xb0, 0xd0, 0xf0 ] #filter = [ 0,1,2,3,7,8 ] #filter = [ 2,3,8 ] #filter = [ 0,1,2,3,4,5,6,7,8,9,10 ] filter = [ 0,1,2,3,4,5,7,8,9 ] if ENABLE_DOWNMIX: filter = [ 0,1,7 ] last_tone3 = 255 for i in range(len(registers[0])): # 11 registers per frame # Tone 0 HL Tone 1 HL Tone 2 HL Tone 3 Vol 0123 for r in range(11): register_data = registers[r][i] # dont update noise register unless different update = True if r == 6: if register_data == last_tone3: update = False else: last_tone3 = register_data if not r in filter: update = False if update: register_data \|= control[r] vgm_stream.extend( struct.pack('B', 0x50) ) # COMMAND vgm_stream.extend( struct.pack('B', register_data) ) # DATA # next frame if sample_interval == 882: # wait 50 vgm_stream.extend( struct.pack('B', 0x63) ) elif sample_interval == 735: # wait 60 vgm_stream.extend( struct.pack('B', 0x62) ) else: vgm_stream.extend( struct.pack('B', 0x61) ) vgm_stream.extend( struct.pack('B', int(sample_interval % 256)) ) vgm_stream.extend( struct.pack('B', int(sample_interval / 256)) ) # END command vgm_stream.extend( struct.pack('B', 0x66) ) vgm.write_vgm(vgm_stream, dst_filename) #output = bytearray() # write the electron vgm file #open(dst_filename, "wb").write( output ) #------------------------------------------------------------------------ # Main() #------------------------------------------------------------------------ import argparse # Determine if running as a script if __name__ == '__main__': print("Vgm2Electron.py : VGM music converter for Acorn Electron") print("Written in 2019 by <NAME>, https://github.com/simondotm/vgm-packer") print("") epilog_string = "" parser = argparse.ArgumentParser( formatter_class=argparse.RawDescriptionHelpFormatter, epilog=epilog_string) parser.add_argument("input", help="VGM source file (must be single SN76489 PSG format) [input]") parser.add_argument("-o", "--output", metavar="<output>", help="write VGC file <output> (default is '[input].vgc')") parser.add_argument("-v", "--verbose", help="Enable verbose mode", action="store_true") parser.add_argument("-a", "--attenuation", default="444", metavar="<nnn>", help="Set attenuation threshold for each channel, 3 character string where each character is 0-F and 0 is loudest, 4 is 50%, F is quietest, default: 444") parser.add_argument("-t", "--transpose", default="000", metavar="<nnn>", help="Set octaves to transpose for each channel, where 1 is +1 octave and F is -1 octave.") parser.add_argument("-c", "--channels", default="123", metavar="[1][2][3]", help="Set which channels will be included in the conversion, default 123, which means all 3 channels") parser.add_argument("-q", "--technique", default=2, metavar="<n>", help="Set which downmix technique to use 1 or 2.") args = parser.parse_args() src = args.input dst = args.output if dst == None: dst = os.path.splitext(src)[0] + ".electron.vgm" # attenuation options attenuation = args.attenuation if (len(attenuation) != 3): print("ERROR: attenuation must be 3 values eg. '444'") sys.exit() #print("attenuation=" + attenuation) VgmElectron.ATTENTUATION_THRESHOLD1 = int(attenuation[0],16) VgmElectron.ATTENTUATION_THRESHOLD2 = int(attenuation[1],16) VgmElectron.ATTENTUATION_THRESHOLD3 = int(attenuation[2],16) # transpose options transpose = args.transpose if (len(transpose) != 3): print("ERROR: transpose must be 3 values eg. '000'") sys.exit() #print("transpose=" + transpose) # 0 1 2 3 4 5 6 7 8 9 a b c d e f ttable = [0,1,2,3,4,5,6,7,-8,-7,-6,-5,-4,-3,-2,-1] VgmElectron.TRANSPOSE_OCTAVES1 = ttable[ int(transpose[0],16) ] VgmElectron.TRANSPOSE_OCTAVES2 = ttable[ int(transpose[1],16) ] VgmElectron.TRANSPOSE_OCTAVES3 = ttable[ int(transpose[2],16) ] # channel options print(args.channels) VgmElectron.ENABLE_CHANNEL1 = args.channels.find("1") >= 0 VgmElectron.ENABLE_CHANNEL2 = args.channels.find("2") >= 0 VgmElectron.ENABLE_CHANNEL3 = args.channels.find("3") >= 0 print("Channel 1: Enabled=" + str(VgmElectron.ENABLE_CHANNEL1) + ", Transpose=" + str(VgmElectron.TRANSPOSE_OCTAVES1) + ", Attenuation="+str(VgmElectron.ATTENTUATION_THRESHOLD1)) print("Channel 2: Enabled=" + str(VgmElectron.ENABLE_CHANNEL2) + ", Transpose=" + str(VgmElectron.TRANSPOSE_OCTAVES2) + ", Attenuation="+str(VgmElectron.ATTENTUATION_THRESHOLD2)) print("Channel 3: Enabled=" + str(VgmElectron.ENABLE_CHANNEL3) + ", Transpose=" + str(VgmElectron.TRANSPOSE_OCTAVES3) + ", Attenuation="+str(VgmElectron.ATTENTUATION_THRESHOLD3)) # technique VgmElectron.USE_TECHNIQUE = int(args.technique) print("Using technique " + str(VgmElectron.USE_TECHNIQUE)) # check for missing files if not os.path.isfile(src): print("ERROR: File '" + src + "' not found") sys.exit() packer = VgmElectron() packer.VERBOSE = args.verbose packer.process(src, dst)	en	0.590557	#!/usr/bin/env python # vgm2electron.py # Tool for converting SN76489-based PSG VGM data to Acorn Electron # By <NAME> (https://github.com/simondotm/) # See https://github.com/simondotm/vgm-packer # # Copyright (c) 2019 <NAME>. All rights reserved. # # "MIT License": # 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. # output raw dumps of the data that was compressed by LZ4/Huffman # 0-3 represents approx the loudest 50% of volumes (=ON), 4-15 are the quietest 50% (=OFF) # define the number of octaves to transpose whole song by, in case too much bass getting lost #-1 #---------------------------------------------------------- # Utilities #---------------------------------------------------------- # split the packed raw data into 11 separate streams # returns array of 11 bytearrays # unpack the raw binary data in 11 arrays of register data without any deltas between them # eg. the raw chip writes to all 11 registers every frame #if verbose: # print " frame byte number=" +str(x) # print " frame byte=" +str(d) # latch # volume # tone # we no longer do any masking here # d & 63 # tone data only contains 6 bits of info anyway, so no need for mask # emit current state of each of the 11 registers to 11 different bytearrays # next packet #print(output_blocks[6]) #IGNORE we no longer do this - let the decoder do it instead. # make sure we only emit tone3 when it changes, or 15 for no-change # this prevents the LFSR from being reset # print(output_blocks[6]) # Add EOF marker (0x08) to tone3 byte stream # 0x08 is an invalid noise tone. # return the split blocks # given an array of data points, serialize it to a bytearray # size is the number of bytes to be used to represent each element in the source array. #---------------------------------------------------------- # Process(filename) # Convert the given VGM file to an electron VGM file #---------------------------------------------------------- # load the VGM file, or alternatively interpret as a binary # parse the header # header size # play rate # packet count LO # duration mm # duration ss # Trim off the header data. The rest is raw data. #---------------------------------------------------------- # Unpack the register data into 11 separate data streams #---------------------------------------------------------- #---------------------------------------------------------- # Begin VGM conversion to Electron #---------------------------------------------------------- # Filter out channels we do not need # Modify all volumes to full or none # Interleave sound to a single channel # output final VGM # given an SN76489 tone register value, return the equivalent Electron ULA register setting # hack to protect against divbyzero # electron # Sound frequency = 1 MHz / [32 * (S + 1)] # f * 32(S+1) = 1Mhz # 32(S+1) = 1Mhz / f # (S+1) = 1Mhz / f32 #print ("SN freq is " + str(hz)) # check we are within range #-------------------------------------------------------------- # conversion settings #-------------------------------------------------------------- # convert the register data to a vgm stream # 882 # 50hz - TODO: use frame rate # True # TODO: make these all parameters # Add channel filter option # Add mix type options # --attentuation 468 --filter 123 --transpose 00F --mix 123 --arpeggio 2 --rate 50 # Add option to clamp or transpose out of range frequencies # Make the .ula output file filename.electron.ula # Add 0x01 as a terminating byte in the output ULA # modulo 2 for interleaving channels # other options # bias for channels # transpose or silence out of range notes #-------------------------------------------------------------- # pre-process music to suit Electron capabilities #-------------------------------------------------------------- #-------------------------------------------------------------- # step 1- map volumes to 1-bit precision #-------------------------------------------------------------- # 11 registers per frame # Tone 0 HL Tone 1 HL Tone 2 HL Tone 3 Vol 0123 # apply the threshold for each channel # if its a volume, map to loudest volume or no volume (using logarithmic scale) # full volume # zero volume # zero volume # zero volume # zero volume #-------------------------------------------------------------- # step 2 - transpose to fit frequency range #-------------------------------------------------------------- # final step - bring tone1 into the frequency range of the electron # if the frequency goes below the range of the ULA capabilities, add an octave #if (octaves == 0): # print(" No transpose performed, octaves set to 0") # return # electron baseline is 122Hz not 244Hz as the AUG states. # target_freq < baseline_freq: # if cant reach baseline freq, transpose once, then silence if still too low :( # better to just clamp low frequencies at the bottom, and risk tuning issues rather than transposition jumps #= 2.0 #target_freq *= 2.0 #retuned += 1 #print(" WARNING: Freq too low - Added " + str(retuned) + " octave(s) - from " + str(tone_freq) + " to " + str(target_freq) + "Hz") # transpose #if TRANSPOSE_OCTAVES > 0: #-------------------------------------------------------------- # Step 3 - mix the 2 primary channels down to 1 channel #-------------------------------------------------------------- # map channel 2 to channel 1 # noise channel is completely ignored #print("Frame " + str(i)) # any channels playing the same frequency are filtered out # interleaving of channels 1+2 is done on odd/even frames for a consistent effect #(i & 1) == 0 # random is no good, thought it might average out but it sounds , well random #mix = random.random() < 0.5 # test code to see if modulo 3 any good, it wasn't # detect if channel 1 needs priority this frame # - its volume is on, and the alternative frame mix flag is good # don't give channel 2 priority if tone is the same and channel1 is playing #diff < 100: #registers[0][i] == registers[2][i] and registers[1][i] == registers[2][i] and vol1 == 0: # replace channel 1 data with channel 2 data # if, channel2 is active, but c1 doesn't have priority this frame # and vol1 != 0: # if no volume on tone1, we can look at channel 3 too #if registers[7][i] == 15: # and not c1p and output_tone != 2: # pick which tone to output # do nothing, because tone1 register frequency already setup # replace tone 1 frequency with tone 2 frequency # replace tone 1 frequency with tone 3 frequency # output ULA data # zero is highest freq. so inaudible, so thats how we handle volume # write to output ULA file #-------------------------------------------------------------- # Final stage - output to vgm #-------------------------------------------------------------- # Tone1----- Tone2----- Tone3----- Tone4 Vol1 Vol2 Vol3 Vol4 #filter = [ 0,1,2,3,7,8 ] #filter = [ 2,3,8 ] #filter = [ 0,1,2,3,4,5,6,7,8,9,10 ] # 11 registers per frame # Tone 0 HL Tone 1 HL Tone 2 HL Tone 3 Vol 0123 # dont update noise register unless different # COMMAND # DATA # next frame # wait 50 # wait 60 # END command #output = bytearray() # write the electron vgm file #open(dst_filename, "wb").write( output ) #------------------------------------------------------------------------ # Main() #------------------------------------------------------------------------ # Determine if running as a script # attenuation options #print("attenuation=" + attenuation) # transpose options #print("transpose=" + transpose) # 0 1 2 3 4 5 6 7 8 9 a b c d e f # channel options # technique # check for missing files	1.670812	2
twitter_sent.py	rthorst/TwitterSentiment	6	10234	<gh_stars>1-10 import webapp2 import tweepy import json import csv import os import statistics import bokeh from bokeh.io import show, output_file from bokeh.plotting import figure from bokeh.models import HoverTool, ColumnDataSource from bokeh.embed import components, json_item from bokeh.resources import INLINE from bokeh.models.glyphs import Line, Text import numpy as np import random import operator from collections import Counter from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer """ ---AUTHOR: --- <NAME> <EMAIL> ---LICENSE: --- MIT License. ---ABOUT: --- Application to get the sentiment of recent tweets based on a keyword. Example: keyword -> "taco bell" retrieve 300 recent tweets mentioning taco bell. get average sentiment. plot distribution of tweets and sentiment. plot most informative words for this application. This script runs based on google app server. Expects Python 2.7 Depenencies need to be included in the lib/ directory (pip install -t lib [PACKAGE_NAME]) The main work is done by the MainPage class. The get() method runs the main pipeline of code and returns HTML as a string. Working online version: https://twittersentiment-247018.appspot.com/ """ def get_tweets(keyword, max_tweets=200): """ Given a keyword as a string (e.g. "data science"), get recent tweets matching that string up to # max_tweets. Return a list of tweets, represented as strings. """ # API keys. consumer_key = "kNOG1klRMMUYbsjMuY5TKl4lE" consumer_secret = "ieghv6WI1qseYly43A0Ra1MPksEw1i5Onma0txfEu5aHantD2v" access_key = "<KEY>" access_secret = "<KEY>" # Initialize tweepy API object and authorize using API key. auth = tweepy.OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_key, access_secret) api = tweepy.API(auth) """ Get tweets.""" alltweets = [] for status in tweepy.Cursor( api.search, q=keyword + " -RT", # the -RT flag excludes retweets. count=1000, result_type="recent", include_entities=True, monitor_rate_limit=True, wait_on_rate_limit=True, lang="en", ).items(): # get text of the tweet, encoding as utf-8. text = str(status.text.encode("utf-8")) # add to the data structure, alltweets, holding the tweets. alltweets.append(text) # if we've reached max_tweets, break. if len(alltweets) >= max_tweets: break return alltweets class VaderSentimentModel: """ Calculate sentiment using a mostly lexicon-based approach that is optimized for social media. Approach is social media aware, for example emoticons are part of the lexicon and tokenization is twitter-sensitive. There are also some basic rules, e.g. it's sensitive to negations. """ def __init__(self): # Initialize a vader_analyzer object which does the work of sentiment analysis. self.vader_analyzer = SentimentIntensityAnalyzer() pass def classify_sentiment(self, tweet): # Classify sentiment of a single tweet. # Input tweet: as string. # Return sentiment score : # range -1 (very negaitve) to +1 (very positive). # score is calculated as p(positive) - p(negative) # normalizing to range from -1 to 1. # calculate sentiment in a dictionary. key is polarity ("pos", "neg", "neut") and value is probability. sentiment_dict = self.vader_analyzer.polarity_scores(tweet) # retrieve the compound sentiment score, which is p(pos) - p(neg), but normalized to range from {-1, 1} score = sentiment_dict["compound"] # compound is the combined score scaled to {-1, 1} return score def plot_tweets(tweets, sentiment_scores): """ Create a histogram-style barplot of tweets and their sentiment. Return a bokeh plot object, expressed as a tuple of (resources, script, div). Where : resources: some CSS, etc. that goes in the head of the webpage for styling the plot. script: javascript for the plot to function. expressed as string. div: html div container for the plot. expressed as string. """ # Sort tweets from negative to positive. # This step is not strictly necessary, but makes it easier to see the overall shape of the data. sorted_indices = np.argsort(sentiment_scores) sentiment_scores = np.array(sentiment_scores)[sorted_indices] tweets = np.array(tweets)[sorted_indices] # Express the data as a bokeh data source object. source = ColumnDataSource(data={ "text": tweets, "sentiment": sentiment_scores, "x": np.arange(len(tweets)), }) """ Create plot. """ # Create plot object. width = 0.9 p = figure(x_axis_label="Tweet", y_axis_label="Sentiment (0 = Neutral)") p.vbar(source=source, x="x", top="sentiment", width=width) # Add hover tool, allowing mouseover to view text and sentiment. hover = HoverTool( tooltips=[ ("text", "@text"), ("sentiment", "@sentiment") ], formatters={ "text": "printf", "sentiment": "printf" }, mode="vline" ) p.add_tools(hover) """ Format plot. """ # axis font size p.xaxis.axis_label_text_font_size = "15pt" p.yaxis.axis_label_text_font_size = "15pt" # remove tick marks from axes p.xaxis.major_tick_line_color = None p.xaxis.minor_tick_line_color = None p.yaxis.major_tick_line_color = None p.yaxis.minor_tick_line_color = None # adjust plot width, height scale = 1.5 p.plot_height = int(250 * scale) p.plot_width = int(450 * scale) # remove toolbar (e.g. move, resize, etc) from right of plot. p.toolbar.logo = None p.toolbar_location = None # remove gridlines p.xgrid.visible = False p.ygrid.visible = False # remove x axis tick labels (done by setting label fontsize to 0 pt) p.xaxis.major_label_text_font_size = '0pt' """ Export plot """ # Create resources string, which is CSS, etc. that goes in the head of resources = INLINE.render() # Get javascript (script) and HTML div (div) for the plot. script, div = components(p) return (resources, script, div) def plot_reason(tweets, sentiment_scores): """ Plot the top words that lead us to the classification as positive or negative. Return: script : javascript for the plot, expressed as string. div : html container for the plot, expressed as string. NOTE: requires the shared resources attribute from plot_tweets() in the HTML header. """ """ Calculate the sentiment of each individual token in the tweets. """ # list tokens, keeping only unique tokens (e.g. remove repeated words). all_toks = [] for tweet in tweets: toks = tweet.lower().split() all_toks.extend(toks) all_toks = [tok for tok in set(all_toks)] # remove duplicates. # calculate sentiment of each token. sm = VaderSentimentModel() toks_sentiment = [sm.classify_sentiment(tok) for tok in all_toks] """ sort tokens by sentiment. if overall valence is negative, sort negative to postitive. if overall valence is positive, sort positive to negative. thus, in any case, the earliest elements in the list are the most informative words. """ nwords = 20 # negative? sort neg -> positive. if np.mean(sentiment_scores) < 0: sorted_indices = np.argsort(toks_sentiment) # else (positive)? sort positive -> negative else: sorted_indices = np.argsort(toks_sentiment)[::-1] # toks_to_plot: shape (nwords, ) list of informative tokens. # sentiment_to_plot: shape (nwords, ) list of sentiment of these tokens. toks_to_plot = np.array(all_toks)[sorted_indices][:nwords] sentiment_to_plot = np.array(toks_sentiment)[sorted_indices][:nwords] # convert all sentiment scores to positive values. # this is for DISPLAY only, to make all plots go from left to right. # we still retain the correct tokens and sorting order. sentiment_to_plot = np.array([abs(v) for v in sentiment_to_plot]) """ Set up plot. - create data source object. - define formatting variables. """ text_offset = 0.1 source = ColumnDataSource(data={ "token": toks_to_plot, "sentiment": sentiment_to_plot, "x": np.arange(len(toks_to_plot))[::-1], "label_x": sentiment_to_plot + text_offset }) """ Make plot. """ # Create initial plot. width = 0.9 xrange = [0, max(sentiment_to_plot) + 1] p2 = figure(x_axis_label="Sentiment", y_axis_label="Word", x_range=xrange) p2.hbar(source=source, y="x", right="sentiment", height=width) """ Format plot. """ # Annotate each bar with the word being represented. glyph = Text(x="label_x", y="x", text="token") p2.add_glyph(source, glyph) # Axis labels. p2.xaxis.axis_label_text_font_size = "15pt" p2.yaxis.axis_label_text_font_size = "15pt" # Remove ticks. p2.xaxis.major_tick_line_color = None p2.xaxis.minor_tick_line_color = None p2.yaxis.major_tick_line_color = None p2.yaxis.minor_tick_line_color = None # Remove y axis tick labels. p2.yaxis.major_label_text_font_size = '0pt' # Plot width, height. scale = 1.5 p2.plot_height = int(250 * scale) p2.plot_width = int(250 * scale) # remove toolbar (e.g. move, resize, etc) from right of plot. p2.toolbar.logo = None p2.toolbar_location = None # remove gridlines p2.xgrid.visible = False p2.ygrid.visible = False # remove x axis tick labels (set font to 0pt) p2.xaxis.major_label_text_font_size = '0pt' # get bokeh component for plot 2. script2, div2 = components(p2) return (script2, div2) class MainPage(webapp2.RequestHandler): """ This class does the work of writing HTML to the google app server. Thus, we allow the get() method to incorporate: our main pipeline (getting tweets, analyzing sentiment, producing graphs) writing html """ def get(self): """ Get tweets and sentiment scores. """ # Retrieve keyword from the HTML form. If no keyword provided, use a random suggested keyword. keyword = self.request.get("keyword") if not keyword: suggested_keywords = ["alarm clocks", "the future", "miller lite", "taco bell", "yoga", "netflix", "life", "traffic", "elon musk", "beards", "world trade", "pepsi", "amazon"] indices = np.arange(len(suggested_keywords)) random.shuffle(indices) keyword = suggested_keywords[indices[0]] # Get recent tweets based on the keyword, up to 300 maximum tweets. tweets = get_tweets(keyword, max_tweets=300) # Compute the sentiment of each tweet. v = VaderSentimentModel() sentiment_scores = [v.classify_sentiment(tw) for tw in tweets] # shape (ntweets,) # Label sentiment categorically, e.g. "negative" or "positive" M_sent = np.mean(sentiment_scores) map = {1 : "positive", 0 : "negative"} valence = map[int(M_sent > 0)] """ Create plots. """ ############# # Plot #1: ############ # Plot the distribution of tweets and sentiment. # Resources is CSS code that goes in the header of the HTML. Shared across all bokeh plots. # Script1 is javascript for this plot. # Div1 is an HTML container for the plot. Goes where you want the plot to appear. resources, script1, div1 = plot_tweets(tweets=tweets, sentiment_scores=sentiment_scores) ############# # Plot #2: ############ # Plot the key words that lead us to this classification. # Script2 is javascript for this plot. # Div2 is an HTML container for this plot. Goes where you want the plot to appear. # Requires the HTML to include the shared resources, generated above, in the <HEAD> script2, div2 = plot_reason(tweets=tweets, sentiment_scores=sentiment_scores) """ Create HTML output. """ # Load HTML template. # This is a functioning webpage, with some placeholders for the keywords and plots we have created. html_p = os.path.join("html", "index.html") html = open(html_p, "r").read() # Fill in placeholders in the HTML with varibles we have created. term_to_value = { "[[!KEYWORD]]" : keyword, "[[!VALENCE]]" : valence, "[[!BOKEH_SCRIPT]]" : script1, "[[!BOKEH_SCRIPT2]]": script2, "[[!BOKEH_DIV]]" : div1, "[[!BOKEH_RESOURCES]]" : resources, "[[!BOKEH_DIV2]]" : div2 } for term, val in term_to_value.items(): html = html.replace(term, val) """ Write a response. This essentially returns HTML to the google app engine. This will render a webpage visible to the user. """ self.response.headers["Content-Type"] = "text/html" self.response.write(html) # Run application. routes = [('/', MainPage)] my_app = webapp2.WSGIApplication(routes, debug=True)	import webapp2 import tweepy import json import csv import os import statistics import bokeh from bokeh.io import show, output_file from bokeh.plotting import figure from bokeh.models import HoverTool, ColumnDataSource from bokeh.embed import components, json_item from bokeh.resources import INLINE from bokeh.models.glyphs import Line, Text import numpy as np import random import operator from collections import Counter from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer """ ---AUTHOR: --- <NAME> <EMAIL> ---LICENSE: --- MIT License. ---ABOUT: --- Application to get the sentiment of recent tweets based on a keyword. Example: keyword -> "taco bell" retrieve 300 recent tweets mentioning taco bell. get average sentiment. plot distribution of tweets and sentiment. plot most informative words for this application. This script runs based on google app server. Expects Python 2.7 Depenencies need to be included in the lib/ directory (pip install -t lib [PACKAGE_NAME]) The main work is done by the MainPage class. The get() method runs the main pipeline of code and returns HTML as a string. Working online version: https://twittersentiment-247018.appspot.com/ """ def get_tweets(keyword, max_tweets=200): """ Given a keyword as a string (e.g. "data science"), get recent tweets matching that string up to # max_tweets. Return a list of tweets, represented as strings. """ # API keys. consumer_key = "kNOG1klRMMUYbsjMuY5TKl4lE" consumer_secret = "ieghv6WI1qseYly43A0Ra1MPksEw1i5Onma0txfEu5aHantD2v" access_key = "<KEY>" access_secret = "<KEY>" # Initialize tweepy API object and authorize using API key. auth = tweepy.OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_key, access_secret) api = tweepy.API(auth) """ Get tweets.""" alltweets = [] for status in tweepy.Cursor( api.search, q=keyword + " -RT", # the -RT flag excludes retweets. count=1000, result_type="recent", include_entities=True, monitor_rate_limit=True, wait_on_rate_limit=True, lang="en", ).items(): # get text of the tweet, encoding as utf-8. text = str(status.text.encode("utf-8")) # add to the data structure, alltweets, holding the tweets. alltweets.append(text) # if we've reached max_tweets, break. if len(alltweets) >= max_tweets: break return alltweets class VaderSentimentModel: """ Calculate sentiment using a mostly lexicon-based approach that is optimized for social media. Approach is social media aware, for example emoticons are part of the lexicon and tokenization is twitter-sensitive. There are also some basic rules, e.g. it's sensitive to negations. """ def __init__(self): # Initialize a vader_analyzer object which does the work of sentiment analysis. self.vader_analyzer = SentimentIntensityAnalyzer() pass def classify_sentiment(self, tweet): # Classify sentiment of a single tweet. # Input tweet: as string. # Return sentiment score : # range -1 (very negaitve) to +1 (very positive). # score is calculated as p(positive) - p(negative) # normalizing to range from -1 to 1. # calculate sentiment in a dictionary. key is polarity ("pos", "neg", "neut") and value is probability. sentiment_dict = self.vader_analyzer.polarity_scores(tweet) # retrieve the compound sentiment score, which is p(pos) - p(neg), but normalized to range from {-1, 1} score = sentiment_dict["compound"] # compound is the combined score scaled to {-1, 1} return score def plot_tweets(tweets, sentiment_scores): """ Create a histogram-style barplot of tweets and their sentiment. Return a bokeh plot object, expressed as a tuple of (resources, script, div). Where : resources: some CSS, etc. that goes in the head of the webpage for styling the plot. script: javascript for the plot to function. expressed as string. div: html div container for the plot. expressed as string. """ # Sort tweets from negative to positive. # This step is not strictly necessary, but makes it easier to see the overall shape of the data. sorted_indices = np.argsort(sentiment_scores) sentiment_scores = np.array(sentiment_scores)[sorted_indices] tweets = np.array(tweets)[sorted_indices] # Express the data as a bokeh data source object. source = ColumnDataSource(data={ "text": tweets, "sentiment": sentiment_scores, "x": np.arange(len(tweets)), }) """ Create plot. """ # Create plot object. width = 0.9 p = figure(x_axis_label="Tweet", y_axis_label="Sentiment (0 = Neutral)") p.vbar(source=source, x="x", top="sentiment", width=width) # Add hover tool, allowing mouseover to view text and sentiment. hover = HoverTool( tooltips=[ ("text", "@text"), ("sentiment", "@sentiment") ], formatters={ "text": "printf", "sentiment": "printf" }, mode="vline" ) p.add_tools(hover) """ Format plot. """ # axis font size p.xaxis.axis_label_text_font_size = "15pt" p.yaxis.axis_label_text_font_size = "15pt" # remove tick marks from axes p.xaxis.major_tick_line_color = None p.xaxis.minor_tick_line_color = None p.yaxis.major_tick_line_color = None p.yaxis.minor_tick_line_color = None # adjust plot width, height scale = 1.5 p.plot_height = int(250 * scale) p.plot_width = int(450 * scale) # remove toolbar (e.g. move, resize, etc) from right of plot. p.toolbar.logo = None p.toolbar_location = None # remove gridlines p.xgrid.visible = False p.ygrid.visible = False # remove x axis tick labels (done by setting label fontsize to 0 pt) p.xaxis.major_label_text_font_size = '0pt' """ Export plot """ # Create resources string, which is CSS, etc. that goes in the head of resources = INLINE.render() # Get javascript (script) and HTML div (div) for the plot. script, div = components(p) return (resources, script, div) def plot_reason(tweets, sentiment_scores): """ Plot the top words that lead us to the classification as positive or negative. Return: script : javascript for the plot, expressed as string. div : html container for the plot, expressed as string. NOTE: requires the shared resources attribute from plot_tweets() in the HTML header. """ """ Calculate the sentiment of each individual token in the tweets. """ # list tokens, keeping only unique tokens (e.g. remove repeated words). all_toks = [] for tweet in tweets: toks = tweet.lower().split() all_toks.extend(toks) all_toks = [tok for tok in set(all_toks)] # remove duplicates. # calculate sentiment of each token. sm = VaderSentimentModel() toks_sentiment = [sm.classify_sentiment(tok) for tok in all_toks] """ sort tokens by sentiment. if overall valence is negative, sort negative to postitive. if overall valence is positive, sort positive to negative. thus, in any case, the earliest elements in the list are the most informative words. """ nwords = 20 # negative? sort neg -> positive. if np.mean(sentiment_scores) < 0: sorted_indices = np.argsort(toks_sentiment) # else (positive)? sort positive -> negative else: sorted_indices = np.argsort(toks_sentiment)[::-1] # toks_to_plot: shape (nwords, ) list of informative tokens. # sentiment_to_plot: shape (nwords, ) list of sentiment of these tokens. toks_to_plot = np.array(all_toks)[sorted_indices][:nwords] sentiment_to_plot = np.array(toks_sentiment)[sorted_indices][:nwords] # convert all sentiment scores to positive values. # this is for DISPLAY only, to make all plots go from left to right. # we still retain the correct tokens and sorting order. sentiment_to_plot = np.array([abs(v) for v in sentiment_to_plot]) """ Set up plot. - create data source object. - define formatting variables. """ text_offset = 0.1 source = ColumnDataSource(data={ "token": toks_to_plot, "sentiment": sentiment_to_plot, "x": np.arange(len(toks_to_plot))[::-1], "label_x": sentiment_to_plot + text_offset }) """ Make plot. """ # Create initial plot. width = 0.9 xrange = [0, max(sentiment_to_plot) + 1] p2 = figure(x_axis_label="Sentiment", y_axis_label="Word", x_range=xrange) p2.hbar(source=source, y="x", right="sentiment", height=width) """ Format plot. """ # Annotate each bar with the word being represented. glyph = Text(x="label_x", y="x", text="token") p2.add_glyph(source, glyph) # Axis labels. p2.xaxis.axis_label_text_font_size = "15pt" p2.yaxis.axis_label_text_font_size = "15pt" # Remove ticks. p2.xaxis.major_tick_line_color = None p2.xaxis.minor_tick_line_color = None p2.yaxis.major_tick_line_color = None p2.yaxis.minor_tick_line_color = None # Remove y axis tick labels. p2.yaxis.major_label_text_font_size = '0pt' # Plot width, height. scale = 1.5 p2.plot_height = int(250 * scale) p2.plot_width = int(250 * scale) # remove toolbar (e.g. move, resize, etc) from right of plot. p2.toolbar.logo = None p2.toolbar_location = None # remove gridlines p2.xgrid.visible = False p2.ygrid.visible = False # remove x axis tick labels (set font to 0pt) p2.xaxis.major_label_text_font_size = '0pt' # get bokeh component for plot 2. script2, div2 = components(p2) return (script2, div2) class MainPage(webapp2.RequestHandler): """ This class does the work of writing HTML to the google app server. Thus, we allow the get() method to incorporate: our main pipeline (getting tweets, analyzing sentiment, producing graphs) writing html """ def get(self): """ Get tweets and sentiment scores. """ # Retrieve keyword from the HTML form. If no keyword provided, use a random suggested keyword. keyword = self.request.get("keyword") if not keyword: suggested_keywords = ["alarm clocks", "the future", "miller lite", "taco bell", "yoga", "netflix", "life", "traffic", "elon musk", "beards", "world trade", "pepsi", "amazon"] indices = np.arange(len(suggested_keywords)) random.shuffle(indices) keyword = suggested_keywords[indices[0]] # Get recent tweets based on the keyword, up to 300 maximum tweets. tweets = get_tweets(keyword, max_tweets=300) # Compute the sentiment of each tweet. v = VaderSentimentModel() sentiment_scores = [v.classify_sentiment(tw) for tw in tweets] # shape (ntweets,) # Label sentiment categorically, e.g. "negative" or "positive" M_sent = np.mean(sentiment_scores) map = {1 : "positive", 0 : "negative"} valence = map[int(M_sent > 0)] """ Create plots. """ ############# # Plot #1: ############ # Plot the distribution of tweets and sentiment. # Resources is CSS code that goes in the header of the HTML. Shared across all bokeh plots. # Script1 is javascript for this plot. # Div1 is an HTML container for the plot. Goes where you want the plot to appear. resources, script1, div1 = plot_tweets(tweets=tweets, sentiment_scores=sentiment_scores) ############# # Plot #2: ############ # Plot the key words that lead us to this classification. # Script2 is javascript for this plot. # Div2 is an HTML container for this plot. Goes where you want the plot to appear. # Requires the HTML to include the shared resources, generated above, in the <HEAD> script2, div2 = plot_reason(tweets=tweets, sentiment_scores=sentiment_scores) """ Create HTML output. """ # Load HTML template. # This is a functioning webpage, with some placeholders for the keywords and plots we have created. html_p = os.path.join("html", "index.html") html = open(html_p, "r").read() # Fill in placeholders in the HTML with varibles we have created. term_to_value = { "[[!KEYWORD]]" : keyword, "[[!VALENCE]]" : valence, "[[!BOKEH_SCRIPT]]" : script1, "[[!BOKEH_SCRIPT2]]": script2, "[[!BOKEH_DIV]]" : div1, "[[!BOKEH_RESOURCES]]" : resources, "[[!BOKEH_DIV2]]" : div2 } for term, val in term_to_value.items(): html = html.replace(term, val) """ Write a response. This essentially returns HTML to the google app engine. This will render a webpage visible to the user. """ self.response.headers["Content-Type"] = "text/html" self.response.write(html) # Run application. routes = [('/', MainPage)] my_app = webapp2.WSGIApplication(routes, debug=True)	en	0.824098	---AUTHOR: --- <NAME> <EMAIL> ---LICENSE: --- MIT License. ---ABOUT: --- Application to get the sentiment of recent tweets based on a keyword. Example: keyword -> "taco bell" retrieve 300 recent tweets mentioning taco bell. get average sentiment. plot distribution of tweets and sentiment. plot most informative words for this application. This script runs based on google app server. Expects Python 2.7 Depenencies need to be included in the lib/ directory (pip install -t lib [PACKAGE_NAME]) The main work is done by the MainPage class. The get() method runs the main pipeline of code and returns HTML as a string. Working online version: https://twittersentiment-247018.appspot.com/ Given a keyword as a string (e.g. "data science"), get recent tweets matching that string up to # max_tweets. Return a list of tweets, represented as strings. # API keys. # Initialize tweepy API object and authorize using API key. Get tweets. # the -RT flag excludes retweets. # get text of the tweet, encoding as utf-8. # add to the data structure, alltweets, holding the tweets. # if we've reached max_tweets, break. Calculate sentiment using a mostly lexicon-based approach that is optimized for social media. Approach is social media aware, for example emoticons are part of the lexicon and tokenization is twitter-sensitive. There are also some basic rules, e.g. it's sensitive to negations. # Initialize a vader_analyzer object which does the work of sentiment analysis. # Classify sentiment of a single tweet. # Input tweet: as string. # Return sentiment score : # range -1 (very negaitve) to +1 (very positive). # score is calculated as p(positive) - p(negative) # normalizing to range from -1 to 1. # calculate sentiment in a dictionary. key is polarity ("pos", "neg", "neut") and value is probability. # retrieve the compound sentiment score, which is p(pos) - p(neg), but normalized to range from {-1, 1} # compound is the combined score scaled to {-1, 1} Create a histogram-style barplot of tweets and their sentiment. Return a bokeh plot object, expressed as a tuple of (resources, script, div). Where : resources: some CSS, etc. that goes in the head of the webpage for styling the plot. script: javascript for the plot to function. expressed as string. div: html div container for the plot. expressed as string. # Sort tweets from negative to positive. # This step is not strictly necessary, but makes it easier to see the overall shape of the data. # Express the data as a bokeh data source object. Create plot. # Create plot object. # Add hover tool, allowing mouseover to view text and sentiment. Format plot. # axis font size # remove tick marks from axes # adjust plot width, height # remove toolbar (e.g. move, resize, etc) from right of plot. # remove gridlines # remove x axis tick labels (done by setting label fontsize to 0 pt) Export plot # Create resources string, which is CSS, etc. that goes in the head of # Get javascript (script) and HTML div (div) for the plot. Plot the top words that lead us to the classification as positive or negative. Return: script : javascript for the plot, expressed as string. div : html container for the plot, expressed as string. NOTE: requires the shared resources attribute from plot_tweets() in the HTML header. Calculate the sentiment of each individual token in the tweets. # list tokens, keeping only unique tokens (e.g. remove repeated words). # remove duplicates. # calculate sentiment of each token. sort tokens by sentiment. if overall valence is negative, sort negative to postitive. if overall valence is positive, sort positive to negative. thus, in any case, the earliest elements in the list are the most informative words. # negative? sort neg -> positive. # else (positive)? sort positive -> negative # toks_to_plot: shape (nwords, ) list of informative tokens. # sentiment_to_plot: shape (nwords, ) list of sentiment of these tokens. # convert all sentiment scores to positive values. # this is for DISPLAY only, to make all plots go from left to right. # we still retain the correct tokens and sorting order. Set up plot. - create data source object. - define formatting variables. Make plot. # Create initial plot. Format plot. # Annotate each bar with the word being represented. # Axis labels. # Remove ticks. # Remove y axis tick labels. # Plot width, height. # remove toolbar (e.g. move, resize, etc) from right of plot. # remove gridlines # remove x axis tick labels (set font to 0pt) # get bokeh component for plot 2. This class does the work of writing HTML to the google app server. Thus, we allow the get() method to incorporate: our main pipeline (getting tweets, analyzing sentiment, producing graphs) writing html Get tweets and sentiment scores. # Retrieve keyword from the HTML form. If no keyword provided, use a random suggested keyword. # Get recent tweets based on the keyword, up to 300 maximum tweets. # Compute the sentiment of each tweet. # shape (ntweets,) # Label sentiment categorically, e.g. "negative" or "positive" Create plots. ############# # Plot #1: ############ # Plot the distribution of tweets and sentiment. # Resources is CSS code that goes in the header of the HTML. Shared across all bokeh plots. # Script1 is javascript for this plot. # Div1 is an HTML container for the plot. Goes where you want the plot to appear. ############# # Plot #2: ############ # Plot the key words that lead us to this classification. # Script2 is javascript for this plot. # Div2 is an HTML container for this plot. Goes where you want the plot to appear. # Requires the HTML to include the shared resources, generated above, in the <HEAD> Create HTML output. # Load HTML template. # This is a functioning webpage, with some placeholders for the keywords and plots we have created. # Fill in placeholders in the HTML with varibles we have created. Write a response. This essentially returns HTML to the google app engine. This will render a webpage visible to the user. # Run application.	2.989811	3
tests/testcgatools.py	ereide/pyga-camcal	5	10235	<reponame>ereide/pyga-camcal<gh_stars>1-10 import unittest import clifford as cl from clifford import g3c from numpy import pi, e import numpy as np from scipy.sparse.linalg.matfuncs import _sinch as sinch from clifford import MultiVector from pygacal.common.cgatools import ( Sandwich, Dilator, Translator, Reflector, inversion, Rotor, Transversor, I3, I5, VectorEquality, Distance, ga_log, ga_exp, MVEqual, Meet, extractBivectorParameters_complicated, ga_exp_complicated, one) from pygacal.geometry import createRandomBivector, createRandomVector, createRandomPoints from pygacal.geometry.lines import createLine from pygacal.geometry.planes import createPlane layout = g3c.layout locals().update(g3c.blades) ep, en, up, down, homo, E0, ninf, no = (g3c.stuff["ep"], g3c.stuff["en"], g3c.stuff["up"], g3c.stuff["down"], g3c.stuff["homo"], g3c.stuff["E0"], g3c.stuff["einf"], -g3c.stuff["eo"]) np.random.seed(2512) def AssertMVEqual(actual, expected, rtol = 1e-5, atol = 1e-6, verbose = False): assert(MVEqual(actual, expected, rtol, atol, verbose)) def AssertMVUnEqual(actual, expected, rtol = 1e-5, atol = 1e-6, verbose = False): assert(not MVEqual(actual, expected, rtol, atol, verbose)) class TestCGAOperators(unittest.TestCase): def testDilator(self): x = 2e1 + 3 e2 + 4e3 X = up(x) assert(down(Sandwich(X, Dilator(0.1))) == x 0.1) def testTranslation(self): x = 2e1 + 3 e2 + 4e3 X = up(x) a = 2 e1 + e3 assert(down(Sandwich(X, Translator(a))) == x + a) def testRotation(self): x = 2e1 + 3 e2 + 4e3 X = up(x) actual = down(Sandwich(X, Rotor(e12, pi/2))) expected = (-3.0)e1 + 2.0e2 + 4.0 e3 assert(actual == expected) def testInversion(self): x = 2e1 + 3 e2 + 4e3 X = up(x) assert(down(inversion(X)) x == 1) def testDistance(self): a = e1 b = e2 A, B = up(a), up(b) assert(Distance(A, B) == np.sqrt(2)) def testMeet(self): A, B, C, D = createRandomPoints(N = 4, scale = 50) L = createLine(A, B) L2 = createLine(A, C) P1 = createPlane(A, B, C) P2 = createPlane(A, B, D) L_actual = Meet(P1, P2) assert(MVEqual(L, L_actual)) #Plane to line Q = (ninf ^ A).normal() P3 = A ^ C ^ D ^ ninf Q_actual = Meet(P3, L).normal() #How do we define order/direction? assert(MVEqual(Q, Q_actual)) def testAssertEqual(self): verbose = False a = createRandomBivector() b = a + 0.01 a2 = b - 0.01 c = a + 1 d = c - a AssertMVEqual(a, a2, verbose = verbose) AssertMVUnEqual(a, b, verbose = verbose) AssertMVEqual(d, 1, verbose = verbose) def testLogarithm(self): verbose = False if verbose: print("\nTest Logarithms and exponents") phi = 0.5 #Rotation amount P = (e12 + 2e23 + 3e13).normal() #Rotation Plane P_n = PI3 t = 2.73 e1 + 3.14e2 #Translation vector t_nor = (P_n \| t) P_n #Decomposition into normal component t_par = t - t_nor #Decomposition into paralel component assert(t_par + t_nor == t) if verbose: print("P = ", P) print("phi = ", phi) print("t = ", t) print("t_nor = ", t_nor) print("t_par = ", t_par) print("") assert(P\|t_nor == 0) #Normal to P assert(P^t_nor != 0) #Normal to P assert(P\|t_par != 0) #Parallel to P assert(P^t_par == 0) #Parallel to P assert(Pt != 0) #Non zero product R_expected = (np.cos(phi) + (np.sin(phi) P))(1 + (t_norninf)) + np.sinc(phi/np.pi)t_par ninf B_expected = phi * P + tninf R_exponential = np.exp(B_expected) R_actual = ga_exp(B_expected, verbose = verbose) B_new = ga_log(R_expected, verbose = verbose) R_ga = ga_exp(B_new) if verbose: print("R_old ", R_expected) print("R_expected ", R_actual) print("R_exponential", R_exponential) print("R_ga ", R_ga) print("B_new ", B_new) print("B_expected ", B_expected) #Rotor properties AssertMVEqual(R_expected ~R_expected, 1, verbose = verbose) AssertMVEqual(R_ga * ~R_ga, 1, verbose = verbose) #Equalities AssertMVEqual(R_actual, R_expected, verbose = verbose) AssertMVEqual(R_exponential, R_expected, verbose = verbose) AssertMVEqual(B_new, B_expected, verbose = verbose) AssertMVEqual(R_ga, R_expected, verbose = verbose) N = 100 #Random bivectors to test this as well for i in range(N): B = createRandomBivector() AssertMVEqual(B, ga_log(ga_exp(B, verbose = verbose), verbose = verbose), verbose = verbose) def testComplicatedLogarithm(self): verbose = True if verbose: print("\nTest Complicated Logarithms and exponents") phi = 0.2 #Rotation amount P = (e12 + 2e23 + 3e13).normal() #Rotation Plane P_n = PI3 #t = 0 t = 2.73 e1 + 3.14e2 #Translation vector t_nor = (P_n \| t) P_n #Decomposition into normal component t_par = t - t_nor #Decomposition into paralel component omega = 0.1 assert(t_par + t_nor == t) if verbose: print("P = ", P) print("phi = ", phi) print("t = ", t) print("t_nor = ", t_nor) print("t_par = ", t_par) print("omega = ", omega) print("") """ assert(P\|t_nor == 0) #Normal to P assert(P^t_nor != 0) #Normal to P assert(P\|t_par != 0) #Parallel to P assert(P^t_par == 0) #Parallel to P assert(Pt != 0) #Non zero product assert(t_par\|t_nor == 0) #Non zero product """ B_expected = (phi P) + (tninf) + (omega E0) k = (omega * omega + phi * phi) R_expected = (np.cos(phi) + np.sin(phi) * P)(np.cosh(omega) + np.sinh(omega) E0 + sinch(omega) * t_norninf) if (k > 0): R_expected += 1/k ( (-omega * np.sin(phi) * np.cosh(omega) + phi * np.cos(phi) * np.sinh(omega)) * P + ( omega * np.cos(phi) * np.sinh(omega) + phi * np.sin(phi) * np.cosh(omega))) * t_par * ninf else: R_expected += t_par * ninf phi_test, P_test, t_nor_test, t_par_test, omega_test = extractBivectorParameters_complicated(B_expected) B_actual = phi_test * P_test + (t_nor_test + t_par_test)ninf + omega_test E0 #Testing some basic properties of the extraction AssertMVEqual(phi(P ~P), phione, verbose = False) AssertMVEqual(phiP, phiP_test, verbose = False) R_exponential = np.exp(B_expected) R_actual = ga_exp_complicated(B_expected, verbose = verbose) #B_new = ga_log(R_expected, verbose = verbose) #R_ga = ga_exp(B_new) if verbose: print("R_expected ", R_expected) print("R_actual ", R_actual) print("R_exponential ", R_exponential) #print("R_ga ", R_ga) #print("B_new ", B_new) print("B_expected ", B_expected) print() #BivectorExtraction AssertMVEqual(B_actual, B_expected, verbose = verbose) AssertMVEqual(R_expected ~R_expected, one, verbose = verbose) #Rotor properties AssertMVEqual(R_actual * ~R_actual, one, verbose = verbose) #Only an approximation AssertMVEqual(R_exponential * ~R_exponential, one, verbose = verbose) #AssertMVEqual(R_expected * ~R_expected, 1, verbose = verbose) #AssertMVEqual(R_ga * ~R_ga, 1, verbose = verbose) #Equalities #AssertMVEqual(R_actual, R_expected, verbose = verbose) AssertMVEqual(R_exponential, R_actual, rtol = 1e-2, atol = 1e-3, verbose = verbose) #AssertMVEqual(B_new, B_expected, verbose = verbose) #AssertMVEqual(R_ga, R_expected, verbose = verbose) #N = 100 #Random bivectors to test this as well #for i in range(N): # B = createRandomBivector() # AssertMVEqual(B, ga_log(ga_exp(B, verbose = verbose), verbose = verbose), verbose = verbose) if __name__ == "__main__": unittest.main()	import unittest import clifford as cl from clifford import g3c from numpy import pi, e import numpy as np from scipy.sparse.linalg.matfuncs import _sinch as sinch from clifford import MultiVector from pygacal.common.cgatools import ( Sandwich, Dilator, Translator, Reflector, inversion, Rotor, Transversor, I3, I5, VectorEquality, Distance, ga_log, ga_exp, MVEqual, Meet, extractBivectorParameters_complicated, ga_exp_complicated, one) from pygacal.geometry import createRandomBivector, createRandomVector, createRandomPoints from pygacal.geometry.lines import createLine from pygacal.geometry.planes import createPlane layout = g3c.layout locals().update(g3c.blades) ep, en, up, down, homo, E0, ninf, no = (g3c.stuff["ep"], g3c.stuff["en"], g3c.stuff["up"], g3c.stuff["down"], g3c.stuff["homo"], g3c.stuff["E0"], g3c.stuff["einf"], -g3c.stuff["eo"]) np.random.seed(2512) def AssertMVEqual(actual, expected, rtol = 1e-5, atol = 1e-6, verbose = False): assert(MVEqual(actual, expected, rtol, atol, verbose)) def AssertMVUnEqual(actual, expected, rtol = 1e-5, atol = 1e-6, verbose = False): assert(not MVEqual(actual, expected, rtol, atol, verbose)) class TestCGAOperators(unittest.TestCase): def testDilator(self): x = 2e1 + 3 e2 + 4e3 X = up(x) assert(down(Sandwich(X, Dilator(0.1))) == x 0.1) def testTranslation(self): x = 2e1 + 3 e2 + 4e3 X = up(x) a = 2 e1 + e3 assert(down(Sandwich(X, Translator(a))) == x + a) def testRotation(self): x = 2e1 + 3 e2 + 4e3 X = up(x) actual = down(Sandwich(X, Rotor(e12, pi/2))) expected = (-3.0)e1 + 2.0e2 + 4.0 e3 assert(actual == expected) def testInversion(self): x = 2e1 + 3 e2 + 4e3 X = up(x) assert(down(inversion(X)) x == 1) def testDistance(self): a = e1 b = e2 A, B = up(a), up(b) assert(Distance(A, B) == np.sqrt(2)) def testMeet(self): A, B, C, D = createRandomPoints(N = 4, scale = 50) L = createLine(A, B) L2 = createLine(A, C) P1 = createPlane(A, B, C) P2 = createPlane(A, B, D) L_actual = Meet(P1, P2) assert(MVEqual(L, L_actual)) #Plane to line Q = (ninf ^ A).normal() P3 = A ^ C ^ D ^ ninf Q_actual = Meet(P3, L).normal() #How do we define order/direction? assert(MVEqual(Q, Q_actual)) def testAssertEqual(self): verbose = False a = createRandomBivector() b = a + 0.01 a2 = b - 0.01 c = a + 1 d = c - a AssertMVEqual(a, a2, verbose = verbose) AssertMVUnEqual(a, b, verbose = verbose) AssertMVEqual(d, 1, verbose = verbose) def testLogarithm(self): verbose = False if verbose: print("\nTest Logarithms and exponents") phi = 0.5 #Rotation amount P = (e12 + 2e23 + 3e13).normal() #Rotation Plane P_n = PI3 t = 2.73 e1 + 3.14e2 #Translation vector t_nor = (P_n \| t) P_n #Decomposition into normal component t_par = t - t_nor #Decomposition into paralel component assert(t_par + t_nor == t) if verbose: print("P = ", P) print("phi = ", phi) print("t = ", t) print("t_nor = ", t_nor) print("t_par = ", t_par) print("") assert(P\|t_nor == 0) #Normal to P assert(P^t_nor != 0) #Normal to P assert(P\|t_par != 0) #Parallel to P assert(P^t_par == 0) #Parallel to P assert(Pt != 0) #Non zero product R_expected = (np.cos(phi) + (np.sin(phi) P))(1 + (t_norninf)) + np.sinc(phi/np.pi)t_par ninf B_expected = phi * P + tninf R_exponential = np.exp(B_expected) R_actual = ga_exp(B_expected, verbose = verbose) B_new = ga_log(R_expected, verbose = verbose) R_ga = ga_exp(B_new) if verbose: print("R_old ", R_expected) print("R_expected ", R_actual) print("R_exponential", R_exponential) print("R_ga ", R_ga) print("B_new ", B_new) print("B_expected ", B_expected) #Rotor properties AssertMVEqual(R_expected ~R_expected, 1, verbose = verbose) AssertMVEqual(R_ga * ~R_ga, 1, verbose = verbose) #Equalities AssertMVEqual(R_actual, R_expected, verbose = verbose) AssertMVEqual(R_exponential, R_expected, verbose = verbose) AssertMVEqual(B_new, B_expected, verbose = verbose) AssertMVEqual(R_ga, R_expected, verbose = verbose) N = 100 #Random bivectors to test this as well for i in range(N): B = createRandomBivector() AssertMVEqual(B, ga_log(ga_exp(B, verbose = verbose), verbose = verbose), verbose = verbose) def testComplicatedLogarithm(self): verbose = True if verbose: print("\nTest Complicated Logarithms and exponents") phi = 0.2 #Rotation amount P = (e12 + 2e23 + 3e13).normal() #Rotation Plane P_n = PI3 #t = 0 t = 2.73 e1 + 3.14e2 #Translation vector t_nor = (P_n \| t) P_n #Decomposition into normal component t_par = t - t_nor #Decomposition into paralel component omega = 0.1 assert(t_par + t_nor == t) if verbose: print("P = ", P) print("phi = ", phi) print("t = ", t) print("t_nor = ", t_nor) print("t_par = ", t_par) print("omega = ", omega) print("") """ assert(P\|t_nor == 0) #Normal to P assert(P^t_nor != 0) #Normal to P assert(P\|t_par != 0) #Parallel to P assert(P^t_par == 0) #Parallel to P assert(Pt != 0) #Non zero product assert(t_par\|t_nor == 0) #Non zero product """ B_expected = (phi P) + (tninf) + (omega E0) k = (omega * omega + phi * phi) R_expected = (np.cos(phi) + np.sin(phi) * P)(np.cosh(omega) + np.sinh(omega) E0 + sinch(omega) * t_norninf) if (k > 0): R_expected += 1/k ( (-omega * np.sin(phi) * np.cosh(omega) + phi * np.cos(phi) * np.sinh(omega)) * P + ( omega * np.cos(phi) * np.sinh(omega) + phi * np.sin(phi) * np.cosh(omega))) * t_par * ninf else: R_expected += t_par * ninf phi_test, P_test, t_nor_test, t_par_test, omega_test = extractBivectorParameters_complicated(B_expected) B_actual = phi_test * P_test + (t_nor_test + t_par_test)ninf + omega_test E0 #Testing some basic properties of the extraction AssertMVEqual(phi(P ~P), phione, verbose = False) AssertMVEqual(phiP, phiP_test, verbose = False) R_exponential = np.exp(B_expected) R_actual = ga_exp_complicated(B_expected, verbose = verbose) #B_new = ga_log(R_expected, verbose = verbose) #R_ga = ga_exp(B_new) if verbose: print("R_expected ", R_expected) print("R_actual ", R_actual) print("R_exponential ", R_exponential) #print("R_ga ", R_ga) #print("B_new ", B_new) print("B_expected ", B_expected) print() #BivectorExtraction AssertMVEqual(B_actual, B_expected, verbose = verbose) AssertMVEqual(R_expected ~R_expected, one, verbose = verbose) #Rotor properties AssertMVEqual(R_actual * ~R_actual, one, verbose = verbose) #Only an approximation AssertMVEqual(R_exponential * ~R_exponential, one, verbose = verbose) #AssertMVEqual(R_expected * ~R_expected, 1, verbose = verbose) #AssertMVEqual(R_ga * ~R_ga, 1, verbose = verbose) #Equalities #AssertMVEqual(R_actual, R_expected, verbose = verbose) AssertMVEqual(R_exponential, R_actual, rtol = 1e-2, atol = 1e-3, verbose = verbose) #AssertMVEqual(B_new, B_expected, verbose = verbose) #AssertMVEqual(R_ga, R_expected, verbose = verbose) #N = 100 #Random bivectors to test this as well #for i in range(N): # B = createRandomBivector() # AssertMVEqual(B, ga_log(ga_exp(B, verbose = verbose), verbose = verbose), verbose = verbose) if __name__ == "__main__": unittest.main()	en	0.775235	#Plane to line #How do we define order/direction? #Rotation amount #Rotation Plane #Translation vector #Decomposition into normal component #Decomposition into paralel component #Normal to P #Normal to P #Parallel to P #Parallel to P #Non zero product #Rotor properties #Equalities #Random bivectors to test this as well #Rotation amount #Rotation Plane #t = 0 #Translation vector #Decomposition into normal component #Decomposition into paralel component assert(P\|t_nor == 0) #Normal to P assert(P^t_nor != 0) #Normal to P assert(P\|t_par != 0) #Parallel to P assert(P^t_par == 0) #Parallel to P assert(Pt != 0) #Non zero product assert(t_par\|t_nor == 0) #Non zero product #Testing some basic properties of the extraction #B_new = ga_log(R_expected, verbose = verbose) #R_ga = ga_exp(B_new) #print("R_ga ", R_ga) #print("B_new ", B_new) #BivectorExtraction #Rotor properties #Only an approximation #AssertMVEqual(R_expected ~R_expected, 1, verbose = verbose) #AssertMVEqual(R_ga * ~R_ga, 1, verbose = verbose) #Equalities #AssertMVEqual(R_actual, R_expected, verbose = verbose) #AssertMVEqual(B_new, B_expected, verbose = verbose) #AssertMVEqual(R_ga, R_expected, verbose = verbose) #N = 100 #Random bivectors to test this as well #for i in range(N): # B = createRandomBivector() # AssertMVEqual(B, ga_log(ga_exp(B, verbose = verbose), verbose = verbose), verbose = verbose)	2.070098	2
router/posts.py	DiegoLing33/prestij.xyz-api	0	10236	# ██╗░░░░░██╗███╗░░██╗░██████╗░░░░██████╗░██╗░░░░░░█████╗░░█████╗░██╗░░██╗ # ██║░░░░░██║████╗░██║██╔════╝░░░░██╔══██╗██║░░░░░██╔══██╗██╔══██╗██║░██╔╝ # ██║░░░░░██║██╔██╗██║██║░░██╗░░░░██████╦╝██║░░░░░███████║██║░░╚═╝█████═╝░ # ██║░░░░░██║██║╚████║██║░░╚██╗░░░██╔══██╗██║░░░░░██╔══██║██║░░██╗██╔═██╗░ # ███████╗██║██║░╚███║╚██████╔╝░░░██████╦╝███████╗██║░░██║╚█████╔╝██║░╚██╗ # ╚══════╝╚═╝╚═╝░░╚══╝░╚═════╝░░░░╚═════╝░╚══════╝╚═╝░░╚═╝░╚════╝░╚═╝░░╚═╝ # # Developed by <NAME> (C) Ling • Black 2020 # @site http://ling.black # ██╗░░░░░██╗███╗░░██╗░██████╗░░░░██████╗░██╗░░░░░░█████╗░░█████╗░██╗░░██╗ # ██║░░░░░██║████╗░██║██╔════╝░░░░██╔══██╗██║░░░░░██╔══██╗██╔══██╗██║░██╔╝ # ██║░░░░░██║██╔██╗██║██║░░██╗░░░░██████╦╝██║░░░░░███████║██║░░╚═╝█████═╝░ # ██║░░░░░██║██║╚████║██║░░╚██╗░░░██╔══██╗██║░░░░░██╔══██║██║░░██╗██╔═██╗░ # ███████╗██║██║░╚███║╚██████╔╝░░░██████╦╝███████╗██║░░██║╚█████╔╝██║░╚██╗ # ╚══════╝╚═╝╚═╝░░╚══╝░╚═════╝░░░░╚═════╝░╚══════╝╚═╝░░╚═╝░╚════╝░╚═╝░░╚═╝ # # Developed by <NAME> (C) Ling • Black 2020 # @site http://ling.black from typing import List from fastapi import APIRouter, Depends, HTTPException from pydantic import BaseModel from core.response import RequestLimit from database import get_db, DatabaseUtils from database.wow.models import PostModel, PostCommentsModel from wow.interface.entity import PostCategory, Post, PostCategoryCreate, PostCreate, PostLikeCreate, PostCommentCreate from wow.utils.posts import PostsUtils from wow.utils.users import BlizzardUsersUtils router = APIRouter() class TokenArgs(BaseModel): token: str class TokenPostIdArgs(BaseModel): token: str post_id: int class CommentIdAndToken(TokenArgs): comment_id: int class PostAPIList(BaseModel): items: List[Post] count: int class PostAPIListResponse(BaseModel): response: PostAPIList request: RequestLimit # ----------------------------------- # CATEGORIES # ----------------------------------- @router.post( "/categories", response_model=PostCategory, summary='Adds the category' ) def add_category(body: PostCategoryCreate): """ Adds the category :param body: :return: """ blizzard_id = BlizzardUsersUtils.id__safe(body.token) return PostsUtils.add_category(user_id=blizzard_id, url=body.url, title=body.title) @router.get( "/categories", response_model=List[PostCategory], summary='Returns the categories' ) def get_categories(): """ Returns the categories list :return: """ return PostsUtils.get_categories() # ----------------------------------- # POSTS # ----------------------------------- @router.get( "/", response_model=PostAPIListResponse, summary='Returns all the posts' ) def get_posts_all(limit: int = 100, offset: int = 0): return PostsUtils.get_posts_limit( limit=limit, offset=offset ) @router.get( "/category/{category_url}", response_model=PostAPIListResponse, summary='Returns the posts in category' ) def get_posts_all(category_url: int, limit: int = 100, offset: int = 0): """ Returns all the posts by category :param category_url: :param limit: :param offset: :return: """ return PostsUtils.get_posts_by_category_limit( category_id=category_url, limit=limit, offset=offset ) @router.get( "/user/{blizzard_id}", response_model=PostAPIListResponse, summary='Returns the posts by users' ) def get_posts_all(blizzard_id: int, limit: int = 100, offset: int = 0): """ Returns all the posts by category :param blizzard_id: :param limit: :param offset: :return: """ return PostsUtils.get_posts_by_blizzard_id( blizzard_id=blizzard_id, limit=limit, offset=offset ) @router.post( "/like", summary='Likes the post', tags=['Лайки'] ) def like_post(body: PostLikeCreate): blizzard_id = BlizzardUsersUtils.id__safe(body.token) return PostsUtils.add_like( user_id=blizzard_id, post_id=body.post_id, ) @router.post( "/unlike", summary='Unlikes the post', tags=['Лайки'] ) def like_post(body: PostLikeCreate): blizzard_id = BlizzardUsersUtils.id__safe(body.token) return PostsUtils.remove_like( user_id=blizzard_id, post_id=body.post_id, ) @router.post( "/comment", summary='Adds the comment', tags=['Комментарии'] ) def like_post(body: PostCommentCreate): blizzard_id = BlizzardUsersUtils.id__safe(body.token) return PostsUtils.add_comment( user_id=blizzard_id, post_id=body.post_id, reply_id=body.reply_id, text=body.text, ) @router.delete( "/comment", summary='Removes the comment', tags=['Комментарии'] ) def removes_post(body: CommentIdAndToken, db=Depends(get_db)): blizzard_id = BlizzardUsersUtils.id__safe(body.token) com = db.query(PostCommentsModel).filter(PostCommentsModel.id == body.comment_id).filter( PostCommentsModel.user_id == blizzard_id) if com.count() > 0: com.delete() db.commit() return True return False @router.post( "/", response_model=Post, summary='Adds the post' ) def add_post(body: PostCreate): """ Adds the post item :param body: :return: """ blizzard_id = BlizzardUsersUtils.id__safe(body.token) return PostsUtils.add_post( user_id=blizzard_id, category_id=body.category_id, title=body.title, content=body.content, tags=body.tags, image=body.image ) @router.delete( "/{post_id}", summary='Deletes the post' ) def delete_post(post_id: int, body: TokenArgs, db=Depends(get_db)): blizzard_id = BlizzardUsersUtils.id__safe(body.token) q = db.query(PostModel).filter(PostModel.id == post_id).filter(PostModel.user_id == blizzard_id) if q.count() == 0: raise HTTPException(status_code=404, detail='Post is undefined') return DatabaseUtils.remove_query(db, q) @router.post( "/{post_id}", summary='Edits the post' ) def edit_post(post_id: int, body: PostCreate, db=Depends(get_db)): blizzard_id = BlizzardUsersUtils.id__safe(body.token) q = db.query(PostModel).filter(PostModel.id == post_id).filter(PostModel.user_id == blizzard_id) if q.count() == 0: raise HTTPException(status_code=404, detail='Post is undefined') q.update({ 'title': body.title, 'content': body.content, 'category_id': body.category_id, 'image': body.image, 'tags': body.tags, }) db.commit() return True @router.get( "/{post_id}", response_model=Post, summary='Returns the post' ) def get_post(post_id: int, db=Depends(get_db)): return db.query(PostModel).filter(PostModel.id == post_id).first()	# ██╗░░░░░██╗███╗░░██╗░██████╗░░░░██████╗░██╗░░░░░░█████╗░░█████╗░██╗░░██╗ # 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----------------------------------- # CATEGORIES # ----------------------------------- @router.post( "/categories", response_model=PostCategory, summary='Adds the category' ) def add_category(body: PostCategoryCreate): """ Adds the category :param body: :return: """ blizzard_id = BlizzardUsersUtils.id__safe(body.token) return PostsUtils.add_category(user_id=blizzard_id, url=body.url, title=body.title) @router.get( "/categories", response_model=List[PostCategory], summary='Returns the categories' ) def get_categories(): """ Returns the categories list :return: """ return PostsUtils.get_categories() # ----------------------------------- # POSTS # ----------------------------------- @router.get( "/", response_model=PostAPIListResponse, summary='Returns all the posts' ) def get_posts_all(limit: int = 100, offset: int = 0): return PostsUtils.get_posts_limit( limit=limit, offset=offset ) @router.get( "/category/{category_url}", response_model=PostAPIListResponse, summary='Returns the posts in category' ) def get_posts_all(category_url: int, limit: int = 100, offset: int = 0): """ Returns all the posts by category :param category_url: :param limit: :param offset: :return: """ return PostsUtils.get_posts_by_category_limit( category_id=category_url, limit=limit, offset=offset ) @router.get( "/user/{blizzard_id}", response_model=PostAPIListResponse, summary='Returns the posts by users' ) def get_posts_all(blizzard_id: int, limit: int = 100, offset: int = 0): """ Returns all the posts by category :param blizzard_id: :param limit: :param offset: :return: """ return PostsUtils.get_posts_by_blizzard_id( blizzard_id=blizzard_id, limit=limit, offset=offset ) @router.post( "/like", summary='Likes the post', tags=['Лайки'] ) def like_post(body: PostLikeCreate): blizzard_id = BlizzardUsersUtils.id__safe(body.token) return PostsUtils.add_like( user_id=blizzard_id, post_id=body.post_id, ) @router.post( "/unlike", summary='Unlikes the post', tags=['Лайки'] ) def like_post(body: PostLikeCreate): blizzard_id = BlizzardUsersUtils.id__safe(body.token) return PostsUtils.remove_like( user_id=blizzard_id, post_id=body.post_id, ) @router.post( "/comment", summary='Adds the comment', tags=['Комментарии'] ) def like_post(body: PostCommentCreate): blizzard_id = BlizzardUsersUtils.id__safe(body.token) return PostsUtils.add_comment( user_id=blizzard_id, post_id=body.post_id, reply_id=body.reply_id, text=body.text, ) @router.delete( "/comment", summary='Removes the comment', tags=['Комментарии'] ) def removes_post(body: CommentIdAndToken, db=Depends(get_db)): blizzard_id = BlizzardUsersUtils.id__safe(body.token) com = db.query(PostCommentsModel).filter(PostCommentsModel.id == body.comment_id).filter( PostCommentsModel.user_id == blizzard_id) if com.count() > 0: com.delete() db.commit() return True return False @router.post( "/", response_model=Post, summary='Adds the post' ) def add_post(body: PostCreate): """ Adds the post item :param body: :return: """ blizzard_id = BlizzardUsersUtils.id__safe(body.token) return PostsUtils.add_post( user_id=blizzard_id, category_id=body.category_id, title=body.title, content=body.content, tags=body.tags, image=body.image ) @router.delete( "/{post_id}", summary='Deletes the post' ) def delete_post(post_id: int, body: TokenArgs, db=Depends(get_db)): blizzard_id = BlizzardUsersUtils.id__safe(body.token) q = db.query(PostModel).filter(PostModel.id == post_id).filter(PostModel.user_id == blizzard_id) if q.count() == 0: raise HTTPException(status_code=404, detail='Post is undefined') return DatabaseUtils.remove_query(db, q) @router.post( "/{post_id}", summary='Edits the post' ) def edit_post(post_id: int, body: PostCreate, db=Depends(get_db)): blizzard_id = BlizzardUsersUtils.id__safe(body.token) q = db.query(PostModel).filter(PostModel.id == post_id).filter(PostModel.user_id == blizzard_id) if q.count() == 0: raise HTTPException(status_code=404, detail='Post is undefined') q.update({ 'title': body.title, 'content': body.content, 'category_id': body.category_id, 'image': body.image, 'tags': body.tags, }) db.commit() return True @router.get( "/{post_id}", response_model=Post, summary='Returns the post' ) def get_post(post_id: int, db=Depends(get_db)): return db.query(PostModel).filter(PostModel.id == post_id).first()	en	0.085405	# ██╗░░░░░██╗███╗░░██╗░██████╗░░░░██████╗░██╗░░░░░░█████╗░░█████╗░██╗░░██╗ # 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CATEGORIES # ----------------------------------- Adds the category :param body: :return: Returns the categories list :return: # ----------------------------------- # POSTS # ----------------------------------- Returns all the posts by category :param category_url: :param limit: :param offset: :return: Returns all the posts by category :param blizzard_id: :param limit: :param offset: :return: Adds the post item :param body: :return:	2.165511	2
toontown/catalog/CatalogChatBalloon.py	CrankySupertoon01/Toontown-2	1	10237	<filename>toontown/catalog/CatalogChatBalloon.py from pandac.PandaModules import * class CatalogChatBalloon: TEXT_SHIFT = (0.1, -0.05, 1.1) TEXT_SHIFT_REVERSED = -0.05 TEXT_SHIFT_PROP = 0.08 NATIVE_WIDTH = 10.0 MIN_WIDTH = 2.5 MIN_HEIGHT = 1 BUBBLE_PADDING = 0.3 BUBBLE_PADDING_PROP = 0.05 BUTTON_SCALE = 6 BUTTON_SHIFT = (-0.2, 0, 0.6) FRAME_SHIFT = (0.2, 1.4) def __init__(self, model): self.model = model def generate(self, text, font, textColor=(0,0,0,1), balloonColor=(1,1,1,1), wordWrap = 10.0, button=None, reversed=False): root = NodePath('balloon') # Add balloon geometry: balloon = self.model.copyTo(root) top = balloon.find('/top') middle = balloon.find('/middle') bottom = balloon.find('*/bottom') balloon.setColor(balloonColor) if balloonColor[3] < 1.0: balloon.setTransparency(1) # Render the text into a TextNode, using the font: t = root.attachNewNode(TextNode('text')) t.node().setFont(font) t.node().setWordwrap(wordWrap) t.node().setText(text) t.node().setTextColor(textColor) width, height = t.node().getWidth(), t.node().getHeight() # Turn off depth write for the text: The place in the depth buffer is # held by the chat bubble anyway, and the text renders after the bubble # so there's no risk of the bubble overwriting the text's pixels. t.setAttrib(DepthWriteAttrib.make(0)) t.setPos(self.TEXT_SHIFT) t.setX(t, self.TEXT_SHIFT_PROPwidth) t.setZ(t, height) if reversed: # The nametag code wants the text on the left side of the axis, # rather than on the right side. Therefore, we move the text to the # opposite side: t.setX(self.TEXT_SHIFT_REVERSED - self.TEXT_SHIFT_PROPwidth - width) # Give the chat bubble a button, if one is requested: if button: np = button.copyTo(root) np.setPos(t, width, 0, -height) np.setPos(np, self.BUTTON_SHIFT) np.setScale(self.BUTTON_SCALE) # Set a minimum width and height for short or empty messages if width < self.MIN_WIDTH: width = self.MIN_WIDTH if reversed: t.setX(t, -width/2.0) else: t.setX(t, width/2.0) t.node().setAlign(TextNode.ACenter) if height < self.MIN_HEIGHT: height = self.MIN_HEIGHT t.setX(t, height/2.0) t.node().setAlign(TextNode.ACenter) # Set the balloon's size: width = 1+self.BUBBLE_PADDING_PROP width += self.BUBBLE_PADDING balloon.setSx(width/self.NATIVE_WIDTH) if reversed: balloon.setSx(-balloon.getSx()) balloon.setTwoSided(True) # Render the backface of the balloon middle.setSz(height) top.setZ(top, height-1) # Calculate the frame occupied by the balloon: left, bottom = self.FRAME_SHIFT if reversed: left = -left - width frame = (left, left+width, bottom, bottom+height+1) return root, frame	<filename>toontown/catalog/CatalogChatBalloon.py from pandac.PandaModules import * class CatalogChatBalloon: TEXT_SHIFT = (0.1, -0.05, 1.1) TEXT_SHIFT_REVERSED = -0.05 TEXT_SHIFT_PROP = 0.08 NATIVE_WIDTH = 10.0 MIN_WIDTH = 2.5 MIN_HEIGHT = 1 BUBBLE_PADDING = 0.3 BUBBLE_PADDING_PROP = 0.05 BUTTON_SCALE = 6 BUTTON_SHIFT = (-0.2, 0, 0.6) FRAME_SHIFT = (0.2, 1.4) def __init__(self, model): self.model = model def generate(self, text, font, textColor=(0,0,0,1), balloonColor=(1,1,1,1), wordWrap = 10.0, button=None, reversed=False): root = NodePath('balloon') # Add balloon geometry: balloon = self.model.copyTo(root) top = balloon.find('/top') middle = balloon.find('/middle') bottom = balloon.find('*/bottom') balloon.setColor(balloonColor) if balloonColor[3] < 1.0: balloon.setTransparency(1) # Render the text into a TextNode, using the font: t = root.attachNewNode(TextNode('text')) t.node().setFont(font) t.node().setWordwrap(wordWrap) t.node().setText(text) t.node().setTextColor(textColor) width, height = t.node().getWidth(), t.node().getHeight() # Turn off depth write for the text: The place in the depth buffer is # held by the chat bubble anyway, and the text renders after the bubble # so there's no risk of the bubble overwriting the text's pixels. t.setAttrib(DepthWriteAttrib.make(0)) t.setPos(self.TEXT_SHIFT) t.setX(t, self.TEXT_SHIFT_PROPwidth) t.setZ(t, height) if reversed: # The nametag code wants the text on the left side of the axis, # rather than on the right side. Therefore, we move the text to the # opposite side: t.setX(self.TEXT_SHIFT_REVERSED - self.TEXT_SHIFT_PROPwidth - width) # Give the chat bubble a button, if one is requested: if button: np = button.copyTo(root) np.setPos(t, width, 0, -height) np.setPos(np, self.BUTTON_SHIFT) np.setScale(self.BUTTON_SCALE) # Set a minimum width and height for short or empty messages if width < self.MIN_WIDTH: width = self.MIN_WIDTH if reversed: t.setX(t, -width/2.0) else: t.setX(t, width/2.0) t.node().setAlign(TextNode.ACenter) if height < self.MIN_HEIGHT: height = self.MIN_HEIGHT t.setX(t, height/2.0) t.node().setAlign(TextNode.ACenter) # Set the balloon's size: width = 1+self.BUBBLE_PADDING_PROP width += self.BUBBLE_PADDING balloon.setSx(width/self.NATIVE_WIDTH) if reversed: balloon.setSx(-balloon.getSx()) balloon.setTwoSided(True) # Render the backface of the balloon middle.setSz(height) top.setZ(top, height-1) # Calculate the frame occupied by the balloon: left, bottom = self.FRAME_SHIFT if reversed: left = -left - width frame = (left, left+width, bottom, bottom+height+1) return root, frame	en	0.842173	# Add balloon geometry: # Render the text into a TextNode, using the font: # Turn off depth write for the text: The place in the depth buffer is # held by the chat bubble anyway, and the text renders after the bubble # so there's no risk of the bubble overwriting the text's pixels. # The nametag code wants the text on the left side of the axis, # rather than on the right side. Therefore, we move the text to the # opposite side: # Give the chat bubble a button, if one is requested: # Set a minimum width and height for short or empty messages # Set the balloon's size: # Render the backface of the balloon # Calculate the frame occupied by the balloon:	2.802248	3
TTS/vocoder/tf/utils/io.py	mightmay/Mien-TTS	0	10238	import datetime import pickle import tensorflow as tf def save_checkpoint(model, current_step, epoch, output_path, **kwargs): """ Save TF Vocoder model """ state = { 'model': model.weights, 'step': current_step, 'epoch': epoch, 'date': datetime.date.today().strftime("%B %d, %Y"), } state.update(kwargs) pickle.dump(state, open(output_path, 'wb')) def load_checkpoint(model, checkpoint_path): """ Load TF Vocoder model """ checkpoint = pickle.load(open(checkpoint_path, 'rb')) chkp_var_dict = {var.name: var.numpy() for var in checkpoint['model']} tf_vars = model.weights for tf_var in tf_vars: layer_name = tf_var.name chkp_var_value = chkp_var_dict[layer_name] tf.keras.backend.set_value(tf_var, chkp_var_value) return model	import datetime import pickle import tensorflow as tf def save_checkpoint(model, current_step, epoch, output_path, **kwargs): """ Save TF Vocoder model """ state = { 'model': model.weights, 'step': current_step, 'epoch': epoch, 'date': datetime.date.today().strftime("%B %d, %Y"), } state.update(kwargs) pickle.dump(state, open(output_path, 'wb')) def load_checkpoint(model, checkpoint_path): """ Load TF Vocoder model """ checkpoint = pickle.load(open(checkpoint_path, 'rb')) chkp_var_dict = {var.name: var.numpy() for var in checkpoint['model']} tf_vars = model.weights for tf_var in tf_vars: layer_name = tf_var.name chkp_var_value = chkp_var_dict[layer_name] tf.keras.backend.set_value(tf_var, chkp_var_value) return model	en	0.399366	Save TF Vocoder model Load TF Vocoder model	2.468851	2
tests/test_path_choice.py	jataware/flee	3	10239	<reponame>jataware/flee from flee import flee """ Generation 1 code. Incorporates only distance, travel always takes one day. """ def test_path_choice(): print("Testing basic data handling and simulation kernel.") flee.SimulationSettings.MinMoveSpeed = 5000.0 flee.SimulationSettings.MaxMoveSpeed = 5000.0 flee.SimulationSettings.MaxWalkSpeed = 5000.0 e = flee.Ecosystem() l1 = e.addLocation(name="A", movechance=1.0) _ = e.addLocation(name="B", movechance=1.0) _ = e.addLocation(name="C1", movechance=1.0) _ = e.addLocation(name="C2", movechance=1.0) _ = e.addLocation(name="D1", movechance=1.0) _ = e.addLocation(name="D2", movechance=1.0) _ = e.addLocation(name="D3", movechance=1.0) # l2 = e.addLocation(name="B", movechance=1.0) # l3 = e.addLocation(name="C1", movechance=1.0) # l4 = e.addLocation(name="C2", movechance=1.0) # l5 = e.addLocation(name="D1", movechance=1.0) # l6 = e.addLocation(name="D2", movechance=1.0) # l7 = e.addLocation(name="D3", movechance=1.0) e.linkUp(endpoint1="A", endpoint2="B", distance=10.0) e.linkUp(endpoint1="A", endpoint2="C1", distance=10.0) e.linkUp(endpoint1="A", endpoint2="D1", distance=10.0) e.linkUp(endpoint1="C1", endpoint2="C2", distance=10.0) e.linkUp(endpoint1="D1", endpoint2="D2", distance=10.0) e.linkUp(endpoint1="D2", endpoint2="D3", distance=10.0) e.addAgent(location=l1) print("Test successful!") if __name__ == "__main__": test_path_choice()	from flee import flee """ Generation 1 code. Incorporates only distance, travel always takes one day. """ def test_path_choice(): print("Testing basic data handling and simulation kernel.") flee.SimulationSettings.MinMoveSpeed = 5000.0 flee.SimulationSettings.MaxMoveSpeed = 5000.0 flee.SimulationSettings.MaxWalkSpeed = 5000.0 e = flee.Ecosystem() l1 = e.addLocation(name="A", movechance=1.0) _ = e.addLocation(name="B", movechance=1.0) _ = e.addLocation(name="C1", movechance=1.0) _ = e.addLocation(name="C2", movechance=1.0) _ = e.addLocation(name="D1", movechance=1.0) _ = e.addLocation(name="D2", movechance=1.0) _ = e.addLocation(name="D3", movechance=1.0) # l2 = e.addLocation(name="B", movechance=1.0) # l3 = e.addLocation(name="C1", movechance=1.0) # l4 = e.addLocation(name="C2", movechance=1.0) # l5 = e.addLocation(name="D1", movechance=1.0) # l6 = e.addLocation(name="D2", movechance=1.0) # l7 = e.addLocation(name="D3", movechance=1.0) e.linkUp(endpoint1="A", endpoint2="B", distance=10.0) e.linkUp(endpoint1="A", endpoint2="C1", distance=10.0) e.linkUp(endpoint1="A", endpoint2="D1", distance=10.0) e.linkUp(endpoint1="C1", endpoint2="C2", distance=10.0) e.linkUp(endpoint1="D1", endpoint2="D2", distance=10.0) e.linkUp(endpoint1="D2", endpoint2="D3", distance=10.0) e.addAgent(location=l1) print("Test successful!") if __name__ == "__main__": test_path_choice()	en	0.281603	Generation 1 code. Incorporates only distance, travel always takes one day. # l2 = e.addLocation(name="B", movechance=1.0) # l3 = e.addLocation(name="C1", movechance=1.0) # l4 = e.addLocation(name="C2", movechance=1.0) # l5 = e.addLocation(name="D1", movechance=1.0) # l6 = e.addLocation(name="D2", movechance=1.0) # l7 = e.addLocation(name="D3", movechance=1.0)	2.96455	3
archive/old_plots/plot_supplemental_divergence_correlations.py	garudlab/mother_infant	2	10240	<reponame>garudlab/mother_infant<filename>archive/old_plots/plot_supplemental_divergence_correlations.py import matplotlib matplotlib.use('Agg') import config import parse_midas_data import parse_HMP_data import os.path import pylab import sys import numpy import diversity_utils import gene_diversity_utils import calculate_substitution_rates import stats_utils import matplotlib.colors as colors import matplotlib.cm as cmx from math import log10,ceil import matplotlib as mpl import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec from numpy.random import randint from scipy.cluster.hierarchy import dendrogram, linkage from scipy.cluster.hierarchy import cophenet from scipy.cluster.hierarchy import fcluster from scipy.stats import gaussian_kde mpl.rcParams['font.size'] = 6 mpl.rcParams['lines.linewidth'] = 0.5 mpl.rcParams['legend.frameon'] = False mpl.rcParams['legend.fontsize'] = 'small' ################################################################################ # # Standard header to read in argument information # ################################################################################ import argparse parser = argparse.ArgumentParser() parser.add_argument("--debug", help="Loads only a subset of SNPs for speed", action="store_true") parser.add_argument("--chunk-size", type=int, help="max number of records to load", default=1000000000) args = parser.parse_args() debug = args.debug chunk_size = args.chunk_size ################################################################################ good_species_list = ['Bacteroides_vulgatus_57955', 'Bacteroides_uniformis_57318', 'Alistipes_putredinis_61533'] #################################################### # # Set up Figure (3 panels, arranged in 1x3 grid) # #################################################### pylab.figure(1,figsize=(7,1.5)) fig = pylab.gcf() # make three panels panels outer_grid = gridspec.GridSpec(1,3,width_ratios=[1,1,1],wspace=0.1) ####### # # SNP divergence vs Gene divergence in B. vulgatus # ####### gene_axis = plt.Subplot(fig, outer_grid[0]) fig.add_subplot(gene_axis) gene_axis.set_ylabel('SNP divergence\n %s' % (good_species_list[0])) gene_axis.set_xlabel('Gene divergence\n %s' % (good_species_list[0])) gene_axis.set_ylim([1e-06,1e-01]) #gene_axis.set_xlim([1e-02,1]) gene_axis.spines['top'].set_visible(False) gene_axis.spines['right'].set_visible(False) gene_axis.get_xaxis().tick_bottom() gene_axis.get_yaxis().tick_left() ####### # # SNP divergence (B vulgatus) vs SNP divergence (A putredinis) # ####### species_axis_1 = plt.Subplot(fig, outer_grid[1]) fig.add_subplot(species_axis_1) species_axis_1.set_xlabel('SNP divergence\n %s' % (good_species_list[1])) species_axis_1.set_ylim([1e-06,1e-01]) species_axis_1.set_xlim([1e-06,1e-01]) species_axis_1.spines['top'].set_visible(False) species_axis_1.spines['right'].set_visible(False) species_axis_1.get_xaxis().tick_bottom() species_axis_1.get_yaxis().tick_left() ####### # # SNP divergence (B vulgatus) vs SNP divergence (A putredinis) # ####### species_axis_2 = plt.Subplot(fig, outer_grid[2]) fig.add_subplot(species_axis_2) species_axis_2.set_xlabel('SNP divergence\n %s' % (good_species_list[2])) species_axis_2.set_ylim([1e-06,1e-01]) species_axis_2.set_xlim([1e-06,1e-01]) species_axis_2.spines['top'].set_visible(False) species_axis_2.spines['right'].set_visible(False) species_axis_2.get_xaxis().tick_bottom() species_axis_2.get_yaxis().tick_left() ######## # # Now do calculation and plot figures # ######## sys.stderr.write("Loading sample metadata...\n") subject_sample_map = parse_HMP_data.parse_subject_sample_map() sample_order_map = parse_HMP_data.parse_sample_order_map() sys.stderr.write("Done!\n") snp_divergence_map = {species_name: {} for species_name in good_species_list} gene_divergence_map = {species_name: {} for species_name in good_species_list} for species_name in good_species_list: sys.stderr.write("Loading haploid samples...\n") snp_samples = diversity_utils.calculate_haploid_samples(species_name, debug=debug) sys.stderr.write("Calculating unique samples...\n") # Only consider one sample per person snp_samples = snp_samples[parse_midas_data.calculate_unique_samples(subject_sample_map, sample_list=snp_samples)] sys.stderr.write("Loading pre-computed substitution rates for %s...\n" % species_name) substitution_rate_map = calculate_substitution_rates.load_substitution_rate_map(species_name) sys.stderr.write("Calculating snp matrix...\n") dummy_samples, snp_difference_matrix, snp_opportunity_matrix = calculate_substitution_rates.calculate_matrices_from_substitution_rate_map(substitution_rate_map, 'core', allowed_samples=snp_samples) snp_samples = dummy_samples sys.stderr.write("Done!\n") sys.stderr.write("Calculating gene matrix...\n") gene_samples, gene_difference_matrix, gene_opportunity_matrix = calculate_substitution_rates.calculate_matrices_from_substitution_rate_map(substitution_rate_map, 'genes', allowed_samples=snp_samples) snp_samples = gene_samples sys.stderr.write("Done!\n") # Focus on the subset of samples that have sufficient gene depth and snp depth desired_samples = gene_samples # Figure out which pairs of indices in desired_samples belong to diff subjects desired_same_sample_idxs, desired_same_subject_idxs, desired_diff_subject_idxs = parse_midas_data.calculate_subject_pairs( subject_sample_map, desired_samples) # Turn these into indices for snp and gene matrices snp_sample_idx_map = parse_midas_data.calculate_sample_idx_map(desired_samples, snp_samples) gene_sample_idx_map = parse_midas_data.calculate_sample_idx_map(desired_samples, gene_samples) same_subject_snp_idxs = parse_midas_data.apply_sample_index_map_to_indices(snp_sample_idx_map, desired_same_subject_idxs) same_subject_gene_idxs = parse_midas_data.apply_sample_index_map_to_indices(gene_sample_idx_map, desired_same_subject_idxs) diff_subject_snp_idxs = parse_midas_data.apply_sample_index_map_to_indices(snp_sample_idx_map, desired_diff_subject_idxs) diff_subject_gene_idxs = parse_midas_data.apply_sample_index_map_to_indices(gene_sample_idx_map, desired_diff_subject_idxs) for sample_pair_idx in xrange(0,len(diff_subject_snp_idxs[0])): snp_i = diff_subject_snp_idxs[0][sample_pair_idx] snp_j = diff_subject_snp_idxs[1][sample_pair_idx] gene_i = diff_subject_gene_idxs[0][sample_pair_idx] gene_j = diff_subject_gene_idxs[1][sample_pair_idx] sample_i = desired_samples[gene_i] sample_j = desired_samples[gene_j] # This will serve as a key in snp_divergence_map sample_pair = frozenset([sample_i,sample_j]) # Focus on pairs of samples with sufficient coverage if snp_opportunity_matrix[snp_i,snp_j]>0: snp_d = snp_difference_matrix[snp_i,snp_j]1.0/snp_opportunity_matrix[snp_i,snp_j] snp_divergence_map[species_name][sample_pair] = snp_d if gene_opportunity_matrix[gene_i, gene_j]>0: gene_d = gene_difference_matrix[gene_i, gene_j]1.0/gene_opportunity_matrix[gene_i, gene_j] gene_divergence_map[species_name][sample_pair] = gene_d ################# # # Plot figures! # ################# # First calculate SNP vs gene divergence in B. vulgatus species_name = good_species_list[0] snp_divergences = [] gene_divergences = [] # Loop over sample pairs that are in both snp_divergence_map and gene_divergence_map for sample_pair in (set(snp_divergence_map[species_name].keys()) & set(gene_divergence_map[species_name].keys()) ): snp_divergences.append( snp_divergence_map[species_name][sample_pair] ) gene_divergences.append( gene_divergence_map[species_name][sample_pair] ) snp_divergences = numpy.array(snp_divergences) gene_divergences = numpy.array(gene_divergences) # Null expectation (medians line up) median_ratio = numpy.median(snp_divergences)/numpy.median(gene_divergences) gene_axis.loglog([1e-02,1],[1e-02median_ratio,1median_ratio],'k-',linewidth=0.25) gene_axis.loglog(gene_divergences, snp_divergences, 'r.', markersize=2,alpha=0.5,markeredgewidth=0, rasterized=True) # Then SNP divergence between two species species_1 = good_species_list[0] species_2 = good_species_list[1] snp_divergences_1 = [] snp_divergences_2 = [] # Loop over sample pairs that are in both snp_divergence_map and gene_divergence_map for sample_pair in (set(snp_divergence_map[species_1].keys()) & set(snp_divergence_map[species_2].keys()) ): snp_divergences_1.append( snp_divergence_map[species_1][sample_pair] ) snp_divergences_2.append( snp_divergence_map[species_2][sample_pair] ) snp_divergences_1 = numpy.array(snp_divergences_1) snp_divergences_2 = numpy.array(snp_divergences_2) # Null expectation (medians line up) median_ratio = numpy.median(snp_divergences_1)/numpy.median(snp_divergences_2) species_axis_1.loglog([1e-06,1e-01],[1e-06median_ratio,1e-01median_ratio],'k-',linewidth=0.25) # Observed values species_axis_1.loglog(snp_divergences_2, snp_divergences_1, 'r.', markersize=2,alpha=0.5,markeredgewidth=0, rasterized=True) # Then SNP divergence between other two species species_1 = good_species_list[0] species_2 = good_species_list[2] snp_divergences_1 = [] snp_divergences_2 = [] # Loop over sample pairs that are in both snp_divergence_map and gene_divergence_map for sample_pair in (set(snp_divergence_map[species_1].keys()) & set(snp_divergence_map[species_2].keys()) ): snp_divergences_1.append( snp_divergence_map[species_1][sample_pair] ) snp_divergences_2.append( snp_divergence_map[species_2][sample_pair] ) snp_divergences_1 = numpy.array(snp_divergences_1) snp_divergences_2 = numpy.array(snp_divergences_2) # Null expectation (medians line up) median_ratio = numpy.median(snp_divergences_1)/numpy.median(snp_divergences_2) species_axis_2.loglog([1e-06,1e-01],[1e-06median_ratio,1e-01median_ratio],'k-',linewidth=0.25) species_axis_2.loglog(snp_divergences_2, snp_divergences_1, 'r.', markersize=2,alpha=0.5,markeredgewidth=0,rasterized=True) # Since y-axes are shared, do not duplicate ticklables species_axis_1.set_yticklabels([]) species_axis_2.set_yticklabels([]) sys.stderr.write("Saving figure...\t") fig.savefig('%s/supplemental_divergence_correlations.pdf' % (parse_midas_data.analysis_directory),bbox_inches='tight',dpi=600) sys.stderr.write("Done!\n")	import matplotlib matplotlib.use('Agg') import config import parse_midas_data import parse_HMP_data import os.path import pylab import sys import numpy import diversity_utils import gene_diversity_utils import calculate_substitution_rates import stats_utils import matplotlib.colors as colors import matplotlib.cm as cmx from math import log10,ceil import matplotlib as mpl import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec from numpy.random import randint from scipy.cluster.hierarchy import dendrogram, linkage from scipy.cluster.hierarchy import cophenet from scipy.cluster.hierarchy import fcluster from scipy.stats import gaussian_kde mpl.rcParams['font.size'] = 6 mpl.rcParams['lines.linewidth'] = 0.5 mpl.rcParams['legend.frameon'] = False mpl.rcParams['legend.fontsize'] = 'small' ################################################################################ # # Standard header to read in argument information # ################################################################################ import argparse parser = argparse.ArgumentParser() parser.add_argument("--debug", help="Loads only a subset of SNPs for speed", action="store_true") parser.add_argument("--chunk-size", type=int, help="max number of records to load", default=1000000000) args = parser.parse_args() debug = args.debug chunk_size = args.chunk_size ################################################################################ good_species_list = ['Bacteroides_vulgatus_57955', 'Bacteroides_uniformis_57318', 'Alistipes_putredinis_61533'] #################################################### # # Set up Figure (3 panels, arranged in 1x3 grid) # #################################################### pylab.figure(1,figsize=(7,1.5)) fig = pylab.gcf() # make three panels panels outer_grid = gridspec.GridSpec(1,3,width_ratios=[1,1,1],wspace=0.1) ####### # # SNP divergence vs Gene divergence in B. vulgatus # ####### gene_axis = plt.Subplot(fig, outer_grid[0]) fig.add_subplot(gene_axis) gene_axis.set_ylabel('SNP divergence\n %s' % (good_species_list[0])) gene_axis.set_xlabel('Gene divergence\n %s' % (good_species_list[0])) gene_axis.set_ylim([1e-06,1e-01]) #gene_axis.set_xlim([1e-02,1]) gene_axis.spines['top'].set_visible(False) gene_axis.spines['right'].set_visible(False) gene_axis.get_xaxis().tick_bottom() gene_axis.get_yaxis().tick_left() ####### # # SNP divergence (B vulgatus) vs SNP divergence (A putredinis) # ####### species_axis_1 = plt.Subplot(fig, outer_grid[1]) fig.add_subplot(species_axis_1) species_axis_1.set_xlabel('SNP divergence\n %s' % (good_species_list[1])) species_axis_1.set_ylim([1e-06,1e-01]) species_axis_1.set_xlim([1e-06,1e-01]) species_axis_1.spines['top'].set_visible(False) species_axis_1.spines['right'].set_visible(False) species_axis_1.get_xaxis().tick_bottom() species_axis_1.get_yaxis().tick_left() ####### # # SNP divergence (B vulgatus) vs SNP divergence (A putredinis) # ####### species_axis_2 = plt.Subplot(fig, outer_grid[2]) fig.add_subplot(species_axis_2) species_axis_2.set_xlabel('SNP divergence\n %s' % (good_species_list[2])) species_axis_2.set_ylim([1e-06,1e-01]) species_axis_2.set_xlim([1e-06,1e-01]) species_axis_2.spines['top'].set_visible(False) species_axis_2.spines['right'].set_visible(False) species_axis_2.get_xaxis().tick_bottom() species_axis_2.get_yaxis().tick_left() ######## # # Now do calculation and plot figures # ######## sys.stderr.write("Loading sample metadata...\n") subject_sample_map = parse_HMP_data.parse_subject_sample_map() sample_order_map = parse_HMP_data.parse_sample_order_map() sys.stderr.write("Done!\n") snp_divergence_map = {species_name: {} for species_name in good_species_list} gene_divergence_map = {species_name: {} for species_name in good_species_list} for species_name in good_species_list: sys.stderr.write("Loading haploid samples...\n") snp_samples = diversity_utils.calculate_haploid_samples(species_name, debug=debug) sys.stderr.write("Calculating unique samples...\n") # Only consider one sample per person snp_samples = snp_samples[parse_midas_data.calculate_unique_samples(subject_sample_map, sample_list=snp_samples)] sys.stderr.write("Loading pre-computed substitution rates for %s...\n" % species_name) substitution_rate_map = calculate_substitution_rates.load_substitution_rate_map(species_name) sys.stderr.write("Calculating snp matrix...\n") dummy_samples, snp_difference_matrix, snp_opportunity_matrix = calculate_substitution_rates.calculate_matrices_from_substitution_rate_map(substitution_rate_map, 'core', allowed_samples=snp_samples) snp_samples = dummy_samples sys.stderr.write("Done!\n") sys.stderr.write("Calculating gene matrix...\n") gene_samples, gene_difference_matrix, gene_opportunity_matrix = calculate_substitution_rates.calculate_matrices_from_substitution_rate_map(substitution_rate_map, 'genes', allowed_samples=snp_samples) snp_samples = gene_samples sys.stderr.write("Done!\n") # Focus on the subset of samples that have sufficient gene depth and snp depth desired_samples = gene_samples # Figure out which pairs of indices in desired_samples belong to diff subjects desired_same_sample_idxs, desired_same_subject_idxs, desired_diff_subject_idxs = parse_midas_data.calculate_subject_pairs( subject_sample_map, desired_samples) # Turn these into indices for snp and gene matrices snp_sample_idx_map = parse_midas_data.calculate_sample_idx_map(desired_samples, snp_samples) gene_sample_idx_map = parse_midas_data.calculate_sample_idx_map(desired_samples, gene_samples) same_subject_snp_idxs = parse_midas_data.apply_sample_index_map_to_indices(snp_sample_idx_map, desired_same_subject_idxs) same_subject_gene_idxs = parse_midas_data.apply_sample_index_map_to_indices(gene_sample_idx_map, desired_same_subject_idxs) diff_subject_snp_idxs = parse_midas_data.apply_sample_index_map_to_indices(snp_sample_idx_map, desired_diff_subject_idxs) diff_subject_gene_idxs = parse_midas_data.apply_sample_index_map_to_indices(gene_sample_idx_map, desired_diff_subject_idxs) for sample_pair_idx in xrange(0,len(diff_subject_snp_idxs[0])): snp_i = diff_subject_snp_idxs[0][sample_pair_idx] snp_j = diff_subject_snp_idxs[1][sample_pair_idx] gene_i = diff_subject_gene_idxs[0][sample_pair_idx] gene_j = diff_subject_gene_idxs[1][sample_pair_idx] sample_i = desired_samples[gene_i] sample_j = desired_samples[gene_j] # This will serve as a key in snp_divergence_map sample_pair = frozenset([sample_i,sample_j]) # Focus on pairs of samples with sufficient coverage if snp_opportunity_matrix[snp_i,snp_j]>0: snp_d = snp_difference_matrix[snp_i,snp_j]1.0/snp_opportunity_matrix[snp_i,snp_j] snp_divergence_map[species_name][sample_pair] = snp_d if gene_opportunity_matrix[gene_i, gene_j]>0: gene_d = gene_difference_matrix[gene_i, gene_j]1.0/gene_opportunity_matrix[gene_i, gene_j] gene_divergence_map[species_name][sample_pair] = gene_d ################# # # Plot figures! # ################# # First calculate SNP vs gene divergence in B. vulgatus species_name = good_species_list[0] snp_divergences = [] gene_divergences = [] # Loop over sample pairs that are in both snp_divergence_map and gene_divergence_map for sample_pair in (set(snp_divergence_map[species_name].keys()) & set(gene_divergence_map[species_name].keys()) ): snp_divergences.append( snp_divergence_map[species_name][sample_pair] ) gene_divergences.append( gene_divergence_map[species_name][sample_pair] ) snp_divergences = numpy.array(snp_divergences) gene_divergences = numpy.array(gene_divergences) # Null expectation (medians line up) median_ratio = numpy.median(snp_divergences)/numpy.median(gene_divergences) gene_axis.loglog([1e-02,1],[1e-02median_ratio,1median_ratio],'k-',linewidth=0.25) gene_axis.loglog(gene_divergences, snp_divergences, 'r.', markersize=2,alpha=0.5,markeredgewidth=0, rasterized=True) # Then SNP divergence between two species species_1 = good_species_list[0] species_2 = good_species_list[1] snp_divergences_1 = [] snp_divergences_2 = [] # Loop over sample pairs that are in both snp_divergence_map and gene_divergence_map for sample_pair in (set(snp_divergence_map[species_1].keys()) & set(snp_divergence_map[species_2].keys()) ): snp_divergences_1.append( snp_divergence_map[species_1][sample_pair] ) snp_divergences_2.append( snp_divergence_map[species_2][sample_pair] ) snp_divergences_1 = numpy.array(snp_divergences_1) snp_divergences_2 = numpy.array(snp_divergences_2) # Null expectation (medians line up) median_ratio = numpy.median(snp_divergences_1)/numpy.median(snp_divergences_2) species_axis_1.loglog([1e-06,1e-01],[1e-06median_ratio,1e-01median_ratio],'k-',linewidth=0.25) # Observed values species_axis_1.loglog(snp_divergences_2, snp_divergences_1, 'r.', markersize=2,alpha=0.5,markeredgewidth=0, rasterized=True) # Then SNP divergence between other two species species_1 = good_species_list[0] species_2 = good_species_list[2] snp_divergences_1 = [] snp_divergences_2 = [] # Loop over sample pairs that are in both snp_divergence_map and gene_divergence_map for sample_pair in (set(snp_divergence_map[species_1].keys()) & set(snp_divergence_map[species_2].keys()) ): snp_divergences_1.append( snp_divergence_map[species_1][sample_pair] ) snp_divergences_2.append( snp_divergence_map[species_2][sample_pair] ) snp_divergences_1 = numpy.array(snp_divergences_1) snp_divergences_2 = numpy.array(snp_divergences_2) # Null expectation (medians line up) median_ratio = numpy.median(snp_divergences_1)/numpy.median(snp_divergences_2) species_axis_2.loglog([1e-06,1e-01],[1e-06median_ratio,1e-01median_ratio],'k-',linewidth=0.25) species_axis_2.loglog(snp_divergences_2, snp_divergences_1, 'r.', markersize=2,alpha=0.5,markeredgewidth=0,rasterized=True) # Since y-axes are shared, do not duplicate ticklables species_axis_1.set_yticklabels([]) species_axis_2.set_yticklabels([]) sys.stderr.write("Saving figure...\t") fig.savefig('%s/supplemental_divergence_correlations.pdf' % (parse_midas_data.analysis_directory),bbox_inches='tight',dpi=600) sys.stderr.write("Done!\n")	en	0.498721	################################################################################ # # Standard header to read in argument information # ################################################################################ ################################################################################ #################################################### # # Set up Figure (3 panels, arranged in 1x3 grid) # #################################################### # make three panels panels ####### # # SNP divergence vs Gene divergence in B. vulgatus # ####### #gene_axis.set_xlim([1e-02,1]) ####### # # SNP divergence (B vulgatus) vs SNP divergence (A putredinis) # ####### ####### # # SNP divergence (B vulgatus) vs SNP divergence (A putredinis) # ####### ######## # # Now do calculation and plot figures # ######## # Only consider one sample per person # Focus on the subset of samples that have sufficient gene depth and snp depth # Figure out which pairs of indices in desired_samples belong to diff subjects # Turn these into indices for snp and gene matrices # This will serve as a key in snp_divergence_map # Focus on pairs of samples with sufficient coverage ################# # # Plot figures! # ################# # First calculate SNP vs gene divergence in B. vulgatus # Loop over sample pairs that are in both snp_divergence_map and gene_divergence_map # Null expectation (medians line up) # Then SNP divergence between two species # Loop over sample pairs that are in both snp_divergence_map and gene_divergence_map # Null expectation (medians line up) # Observed values # Then SNP divergence between other two species # Loop over sample pairs that are in both snp_divergence_map and gene_divergence_map # Null expectation (medians line up) # Since y-axes are shared, do not duplicate ticklables	2.018415	2
multivis/plotFeatures.py	brettChapman/cimcb_vis	1	10241	import sys import copy import matplotlib import matplotlib.pyplot as plt import seaborn as sns from collections import Counter from .utils import * import numpy as np import pandas as pd class plotFeatures: usage = """Produces different feature plots given a data table and peak table. Initial_Parameters ---------- peaktable : Pandas dataframe containing peak data. Must contain 'Name' and 'Label'. datatable : Pandas dataframe containing matrix of values to plot (N samples x N features). Columns/features must be same as 'Name' from Peak Table. Methods ------- set_params : Set parameters - plot_type: The type of plot. Either "point", "violin", "box", "swarm", "violin-swarm" or "box-swarm" (default: 'point') column_numbers: The number of columns to display in the plots (default: 4) log_data: Perform a log ('natural', base 2 or base 10) on all data (default: (True, 2)) scale_data: Scale the data ('standard' (centers to the mean and scales to unit variance), 'minmax' (scales between 0 and 1), 'maxabs' (scales to the absolute maximum value), 'robust' (centers to the median and scales to between 25th and 75th quantile range) (default: (True, 'minmax')) impute_data: Impute any missing values using KNN impute with a set number of nearest neighbours (default: (True, 3)) style: Set the matplotlib style (see https://matplotlib.org/stable/tutorials/introductory/customizing.html) (default: 'seaborn-white') transparent: Setting to 'True' will make the background transparent (default: False) figSize: The figure size as a tuple (width,height) (default: (15,10)) fontSize: The font size for all text (default: 12) colour_palette: The colour palette to use for the plot (default: None) y_axis_label: The label to customise the y axis (default: None) x_axis_rotation: Rotate the x axis labels this number of degrees (default: 0) group_column_name: The group column name used in the datatable (e.g. 'Class') (default: None) point_estimator: The statistical function to use for the point plot. Either "mean" or "median" (default: 'mean') point_ci: The bootstrapped confidence interval for the point plot. Can also be standard deviation ("sd") (default: 95) violin_distribution_type: The representation of the distribution of data points within the violin plot. Either "quartile", "box", "point", "stick" or None (default: 'box') violin_width_scale: The method used to scale the width of the violin plot. Either "area", "count" or "width" (default: "width") box_iqr: The proportion past the lower and upper quartiles to extend the plot whiskers for the box plot. Points outside this range will be identified as outliers (default: 1.5) saveImage: Setting to 'True' will save the image to file (default: True) imageFileName: The image file name to save to (default: [plot_type]_features.png') dpi: The number of Dots Per Inch (DPI) for the image (default: 200) help : Print this help text plot : Generates feature plots """ def __init__(self, peaktable, datatable): peaktable = self.__checkPeakTable(self.__checkData(peaktable)) datatable = self.__checkData(datatable) # Slice the meta-data, and select only peaks from the peaktable for processing, and add the meta-data back meta = datatable.T[~datatable.T.index.isin(peaktable['Name'])].T.reset_index(drop=True) dat = datatable[peaktable['Name']].reset_index() datatable = pd.concat([meta, dat], axis=1).set_index(['index']) datatable.index.name = None self.__peaktable = peaktable # Search for duplicate labels and amend with a suffix, to avoid issues when relabelling the datatable labels = copy.deepcopy(list(peaktable['Label'])) label_counts = {k: v for k, v in Counter(labels).items() if v > 1} for i in reversed(range(len(labels))): item = str(labels[i]) if item in label_counts and label_counts[item]: labels[i] += "_" + str(label_counts[item]) label_counts[item] -= 1 #Label datatable with peak labels instead of names for ease of feature plotting col_label_dict = dict(zip(list(peaktable['Name']), labels)) datatable.rename(columns=col_label_dict, inplace=True) self.__peak_labels = labels self.__datatable = datatable self.set_params() def help(self): print(plotFeatures.usage) def set_params(self, plot_type='point', column_numbers=4, log_data=(True, 2), scale_data=(True, 'minmax'), impute_data=(True, 3), style='seaborn-white', transparent=False, figSize = (15, 10), fontSize = 12, colour_palette=None, y_axis_label=None, x_axis_rotation=0, group_column_name=None, point_estimator='mean', point_ci=95, violin_distribution_type='box', violin_width_scale='width', box_iqr=1.5, saveImage=True, imageFileName='_features.png', dpi = 200): plot_type, column_numbers, log_data, scale_data, impute_data, style, transparent, figSize, fontSize, colour_palette, y_axis_label, x_axis_rotation, group_column_name, point_estimator, point_ci, violin_distribution_type, violin_width_scale, box_iqr, saveImage, imageFileName, dpi = self.__paramCheck(plot_type, column_numbers, log_data, scale_data, impute_data, style, transparent, figSize, fontSize, colour_palette, y_axis_label, x_axis_rotation, group_column_name, point_estimator, point_ci, violin_distribution_type, violin_width_scale, box_iqr, saveImage, imageFileName, dpi) self.__plot_type = plot_type; self.__column_numbers = column_numbers; self.__log_data = log_data; self.__scale_data = scale_data; self.__impute_data = impute_data; self.__style = style; self.__transparent = transparent; self.__figSize = figSize; self.__fontSize = fontSize; self.__colour_palette = colour_palette; self.__y_axis_label = y_axis_label; self.__x_axis_rotation = x_axis_rotation; self.__group_column_name = group_column_name; self.__point_estimator = point_estimator; self.__point_ci = point_ci; self.__violin_distribution_type = violin_distribution_type; self.__violin_width_scale = violin_width_scale; self.__box_iqr = box_iqr; self.__saveImage = saveImage; self.__imageFileName = imageFileName; self.__dpi = dpi; def plot(self): datatable = copy.deepcopy(self.__datatable) labels = self.__peak_labels plot_type = self.__plot_type group_column_name = self.__group_column_name column_numbers = self.__column_numbers colour_palette = self.__colour_palette point_ci = self.__point_ci point_estimator = self.__point_estimator log_data = self.__log_data scale_data = self.__scale_data impute_data = self.__impute_data x_axis_rotation = self.__x_axis_rotation y_axis_label = self.__y_axis_label violin_distribution_type = self.__violin_distribution_type violin_width_scale = self.__violin_width_scale box_iqr = self.__box_iqr imageFileName = self.__imageFileName saveImage = self.__saveImage fontSize = self.__fontSize style = self.__style transparent = self.__transparent dpi = self.__dpi figSize = self.__figSize meta = datatable.T[~datatable.T.index.isin(labels)].T.reset_index(drop=True) X = datatable[labels].reset_index(drop=True) (log_bool, log_base) = log_data; if log_bool: if isinstance(log_base, str) and log_base.lower() == 'natural': X = X.applymap(np.log); elif log_base == 2: X = X.applymap(np.log2); elif log_base == 10: X = X.applymap(np.log10); else: print("Error: The chosen log type is invalid.") sys.exit() (scale_bool, scale_type) = scale_data if scale_bool: if isinstance(scale_type, str) and scale_type.lower() == 'standard': X = scaler(X, type=scale_type.lower()).reset_index(drop=True) elif isinstance(scale_type, str) and scale_type.lower() == 'minmax': X = scaler(X, type=scale_type.lower()).reset_index(drop=True) elif isinstance(scale_type, str) and scale_type.lower() == 'maxabs': X = scaler(X, type=scale_type.lower()).reset_index(drop=True) elif isinstance(scale_type, str) and scale_type.lower() == 'robust': X = scaler(X, type=scale_type.lower()).reset_index(drop=True) else: print("Error: The chosen scale type is invalid.") sys.exit() (impute_bool, k) = impute_data; if impute_bool: X = imputeData(X, k=k).reset_index(drop=True) if not isinstance(X, pd.DataFrame): X = pd.DataFrame(X, columns=labels) # Add the meta data back in with the logged, scaled, or imputed data datatable = pd.concat([meta, X], axis=1).reset_index(drop=True) with plt.style.context(style): fig, axes = plt.subplots(nrows=int(np.ceil(float(len(labels) / column_numbers))), ncols=column_numbers, sharey=True, figsize=figSize) if plot_type == 'point': for peak_index, peak in enumerate(labels): if point_estimator.lower() == 'mean': point_estimator = 'Mean' ax = sns.pointplot(data=datatable, x=group_column_name, y=peak, estimator=np.nanmean, capsize=0.1, ci=point_ci, palette=colour_palette, ax=axes.flat[peak_index]) elif point_estimator.lower() == 'median': point_estimator = 'Median' ax = sns.pointplot(data=datatable, x=group_column_name, y=peak, estimator=np.nanmedian, capsize=0.1, ci=point_ci, palette=colour_palette, ax=axes.flat[peak_index]) else: print("Error: Invalid point plot estimator type.") sys.exit() ax.tick_params(labelrotation=x_axis_rotation, labelsize=fontSize) if log_bool: if scale_data: if isinstance(point_ci, str): if point_ci == 'sd': ax.set_title(peak + ' within SD', fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) {} Peak Area within SD'.format(log_base, scale_type, point_estimator), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: ax.set_title(peak + ' with {}% CI'.format(point_ci), fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) {} Peak Area & {}% CI'.format(log_base, scale_type, point_estimator, point_ci), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if isinstance(point_ci, str): if point_ci == 'sd': ax.set_title(peak + ' within SD', fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('Log({}) {} Peak Area within SD'.format(log_base, point_estimator), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: ax.set_title(peak + ' with {}% CI'.format(point_ci), fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('Log({}) {} Peak Area & {}% CI'.format(log_base, point_estimator, point_ci), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if scale_data: if isinstance(point_ci, str): if point_ci == 'sd': ax.set_title(peak + ' within SD', fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('Scaled ({}) {} Peak Area within SD'.format(scale_type, point_estimator), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: ax.set_title(peak + ' with {}% CI'.format(point_ci), fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('Scaled ({}) {} Peak Area & {}% CI'.format(scale_type, point_estimator, point_ci), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if isinstance(point_ci, str): if point_ci == 'sd': ax.set_title(peak + ' within SD', fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('{} Peak Area within SD'.format(point_estimator), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: ax.set_title(peak + ' with {}% CI'.format(point_ci), fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('{} Peak Area & {}% CI'.format(point_estimator, point_ci), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) elif plot_type.lower() == 'violin': for peak_index, peak in enumerate(labels): ax = sns.violinplot(data=datatable, x=group_column_name, y=peak, linewidth=1, inner=violin_distribution_type, scale=violin_width_scale, palette=colour_palette, ax=axes.flat[peak_index]) ax.tick_params(labelrotation=x_axis_rotation, labelsize=fontSize) ax.set_title(peak, fontsize=fontSize) ax.set_xlabel('') if log_bool: if scale_data: if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) Peak Area'.format(log_base, scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Log({}) Peak Area'.format(log_base), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if scale_data: if y_axis_label is None: ax.set_ylabel('Scaled ({}) Peak Area'.format(scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Peak Area', fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) elif plot_type.lower() == 'box': for peak_index, peak in enumerate(labels): ax = sns.boxplot(data=datatable, x=group_column_name, y=peak, palette=colour_palette, whis=box_iqr, ax=axes.flat[peak_index]) ax.tick_params(labelrotation=x_axis_rotation, labelsize=fontSize) ax.set_title(peak, fontsize=fontSize) ax.set_xlabel('') if log_bool: if scale_data: if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) Peak Area'.format(log_base, scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Log({}) Peak Area'.format(log_base), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if scale_data: if y_axis_label is None: ax.set_ylabel('Scaled ({}) Peak Area'.format(scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Peak Area', fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) elif plot_type.lower() == 'swarm': for peak_index, peak in enumerate(labels): ax = sns.swarmplot(data=datatable, x=group_column_name, y=peak, size=10, palette=colour_palette, ax=axes.flat[peak_index]) ax.tick_params(labelrotation=x_axis_rotation, labelsize=fontSize) ax.set_title(peak, fontsize=fontSize) ax.set_xlabel('') if log_bool: if scale_data: if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) Peak Area'.format(log_base, scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Log({}) Peak Area'.format(log_base), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if scale_data: if y_axis_label is None: ax.set_ylabel('Scaled ({}) Peak Area'.format(scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Peak Area', fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) elif plot_type.lower() == 'violin-swarm': for peak_index, peak in enumerate(labels): ax = sns.violinplot(data=datatable, x=group_column_name, y=peak, linewidth=1, inner=None, scale=violin_width_scale, palette=colour_palette, ax=axes.flat[peak_index]) ax = sns.swarmplot(data=datatable, x=group_column_name, y=peak, color="white", edgecolor="gray", ax=axes.flat[peak_index]) ax.tick_params(labelrotation=x_axis_rotation, labelsize=fontSize) ax.set_title(peak, fontsize=fontSize) ax.set_xlabel('') if log_bool: if scale_data: if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) Peak Area'.format(log_base, scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Log({}) Peak Area'.format(log_base), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if scale_data: if y_axis_label is None: ax.set_ylabel('Scaled ({}) Peak Area'.format(scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Peak Area', fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) elif plot_type.lower() == 'box-swarm': for peak_index, peak in enumerate(labels): ax = sns.boxplot(data=datatable, x=group_column_name, y=peak, palette=colour_palette, whis=np.inf, ax=axes.flat[peak_index]) ax = sns.swarmplot(data=datatable, x=group_column_name, y=peak, color="0.2", ax=axes.flat[peak_index]) ax.tick_params(labelrotation=x_axis_rotation, labelsize=fontSize) ax.set_title(peak, fontsize=fontSize) ax.set_xlabel('') if log_bool: if scale_data: if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) Peak Area'.format(log_base, scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Log({}) Peak Area'.format(log_base), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if scale_data: if y_axis_label is None: ax.set_ylabel('Scaled ({}) Peak Area'.format(scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Peak Area', fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) fig.tight_layout(h_pad=5, w_pad=2) if saveImage: plt.savefig(plot_type + 'Plot' + imageFileName, dpi=dpi, transparent=transparent) plt.show() def __paramCheck(self, plot_type, column_numbers, log_data, scale_data, impute_data, style, transparent, figSize, fontSize, colour_palette, y_axis_label, x_axis_rotation, group_column_name, point_estimator, point_ci, violin_distribution_type, violin_width_scale, box_iqr, saveImage, imageFileName, dpi): cmap_list = list(matplotlib.cm.cmaps_listed) + list(matplotlib.cm.datad) cmap_list_r = [cmap + '_r' for cmap in cmap_list] cmap_list = cmap_list + cmap_list_r plot_types = ['point', 'violin', 'box', 'swarm', 'violin-swarm', 'box-swarm'] estimator_types = ['mean', 'median'] datatable = self.__datatable if plot_type.lower() not in plot_types: print("Error: Plot type is not valid. Choose one of the following: {}.".format(', '.join(plot_types))) sys.exit() if not isinstance(column_numbers, int): print("Error: Column numbers is not valid. Choose a integer value.") sys.exit() if not isinstance(log_data, tuple): print("Error: Log data type if not a tuple. Please ensure the value is a tuple (e.g. (True, 2).") sys.exit() else: (log_bool, log_base) = log_data if not isinstance(log_bool, bool): print("Error: Log data first tuple item is not a boolean value. Choose either \"True\" or \"False\".") sys.exit() base_types = ['natural', 2, 10] if isinstance(log_base, str): log_base = log_base.lower() if log_base not in base_types: print("Error: Log data second tuple item is not valid. Choose one of {}.".format(', '.join(base_types))) sys.exit() if not isinstance(scale_data, tuple): print("Error: Scale data type if not a tuple. Please ensure the value is a tuple (e.g. (True, 'standard').") sys.exit() else: (scale_bool, scale_type) = scale_data if not isinstance(scale_bool, bool): print("Error: Scale data first tuple item is not a boolean value. Choose either \"True\" or \"False\".") sys.exit() scale_types = ['standard', 'minmax', 'maxabs', 'robust'] if isinstance(scale_type, str): scale_type = scale_type.lower() if scale_type not in scale_types: print("Error: Scale data second tuple item is not valid. Choose one of {}.".format(', '.join(scale_types))) sys.exit() if not isinstance(impute_data, tuple): print("Error: Impute data type if not a tuple. Please ensure the value is a tuple (e.g. (True, 3).") sys.exit() else: (impute_bool, k) = impute_data if not isinstance(impute_bool, bool): print("Error: Impute data first tuple item is not a boolean value. Choose either \"True\" or \"False\".") sys.exit() if not isinstance(k, float): if not isinstance(k, int): print("Error: Impute data second tuple item, the nearest neighbours k value, is not valid. Choose a float or integer value.") sys.exit() if not isinstance(style, str): print("Error: Seaborn style is not valid. Choose a string value.") sys.exit() else: styleList = list(plt.style.available) if style not in styleList: print("Error: Chosen style is not valid. Choose one of the following: {}.".format(', '.join(styleList))) sys.exit() if not isinstance(transparent, bool): print("Error: The transparent value is not valid. Choose either \"True\" or \"False\".") sys.exit() if not isinstance(figSize, tuple): print("Error: Figure size is not valid. Choose a tuple of length 2.") sys.exit() else: for length in figSize: if not isinstance(length, float): if not isinstance(length, int): print("Error: Figure size value is not valid. Choose a float or integer value.") sys.exit() if not isinstance(fontSize, float): if not isinstance(fontSize, int): print("Error: Font size is not valid. Choose a float or integer value.") sys.exit() if colour_palette is not None: if not isinstance(colour_palette, str): print("Error: The colour palette is not valid. Choose a string value.") sys.exit() else: if colour_palette not in cmap_list: print("Error: The colour palette is not valid. Choose one of the following: {}.".format(', '.join(cmap_list))) sys.exit() if y_axis_label is not None: if isinstance(y_axis_label, str): print("Error: The y axis label is not valid. Choose a string value.") sys.exit() if not isinstance(x_axis_rotation, float): if not isinstance(x_axis_rotation, int): print("Error: The x axis rotation value is not valid. Choose a float or integer value.") sys.exit() if ((x_axis_rotation < 0) or (x_axis_rotation > 360)): print("Error: The x axis rotation value is not valid. Choose a value >=0 or <= 360.") sys.exit() if group_column_name is not None: if not isinstance(group_column_name, str): print("Error: Group column name is not valid. Choose a string value.") sys.exit() else: if group_column_name not in list(datatable.columns): print("Error: Group column name not valid. Choose one of {}.".format(', '.join(list(datatable.columns)))) sys.exit() if point_estimator.lower() not in estimator_types: print("Error: The chosen point plot estimator is invalid. Choose one of \"{}\".".format('\" or \"'.join(estimator_types))) sys.exit() if isinstance(point_ci, str): if point_ci != 'sd': print("Error: The string value for point plot ci is invalid. Choose a float, integer or 'sd' value for standard deviation.") sys.exit() else: if not isinstance(point_ci, float): if not isinstance(point_ci, int): print("Error: The value for point plot ci is invalid. Choose a float, integer or 'sd' value for standard deviation.") sys.exit() violin_distribution_types = ['quartile', 'box', 'point', 'stick', None] violin_width_scale_types = ['area', 'count', 'width'] if plot_type.lower() == "violin": if violin_distribution_type not in violin_distribution_types: print("Error: Violin distribution type not valid. Choose one of the following: {}.".format(', '.join(violin_distribution_types))) sys.exit() if violin_width_scale not in violin_width_scale_types: print("Error: Violin width scale type not valid. Choose one of the following: {}.".format(', '.join(violin_width_scale_types))) sys.exit() if plot_type.lower == "box": if not isinstance(box_iqr, float): if not isinstance(box_iqr, int): print( "Error: The box plot interquartile range extension beyond whiskers is not valid. Choose a float or integer value.") sys.exit() if not isinstance(saveImage, bool): print("Error: Save image is not valid. Choose either \"True\" or \"False\".") sys.exit() if not isinstance(imageFileName, str): print("Error: Image file name is not valid. Choose a string value.") sys.exit() if not isinstance(dpi, float): if not isinstance(dpi, int): print("Error: Dpi is not valid. Choose a float or integer value.") sys.exit() return plot_type, column_numbers, log_data, scale_data, impute_data, style, transparent, figSize, fontSize, colour_palette, y_axis_label, x_axis_rotation, group_column_name, point_estimator, point_ci, violin_distribution_type, violin_width_scale, box_iqr, saveImage, imageFileName, dpi def __checkData(self, df): if not isinstance(df, pd.DataFrame): print("Error: A dataframe was not entered. Please check your data.") return df def __checkPeakTable(self, PeakTable): if "Name" not in PeakTable.columns: print("Error: \"Name\" column not in Peak Table. Please check your data.") sys.exit() if "Label" not in PeakTable.columns: print("Error: \"Label\" column not in Peak Table. Please check your data.") sys.exit() # Do not assume the peaks/nodes have been indexed correctly. Remove any index columns and reindex. column_list = [column.lower() for column in PeakTable.columns] if 'idx' in column_list: index = column_list.index('idx') column_name = PeakTable.columns[index] PeakTable = PeakTable.drop(columns=[column_name]) if 'index' in column_list: index = column_list.index('index') column_name = PeakTable.columns[index] PeakTable = PeakTable.drop(columns=[column_name]) PeakTable = PeakTable.reset_index(drop=True) PeakTable.index.name = 'Idx' PeakTable = PeakTable.reset_index() return PeakTable	import sys import copy import matplotlib import matplotlib.pyplot as plt import seaborn as sns from collections import Counter from .utils import * import numpy as np import pandas as pd class plotFeatures: usage = """Produces different feature plots given a data table and peak table. Initial_Parameters ---------- peaktable : Pandas dataframe containing peak data. Must contain 'Name' and 'Label'. datatable : Pandas dataframe containing matrix of values to plot (N samples x N features). Columns/features must be same as 'Name' from Peak Table. Methods ------- set_params : Set parameters - plot_type: The type of plot. Either "point", "violin", "box", "swarm", "violin-swarm" or "box-swarm" (default: 'point') column_numbers: The number of columns to display in the plots (default: 4) log_data: Perform a log ('natural', base 2 or base 10) on all data (default: (True, 2)) scale_data: Scale the data ('standard' (centers to the mean and scales to unit variance), 'minmax' (scales between 0 and 1), 'maxabs' (scales to the absolute maximum value), 'robust' (centers to the median and scales to between 25th and 75th quantile range) (default: (True, 'minmax')) impute_data: Impute any missing values using KNN impute with a set number of nearest neighbours (default: (True, 3)) style: Set the matplotlib style (see https://matplotlib.org/stable/tutorials/introductory/customizing.html) (default: 'seaborn-white') transparent: Setting to 'True' will make the background transparent (default: False) figSize: The figure size as a tuple (width,height) (default: (15,10)) fontSize: The font size for all text (default: 12) colour_palette: The colour palette to use for the plot (default: None) y_axis_label: The label to customise the y axis (default: None) x_axis_rotation: Rotate the x axis labels this number of degrees (default: 0) group_column_name: The group column name used in the datatable (e.g. 'Class') (default: None) point_estimator: The statistical function to use for the point plot. Either "mean" or "median" (default: 'mean') point_ci: The bootstrapped confidence interval for the point plot. Can also be standard deviation ("sd") (default: 95) violin_distribution_type: The representation of the distribution of data points within the violin plot. Either "quartile", "box", "point", "stick" or None (default: 'box') violin_width_scale: The method used to scale the width of the violin plot. Either "area", "count" or "width" (default: "width") box_iqr: The proportion past the lower and upper quartiles to extend the plot whiskers for the box plot. Points outside this range will be identified as outliers (default: 1.5) saveImage: Setting to 'True' will save the image to file (default: True) imageFileName: The image file name to save to (default: [plot_type]_features.png') dpi: The number of Dots Per Inch (DPI) for the image (default: 200) help : Print this help text plot : Generates feature plots """ def __init__(self, peaktable, datatable): peaktable = self.__checkPeakTable(self.__checkData(peaktable)) datatable = self.__checkData(datatable) # Slice the meta-data, and select only peaks from the peaktable for processing, and add the meta-data back meta = datatable.T[~datatable.T.index.isin(peaktable['Name'])].T.reset_index(drop=True) dat = datatable[peaktable['Name']].reset_index() datatable = pd.concat([meta, dat], axis=1).set_index(['index']) datatable.index.name = None self.__peaktable = peaktable # Search for duplicate labels and amend with a suffix, to avoid issues when relabelling the datatable labels = copy.deepcopy(list(peaktable['Label'])) label_counts = {k: v for k, v in Counter(labels).items() if v > 1} for i in reversed(range(len(labels))): item = str(labels[i]) if item in label_counts and label_counts[item]: labels[i] += "_" + str(label_counts[item]) label_counts[item] -= 1 #Label datatable with peak labels instead of names for ease of feature plotting col_label_dict = dict(zip(list(peaktable['Name']), labels)) datatable.rename(columns=col_label_dict, inplace=True) self.__peak_labels = labels self.__datatable = datatable self.set_params() def help(self): print(plotFeatures.usage) def set_params(self, plot_type='point', column_numbers=4, log_data=(True, 2), scale_data=(True, 'minmax'), impute_data=(True, 3), style='seaborn-white', transparent=False, figSize = (15, 10), fontSize = 12, colour_palette=None, y_axis_label=None, x_axis_rotation=0, group_column_name=None, point_estimator='mean', point_ci=95, violin_distribution_type='box', violin_width_scale='width', box_iqr=1.5, saveImage=True, imageFileName='_features.png', dpi = 200): plot_type, column_numbers, log_data, scale_data, impute_data, style, transparent, figSize, fontSize, colour_palette, y_axis_label, x_axis_rotation, group_column_name, point_estimator, point_ci, violin_distribution_type, violin_width_scale, box_iqr, saveImage, imageFileName, dpi = self.__paramCheck(plot_type, column_numbers, log_data, scale_data, impute_data, style, transparent, figSize, fontSize, colour_palette, y_axis_label, x_axis_rotation, group_column_name, point_estimator, point_ci, violin_distribution_type, violin_width_scale, box_iqr, saveImage, imageFileName, dpi) self.__plot_type = plot_type; self.__column_numbers = column_numbers; self.__log_data = log_data; self.__scale_data = scale_data; self.__impute_data = impute_data; self.__style = style; self.__transparent = transparent; self.__figSize = figSize; self.__fontSize = fontSize; self.__colour_palette = colour_palette; self.__y_axis_label = y_axis_label; self.__x_axis_rotation = x_axis_rotation; self.__group_column_name = group_column_name; self.__point_estimator = point_estimator; self.__point_ci = point_ci; self.__violin_distribution_type = violin_distribution_type; self.__violin_width_scale = violin_width_scale; self.__box_iqr = box_iqr; self.__saveImage = saveImage; self.__imageFileName = imageFileName; self.__dpi = dpi; def plot(self): datatable = copy.deepcopy(self.__datatable) labels = self.__peak_labels plot_type = self.__plot_type group_column_name = self.__group_column_name column_numbers = self.__column_numbers colour_palette = self.__colour_palette point_ci = self.__point_ci point_estimator = self.__point_estimator log_data = self.__log_data scale_data = self.__scale_data impute_data = self.__impute_data x_axis_rotation = self.__x_axis_rotation y_axis_label = self.__y_axis_label violin_distribution_type = self.__violin_distribution_type violin_width_scale = self.__violin_width_scale box_iqr = self.__box_iqr imageFileName = self.__imageFileName saveImage = self.__saveImage fontSize = self.__fontSize style = self.__style transparent = self.__transparent dpi = self.__dpi figSize = self.__figSize meta = datatable.T[~datatable.T.index.isin(labels)].T.reset_index(drop=True) X = datatable[labels].reset_index(drop=True) (log_bool, log_base) = log_data; if log_bool: if isinstance(log_base, str) and log_base.lower() == 'natural': X = X.applymap(np.log); elif log_base == 2: X = X.applymap(np.log2); elif log_base == 10: X = X.applymap(np.log10); else: print("Error: The chosen log type is invalid.") sys.exit() (scale_bool, scale_type) = scale_data if scale_bool: if isinstance(scale_type, str) and scale_type.lower() == 'standard': X = scaler(X, type=scale_type.lower()).reset_index(drop=True) elif isinstance(scale_type, str) and scale_type.lower() == 'minmax': X = scaler(X, type=scale_type.lower()).reset_index(drop=True) elif isinstance(scale_type, str) and scale_type.lower() == 'maxabs': X = scaler(X, type=scale_type.lower()).reset_index(drop=True) elif isinstance(scale_type, str) and scale_type.lower() == 'robust': X = scaler(X, type=scale_type.lower()).reset_index(drop=True) else: print("Error: The chosen scale type is invalid.") sys.exit() (impute_bool, k) = impute_data; if impute_bool: X = imputeData(X, k=k).reset_index(drop=True) if not isinstance(X, pd.DataFrame): X = pd.DataFrame(X, columns=labels) # Add the meta data back in with the logged, scaled, or imputed data datatable = pd.concat([meta, X], axis=1).reset_index(drop=True) with plt.style.context(style): fig, axes = plt.subplots(nrows=int(np.ceil(float(len(labels) / column_numbers))), ncols=column_numbers, sharey=True, figsize=figSize) if plot_type == 'point': for peak_index, peak in enumerate(labels): if point_estimator.lower() == 'mean': point_estimator = 'Mean' ax = sns.pointplot(data=datatable, x=group_column_name, y=peak, estimator=np.nanmean, capsize=0.1, ci=point_ci, palette=colour_palette, ax=axes.flat[peak_index]) elif point_estimator.lower() == 'median': point_estimator = 'Median' ax = sns.pointplot(data=datatable, x=group_column_name, y=peak, estimator=np.nanmedian, capsize=0.1, ci=point_ci, palette=colour_palette, ax=axes.flat[peak_index]) else: print("Error: Invalid point plot estimator type.") sys.exit() ax.tick_params(labelrotation=x_axis_rotation, labelsize=fontSize) if log_bool: if scale_data: if isinstance(point_ci, str): if point_ci == 'sd': ax.set_title(peak + ' within SD', fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) {} Peak Area within SD'.format(log_base, scale_type, point_estimator), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: ax.set_title(peak + ' with {}% CI'.format(point_ci), fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) {} Peak Area & {}% CI'.format(log_base, scale_type, point_estimator, point_ci), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if isinstance(point_ci, str): if point_ci == 'sd': ax.set_title(peak + ' within SD', fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('Log({}) {} Peak Area within SD'.format(log_base, point_estimator), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: ax.set_title(peak + ' with {}% CI'.format(point_ci), fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('Log({}) {} Peak Area & {}% CI'.format(log_base, point_estimator, point_ci), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if scale_data: if isinstance(point_ci, str): if point_ci == 'sd': ax.set_title(peak + ' within SD', fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('Scaled ({}) {} Peak Area within SD'.format(scale_type, point_estimator), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: ax.set_title(peak + ' with {}% CI'.format(point_ci), fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('Scaled ({}) {} Peak Area & {}% CI'.format(scale_type, point_estimator, point_ci), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if isinstance(point_ci, str): if point_ci == 'sd': ax.set_title(peak + ' within SD', fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('{} Peak Area within SD'.format(point_estimator), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: ax.set_title(peak + ' with {}% CI'.format(point_ci), fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('{} Peak Area & {}% CI'.format(point_estimator, point_ci), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) elif plot_type.lower() == 'violin': for peak_index, peak in enumerate(labels): ax = sns.violinplot(data=datatable, x=group_column_name, y=peak, linewidth=1, inner=violin_distribution_type, scale=violin_width_scale, palette=colour_palette, ax=axes.flat[peak_index]) ax.tick_params(labelrotation=x_axis_rotation, labelsize=fontSize) ax.set_title(peak, fontsize=fontSize) ax.set_xlabel('') if log_bool: if scale_data: if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) Peak Area'.format(log_base, scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Log({}) Peak Area'.format(log_base), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if scale_data: if y_axis_label is None: ax.set_ylabel('Scaled ({}) Peak Area'.format(scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Peak Area', fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) elif plot_type.lower() == 'box': for peak_index, peak in enumerate(labels): ax = sns.boxplot(data=datatable, x=group_column_name, y=peak, palette=colour_palette, whis=box_iqr, ax=axes.flat[peak_index]) ax.tick_params(labelrotation=x_axis_rotation, labelsize=fontSize) ax.set_title(peak, fontsize=fontSize) ax.set_xlabel('') if log_bool: if scale_data: if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) Peak Area'.format(log_base, scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Log({}) Peak Area'.format(log_base), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if scale_data: if y_axis_label is None: ax.set_ylabel('Scaled ({}) Peak Area'.format(scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Peak Area', fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) elif plot_type.lower() == 'swarm': for peak_index, peak in enumerate(labels): ax = sns.swarmplot(data=datatable, x=group_column_name, y=peak, size=10, palette=colour_palette, ax=axes.flat[peak_index]) ax.tick_params(labelrotation=x_axis_rotation, labelsize=fontSize) ax.set_title(peak, fontsize=fontSize) ax.set_xlabel('') if log_bool: if scale_data: if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) Peak Area'.format(log_base, scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Log({}) Peak Area'.format(log_base), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if scale_data: if y_axis_label is None: ax.set_ylabel('Scaled ({}) Peak Area'.format(scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Peak Area', fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) elif plot_type.lower() == 'violin-swarm': for peak_index, peak in enumerate(labels): ax = sns.violinplot(data=datatable, x=group_column_name, y=peak, linewidth=1, inner=None, scale=violin_width_scale, palette=colour_palette, ax=axes.flat[peak_index]) ax = sns.swarmplot(data=datatable, x=group_column_name, y=peak, color="white", edgecolor="gray", ax=axes.flat[peak_index]) ax.tick_params(labelrotation=x_axis_rotation, labelsize=fontSize) ax.set_title(peak, fontsize=fontSize) ax.set_xlabel('') if log_bool: if scale_data: if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) Peak Area'.format(log_base, scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Log({}) Peak Area'.format(log_base), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if scale_data: if y_axis_label is None: ax.set_ylabel('Scaled ({}) Peak Area'.format(scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Peak Area', fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) elif plot_type.lower() == 'box-swarm': for peak_index, peak in enumerate(labels): ax = sns.boxplot(data=datatable, x=group_column_name, y=peak, palette=colour_palette, whis=np.inf, ax=axes.flat[peak_index]) ax = sns.swarmplot(data=datatable, x=group_column_name, y=peak, color="0.2", ax=axes.flat[peak_index]) ax.tick_params(labelrotation=x_axis_rotation, labelsize=fontSize) ax.set_title(peak, fontsize=fontSize) ax.set_xlabel('') if log_bool: if scale_data: if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) Peak Area'.format(log_base, scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Log({}) Peak Area'.format(log_base), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if scale_data: if y_axis_label is None: ax.set_ylabel('Scaled ({}) Peak Area'.format(scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Peak Area', fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) fig.tight_layout(h_pad=5, w_pad=2) if saveImage: plt.savefig(plot_type + 'Plot' + imageFileName, dpi=dpi, transparent=transparent) plt.show() def __paramCheck(self, plot_type, column_numbers, log_data, scale_data, impute_data, style, transparent, figSize, fontSize, colour_palette, y_axis_label, x_axis_rotation, group_column_name, point_estimator, point_ci, violin_distribution_type, violin_width_scale, box_iqr, saveImage, imageFileName, dpi): cmap_list = list(matplotlib.cm.cmaps_listed) + list(matplotlib.cm.datad) cmap_list_r = [cmap + '_r' for cmap in cmap_list] cmap_list = cmap_list + cmap_list_r plot_types = ['point', 'violin', 'box', 'swarm', 'violin-swarm', 'box-swarm'] estimator_types = ['mean', 'median'] datatable = self.__datatable if plot_type.lower() not in plot_types: print("Error: Plot type is not valid. Choose one of the following: {}.".format(', '.join(plot_types))) sys.exit() if not isinstance(column_numbers, int): print("Error: Column numbers is not valid. Choose a integer value.") sys.exit() if not isinstance(log_data, tuple): print("Error: Log data type if not a tuple. Please ensure the value is a tuple (e.g. (True, 2).") sys.exit() else: (log_bool, log_base) = log_data if not isinstance(log_bool, bool): print("Error: Log data first tuple item is not a boolean value. Choose either \"True\" or \"False\".") sys.exit() base_types = ['natural', 2, 10] if isinstance(log_base, str): log_base = log_base.lower() if log_base not in base_types: print("Error: Log data second tuple item is not valid. Choose one of {}.".format(', '.join(base_types))) sys.exit() if not isinstance(scale_data, tuple): print("Error: Scale data type if not a tuple. Please ensure the value is a tuple (e.g. (True, 'standard').") sys.exit() else: (scale_bool, scale_type) = scale_data if not isinstance(scale_bool, bool): print("Error: Scale data first tuple item is not a boolean value. Choose either \"True\" or \"False\".") sys.exit() scale_types = ['standard', 'minmax', 'maxabs', 'robust'] if isinstance(scale_type, str): scale_type = scale_type.lower() if scale_type not in scale_types: print("Error: Scale data second tuple item is not valid. Choose one of {}.".format(', '.join(scale_types))) sys.exit() if not isinstance(impute_data, tuple): print("Error: Impute data type if not a tuple. Please ensure the value is a tuple (e.g. (True, 3).") sys.exit() else: (impute_bool, k) = impute_data if not isinstance(impute_bool, bool): print("Error: Impute data first tuple item is not a boolean value. Choose either \"True\" or \"False\".") sys.exit() if not isinstance(k, float): if not isinstance(k, int): print("Error: Impute data second tuple item, the nearest neighbours k value, is not valid. Choose a float or integer value.") sys.exit() if not isinstance(style, str): print("Error: Seaborn style is not valid. Choose a string value.") sys.exit() else: styleList = list(plt.style.available) if style not in styleList: print("Error: Chosen style is not valid. Choose one of the following: {}.".format(', '.join(styleList))) sys.exit() if not isinstance(transparent, bool): print("Error: The transparent value is not valid. Choose either \"True\" or \"False\".") sys.exit() if not isinstance(figSize, tuple): print("Error: Figure size is not valid. Choose a tuple of length 2.") sys.exit() else: for length in figSize: if not isinstance(length, float): if not isinstance(length, int): print("Error: Figure size value is not valid. Choose a float or integer value.") sys.exit() if not isinstance(fontSize, float): if not isinstance(fontSize, int): print("Error: Font size is not valid. Choose a float or integer value.") sys.exit() if colour_palette is not None: if not isinstance(colour_palette, str): print("Error: The colour palette is not valid. Choose a string value.") sys.exit() else: if colour_palette not in cmap_list: print("Error: The colour palette is not valid. Choose one of the following: {}.".format(', '.join(cmap_list))) sys.exit() if y_axis_label is not None: if isinstance(y_axis_label, str): print("Error: The y axis label is not valid. Choose a string value.") sys.exit() if not isinstance(x_axis_rotation, float): if not isinstance(x_axis_rotation, int): print("Error: The x axis rotation value is not valid. Choose a float or integer value.") sys.exit() if ((x_axis_rotation < 0) or (x_axis_rotation > 360)): print("Error: The x axis rotation value is not valid. Choose a value >=0 or <= 360.") sys.exit() if group_column_name is not None: if not isinstance(group_column_name, str): print("Error: Group column name is not valid. Choose a string value.") sys.exit() else: if group_column_name not in list(datatable.columns): print("Error: Group column name not valid. Choose one of {}.".format(', '.join(list(datatable.columns)))) sys.exit() if point_estimator.lower() not in estimator_types: print("Error: The chosen point plot estimator is invalid. Choose one of \"{}\".".format('\" or \"'.join(estimator_types))) sys.exit() if isinstance(point_ci, str): if point_ci != 'sd': print("Error: The string value for point plot ci is invalid. Choose a float, integer or 'sd' value for standard deviation.") sys.exit() else: if not isinstance(point_ci, float): if not isinstance(point_ci, int): print("Error: The value for point plot ci is invalid. Choose a float, integer or 'sd' value for standard deviation.") sys.exit() violin_distribution_types = ['quartile', 'box', 'point', 'stick', None] violin_width_scale_types = ['area', 'count', 'width'] if plot_type.lower() == "violin": if violin_distribution_type not in violin_distribution_types: print("Error: Violin distribution type not valid. Choose one of the following: {}.".format(', '.join(violin_distribution_types))) sys.exit() if violin_width_scale not in violin_width_scale_types: print("Error: Violin width scale type not valid. Choose one of the following: {}.".format(', '.join(violin_width_scale_types))) sys.exit() if plot_type.lower == "box": if not isinstance(box_iqr, float): if not isinstance(box_iqr, int): print( "Error: The box plot interquartile range extension beyond whiskers is not valid. Choose a float or integer value.") sys.exit() if not isinstance(saveImage, bool): print("Error: Save image is not valid. Choose either \"True\" or \"False\".") sys.exit() if not isinstance(imageFileName, str): print("Error: Image file name is not valid. Choose a string value.") sys.exit() if not isinstance(dpi, float): if not isinstance(dpi, int): print("Error: Dpi is not valid. Choose a float or integer value.") sys.exit() return plot_type, column_numbers, log_data, scale_data, impute_data, style, transparent, figSize, fontSize, colour_palette, y_axis_label, x_axis_rotation, group_column_name, point_estimator, point_ci, violin_distribution_type, violin_width_scale, box_iqr, saveImage, imageFileName, dpi def __checkData(self, df): if not isinstance(df, pd.DataFrame): print("Error: A dataframe was not entered. Please check your data.") return df def __checkPeakTable(self, PeakTable): if "Name" not in PeakTable.columns: print("Error: \"Name\" column not in Peak Table. Please check your data.") sys.exit() if "Label" not in PeakTable.columns: print("Error: \"Label\" column not in Peak Table. Please check your data.") sys.exit() # Do not assume the peaks/nodes have been indexed correctly. Remove any index columns and reindex. column_list = [column.lower() for column in PeakTable.columns] if 'idx' in column_list: index = column_list.index('idx') column_name = PeakTable.columns[index] PeakTable = PeakTable.drop(columns=[column_name]) if 'index' in column_list: index = column_list.index('index') column_name = PeakTable.columns[index] PeakTable = PeakTable.drop(columns=[column_name]) PeakTable = PeakTable.reset_index(drop=True) PeakTable.index.name = 'Idx' PeakTable = PeakTable.reset_index() return PeakTable	en	0.600224	Produces different feature plots given a data table and peak table. Initial_Parameters ---------- peaktable : Pandas dataframe containing peak data. Must contain 'Name' and 'Label'. datatable : Pandas dataframe containing matrix of values to plot (N samples x N features). Columns/features must be same as 'Name' from Peak Table. Methods ------- set_params : Set parameters - plot_type: The type of plot. Either "point", "violin", "box", "swarm", "violin-swarm" or "box-swarm" (default: 'point') column_numbers: The number of columns to display in the plots (default: 4) log_data: Perform a log ('natural', base 2 or base 10) on all data (default: (True, 2)) scale_data: Scale the data ('standard' (centers to the mean and scales to unit variance), 'minmax' (scales between 0 and 1), 'maxabs' (scales to the absolute maximum value), 'robust' (centers to the median and scales to between 25th and 75th quantile range) (default: (True, 'minmax')) impute_data: Impute any missing values using KNN impute with a set number of nearest neighbours (default: (True, 3)) style: Set the matplotlib style (see https://matplotlib.org/stable/tutorials/introductory/customizing.html) (default: 'seaborn-white') transparent: Setting to 'True' will make the background transparent (default: False) figSize: The figure size as a tuple (width,height) (default: (15,10)) fontSize: The font size for all text (default: 12) colour_palette: The colour palette to use for the plot (default: None) y_axis_label: The label to customise the y axis (default: None) x_axis_rotation: Rotate the x axis labels this number of degrees (default: 0) group_column_name: The group column name used in the datatable (e.g. 'Class') (default: None) point_estimator: The statistical function to use for the point plot. Either "mean" or "median" (default: 'mean') point_ci: The bootstrapped confidence interval for the point plot. Can also be standard deviation ("sd") (default: 95) violin_distribution_type: The representation of the distribution of data points within the violin plot. Either "quartile", "box", "point", "stick" or None (default: 'box') violin_width_scale: The method used to scale the width of the violin plot. Either "area", "count" or "width" (default: "width") box_iqr: The proportion past the lower and upper quartiles to extend the plot whiskers for the box plot. Points outside this range will be identified as outliers (default: 1.5) saveImage: Setting to 'True' will save the image to file (default: True) imageFileName: The image file name to save to (default: [plot_type]_features.png') dpi: The number of Dots Per Inch (DPI) for the image (default: 200) help : Print this help text plot : Generates feature plots # Slice the meta-data, and select only peaks from the peaktable for processing, and add the meta-data back # Search for duplicate labels and amend with a suffix, to avoid issues when relabelling the datatable #Label datatable with peak labels instead of names for ease of feature plotting # Add the meta data back in with the logged, scaled, or imputed data # Do not assume the peaks/nodes have been indexed correctly. Remove any index columns and reindex.	3.178132	3
core/data/DataWriter.py	berendkleinhaneveld/Registrationshop	25	10242	<reponame>berendkleinhaneveld/Registrationshop<gh_stars>10-100 """ DataWriter.py """ from DataController import DataController from DataReader import DataReader from vtk import vtkMetaImageWriter from vtk import vtkXMLImageDataWriter class DataWriter(DataController): """ DataWriter writes an image data object to disk using the provided format. """ def __init__(self): super(DataWriter, self).__init__() self.supportedExtensions = [DataReader.TypeMHD, DataReader.TypeVTI, DataReader.TypeMHA] def WriteToFile(self, imageData, exportFileName, fileType): if fileType == DataReader.TypeMHD: if not exportFileName.endswith(".mhd"): exportFileName = exportFileName + ".mhd" writer = vtkMetaImageWriter() writer.SetFileName(exportFileName) writer.SetInputData(imageData) writer.Write() elif fileType == DataReader.TypeVTI: writer = vtkXMLImageDataWriter() writer.SetFileName(exportFileName) writer.SetInputData(imageData) writer.Write() elif fileType == DataReader.TypeMHA: writer = vtkMetaImageWriter() writer.SetFileName(exportFileName) writer.SetInputData(imageData) writer.Write() else: raise NotImplementedError("No writing support for type " + str(fileType))	""" DataWriter.py """ from DataController import DataController from DataReader import DataReader from vtk import vtkMetaImageWriter from vtk import vtkXMLImageDataWriter class DataWriter(DataController): """ DataWriter writes an image data object to disk using the provided format. """ def __init__(self): super(DataWriter, self).__init__() self.supportedExtensions = [DataReader.TypeMHD, DataReader.TypeVTI, DataReader.TypeMHA] def WriteToFile(self, imageData, exportFileName, fileType): if fileType == DataReader.TypeMHD: if not exportFileName.endswith(".mhd"): exportFileName = exportFileName + ".mhd" writer = vtkMetaImageWriter() writer.SetFileName(exportFileName) writer.SetInputData(imageData) writer.Write() elif fileType == DataReader.TypeVTI: writer = vtkXMLImageDataWriter() writer.SetFileName(exportFileName) writer.SetInputData(imageData) writer.Write() elif fileType == DataReader.TypeMHA: writer = vtkMetaImageWriter() writer.SetFileName(exportFileName) writer.SetInputData(imageData) writer.Write() else: raise NotImplementedError("No writing support for type " + str(fileType))	en	0.563685	DataWriter.py DataWriter writes an image data object to disk using the provided format.	2.945427	3
parkings/models/permit.py	klemmari1/parkkihubi	12	10243	from itertools import chain from django.conf import settings from django.contrib.gis.db import models as gis_models from django.db import models, router, transaction from django.utils import timezone from django.utils.translation import gettext_lazy as _ from ..fields import CleaningJsonField from ..validators import DictListValidator, TextField, TimestampField from .constants import GK25FIN_SRID from .enforcement_domain import EnforcementDomain from .mixins import TimestampedModelMixin from .parking import Parking class PermitArea(TimestampedModelMixin): name = models.CharField(max_length=40, verbose_name=_('name')) domain = models.ForeignKey( EnforcementDomain, on_delete=models.PROTECT, related_name='permit_areas') identifier = models.CharField(max_length=10, verbose_name=_('identifier')) geom = gis_models.MultiPolygonField( srid=GK25FIN_SRID, verbose_name=_('geometry')) permitted_user = models.ForeignKey( settings.AUTH_USER_MODEL, on_delete=models.PROTECT, verbose_name=_("permitted_user")) class Meta: unique_together = [('domain', 'identifier')] ordering = ('identifier',) def __str__(self): return '{}/{}: {}'.format(self.domain.code, self.identifier, self.name) class PermitSeriesQuerySet(models.QuerySet): def active(self): return self.filter(active=True) def latest_active(self): return self.active().order_by('-modified_at').first() def prunable(self, time_limit=None): limit = time_limit or ( timezone.now() - settings.PARKKIHUBI_PERMITS_PRUNABLE_AFTER) return self.filter(created_at__lt=limit, active=False) class PermitSeries(TimestampedModelMixin, models.Model): active = models.BooleanField(default=False) owner = models.ForeignKey( settings.AUTH_USER_MODEL, on_delete=models.PROTECT, verbose_name=_("owner")) objects = PermitSeriesQuerySet.as_manager() class Meta: ordering = ('created_at', 'id') verbose_name = _("permit series") verbose_name_plural = _("permit series") @classmethod def delete_prunable_series(cls, time_limit=None): prunable = cls.objects.prunable(time_limit) Permit.objects.filter(series__in=prunable).delete() prunable.delete() def __str__(self): return str(self.id) class PermitQuerySet(models.QuerySet): def active(self): return self.filter(series__active=True) def by_time(self, timestamp): lookup_items = PermitLookupItem.objects.by_time(timestamp) return self.filter(lookup_items__in=lookup_items).distinct() def by_subject(self, registration_number): lookup_items = PermitLookupItem.objects.by_subject(registration_number) return self.filter(lookup_items__in=lookup_items).distinct() def by_area(self, area): lookup_items = PermitLookupItem.objects.by_area(area) return self.filter(lookup_items__in=lookup_items).distinct() def bulk_create(self, permits, args, kwargs): for permit in permits: assert isinstance(permit, Permit) permit.full_clean() with transaction.atomic(using=self.db, savepoint=False): created_permits = super().bulk_create(permits, args, *kwargs) PermitLookupItem.objects.using(self.db).bulk_create( chain((x._make_lookup_items() for x in created_permits))) return created_permits class Permit(TimestampedModelMixin, models.Model): domain = models.ForeignKey( EnforcementDomain, on_delete=models.PROTECT, related_name='permits') series = models.ForeignKey(PermitSeries, on_delete=models.PROTECT) external_id = models.CharField(max_length=50, null=True, blank=True) subjects = CleaningJsonField(blank=True, validators=[DictListValidator({ 'start_time': TimestampField(), 'end_time': TimestampField(), 'registration_number': TextField(max_length=20), })]) areas = CleaningJsonField(blank=True, validators=[DictListValidator({ 'start_time': TimestampField(), 'end_time': TimestampField(), 'area': TextField(max_length=10), })]) objects = PermitQuerySet.as_manager() class Meta: unique_together = [('series', 'external_id')] indexes = [ models.Index(fields=['series', 'id']), ] ordering = ('series', 'id') def __str__(self): return 'Permit {id} ({series}{active}/{external_id} {dom})'.format( id=self.id, dom=self.domain.code, series=self.series, active='' if self.series.active else '', external_id=self.external_id) def save(self, using=None, args, *kwargs): self.full_clean() using = using or router.db_for_write(type(self), instance=self) with transaction.atomic(using=using, savepoint=False): super(Permit, self).save(using=using, args, **kwargs) self.lookup_items.all().using(using).delete() new_lookup_items = self._make_lookup_items() PermitLookupItem.objects.using(using).bulk_create(new_lookup_items) def _make_lookup_items(self): for area in self.areas: for subject in self.subjects: max_start_time = max(subject['start_time'], area['start_time']) min_end_time = min(subject['end_time'], area['end_time']) if max_start_time >= min_end_time: continue yield PermitLookupItem( permit=self, registration_number=Parking.normalize_reg_num( subject['registration_number']), area=PermitArea.objects.get(identifier=area['area'], domain=self.domain), start_time=max_start_time, end_time=min_end_time ) class PermitLookupItemQuerySet(models.QuerySet): def active(self): return self.filter(permit__series__active=True) def by_time(self, timestamp): return self.filter(start_time__lte=timestamp, end_time__gte=timestamp) def by_subject(self, registration_number): normalized_reg_num = Parking.normalize_reg_num(registration_number) return self.filter(registration_number=normalized_reg_num) def by_area(self, area): return self.filter(area=area) class PermitLookupItem(models.Model): permit = models.ForeignKey( Permit, related_name="lookup_items", on_delete=models.CASCADE) registration_number = models.CharField(max_length=20) area = models.ForeignKey(PermitArea, on_delete=models.PROTECT, default=None, null=True, blank=True) start_time = models.DateTimeField() end_time = models.DateTimeField() objects = PermitLookupItemQuerySet.as_manager() class Meta: indexes = [ models.Index(fields=[ 'registration_number', 'start_time', 'end_time', 'area', 'permit']), ] ordering = ('registration_number', 'start_time', 'end_time') def __str__(self): return ( '{start_time:%Y-%m-%d %H:%M} -- {end_time:%Y-%m-%d %H:%M} / ' '{registration_number} / {area}' ).format( start_time=self.start_time, end_time=self.end_time, registration_number=self.registration_number, area=self.area.identifier)	from itertools import chain from django.conf import settings from django.contrib.gis.db import models as gis_models from django.db import models, router, transaction from django.utils import timezone from django.utils.translation import gettext_lazy as _ from ..fields import CleaningJsonField from ..validators import DictListValidator, TextField, TimestampField from .constants import GK25FIN_SRID from .enforcement_domain import EnforcementDomain from .mixins import TimestampedModelMixin from .parking import Parking class PermitArea(TimestampedModelMixin): name = models.CharField(max_length=40, verbose_name=_('name')) domain = models.ForeignKey( EnforcementDomain, on_delete=models.PROTECT, related_name='permit_areas') identifier = models.CharField(max_length=10, verbose_name=_('identifier')) geom = gis_models.MultiPolygonField( srid=GK25FIN_SRID, verbose_name=_('geometry')) permitted_user = models.ForeignKey( settings.AUTH_USER_MODEL, on_delete=models.PROTECT, verbose_name=_("permitted_user")) class Meta: unique_together = [('domain', 'identifier')] ordering = ('identifier',) def __str__(self): return '{}/{}: {}'.format(self.domain.code, self.identifier, self.name) class PermitSeriesQuerySet(models.QuerySet): def active(self): return self.filter(active=True) def latest_active(self): return self.active().order_by('-modified_at').first() def prunable(self, time_limit=None): limit = time_limit or ( timezone.now() - settings.PARKKIHUBI_PERMITS_PRUNABLE_AFTER) return self.filter(created_at__lt=limit, active=False) class PermitSeries(TimestampedModelMixin, models.Model): active = models.BooleanField(default=False) owner = models.ForeignKey( settings.AUTH_USER_MODEL, on_delete=models.PROTECT, verbose_name=_("owner")) objects = PermitSeriesQuerySet.as_manager() class Meta: ordering = ('created_at', 'id') verbose_name = _("permit series") verbose_name_plural = _("permit series") @classmethod def delete_prunable_series(cls, time_limit=None): prunable = cls.objects.prunable(time_limit) Permit.objects.filter(series__in=prunable).delete() prunable.delete() def __str__(self): return str(self.id) class PermitQuerySet(models.QuerySet): def active(self): return self.filter(series__active=True) def by_time(self, timestamp): lookup_items = PermitLookupItem.objects.by_time(timestamp) return self.filter(lookup_items__in=lookup_items).distinct() def by_subject(self, registration_number): lookup_items = PermitLookupItem.objects.by_subject(registration_number) return self.filter(lookup_items__in=lookup_items).distinct() def by_area(self, area): lookup_items = PermitLookupItem.objects.by_area(area) return self.filter(lookup_items__in=lookup_items).distinct() def bulk_create(self, permits, args, kwargs): for permit in permits: assert isinstance(permit, Permit) permit.full_clean() with transaction.atomic(using=self.db, savepoint=False): created_permits = super().bulk_create(permits, args, *kwargs) PermitLookupItem.objects.using(self.db).bulk_create( chain((x._make_lookup_items() for x in created_permits))) return created_permits class Permit(TimestampedModelMixin, models.Model): domain = models.ForeignKey( EnforcementDomain, on_delete=models.PROTECT, related_name='permits') series = models.ForeignKey(PermitSeries, on_delete=models.PROTECT) external_id = models.CharField(max_length=50, null=True, blank=True) subjects = CleaningJsonField(blank=True, validators=[DictListValidator({ 'start_time': TimestampField(), 'end_time': TimestampField(), 'registration_number': TextField(max_length=20), })]) areas = CleaningJsonField(blank=True, validators=[DictListValidator({ 'start_time': TimestampField(), 'end_time': TimestampField(), 'area': TextField(max_length=10), })]) objects = PermitQuerySet.as_manager() class Meta: unique_together = [('series', 'external_id')] indexes = [ models.Index(fields=['series', 'id']), ] ordering = ('series', 'id') def __str__(self): return 'Permit {id} ({series}{active}/{external_id} {dom})'.format( id=self.id, dom=self.domain.code, series=self.series, active='' if self.series.active else '', external_id=self.external_id) def save(self, using=None, args, *kwargs): self.full_clean() using = using or router.db_for_write(type(self), instance=self) with transaction.atomic(using=using, savepoint=False): super(Permit, self).save(using=using, args, **kwargs) self.lookup_items.all().using(using).delete() new_lookup_items = self._make_lookup_items() PermitLookupItem.objects.using(using).bulk_create(new_lookup_items) def _make_lookup_items(self): for area in self.areas: for subject in self.subjects: max_start_time = max(subject['start_time'], area['start_time']) min_end_time = min(subject['end_time'], area['end_time']) if max_start_time >= min_end_time: continue yield PermitLookupItem( permit=self, registration_number=Parking.normalize_reg_num( subject['registration_number']), area=PermitArea.objects.get(identifier=area['area'], domain=self.domain), start_time=max_start_time, end_time=min_end_time ) class PermitLookupItemQuerySet(models.QuerySet): def active(self): return self.filter(permit__series__active=True) def by_time(self, timestamp): return self.filter(start_time__lte=timestamp, end_time__gte=timestamp) def by_subject(self, registration_number): normalized_reg_num = Parking.normalize_reg_num(registration_number) return self.filter(registration_number=normalized_reg_num) def by_area(self, area): return self.filter(area=area) class PermitLookupItem(models.Model): permit = models.ForeignKey( Permit, related_name="lookup_items", on_delete=models.CASCADE) registration_number = models.CharField(max_length=20) area = models.ForeignKey(PermitArea, on_delete=models.PROTECT, default=None, null=True, blank=True) start_time = models.DateTimeField() end_time = models.DateTimeField() objects = PermitLookupItemQuerySet.as_manager() class Meta: indexes = [ models.Index(fields=[ 'registration_number', 'start_time', 'end_time', 'area', 'permit']), ] ordering = ('registration_number', 'start_time', 'end_time') def __str__(self): return ( '{start_time:%Y-%m-%d %H:%M} -- {end_time:%Y-%m-%d %H:%M} / ' '{registration_number} / {area}' ).format( start_time=self.start_time, end_time=self.end_time, registration_number=self.registration_number, area=self.area.identifier)	none	1		1.983396	2
poi_mining/biz/LSA/logEntropy.py	yummydeli/machine_learning	1	10244	<filename>poi_mining/biz/LSA/logEntropy.py #!/usr/bin/env python # encoding:utf-8 # ############################################################################## # The MIT License (MIT) # # Copyright (c) [2015] [baidu.com] # # 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. # ############################################################################## """ 生成LogEntropy矩阵并筛选出合适的词汇 """ import glob import collections import pandas from sklearn.feature_extraction.text import CountVectorizer import math class LogEntropy(object): """计算logentropy，得到类别关键字""" def __init__(self): self.fnames = glob.glob('data/segs/names.') def extract_segs(self): """分词文件中获取分词结果""" idx = [] words = [] for f in self.fnames: lines = [] for i, line in enumerate(open(f)): if i % 2 == 1: non_int = '\t'.join([e for e in line.decode('GBK').rstrip('\n').split('\t') \ if not e.isdigit()]) lines.append(non_int) words.append('\t'.join(lines)) idx.append(f.split('.')[1][1:]) return words, idx def mk_document_term_matrix(self): """生成TDM矩阵""" words, idx = self.extract_segs() countvec = CountVectorizer() dtm = pandas.DataFrame(countvec.fit_transform(words).toarray(), columns=countvec.get_feature_names(), index=idx) """ canting faguo riben zhongwen 1001 1 0 0 1 991 1 0 1 0 203 1 1 0 0 """ return dtm def global_weighting(self, dtm): """ 1 - Entropy(words) / log(N) """ # normalized entropy for word pdtm = (dtm / dtm.sum(axis=0)) ndocs = pdtm.shape[0] gw = 1 + (pdtm.applymap(lambda x: x math.log(x) if x != 0 else 0).sum() / math.log(ndocs)) """ canting 2.220446e-16 faguo 1.000000e+00 riben 1.000000e+00 zhongwen 1.000000e+00 """ return gw def local_weighting(self, dtm): """ math.log(freq + 1)""" lw = dtm.applymap(lambda freq: math.log(freq + 1)) """ canting faguo riben zhongwen 1001 0.693147 0.000000 0.000000 0.693147 991 0.693147 0.000000 0.693147 0.000000 203 0.693147 0.693147 0.000000 0.000000 """ return lw def logEntropyWeighting(self): """计算最终的logentropy得分""" dtm = self.mk_document_term_matrix() """ canting faguo riben zhongwen 1001 1.539096e-16 0.000000 0.000000 0.693147 991 1.539096e-16 0.000000 0.693147 0.000000 203 1.539096e-16 0.693147 0.000000 0.000000 """ logEntro = (self.global_weighting(dtm.copy()) * self.local_weighting(dtm)).applymap( lambda x: 0 if x < 0.001 else x ) logEntro.T.to_csv('data/keyWords.cates', sep='\t', encoding='UTF-8') if __name__ == '__main__': lsaEntropy = LogEntropy() lsaEntropy.logEntropyWeighting()	<filename>poi_mining/biz/LSA/logEntropy.py #!/usr/bin/env python # encoding:utf-8 # ############################################################################## # The MIT License (MIT) # # Copyright (c) [2015] [baidu.com] # # 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. # ############################################################################## """ 生成LogEntropy矩阵并筛选出合适的词汇 """ import glob import collections import pandas from sklearn.feature_extraction.text import CountVectorizer import math class LogEntropy(object): """计算logentropy，得到类别关键字""" def __init__(self): self.fnames = glob.glob('data/segs/names.') def extract_segs(self): """分词文件中获取分词结果""" idx = [] words = [] for f in self.fnames: lines = [] for i, line in enumerate(open(f)): if i % 2 == 1: non_int = '\t'.join([e for e in line.decode('GBK').rstrip('\n').split('\t') \ if not e.isdigit()]) lines.append(non_int) words.append('\t'.join(lines)) idx.append(f.split('.')[1][1:]) return words, idx def mk_document_term_matrix(self): """生成TDM矩阵""" words, idx = self.extract_segs() countvec = CountVectorizer() dtm = pandas.DataFrame(countvec.fit_transform(words).toarray(), columns=countvec.get_feature_names(), index=idx) """ canting faguo riben zhongwen 1001 1 0 0 1 991 1 0 1 0 203 1 1 0 0 """ return dtm def global_weighting(self, dtm): """ 1 - Entropy(words) / log(N) """ # normalized entropy for word pdtm = (dtm / dtm.sum(axis=0)) ndocs = pdtm.shape[0] gw = 1 + (pdtm.applymap(lambda x: x math.log(x) if x != 0 else 0).sum() / math.log(ndocs)) """ canting 2.220446e-16 faguo 1.000000e+00 riben 1.000000e+00 zhongwen 1.000000e+00 """ return gw def local_weighting(self, dtm): """ math.log(freq + 1)""" lw = dtm.applymap(lambda freq: math.log(freq + 1)) """ canting faguo riben zhongwen 1001 0.693147 0.000000 0.000000 0.693147 991 0.693147 0.000000 0.693147 0.000000 203 0.693147 0.693147 0.000000 0.000000 """ return lw def logEntropyWeighting(self): """计算最终的logentropy得分""" dtm = self.mk_document_term_matrix() """ canting faguo riben zhongwen 1001 1.539096e-16 0.000000 0.000000 0.693147 991 1.539096e-16 0.000000 0.693147 0.000000 203 1.539096e-16 0.693147 0.000000 0.000000 """ logEntro = (self.global_weighting(dtm.copy()) * self.local_weighting(dtm)).applymap( lambda x: 0 if x < 0.001 else x ) logEntro.T.to_csv('data/keyWords.cates', sep='\t', encoding='UTF-8') if __name__ == '__main__': lsaEntropy = LogEntropy() lsaEntropy.logEntropyWeighting()	en	0.504559	#!/usr/bin/env python # encoding:utf-8 # ############################################################################## # The MIT License (MIT) # # Copyright (c) [2015] [baidu.com] # # 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. # ############################################################################## 生成LogEntropy矩阵并筛选出合适的词汇计算logentropy，得到类别关键字分词文件中获取分词结果生成TDM矩阵 canting faguo riben zhongwen 1001 1 0 0 1 991 1 0 1 0 203 1 1 0 0 1 - Entropy(words) / log(N) # normalized entropy for word canting 2.220446e-16 faguo 1.000000e+00 riben 1.000000e+00 zhongwen 1.000000e+00 math.log(freq + 1) canting faguo riben zhongwen 1001 0.693147 0.000000 0.000000 0.693147 991 0.693147 0.000000 0.693147 0.000000 203 0.693147 0.693147 0.000000 0.000000 计算最终的logentropy得分 canting faguo riben zhongwen 1001 1.539096e-16 0.000000 0.000000 0.693147 991 1.539096e-16 0.000000 0.693147 0.000000 203 1.539096e-16 0.693147 0.000000 0.000000	1.218488	1
Python/swap_numbers.py	saurabhcommand/Hello-world	1,428	10245	a = 5 b = 7 a,b = b,a print a print b	a = 5 b = 7 a,b = b,a print a print b	none	1		2.308451	2
algorithms/tests/test_unionfind.py	tommyod/PythonAlgorithms	1	10246	#!/usr/bin/env python3 # -- coding: utf-8 -- """ Tests for the union find data structure. """ try: from ..unionfind import UnionFind except ValueError: pass def test_unionfind_basics(): """ Test the basic properties of unionfind. """ u = UnionFind([1, 2, 3]) assert u.in_same_set(1, 2) is False assert u.in_same_set(2, 3) is False u.union(1, 3) assert u.in_same_set(1, 2) is False assert u.in_same_set(3, 1) assert u.get_root(1) == u.get_root(3) def test_unionfind_adding_elements(): """ Test adding operations, mostly syntactic sugar. """ u = UnionFind([1, 2]) u.add(['a', 'b']) assert 1 in u assert 'a' in u def test_unionfind_example(): """ Test on a slightly more invovled example. """ u = UnionFind([1, 2, 3, 4, 5]) u.union(1, 3) u.union(2, 4) assert u.in_same_set(1, 3) assert u.in_same_set(4, 2) assert not u.in_same_set(2, 5) assert not u.in_same_set(2, 1) assert not u.in_same_set(1, 4) u.union(5, 1) assert u.in_same_set(3, 5) def test_unionfind_several(): """ Test that we can take union of more than two elements. """ u = UnionFind([1, 2, 3, 4, 5, 6, 7, 8]) u.union([1, 2, 3]) u.union([4, 5, 6]) u.union([7, 8]) assert u.in_same_set(1, 3) assert u.in_same_set(6, 4) assert u.in_same_set(7, 8) assert not u.in_same_set(2, 5) assert not u.in_same_set(4, 8) def test_unionfind_compression(): """ Test path compression and the union by rank. """ # Test the ranking elements = list(range(100)) u = UnionFind(elements) for i in range(len(elements) - 1): u.union(elements[i], elements[i + 1]) assert max(u._rank.values()) == 1 # Test path compression parent_nodes = list(u._parent.values()) assert all(parent == parent_nodes[0] for parent in parent_nodes) if __name__ == "__main__": import pytest # --durations=10 <- May be used to show potentially slow tests pytest.main(args=['.', '--doctest-modules', '-v'])	#!/usr/bin/env python3 # -- coding: utf-8 -- """ Tests for the union find data structure. """ try: from ..unionfind import UnionFind except ValueError: pass def test_unionfind_basics(): """ Test the basic properties of unionfind. """ u = UnionFind([1, 2, 3]) assert u.in_same_set(1, 2) is False assert u.in_same_set(2, 3) is False u.union(1, 3) assert u.in_same_set(1, 2) is False assert u.in_same_set(3, 1) assert u.get_root(1) == u.get_root(3) def test_unionfind_adding_elements(): """ Test adding operations, mostly syntactic sugar. """ u = UnionFind([1, 2]) u.add(['a', 'b']) assert 1 in u assert 'a' in u def test_unionfind_example(): """ Test on a slightly more invovled example. """ u = UnionFind([1, 2, 3, 4, 5]) u.union(1, 3) u.union(2, 4) assert u.in_same_set(1, 3) assert u.in_same_set(4, 2) assert not u.in_same_set(2, 5) assert not u.in_same_set(2, 1) assert not u.in_same_set(1, 4) u.union(5, 1) assert u.in_same_set(3, 5) def test_unionfind_several(): """ Test that we can take union of more than two elements. """ u = UnionFind([1, 2, 3, 4, 5, 6, 7, 8]) u.union([1, 2, 3]) u.union([4, 5, 6]) u.union([7, 8]) assert u.in_same_set(1, 3) assert u.in_same_set(6, 4) assert u.in_same_set(7, 8) assert not u.in_same_set(2, 5) assert not u.in_same_set(4, 8) def test_unionfind_compression(): """ Test path compression and the union by rank. """ # Test the ranking elements = list(range(100)) u = UnionFind(elements) for i in range(len(elements) - 1): u.union(elements[i], elements[i + 1]) assert max(u._rank.values()) == 1 # Test path compression parent_nodes = list(u._parent.values()) assert all(parent == parent_nodes[0] for parent in parent_nodes) if __name__ == "__main__": import pytest # --durations=10 <- May be used to show potentially slow tests pytest.main(args=['.', '--doctest-modules', '-v'])	en	0.808516	#!/usr/bin/env python3 # -- coding: utf-8 -- Tests for the union find data structure. Test the basic properties of unionfind. Test adding operations, mostly syntactic sugar. Test on a slightly more invovled example. Test that we can take union of more than two elements. Test path compression and the union by rank. # Test the ranking # Test path compression # --durations=10 <- May be used to show potentially slow tests	3.456439	3
a3/ga.py	mishless/LearningSystems	1	10247	<filename>a3/ga.py # Genetic Algorithm for solving the Traveling Salesman problem # Authors: <NAME>, <NAME> # Includes import configparser import math import matplotlib.pyplot as plt import numpy import random import sys from operator import itemgetter #Global variables(yay!) # Configuration variables(read from config.txt) mutation_rate = 0; population_size = 0; elitism_rate = 0; tournament_rate = 0; max_iterations = 0; input_file_name = ""; parent_rate = 0; # General global variables cities = {}; number_of_cities = 0; parent_number = 0; tournament_size = 0; elite_number = 0; crossover_number = 0; def read_config(): global mutation_rate; global elitism_rate; global tournament_rate; global population_size; global input_file_name; global max_iterations; global parent_rate; global parent_number; global tournament_size; global elite_number; global crossover_number; config = configparser.ConfigParser(); config.read("config.txt"); mutation_rate = float(config['general']['mutation_rate']); population_size = int(config['general']['population_size']); elitism_rate = float(config['general']['elitism_rate']); tournament_rate = float(config['general']['tournament_rate']); max_iterations = int(config['general']['max_iterations']); parent_rate = float(config['general']['parent_rate']); input_file_name = config['general']['input_file_name']; parent_number = int(population_size * parent_rate); elite_number = int(population_size * elitism_rate); tournament_size = int(population_size * tournament_rate); crossover_number = population_size - elite_number; def print_config(): print("*** CONFIGURATION *"); print_var("Population size", population_size); print_var("Elitism rate", elitism_rate); print_var("Tournament rate", tournament_rate); print_var("Mutation rate", mutation_rate); print_var("Parent rate", parent_rate); print_var("Iteration number", max_iterations); print(""); print_var("Tournament size", tournament_size); print_var("Parent number", parent_number); print_var("Elite number", elite_number); print_var("Crossover number", crossover_number); print(""); def read_input_file(): global number_of_cities; file = open(input_file_name, "r"); file_lines = file.readlines(); file.close(); for file_line in file_lines: temp = file_line.split(); cities[int(temp[0])] = {'x' : float(temp[1]), 'y' : float(temp[2])}; number_of_cities = len(cities); def get_distance(city1, city2): return math.sqrt( ((city1['x']-city2['x'])2) + ((city1['y']-city2['y'])2)); def print_cities(): print("* CITIES **"); for key, city in cities.items(): print("#" + "%2s" % str(key) + ": (" + "%6s" % str(city['x']) + ', ' + "%6s" % str(city['y']) + ')'); print(""); def print_var(name, var): print(name + ":" + " "(17-len(name)) + str(var)); def init(): read_config(); read_input_file(); print_config(); def create_random_individual(): individual = []; # We must begin at first city individual.append(1); # Create list of city indexes indexes = list(range(2,number_of_cities+1)); while len(indexes) > 0: picked_index = random.choice(indexes); indexes.remove(picked_index); individual.append(picked_index); # We must end at first city individual.append(1); return individual; def print_population(population, name): print("*** POPULATION: " + name + " *"); print("Population size = " + str(len(population))); i = 0; for individual in population: print("IND #" + str(i) + ": " + str(individual)); i += 1; def print_population_2(population, name): print("* POPULATION: " + name + " *"); print("Population size = " + str(len(population))); i = 0; for individual in population: print("IND #" + str(i) + " distance = " + str(evaluate_individual(individual))); i += 1; print(""); def print_population_3(population, name): print("* POPULATION: " + name + " *"); print("Population size = " + str(len(population))); for individual in population: print(str(individual) + ": distance = " + str(evaluate_individual(individual))); print(""); def create_random_population(population_size): population = []; for i in range(0, population_size): population.append(create_random_individual()); return population; def evaluate_individual(individual): distance_traveled = 0; for i in range(0, len(individual)-1): distance_traveled = (distance_traveled + get_distance(cities[individual[i]], cities[individual[i+1]])); return distance_traveled; def evaluate_population(population): evaluations = []; for individual in population: evaluations.append((evaluate_individual(individual), individual)); return evaluations; def select_tournament_pool(data): tournament_pool = []; indexes = list(range(0, len(data))); for i in range(0, tournament_size): chosen_index = random.choice(indexes); tournament_pool.append(data[chosen_index]); indexes.remove(chosen_index); return tournament_pool; def best_solution(pool): best_individual = {'eval' : sys.float_info.max}; for individual in pool: if individual['eval'] < best_individual['eval']: best_individual = individual; return best_individual; def run_tournament(pool): return best_solution(pool); def merge_popul_and_eval(population, evaluations): data = []; for i in range(0, len(population)): data.append({'ind' : population[i], 'eval' : evaluations[i]}); return data; def select_parent_pool(population, evaluations): parent_pool = []; data = merge_popul_and_eval(population, evaluations); for i in range(0, parent_number): tournament_pool = select_tournament_pool(data); parent = run_tournament(tournament_pool); parent_pool.append(parent['ind']); data.remove(parent); return parent_pool; def is_individual_valid(individual): if(len(individual) != (number_of_cities+1)): print("INVALID " + str(individual)); return False; if(individual[0] != 1): print("INVALID " + str(individual)); return False; if(individual[-1] != 1): print("INVALID " + str(individual)); return False; for city in individual: if city == 1: if individual.count(city) != 2: print("INVALID " + str(individual)); return False; else: if individual.count(city) != 1: print("INVALID " + str(individual)); return False; return True; def is_population_valid(population): for individual in population: if is_individual_valid(individual) == False: return False; return True; def create_child(parent1, parent2): l = len(parent1); x = random.randint(1, l-1); y = random.randint(x, l-1); child = []; extract = parent1[x:y]; """print_var("P1", parent1); print_var("P2", parent2); print_var("x", x); print_var("y", y); print_var("Extract", extract);""" i = 0; for j in range(0, x): while(parent2[i] in extract): i += 1; child.append(parent2[i]); i += 1; child.extend(extract); for j in range(y, l): while(parent2[i] in extract): i += 1; child.append(parent2[i]); i += 1; return child; def generate_children(parent_pool, child_num): children = []; for i in range(0, child_num): parent1 = random.choice(parent_pool); parent_pool.remove(parent1); parent2 = random.choice(parent_pool); parent_pool.append(parent1); new_child = create_child(parent1, parent2); children.append(new_child); return children; def generate_elites(population, evaluations, number): data = merge_popul_and_eval(population, evaluations); elites = []; for i in range(0, number): best = best_solution(data); elites.append(best['ind']); data.remove(best); return elites; def mutate_individual(individual): i = random.randint(1, len(individual)-2); j = i; while j == i: j = random.randint(1, len(individual)-2); individual[i], individual[j] = individual[j], individual[i]; def mutate_population(population): for individual in population: if random.random() < mutation_rate: mutate_individual(individual); def test_stuff(): """ p1 = "abcdefg"; p2 = "1234567"; for i in range(0,10): print(create_child(p1,p2)); ind = [1,2,3,4,5,6]; print("Before", ind); mutate_individual(ind); print("After", ind); exit();""" def perform_GA(): best_solutions = []; best_individuals = []; best_solution = None; #print("* ALGORITHM START *"); population = create_random_population(population_size); iteration_counter = 1; while True: #print("Running iteration " + str(iteration_counter) + ":"); evaluations = evaluate_population(population); best_solution = min(evaluations, key=lambda evaluation:evaluation[0]) best_solutions.append(best_solution[0]); best_individuals.append(best_solution[1]); evaluations = [evaluation[0] for evaluation in evaluations] if iteration_counter == max_iterations: break; parent_pool = select_parent_pool(population, evaluations); children = generate_children(parent_pool, crossover_number); mutate_population(children); elites = generate_elites(population, evaluations, elite_number); # Prepare population for the next iteration population = children + elites; iteration_counter += 1; if is_population_valid(population) == False: break; return (best_solutions, best_individuals); def do_what_needs_to_be_done(): results = []; bests = []; print("* ALGORITHM START *"); sys.stdout.flush() for i in range(0, 10): print("Starting cycle " + str(i+1)); results.append(perform_GA()); bests.append((results[i][0][-1], results[i][1][-1])); best_ind = bests.index(min(bests, key=lambda best:best[0])); print(str(best_ind)); print("* RESULTS ***"); print("Best result is " + str(bests[best_ind][0])); print("Best result is " + str(bests[best_ind][1])); plt.plot(results[best_ind][0]); plt.show(); #main init(); do_what_needs_to_be_done()	<filename>a3/ga.py # Genetic Algorithm for solving the Traveling Salesman problem # Authors: <NAME>, <NAME> # Includes import configparser import math import matplotlib.pyplot as plt import numpy import random import sys from operator import itemgetter #Global variables(yay!) # Configuration variables(read from config.txt) mutation_rate = 0; population_size = 0; elitism_rate = 0; tournament_rate = 0; max_iterations = 0; input_file_name = ""; parent_rate = 0; # General global variables cities = {}; number_of_cities = 0; parent_number = 0; tournament_size = 0; elite_number = 0; crossover_number = 0; def read_config(): global mutation_rate; global elitism_rate; global tournament_rate; global population_size; global input_file_name; global max_iterations; global parent_rate; global parent_number; global tournament_size; global elite_number; global crossover_number; config = configparser.ConfigParser(); config.read("config.txt"); mutation_rate = float(config['general']['mutation_rate']); population_size = int(config['general']['population_size']); elitism_rate = float(config['general']['elitism_rate']); tournament_rate = float(config['general']['tournament_rate']); max_iterations = int(config['general']['max_iterations']); parent_rate = float(config['general']['parent_rate']); input_file_name = config['general']['input_file_name']; parent_number = int(population_size * parent_rate); elite_number = int(population_size * elitism_rate); tournament_size = int(population_size * tournament_rate); crossover_number = population_size - elite_number; def print_config(): print("*** CONFIGURATION *"); print_var("Population size", population_size); print_var("Elitism rate", elitism_rate); print_var("Tournament rate", tournament_rate); print_var("Mutation rate", mutation_rate); print_var("Parent rate", parent_rate); print_var("Iteration number", max_iterations); print(""); print_var("Tournament size", tournament_size); print_var("Parent number", parent_number); print_var("Elite number", elite_number); print_var("Crossover number", crossover_number); print(""); def read_input_file(): global number_of_cities; file = open(input_file_name, "r"); file_lines = file.readlines(); file.close(); for file_line in file_lines: temp = file_line.split(); cities[int(temp[0])] = {'x' : float(temp[1]), 'y' : float(temp[2])}; number_of_cities = len(cities); def get_distance(city1, city2): return math.sqrt( ((city1['x']-city2['x'])2) + ((city1['y']-city2['y'])2)); def print_cities(): print("* CITIES **"); for key, city in cities.items(): print("#" + "%2s" % str(key) + ": (" + "%6s" % str(city['x']) + ', ' + "%6s" % str(city['y']) + ')'); print(""); def print_var(name, var): print(name + ":" + " "(17-len(name)) + str(var)); def init(): read_config(); read_input_file(); print_config(); def create_random_individual(): individual = []; # We must begin at first city individual.append(1); # Create list of city indexes indexes = list(range(2,number_of_cities+1)); while len(indexes) > 0: picked_index = random.choice(indexes); indexes.remove(picked_index); individual.append(picked_index); # We must end at first city individual.append(1); return individual; def print_population(population, name): print("*** POPULATION: " + name + " *"); print("Population size = " + str(len(population))); i = 0; for individual in population: print("IND #" + str(i) + ": " + str(individual)); i += 1; def print_population_2(population, name): print("* POPULATION: " + name + " *"); print("Population size = " + str(len(population))); i = 0; for individual in population: print("IND #" + str(i) + " distance = " + str(evaluate_individual(individual))); i += 1; print(""); def print_population_3(population, name): print("* POPULATION: " + name + " *"); print("Population size = " + str(len(population))); for individual in population: print(str(individual) + ": distance = " + str(evaluate_individual(individual))); print(""); def create_random_population(population_size): population = []; for i in range(0, population_size): population.append(create_random_individual()); return population; def evaluate_individual(individual): distance_traveled = 0; for i in range(0, len(individual)-1): distance_traveled = (distance_traveled + get_distance(cities[individual[i]], cities[individual[i+1]])); return distance_traveled; def evaluate_population(population): evaluations = []; for individual in population: evaluations.append((evaluate_individual(individual), individual)); return evaluations; def select_tournament_pool(data): tournament_pool = []; indexes = list(range(0, len(data))); for i in range(0, tournament_size): chosen_index = random.choice(indexes); tournament_pool.append(data[chosen_index]); indexes.remove(chosen_index); return tournament_pool; def best_solution(pool): best_individual = {'eval' : sys.float_info.max}; for individual in pool: if individual['eval'] < best_individual['eval']: best_individual = individual; return best_individual; def run_tournament(pool): return best_solution(pool); def merge_popul_and_eval(population, evaluations): data = []; for i in range(0, len(population)): data.append({'ind' : population[i], 'eval' : evaluations[i]}); return data; def select_parent_pool(population, evaluations): parent_pool = []; data = merge_popul_and_eval(population, evaluations); for i in range(0, parent_number): tournament_pool = select_tournament_pool(data); parent = run_tournament(tournament_pool); parent_pool.append(parent['ind']); data.remove(parent); return parent_pool; def is_individual_valid(individual): if(len(individual) != (number_of_cities+1)): print("INVALID " + str(individual)); return False; if(individual[0] != 1): print("INVALID " + str(individual)); return False; if(individual[-1] != 1): print("INVALID " + str(individual)); return False; for city in individual: if city == 1: if individual.count(city) != 2: print("INVALID " + str(individual)); return False; else: if individual.count(city) != 1: print("INVALID " + str(individual)); return False; return True; def is_population_valid(population): for individual in population: if is_individual_valid(individual) == False: return False; return True; def create_child(parent1, parent2): l = len(parent1); x = random.randint(1, l-1); y = random.randint(x, l-1); child = []; extract = parent1[x:y]; """print_var("P1", parent1); print_var("P2", parent2); print_var("x", x); print_var("y", y); print_var("Extract", extract);""" i = 0; for j in range(0, x): while(parent2[i] in extract): i += 1; child.append(parent2[i]); i += 1; child.extend(extract); for j in range(y, l): while(parent2[i] in extract): i += 1; child.append(parent2[i]); i += 1; return child; def generate_children(parent_pool, child_num): children = []; for i in range(0, child_num): parent1 = random.choice(parent_pool); parent_pool.remove(parent1); parent2 = random.choice(parent_pool); parent_pool.append(parent1); new_child = create_child(parent1, parent2); children.append(new_child); return children; def generate_elites(population, evaluations, number): data = merge_popul_and_eval(population, evaluations); elites = []; for i in range(0, number): best = best_solution(data); elites.append(best['ind']); data.remove(best); return elites; def mutate_individual(individual): i = random.randint(1, len(individual)-2); j = i; while j == i: j = random.randint(1, len(individual)-2); individual[i], individual[j] = individual[j], individual[i]; def mutate_population(population): for individual in population: if random.random() < mutation_rate: mutate_individual(individual); def test_stuff(): """ p1 = "abcdefg"; p2 = "1234567"; for i in range(0,10): print(create_child(p1,p2)); ind = [1,2,3,4,5,6]; print("Before", ind); mutate_individual(ind); print("After", ind); exit();""" def perform_GA(): best_solutions = []; best_individuals = []; best_solution = None; #print("* ALGORITHM START *"); population = create_random_population(population_size); iteration_counter = 1; while True: #print("Running iteration " + str(iteration_counter) + ":"); evaluations = evaluate_population(population); best_solution = min(evaluations, key=lambda evaluation:evaluation[0]) best_solutions.append(best_solution[0]); best_individuals.append(best_solution[1]); evaluations = [evaluation[0] for evaluation in evaluations] if iteration_counter == max_iterations: break; parent_pool = select_parent_pool(population, evaluations); children = generate_children(parent_pool, crossover_number); mutate_population(children); elites = generate_elites(population, evaluations, elite_number); # Prepare population for the next iteration population = children + elites; iteration_counter += 1; if is_population_valid(population) == False: break; return (best_solutions, best_individuals); def do_what_needs_to_be_done(): results = []; bests = []; print("* ALGORITHM START *"); sys.stdout.flush() for i in range(0, 10): print("Starting cycle " + str(i+1)); results.append(perform_GA()); bests.append((results[i][0][-1], results[i][1][-1])); best_ind = bests.index(min(bests, key=lambda best:best[0])); print(str(best_ind)); print("* RESULTS ***"); print("Best result is " + str(bests[best_ind][0])); print("Best result is " + str(bests[best_ind][1])); plt.plot(results[best_ind][0]); plt.show(); #main init(); do_what_needs_to_be_done()	en	0.516051	# Genetic Algorithm for solving the Traveling Salesman problem # Authors: <NAME>, <NAME> # Includes #Global variables(yay!) # Configuration variables(read from config.txt) # General global variables # We must begin at first city # Create list of city indexes # We must end at first city #" + str(i) + ": " + str(individual)); #" + str(i) + " distance = " + print_var("P1", parent1); print_var("P2", parent2); print_var("x", x); print_var("y", y); print_var("Extract", extract); p1 = "abcdefg"; p2 = "1234567"; for i in range(0,10): print(create_child(p1,p2)); ind = [1,2,3,4,5,6]; print("Before", ind); mutate_individual(ind); print("After", ind); exit(); #print("*** ALGORITHM START ***"); #print("Running iteration " + str(iteration_counter) + ":"); # Prepare population for the next iteration #main	2.929989	3
products/migrations/0010_remove_product_updated_at.py	UB-ES-2021-A1/wannasell-backend	0	10248	# Generated by Django 3.2.8 on 2021-11-25 17:50 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('products', '0009_auto_20211125_1846'), ] operations = [ migrations.RemoveField( model_name='product', name='updated_at', ), ]	# Generated by Django 3.2.8 on 2021-11-25 17:50 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('products', '0009_auto_20211125_1846'), ] operations = [ migrations.RemoveField( model_name='product', name='updated_at', ), ]	en	0.882256	# Generated by Django 3.2.8 on 2021-11-25 17:50	1.295391	1
ResumeAnalyser/apps.py	samyakj2307/recruitai_resume_backend	0	10249	<gh_stars>0 from django.apps import AppConfig class ResumeanalyserConfig(AppConfig): default_auto_field = 'django.db.models.BigAutoField' name = 'ResumeAnalyser'	from django.apps import AppConfig class ResumeanalyserConfig(AppConfig): default_auto_field = 'django.db.models.BigAutoField' name = 'ResumeAnalyser'	none	1		1.263755	1
plugins/core/player_manager_plugin/__init__.py	StarryPy/StarryPy-Historic	38	10250	<filename>plugins/core/player_manager_plugin/__init__.py<gh_stars>10-100 from plugins.core.player_manager_plugin.plugin import PlayerManagerPlugin from plugins.core.player_manager_plugin.manager import ( Banned, UserLevels, permissions, PlayerManager )	<filename>plugins/core/player_manager_plugin/__init__.py<gh_stars>10-100 from plugins.core.player_manager_plugin.plugin import PlayerManagerPlugin from plugins.core.player_manager_plugin.manager import ( Banned, UserLevels, permissions, PlayerManager )	none	1		1.208188	1
src/config/svc-monitor/svc_monitor/services/loadbalancer/drivers/ha_proxy/custom_attributes/haproxy_validator.py	jnpr-pranav/contrail-controller	37	10251	from builtins import str from builtins import range from builtins import object import logging import inspect import os class CustomAttr(object): """This type handles non-flat data-types like int, str, bool. """ def __init__(self, key, value): self._value = value self._key = key def validate(self): pass def post_validation(self): pass class CustomAttrTlsContainer(CustomAttr): def __init__(self, key, value): super(CustomAttrTlsContainer, self).__init__(key, value) def validate(self): return True def post_validation(self): return self._value def validate_custom_attributes(custom_attributes_dict, section, custom_attributes): section_dict = {} if custom_attributes and section in custom_attributes_dict: for key, value in list(custom_attributes.items()): if key in custom_attributes_dict[section]: #Sanitize the value try: type_attr = custom_attributes_dict[section][key]['type'] limits = custom_attributes_dict[section][key]['limits'] if type_attr == 'int': value = int(value) if value in range(limits[0], limits[1]): section_dict.update({key:value}) else: logging.info("Skipping key: %s, value: %s due to" \ "validation failure" % (key, value)) elif type_attr == 'str': if len(value) in range(limits[0], limits[1]): section_dict.update({key:value}) else: logging.info("Skipping key: %s, value: %s due to" \ "validation failure" % (key, value)) elif type_attr == 'bool': if value in limits: if value == 'True': value = '' elif value == 'False': value = 'no ' section_dict.update({key:value}) else: logging.info("Skipping key: %s, value: %s due to" \ "validation failure" % (key, value)) elif inspect.isclass(eval(type_attr)): new_custom_attr = eval(type_attr)(key, value) if new_custom_attr.validate(): value = new_custom_attr.post_validation() section_dict.update({key:value}) else: logging.info("Skipping key: %s, value: %s due to" \ "validation failure" % (key, value)) except Exception as e: logging.error(str(e)) continue return section_dict	from builtins import str from builtins import range from builtins import object import logging import inspect import os class CustomAttr(object): """This type handles non-flat data-types like int, str, bool. """ def __init__(self, key, value): self._value = value self._key = key def validate(self): pass def post_validation(self): pass class CustomAttrTlsContainer(CustomAttr): def __init__(self, key, value): super(CustomAttrTlsContainer, self).__init__(key, value) def validate(self): return True def post_validation(self): return self._value def validate_custom_attributes(custom_attributes_dict, section, custom_attributes): section_dict = {} if custom_attributes and section in custom_attributes_dict: for key, value in list(custom_attributes.items()): if key in custom_attributes_dict[section]: #Sanitize the value try: type_attr = custom_attributes_dict[section][key]['type'] limits = custom_attributes_dict[section][key]['limits'] if type_attr == 'int': value = int(value) if value in range(limits[0], limits[1]): section_dict.update({key:value}) else: logging.info("Skipping key: %s, value: %s due to" \ "validation failure" % (key, value)) elif type_attr == 'str': if len(value) in range(limits[0], limits[1]): section_dict.update({key:value}) else: logging.info("Skipping key: %s, value: %s due to" \ "validation failure" % (key, value)) elif type_attr == 'bool': if value in limits: if value == 'True': value = '' elif value == 'False': value = 'no ' section_dict.update({key:value}) else: logging.info("Skipping key: %s, value: %s due to" \ "validation failure" % (key, value)) elif inspect.isclass(eval(type_attr)): new_custom_attr = eval(type_attr)(key, value) if new_custom_attr.validate(): value = new_custom_attr.post_validation() section_dict.update({key:value}) else: logging.info("Skipping key: %s, value: %s due to" \ "validation failure" % (key, value)) except Exception as e: logging.error(str(e)) continue return section_dict	en	0.725463	This type handles non-flat data-types like int, str, bool. #Sanitize the value	2.978925	3
python/janitor/typecache.py	monkeyman79/janitor	2	10252	import gdb class TypeCache(object): def __init__(self): self.cache = {} self.intptr_type = False def clear(self): self.cache = {} self.intptr_type = False def get_type(self, typename): if typename in self.cache: return self.cache[typename] try: gdb_type = gdb.lookup_type(typename) self.cache[typename] = gdb_type return gdb_type except: pass try: proto = gdb.parse_and_eval("(%s)0" % typename) gdb_type = proto.type.target() self.cache[typename] = gdb_type return gdb_type except: pass return None def get_intptr_type(self): if self.intptr_type != False: return self.intptr_type ptr_type = self.get_type("void") if ptr_type == None: self.intptr_type = None return None ulong_type = self.get_type("unsigned long") if ulong_type == None: self.intptr_type = None return None if ulong_type.sizeof >= ptr_type.sizeof: self.intptr_type = ulong_type return ulong_type ullong_type = self.get_type("unsigned long long") self.intptr_type = ullong_type return ullong_type cache = TypeCache()	import gdb class TypeCache(object): def __init__(self): self.cache = {} self.intptr_type = False def clear(self): self.cache = {} self.intptr_type = False def get_type(self, typename): if typename in self.cache: return self.cache[typename] try: gdb_type = gdb.lookup_type(typename) self.cache[typename] = gdb_type return gdb_type except: pass try: proto = gdb.parse_and_eval("(%s)0" % typename) gdb_type = proto.type.target() self.cache[typename] = gdb_type return gdb_type except: pass return None def get_intptr_type(self): if self.intptr_type != False: return self.intptr_type ptr_type = self.get_type("void") if ptr_type == None: self.intptr_type = None return None ulong_type = self.get_type("unsigned long") if ulong_type == None: self.intptr_type = None return None if ulong_type.sizeof >= ptr_type.sizeof: self.intptr_type = ulong_type return ulong_type ullong_type = self.get_type("unsigned long long") self.intptr_type = ullong_type return ullong_type cache = TypeCache()	none	1		2.447621	2
key_phrase.py	Santara/autoSLR	1	10253	import os import sys directory = sys.argv[1] outfile = open("key_phrases.csv","w") files = {} for filename in os.listdir(directory): text=[] with open(os.path.join(directory, filename)) as f: text=[l.strip() for l in f if len(l.strip())>2] data='' for t in text: if len(t.split()) > 1: data = data+'. '+t.strip() whitelist = set('abcdefghijklmnopqrstuvwxy ABCDEFGHIJKLMNOPQRSTUVWXYZ') answer = ''.join(filter(whitelist.__contains__, data)) answer=' '.join(answer.split()) import rake import operator rake_object = rake.Rake("/home/ashutosh/Sudeshna/RAKE-tutorial/data/stoplists/SmartStoplist.txt", 3,3,1) import pprint pp = pprint.PrettyPrinter() keywords = rake_object.run(answer) for entry in keywords: outfile.write("%s, %s\n" % (entry[0], str(entry[1])) ) outfile.close()	import os import sys directory = sys.argv[1] outfile = open("key_phrases.csv","w") files = {} for filename in os.listdir(directory): text=[] with open(os.path.join(directory, filename)) as f: text=[l.strip() for l in f if len(l.strip())>2] data='' for t in text: if len(t.split()) > 1: data = data+'. '+t.strip() whitelist = set('abcdefghijklmnopqrstuvwxy ABCDEFGHIJKLMNOPQRSTUVWXYZ') answer = ''.join(filter(whitelist.__contains__, data)) answer=' '.join(answer.split()) import rake import operator rake_object = rake.Rake("/home/ashutosh/Sudeshna/RAKE-tutorial/data/stoplists/SmartStoplist.txt", 3,3,1) import pprint pp = pprint.PrettyPrinter() keywords = rake_object.run(answer) for entry in keywords: outfile.write("%s, %s\n" % (entry[0], str(entry[1])) ) outfile.close()	none	1		2.646277	3
tests/test_utils.py	aced-differentiate/dft-input-gen	1	10254	"""Unit tests for helper utilities in :mod:`dftinputgen.utils`.""" import os import pytest from ase import io as ase_io from dftinputgen.utils import get_elem_symbol from dftinputgen.utils import read_crystal_structure from dftinputgen.utils import get_kpoint_grid_from_spacing from dftinputgen.utils import DftInputGeneratorUtilsError test_base_dir = os.path.dirname(__file__) feo_conv_file = os.path.join(test_base_dir, "qe", "files", "feo_conv.vasp") feo_conv = ase_io.read(feo_conv_file) def test_get_elem_symbol(): assert get_elem_symbol("Fe-34") == "Fe" assert get_elem_symbol("3RGe-34") == "Ge" with pytest.raises(DftInputGeneratorUtilsError): get_elem_symbol("G23") def test_read_crystal_structure(): # str with path to crystal structure file is OK cs = read_crystal_structure(feo_conv_file) assert cs == feo_conv # any other type of input should throw an error with pytest.raises(TypeError): read_crystal_structure(feo_conv) def test_kpoint_grid_from_spacing(): assert get_kpoint_grid_from_spacing(feo_conv, 0.2) == pytest.approx( [7, 7, 7] )	"""Unit tests for helper utilities in :mod:`dftinputgen.utils`.""" import os import pytest from ase import io as ase_io from dftinputgen.utils import get_elem_symbol from dftinputgen.utils import read_crystal_structure from dftinputgen.utils import get_kpoint_grid_from_spacing from dftinputgen.utils import DftInputGeneratorUtilsError test_base_dir = os.path.dirname(__file__) feo_conv_file = os.path.join(test_base_dir, "qe", "files", "feo_conv.vasp") feo_conv = ase_io.read(feo_conv_file) def test_get_elem_symbol(): assert get_elem_symbol("Fe-34") == "Fe" assert get_elem_symbol("3RGe-34") == "Ge" with pytest.raises(DftInputGeneratorUtilsError): get_elem_symbol("G23") def test_read_crystal_structure(): # str with path to crystal structure file is OK cs = read_crystal_structure(feo_conv_file) assert cs == feo_conv # any other type of input should throw an error with pytest.raises(TypeError): read_crystal_structure(feo_conv) def test_kpoint_grid_from_spacing(): assert get_kpoint_grid_from_spacing(feo_conv, 0.2) == pytest.approx( [7, 7, 7] )	en	0.726543	Unit tests for helper utilities in :mod:`dftinputgen.utils`. # str with path to crystal structure file is OK # any other type of input should throw an error	2.515499	3
core/models.py	nforesperance/Django-Channels-ChatApp	2	10255	from django.contrib.auth.models import User from django.db.models import (Model, TextField, DateTimeField, ForeignKey, CASCADE) from asgiref.sync import async_to_sync from channels.layers import get_channel_layer from django.db import models import json class MessageModel(Model): """ This class represents a chat message. It has a owner (user), timestamp and the message body. """ user = ForeignKey(User, on_delete=CASCADE, verbose_name='user', related_name='from_user', db_index=True) recipient = ForeignKey(User, on_delete=CASCADE, verbose_name='recipient', related_name='to_user', db_index=True) timestamp = DateTimeField('timestamp', auto_now_add=True, editable=False, db_index=True) body = TextField('body') def __str__(self): return str(self.id) def characters(self): """ Toy function to count body characters. :return: body's char number """ return len(self.body) def notify_ws_clients(self): """ Inform client there is a new message. """ notification = { 'type': 'chat_message', 'message': '{}'.format(self.id) } channel_layer = get_channel_layer() print("user.id {}".format(self.user.id)) print("user.id {}".format(self.recipient.id)) async_to_sync(channel_layer.group_send)("{}".format(self.user.id), notification) async_to_sync(channel_layer.group_send)("{}".format(self.recipient.id), notification) def save(self, args, kwargs): """ Trims white spaces, saves the message and notifies the recipient via WS if the message is new. """ new = self.id self.body = self.body.strip() # Trimming whitespaces from the body super(MessageModel, self).save(args, *kwargs) if new is None: self.notify_ws_clients() # Meta class Meta: app_label = 'core' verbose_name = 'message' verbose_name_plural = 'messages' ordering = ('-timestamp',) class Group(models.Model): name = models.CharField(max_length = 20) members = models.TextField() messages = models.TextField () def set_members(self,user_id_list): self.members = json.dumps(user_id_list) def get_members(self): return json.loads(self.members) def add(self,user_id): current_list = self.get_members() if user_id in current_list: print("user is already in the group") else: new_list = current_list.append(user_id) self.set_members(new_list) def remove(self,user_id): current_list = self.get_members() if user_id in current_list: new_list = current_list.remove(user_id) self.set_members(new_list) else: print("User is not a member of theis group") def has(self,user_id): current_list = self.get_members() return(user_id in current_list) # Set of functions for dealing with group messages def set_messages(self,message_id_list): self.messages = json.dumps(message_id_list) def get_messages(self): return json.loads(self.messages) def add_message(self,message_id): current_list = self.get_messages() new_list = current_list.append(message_id) self.set_messages(new_list) def delete_message(self,message_id): current_list = self.get_messages() if message_id in current_list: new_list = current_list.remove(message_id) self.set_messages(new_list) def save(self, args, *kwargs): if self.pk is None or self.members is None or self.members == '': self.set_members([]) if self.pk is None or self.messages is None or self.messages == '': self.set_messages([]) super(Group, self).save(args, *kwargs) def __str__(self): return self.name+" ID: "+str(self.id) # Meta class Meta: app_label = 'core' verbose_name = 'Group' verbose_name_plural = 'Groups' ordering = ('name',) class GroupMessage(Model): """ This class represents a chat message. It has a owner (user), timestamp and the message body. """ sender = ForeignKey(User, on_delete=CASCADE, verbose_name='sender', related_name='from_sender', db_index=True) group = ForeignKey(Group, on_delete=CASCADE, verbose_name='group', related_name='to_group', db_index=True) time = DateTimeField('time', auto_now_add=True, editable=False, db_index=True) body = TextField('body') def __str__(self): return str(self.id) def characters(self): """ Toy function to count body characters. :return: body's char number """ return len(self.body) def notify_ws_clients(self): """ Inform client there is a new message. """ notification = { 'type': 'group_message', 'group': '{}'.format(self.id) } channel_layer = get_channel_layer() group_id = "group"+str(self.group.id) print("group.id {}".format(group_id)) async_to_sync(channel_layer.group_send)(group_id, notification) def save(self, args, *kwargs): """ Trims white spaces, saves the message and notifies the recipient via WS if the message is new. """ new = self.id self.body = self.body.strip() # Trimming whitespaces from the body super(GroupMessage, self).save(args, **kwargs) if new is None: self.notify_ws_clients() # Meta class Meta: app_label = 'core' verbose_name = 'group message' verbose_name_plural = 'group messags' ordering = ('-time',)	from django.contrib.auth.models import User from django.db.models import (Model, TextField, DateTimeField, ForeignKey, CASCADE) from asgiref.sync import async_to_sync from channels.layers import get_channel_layer from django.db import models import json class MessageModel(Model): """ This class represents a chat message. It has a owner (user), timestamp and the message body. """ user = ForeignKey(User, on_delete=CASCADE, verbose_name='user', related_name='from_user', db_index=True) recipient = ForeignKey(User, on_delete=CASCADE, verbose_name='recipient', related_name='to_user', db_index=True) timestamp = DateTimeField('timestamp', auto_now_add=True, editable=False, db_index=True) body = TextField('body') def __str__(self): return str(self.id) def characters(self): """ Toy function to count body characters. :return: body's char number """ return len(self.body) def notify_ws_clients(self): """ Inform client there is a new message. """ notification = { 'type': 'chat_message', 'message': '{}'.format(self.id) } channel_layer = get_channel_layer() print("user.id {}".format(self.user.id)) print("user.id {}".format(self.recipient.id)) async_to_sync(channel_layer.group_send)("{}".format(self.user.id), notification) async_to_sync(channel_layer.group_send)("{}".format(self.recipient.id), notification) def save(self, args, kwargs): """ Trims white spaces, saves the message and notifies the recipient via WS if the message is new. """ new = self.id self.body = self.body.strip() # Trimming whitespaces from the body super(MessageModel, self).save(args, *kwargs) if new is None: self.notify_ws_clients() # Meta class Meta: app_label = 'core' verbose_name = 'message' verbose_name_plural = 'messages' ordering = ('-timestamp',) class Group(models.Model): name = models.CharField(max_length = 20) members = models.TextField() messages = models.TextField () def set_members(self,user_id_list): self.members = json.dumps(user_id_list) def get_members(self): return json.loads(self.members) def add(self,user_id): current_list = self.get_members() if user_id in current_list: print("user is already in the group") else: new_list = current_list.append(user_id) self.set_members(new_list) def remove(self,user_id): current_list = self.get_members() if user_id in current_list: new_list = current_list.remove(user_id) self.set_members(new_list) else: print("User is not a member of theis group") def has(self,user_id): current_list = self.get_members() return(user_id in current_list) # Set of functions for dealing with group messages def set_messages(self,message_id_list): self.messages = json.dumps(message_id_list) def get_messages(self): return json.loads(self.messages) def add_message(self,message_id): current_list = self.get_messages() new_list = current_list.append(message_id) self.set_messages(new_list) def delete_message(self,message_id): current_list = self.get_messages() if message_id in current_list: new_list = current_list.remove(message_id) self.set_messages(new_list) def save(self, args, *kwargs): if self.pk is None or self.members is None or self.members == '': self.set_members([]) if self.pk is None or self.messages is None or self.messages == '': self.set_messages([]) super(Group, self).save(args, *kwargs) def __str__(self): return self.name+" ID: "+str(self.id) # Meta class Meta: app_label = 'core' verbose_name = 'Group' verbose_name_plural = 'Groups' ordering = ('name',) class GroupMessage(Model): """ This class represents a chat message. It has a owner (user), timestamp and the message body. """ sender = ForeignKey(User, on_delete=CASCADE, verbose_name='sender', related_name='from_sender', db_index=True) group = ForeignKey(Group, on_delete=CASCADE, verbose_name='group', related_name='to_group', db_index=True) time = DateTimeField('time', auto_now_add=True, editable=False, db_index=True) body = TextField('body') def __str__(self): return str(self.id) def characters(self): """ Toy function to count body characters. :return: body's char number """ return len(self.body) def notify_ws_clients(self): """ Inform client there is a new message. """ notification = { 'type': 'group_message', 'group': '{}'.format(self.id) } channel_layer = get_channel_layer() group_id = "group"+str(self.group.id) print("group.id {}".format(group_id)) async_to_sync(channel_layer.group_send)(group_id, notification) def save(self, args, *kwargs): """ Trims white spaces, saves the message and notifies the recipient via WS if the message is new. """ new = self.id self.body = self.body.strip() # Trimming whitespaces from the body super(GroupMessage, self).save(args, **kwargs) if new is None: self.notify_ws_clients() # Meta class Meta: app_label = 'core' verbose_name = 'group message' verbose_name_plural = 'group messags' ordering = ('-time',)	en	0.771246	This class represents a chat message. It has a owner (user), timestamp and the message body. Toy function to count body characters. :return: body's char number Inform client there is a new message. Trims white spaces, saves the message and notifies the recipient via WS if the message is new. # Trimming whitespaces from the body # Meta # Set of functions for dealing with group messages # Meta This class represents a chat message. It has a owner (user), timestamp and the message body. Toy function to count body characters. :return: body's char number Inform client there is a new message. Trims white spaces, saves the message and notifies the recipient via WS if the message is new. # Trimming whitespaces from the body # Meta	2.333341	2
backup/model.py	jsikyoon/ASNP-RMR	8	10256	<reponame>jsikyoon/ASNP-RMR import tensorflow as tf import numpy as np # utility methods def batch_mlp(input, output_sizes, variable_scope): """Apply MLP to the final axis of a 3D tensor (reusing already defined MLPs). Args: input: input tensor of shape [B,n,d_in]. output_sizes: An iterable containing the output sizes of the MLP as defined in `basic.Linear`. variable_scope: String giving the name of the variable scope. If this is set to be the same as a previously defined MLP, then the weights are reused. Returns: tensor of shape [B,n,d_out] where d_out=output_sizes[-1] """ # Get the shapes of the input and reshape to parallelise across observations batch_size, _, filter_size = input.shape.as_list() output = tf.reshape(input, (-1, filter_size)) output.set_shape((None, filter_size)) # Pass through MLP with tf.variable_scope(variable_scope, reuse=tf.AUTO_REUSE): for i, size in enumerate(output_sizes[:-1]): output = tf.nn.relu( tf.layers.dense(output, size, name="layer_{}".format(i))) # Last layer without a ReLu output = tf.layers.dense( output, output_sizes[-1], name="layer_{}".format(i + 1)) # Bring back into original shape output = tf.reshape(output, (batch_size, -1, output_sizes[-1])) return output class DeterministicEncoder(object): """The Deterministic Encoder.""" def __init__(self, output_sizes, attention): """(A)NP deterministic encoder. Args: output_sizes: An iterable containing the output sizes of the encoding MLP. attention: The attention module. """ self._output_sizes = output_sizes self._attention = attention def __call__(self, context_x, context_y, target_x): """Encodes the inputs into one representation. Args: context_x: Tensor of shape [B,observations,d_x]. For this 1D regression task this corresponds to the x-values. context_y: Tensor of shape [B,observations,d_y]. For this 1D regression task this corresponds to the y-values. target_x: Tensor of shape [B,target_observations,d_x]. For this 1D regression task this corresponds to the x-values. Returns: The encoded representation. Tensor of shape [B,target_observations,d] """ # Concatenate x and y along the filter axes encoder_input = tf.concat([context_x, context_y], axis=-1) # Pass final axis through MLP hidden = batch_mlp(encoder_input, self._output_sizes, "deterministic_encoder") # Apply attention with tf.variable_scope("deterministic_encoder", reuse=tf.AUTO_REUSE): hidden = self._attention(context_x, target_x, hidden) return hidden class LatentEncoder(object): """The Latent Encoder.""" def __init__(self, output_sizes, num_latents): """(A)NP latent encoder. Args: output_sizes: An iterable containing the output sizes of the encoding MLP. num_latents: The latent dimensionality. """ self._output_sizes = output_sizes self._num_latents = num_latents def __call__(self, x, y): """Encodes the inputs into one representation. Args: x: Tensor of shape [B,observations,d_x]. For this 1D regression task this corresponds to the x-values. y: Tensor of shape [B,observations,d_y]. For this 1D regression task this corresponds to the y-values. Returns: A normal distribution over tensors of shape [B, num_latents] """ # Concatenate x and y along the filter axes encoder_input = tf.concat([x, y], axis=-1) # Pass final axis through MLP hidden = batch_mlp(encoder_input, self._output_sizes, "latent_encoder") # Aggregator: take the mean over all points hidden = tf.reduce_mean(hidden, axis=1) # Have further MLP layers that map to the parameters of the Gaussian latent with tf.variable_scope("latent_encoder", reuse=tf.AUTO_REUSE): # First apply intermediate relu layer hidden = tf.nn.relu( tf.layers.dense(hidden, (self._output_sizes[-1] + self._num_latents)/2, name="penultimate_layer")) # Then apply further linear layers to output latent mu and log sigma mu = tf.layers.dense(hidden, self._num_latents, name="mean_layer") log_sigma = tf.layers.dense(hidden, self._num_latents, name="std_layer") # Compute sigma sigma = 0.1 + 0.9 * tf.sigmoid(log_sigma) return tf.contrib.distributions.Normal(loc=mu, scale=sigma) class Decoder(object): """The Decoder.""" def __init__(self, output_sizes): """(A)NP decoder. Args: output_sizes: An iterable containing the output sizes of the decoder MLP as defined in `basic.Linear`. """ self._output_sizes = output_sizes def __call__(self, representation, target_x): """Decodes the individual targets. Args: representation: The representation of the context for target predictions. Tensor of shape [B,target_observations,?]. target_x: The x locations for the target query. Tensor of shape [B,target_observations,d_x]. Returns: dist: A multivariate Gaussian over the target points. A distribution over tensors of shape [B,target_observations,d_y]. mu: The mean of the multivariate Gaussian. Tensor of shape [B,target_observations,d_x]. sigma: The standard deviation of the multivariate Gaussian. Tensor of shape [B,target_observations,d_x]. """ # concatenate target_x and representation hidden = tf.concat([representation, target_x], axis=-1) # Pass final axis through MLP hidden = batch_mlp(hidden, self._output_sizes, "decoder") # Get the mean an the variance mu, log_sigma = tf.split(hidden, 2, axis=-1) # Bound the variance sigma = 0.1 + 0.9 * tf.nn.softplus(log_sigma) # Get the distribution dist = tf.contrib.distributions.MultivariateNormalDiag( loc=mu, scale_diag=sigma) return dist, mu, sigma class LatentModel(object): """The (A)NP model.""" def __init__(self, latent_encoder_output_sizes, num_latents, decoder_output_sizes, use_deterministic_path=True, deterministic_encoder_output_sizes=None, attention=None): """Initialises the model. Args: latent_encoder_output_sizes: An iterable containing the sizes of hidden layers of the latent encoder. num_latents: The latent dimensionality. decoder_output_sizes: An iterable containing the sizes of hidden layers of the decoder. The last element should correspond to d_y * 2 (it encodes both mean and variance concatenated) use_deterministic_path: a boolean that indicates whether the deterministic encoder is used or not. deterministic_encoder_output_sizes: An iterable containing the sizes of hidden layers of the deterministic encoder. The last one is the size of the deterministic representation r. attention: The attention module used in the deterministic encoder. Only relevant when use_deterministic_path=True. """ self._latent_encoder = LatentEncoder(latent_encoder_output_sizes, num_latents) self._decoder = Decoder(decoder_output_sizes) self._use_deterministic_path = use_deterministic_path if use_deterministic_path: self._deterministic_encoder = DeterministicEncoder( deterministic_encoder_output_sizes, attention) def __call__(self, query, num_targets, target_y=None): """Returns the predicted mean and variance at the target points. Args: query: Array containing ((context_x, context_y), target_x) where: context_x: Tensor of shape [B,num_contexts,d_x]. Contains the x values of the context points. context_y: Tensor of shape [B,num_contexts,d_y]. Contains the y values of the context points. target_x: Tensor of shape [B,num_targets,d_x]. Contains the x values of the target points. num_targets: Number of target points. target_y: The ground truth y values of the target y. Tensor of shape [B,num_targets,d_y]. Returns: log_p: The log_probability of the target_y given the predicted distribution. Tensor of shape [B,num_targets]. mu: The mean of the predicted distribution. Tensor of shape [B,num_targets,d_y]. sigma: The variance of the predicted distribution. Tensor of shape [B,num_targets,d_y]. """ (context_x, context_y), target_x = query # Pass query through the encoder and the decoder prior = self._latent_encoder(context_x, context_y) # For training, when target_y is available, use targets for latent encoder. # Note that targets contain contexts by design. if target_y is None: latent_rep = prior.sample() # For testing, when target_y unavailable, use contexts for latent encoder. else: posterior = self._latent_encoder(target_x, target_y) latent_rep = posterior.sample() latent_rep = tf.tile(tf.expand_dims(latent_rep, axis=1), [1, num_targets, 1]) if self._use_deterministic_path: deterministic_rep = self._deterministic_encoder(context_x, context_y, target_x) representation = tf.concat([deterministic_rep, latent_rep], axis=-1) else: representation = latent_rep dist, mu, sigma = self._decoder(representation, target_x) # If we want to calculate the log_prob for training we will make use of the # target_y. At test time the target_y is not available so we return None. if target_y is not None: log_p = dist.log_prob(target_y) posterior = self._latent_encoder(target_x, target_y) kl = tf.reduce_sum( tf.contrib.distributions.kl_divergence(posterior, prior), axis=-1, keepdims=True) kl = tf.tile(kl, [1, num_targets]) loss = - tf.reduce_mean(log_p - kl / tf.cast(num_targets, tf.float32)) else: log_p = None kl = None loss = None return mu, sigma, log_p, kl, loss def uniform_attention(q, v): """Uniform attention. Equivalent to np. Args: q: queries. tensor of shape [B,m,d_k]. v: values. tensor of shape [B,n,d_v]. Returns: tensor of shape [B,m,d_v]. """ total_points = tf.shape(q)[1] rep = tf.reduce_mean(v, axis=1, keepdims=True) # [B,1,d_v] rep = tf.tile(rep, [1, total_points, 1]) return rep def laplace_attention(q, k, v, scale, normalise): """Computes laplace exponential attention. Args: q: queries. tensor of shape [B,m,d_k]. k: keys. tensor of shape [B,n,d_k]. v: values. tensor of shape [B,n,d_v]. scale: float that scales the L1 distance. normalise: Boolean that determines whether weights sum to 1. Returns: tensor of shape [B,m,d_v]. """ k = tf.expand_dims(k, axis=1) # [B,1,n,d_k] q = tf.expand_dims(q, axis=2) # [B,m,1,d_k] unnorm_weights = - tf.abs((k - q) / scale) # [B,m,n,d_k] unnorm_weights = tf.reduce_sum(unnorm_weights, axis=-1) # [B,m,n] if normalise: weight_fn = tf.nn.softmax else: weight_fn = lambda x: 1 + tf.tanh(x) weights = weight_fn(unnorm_weights) # [B,m,n] rep = tf.einsum('bik,bkj->bij', weights, v) # [B,m,d_v] return rep def dot_product_attention(q, k, v, normalise): """Computes dot product attention. Args: q: queries. tensor of shape [B,m,d_k]. k: keys. tensor of shape [B,n,d_k]. v: values. tensor of shape [B,n,d_v]. normalise: Boolean that determines whether weights sum to 1. Returns: tensor of shape [B,m,d_v]. """ d_k = tf.shape(q)[-1] scale = tf.sqrt(tf.cast(d_k, tf.float32)) unnorm_weights = tf.einsum('bjk,bik->bij', k, q) / scale # [B,m,n] if normalise: weight_fn = tf.nn.softmax else: weight_fn = tf.sigmoid weights = weight_fn(unnorm_weights) # [B,m,n] rep = tf.einsum('bik,bkj->bij', weights, v) # [B,m,d_v] return rep def multihead_attention(q, k, v, num_heads=8): """Computes multi-head attention. Args: q: queries. tensor of shape [B,m,d_k]. k: keys. tensor of shape [B,n,d_k]. v: values. tensor of shape [B,n,d_v]. num_heads: number of heads. Should divide d_v. Returns: tensor of shape [B,m,d_v]. """ d_k = q.get_shape().as_list()[-1] d_v = v.get_shape().as_list()[-1] head_size = d_v / num_heads key_initializer = tf.random_normal_initializer(stddev=d_k-0.5) value_initializer = tf.random_normal_initializer(stddev=d_v-0.5) rep = tf.constant(0.0) for h in range(num_heads): o = dot_product_attention( tf.layers.Conv1D(head_size, 1, kernel_initializer=key_initializer, name='wq%d' % h, use_bias=False, padding='VALID')(q), tf.layers.Conv1D(head_size, 1, kernel_initializer=key_initializer, name='wk%d' % h, use_bias=False, padding='VALID')(k), tf.layers.Conv1D(head_size, 1, kernel_initializer=key_initializer, name='wv%d' % h, use_bias=False, padding='VALID')(v), normalise=True) rep += tf.layers.Conv1D(d_v, 1, kernel_initializer=value_initializer, name='wo%d' % h, use_bias=False, padding='VALID')(o) return rep class Attention(object): """The Attention module.""" def __init__(self, rep, output_sizes, att_type, scale=1., normalise=True, num_heads=8): """Create attention module. Takes in context inputs, target inputs and representations of each context input/output pair to output an aggregated representation of the context data. Args: rep: transformation to apply to contexts before computing attention. One of: ['identity','mlp']. output_sizes: list of number of hidden units per layer of mlp. Used only if rep == 'mlp'. att_type: type of attention. One of the following: ['uniform','laplace','dot_product','multihead'] scale: scale of attention. normalise: Boolean determining whether to: 1. apply softmax to weights so that they sum to 1 across context pts or 2. apply custom transformation to have weights in [0,1]. num_heads: number of heads for multihead. """ self._rep = rep self._output_sizes = output_sizes self._type = att_type self._scale = scale self._normalise = normalise if self._type == 'multihead': self._num_heads = num_heads def __call__(self, x1, x2, r): """Apply attention to create aggregated representation of r. Args: x1: tensor of shape [B,n1,d_x]. x2: tensor of shape [B,n2,d_x]. r: tensor of shape [B,n1,d]. Returns: tensor of shape [B,n2,d] Raises: NameError: The argument for rep/type was invalid. """ if self._rep == 'identity': k, q = (x1, x2) elif self._rep == 'mlp': # Pass through MLP k = batch_mlp(x1, self._output_sizes, "attention") q = batch_mlp(x2, self._output_sizes, "attention") else: raise NameError("'rep' not among ['identity','mlp']") if self._type == 'uniform': rep = uniform_attention(q, r) elif self._type == 'laplace': rep = laplace_attention(q, k, r, self._scale, self._normalise) elif self._type == 'dot_product': rep = dot_product_attention(q, k, r, self._normalise) elif self._type == 'multihead': rep = multihead_attention(q, k, r, self._num_heads) else: raise NameError(("'att_type' not among ['uniform','laplace','dot_product'" ",'multihead']")) return rep	import tensorflow as tf import numpy as np # utility methods def batch_mlp(input, output_sizes, variable_scope): """Apply MLP to the final axis of a 3D tensor (reusing already defined MLPs). Args: input: input tensor of shape [B,n,d_in]. output_sizes: An iterable containing the output sizes of the MLP as defined in `basic.Linear`. variable_scope: String giving the name of the variable scope. If this is set to be the same as a previously defined MLP, then the weights are reused. Returns: tensor of shape [B,n,d_out] where d_out=output_sizes[-1] """ # Get the shapes of the input and reshape to parallelise across observations batch_size, _, filter_size = input.shape.as_list() output = tf.reshape(input, (-1, filter_size)) output.set_shape((None, filter_size)) # Pass through MLP with tf.variable_scope(variable_scope, reuse=tf.AUTO_REUSE): for i, size in enumerate(output_sizes[:-1]): output = tf.nn.relu( tf.layers.dense(output, size, name="layer_{}".format(i))) # Last layer without a ReLu output = tf.layers.dense( output, output_sizes[-1], name="layer_{}".format(i + 1)) # Bring back into original shape output = tf.reshape(output, (batch_size, -1, output_sizes[-1])) return output class DeterministicEncoder(object): """The Deterministic Encoder.""" def __init__(self, output_sizes, attention): """(A)NP deterministic encoder. Args: output_sizes: An iterable containing the output sizes of the encoding MLP. attention: The attention module. """ self._output_sizes = output_sizes self._attention = attention def __call__(self, context_x, context_y, target_x): """Encodes the inputs into one representation. Args: context_x: Tensor of shape [B,observations,d_x]. For this 1D regression task this corresponds to the x-values. context_y: Tensor of shape [B,observations,d_y]. For this 1D regression task this corresponds to the y-values. target_x: Tensor of shape [B,target_observations,d_x]. For this 1D regression task this corresponds to the x-values. Returns: The encoded representation. Tensor of shape [B,target_observations,d] """ # Concatenate x and y along the filter axes encoder_input = tf.concat([context_x, context_y], axis=-1) # Pass final axis through MLP hidden = batch_mlp(encoder_input, self._output_sizes, "deterministic_encoder") # Apply attention with tf.variable_scope("deterministic_encoder", reuse=tf.AUTO_REUSE): hidden = self._attention(context_x, target_x, hidden) return hidden class LatentEncoder(object): """The Latent Encoder.""" def __init__(self, output_sizes, num_latents): """(A)NP latent encoder. Args: output_sizes: An iterable containing the output sizes of the encoding MLP. num_latents: The latent dimensionality. """ self._output_sizes = output_sizes self._num_latents = num_latents def __call__(self, x, y): """Encodes the inputs into one representation. Args: x: Tensor of shape [B,observations,d_x]. For this 1D regression task this corresponds to the x-values. y: Tensor of shape [B,observations,d_y]. For this 1D regression task this corresponds to the y-values. Returns: A normal distribution over tensors of shape [B, num_latents] """ # Concatenate x and y along the filter axes encoder_input = tf.concat([x, y], axis=-1) # Pass final axis through MLP hidden = batch_mlp(encoder_input, self._output_sizes, "latent_encoder") # Aggregator: take the mean over all points hidden = tf.reduce_mean(hidden, axis=1) # Have further MLP layers that map to the parameters of the Gaussian latent with tf.variable_scope("latent_encoder", reuse=tf.AUTO_REUSE): # First apply intermediate relu layer hidden = tf.nn.relu( tf.layers.dense(hidden, (self._output_sizes[-1] + self._num_latents)/2, name="penultimate_layer")) # Then apply further linear layers to output latent mu and log sigma mu = tf.layers.dense(hidden, self._num_latents, name="mean_layer") log_sigma = tf.layers.dense(hidden, self._num_latents, name="std_layer") # Compute sigma sigma = 0.1 + 0.9 * tf.sigmoid(log_sigma) return tf.contrib.distributions.Normal(loc=mu, scale=sigma) class Decoder(object): """The Decoder.""" def __init__(self, output_sizes): """(A)NP decoder. Args: output_sizes: An iterable containing the output sizes of the decoder MLP as defined in `basic.Linear`. """ self._output_sizes = output_sizes def __call__(self, representation, target_x): """Decodes the individual targets. Args: representation: The representation of the context for target predictions. Tensor of shape [B,target_observations,?]. target_x: The x locations for the target query. Tensor of shape [B,target_observations,d_x]. Returns: dist: A multivariate Gaussian over the target points. A distribution over tensors of shape [B,target_observations,d_y]. mu: The mean of the multivariate Gaussian. Tensor of shape [B,target_observations,d_x]. sigma: The standard deviation of the multivariate Gaussian. Tensor of shape [B,target_observations,d_x]. """ # concatenate target_x and representation hidden = tf.concat([representation, target_x], axis=-1) # Pass final axis through MLP hidden = batch_mlp(hidden, self._output_sizes, "decoder") # Get the mean an the variance mu, log_sigma = tf.split(hidden, 2, axis=-1) # Bound the variance sigma = 0.1 + 0.9 * tf.nn.softplus(log_sigma) # Get the distribution dist = tf.contrib.distributions.MultivariateNormalDiag( loc=mu, scale_diag=sigma) return dist, mu, sigma class LatentModel(object): """The (A)NP model.""" def __init__(self, latent_encoder_output_sizes, num_latents, decoder_output_sizes, use_deterministic_path=True, deterministic_encoder_output_sizes=None, attention=None): """Initialises the model. Args: latent_encoder_output_sizes: An iterable containing the sizes of hidden layers of the latent encoder. num_latents: The latent dimensionality. decoder_output_sizes: An iterable containing the sizes of hidden layers of the decoder. The last element should correspond to d_y * 2 (it encodes both mean and variance concatenated) use_deterministic_path: a boolean that indicates whether the deterministic encoder is used or not. deterministic_encoder_output_sizes: An iterable containing the sizes of hidden layers of the deterministic encoder. The last one is the size of the deterministic representation r. attention: The attention module used in the deterministic encoder. Only relevant when use_deterministic_path=True. """ self._latent_encoder = LatentEncoder(latent_encoder_output_sizes, num_latents) self._decoder = Decoder(decoder_output_sizes) self._use_deterministic_path = use_deterministic_path if use_deterministic_path: self._deterministic_encoder = DeterministicEncoder( deterministic_encoder_output_sizes, attention) def __call__(self, query, num_targets, target_y=None): """Returns the predicted mean and variance at the target points. Args: query: Array containing ((context_x, context_y), target_x) where: context_x: Tensor of shape [B,num_contexts,d_x]. Contains the x values of the context points. context_y: Tensor of shape [B,num_contexts,d_y]. Contains the y values of the context points. target_x: Tensor of shape [B,num_targets,d_x]. Contains the x values of the target points. num_targets: Number of target points. target_y: The ground truth y values of the target y. Tensor of shape [B,num_targets,d_y]. Returns: log_p: The log_probability of the target_y given the predicted distribution. Tensor of shape [B,num_targets]. mu: The mean of the predicted distribution. Tensor of shape [B,num_targets,d_y]. sigma: The variance of the predicted distribution. Tensor of shape [B,num_targets,d_y]. """ (context_x, context_y), target_x = query # Pass query through the encoder and the decoder prior = self._latent_encoder(context_x, context_y) # For training, when target_y is available, use targets for latent encoder. # Note that targets contain contexts by design. if target_y is None: latent_rep = prior.sample() # For testing, when target_y unavailable, use contexts for latent encoder. else: posterior = self._latent_encoder(target_x, target_y) latent_rep = posterior.sample() latent_rep = tf.tile(tf.expand_dims(latent_rep, axis=1), [1, num_targets, 1]) if self._use_deterministic_path: deterministic_rep = self._deterministic_encoder(context_x, context_y, target_x) representation = tf.concat([deterministic_rep, latent_rep], axis=-1) else: representation = latent_rep dist, mu, sigma = self._decoder(representation, target_x) # If we want to calculate the log_prob for training we will make use of the # target_y. At test time the target_y is not available so we return None. if target_y is not None: log_p = dist.log_prob(target_y) posterior = self._latent_encoder(target_x, target_y) kl = tf.reduce_sum( tf.contrib.distributions.kl_divergence(posterior, prior), axis=-1, keepdims=True) kl = tf.tile(kl, [1, num_targets]) loss = - tf.reduce_mean(log_p - kl / tf.cast(num_targets, tf.float32)) else: log_p = None kl = None loss = None return mu, sigma, log_p, kl, loss def uniform_attention(q, v): """Uniform attention. Equivalent to np. Args: q: queries. tensor of shape [B,m,d_k]. v: values. tensor of shape [B,n,d_v]. Returns: tensor of shape [B,m,d_v]. """ total_points = tf.shape(q)[1] rep = tf.reduce_mean(v, axis=1, keepdims=True) # [B,1,d_v] rep = tf.tile(rep, [1, total_points, 1]) return rep def laplace_attention(q, k, v, scale, normalise): """Computes laplace exponential attention. Args: q: queries. tensor of shape [B,m,d_k]. k: keys. tensor of shape [B,n,d_k]. v: values. tensor of shape [B,n,d_v]. scale: float that scales the L1 distance. normalise: Boolean that determines whether weights sum to 1. Returns: tensor of shape [B,m,d_v]. """ k = tf.expand_dims(k, axis=1) # [B,1,n,d_k] q = tf.expand_dims(q, axis=2) # [B,m,1,d_k] unnorm_weights = - tf.abs((k - q) / scale) # [B,m,n,d_k] unnorm_weights = tf.reduce_sum(unnorm_weights, axis=-1) # [B,m,n] if normalise: weight_fn = tf.nn.softmax else: weight_fn = lambda x: 1 + tf.tanh(x) weights = weight_fn(unnorm_weights) # [B,m,n] rep = tf.einsum('bik,bkj->bij', weights, v) # [B,m,d_v] return rep def dot_product_attention(q, k, v, normalise): """Computes dot product attention. Args: q: queries. tensor of shape [B,m,d_k]. k: keys. tensor of shape [B,n,d_k]. v: values. tensor of shape [B,n,d_v]. normalise: Boolean that determines whether weights sum to 1. Returns: tensor of shape [B,m,d_v]. """ d_k = tf.shape(q)[-1] scale = tf.sqrt(tf.cast(d_k, tf.float32)) unnorm_weights = tf.einsum('bjk,bik->bij', k, q) / scale # [B,m,n] if normalise: weight_fn = tf.nn.softmax else: weight_fn = tf.sigmoid weights = weight_fn(unnorm_weights) # [B,m,n] rep = tf.einsum('bik,bkj->bij', weights, v) # [B,m,d_v] return rep def multihead_attention(q, k, v, num_heads=8): """Computes multi-head attention. Args: q: queries. tensor of shape [B,m,d_k]. k: keys. tensor of shape [B,n,d_k]. v: values. tensor of shape [B,n,d_v]. num_heads: number of heads. Should divide d_v. Returns: tensor of shape [B,m,d_v]. """ d_k = q.get_shape().as_list()[-1] d_v = v.get_shape().as_list()[-1] head_size = d_v / num_heads key_initializer = tf.random_normal_initializer(stddev=d_k-0.5) value_initializer = tf.random_normal_initializer(stddev=d_v-0.5) rep = tf.constant(0.0) for h in range(num_heads): o = dot_product_attention( tf.layers.Conv1D(head_size, 1, kernel_initializer=key_initializer, name='wq%d' % h, use_bias=False, padding='VALID')(q), tf.layers.Conv1D(head_size, 1, kernel_initializer=key_initializer, name='wk%d' % h, use_bias=False, padding='VALID')(k), tf.layers.Conv1D(head_size, 1, kernel_initializer=key_initializer, name='wv%d' % h, use_bias=False, padding='VALID')(v), normalise=True) rep += tf.layers.Conv1D(d_v, 1, kernel_initializer=value_initializer, name='wo%d' % h, use_bias=False, padding='VALID')(o) return rep class Attention(object): """The Attention module.""" def __init__(self, rep, output_sizes, att_type, scale=1., normalise=True, num_heads=8): """Create attention module. Takes in context inputs, target inputs and representations of each context input/output pair to output an aggregated representation of the context data. Args: rep: transformation to apply to contexts before computing attention. One of: ['identity','mlp']. output_sizes: list of number of hidden units per layer of mlp. Used only if rep == 'mlp'. att_type: type of attention. One of the following: ['uniform','laplace','dot_product','multihead'] scale: scale of attention. normalise: Boolean determining whether to: 1. apply softmax to weights so that they sum to 1 across context pts or 2. apply custom transformation to have weights in [0,1]. num_heads: number of heads for multihead. """ self._rep = rep self._output_sizes = output_sizes self._type = att_type self._scale = scale self._normalise = normalise if self._type == 'multihead': self._num_heads = num_heads def __call__(self, x1, x2, r): """Apply attention to create aggregated representation of r. Args: x1: tensor of shape [B,n1,d_x]. x2: tensor of shape [B,n2,d_x]. r: tensor of shape [B,n1,d]. Returns: tensor of shape [B,n2,d] Raises: NameError: The argument for rep/type was invalid. """ if self._rep == 'identity': k, q = (x1, x2) elif self._rep == 'mlp': # Pass through MLP k = batch_mlp(x1, self._output_sizes, "attention") q = batch_mlp(x2, self._output_sizes, "attention") else: raise NameError("'rep' not among ['identity','mlp']") if self._type == 'uniform': rep = uniform_attention(q, r) elif self._type == 'laplace': rep = laplace_attention(q, k, r, self._scale, self._normalise) elif self._type == 'dot_product': rep = dot_product_attention(q, k, r, self._normalise) elif self._type == 'multihead': rep = multihead_attention(q, k, r, self._num_heads) else: raise NameError(("'att_type' not among ['uniform','laplace','dot_product'" ",'multihead']")) return rep	en	0.72758	# utility methods Apply MLP to the final axis of a 3D tensor (reusing already defined MLPs). Args: input: input tensor of shape [B,n,d_in]. output_sizes: An iterable containing the output sizes of the MLP as defined in `basic.Linear`. variable_scope: String giving the name of the variable scope. If this is set to be the same as a previously defined MLP, then the weights are reused. Returns: tensor of shape [B,n,d_out] where d_out=output_sizes[-1] # Get the shapes of the input and reshape to parallelise across observations # Pass through MLP # Last layer without a ReLu # Bring back into original shape The Deterministic Encoder. (A)NP deterministic encoder. Args: output_sizes: An iterable containing the output sizes of the encoding MLP. attention: The attention module. Encodes the inputs into one representation. Args: context_x: Tensor of shape [B,observations,d_x]. For this 1D regression task this corresponds to the x-values. context_y: Tensor of shape [B,observations,d_y]. For this 1D regression task this corresponds to the y-values. target_x: Tensor of shape [B,target_observations,d_x]. For this 1D regression task this corresponds to the x-values. Returns: The encoded representation. Tensor of shape [B,target_observations,d] # Concatenate x and y along the filter axes # Pass final axis through MLP # Apply attention The Latent Encoder. (A)NP latent encoder. Args: output_sizes: An iterable containing the output sizes of the encoding MLP. num_latents: The latent dimensionality. Encodes the inputs into one representation. Args: x: Tensor of shape [B,observations,d_x]. For this 1D regression task this corresponds to the x-values. y: Tensor of shape [B,observations,d_y]. For this 1D regression task this corresponds to the y-values. Returns: A normal distribution over tensors of shape [B, num_latents] # Concatenate x and y along the filter axes # Pass final axis through MLP # Aggregator: take the mean over all points # Have further MLP layers that map to the parameters of the Gaussian latent # First apply intermediate relu layer # Then apply further linear layers to output latent mu and log sigma # Compute sigma The Decoder. (A)NP decoder. Args: output_sizes: An iterable containing the output sizes of the decoder MLP as defined in `basic.Linear`. Decodes the individual targets. Args: representation: The representation of the context for target predictions. Tensor of shape [B,target_observations,?]. target_x: The x locations for the target query. Tensor of shape [B,target_observations,d_x]. Returns: dist: A multivariate Gaussian over the target points. A distribution over tensors of shape [B,target_observations,d_y]. mu: The mean of the multivariate Gaussian. Tensor of shape [B,target_observations,d_x]. sigma: The standard deviation of the multivariate Gaussian. Tensor of shape [B,target_observations,d_x]. # concatenate target_x and representation # Pass final axis through MLP # Get the mean an the variance # Bound the variance # Get the distribution The (A)NP model. Initialises the model. Args: latent_encoder_output_sizes: An iterable containing the sizes of hidden layers of the latent encoder. num_latents: The latent dimensionality. decoder_output_sizes: An iterable containing the sizes of hidden layers of the decoder. The last element should correspond to d_y * 2 (it encodes both mean and variance concatenated) use_deterministic_path: a boolean that indicates whether the deterministic encoder is used or not. deterministic_encoder_output_sizes: An iterable containing the sizes of hidden layers of the deterministic encoder. The last one is the size of the deterministic representation r. attention: The attention module used in the deterministic encoder. Only relevant when use_deterministic_path=True. Returns the predicted mean and variance at the target points. Args: query: Array containing ((context_x, context_y), target_x) where: context_x: Tensor of shape [B,num_contexts,d_x]. Contains the x values of the context points. context_y: Tensor of shape [B,num_contexts,d_y]. Contains the y values of the context points. target_x: Tensor of shape [B,num_targets,d_x]. Contains the x values of the target points. num_targets: Number of target points. target_y: The ground truth y values of the target y. Tensor of shape [B,num_targets,d_y]. Returns: log_p: The log_probability of the target_y given the predicted distribution. Tensor of shape [B,num_targets]. mu: The mean of the predicted distribution. Tensor of shape [B,num_targets,d_y]. sigma: The variance of the predicted distribution. Tensor of shape [B,num_targets,d_y]. # Pass query through the encoder and the decoder # For training, when target_y is available, use targets for latent encoder. # Note that targets contain contexts by design. # For testing, when target_y unavailable, use contexts for latent encoder. # If we want to calculate the log_prob for training we will make use of the # target_y. At test time the target_y is not available so we return None. Uniform attention. Equivalent to np. Args: q: queries. tensor of shape [B,m,d_k]. v: values. tensor of shape [B,n,d_v]. Returns: tensor of shape [B,m,d_v]. # [B,1,d_v] Computes laplace exponential attention. Args: q: queries. tensor of shape [B,m,d_k]. k: keys. tensor of shape [B,n,d_k]. v: values. tensor of shape [B,n,d_v]. scale: float that scales the L1 distance. normalise: Boolean that determines whether weights sum to 1. Returns: tensor of shape [B,m,d_v]. # [B,1,n,d_k] # [B,m,1,d_k] # [B,m,n,d_k] # [B,m,n] # [B,m,n] # [B,m,d_v] Computes dot product attention. Args: q: queries. tensor of shape [B,m,d_k]. k: keys. tensor of shape [B,n,d_k]. v: values. tensor of shape [B,n,d_v]. normalise: Boolean that determines whether weights sum to 1. Returns: tensor of shape [B,m,d_v]. # [B,m,n] # [B,m,n] # [B,m,d_v] Computes multi-head attention. Args: q: queries. tensor of shape [B,m,d_k]. k: keys. tensor of shape [B,n,d_k]. v: values. tensor of shape [B,n,d_v]. num_heads: number of heads. Should divide d_v. Returns: tensor of shape [B,m,d_v]. The Attention module. Create attention module. Takes in context inputs, target inputs and representations of each context input/output pair to output an aggregated representation of the context data. Args: rep: transformation to apply to contexts before computing attention. One of: ['identity','mlp']. output_sizes: list of number of hidden units per layer of mlp. Used only if rep == 'mlp'. att_type: type of attention. One of the following: ['uniform','laplace','dot_product','multihead'] scale: scale of attention. normalise: Boolean determining whether to: 1. apply softmax to weights so that they sum to 1 across context pts or 2. apply custom transformation to have weights in [0,1]. num_heads: number of heads for multihead. Apply attention to create aggregated representation of r. Args: x1: tensor of shape [B,n1,d_x]. x2: tensor of shape [B,n2,d_x]. r: tensor of shape [B,n1,d]. Returns: tensor of shape [B,n2,d] Raises: NameError: The argument for rep/type was invalid. # Pass through MLP	3.012563	3
minotaur/_minotaur.py	giannitedesco/minotaur	172	10257	<filename>minotaur/_minotaur.py from typing import Dict, Tuple, Optional from pathlib import Path import asyncio from ._mask import Mask from ._event import Event from ._base import InotifyBase __all__ = ('Minotaur',) class Notification: __slots__ = ( '_path', '_type', '_isdir', '_unmount', '_qoverflow', ) def __init__(self, path: Path, type: Mask, isdir: bool, unmount: bool, qoverflow: bool = False): self._path = path self._type = type self._isdir = bool(isdir) self._unmount = bool(unmount) self._qoverflow = bool(qoverflow) @property def isdir(self) -> bool: return self._isdir @property def unmount(self) -> bool: return self._unmount @property def qoverflow(self) -> bool: return self._qoverflow @property def path(self) -> Path: return self._path def __repr__(self) -> str: t = self._isdir and 'dir' or 'file' return f'{type(self).__name__}({self._type.name} {t} {self._path})' @classmethod def create(cls, path: Path, mask: Mask) -> 'Notification': return cls(path, mask & Mask.EVENT_TYPE, bool(mask & Mask.ISDIR), bool(mask & Mask.UNMOUNT), bool(mask & Mask.Q_OVERFLOW)) class Minotaur(InotifyBase): """ Fancy interface for Inotify which does questionable things like: 1. Resolve watch-descriptors back to paths (which races with renames of original paths and can't be used safely, but other inotify packages provide this feature, so here it is for your delectation). 2. Link rename_from/rename_to events together. This feature would be useful but isn't yet actually implemented. Working on it... """ __slots__ = ( '_wdmap', '_cmap', ) _wdmap: Dict[int, Path] _cmap: Dict[Tuple[int, int], Event] def __init__(self, blocking: bool = True, cloexec: bool = True, loop: Optional[asyncio.AbstractEventLoop] = None, ) -> None: super().__init__(blocking, cloexec, loop) self._wdmap = {} self._cmap = {} def add_watch(self, p: Path, mask: Mask) -> int: try: wd = super().add_watch(p, mask) except Exception: raise else: self._wdmap[wd] = p.resolve() return wd def rm_watch(self, wd: int) -> int: try: return super().rm_watch(wd) except Exception: raise else: del self._wdmap[wd] def _resolve_path(self, wd: int, name: Path) -> Path: try: base_dir = self._wdmap[wd] except KeyError: path = name else: path = base_dir / name return path def __next__(self) -> Notification: evt = super()._next_event() if evt is None: raise StopIteration # TODO: Link rename_from/rename_to together if we have them path = self._resolve_path(evt.wd, evt.name) return Notification.create(path, evt.mask) async def __anext__(self) -> Notification: evt = await super()._next_event_async() if evt is None: raise StopIteration path = self._resolve_path(evt.wd, evt.name) return Notification.create(path, evt.mask)	<filename>minotaur/_minotaur.py from typing import Dict, Tuple, Optional from pathlib import Path import asyncio from ._mask import Mask from ._event import Event from ._base import InotifyBase __all__ = ('Minotaur',) class Notification: __slots__ = ( '_path', '_type', '_isdir', '_unmount', '_qoverflow', ) def __init__(self, path: Path, type: Mask, isdir: bool, unmount: bool, qoverflow: bool = False): self._path = path self._type = type self._isdir = bool(isdir) self._unmount = bool(unmount) self._qoverflow = bool(qoverflow) @property def isdir(self) -> bool: return self._isdir @property def unmount(self) -> bool: return self._unmount @property def qoverflow(self) -> bool: return self._qoverflow @property def path(self) -> Path: return self._path def __repr__(self) -> str: t = self._isdir and 'dir' or 'file' return f'{type(self).__name__}({self._type.name} {t} {self._path})' @classmethod def create(cls, path: Path, mask: Mask) -> 'Notification': return cls(path, mask & Mask.EVENT_TYPE, bool(mask & Mask.ISDIR), bool(mask & Mask.UNMOUNT), bool(mask & Mask.Q_OVERFLOW)) class Minotaur(InotifyBase): """ Fancy interface for Inotify which does questionable things like: 1. Resolve watch-descriptors back to paths (which races with renames of original paths and can't be used safely, but other inotify packages provide this feature, so here it is for your delectation). 2. Link rename_from/rename_to events together. This feature would be useful but isn't yet actually implemented. Working on it... """ __slots__ = ( '_wdmap', '_cmap', ) _wdmap: Dict[int, Path] _cmap: Dict[Tuple[int, int], Event] def __init__(self, blocking: bool = True, cloexec: bool = True, loop: Optional[asyncio.AbstractEventLoop] = None, ) -> None: super().__init__(blocking, cloexec, loop) self._wdmap = {} self._cmap = {} def add_watch(self, p: Path, mask: Mask) -> int: try: wd = super().add_watch(p, mask) except Exception: raise else: self._wdmap[wd] = p.resolve() return wd def rm_watch(self, wd: int) -> int: try: return super().rm_watch(wd) except Exception: raise else: del self._wdmap[wd] def _resolve_path(self, wd: int, name: Path) -> Path: try: base_dir = self._wdmap[wd] except KeyError: path = name else: path = base_dir / name return path def __next__(self) -> Notification: evt = super()._next_event() if evt is None: raise StopIteration # TODO: Link rename_from/rename_to together if we have them path = self._resolve_path(evt.wd, evt.name) return Notification.create(path, evt.mask) async def __anext__(self) -> Notification: evt = await super()._next_event_async() if evt is None: raise StopIteration path = self._resolve_path(evt.wd, evt.name) return Notification.create(path, evt.mask)	en	0.932626	Fancy interface for Inotify which does questionable things like: 1. Resolve watch-descriptors back to paths (which races with renames of original paths and can't be used safely, but other inotify packages provide this feature, so here it is for your delectation). 2. Link rename_from/rename_to events together. This feature would be useful but isn't yet actually implemented. Working on it... # TODO: Link rename_from/rename_to together if we have them	2.328925	2
pyclustering/container/examples/__init__.py	JosephChataignon/pyclustering	1,013	10258	<reponame>JosephChataignon/pyclustering """! @brief Collection of examples devoted to containers. @authors <NAME> (<EMAIL>) @date 2014-2020 @copyright BSD-3-Clause """	"""! @brief Collection of examples devoted to containers. @authors <NAME> (<EMAIL>) @date 2014-2020 @copyright BSD-3-Clause """	en	0.532854	! @brief Collection of examples devoted to containers. @authors <NAME> (<EMAIL>) @date 2014-2020 @copyright BSD-3-Clause	1.233931	1
novelty-detection/train_wood_vgg19.py	matherm/python-data-science	1	10259	<reponame>matherm/python-data-science import argparse import sys import torch import numpy as np import torch.nn as nn from torch.utils.data import DataLoader from torchvision.datasets import MNIST from torchvision.datasets import CIFAR10 import torchvision.transforms as transforms import matplotlib.pyplot as plt parser = argparse.ArgumentParser(description='PyTorch Novelty Detection') # TRAINING PARAMS parser.add_argument('--epochs', type=int, default=100, metavar='', help='Amount of epochs for training (default: 100)') parser.add_argument('--batch_size', type=int, default=1000, metavar='', help='Batch size for SGD (default: 100)') parser.add_argument('--lrate', type=float, default=0.0001, metavar="", help="Learning rate (default: 0.001") parser.add_argument('--with_cuda', action='store_true', dest='use_cuda', help="Shall cuda be used (default: False)") parser.add_argument('--model', type=int, default=0, help="Which model to train (0=KLminimizer, 1=Euclidean-Minimizer) (default: 0)") parser.add_argument('--plots', action='store_true', dest='plots', help="Shall matplotlib be used (default: False)") parser.add_argument('--grid', action='store_true', dest='grid', help="Grid search (default: False)") argv = parser.parse_args() sys.argv = [sys.argv[0]] from ummon import * from negvarbound import * from model import * from helpers import Evaluator import helpers torch.manual_seed(4) if __name__ == '__main__': # WOOD transform = transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), VGG19Features("pool4"), helpers.flatten_transform]) wood_data = ImagePatches("/ext/data/Wood-0035.png", mode='rgb', train=True, stride_y=14, stride_x=14, window_size=28, transform=transform) wood_data_test = AnomalyImagePatches("/ext/data/Wood-0035.png", mode='rgb', train=True, stride_y=14, stride_x=14, window_size=28, transform=transform, propability=1.0, anomaly=SquareAnomaly(size=8, color=255)) wood_data = [wood_data[i][0].data for i in range(len(wood_data))] wood_data = torch.stack(wood_data).numpy() / 10 wood_data_test = [wood_data_test[i][0].data for i in range(len(wood_data_test))] wood_data_test = torch.stack(wood_data_test).numpy() / 10 # Novelty data_novelty = wood_data_test # Train data_train = wood_data # Val data_val = data_train ###################################################### # NORMAL DISTRIBUTION ###################################################### # Model model = ModelNormal(input_features = data_train.shape[1], hidden_layer=20, latent_features=20) torch.manual_seed(4) # LOSS criterion = KLLoss(model=model, size_average=False) # INSTANTIATE OPTIMIZER optimizer = torch.optim.SGD(model.parameters(), lr=argv.lrate, weight_decay=1) #Evaluator evaluator = Evaluator(model, data_train, data_val, data_novelty) # Activate matplotlib argv.plots = True with Logger(loglevel=10, log_batch_interval=601) as lg: # CREATE A TRAINER my_trainer = UnsupervisedTrainer(lg, model, criterion, optimizer, trainingstate = Trainingstate(), model_filename="KL_MIN", use_cuda= argv.use_cuda, profile = False, convergence_eps = 1e-5) # START TRAINING my_trainer.fit(dataloader_training=(wood_data, 20), epochs=200) evaluator.evaluate_model(argv) ###################################################### # LOGNORMAL ###################################################### # Model model = ModelLogNormal(input_features = data_train.shape[1], hidden_layer=20, latent_features=20) torch.manual_seed(4) # LOSS criterion = KLLoss_lognormal(model=model, size_average=False) # INSTANTIATE OPTIMIZER optimizer = torch.optim.SGD(model.parameters(), lr=argv.lrate, weight_decay=1) #Evaluator evaluator = Evaluator(model, data_train, data_val, data_novelty) # Activate matplotlib argv.plots = True with Logger(loglevel=10, log_batch_interval=601) as lg: # CREATE A TRAINER my_trainer = UnsupervisedTrainer(lg, model, criterion, optimizer, trainingstate = Trainingstate(), model_filename="KL_MIN", use_cuda= argv.use_cuda, profile = False, convergence_eps = 1e-5) # START TRAINING my_trainer.fit(dataloader_training=(data_train, 20), epochs=argv.epochs) evaluator.evaluate_model(argv) ###################################################### # LAPLACE ###################################################### # Model model = ModelLaplace(input_features = data_train.shape[1], hidden_layer=20, latent_features=20) torch.manual_seed(4) # LOSS criterion = KLLoss_laplace(model=model, size_average=False, mean=2, scale=0.5) # INSTANTIATE OPTIMIZER optimizer = torch.optim.SGD(model.parameters(), lr=0.000001, weight_decay=1) #Evaluator evaluator = Evaluator(model, data_train, data_val, data_novelty) # Activate matplotlib argv.plots = True with Logger(loglevel=10, log_batch_interval=601) as lg: # CREATE A TRAINER my_trainer = UnsupervisedTrainer(lg, model, criterion, optimizer, trainingstate = Trainingstate(), model_filename="KL_MIN", use_cuda= argv.use_cuda, profile = False, convergence_eps = 1e-1) # START TRAINING my_trainer.fit(dataloader_training=(data_train, 20), epochs=300) evaluator.evaluate_model(argv) # {'AUROC LAT (TRAIN)': 0.8743801652892562, # 'AUROC LAT (VAL)': 0.8661157024793389, # 'AUROC REC (TRAIN)': 0.86900826446281, # 'AUROC REC (VAL)': 0.8528925619834712} ###################################################### # LAPLACE WITH R-SHIFT ###################################################### class CombinedLoss(nn.Module): def __init__(self, model, args, *kwargs): super(CombinedLoss, self).__init__() self.model = model self.r_shift = KLLoss_shift_r(model=model, size_average=False) self.kl_loss = KLLoss_laplace(model=model, size_average=False, mean=10, scale=0.3) def forward(self, inpt, outpt): self.r_shift() return self.kl_loss(inpt,outpt) # Model model = ModelLaplace(input_features = data_train.shape[1], hidden_layer=20, latent_features=20) torch.manual_seed(4) # LOSS criterion = CombinedLoss(model) # INSTANTIATE OPTIMIZER optimizer = torch.optim.SGD(model.parameters(), lr=argv.lrate, weight_decay=1) #Evaluator evaluator = Evaluator(model, data_train, data_val, data_novelty) # Activate matplotlib argv.plots = True with Logger(loglevel=10, log_batch_interval=601) as lg: # CREATE A TRAINER my_trainer = UnsupervisedTrainer(lg, model, criterion, optimizer, trainingstate = Trainingstate(), model_filename="KL_MIN", use_cuda= argv.use_cuda, profile = False, convergence_eps = 1e-3) # START TRAINING my_trainer.fit(dataloader_training=(data_train, 20), epochs=200) evaluator.evaluate_model(argv) # {'AUROC LAT (TRAIN)': 0.8590909090909091, # 'AUROC LAT (VAL)': 0.8752066115702479, # 'AUROC REC (TRAIN)': 0.8677685950413224, # 'AUROC REC (VAL)': 0.8619834710743801}	import argparse import sys import torch import numpy as np import torch.nn as nn from torch.utils.data import DataLoader from torchvision.datasets import MNIST from torchvision.datasets import CIFAR10 import torchvision.transforms as transforms import matplotlib.pyplot as plt parser = argparse.ArgumentParser(description='PyTorch Novelty Detection') # TRAINING PARAMS parser.add_argument('--epochs', type=int, default=100, metavar='', help='Amount of epochs for training (default: 100)') parser.add_argument('--batch_size', type=int, default=1000, metavar='', help='Batch size for SGD (default: 100)') parser.add_argument('--lrate', type=float, default=0.0001, metavar="", help="Learning rate (default: 0.001") parser.add_argument('--with_cuda', action='store_true', dest='use_cuda', help="Shall cuda be used (default: False)") parser.add_argument('--model', type=int, default=0, help="Which model to train (0=KLminimizer, 1=Euclidean-Minimizer) (default: 0)") parser.add_argument('--plots', action='store_true', dest='plots', help="Shall matplotlib be used (default: False)") parser.add_argument('--grid', action='store_true', dest='grid', help="Grid search (default: False)") argv = parser.parse_args() sys.argv = [sys.argv[0]] from ummon import * from negvarbound import * from model import * from helpers import Evaluator import helpers torch.manual_seed(4) if __name__ == '__main__': # WOOD transform = transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), VGG19Features("pool4"), helpers.flatten_transform]) wood_data = ImagePatches("/ext/data/Wood-0035.png", mode='rgb', train=True, stride_y=14, stride_x=14, window_size=28, transform=transform) wood_data_test = AnomalyImagePatches("/ext/data/Wood-0035.png", mode='rgb', train=True, stride_y=14, stride_x=14, window_size=28, transform=transform, propability=1.0, anomaly=SquareAnomaly(size=8, color=255)) wood_data = [wood_data[i][0].data for i in range(len(wood_data))] wood_data = torch.stack(wood_data).numpy() / 10 wood_data_test = [wood_data_test[i][0].data for i in range(len(wood_data_test))] wood_data_test = torch.stack(wood_data_test).numpy() / 10 # Novelty data_novelty = wood_data_test # Train data_train = wood_data # Val data_val = data_train ###################################################### # NORMAL DISTRIBUTION ###################################################### # Model model = ModelNormal(input_features = data_train.shape[1], hidden_layer=20, latent_features=20) torch.manual_seed(4) # LOSS criterion = KLLoss(model=model, size_average=False) # INSTANTIATE OPTIMIZER optimizer = torch.optim.SGD(model.parameters(), lr=argv.lrate, weight_decay=1) #Evaluator evaluator = Evaluator(model, data_train, data_val, data_novelty) # Activate matplotlib argv.plots = True with Logger(loglevel=10, log_batch_interval=601) as lg: # CREATE A TRAINER my_trainer = UnsupervisedTrainer(lg, model, criterion, optimizer, trainingstate = Trainingstate(), model_filename="KL_MIN", use_cuda= argv.use_cuda, profile = False, convergence_eps = 1e-5) # START TRAINING my_trainer.fit(dataloader_training=(wood_data, 20), epochs=200) evaluator.evaluate_model(argv) ###################################################### # LOGNORMAL ###################################################### # Model model = ModelLogNormal(input_features = data_train.shape[1], hidden_layer=20, latent_features=20) torch.manual_seed(4) # LOSS criterion = KLLoss_lognormal(model=model, size_average=False) # INSTANTIATE OPTIMIZER optimizer = torch.optim.SGD(model.parameters(), lr=argv.lrate, weight_decay=1) #Evaluator evaluator = Evaluator(model, data_train, data_val, data_novelty) # Activate matplotlib argv.plots = True with Logger(loglevel=10, log_batch_interval=601) as lg: # CREATE A TRAINER my_trainer = UnsupervisedTrainer(lg, model, criterion, optimizer, trainingstate = Trainingstate(), model_filename="KL_MIN", use_cuda= argv.use_cuda, profile = False, convergence_eps = 1e-5) # START TRAINING my_trainer.fit(dataloader_training=(data_train, 20), epochs=argv.epochs) evaluator.evaluate_model(argv) ###################################################### # LAPLACE ###################################################### # Model model = ModelLaplace(input_features = data_train.shape[1], hidden_layer=20, latent_features=20) torch.manual_seed(4) # LOSS criterion = KLLoss_laplace(model=model, size_average=False, mean=2, scale=0.5) # INSTANTIATE OPTIMIZER optimizer = torch.optim.SGD(model.parameters(), lr=0.000001, weight_decay=1) #Evaluator evaluator = Evaluator(model, data_train, data_val, data_novelty) # Activate matplotlib argv.plots = True with Logger(loglevel=10, log_batch_interval=601) as lg: # CREATE A TRAINER my_trainer = UnsupervisedTrainer(lg, model, criterion, optimizer, trainingstate = Trainingstate(), model_filename="KL_MIN", use_cuda= argv.use_cuda, profile = False, convergence_eps = 1e-1) # START TRAINING my_trainer.fit(dataloader_training=(data_train, 20), epochs=300) evaluator.evaluate_model(argv) # {'AUROC LAT (TRAIN)': 0.8743801652892562, # 'AUROC LAT (VAL)': 0.8661157024793389, # 'AUROC REC (TRAIN)': 0.86900826446281, # 'AUROC REC (VAL)': 0.8528925619834712} ###################################################### # LAPLACE WITH R-SHIFT ###################################################### class CombinedLoss(nn.Module): def __init__(self, model, args, *kwargs): super(CombinedLoss, self).__init__() self.model = model self.r_shift = KLLoss_shift_r(model=model, size_average=False) self.kl_loss = KLLoss_laplace(model=model, size_average=False, mean=10, scale=0.3) def forward(self, inpt, outpt): self.r_shift() return self.kl_loss(inpt,outpt) # Model model = ModelLaplace(input_features = data_train.shape[1], hidden_layer=20, latent_features=20) torch.manual_seed(4) # LOSS criterion = CombinedLoss(model) # INSTANTIATE OPTIMIZER optimizer = torch.optim.SGD(model.parameters(), lr=argv.lrate, weight_decay=1) #Evaluator evaluator = Evaluator(model, data_train, data_val, data_novelty) # Activate matplotlib argv.plots = True with Logger(loglevel=10, log_batch_interval=601) as lg: # CREATE A TRAINER my_trainer = UnsupervisedTrainer(lg, model, criterion, optimizer, trainingstate = Trainingstate(), model_filename="KL_MIN", use_cuda= argv.use_cuda, profile = False, convergence_eps = 1e-3) # START TRAINING my_trainer.fit(dataloader_training=(data_train, 20), epochs=200) evaluator.evaluate_model(argv) # {'AUROC LAT (TRAIN)': 0.8590909090909091, # 'AUROC LAT (VAL)': 0.8752066115702479, # 'AUROC REC (TRAIN)': 0.8677685950413224, # 'AUROC REC (VAL)': 0.8619834710743801}	de	0.33531	# TRAINING PARAMS # WOOD # Novelty # Train # Val ###################################################### # NORMAL DISTRIBUTION ###################################################### # Model # LOSS # INSTANTIATE OPTIMIZER #Evaluator # Activate matplotlib # CREATE A TRAINER # START TRAINING ###################################################### # LOGNORMAL ###################################################### # Model # LOSS # INSTANTIATE OPTIMIZER #Evaluator # Activate matplotlib # CREATE A TRAINER # START TRAINING ###################################################### # LAPLACE ###################################################### # Model # LOSS # INSTANTIATE OPTIMIZER #Evaluator # Activate matplotlib # CREATE A TRAINER # START TRAINING # {'AUROC LAT (TRAIN)': 0.8743801652892562, # 'AUROC LAT (VAL)': 0.8661157024793389, # 'AUROC REC (TRAIN)': 0.86900826446281, # 'AUROC REC (VAL)': 0.8528925619834712} ###################################################### # LAPLACE WITH R-SHIFT ###################################################### # Model # LOSS # INSTANTIATE OPTIMIZER #Evaluator # Activate matplotlib # CREATE A TRAINER # START TRAINING # {'AUROC LAT (TRAIN)': 0.8590909090909091, # 'AUROC LAT (VAL)': 0.8752066115702479, # 'AUROC REC (TRAIN)': 0.8677685950413224, # 'AUROC REC (VAL)': 0.8619834710743801}	2.441559	2
sample_architectures/cnn.py	hvarS/PyTorch-Refer	0	10260	# -- coding: utf-8 -- """CNN.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1Tq6HUya2PrC0SmyOIFo2c_eVtguRED2q """ import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F from torch.utils.data import DataLoader import torchvision.datasets as datasets import torchvision.transforms as transforms class CNN(nn.Module): def __init__(self,in_channels = 1,num_classes = 10): super(CNN,self).__init__() self.conv1 = nn.Conv2d(in_channels= in_channels,out_channels = 8,kernel_size =(3,3),stride = (1,1),padding = (1,1)) self.pool1 = nn.MaxPool2d(kernel_size=(2,2),stride=(2,2)) self.conv2 = nn.Conv2d(in_channels= 8,out_channels = 16,kernel_size =(3,3),stride = (1,1),padding = (1,1)) self.pool2 = nn.MaxPool2d(kernel_size=(2,2),stride=(2,2)) self.fc1 = nn.Linear(1677,num_classes) def forward(self,x): x = F.relu(self.conv1(x)) x = self.pool1(x) x = F.relu(self.conv2(x)) x = self.pool2(x) x = x.reshape(x.shape[0],-1) x = self.fc1(x) return x model = CNN(1,10) x = torch.randn((64,1,28,28)) print(model(x).shape) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device in_channels = 1 num_classes = 10 learning_rate = 0.001 batch_size = 64 num_epochs = 4 train_dataset = datasets.MNIST(root = "dataset/",train = True,transform = transforms.ToTensor(),download = True) train_loader = DataLoader(dataset=train_dataset,batch_size=64,shuffle=True) test_dataset = train_dataset = datasets.MNIST(root = "dataset/",train = False,transform = transforms.ToTensor(),download = True) test_loader = DataLoader(dataset = test_dataset,batch_size = batch_size,shuffle = True) model = CNN(1,10).to(device = device) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(),lr = learning_rate) for epoch in range(num_epochs): for batch_idx,(data,targets) in enumerate(train_loader): #get data to cuda if possible data = data.cuda() targets = targets.cuda() scores = model(data) loss = criterion(scores,targets) #backward optimizer.zero_grad() loss.backward() #gradient_descent or adam-step optimizer.step() # Check the accuracy for the training step def check_accuracy(loader,model): if loader.dataset.train: print("Checking accuracy on training data") else: print("Checking accuracy on test data") num_correct = 0 num_samples = 0 model.eval() with torch.no_grad(): for x,y in loader: x = x.cuda() y = y.cuda() scores = model(x) _,predictions = scores.max(1) num_correct += (predictions == y).sum() num_samples += predictions.size(0) print(f' Got {num_correct}/{num_samples} with accuracy ={float(num_correct)/float(num_samples)*100:.2f} ') model.train() check_accuracy(train_loader,model) check_accuracy(test_loader,model)	# -- coding: utf-8 -- """CNN.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1Tq6HUya2PrC0SmyOIFo2c_eVtguRED2q """ import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F from torch.utils.data import DataLoader import torchvision.datasets as datasets import torchvision.transforms as transforms class CNN(nn.Module): def __init__(self,in_channels = 1,num_classes = 10): super(CNN,self).__init__() self.conv1 = nn.Conv2d(in_channels= in_channels,out_channels = 8,kernel_size =(3,3),stride = (1,1),padding = (1,1)) self.pool1 = nn.MaxPool2d(kernel_size=(2,2),stride=(2,2)) self.conv2 = nn.Conv2d(in_channels= 8,out_channels = 16,kernel_size =(3,3),stride = (1,1),padding = (1,1)) self.pool2 = nn.MaxPool2d(kernel_size=(2,2),stride=(2,2)) self.fc1 = nn.Linear(1677,num_classes) def forward(self,x): x = F.relu(self.conv1(x)) x = self.pool1(x) x = F.relu(self.conv2(x)) x = self.pool2(x) x = x.reshape(x.shape[0],-1) x = self.fc1(x) return x model = CNN(1,10) x = torch.randn((64,1,28,28)) print(model(x).shape) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device in_channels = 1 num_classes = 10 learning_rate = 0.001 batch_size = 64 num_epochs = 4 train_dataset = datasets.MNIST(root = "dataset/",train = True,transform = transforms.ToTensor(),download = True) train_loader = DataLoader(dataset=train_dataset,batch_size=64,shuffle=True) test_dataset = train_dataset = datasets.MNIST(root = "dataset/",train = False,transform = transforms.ToTensor(),download = True) test_loader = DataLoader(dataset = test_dataset,batch_size = batch_size,shuffle = True) model = CNN(1,10).to(device = device) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(),lr = learning_rate) for epoch in range(num_epochs): for batch_idx,(data,targets) in enumerate(train_loader): #get data to cuda if possible data = data.cuda() targets = targets.cuda() scores = model(data) loss = criterion(scores,targets) #backward optimizer.zero_grad() loss.backward() #gradient_descent or adam-step optimizer.step() # Check the accuracy for the training step def check_accuracy(loader,model): if loader.dataset.train: print("Checking accuracy on training data") else: print("Checking accuracy on test data") num_correct = 0 num_samples = 0 model.eval() with torch.no_grad(): for x,y in loader: x = x.cuda() y = y.cuda() scores = model(x) _,predictions = scores.max(1) num_correct += (predictions == y).sum() num_samples += predictions.size(0) print(f' Got {num_correct}/{num_samples} with accuracy ={float(num_correct)/float(num_samples)*100:.2f} ') model.train() check_accuracy(train_loader,model) check_accuracy(test_loader,model)	en	0.798453	# -- coding: utf-8 -- CNN.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1Tq6HUya2PrC0SmyOIFo2c_eVtguRED2q #get data to cuda if possible #backward #gradient_descent or adam-step # Check the accuracy for the training step	3.112416	3
aispace/layers/callbacks/qa_evaluators.py	SmileGoat/AiSpace	32	10261	<reponame>SmileGoat/AiSpace # -- coding: utf-8 -- # @Time : 2020-07-30 15:06 # @Author : yingyuankai # @Email : <EMAIL> # @File : qa_evaluators.py import os import logging import numpy as np import tensorflow as tf import json from pprint import pprint from collections import defaultdict from aispace.utils.eval_utils import calc_em_score, calc_f1_score from aispace.utils.io_utils import save_json from aispace.utils.print_utils import print_boxed from aispace.utils.metrics_utils import ConfusionMatrix __all__ = [ 'EvaluatorForQaSimple', 'EvaluatorForQaWithImpossible' ] logger = logging.getLogger(__name__) class EvaluatorForQaSimple(tf.keras.callbacks.Callback): """ start_top_log_prob and end_top_log_prob's shape is [batch, k] ref: https://keras.io/examples/nlp/text_extraction_with_bert/ """ def __init__(self, validation_dataset, validation_steps, test_dataset, test_steps, report_dir, max_answer_length=64, n_best_size=5): self.validation_dataset = validation_dataset self.validation_steps = validation_steps self.test_dataset = test_dataset self.test_steps = test_steps self.max_answer_length = max_answer_length self.n_best_size = n_best_size self.report_dir = report_dir def on_epoch_end(self, epoch, logs=None): new_logs = self.eval_process(self.validation_dataset, self.validation_steps) logs = logs or {} logs.update(new_logs) print(f"Epoch: {epoch + 1}, val_f1_score: {logs['val_f1_score']:.4f}, val_em_score: {logs['val_em_score']:.4f}, " f"val_f1_em_avg_score: {logs['val_f1_em_avg_score']:.4f}") def on_train_end(self, logs=None): logger.info("Start Evaluate.") if not os.path.exists(self.report_dir): os.makedirs(self.report_dir) new_logs = self.eval_process(self.test_dataset, self.test_steps) save_json(os.path.join(self.report_dir, 'performance.json'), new_logs) print_boxed(f"Question Answer Evaluation") pprint(new_logs) logger.info(f"Save question answer reports in {self.report_dir}") def eval_process(self, dataset, n_steps=None): f1 = 0 em = 0 total_count = 0 skip_count = 0 start_top_res, end_top_res, unique_id_res = self.model.predict(dataset, steps=n_steps) start_top_log_prob, start_top_index = start_top_res[:, :, 0], start_top_res[:, :, 1].astype(np.int) # [b, k] end_top_log_prob, end_top_index = end_top_res[:, :, 0], end_top_res[:, :, 1].astype(np.int) # [b, k] unique_id_res = unique_id_res.astype(np.int) # predict results results = {} for i in range(end_top_index.shape[0]): unique_id = unique_id_res[i][0] itm = { 'unique_id': unique_id, 'start_top_log_prob': start_top_log_prob[i], 'start_top_index': start_top_index[i], 'end_top_log_prob': end_top_log_prob[i], 'end_top_index': end_top_index[i], } results[unique_id] = itm # raw inputs start_n_top, end_n_top = start_top_index.shape[-1], end_top_index.shape[-1] qas_id_to_examples = defaultdict(list) unique_id_to_examples = {} for idx, (inputs, outputs) in enumerate(dataset): if n_steps is not None and idx >= n_steps: break unique_ids = inputs['unique_id'].numpy().astype(np.int).tolist() offsets = inputs['offset'].numpy().astype(np.int).tolist() qas_ids = inputs['qas_id'].numpy().astype(str).tolist() doc_token2char_raw_start_indexs = inputs['doc_token2char_raw_start_index'].numpy().astype(str).tolist() doc_token2char_raw_end_indexs = inputs['doc_token2char_raw_end_index'].numpy().astype(str).tolist() doc_token2doc_indexs = inputs['doc_token2doc_index'].numpy().astype(str).tolist() all_answers = inputs['all_answers'].numpy().astype(str).tolist() answer_texts = inputs['answer_text'].numpy().tolist() context_texts = inputs['context_text'].numpy().tolist() question_texts = inputs['question_text'].numpy().tolist() is_impossibles = inputs['is_impossible'].numpy().tolist() p_masks = inputs['p_mask'].numpy().astype(np.int).tolist() for t in range(len(unique_ids)): itm = { 'unique_id': unique_ids[t], 'qas_id': qas_ids[t], 'question_text': question_texts[t].decode("utf8"), 'context_text': context_texts[t].decode("utf8"), 'answer_text': answer_texts[t].decode("utf8"), 'all_answers': json.loads(all_answers[t]), 'doc_token2char_raw_start_index': json.loads(doc_token2char_raw_start_indexs[t]), 'doc_token2char_raw_end_index': json.loads(doc_token2char_raw_end_indexs[t]), 'doc_token2doc_index': json.loads(doc_token2doc_indexs[t]), 'is_impossible': is_impossibles[t], 'p_mask': p_masks[t], 'offset': offsets[t] } unique_id_to_examples[unique_ids[t]] = itm qas_id_to_examples[qas_ids[t]].append(itm) for qas_id, examples in qas_id_to_examples.items(): example_all_predicts = [] answers = set() for example in examples: cur_unique_id = example['unique_id'] if cur_unique_id not in results: continue if example['is_impossible'] == 1: continue # if example['answer_text'] not in answers: # answers.append(example['answer_text']) answers \|= set(example['all_answers']) cur_result = results.get(cur_unique_id) cur_start_top_log_prob = cur_result['start_top_log_prob'] cur_start_top_index = cur_result['start_top_index'] cur_end_top_log_prob = cur_result['end_top_log_prob'] cur_end_top_index = cur_result['end_top_index'] cur_p_mask = example['p_mask'] for i in range(start_n_top): start_prob = cur_start_top_log_prob[i] start_index = cur_start_top_index[i] if not cur_p_mask[start_index]: continue for j in range(end_n_top): end_prob = cur_end_top_log_prob[j] end_index = cur_end_top_index[j] if not cur_p_mask[end_index]: continue answer_length = end_index - start_index + 1 if end_index < start_index or answer_length > self.max_answer_length: continue itm = { 'unique_id': cur_unique_id, 'start_prob': start_prob, 'start_index': start_index, 'end_prob': end_prob, 'end_index': end_index, 'predict_score': np.log(start_prob) + np.log(end_prob) } example_all_predicts.append(itm) if len(answers) != 0: total_count += 1 else: skip_count += 1 continue example_all_predicts.sort(key=lambda s: s['predict_score'], reverse=True) example_top_predicts = [] is_visited = set() for example_predict in example_all_predicts: if len(example_top_predicts) >= self.n_best_size: break example_feature = unique_id_to_examples[example_predict['unique_id']] if example_predict['start_index'] - example_feature['offset'] < 0 or example_predict['end_index'] - example_feature['offset'] < 0: predict_text = "" else: predict_start = example_feature['doc_token2char_raw_start_index'][ example_predict['start_index'] - example_feature['offset']] predict_end = example_feature['doc_token2char_raw_end_index'][ example_predict['end_index'] - example_feature['offset']] predict_text = example_feature['context_text'][predict_start: predict_end + 1].strip() if predict_text in is_visited: continue is_visited.add(predict_text) itm = { 'predict_text': predict_text, 'start_prob': example_predict['start_prob'], 'end_prob': example_predict['end_prob'], 'predict_score': example_predict['predict_score'] } example_top_predicts.append(itm) if len(example_top_predicts) == 0: example_top_predicts.append( { 'predict_text': "", 'start_prob': 0., 'end_prob': 0., 'predict_score': 0. } ) example_best_predict = example_top_predicts[0] cur_f1 = calc_f1_score(list(answers), example_best_predict['predict_text']) cur_em = calc_em_score(list(answers), example_best_predict['predict_text']) f1 += cur_f1 em += cur_em # debug if cur_f1 == 0 or cur_em == 0: example_output = {} example_output.update(example_best_predict) example_output['question'] = examples[0]['question_text'] example_output['answer'] = answers example_output['f1'] = cur_f1 example_output['em'] = cur_em print(example_output) # total_count = len(qas_id_to_examples) f1_score = f1 / total_count em_score = em / total_count logs = {} logs['skip_count'] = skip_count logs['total'] = total_count logs['val_f1_score'] = f1_score logs['val_em_score'] = em_score logs['val_f1_em_avg_score'] = (em_score + f1_score) / 2. return logs class EvaluatorForQaWithImpossible(tf.keras.callbacks.Callback): """ start_top_log_prob and end_top_log_prob's shape is [batch, k, k] ref: https://keras.io/examples/nlp/text_extraction_with_bert/ """ def __init__(self, validation_dataset, validation_steps, test_dataset, test_steps, report_dir, max_answer_length=64, n_best_size=5, is_impossible_threshold=0.5, weights=[1., 1., 1.]): self.validation_dataset = validation_dataset self.validation_steps = validation_steps self.test_dataset = test_dataset self.test_steps = test_steps self.max_answer_length = max_answer_length self.n_best_size = n_best_size self.report_dir = report_dir self.is_impossible_threshold = is_impossible_threshold self.weights = weights def on_epoch_end(self, epoch, logs=None): new_logs = self.eval_process(self.validation_dataset, self.validation_steps) logs = logs or {} logs.update(new_logs) print(f"\nEpoch: {epoch + 1}, val_f1_score: {logs['val_f1_score']:.4f}, " f"val_em_score: {logs['val_em_score']:.4f}, " f"val_f1_em_avg_score: {logs['val_f1_em_avg_score']:.4f}," f" val_f1_for_impossible: {logs['val_f1_for_impossible']:.4f}," f" val_f1_avg_score: {logs['val_f1_avg_score']:.4f},") def on_train_end(self, logs=None): logger.info("Start Evaluate.") if not os.path.exists(self.report_dir): os.makedirs(self.report_dir) new_logs = self.eval_process(self.test_dataset, self.test_steps) save_json(os.path.join(self.report_dir, 'performance.json'), new_logs) print_boxed(f"Question Answer Evaluation") pprint(new_logs) logger.info(f"Save question answer reports in {self.report_dir}") def eval_process(self, dataset, n_steps=None): f1 = 0 em = 0 total_count = 0 skip_count = 0 start_top_res, end_top_res, answer_prob, unique_id_res = self.model.predict(dataset, steps=n_steps) start_top_log_prob, start_top_index = start_top_res[:, :, 0], start_top_res[:, :, 1].astype(np.int) # [b, k] end_top_log_prob, end_top_index = end_top_res[:, :, :, 0], end_top_res[:, :, :, 1].astype(np.int) # [b, k, k] unique_id_res = unique_id_res.astype(np.int) # predict results results = {} for i in range(end_top_index.shape[0]): unique_id = unique_id_res[i][0] itm = { 'unique_id': unique_id, 'start_top_log_prob': start_top_log_prob[i], 'start_top_index': start_top_index[i], 'end_top_log_prob': end_top_log_prob[i], 'end_top_index': end_top_index[i], 'is_impossible_prob': answer_prob[i][0] } results[unique_id] = itm # raw inputs start_n_top, end_n_top = end_top_index.shape[1:] qas_id_to_examples = defaultdict(list) unique_id_to_examples = {} for idx, (inputs, outputs) in enumerate(dataset): if n_steps is not None and idx >= n_steps: break unique_ids = inputs['unique_id'].numpy().astype(np.int).tolist() offsets = inputs['offset'].numpy().astype(np.int).tolist() qas_ids = inputs['qas_id'].numpy().astype(str).tolist() doc_token2char_raw_start_indexs = inputs['doc_token2char_raw_start_index'].numpy().astype(str).tolist() doc_token2char_raw_end_indexs = inputs['doc_token2char_raw_end_index'].numpy().astype(str).tolist() doc_token2doc_indexs = inputs['doc_token2doc_index'].numpy().astype(str).tolist() all_answers = inputs['all_answers'].numpy().astype(str).tolist() answer_texts = inputs['answer_text'].numpy().tolist() context_texts = inputs['context_text'].numpy().tolist() question_texts = inputs['question_text'].numpy().tolist() is_impossibles = inputs['is_impossible'].numpy().tolist() p_masks = inputs['p_mask'].numpy().astype(np.int).tolist() for t in range(len(unique_ids)): itm = { 'unique_id': unique_ids[t], 'qas_id': qas_ids[t], 'question_text': question_texts[t].decode("utf8"), 'context_text': context_texts[t].decode("utf8"), 'answer_text': answer_texts[t].decode("utf8"), 'all_answers': json.loads(all_answers[t]), 'doc_token2char_raw_start_index': json.loads(doc_token2char_raw_start_indexs[t]), 'doc_token2char_raw_end_index': json.loads(doc_token2char_raw_end_indexs[t]), 'doc_token2doc_index': json.loads(doc_token2doc_indexs[t]), 'is_impossible': is_impossibles[t], 'p_mask': p_masks[t], 'offset': offsets[t] } unique_id_to_examples[unique_ids[t]] = itm qas_id_to_examples[qas_ids[t]].append(itm) ground_truth_for_impossible, predictions_for_impossible = [], [] for qas_id, examples in qas_id_to_examples.items(): example_all_predicts = [] answers = set() for example in examples: cur_unique_id = example['unique_id'] if cur_unique_id not in results: continue # if example['answer_text'] not in answers: # answers.append(example['answer_text']) answers \|= set(example['all_answers']) cur_result = results.get(cur_unique_id) cur_start_top_log_prob = cur_result['start_top_log_prob'] cur_start_top_index = cur_result['start_top_index'] cur_end_top_log_prob = cur_result['end_top_log_prob'] cur_end_top_index = cur_result['end_top_index'] ground_truth_for_impossible.append(example['is_impossible']) predictions_for_impossible.append(int(cur_result['is_impossible_prob'] >= self.is_impossible_threshold)) if example['is_impossible'] == 1: continue cur_p_mask = example['p_mask'] for i in range(start_n_top): start_prob = cur_start_top_log_prob[i] start_index = cur_start_top_index[i] if not cur_p_mask[start_index]: continue for j in range(end_n_top): end_prob = cur_end_top_log_prob[i, j] end_index = cur_end_top_index[i, j] if not cur_p_mask[end_index]: continue answer_length = end_index - start_index + 1 if end_index < start_index or answer_length > self.max_answer_length: continue itm = { 'unique_id': cur_unique_id, 'start_prob': start_prob, 'start_index': start_index, 'end_prob': end_prob, 'end_index': end_index, 'predict_score': np.log(end_prob) } example_all_predicts.append(itm) if len(answers) != 0 and "" not in answers: total_count += 1 else: skip_count += 1 continue example_all_predicts.sort(key=lambda s: s['predict_score'], reverse=True) example_top_predicts = [] is_visited = set() for example_predict in example_all_predicts: if len(example_top_predicts) >= self.n_best_size: break example_feature = unique_id_to_examples[example_predict['unique_id']] if example_predict['start_index'] - example_feature['offset'] < 0 or example_predict['end_index'] - example_feature['offset'] < 0: predict_text = "" else: predict_start = example_feature['doc_token2char_raw_start_index'][ example_predict['start_index'] - example_feature['offset']] predict_end = example_feature['doc_token2char_raw_end_index'][ example_predict['end_index'] - example_feature['offset']] predict_text = example_feature['context_text'][predict_start: predict_end + 1].strip() if predict_text in is_visited: continue is_visited.add(predict_text) itm = { 'predict_text': predict_text, 'start_prob': example_predict['start_prob'], 'end_prob': example_predict['end_prob'], 'predict_score': example_predict['predict_score'] } example_top_predicts.append(itm) if len(example_top_predicts) == 0: example_top_predicts.append( { 'predict_text': "", 'start_prob': 0., 'end_prob': 0., 'predict_score': 0. } ) example_best_predict = example_top_predicts[0] cur_f1 = calc_f1_score(list(answers), example_best_predict['predict_text']) cur_em = calc_em_score(list(answers), example_best_predict['predict_text']) f1 += cur_f1 em += cur_em # debug if cur_f1 == 0 or cur_em == 0: example_output = {} example_output.update(example_best_predict) example_output['question'] = examples[0]['question_text'] example_output['answer'] = answers example_output['f1'] = cur_f1 example_output['em'] = cur_em print(example_output) # total_count = len(qas_id_to_examples) f1_score = f1 / total_count em_score = em / total_count cm = ConfusionMatrix(ground_truth_for_impossible, predictions_for_impossible) logs = {} logs['skip_count'] = skip_count logs['total'] = total_count logs['val_f1_score'] = f1_score logs['val_em_score'] = em_score logs['val_f1_em_avg_score'] = (em_score * self.weights[0] + f1_score * self.weights[1]) / sum(self.weights[:2]) logs['val_f1_for_impossible'] = cm.avg_f1_score(average='macro') logs['val_accuracy_for_impossible'] = cm.overall_accuracy() logs['val_f1_avg_score'] = (em_score * self.weights[0] + f1_score * self.weights[1] + logs['val_f1_for_impossible'] * self.weights[2]) / sum(self.weights) return logs	# -- coding: utf-8 -- # @Time : 2020-07-30 15:06 # @Author : yingyuankai # @Email : <EMAIL> # @File : qa_evaluators.py import os import logging import numpy as np import tensorflow as tf import json from pprint import pprint from collections import defaultdict from aispace.utils.eval_utils import calc_em_score, calc_f1_score from aispace.utils.io_utils import save_json from aispace.utils.print_utils import print_boxed from aispace.utils.metrics_utils import ConfusionMatrix __all__ = [ 'EvaluatorForQaSimple', 'EvaluatorForQaWithImpossible' ] logger = logging.getLogger(__name__) class EvaluatorForQaSimple(tf.keras.callbacks.Callback): """ start_top_log_prob and end_top_log_prob's shape is [batch, k] ref: https://keras.io/examples/nlp/text_extraction_with_bert/ """ def __init__(self, validation_dataset, validation_steps, test_dataset, test_steps, report_dir, max_answer_length=64, n_best_size=5): self.validation_dataset = validation_dataset self.validation_steps = validation_steps self.test_dataset = test_dataset self.test_steps = test_steps self.max_answer_length = max_answer_length self.n_best_size = n_best_size self.report_dir = report_dir def on_epoch_end(self, epoch, logs=None): new_logs = self.eval_process(self.validation_dataset, self.validation_steps) logs = logs or {} logs.update(new_logs) print(f"Epoch: {epoch + 1}, val_f1_score: {logs['val_f1_score']:.4f}, val_em_score: {logs['val_em_score']:.4f}, " f"val_f1_em_avg_score: {logs['val_f1_em_avg_score']:.4f}") def on_train_end(self, logs=None): logger.info("Start Evaluate.") if not os.path.exists(self.report_dir): os.makedirs(self.report_dir) new_logs = self.eval_process(self.test_dataset, self.test_steps) save_json(os.path.join(self.report_dir, 'performance.json'), new_logs) print_boxed(f"Question Answer Evaluation") pprint(new_logs) logger.info(f"Save question answer reports in {self.report_dir}") def eval_process(self, dataset, n_steps=None): f1 = 0 em = 0 total_count = 0 skip_count = 0 start_top_res, end_top_res, unique_id_res = self.model.predict(dataset, steps=n_steps) start_top_log_prob, start_top_index = start_top_res[:, :, 0], start_top_res[:, :, 1].astype(np.int) # [b, k] end_top_log_prob, end_top_index = end_top_res[:, :, 0], end_top_res[:, :, 1].astype(np.int) # [b, k] unique_id_res = unique_id_res.astype(np.int) # predict results results = {} for i in range(end_top_index.shape[0]): unique_id = unique_id_res[i][0] itm = { 'unique_id': unique_id, 'start_top_log_prob': start_top_log_prob[i], 'start_top_index': start_top_index[i], 'end_top_log_prob': end_top_log_prob[i], 'end_top_index': end_top_index[i], } results[unique_id] = itm # raw inputs start_n_top, end_n_top = start_top_index.shape[-1], end_top_index.shape[-1] qas_id_to_examples = defaultdict(list) unique_id_to_examples = {} for idx, (inputs, outputs) in enumerate(dataset): if n_steps is not None and idx >= n_steps: break unique_ids = inputs['unique_id'].numpy().astype(np.int).tolist() offsets = inputs['offset'].numpy().astype(np.int).tolist() qas_ids = inputs['qas_id'].numpy().astype(str).tolist() doc_token2char_raw_start_indexs = inputs['doc_token2char_raw_start_index'].numpy().astype(str).tolist() doc_token2char_raw_end_indexs = inputs['doc_token2char_raw_end_index'].numpy().astype(str).tolist() doc_token2doc_indexs = inputs['doc_token2doc_index'].numpy().astype(str).tolist() all_answers = inputs['all_answers'].numpy().astype(str).tolist() answer_texts = inputs['answer_text'].numpy().tolist() context_texts = inputs['context_text'].numpy().tolist() question_texts = inputs['question_text'].numpy().tolist() is_impossibles = inputs['is_impossible'].numpy().tolist() p_masks = inputs['p_mask'].numpy().astype(np.int).tolist() for t in range(len(unique_ids)): itm = { 'unique_id': unique_ids[t], 'qas_id': qas_ids[t], 'question_text': question_texts[t].decode("utf8"), 'context_text': context_texts[t].decode("utf8"), 'answer_text': answer_texts[t].decode("utf8"), 'all_answers': json.loads(all_answers[t]), 'doc_token2char_raw_start_index': json.loads(doc_token2char_raw_start_indexs[t]), 'doc_token2char_raw_end_index': json.loads(doc_token2char_raw_end_indexs[t]), 'doc_token2doc_index': json.loads(doc_token2doc_indexs[t]), 'is_impossible': is_impossibles[t], 'p_mask': p_masks[t], 'offset': offsets[t] } unique_id_to_examples[unique_ids[t]] = itm qas_id_to_examples[qas_ids[t]].append(itm) for qas_id, examples in qas_id_to_examples.items(): example_all_predicts = [] answers = set() for example in examples: cur_unique_id = example['unique_id'] if cur_unique_id not in results: continue if example['is_impossible'] == 1: continue # if example['answer_text'] not in answers: # answers.append(example['answer_text']) answers \|= set(example['all_answers']) cur_result = results.get(cur_unique_id) cur_start_top_log_prob = cur_result['start_top_log_prob'] cur_start_top_index = cur_result['start_top_index'] cur_end_top_log_prob = cur_result['end_top_log_prob'] cur_end_top_index = cur_result['end_top_index'] cur_p_mask = example['p_mask'] for i in range(start_n_top): start_prob = cur_start_top_log_prob[i] start_index = cur_start_top_index[i] if not cur_p_mask[start_index]: continue for j in range(end_n_top): end_prob = cur_end_top_log_prob[j] end_index = cur_end_top_index[j] if not cur_p_mask[end_index]: continue answer_length = end_index - start_index + 1 if end_index < start_index or answer_length > self.max_answer_length: continue itm = { 'unique_id': cur_unique_id, 'start_prob': start_prob, 'start_index': start_index, 'end_prob': end_prob, 'end_index': end_index, 'predict_score': np.log(start_prob) + np.log(end_prob) } example_all_predicts.append(itm) if len(answers) != 0: total_count += 1 else: skip_count += 1 continue example_all_predicts.sort(key=lambda s: s['predict_score'], reverse=True) example_top_predicts = [] is_visited = set() for example_predict in example_all_predicts: if len(example_top_predicts) >= self.n_best_size: break example_feature = unique_id_to_examples[example_predict['unique_id']] if example_predict['start_index'] - example_feature['offset'] < 0 or example_predict['end_index'] - example_feature['offset'] < 0: predict_text = "" else: predict_start = example_feature['doc_token2char_raw_start_index'][ example_predict['start_index'] - example_feature['offset']] predict_end = example_feature['doc_token2char_raw_end_index'][ example_predict['end_index'] - example_feature['offset']] predict_text = example_feature['context_text'][predict_start: predict_end + 1].strip() if predict_text in is_visited: continue is_visited.add(predict_text) itm = { 'predict_text': predict_text, 'start_prob': example_predict['start_prob'], 'end_prob': example_predict['end_prob'], 'predict_score': example_predict['predict_score'] } example_top_predicts.append(itm) if len(example_top_predicts) == 0: example_top_predicts.append( { 'predict_text': "", 'start_prob': 0., 'end_prob': 0., 'predict_score': 0. } ) example_best_predict = example_top_predicts[0] cur_f1 = calc_f1_score(list(answers), example_best_predict['predict_text']) cur_em = calc_em_score(list(answers), example_best_predict['predict_text']) f1 += cur_f1 em += cur_em # debug if cur_f1 == 0 or cur_em == 0: example_output = {} example_output.update(example_best_predict) example_output['question'] = examples[0]['question_text'] example_output['answer'] = answers example_output['f1'] = cur_f1 example_output['em'] = cur_em print(example_output) # total_count = len(qas_id_to_examples) f1_score = f1 / total_count em_score = em / total_count logs = {} logs['skip_count'] = skip_count logs['total'] = total_count logs['val_f1_score'] = f1_score logs['val_em_score'] = em_score logs['val_f1_em_avg_score'] = (em_score + f1_score) / 2. return logs class EvaluatorForQaWithImpossible(tf.keras.callbacks.Callback): """ start_top_log_prob and end_top_log_prob's shape is [batch, k, k] ref: https://keras.io/examples/nlp/text_extraction_with_bert/ """ def __init__(self, validation_dataset, validation_steps, test_dataset, test_steps, report_dir, max_answer_length=64, n_best_size=5, is_impossible_threshold=0.5, weights=[1., 1., 1.]): self.validation_dataset = validation_dataset self.validation_steps = validation_steps self.test_dataset = test_dataset self.test_steps = test_steps self.max_answer_length = max_answer_length self.n_best_size = n_best_size self.report_dir = report_dir self.is_impossible_threshold = is_impossible_threshold self.weights = weights def on_epoch_end(self, epoch, logs=None): new_logs = self.eval_process(self.validation_dataset, self.validation_steps) logs = logs or {} logs.update(new_logs) print(f"\nEpoch: {epoch + 1}, val_f1_score: {logs['val_f1_score']:.4f}, " f"val_em_score: {logs['val_em_score']:.4f}, " f"val_f1_em_avg_score: {logs['val_f1_em_avg_score']:.4f}," f" val_f1_for_impossible: {logs['val_f1_for_impossible']:.4f}," f" val_f1_avg_score: {logs['val_f1_avg_score']:.4f},") def on_train_end(self, logs=None): logger.info("Start Evaluate.") if not os.path.exists(self.report_dir): os.makedirs(self.report_dir) new_logs = self.eval_process(self.test_dataset, self.test_steps) save_json(os.path.join(self.report_dir, 'performance.json'), new_logs) print_boxed(f"Question Answer Evaluation") pprint(new_logs) logger.info(f"Save question answer reports in {self.report_dir}") def eval_process(self, dataset, n_steps=None): f1 = 0 em = 0 total_count = 0 skip_count = 0 start_top_res, end_top_res, answer_prob, unique_id_res = self.model.predict(dataset, steps=n_steps) start_top_log_prob, start_top_index = start_top_res[:, :, 0], start_top_res[:, :, 1].astype(np.int) # [b, k] end_top_log_prob, end_top_index = end_top_res[:, :, :, 0], end_top_res[:, :, :, 1].astype(np.int) # [b, k, k] unique_id_res = unique_id_res.astype(np.int) # predict results results = {} for i in range(end_top_index.shape[0]): unique_id = unique_id_res[i][0] itm = { 'unique_id': unique_id, 'start_top_log_prob': start_top_log_prob[i], 'start_top_index': start_top_index[i], 'end_top_log_prob': end_top_log_prob[i], 'end_top_index': end_top_index[i], 'is_impossible_prob': answer_prob[i][0] } results[unique_id] = itm # raw inputs start_n_top, end_n_top = end_top_index.shape[1:] qas_id_to_examples = defaultdict(list) unique_id_to_examples = {} for idx, (inputs, outputs) in enumerate(dataset): if n_steps is not None and idx >= n_steps: break unique_ids = inputs['unique_id'].numpy().astype(np.int).tolist() offsets = inputs['offset'].numpy().astype(np.int).tolist() qas_ids = inputs['qas_id'].numpy().astype(str).tolist() doc_token2char_raw_start_indexs = inputs['doc_token2char_raw_start_index'].numpy().astype(str).tolist() doc_token2char_raw_end_indexs = inputs['doc_token2char_raw_end_index'].numpy().astype(str).tolist() doc_token2doc_indexs = inputs['doc_token2doc_index'].numpy().astype(str).tolist() all_answers = inputs['all_answers'].numpy().astype(str).tolist() answer_texts = inputs['answer_text'].numpy().tolist() context_texts = inputs['context_text'].numpy().tolist() question_texts = inputs['question_text'].numpy().tolist() is_impossibles = inputs['is_impossible'].numpy().tolist() p_masks = inputs['p_mask'].numpy().astype(np.int).tolist() for t in range(len(unique_ids)): itm = { 'unique_id': unique_ids[t], 'qas_id': qas_ids[t], 'question_text': question_texts[t].decode("utf8"), 'context_text': context_texts[t].decode("utf8"), 'answer_text': answer_texts[t].decode("utf8"), 'all_answers': json.loads(all_answers[t]), 'doc_token2char_raw_start_index': json.loads(doc_token2char_raw_start_indexs[t]), 'doc_token2char_raw_end_index': json.loads(doc_token2char_raw_end_indexs[t]), 'doc_token2doc_index': json.loads(doc_token2doc_indexs[t]), 'is_impossible': is_impossibles[t], 'p_mask': p_masks[t], 'offset': offsets[t] } unique_id_to_examples[unique_ids[t]] = itm qas_id_to_examples[qas_ids[t]].append(itm) ground_truth_for_impossible, predictions_for_impossible = [], [] for qas_id, examples in qas_id_to_examples.items(): example_all_predicts = [] answers = set() for example in examples: cur_unique_id = example['unique_id'] if cur_unique_id not in results: continue # if example['answer_text'] not in answers: # answers.append(example['answer_text']) answers \|= set(example['all_answers']) cur_result = results.get(cur_unique_id) cur_start_top_log_prob = cur_result['start_top_log_prob'] cur_start_top_index = cur_result['start_top_index'] cur_end_top_log_prob = cur_result['end_top_log_prob'] cur_end_top_index = cur_result['end_top_index'] ground_truth_for_impossible.append(example['is_impossible']) predictions_for_impossible.append(int(cur_result['is_impossible_prob'] >= self.is_impossible_threshold)) if example['is_impossible'] == 1: continue cur_p_mask = example['p_mask'] for i in range(start_n_top): start_prob = cur_start_top_log_prob[i] start_index = cur_start_top_index[i] if not cur_p_mask[start_index]: continue for j in range(end_n_top): end_prob = cur_end_top_log_prob[i, j] end_index = cur_end_top_index[i, j] if not cur_p_mask[end_index]: continue answer_length = end_index - start_index + 1 if end_index < start_index or answer_length > self.max_answer_length: continue itm = { 'unique_id': cur_unique_id, 'start_prob': start_prob, 'start_index': start_index, 'end_prob': end_prob, 'end_index': end_index, 'predict_score': np.log(end_prob) } example_all_predicts.append(itm) if len(answers) != 0 and "" not in answers: total_count += 1 else: skip_count += 1 continue example_all_predicts.sort(key=lambda s: s['predict_score'], reverse=True) example_top_predicts = [] is_visited = set() for example_predict in example_all_predicts: if len(example_top_predicts) >= self.n_best_size: break example_feature = unique_id_to_examples[example_predict['unique_id']] if example_predict['start_index'] - example_feature['offset'] < 0 or example_predict['end_index'] - example_feature['offset'] < 0: predict_text = "" else: predict_start = example_feature['doc_token2char_raw_start_index'][ example_predict['start_index'] - example_feature['offset']] predict_end = example_feature['doc_token2char_raw_end_index'][ example_predict['end_index'] - example_feature['offset']] predict_text = example_feature['context_text'][predict_start: predict_end + 1].strip() if predict_text in is_visited: continue is_visited.add(predict_text) itm = { 'predict_text': predict_text, 'start_prob': example_predict['start_prob'], 'end_prob': example_predict['end_prob'], 'predict_score': example_predict['predict_score'] } example_top_predicts.append(itm) if len(example_top_predicts) == 0: example_top_predicts.append( { 'predict_text': "", 'start_prob': 0., 'end_prob': 0., 'predict_score': 0. } ) example_best_predict = example_top_predicts[0] cur_f1 = calc_f1_score(list(answers), example_best_predict['predict_text']) cur_em = calc_em_score(list(answers), example_best_predict['predict_text']) f1 += cur_f1 em += cur_em # debug if cur_f1 == 0 or cur_em == 0: example_output = {} example_output.update(example_best_predict) example_output['question'] = examples[0]['question_text'] example_output['answer'] = answers example_output['f1'] = cur_f1 example_output['em'] = cur_em print(example_output) # total_count = len(qas_id_to_examples) f1_score = f1 / total_count em_score = em / total_count cm = ConfusionMatrix(ground_truth_for_impossible, predictions_for_impossible) logs = {} logs['skip_count'] = skip_count logs['total'] = total_count logs['val_f1_score'] = f1_score logs['val_em_score'] = em_score logs['val_f1_em_avg_score'] = (em_score * self.weights[0] + f1_score * self.weights[1]) / sum(self.weights[:2]) logs['val_f1_for_impossible'] = cm.avg_f1_score(average='macro') logs['val_accuracy_for_impossible'] = cm.overall_accuracy() logs['val_f1_avg_score'] = (em_score * self.weights[0] + f1_score * self.weights[1] + logs['val_f1_for_impossible'] * self.weights[2]) / sum(self.weights) return logs	en	0.509923	# -- coding: utf-8 -- # @Time : 2020-07-30 15:06 # @Author : yingyuankai # @Email : <EMAIL> # @File : qa_evaluators.py start_top_log_prob and end_top_log_prob's shape is [batch, k] ref: https://keras.io/examples/nlp/text_extraction_with_bert/ # [b, k] # [b, k] # predict results # raw inputs # if example['answer_text'] not in answers: # answers.append(example['answer_text']) # debug # total_count = len(qas_id_to_examples) start_top_log_prob and end_top_log_prob's shape is [batch, k, k] ref: https://keras.io/examples/nlp/text_extraction_with_bert/ # [b, k] # [b, k, k] # predict results # raw inputs # if example['answer_text'] not in answers: # answers.append(example['answer_text']) # debug # total_count = len(qas_id_to_examples)	2.171053	2
tests/conftest.py	junjunjunk/torchgpipe	532	10262	<reponame>junjunjunk/torchgpipe import pytest import torch @pytest.fixture(autouse=True) def manual_seed_zero(): torch.manual_seed(0) @pytest.fixture(scope='session') def cuda_sleep(): # Warm-up CUDA. torch.empty(1, device='cuda') # From test/test_cuda.py in PyTorch. start = torch.cuda.Event(enable_timing=True) end = torch.cuda.Event(enable_timing=True) start.record() torch.cuda._sleep(1000000) end.record() end.synchronize() cycles_per_ms = 1000000 / start.elapsed_time(end) def cuda_sleep(seconds): torch.cuda._sleep(int(seconds * cycles_per_ms * 1000)) return cuda_sleep def pytest_report_header(): return f'torch: {torch.__version__}'	import pytest import torch @pytest.fixture(autouse=True) def manual_seed_zero(): torch.manual_seed(0) @pytest.fixture(scope='session') def cuda_sleep(): # Warm-up CUDA. torch.empty(1, device='cuda') # From test/test_cuda.py in PyTorch. start = torch.cuda.Event(enable_timing=True) end = torch.cuda.Event(enable_timing=True) start.record() torch.cuda._sleep(1000000) end.record() end.synchronize() cycles_per_ms = 1000000 / start.elapsed_time(end) def cuda_sleep(seconds): torch.cuda._sleep(int(seconds * cycles_per_ms * 1000)) return cuda_sleep def pytest_report_header(): return f'torch: {torch.__version__}'	en	0.545309	# Warm-up CUDA. # From test/test_cuda.py in PyTorch.	2.126187	2
lib/python/treadmill/scheduler/__init__.py	drienyov/treadmill	0	10263	"""Treadmill hierarchical scheduler. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import abc import collections import datetime import heapq import itertools import logging import operator import sys import time import enum import numpy as np import six _LOGGER = logging.getLogger(__name__) MAX_PRIORITY = 100 DEFAULT_RANK = 100 _UNPLACED_RANK = sys.maxsize DIMENSION_COUNT = None _MAX_UTILIZATION = float('inf') _GLOBAL_ORDER_BASE = time.mktime((2014, 1, 1, 0, 0, 0, 0, 0, 0)) # 21 day DEFAULT_SERVER_UPTIME = 21 * 24 * 60 * 60 # 1 day MIN_SERVER_UPTIME = 1 * 24 * 60 * 60 # 7 days DEFAULT_MAX_APP_LEASE = 7 * 24 * 60 * 60 # Default partition threshold DEFAULT_THRESHOLD = 0.9 # pylint: disable=C0302,too-many-lines def _bit_count(value): """Returns number of bits set. """ count = 0 while value: value &= value - 1 count += 1 return count def zero_capacity(): """Returns zero capacity vector. """ assert DIMENSION_COUNT is not None, 'Dimension count not set.' return np.zeros(DIMENSION_COUNT) def eps_capacity(): """Returns eps capacity vector. """ assert DIMENSION_COUNT is not None, 'Dimension count not set.' return np.array( [np.finfo(float).eps for _x in range(0, DIMENSION_COUNT)] ) def _global_order(): """Use timestamp in nanoseconds, from Jan 1st 2014, to break tie in scheduling conflicts for apps of the same priority, in a FIFO fashion. """ # Take the current EPOCH in nanosec global_order = int(time.time() * 1000000) - _GLOBAL_ORDER_BASE return global_order def utilization(demand, allocated, available): """Calculates utilization score. """ return np.max(np.subtract(demand, allocated) / available) def _all(oper, left, right): """Short circuit all for ndarray. """ return all( oper(ai, bi) for ai, bi in six.moves.zip(left, right) ) def _any(oper, left, right): """Short circuit any for ndarray. """ return any( oper(ai, bi) for ai, bi in six.moves.zip(left, right) ) def _any_eq(left, right): """Short circuit any eq for ndarray. """ return _any(operator.eq, left, right) def _any_isclose(left, right): """Short circuit any isclose for ndarray. """ return _any(np.isclose, left, right) def _any_lt(left, right): """Short circuit any lt for ndarray. """ return _any(operator.lt, left, right) def _any_le(left, right): """Short circuit any le for ndarray. """ return _any(operator.le, left, right) def _any_gt(left, right): """Short circuit any gt for ndarray. """ return _any(operator.gt, left, right) def _any_ge(left, right): """Short circuit any ge for ndarray. """ return _any(operator.ge, left, right) def _all_eq(left, right): """Short circuit all eq for ndarray. """ return _all(operator.eq, left, right) def _all_isclose(left, right): """Short circuit all isclose for ndarray. """ return _all(np.isclose, left, right) def _all_lt(left, right): """Short circuit all lt for ndarray. """ return _all(operator.lt, left, right) def _all_le(left, right): """Short circuit all le for ndarray. """ return _all(operator.le, left, right) def _all_gt(left, right): """Short circuit all gt for ndarray. """ return _all(operator.gt, left, right) def _all_ge(left, right): """Short circuit all ge for ndarray. """ return _all(operator.ge, left, right) class IdentityGroup: """Identity group. """ __slots__ = ( 'available', 'count', ) def __init__(self, count=0): self.count = count self.available = set(range(0, count)) def acquire(self): """Return next available identity or None. """ if self.available: return self.available.pop() else: return None def release(self, ident): """Mark identity as available. """ if ident < self.count: self.available.add(ident) def adjust(self, count): """Adjust identities with new count. If count is larger, add additional identities to the set. If count is lower, remove identities that are no longer valid. All apps that have invalid identities will be adjusted in the schedule cycle. """ if count >= self.count: self.available ^= set(six.moves.xrange(self.count, count)) else: self.available -= set(six.moves.xrange(count, self.count)) self.count = count class State(enum.Enum): """Enumeration of node/server states. """ # Ready to accept new applications. # TODO: Fix attribute name up = 'up' # pylint: disable=invalid-name # Applications need to be migrated. down = 'down' # Existing applications can stay, but will not accept new. frozen = 'frozen' class Affinity: """Model affinity and affinity limits. """ __slots__ = ( 'name', 'limits', 'constraints', ) def __init__(self, name, limits=None): self.name = name self.limits = collections.defaultdict(lambda: float('inf')) if limits: self.limits.update(limits) # freeze affinity shape constraints. self.constraints = tuple([self.name] + sorted(self.limits.values())) class Application: """Application object. """ __slots__ = ( 'global_order', 'name', 'demand', 'affinity', 'priority', 'allocation', 'data_retention_timeout', 'server', 'lease', 'identity', 'identity_group', 'identity_group_ref', 'schedule_once', 'evicted', 'placement_expiry', 'renew', 'unschedule', 'final_rank', 'final_util', 'constraints', ) def __init__(self, name, priority, demand, affinity, affinity_limits=None, data_retention_timeout=0, lease=0, identity_group=None, identity=None, schedule_once=False): self.global_order = _global_order() self.allocation = None self.server = None self.name = name self.affinity = Affinity(affinity, affinity_limits) self.priority = priority self.demand = np.array(demand, dtype=float) self.data_retention_timeout = data_retention_timeout self.lease = lease self.identity_group = identity_group self.identity = identity self.identity_group_ref = None self.schedule_once = schedule_once self.evicted = False self.unschedule = False self.placement_expiry = None self.renew = False def shape(self): """Return tuple of application (constraints, demand). Application shape is tuple of constraints that affect application placement. Currently this includes affinity constraints and app lease time. """ constraints = (self.affinity.constraints + (self.lease,)) if self.allocation: constraints += self.allocation.constraints return constraints, self.demand def acquire_identity(self): """Try to acquire identity if belong to the group. Returns True if successfull or if identity group is none. """ if not self.identity_group_ref: return True if self.identity is None: self.identity = self.identity_group_ref.acquire() _LOGGER.info('Acquired identity: %s: %s - %s', self.name, self.identity_group, self.identity) return self.identity is not None def release_identity(self): """Release app identity. """ if self.identity_group_ref and self.identity is not None: self.identity_group_ref.release(self.identity) self.identity = None def force_set_identity(self, identity): """Force identity of the app. """ if identity is not None: assert self.identity_group_ref self.identity = identity self.identity_group_ref.available.discard(identity) def has_identity(self): """Checks if app has identity if identity group is specified. """ return self.identity_group_ref is None or self.identity is not None @property def traits(self): """The app traits are derived from allocation. """ if self.allocation is None: return 0 else: return self.allocation.traits @six.add_metaclass(abc.ABCMeta) class Strategy: """Base class for all placement strategies. """ @abc.abstractmethod def suggested_node(self): """Suggested node that should be tried first. """ pass @abc.abstractmethod def next_node(self): """Next node to try, if previous suggestion was rejected. """ pass class SpreadStrategy(Strategy): """Spread strategy will suggest new node for each subsequent placement. """ __slots__ = ( 'current_idx', 'node', ) def __init__(self, node): self.current_idx = 0 self.node = node def suggested_node(self): """Suggest next node from the cycle. """ for _ in six.moves.xrange(0, len(self.node.children)): if self.current_idx == len(self.node.children): self.current_idx = 0 current = self.node.children[self.current_idx] self.current_idx += 1 if current: return current # Not a single non-none node. return None def next_node(self): """Suggest next node from the cycle. """ return self.suggested_node() class PackStrategy(Strategy): """Pack strategy will suggest same node until it is full. """ __slots__ = ( 'current_idx', 'node', ) def __init__(self, node): self.current_idx = 0 self.node = node def suggested_node(self): """Suggest same node as previous placement. """ for _ in six.moves.xrange(0, len(self.node.children)): if self.current_idx == len(self.node.children): self.current_idx = 0 node = self.node.children[self.current_idx] if node: return node return None def next_node(self): """Suggest next node from the cycle. """ self.current_idx += 1 return self.suggested_node() class TraitSet: """Hierarchical set of traits. """ __slots__ = ( 'self_traits', 'children_traits', 'traits', ) def __init__(self, traits=0): if not traits: traits = 0 # Private traits. assert isinstance(traits, six.integer_types) self.self_traits = traits # Union of all children traits. self.children_traits = dict() self._recalculate() def _recalculate(self): """Calculate combined set of all traits. """ self.traits = self.self_traits for trait in six.itervalues(self.children_traits): self.traits \|= trait def has(self, traits): """Check if all traits are present. """ return (self.traits & traits) == traits def add(self, child, traits): """Add a child with given traits. """ # Update children traits. self.children_traits[child] = traits self._recalculate() def remove(self, child): """Remove child traits from the list. """ if child in self.children_traits: del self.children_traits[child] self._recalculate() def is_same(self, other): """Compares own traits, ignore child. """ return self.self_traits == other.self_traits class AffinityCounter: """Manages affinity count. """ __slots__ = ( 'affinity_counter', ) def __init__(self): self.affinity_counter = collections.Counter() class Node: """Abstract placement node. """ __slots__ = ( 'name', 'level', 'free_capacity', 'parent', 'children', 'children_by_name', 'traits', 'labels', 'affinity_counters', 'valid_until', '_state', '_state_since', ) def __init__(self, name, traits, level, valid_until=0): self.name = name self.level = level self.free_capacity = zero_capacity() self.parent = None self.children = list() self.children_by_name = dict() self.traits = TraitSet(traits) self.labels = set() self.affinity_counters = collections.Counter() self.valid_until = valid_until self._state = State.up self._state_since = time.time() def empty(self): """Return true if there are no children. """ return not bool(self.children_by_name) def children_iter(self): """Iterate over active children. """ for child in self.children: if child: yield child def get_state(self): """Returns tuple of (state, since). """ return self. _state, self._state_since def set_state(self, state, since): """Sets the state and time since. """ if self._state is not state: self._state_since = since self._state = state _LOGGER.debug('state: %s - (%s, %s)', self.name, self._state, self._state_since) @property def state(self): """Return current state. """ return self._state @state.setter def state(self, new_state): """Set node state and records time. """ self.set_state(new_state, time.time()) def add_child_traits(self, node): """Recursively add child traits up. """ self.traits.add(node.name, node.traits.traits) if self.parent: self.parent.remove_child_traits(self.name) self.parent.add_child_traits(self) def adjust_valid_until(self, child_valid_until): """Recursively adjust valid until time. """ if child_valid_until: self.valid_until = max(self.valid_until, child_valid_until) else: if self.empty(): self.valid_until = 0 else: self.valid_until = max([node.valid_until for node in self.children_iter()]) if self.parent: self.parent.adjust_valid_until(child_valid_until) def remove_child_traits(self, node_name): """Recursively remove child traits up. """ self.traits.remove(node_name) if self.parent: self.parent.remove_child_traits(self.name) self.parent.add_child_traits(self) def reset_children(self): """Reset children to empty list. """ for child in self.children_iter(): child.parent = None self.children = list() self.children_by_name = dict() def add_node(self, node): """Add child node, set the traits and propagate traits up. """ assert node.parent is None assert node.name not in self.children_by_name node.parent = self self.children.append(node) self.children_by_name[node.name] = node self.add_child_traits(node) self.increment_affinity(node.affinity_counters) self.add_labels(node.labels) self.adjust_valid_until(node.valid_until) def add_labels(self, labels): """Recursively add labels to self and parents. """ self.labels.update(labels) if self.parent: self.parent.add_labels(self.labels) def remove_node(self, node): """Remove child node and adjust the traits. """ assert node.name in self.children_by_name del self.children_by_name[node.name] for idx in six.moves.xrange(0, len(self.children)): if self.children[idx] == node: self.children[idx] = None self.remove_child_traits(node.name) self.decrement_affinity(node.affinity_counters) self.adjust_valid_until(None) node.parent = None return node def remove_node_by_name(self, nodename): """Removes node by name. """ assert nodename in self.children_by_name return self.remove_node(self.children_by_name[nodename]) def check_app_constraints(self, app): """Find app placement on the node. """ if app.allocation is not None: if app.allocation.label not in self.labels: _LOGGER.info('Missing label: %s on %s', app.allocation.label, self.name) return False if app.traits != 0 and not self.traits.has(app.traits): _LOGGER.info('Missing traits: %s on %s', app.traits, self.name) return False if not self.check_app_affinity_limit(app): return False if _any_gt(app.demand, self.free_capacity): _LOGGER.info('Not enough free capacity: %s', self.free_capacity) return False return True def check_app_affinity_limit(self, app): """Check app affinity limits """ count = self.affinity_counters[app.affinity.name] limit = app.affinity.limits[self.level] return count < limit def put(self, _app): """Abstract method, should never be called. """ raise Exception('Not implemented.') def size(self, label): """Returns total capacity of the children. """ if self.empty() or label not in self.labels: return eps_capacity() return np.sum([ n.size(label) for n in self.children_iter()], 0) def members(self): """Return set of all leaf node names. """ names = dict() for node in self.children_iter(): names.update(node.members()) return names def increment_affinity(self, counters): """Increment affinity counters recursively. """ self.affinity_counters.update(counters) if self.parent: self.parent.increment_affinity(counters) def decrement_affinity(self, counters): """Decrement affinity counters recursively. """ self.affinity_counters.subtract(counters) if self.parent: self.parent.decrement_affinity(counters) class Bucket(Node): """Collection of nodes/buckets. """ __slots__ = ( 'affinity_strategies', 'traits', ) _default_strategy_t = SpreadStrategy def __init__(self, name, traits=0, level=None): super(Bucket, self).__init__(name, traits, level) self.affinity_strategies = dict() self.traits = TraitSet(traits) def set_affinity_strategy(self, affinity, strategy_t): """Initilaizes placement strategy for given affinity. """ self.affinity_strategies[affinity] = strategy_t(self) def get_affinity_strategy(self, affinity): """Returns placement strategy for the affinity, defaults to spread. """ if affinity not in self.affinity_strategies: self.set_affinity_strategy(affinity, Bucket._default_strategy_t) return self.affinity_strategies[affinity] def adjust_capacity_up(self, new_capacity): """Node can only increase capacity. """ self.free_capacity = np.maximum(self.free_capacity, new_capacity) if self.parent: self.parent.adjust_capacity_up(self.free_capacity) def adjust_capacity_down(self, prev_capacity=None): """Called when capacity is decreased. """ if self.empty(): self.free_capacity = zero_capacity() if self.parent: self.parent.adjust_capacity_down() else: if prev_capacity is not None and _all_lt(prev_capacity, self.free_capacity): return free_capacity = zero_capacity() for child_node in self.children_iter(): if child_node.state is not State.up: continue free_capacity = np.maximum(free_capacity, child_node.free_capacity) # If resulting free_capacity is less the previous, we need to # adjust the parent, otherwise, nothing needs to be done. prev_capacity = self.free_capacity.copy() if _any_lt(free_capacity, self.free_capacity): self.free_capacity = free_capacity if self.parent: self.parent.adjust_capacity_down(prev_capacity) def add_node(self, node): """Adds node to the bucket. """ super(Bucket, self).add_node(node) self.adjust_capacity_up(node.free_capacity) def remove_node(self, node): """Removes node from the bucket. """ super(Bucket, self).remove_node(node) # if _any_isclose(self.free_capacity, node.free_capacity): self.adjust_capacity_down(node.free_capacity) return node def put(self, app): """Try to put app on one of the nodes that belong to the bucket. """ # Check if it is feasible to put app on some node low in the # hierarchy _LOGGER.debug('bucket.put: %s => %s', app.name, self.name) if not self.check_app_constraints(app): return False strategy = self.get_affinity_strategy(app.affinity.name) node = strategy.suggested_node() if node is None: _LOGGER.debug('All nodes in the bucket deleted.') return False nodename0 = node.name first = True while True: # End of iteration. if not first and node.name == nodename0: _LOGGER.debug('Finished iterating on: %s.', self.name) break first = False _LOGGER.debug('Trying node: %s:', node.name) if node.state is not State.up: _LOGGER.debug('Node not up: %s, %s', node.name, node.state) else: if node.put(app): return True node = strategy.next_node() return False class Server(Node): """Server object, final app placement. """ __slots__ = ( 'init_capacity', 'apps', 'up_since', 'presence_id', ) def __init__(self, name, capacity, up_since=0, valid_until=0, traits=0, label=None, presence_id=None): super(Server, self).__init__(name, traits=traits, level='server', valid_until=valid_until) self.labels = set([label]) self.init_capacity = np.array(capacity, dtype=float) self.free_capacity = self.init_capacity.copy() self.apps = dict() self.up_since = up_since self.presence_id = presence_id def __str__(self): return 'server: %s %s' % (self.name, self.init_capacity) def is_same(self, other): """Compares capacity and traits against another server. valid_until is ignored, as server comes up after reboot will have different valid_until value. """ return (self.labels == other.labels and _all_eq(self.init_capacity, other.init_capacity) and self.traits.is_same(other.traits)) def put(self, app): """Tries to put the app on the server. """ assert app.name not in self.apps _LOGGER.debug('server.put: %s => %s', app.name, self.name) if not self.check_app_lifetime(app): return False if not self.check_app_constraints(app): return False prev_capacity = self.free_capacity.copy() self.free_capacity -= app.demand self.apps[app.name] = app self.increment_affinity([app.affinity.name]) app.server = self.name if self.parent: self.parent.adjust_capacity_down(prev_capacity) if app.placement_expiry is None: app.placement_expiry = time.time() + app.lease return True def restore(self, app, placement_expiry=None): """Put app back on the server, ignore app lifetime. """ _LOGGER.debug('server.restore: %s => %s (%s)', app.name, self.name, placement_expiry) lease = app.lease # If not explicit if placement_expiry is None: placement_expiry = app.placement_expiry app.lease = 0 rc = self.put(app) app.lease = lease app.placement_expiry = placement_expiry return rc def renew(self, app): """Try to extend the placement for app lease. """ can_renew = self.check_app_lifetime(app) if can_renew: app.placement_expiry = time.time() + app.lease return can_renew def check_app_lifetime(self, app): """Check if the app lease fits until server is rebooted. """ # app with 0 lease can be placed anywhere (ignore potentially # expired servers) if not app.lease: return True return time.time() + app.lease < self.valid_until def remove(self, app_name): """Removes app from the server. """ assert app_name in self.apps app = self.apps[app_name] del self.apps[app_name] app.server = None app.evicted = True app.unschedule = False app.placement_expiry = None self.free_capacity += app.demand self.decrement_affinity([app.affinity.name]) if self.parent: self.parent.adjust_capacity_up(self.free_capacity) def remove_all(self): """Remove all apps. """ # iterate over copy of the keys, as we are removing them in the loop. for appname in list(self.apps): self.remove(appname) def size(self, label): """Return server capacity. """ if label not in self.labels: return eps_capacity() return self.init_capacity def members(self): """Return set of all leaf node names. """ return {self.name: self} def set_state(self, state, since): """Change host state. """ if self.state is state: return super(Server, self).set_state(state, since) if state == State.up: if self.parent: self.parent.adjust_capacity_up(self.free_capacity) elif state in (State.down, State.frozen): if self.parent: self.parent.adjust_capacity_down(self.free_capacity) else: raise Exception('Invalid state: ' % state) class Allocation: """Allocation manages queue of apps sharing same reserved capacity. In reality allocation is tied to grn via application proid. Applications within the allocation are organized by application priority. Allocations are ranked, and the rank is used to globally order applications from different allocations into global queue. Default allocation has rank 100. Defining allocation with lower rank will result in all it's applications to be evaluated first regardless of utilization. This is used to model "system" applications that should be always present regardless of utilization. Allocation queue can be capped with max_utilization parameter. If set, it will specify the max_utilization which will be considered for scheduling. """ __slots__ = ( 'reserved', 'rank', 'rank_adjustment', 'traits', 'label', 'max_utilization', 'apps', 'sub_allocations', 'path', 'constraints', ) def __init__(self, reserved=None, rank=None, traits=None, max_utilization=None, partition=None): self.set_reserved(reserved) self.rank = None self.rank_adjustment = 0 self.traits = 0 self.label = partition self.max_utilization = _MAX_UTILIZATION self.reserved = zero_capacity() self.set_max_utilization(max_utilization) self.set_traits(traits) self.update(reserved, rank, 0) self.apps = dict() self.sub_allocations = dict() self.path = [] # Freeze shape constraintes. self.constraints = (self.label, self.traits,) @property def name(self): """Returns full allocation name. """ return '/'.join(self.path) def set_reserved(self, reserved): """Update reserved capacity. """ if reserved is None: self.reserved = zero_capacity() elif isinstance(reserved, int): assert reserved == 0 self.reserved = zero_capacity() elif isinstance(reserved, float): assert reserved == 0.0 self.reserved = zero_capacity() elif isinstance(reserved, list): assert len(reserved) == DIMENSION_COUNT self.reserved = np.array(reserved, dtype=float) elif isinstance(reserved, np.ndarray): self.reserved = reserved else: assert 'Unsupported type: %r' % type(reserved) def update(self, reserved, rank, rank_adjustment, max_utilization=None): """Updates allocation. """ if rank is not None: self.rank = rank else: self.rank = DEFAULT_RANK if rank_adjustment is not None: self.rank_adjustment = rank_adjustment self.set_reserved(reserved) self.set_max_utilization(max_utilization) def set_max_utilization(self, max_utilization): """Sets max_utilization, accounting for default None value. """ if max_utilization is not None: self.max_utilization = max_utilization else: self.max_utilization = _MAX_UTILIZATION def set_traits(self, traits): """Set traits, account for default None value. """ if not traits: self.traits = 0 else: self.traits = traits def add(self, app): """Add application to the allocation queue. Once added, the scheduler will make an attempt to place the app on one of the cell nodes. """ # Check that there are no duplicate app names. if app.name in self.apps: _LOGGER.warning( 'Duplicate app on alllocation queue: %s', app.name ) return app.allocation = self self.apps[app.name] = app def remove(self, name): """Remove application from the allocation queue. """ if name in self.apps: self.apps[name].allocation = None del self.apps[name] def priv_utilization_queue(self): """Returns tuples for sorted by global utilization. Apps in the queue are ordered by priority, insertion order. Adding or removing maintains invariant that apps utilization monotonically increases as well. Returns local prioritization queue in a tuple where first element is utilization ratio, so that this queue is suitable for merging into global priority queue. """ def _app_key(app): """Compares apps by priority, state, global index """ return (-app.priority, 0 if app.server else 1, app.global_order, app.name) prio_queue = sorted(six.viewvalues(self.apps), key=_app_key) acc_demand = zero_capacity() available = self.reserved + np.finfo(float).eps util_before = utilization(acc_demand, self.reserved, available) for app in prio_queue: acc_demand = acc_demand + app.demand util_after = utilization(acc_demand, self.reserved, available) # Priority 0 apps are treated specially - utilization is set to # max float. # # This ensures that they are at the end of the all queues. if app.priority == 0: util_before = _MAX_UTILIZATION util_after = _MAX_UTILIZATION # All things equal, already scheduled applications have priority # over pending. pending = 0 if app.server else 1 if util_after <= self.max_utilization - 1: rank = self.rank if util_before < 0: rank -= self.rank_adjustment else: rank = _UNPLACED_RANK entry = (rank, util_before, util_after, pending, app.global_order, app) util_before = util_after yield entry def utilization_queue(self, free_capacity, visitor=None): """Returns utilization queue including the sub-allocs. All app queues from self and sub-allocs are merged in standard order, and then utilization is recalculated based on total reserved capacity of this alloc and sub-allocs combined. The function maintains invariant that any app (self or inside sub-alloc with utilization < 1 will remain with utilzation < 1. """ total_reserved = self.total_reserved() queues = [ alloc.utilization_queue(free_capacity, visitor) for alloc in six.itervalues(self.sub_allocations) ] queues.append(self.priv_utilization_queue()) acc_demand = zero_capacity() available = total_reserved + free_capacity + np.finfo(float).eps util_before = utilization(acc_demand, total_reserved, available) for item in heapq.merge(*queues): rank, _u_before, _u_after, pending, order, app = item acc_demand = acc_demand + app.demand util_after = utilization(acc_demand, total_reserved, available) if app.priority == 0: util_before = _MAX_UTILIZATION util_after = _MAX_UTILIZATION # - lower rank allocations take precedence. # - for same rank, utilization takes precedence # - False < True, so for apps with same utilization we prefer # those that already running (False == not pending) # - Global order entry = (rank, util_before, util_after, pending, order, app) if visitor: visitor(self, entry, acc_demand) util_before = util_after yield entry def total_reserved(self): """Total reserved capacity including sub-allocs. """ return six.moves.reduce( lambda acc, alloc: acc + alloc.total_reserved(), six.itervalues(self.sub_allocations), self.reserved ) def add_sub_alloc(self, name, alloc): """Add child allocation. """ self.sub_allocations[name] = alloc assert not alloc.path alloc.path = self.path + [name] alloc.label = self.label def remove_sub_alloc(self, name): """Remove chlid allocation. """ if name in self.sub_allocations: del self.sub_allocations[name] def get_sub_alloc(self, name): """Return sub allocation, create empty if it does not exist. """ if name not in self.sub_allocations: self.add_sub_alloc(name, Allocation()) return self.sub_allocations[name] def all_apps(self): """Return all apps in allocation and sub-allocations.""" all_apps = list(six.itervalues(self.apps)) for alloc in six.itervalues(self.sub_allocations): all_apps.extend(alloc.all_apps()) return all_apps class Partition: """Cell partition. """ __slots__ = ( 'allocation', 'max_server_uptime', 'max_lease', 'threshold', 'label', '_reboot_buckets', '_reboot_dates', '_reboot_last', ) def __init__(self, max_server_uptime=None, max_lease=None, threshold=None, label=None, reboot_schedule=None, now=None): self.label = label self.allocation = Allocation(partition=label) # Default - if not max_server_uptime: max_server_uptime = DEFAULT_SERVER_UPTIME if not max_lease: max_lease = DEFAULT_MAX_APP_LEASE if not threshold: threshold = DEFAULT_THRESHOLD self.max_server_uptime = max_server_uptime self.max_lease = max_lease self.threshold = threshold if not reboot_schedule: # reboot every day reboot_schedule = {day: (23, 59, 59) for day in range(7)} if not now: now = time.time() self._reboot_dates = reboot_dates( reboot_schedule, start_date=datetime.date.fromtimestamp(now) ) self._reboot_buckets = [] self._reboot_last = now self.tick(now) def _find_bucket(self, timestamp): """Try to find bucket with given timestamp. """ for bucket in self._reboot_buckets: if bucket.timestamp == timestamp: return bucket return None def add(self, server, timestamp=None): """Add server. """ bucket = None if timestamp: bucket = self._find_bucket(timestamp) # servers with larger than max lifetime should be rebooted at # the next opportunity if (self._reboot_buckets[0].timestamp > server.up_since + DEFAULT_SERVER_UPTIME): bucket = self._reboot_buckets[0] if not bucket: bucket = min(reversed(self._reboot_buckets), key=lambda b: b.cost(server)) bucket.add(server) def remove(self, server): """Remove server. """ for bucket in self._reboot_buckets: bucket.remove(server) def tick(self, now): """Do per-tick-bookkeeping. """ while self._reboot_last <= now + DEFAULT_SERVER_UPTIME: bucket = RebootBucket(next(self._reboot_dates)) self._reboot_buckets.append(bucket) self._reboot_last = bucket.timestamp while self._reboot_buckets[0].timestamp < now: self._reboot_buckets.pop(0) class PartitionDict(dict): """Dict that creates partitions on demand. We use this instead of collections.defaultdict so that we can provide the new partition with its label, to be propagated to its allocations. """ def __missing__(self, label): """Create a new partition, passing the label to its constructor. """ self[label] = Partition(label=label) return self[label] # pylint: disable=invalid-name def reboot_dates(schedule, start_date=None): """Generate list of valid reboot dates. """ date = datetime.date.today() if start_date: date = start_date while True: weekday = date.weekday() if weekday in schedule: h, m, s = schedule[weekday] yield time.mktime((date.year, date.month, date.day, h, m, s, 0, 0, 0)) date += datetime.timedelta(days=1) class RebootBucket: """Bucket of servers to be rebooted at the same time. """ __slots__ = ( 'timestamp', 'servers', ) def __init__(self, timestamp): self.timestamp = timestamp self.servers = [] def add(self, server): """Add server to this bucket. """ self.servers.append(server) server.valid_until = self.timestamp _LOGGER.info('Setting valid until on server: %s %s', server.name, server.valid_until) def remove(self, server): """Remove server from this bucket. """ try: self.servers.remove(server) except ValueError: pass def cost(self, server): """The cost of adding server to this bucket. """ if self.timestamp > server.up_since + DEFAULT_SERVER_UPTIME: return float('inf') if self.timestamp < server.up_since + MIN_SERVER_UPTIME: return float('inf') return len(self.servers) class PlacementFeasibilityTracker: """Tracks similar apps placement failures.""" def __init__(self): self.recorder = dict() def feasible(self, app): """Checks if it is feasible to satisfy demand.""" constraints, demand = app.shape() if constraints in self.recorder: # If demand is >= than recorded failure, placement is not feasible. if _all_ge(demand, self.recorder[constraints]): return False return True def adjust(self, app): """Adjust info about failed placement.""" constraints, demand = app.shape() if constraints not in self.recorder: self.recorder[constraints] = demand else: if _all_le(demand, self.recorder[constraints]): self.recorder[constraints] = demand class Cell(Bucket): """Top level node. """ __slots__ = ( 'partitions', 'next_event_at', 'apps', 'identity_groups', ) def __init__(self, name): super(Cell, self).__init__(name, traits=0, level='cell') self.partitions = PartitionDict() self.apps = dict() self.identity_groups = collections.defaultdict(IdentityGroup) self.next_event_at = np.inf def add_app(self, allocation, app): """Adds application to the scheduled list. """ assert allocation is not None if app.allocation: app.allocation.remove(app.name) allocation.add(app) self.apps[app.name] = app if app.identity_group: app.identity_group_ref = self.identity_groups[app.identity_group] def remove_app(self, appname): """Remove app from scheduled list. """ if appname not in self.apps: return app = self.apps[appname] servers = self.members() if app.server in servers: servers[app.server].remove(app.name) if app.allocation: app.allocation.remove(app.name) app.release_identity() del self.apps[appname] def configure_identity_group(self, name, count): """Add identity group to the cell. """ if name not in self.identity_groups: self.identity_groups[name] = IdentityGroup(count) else: self.identity_groups[name].adjust(count) def remove_identity_group(self, name): """Remove identity group. """ ident_group = self.identity_groups.get(name) if ident_group: in_use = False for app in six.itervalues(self.apps): if app.identity_group_ref == ident_group: ident_group.adjust(0) in_use = True break if not in_use: del self.identity_groups[name] def _fix_invalid_placements(self, queue, servers): """If app is placed on non-existent server, set server to None. """ for app in queue: if app.server and app.server not in servers: app.server = None app.evicted = True app.release_identity() def _record_rank_and_util(self, queue): """Set final rank and utilization for all apps in the queue. """ for item in queue: rank = item[0] util = item[1] app = item[-1] app.final_rank = rank app.final_util = util def _fix_invalid_identities(self, queue, servers): """Check that app identity is valid for given identity group. """ for app in queue: if app.identity is not None and app.identity_group_ref is not None: # Can happen if identity group was adjusted to lower count. if app.identity >= app.identity_group_ref.count: # Can't release identity as it is invalid. _LOGGER.info('Identity exceeds limit: %s - %s, limit %s', app.name, app.identity, app.identity_group_ref.count) app.identity = None # Invalidate any existing placement. if app.server: servers[app.server].remove(app.name) def _handle_inactive_servers(self, servers): """Migrate apps from inactive servers. """ self.next_event_at = np.inf for server in six.itervalues(servers): state, since = server.get_state() to_be_moved = [] if state == State.down: _LOGGER.debug('Server state is down: %s', server.name) for name, app in six.iteritems(server.apps): if app.data_retention_timeout is None: expires_at = 0 else: expires_at = since + app.data_retention_timeout if expires_at <= time.time(): _LOGGER.debug('Expired placement: %s', name) app.release_identity() to_be_moved.append(name) else: _LOGGER.debug('Keep placement: %s until %s', name, expires_at) self.next_event_at = min(expires_at, self.next_event_at) elif state == State.frozen: _LOGGER.debug('Server state is frozen: %s', server.name) to_be_moved = [ name for name, app in six.iteritems(server.apps) if app.unschedule ] for name in to_be_moved: server.remove(name) def _find_placements(self, queue, servers): """Run the queue and find placements. """ # TODO: refactor to get rid of warnings. # # pylint: disable=too-many-branches,too-many-statements # # At this point, if app.server is defined, it points to attached # server. evicted = dict() reversed_queue = queue[::-1] placement_tracker = PlacementFeasibilityTracker() for app in queue: _LOGGER.debug('scheduling %s', app.name) if app.final_rank == _UNPLACED_RANK: if app.server: assert app.server in servers assert app.has_identity() servers[app.server].remove(app.name) app.release_identity() continue restore = {} if app.renew: assert app.server assert app.has_identity() assert app.server in servers server = servers[app.server] if not server.renew(app): # Save information that will be used to restore placement # in case renewal fails. _LOGGER.debug('Cannot renew app %s on server %s', app.name, app.server) restore['server'] = server restore['placement_expiry'] = app.placement_expiry server.remove(app.name) # At this point app was either renewed on the same server, or # temporarily removed from server if renew failed. # # If placement will be found, renew should remain False. If # placement will not be found, renew will be set to True when # placement is restored to the server it was running. app.renew = False if app.server: assert app.server in servers assert app.has_identity() continue assert app.server is None if not app.acquire_identity(): _LOGGER.info('Unable to acquire identity: %s, %s', app.name, app.identity_group) continue # If app was evicted before, try to restore to the same node. if app in evicted: assert app.has_identity() evicted_from, app_expiry = evicted[app] del evicted[app] if evicted_from.restore(app, app_expiry): app.evicted = False continue assert app.server is None if app.schedule_once and app.evicted: continue # Check if placement is feasible. if not placement_tracker.feasible(app): _LOGGER.info( 'Placement not feasible: %s %r', app.name, app.shape() ) continue if not self.put(app): # There is not enough capacity, from the end of the queue, # evict apps, freeing capacity. for evicted_app in reversed_queue: # We reached the app we can't place if evicted_app == app: break # The app is not yet placed, skip if not evicted_app.server: continue assert evicted_app.server in servers evicted_app_server = servers[evicted_app.server] # Do not consider servers that are not up. if evicted_app_server.state is not State.up: continue evicted[evicted_app] = (evicted_app_server, evicted_app.placement_expiry) evicted_app_server.remove(evicted_app.name) # TODO: we need to check affinity limit constraints on # each level, all the way to the top. if evicted_app_server.put(app): break # Placement failed. if not app.server: # If renewal attempt failed, restore previous placement and # expiry date. if restore: restore['server'].restore(app, restore['placement_expiry']) app.renew = True else: app.release_identity() placement_tracker.adjust(app) def schedule_alloc(self, allocation, servers): """Run the scheduler for given allocation. """ begin = time.time() size = self.size(allocation.label) util_queue = list(allocation.utilization_queue(size)) self._record_rank_and_util(util_queue) queue = [item[-1] for item in util_queue] self._find_placements(queue, servers) _LOGGER.info('Scheduled %s (%d) apps in %r', allocation.label, len(queue), time.time() - begin) def schedule(self): """Run the scheduler. """ begin = time.time() all_apps = [] for label, partition in six.iteritems(self.partitions): allocation = partition.allocation all_apps.extend(allocation.all_apps()) before = [(app.name, app.server, app.placement_expiry) for app in all_apps] servers = self.members() self._fix_invalid_placements(six.viewvalues(self.apps), servers) self._handle_inactive_servers(servers) self._fix_invalid_identities(six.viewvalues(self.apps), servers) for label, partition in six.iteritems(self.partitions): allocation = partition.allocation allocation.label = label self.schedule_alloc(allocation, servers) after = [(app.server, app.placement_expiry) for app in all_apps] placement = [ tuple(itertools.chain(b, a)) for b, a in six.moves.zip(before, after) ] for appname, s_before, exp_before, s_after, exp_after in placement: if s_before != s_after: _LOGGER.info('New placement: %s - %s => %s', appname, s_before, s_after) else: if exp_before != exp_after: _LOGGER.info('Renewed: %s [%s] - %s => %s', appname, s_before, exp_before, exp_after) _LOGGER.info('Total scheduler time for %s apps: %r (sec)', len(all_apps), time.time() - begin) return placement def resolve_reboot_conflicts(self): """Adjust server exipiration time to avoid conflicts. """ pass def dumps(cell): """Serializes cell to string. """ del cell return '' def loads(data): """Loads scheduler from string. """ del data assert False, 'not implemented.'	"""Treadmill hierarchical scheduler. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import abc import collections import datetime import heapq import itertools import logging import operator import sys import time import enum import numpy as np import six _LOGGER = logging.getLogger(__name__) MAX_PRIORITY = 100 DEFAULT_RANK = 100 _UNPLACED_RANK = sys.maxsize DIMENSION_COUNT = None _MAX_UTILIZATION = float('inf') _GLOBAL_ORDER_BASE = time.mktime((2014, 1, 1, 0, 0, 0, 0, 0, 0)) # 21 day DEFAULT_SERVER_UPTIME = 21 * 24 * 60 * 60 # 1 day MIN_SERVER_UPTIME = 1 * 24 * 60 * 60 # 7 days DEFAULT_MAX_APP_LEASE = 7 * 24 * 60 * 60 # Default partition threshold DEFAULT_THRESHOLD = 0.9 # pylint: disable=C0302,too-many-lines def _bit_count(value): """Returns number of bits set. """ count = 0 while value: value &= value - 1 count += 1 return count def zero_capacity(): """Returns zero capacity vector. """ assert DIMENSION_COUNT is not None, 'Dimension count not set.' return np.zeros(DIMENSION_COUNT) def eps_capacity(): """Returns eps capacity vector. """ assert DIMENSION_COUNT is not None, 'Dimension count not set.' return np.array( [np.finfo(float).eps for _x in range(0, DIMENSION_COUNT)] ) def _global_order(): """Use timestamp in nanoseconds, from Jan 1st 2014, to break tie in scheduling conflicts for apps of the same priority, in a FIFO fashion. """ # Take the current EPOCH in nanosec global_order = int(time.time() * 1000000) - _GLOBAL_ORDER_BASE return global_order def utilization(demand, allocated, available): """Calculates utilization score. """ return np.max(np.subtract(demand, allocated) / available) def _all(oper, left, right): """Short circuit all for ndarray. """ return all( oper(ai, bi) for ai, bi in six.moves.zip(left, right) ) def _any(oper, left, right): """Short circuit any for ndarray. """ return any( oper(ai, bi) for ai, bi in six.moves.zip(left, right) ) def _any_eq(left, right): """Short circuit any eq for ndarray. """ return _any(operator.eq, left, right) def _any_isclose(left, right): """Short circuit any isclose for ndarray. """ return _any(np.isclose, left, right) def _any_lt(left, right): """Short circuit any lt for ndarray. """ return _any(operator.lt, left, right) def _any_le(left, right): """Short circuit any le for ndarray. """ return _any(operator.le, left, right) def _any_gt(left, right): """Short circuit any gt for ndarray. """ return _any(operator.gt, left, right) def _any_ge(left, right): """Short circuit any ge for ndarray. """ return _any(operator.ge, left, right) def _all_eq(left, right): """Short circuit all eq for ndarray. """ return _all(operator.eq, left, right) def _all_isclose(left, right): """Short circuit all isclose for ndarray. """ return _all(np.isclose, left, right) def _all_lt(left, right): """Short circuit all lt for ndarray. """ return _all(operator.lt, left, right) def _all_le(left, right): """Short circuit all le for ndarray. """ return _all(operator.le, left, right) def _all_gt(left, right): """Short circuit all gt for ndarray. """ return _all(operator.gt, left, right) def _all_ge(left, right): """Short circuit all ge for ndarray. """ return _all(operator.ge, left, right) class IdentityGroup: """Identity group. """ __slots__ = ( 'available', 'count', ) def __init__(self, count=0): self.count = count self.available = set(range(0, count)) def acquire(self): """Return next available identity or None. """ if self.available: return self.available.pop() else: return None def release(self, ident): """Mark identity as available. """ if ident < self.count: self.available.add(ident) def adjust(self, count): """Adjust identities with new count. If count is larger, add additional identities to the set. If count is lower, remove identities that are no longer valid. All apps that have invalid identities will be adjusted in the schedule cycle. """ if count >= self.count: self.available ^= set(six.moves.xrange(self.count, count)) else: self.available -= set(six.moves.xrange(count, self.count)) self.count = count class State(enum.Enum): """Enumeration of node/server states. """ # Ready to accept new applications. # TODO: Fix attribute name up = 'up' # pylint: disable=invalid-name # Applications need to be migrated. down = 'down' # Existing applications can stay, but will not accept new. frozen = 'frozen' class Affinity: """Model affinity and affinity limits. """ __slots__ = ( 'name', 'limits', 'constraints', ) def __init__(self, name, limits=None): self.name = name self.limits = collections.defaultdict(lambda: float('inf')) if limits: self.limits.update(limits) # freeze affinity shape constraints. self.constraints = tuple([self.name] + sorted(self.limits.values())) class Application: """Application object. """ __slots__ = ( 'global_order', 'name', 'demand', 'affinity', 'priority', 'allocation', 'data_retention_timeout', 'server', 'lease', 'identity', 'identity_group', 'identity_group_ref', 'schedule_once', 'evicted', 'placement_expiry', 'renew', 'unschedule', 'final_rank', 'final_util', 'constraints', ) def __init__(self, name, priority, demand, affinity, affinity_limits=None, data_retention_timeout=0, lease=0, identity_group=None, identity=None, schedule_once=False): self.global_order = _global_order() self.allocation = None self.server = None self.name = name self.affinity = Affinity(affinity, affinity_limits) self.priority = priority self.demand = np.array(demand, dtype=float) self.data_retention_timeout = data_retention_timeout self.lease = lease self.identity_group = identity_group self.identity = identity self.identity_group_ref = None self.schedule_once = schedule_once self.evicted = False self.unschedule = False self.placement_expiry = None self.renew = False def shape(self): """Return tuple of application (constraints, demand). Application shape is tuple of constraints that affect application placement. Currently this includes affinity constraints and app lease time. """ constraints = (self.affinity.constraints + (self.lease,)) if self.allocation: constraints += self.allocation.constraints return constraints, self.demand def acquire_identity(self): """Try to acquire identity if belong to the group. Returns True if successfull or if identity group is none. """ if not self.identity_group_ref: return True if self.identity is None: self.identity = self.identity_group_ref.acquire() _LOGGER.info('Acquired identity: %s: %s - %s', self.name, self.identity_group, self.identity) return self.identity is not None def release_identity(self): """Release app identity. """ if self.identity_group_ref and self.identity is not None: self.identity_group_ref.release(self.identity) self.identity = None def force_set_identity(self, identity): """Force identity of the app. """ if identity is not None: assert self.identity_group_ref self.identity = identity self.identity_group_ref.available.discard(identity) def has_identity(self): """Checks if app has identity if identity group is specified. """ return self.identity_group_ref is None or self.identity is not None @property def traits(self): """The app traits are derived from allocation. """ if self.allocation is None: return 0 else: return self.allocation.traits @six.add_metaclass(abc.ABCMeta) class Strategy: """Base class for all placement strategies. """ @abc.abstractmethod def suggested_node(self): """Suggested node that should be tried first. """ pass @abc.abstractmethod def next_node(self): """Next node to try, if previous suggestion was rejected. """ pass class SpreadStrategy(Strategy): """Spread strategy will suggest new node for each subsequent placement. """ __slots__ = ( 'current_idx', 'node', ) def __init__(self, node): self.current_idx = 0 self.node = node def suggested_node(self): """Suggest next node from the cycle. """ for _ in six.moves.xrange(0, len(self.node.children)): if self.current_idx == len(self.node.children): self.current_idx = 0 current = self.node.children[self.current_idx] self.current_idx += 1 if current: return current # Not a single non-none node. return None def next_node(self): """Suggest next node from the cycle. """ return self.suggested_node() class PackStrategy(Strategy): """Pack strategy will suggest same node until it is full. """ __slots__ = ( 'current_idx', 'node', ) def __init__(self, node): self.current_idx = 0 self.node = node def suggested_node(self): """Suggest same node as previous placement. """ for _ in six.moves.xrange(0, len(self.node.children)): if self.current_idx == len(self.node.children): self.current_idx = 0 node = self.node.children[self.current_idx] if node: return node return None def next_node(self): """Suggest next node from the cycle. """ self.current_idx += 1 return self.suggested_node() class TraitSet: """Hierarchical set of traits. """ __slots__ = ( 'self_traits', 'children_traits', 'traits', ) def __init__(self, traits=0): if not traits: traits = 0 # Private traits. assert isinstance(traits, six.integer_types) self.self_traits = traits # Union of all children traits. self.children_traits = dict() self._recalculate() def _recalculate(self): """Calculate combined set of all traits. """ self.traits = self.self_traits for trait in six.itervalues(self.children_traits): self.traits \|= trait def has(self, traits): """Check if all traits are present. """ return (self.traits & traits) == traits def add(self, child, traits): """Add a child with given traits. """ # Update children traits. self.children_traits[child] = traits self._recalculate() def remove(self, child): """Remove child traits from the list. """ if child in self.children_traits: del self.children_traits[child] self._recalculate() def is_same(self, other): """Compares own traits, ignore child. """ return self.self_traits == other.self_traits class AffinityCounter: """Manages affinity count. """ __slots__ = ( 'affinity_counter', ) def __init__(self): self.affinity_counter = collections.Counter() class Node: """Abstract placement node. """ __slots__ = ( 'name', 'level', 'free_capacity', 'parent', 'children', 'children_by_name', 'traits', 'labels', 'affinity_counters', 'valid_until', '_state', '_state_since', ) def __init__(self, name, traits, level, valid_until=0): self.name = name self.level = level self.free_capacity = zero_capacity() self.parent = None self.children = list() self.children_by_name = dict() self.traits = TraitSet(traits) self.labels = set() self.affinity_counters = collections.Counter() self.valid_until = valid_until self._state = State.up self._state_since = time.time() def empty(self): """Return true if there are no children. """ return not bool(self.children_by_name) def children_iter(self): """Iterate over active children. """ for child in self.children: if child: yield child def get_state(self): """Returns tuple of (state, since). """ return self. _state, self._state_since def set_state(self, state, since): """Sets the state and time since. """ if self._state is not state: self._state_since = since self._state = state _LOGGER.debug('state: %s - (%s, %s)', self.name, self._state, self._state_since) @property def state(self): """Return current state. """ return self._state @state.setter def state(self, new_state): """Set node state and records time. """ self.set_state(new_state, time.time()) def add_child_traits(self, node): """Recursively add child traits up. """ self.traits.add(node.name, node.traits.traits) if self.parent: self.parent.remove_child_traits(self.name) self.parent.add_child_traits(self) def adjust_valid_until(self, child_valid_until): """Recursively adjust valid until time. """ if child_valid_until: self.valid_until = max(self.valid_until, child_valid_until) else: if self.empty(): self.valid_until = 0 else: self.valid_until = max([node.valid_until for node in self.children_iter()]) if self.parent: self.parent.adjust_valid_until(child_valid_until) def remove_child_traits(self, node_name): """Recursively remove child traits up. """ self.traits.remove(node_name) if self.parent: self.parent.remove_child_traits(self.name) self.parent.add_child_traits(self) def reset_children(self): """Reset children to empty list. """ for child in self.children_iter(): child.parent = None self.children = list() self.children_by_name = dict() def add_node(self, node): """Add child node, set the traits and propagate traits up. """ assert node.parent is None assert node.name not in self.children_by_name node.parent = self self.children.append(node) self.children_by_name[node.name] = node self.add_child_traits(node) self.increment_affinity(node.affinity_counters) self.add_labels(node.labels) self.adjust_valid_until(node.valid_until) def add_labels(self, labels): """Recursively add labels to self and parents. """ self.labels.update(labels) if self.parent: self.parent.add_labels(self.labels) def remove_node(self, node): """Remove child node and adjust the traits. """ assert node.name in self.children_by_name del self.children_by_name[node.name] for idx in six.moves.xrange(0, len(self.children)): if self.children[idx] == node: self.children[idx] = None self.remove_child_traits(node.name) self.decrement_affinity(node.affinity_counters) self.adjust_valid_until(None) node.parent = None return node def remove_node_by_name(self, nodename): """Removes node by name. """ assert nodename in self.children_by_name return self.remove_node(self.children_by_name[nodename]) def check_app_constraints(self, app): """Find app placement on the node. """ if app.allocation is not None: if app.allocation.label not in self.labels: _LOGGER.info('Missing label: %s on %s', app.allocation.label, self.name) return False if app.traits != 0 and not self.traits.has(app.traits): _LOGGER.info('Missing traits: %s on %s', app.traits, self.name) return False if not self.check_app_affinity_limit(app): return False if _any_gt(app.demand, self.free_capacity): _LOGGER.info('Not enough free capacity: %s', self.free_capacity) return False return True def check_app_affinity_limit(self, app): """Check app affinity limits """ count = self.affinity_counters[app.affinity.name] limit = app.affinity.limits[self.level] return count < limit def put(self, _app): """Abstract method, should never be called. """ raise Exception('Not implemented.') def size(self, label): """Returns total capacity of the children. """ if self.empty() or label not in self.labels: return eps_capacity() return np.sum([ n.size(label) for n in self.children_iter()], 0) def members(self): """Return set of all leaf node names. """ names = dict() for node in self.children_iter(): names.update(node.members()) return names def increment_affinity(self, counters): """Increment affinity counters recursively. """ self.affinity_counters.update(counters) if self.parent: self.parent.increment_affinity(counters) def decrement_affinity(self, counters): """Decrement affinity counters recursively. """ self.affinity_counters.subtract(counters) if self.parent: self.parent.decrement_affinity(counters) class Bucket(Node): """Collection of nodes/buckets. """ __slots__ = ( 'affinity_strategies', 'traits', ) _default_strategy_t = SpreadStrategy def __init__(self, name, traits=0, level=None): super(Bucket, self).__init__(name, traits, level) self.affinity_strategies = dict() self.traits = TraitSet(traits) def set_affinity_strategy(self, affinity, strategy_t): """Initilaizes placement strategy for given affinity. """ self.affinity_strategies[affinity] = strategy_t(self) def get_affinity_strategy(self, affinity): """Returns placement strategy for the affinity, defaults to spread. """ if affinity not in self.affinity_strategies: self.set_affinity_strategy(affinity, Bucket._default_strategy_t) return self.affinity_strategies[affinity] def adjust_capacity_up(self, new_capacity): """Node can only increase capacity. """ self.free_capacity = np.maximum(self.free_capacity, new_capacity) if self.parent: self.parent.adjust_capacity_up(self.free_capacity) def adjust_capacity_down(self, prev_capacity=None): """Called when capacity is decreased. """ if self.empty(): self.free_capacity = zero_capacity() if self.parent: self.parent.adjust_capacity_down() else: if prev_capacity is not None and _all_lt(prev_capacity, self.free_capacity): return free_capacity = zero_capacity() for child_node in self.children_iter(): if child_node.state is not State.up: continue free_capacity = np.maximum(free_capacity, child_node.free_capacity) # If resulting free_capacity is less the previous, we need to # adjust the parent, otherwise, nothing needs to be done. prev_capacity = self.free_capacity.copy() if _any_lt(free_capacity, self.free_capacity): self.free_capacity = free_capacity if self.parent: self.parent.adjust_capacity_down(prev_capacity) def add_node(self, node): """Adds node to the bucket. """ super(Bucket, self).add_node(node) self.adjust_capacity_up(node.free_capacity) def remove_node(self, node): """Removes node from the bucket. """ super(Bucket, self).remove_node(node) # if _any_isclose(self.free_capacity, node.free_capacity): self.adjust_capacity_down(node.free_capacity) return node def put(self, app): """Try to put app on one of the nodes that belong to the bucket. """ # Check if it is feasible to put app on some node low in the # hierarchy _LOGGER.debug('bucket.put: %s => %s', app.name, self.name) if not self.check_app_constraints(app): return False strategy = self.get_affinity_strategy(app.affinity.name) node = strategy.suggested_node() if node is None: _LOGGER.debug('All nodes in the bucket deleted.') return False nodename0 = node.name first = True while True: # End of iteration. if not first and node.name == nodename0: _LOGGER.debug('Finished iterating on: %s.', self.name) break first = False _LOGGER.debug('Trying node: %s:', node.name) if node.state is not State.up: _LOGGER.debug('Node not up: %s, %s', node.name, node.state) else: if node.put(app): return True node = strategy.next_node() return False class Server(Node): """Server object, final app placement. """ __slots__ = ( 'init_capacity', 'apps', 'up_since', 'presence_id', ) def __init__(self, name, capacity, up_since=0, valid_until=0, traits=0, label=None, presence_id=None): super(Server, self).__init__(name, traits=traits, level='server', valid_until=valid_until) self.labels = set([label]) self.init_capacity = np.array(capacity, dtype=float) self.free_capacity = self.init_capacity.copy() self.apps = dict() self.up_since = up_since self.presence_id = presence_id def __str__(self): return 'server: %s %s' % (self.name, self.init_capacity) def is_same(self, other): """Compares capacity and traits against another server. valid_until is ignored, as server comes up after reboot will have different valid_until value. """ return (self.labels == other.labels and _all_eq(self.init_capacity, other.init_capacity) and self.traits.is_same(other.traits)) def put(self, app): """Tries to put the app on the server. """ assert app.name not in self.apps _LOGGER.debug('server.put: %s => %s', app.name, self.name) if not self.check_app_lifetime(app): return False if not self.check_app_constraints(app): return False prev_capacity = self.free_capacity.copy() self.free_capacity -= app.demand self.apps[app.name] = app self.increment_affinity([app.affinity.name]) app.server = self.name if self.parent: self.parent.adjust_capacity_down(prev_capacity) if app.placement_expiry is None: app.placement_expiry = time.time() + app.lease return True def restore(self, app, placement_expiry=None): """Put app back on the server, ignore app lifetime. """ _LOGGER.debug('server.restore: %s => %s (%s)', app.name, self.name, placement_expiry) lease = app.lease # If not explicit if placement_expiry is None: placement_expiry = app.placement_expiry app.lease = 0 rc = self.put(app) app.lease = lease app.placement_expiry = placement_expiry return rc def renew(self, app): """Try to extend the placement for app lease. """ can_renew = self.check_app_lifetime(app) if can_renew: app.placement_expiry = time.time() + app.lease return can_renew def check_app_lifetime(self, app): """Check if the app lease fits until server is rebooted. """ # app with 0 lease can be placed anywhere (ignore potentially # expired servers) if not app.lease: return True return time.time() + app.lease < self.valid_until def remove(self, app_name): """Removes app from the server. """ assert app_name in self.apps app = self.apps[app_name] del self.apps[app_name] app.server = None app.evicted = True app.unschedule = False app.placement_expiry = None self.free_capacity += app.demand self.decrement_affinity([app.affinity.name]) if self.parent: self.parent.adjust_capacity_up(self.free_capacity) def remove_all(self): """Remove all apps. """ # iterate over copy of the keys, as we are removing them in the loop. for appname in list(self.apps): self.remove(appname) def size(self, label): """Return server capacity. """ if label not in self.labels: return eps_capacity() return self.init_capacity def members(self): """Return set of all leaf node names. """ return {self.name: self} def set_state(self, state, since): """Change host state. """ if self.state is state: return super(Server, self).set_state(state, since) if state == State.up: if self.parent: self.parent.adjust_capacity_up(self.free_capacity) elif state in (State.down, State.frozen): if self.parent: self.parent.adjust_capacity_down(self.free_capacity) else: raise Exception('Invalid state: ' % state) class Allocation: """Allocation manages queue of apps sharing same reserved capacity. In reality allocation is tied to grn via application proid. Applications within the allocation are organized by application priority. Allocations are ranked, and the rank is used to globally order applications from different allocations into global queue. Default allocation has rank 100. Defining allocation with lower rank will result in all it's applications to be evaluated first regardless of utilization. This is used to model "system" applications that should be always present regardless of utilization. Allocation queue can be capped with max_utilization parameter. If set, it will specify the max_utilization which will be considered for scheduling. """ __slots__ = ( 'reserved', 'rank', 'rank_adjustment', 'traits', 'label', 'max_utilization', 'apps', 'sub_allocations', 'path', 'constraints', ) def __init__(self, reserved=None, rank=None, traits=None, max_utilization=None, partition=None): self.set_reserved(reserved) self.rank = None self.rank_adjustment = 0 self.traits = 0 self.label = partition self.max_utilization = _MAX_UTILIZATION self.reserved = zero_capacity() self.set_max_utilization(max_utilization) self.set_traits(traits) self.update(reserved, rank, 0) self.apps = dict() self.sub_allocations = dict() self.path = [] # Freeze shape constraintes. self.constraints = (self.label, self.traits,) @property def name(self): """Returns full allocation name. """ return '/'.join(self.path) def set_reserved(self, reserved): """Update reserved capacity. """ if reserved is None: self.reserved = zero_capacity() elif isinstance(reserved, int): assert reserved == 0 self.reserved = zero_capacity() elif isinstance(reserved, float): assert reserved == 0.0 self.reserved = zero_capacity() elif isinstance(reserved, list): assert len(reserved) == DIMENSION_COUNT self.reserved = np.array(reserved, dtype=float) elif isinstance(reserved, np.ndarray): self.reserved = reserved else: assert 'Unsupported type: %r' % type(reserved) def update(self, reserved, rank, rank_adjustment, max_utilization=None): """Updates allocation. """ if rank is not None: self.rank = rank else: self.rank = DEFAULT_RANK if rank_adjustment is not None: self.rank_adjustment = rank_adjustment self.set_reserved(reserved) self.set_max_utilization(max_utilization) def set_max_utilization(self, max_utilization): """Sets max_utilization, accounting for default None value. """ if max_utilization is not None: self.max_utilization = max_utilization else: self.max_utilization = _MAX_UTILIZATION def set_traits(self, traits): """Set traits, account for default None value. """ if not traits: self.traits = 0 else: self.traits = traits def add(self, app): """Add application to the allocation queue. Once added, the scheduler will make an attempt to place the app on one of the cell nodes. """ # Check that there are no duplicate app names. if app.name in self.apps: _LOGGER.warning( 'Duplicate app on alllocation queue: %s', app.name ) return app.allocation = self self.apps[app.name] = app def remove(self, name): """Remove application from the allocation queue. """ if name in self.apps: self.apps[name].allocation = None del self.apps[name] def priv_utilization_queue(self): """Returns tuples for sorted by global utilization. Apps in the queue are ordered by priority, insertion order. Adding or removing maintains invariant that apps utilization monotonically increases as well. Returns local prioritization queue in a tuple where first element is utilization ratio, so that this queue is suitable for merging into global priority queue. """ def _app_key(app): """Compares apps by priority, state, global index """ return (-app.priority, 0 if app.server else 1, app.global_order, app.name) prio_queue = sorted(six.viewvalues(self.apps), key=_app_key) acc_demand = zero_capacity() available = self.reserved + np.finfo(float).eps util_before = utilization(acc_demand, self.reserved, available) for app in prio_queue: acc_demand = acc_demand + app.demand util_after = utilization(acc_demand, self.reserved, available) # Priority 0 apps are treated specially - utilization is set to # max float. # # This ensures that they are at the end of the all queues. if app.priority == 0: util_before = _MAX_UTILIZATION util_after = _MAX_UTILIZATION # All things equal, already scheduled applications have priority # over pending. pending = 0 if app.server else 1 if util_after <= self.max_utilization - 1: rank = self.rank if util_before < 0: rank -= self.rank_adjustment else: rank = _UNPLACED_RANK entry = (rank, util_before, util_after, pending, app.global_order, app) util_before = util_after yield entry def utilization_queue(self, free_capacity, visitor=None): """Returns utilization queue including the sub-allocs. All app queues from self and sub-allocs are merged in standard order, and then utilization is recalculated based on total reserved capacity of this alloc and sub-allocs combined. The function maintains invariant that any app (self or inside sub-alloc with utilization < 1 will remain with utilzation < 1. """ total_reserved = self.total_reserved() queues = [ alloc.utilization_queue(free_capacity, visitor) for alloc in six.itervalues(self.sub_allocations) ] queues.append(self.priv_utilization_queue()) acc_demand = zero_capacity() available = total_reserved + free_capacity + np.finfo(float).eps util_before = utilization(acc_demand, total_reserved, available) for item in heapq.merge(*queues): rank, _u_before, _u_after, pending, order, app = item acc_demand = acc_demand + app.demand util_after = utilization(acc_demand, total_reserved, available) if app.priority == 0: util_before = _MAX_UTILIZATION util_after = _MAX_UTILIZATION # - lower rank allocations take precedence. # - for same rank, utilization takes precedence # - False < True, so for apps with same utilization we prefer # those that already running (False == not pending) # - Global order entry = (rank, util_before, util_after, pending, order, app) if visitor: visitor(self, entry, acc_demand) util_before = util_after yield entry def total_reserved(self): """Total reserved capacity including sub-allocs. """ return six.moves.reduce( lambda acc, alloc: acc + alloc.total_reserved(), six.itervalues(self.sub_allocations), self.reserved ) def add_sub_alloc(self, name, alloc): """Add child allocation. """ self.sub_allocations[name] = alloc assert not alloc.path alloc.path = self.path + [name] alloc.label = self.label def remove_sub_alloc(self, name): """Remove chlid allocation. """ if name in self.sub_allocations: del self.sub_allocations[name] def get_sub_alloc(self, name): """Return sub allocation, create empty if it does not exist. """ if name not in self.sub_allocations: self.add_sub_alloc(name, Allocation()) return self.sub_allocations[name] def all_apps(self): """Return all apps in allocation and sub-allocations.""" all_apps = list(six.itervalues(self.apps)) for alloc in six.itervalues(self.sub_allocations): all_apps.extend(alloc.all_apps()) return all_apps class Partition: """Cell partition. """ __slots__ = ( 'allocation', 'max_server_uptime', 'max_lease', 'threshold', 'label', '_reboot_buckets', '_reboot_dates', '_reboot_last', ) def __init__(self, max_server_uptime=None, max_lease=None, threshold=None, label=None, reboot_schedule=None, now=None): self.label = label self.allocation = Allocation(partition=label) # Default - if not max_server_uptime: max_server_uptime = DEFAULT_SERVER_UPTIME if not max_lease: max_lease = DEFAULT_MAX_APP_LEASE if not threshold: threshold = DEFAULT_THRESHOLD self.max_server_uptime = max_server_uptime self.max_lease = max_lease self.threshold = threshold if not reboot_schedule: # reboot every day reboot_schedule = {day: (23, 59, 59) for day in range(7)} if not now: now = time.time() self._reboot_dates = reboot_dates( reboot_schedule, start_date=datetime.date.fromtimestamp(now) ) self._reboot_buckets = [] self._reboot_last = now self.tick(now) def _find_bucket(self, timestamp): """Try to find bucket with given timestamp. """ for bucket in self._reboot_buckets: if bucket.timestamp == timestamp: return bucket return None def add(self, server, timestamp=None): """Add server. """ bucket = None if timestamp: bucket = self._find_bucket(timestamp) # servers with larger than max lifetime should be rebooted at # the next opportunity if (self._reboot_buckets[0].timestamp > server.up_since + DEFAULT_SERVER_UPTIME): bucket = self._reboot_buckets[0] if not bucket: bucket = min(reversed(self._reboot_buckets), key=lambda b: b.cost(server)) bucket.add(server) def remove(self, server): """Remove server. """ for bucket in self._reboot_buckets: bucket.remove(server) def tick(self, now): """Do per-tick-bookkeeping. """ while self._reboot_last <= now + DEFAULT_SERVER_UPTIME: bucket = RebootBucket(next(self._reboot_dates)) self._reboot_buckets.append(bucket) self._reboot_last = bucket.timestamp while self._reboot_buckets[0].timestamp < now: self._reboot_buckets.pop(0) class PartitionDict(dict): """Dict that creates partitions on demand. We use this instead of collections.defaultdict so that we can provide the new partition with its label, to be propagated to its allocations. """ def __missing__(self, label): """Create a new partition, passing the label to its constructor. """ self[label] = Partition(label=label) return self[label] # pylint: disable=invalid-name def reboot_dates(schedule, start_date=None): """Generate list of valid reboot dates. """ date = datetime.date.today() if start_date: date = start_date while True: weekday = date.weekday() if weekday in schedule: h, m, s = schedule[weekday] yield time.mktime((date.year, date.month, date.day, h, m, s, 0, 0, 0)) date += datetime.timedelta(days=1) class RebootBucket: """Bucket of servers to be rebooted at the same time. """ __slots__ = ( 'timestamp', 'servers', ) def __init__(self, timestamp): self.timestamp = timestamp self.servers = [] def add(self, server): """Add server to this bucket. """ self.servers.append(server) server.valid_until = self.timestamp _LOGGER.info('Setting valid until on server: %s %s', server.name, server.valid_until) def remove(self, server): """Remove server from this bucket. """ try: self.servers.remove(server) except ValueError: pass def cost(self, server): """The cost of adding server to this bucket. """ if self.timestamp > server.up_since + DEFAULT_SERVER_UPTIME: return float('inf') if self.timestamp < server.up_since + MIN_SERVER_UPTIME: return float('inf') return len(self.servers) class PlacementFeasibilityTracker: """Tracks similar apps placement failures.""" def __init__(self): self.recorder = dict() def feasible(self, app): """Checks if it is feasible to satisfy demand.""" constraints, demand = app.shape() if constraints in self.recorder: # If demand is >= than recorded failure, placement is not feasible. if _all_ge(demand, self.recorder[constraints]): return False return True def adjust(self, app): """Adjust info about failed placement.""" constraints, demand = app.shape() if constraints not in self.recorder: self.recorder[constraints] = demand else: if _all_le(demand, self.recorder[constraints]): self.recorder[constraints] = demand class Cell(Bucket): """Top level node. """ __slots__ = ( 'partitions', 'next_event_at', 'apps', 'identity_groups', ) def __init__(self, name): super(Cell, self).__init__(name, traits=0, level='cell') self.partitions = PartitionDict() self.apps = dict() self.identity_groups = collections.defaultdict(IdentityGroup) self.next_event_at = np.inf def add_app(self, allocation, app): """Adds application to the scheduled list. """ assert allocation is not None if app.allocation: app.allocation.remove(app.name) allocation.add(app) self.apps[app.name] = app if app.identity_group: app.identity_group_ref = self.identity_groups[app.identity_group] def remove_app(self, appname): """Remove app from scheduled list. """ if appname not in self.apps: return app = self.apps[appname] servers = self.members() if app.server in servers: servers[app.server].remove(app.name) if app.allocation: app.allocation.remove(app.name) app.release_identity() del self.apps[appname] def configure_identity_group(self, name, count): """Add identity group to the cell. """ if name not in self.identity_groups: self.identity_groups[name] = IdentityGroup(count) else: self.identity_groups[name].adjust(count) def remove_identity_group(self, name): """Remove identity group. """ ident_group = self.identity_groups.get(name) if ident_group: in_use = False for app in six.itervalues(self.apps): if app.identity_group_ref == ident_group: ident_group.adjust(0) in_use = True break if not in_use: del self.identity_groups[name] def _fix_invalid_placements(self, queue, servers): """If app is placed on non-existent server, set server to None. """ for app in queue: if app.server and app.server not in servers: app.server = None app.evicted = True app.release_identity() def _record_rank_and_util(self, queue): """Set final rank and utilization for all apps in the queue. """ for item in queue: rank = item[0] util = item[1] app = item[-1] app.final_rank = rank app.final_util = util def _fix_invalid_identities(self, queue, servers): """Check that app identity is valid for given identity group. """ for app in queue: if app.identity is not None and app.identity_group_ref is not None: # Can happen if identity group was adjusted to lower count. if app.identity >= app.identity_group_ref.count: # Can't release identity as it is invalid. _LOGGER.info('Identity exceeds limit: %s - %s, limit %s', app.name, app.identity, app.identity_group_ref.count) app.identity = None # Invalidate any existing placement. if app.server: servers[app.server].remove(app.name) def _handle_inactive_servers(self, servers): """Migrate apps from inactive servers. """ self.next_event_at = np.inf for server in six.itervalues(servers): state, since = server.get_state() to_be_moved = [] if state == State.down: _LOGGER.debug('Server state is down: %s', server.name) for name, app in six.iteritems(server.apps): if app.data_retention_timeout is None: expires_at = 0 else: expires_at = since + app.data_retention_timeout if expires_at <= time.time(): _LOGGER.debug('Expired placement: %s', name) app.release_identity() to_be_moved.append(name) else: _LOGGER.debug('Keep placement: %s until %s', name, expires_at) self.next_event_at = min(expires_at, self.next_event_at) elif state == State.frozen: _LOGGER.debug('Server state is frozen: %s', server.name) to_be_moved = [ name for name, app in six.iteritems(server.apps) if app.unschedule ] for name in to_be_moved: server.remove(name) def _find_placements(self, queue, servers): """Run the queue and find placements. """ # TODO: refactor to get rid of warnings. # # pylint: disable=too-many-branches,too-many-statements # # At this point, if app.server is defined, it points to attached # server. evicted = dict() reversed_queue = queue[::-1] placement_tracker = PlacementFeasibilityTracker() for app in queue: _LOGGER.debug('scheduling %s', app.name) if app.final_rank == _UNPLACED_RANK: if app.server: assert app.server in servers assert app.has_identity() servers[app.server].remove(app.name) app.release_identity() continue restore = {} if app.renew: assert app.server assert app.has_identity() assert app.server in servers server = servers[app.server] if not server.renew(app): # Save information that will be used to restore placement # in case renewal fails. _LOGGER.debug('Cannot renew app %s on server %s', app.name, app.server) restore['server'] = server restore['placement_expiry'] = app.placement_expiry server.remove(app.name) # At this point app was either renewed on the same server, or # temporarily removed from server if renew failed. # # If placement will be found, renew should remain False. If # placement will not be found, renew will be set to True when # placement is restored to the server it was running. app.renew = False if app.server: assert app.server in servers assert app.has_identity() continue assert app.server is None if not app.acquire_identity(): _LOGGER.info('Unable to acquire identity: %s, %s', app.name, app.identity_group) continue # If app was evicted before, try to restore to the same node. if app in evicted: assert app.has_identity() evicted_from, app_expiry = evicted[app] del evicted[app] if evicted_from.restore(app, app_expiry): app.evicted = False continue assert app.server is None if app.schedule_once and app.evicted: continue # Check if placement is feasible. if not placement_tracker.feasible(app): _LOGGER.info( 'Placement not feasible: %s %r', app.name, app.shape() ) continue if not self.put(app): # There is not enough capacity, from the end of the queue, # evict apps, freeing capacity. for evicted_app in reversed_queue: # We reached the app we can't place if evicted_app == app: break # The app is not yet placed, skip if not evicted_app.server: continue assert evicted_app.server in servers evicted_app_server = servers[evicted_app.server] # Do not consider servers that are not up. if evicted_app_server.state is not State.up: continue evicted[evicted_app] = (evicted_app_server, evicted_app.placement_expiry) evicted_app_server.remove(evicted_app.name) # TODO: we need to check affinity limit constraints on # each level, all the way to the top. if evicted_app_server.put(app): break # Placement failed. if not app.server: # If renewal attempt failed, restore previous placement and # expiry date. if restore: restore['server'].restore(app, restore['placement_expiry']) app.renew = True else: app.release_identity() placement_tracker.adjust(app) def schedule_alloc(self, allocation, servers): """Run the scheduler for given allocation. """ begin = time.time() size = self.size(allocation.label) util_queue = list(allocation.utilization_queue(size)) self._record_rank_and_util(util_queue) queue = [item[-1] for item in util_queue] self._find_placements(queue, servers) _LOGGER.info('Scheduled %s (%d) apps in %r', allocation.label, len(queue), time.time() - begin) def schedule(self): """Run the scheduler. """ begin = time.time() all_apps = [] for label, partition in six.iteritems(self.partitions): allocation = partition.allocation all_apps.extend(allocation.all_apps()) before = [(app.name, app.server, app.placement_expiry) for app in all_apps] servers = self.members() self._fix_invalid_placements(six.viewvalues(self.apps), servers) self._handle_inactive_servers(servers) self._fix_invalid_identities(six.viewvalues(self.apps), servers) for label, partition in six.iteritems(self.partitions): allocation = partition.allocation allocation.label = label self.schedule_alloc(allocation, servers) after = [(app.server, app.placement_expiry) for app in all_apps] placement = [ tuple(itertools.chain(b, a)) for b, a in six.moves.zip(before, after) ] for appname, s_before, exp_before, s_after, exp_after in placement: if s_before != s_after: _LOGGER.info('New placement: %s - %s => %s', appname, s_before, s_after) else: if exp_before != exp_after: _LOGGER.info('Renewed: %s [%s] - %s => %s', appname, s_before, exp_before, exp_after) _LOGGER.info('Total scheduler time for %s apps: %r (sec)', len(all_apps), time.time() - begin) return placement def resolve_reboot_conflicts(self): """Adjust server exipiration time to avoid conflicts. """ pass def dumps(cell): """Serializes cell to string. """ del cell return '' def loads(data): """Loads scheduler from string. """ del data assert False, 'not implemented.'	en	0.88203	Treadmill hierarchical scheduler. # 21 day # 1 day # 7 days # Default partition threshold # pylint: disable=C0302,too-many-lines Returns number of bits set. Returns zero capacity vector. Returns eps capacity vector. Use timestamp in nanoseconds, from Jan 1st 2014, to break tie in scheduling conflicts for apps of the same priority, in a FIFO fashion. # Take the current EPOCH in nanosec Calculates utilization score. Short circuit all for ndarray. Short circuit any for ndarray. Short circuit any eq for ndarray. Short circuit any isclose for ndarray. Short circuit any lt for ndarray. Short circuit any le for ndarray. Short circuit any gt for ndarray. Short circuit any ge for ndarray. Short circuit all eq for ndarray. Short circuit all isclose for ndarray. Short circuit all lt for ndarray. Short circuit all le for ndarray. Short circuit all gt for ndarray. Short circuit all ge for ndarray. Identity group. Return next available identity or None. Mark identity as available. Adjust identities with new count. If count is larger, add additional identities to the set. If count is lower, remove identities that are no longer valid. All apps that have invalid identities will be adjusted in the schedule cycle. Enumeration of node/server states. # Ready to accept new applications. # TODO: Fix attribute name # pylint: disable=invalid-name # Applications need to be migrated. # Existing applications can stay, but will not accept new. Model affinity and affinity limits. # freeze affinity shape constraints. Application object. Return tuple of application (constraints, demand). Application shape is tuple of constraints that affect application placement. Currently this includes affinity constraints and app lease time. Try to acquire identity if belong to the group. Returns True if successfull or if identity group is none. Release app identity. Force identity of the app. Checks if app has identity if identity group is specified. The app traits are derived from allocation. Base class for all placement strategies. Suggested node that should be tried first. Next node to try, if previous suggestion was rejected. Spread strategy will suggest new node for each subsequent placement. Suggest next node from the cycle. # Not a single non-none node. Suggest next node from the cycle. Pack strategy will suggest same node until it is full. Suggest same node as previous placement. Suggest next node from the cycle. Hierarchical set of traits. # Private traits. # Union of all children traits. Calculate combined set of all traits. Check if all traits are present. Add a child with given traits. # Update children traits. Remove child traits from the list. Compares own traits, ignore child. Manages affinity count. Abstract placement node. Return true if there are no children. Iterate over active children. Returns tuple of (state, since). Sets the state and time since. Return current state. Set node state and records time. Recursively add child traits up. Recursively adjust valid until time. Recursively remove child traits up. Reset children to empty list. Add child node, set the traits and propagate traits up. Recursively add labels to self and parents. Remove child node and adjust the traits. Removes node by name. Find app placement on the node. Check app affinity limits Abstract method, should never be called. Returns total capacity of the children. Return set of all leaf node names. Increment affinity counters recursively. Decrement affinity counters recursively. Collection of nodes/buckets. Initilaizes placement strategy for given affinity. Returns placement strategy for the affinity, defaults to spread. Node can only increase capacity. Called when capacity is decreased. # If resulting free_capacity is less the previous, we need to # adjust the parent, otherwise, nothing needs to be done. Adds node to the bucket. Removes node from the bucket. # if _any_isclose(self.free_capacity, node.free_capacity): Try to put app on one of the nodes that belong to the bucket. # Check if it is feasible to put app on some node low in the # hierarchy # End of iteration. Server object, final app placement. Compares capacity and traits against another server. valid_until is ignored, as server comes up after reboot will have different valid_until value. Tries to put the app on the server. Put app back on the server, ignore app lifetime. # If not explicit Try to extend the placement for app lease. Check if the app lease fits until server is rebooted. # app with 0 lease can be placed anywhere (ignore potentially # expired servers) Removes app from the server. Remove all apps. # iterate over copy of the keys, as we are removing them in the loop. Return server capacity. Return set of all leaf node names. Change host state. Allocation manages queue of apps sharing same reserved capacity. In reality allocation is tied to grn via application proid. Applications within the allocation are organized by application priority. Allocations are ranked, and the rank is used to globally order applications from different allocations into global queue. Default allocation has rank 100. Defining allocation with lower rank will result in all it's applications to be evaluated first regardless of utilization. This is used to model "system" applications that should be always present regardless of utilization. Allocation queue can be capped with max_utilization parameter. If set, it will specify the max_utilization which will be considered for scheduling. # Freeze shape constraintes. Returns full allocation name. Update reserved capacity. Updates allocation. Sets max_utilization, accounting for default None value. Set traits, account for default None value. Add application to the allocation queue. Once added, the scheduler will make an attempt to place the app on one of the cell nodes. # Check that there are no duplicate app names. Remove application from the allocation queue. Returns tuples for sorted by global utilization. Apps in the queue are ordered by priority, insertion order. Adding or removing maintains invariant that apps utilization monotonically increases as well. Returns local prioritization queue in a tuple where first element is utilization ratio, so that this queue is suitable for merging into global priority queue. Compares apps by priority, state, global index # Priority 0 apps are treated specially - utilization is set to # max float. # # This ensures that they are at the end of the all queues. # All things equal, already scheduled applications have priority # over pending. Returns utilization queue including the sub-allocs. All app queues from self and sub-allocs are merged in standard order, and then utilization is recalculated based on total reserved capacity of this alloc and sub-allocs combined. The function maintains invariant that any app (self or inside sub-alloc with utilization < 1 will remain with utilzation < 1. # - lower rank allocations take precedence. # - for same rank, utilization takes precedence # - False < True, so for apps with same utilization we prefer # those that already running (False == not pending) # - Global order Total reserved capacity including sub-allocs. Add child allocation. Remove chlid allocation. Return sub allocation, create empty if it does not exist. Return all apps in allocation and sub-allocations. Cell partition. # Default - # reboot every day Try to find bucket with given timestamp. Add server. # servers with larger than max lifetime should be rebooted at # the next opportunity Remove server. Do per-tick-bookkeeping. Dict that creates partitions on demand. We use this instead of collections.defaultdict so that we can provide the new partition with its label, to be propagated to its allocations. Create a new partition, passing the label to its constructor. # pylint: disable=invalid-name Generate list of valid reboot dates. Bucket of servers to be rebooted at the same time. Add server to this bucket. Remove server from this bucket. The cost of adding server to this bucket. Tracks similar apps placement failures. Checks if it is feasible to satisfy demand. # If demand is >= than recorded failure, placement is not feasible. Adjust info about failed placement. Top level node. Adds application to the scheduled list. Remove app from scheduled list. Add identity group to the cell. Remove identity group. If app is placed on non-existent server, set server to None. Set final rank and utilization for all apps in the queue. Check that app identity is valid for given identity group. # Can happen if identity group was adjusted to lower count. # Can't release identity as it is invalid. # Invalidate any existing placement. Migrate apps from inactive servers. Run the queue and find placements. # TODO: refactor to get rid of warnings. # # pylint: disable=too-many-branches,too-many-statements # # At this point, if app.server is defined, it points to attached # server. # Save information that will be used to restore placement # in case renewal fails. # At this point app was either renewed on the same server, or # temporarily removed from server if renew failed. # # If placement will be found, renew should remain False. If # placement will not be found, renew will be set to True when # placement is restored to the server it was running. # If app was evicted before, try to restore to the same node. # Check if placement is feasible. # There is not enough capacity, from the end of the queue, # evict apps, freeing capacity. # We reached the app we can't place # The app is not yet placed, skip # Do not consider servers that are not up. # TODO: we need to check affinity limit constraints on # each level, all the way to the top. # Placement failed. # If renewal attempt failed, restore previous placement and # expiry date. Run the scheduler for given allocation. Run the scheduler. Adjust server exipiration time to avoid conflicts. Serializes cell to string. Loads scheduler from string.	2.319293	2
banners/bannerRan.py	gothyyy/AIDungeon	1	10264	<filename>banners/bannerRan.py import random import sys import time import json import os import warnings import numpy as np import glob, os stat_mini = 1 stat_max = 0 listBanners = [] #HOW TO USE IT: #1 copy the opening.txt #2 remove the graphic (but do keep top logo for consistency) #3 add ASCII art that is 78 or less characters in width #4 save txt file under a complete new name class bannerRan: def __init__(self): banner_number = load_banner() #insert function to get random self.banner_number = banner_number def load_banner(): global stat_max global stat_mini global listBanners hey = scanBanners() #load text and get proper numbers choose_between = r(stat_mini, stat_max) x = random.choice(listBanners) return x def r(x,y): #randmom, picks between X and Y return int(str(random.randint(x,y))) def scanBanners(): global stat_max global listBanners dir_path = os.path.dirname(os.path.realpath(__file__)) # directory of banners path #os.chdir("") i = 0 for file in glob.glob("banners/*.txt"): i+=1 listBanners.append(file) #print(str(i), file) stat_max = i x = dir_path return x	<filename>banners/bannerRan.py import random import sys import time import json import os import warnings import numpy as np import glob, os stat_mini = 1 stat_max = 0 listBanners = [] #HOW TO USE IT: #1 copy the opening.txt #2 remove the graphic (but do keep top logo for consistency) #3 add ASCII art that is 78 or less characters in width #4 save txt file under a complete new name class bannerRan: def __init__(self): banner_number = load_banner() #insert function to get random self.banner_number = banner_number def load_banner(): global stat_max global stat_mini global listBanners hey = scanBanners() #load text and get proper numbers choose_between = r(stat_mini, stat_max) x = random.choice(listBanners) return x def r(x,y): #randmom, picks between X and Y return int(str(random.randint(x,y))) def scanBanners(): global stat_max global listBanners dir_path = os.path.dirname(os.path.realpath(__file__)) # directory of banners path #os.chdir("") i = 0 for file in glob.glob("banners/*.txt"): i+=1 listBanners.append(file) #print(str(i), file) stat_max = i x = dir_path return x	en	0.72102	#HOW TO USE IT: #1 copy the opening.txt #2 remove the graphic (but do keep top logo for consistency) #3 add ASCII art that is 78 or less characters in width #4 save txt file under a complete new name #insert function to get random #load text and get proper numbers #randmom, picks between X and Y # directory of banners path #os.chdir("") #print(str(i), file)	3.18566	3
BondMarket/app/theme_lib.py	Meith0717/BondMarket	0	10265	<reponame>Meith0717/BondMarket<filename>BondMarket/app/theme_lib.py from dataclasses import dataclass @dataclass class theme: name : str bg_color : str fg_color : str lb_color : str ttk_theme : str LIGHT = theme( name='LIGHT', bg_color=None, fg_color='black', lb_color='#f0f0f0', ttk_theme='xpnative' ) DARK = theme( name='DARK', bg_color='#424242', fg_color='white', lb_color='#424242', ttk_theme='black' )	from dataclasses import dataclass @dataclass class theme: name : str bg_color : str fg_color : str lb_color : str ttk_theme : str LIGHT = theme( name='LIGHT', bg_color=None, fg_color='black', lb_color='#f0f0f0', ttk_theme='xpnative' ) DARK = theme( name='DARK', bg_color='#424242', fg_color='white', lb_color='#424242', ttk_theme='black' )	none	1		2.749645	3
run.py	rimijoker/CA-MTL	1	10266	import os import sys import re import json import logging import torch from transformers import ( HfArgumentParser, set_seed, AutoTokenizer, AutoConfig, EvalPrediction, ) from src.model.ca_mtl import CaMtl, CaMtlArguments from src.utils.misc import MultiTaskDataArguments, Split from src.mtl_trainer import MultiTaskTrainer, MultiTaskTrainingArguments from src.data.mtl_dataset import MultiTaskDataset from src.data.task_dataset import TaskDataset logger = logging.getLogger(__name__) def setup_logging(training_args): logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN, ) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", training_args.local_rank, training_args.device, training_args.n_gpu, bool(training_args.local_rank != -1), training_args.fp16, ) def parse_cmd_args(): parser = HfArgumentParser( ( CaMtlArguments, MultiTaskDataArguments, MultiTaskTrainingArguments, ) ) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): model_args, data_args, training_args = parser.parse_json_file( json_file=os.path.abspath(sys.argv[1]) ) else: ( model_args, data_args, training_args, ) = parser.parse_args_into_dataclasses() logger.info("Training/evaluation parameters %s", training_args) return model_args, data_args, training_args def create_eval_datasets(mode, data_args, tokenizer): eval_datasets = {} for task_id, task_name in enumerate(data_args.tasks): eval_datasets[task_name] = TaskDataset( task_name, task_id, data_args, tokenizer, mode=mode ) if task_name == "mnli": # Loop to handle MNLI double evaluation (matched, mis-matched) eval_datasets["mnli-mm"] = TaskDataset( "mnli-mm", task_id, data_args, tokenizer, mode=mode ) return eval_datasets def main(): model_args, data_args, training_args = parse_cmd_args() setup_logging(training_args) set_seed(training_args.seed) config = AutoConfig.from_pretrained( CaMtl.get_base_model(model_args.model_name_or_path), ) model = CaMtl.from_pretrained( CaMtl.get_base_model(model_args.model_name_or_path), model_args, data_args, config=config) model.freeze_encoder_layers(model_args) logger.info(model) tokenizer = AutoTokenizer.from_pretrained( CaMtl.get_base_model(model_args.model_name_or_path), ) logger.info("Training tasks: %s", ", ".join([t for t in data_args.tasks])) trainer = MultiTaskTrainer( tokenizer, data_args, model=model, args=training_args, train_dataset=MultiTaskDataset(data_args, tokenizer, limit_length=50) if training_args.do_train else None, eval_datasets=create_eval_datasets(Split.dev, data_args, tokenizer) if training_args.do_eval or training_args.evaluate_during_training else None, test_datasets=create_eval_datasets(Split.test, data_args, tokenizer) if training_args.do_predict else None, ) if training_args.do_train: trainer.train( model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None ) if training_args.do_eval: trainer.evaluate() if training_args.do_predict: trainer.predict() def _mp_fn(index): # For xla_spawn (TPUs) main() if __name__ == "__main__": main()	import os import sys import re import json import logging import torch from transformers import ( HfArgumentParser, set_seed, AutoTokenizer, AutoConfig, EvalPrediction, ) from src.model.ca_mtl import CaMtl, CaMtlArguments from src.utils.misc import MultiTaskDataArguments, Split from src.mtl_trainer import MultiTaskTrainer, MultiTaskTrainingArguments from src.data.mtl_dataset import MultiTaskDataset from src.data.task_dataset import TaskDataset logger = logging.getLogger(__name__) def setup_logging(training_args): logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN, ) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", training_args.local_rank, training_args.device, training_args.n_gpu, bool(training_args.local_rank != -1), training_args.fp16, ) def parse_cmd_args(): parser = HfArgumentParser( ( CaMtlArguments, MultiTaskDataArguments, MultiTaskTrainingArguments, ) ) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): model_args, data_args, training_args = parser.parse_json_file( json_file=os.path.abspath(sys.argv[1]) ) else: ( model_args, data_args, training_args, ) = parser.parse_args_into_dataclasses() logger.info("Training/evaluation parameters %s", training_args) return model_args, data_args, training_args def create_eval_datasets(mode, data_args, tokenizer): eval_datasets = {} for task_id, task_name in enumerate(data_args.tasks): eval_datasets[task_name] = TaskDataset( task_name, task_id, data_args, tokenizer, mode=mode ) if task_name == "mnli": # Loop to handle MNLI double evaluation (matched, mis-matched) eval_datasets["mnli-mm"] = TaskDataset( "mnli-mm", task_id, data_args, tokenizer, mode=mode ) return eval_datasets def main(): model_args, data_args, training_args = parse_cmd_args() setup_logging(training_args) set_seed(training_args.seed) config = AutoConfig.from_pretrained( CaMtl.get_base_model(model_args.model_name_or_path), ) model = CaMtl.from_pretrained( CaMtl.get_base_model(model_args.model_name_or_path), model_args, data_args, config=config) model.freeze_encoder_layers(model_args) logger.info(model) tokenizer = AutoTokenizer.from_pretrained( CaMtl.get_base_model(model_args.model_name_or_path), ) logger.info("Training tasks: %s", ", ".join([t for t in data_args.tasks])) trainer = MultiTaskTrainer( tokenizer, data_args, model=model, args=training_args, train_dataset=MultiTaskDataset(data_args, tokenizer, limit_length=50) if training_args.do_train else None, eval_datasets=create_eval_datasets(Split.dev, data_args, tokenizer) if training_args.do_eval or training_args.evaluate_during_training else None, test_datasets=create_eval_datasets(Split.test, data_args, tokenizer) if training_args.do_predict else None, ) if training_args.do_train: trainer.train( model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None ) if training_args.do_eval: trainer.evaluate() if training_args.do_predict: trainer.predict() def _mp_fn(index): # For xla_spawn (TPUs) main() if __name__ == "__main__": main()	en	0.717475	# Loop to handle MNLI double evaluation (matched, mis-matched) # For xla_spawn (TPUs)	1.848349	2
examples/readWebsocket.py	uadlq/PhyPiDAQ-PiOS11	0	10267	#!/usr/bin/env python3 """Read data in CSV format from websocket """ import sys import asyncio import websockets # read url from command line if len(sys.argv) >= 2: uri = sys.argv[1] else: # host url and port uri = "ws://localhost:8314" print("== ", sys.argv[0], " Lese Daten von url ", uri) async def read_ws(): """asynchronous read from websocket """ async with websockets.connect(uri, ping_interval=None) as websocket: # test connection await websocket.send("req_connect") answ = await websocket.recv() if answ == "ack_connect": print("** connected to websocket ", uri) # get data await websocket.send("getData") while True: inp = await websocket.recv() if inp == '\n': # empty record, end print("empty input - closing") sys.exit(0) else: print('read: %s ' % inp, end='') # run web client asyncio.get_event_loop().run_until_complete(read_ws())	#!/usr/bin/env python3 """Read data in CSV format from websocket """ import sys import asyncio import websockets # read url from command line if len(sys.argv) >= 2: uri = sys.argv[1] else: # host url and port uri = "ws://localhost:8314" print("== ", sys.argv[0], " Lese Daten von url ", uri) async def read_ws(): """asynchronous read from websocket """ async with websockets.connect(uri, ping_interval=None) as websocket: # test connection await websocket.send("req_connect") answ = await websocket.recv() if answ == "ack_connect": print("** connected to websocket ", uri) # get data await websocket.send("getData") while True: inp = await websocket.recv() if inp == '\n': # empty record, end print("empty input - closing") sys.exit(0) else: print('read: %s ' % inp, end='') # run web client asyncio.get_event_loop().run_until_complete(read_ws())	en	0.68723	#!/usr/bin/env python3 Read data in CSV format from websocket # read url from command line # host url and port asynchronous read from websocket # test connection # get data # empty record, end # run web client	3.16624	3
settings/__init__.py	arcana261/python-grpc-boilerplate	0	10268	<gh_stars>0 import os import sys import itertools import json _NONE = object() class SettingManager: _sentry = object() def __init__(self): self.env = os.getenv('ENV', 'prd') try: self._default = __import__('settings.default', fromlist=['']) except ModuleNotFoundError: self._default = object() try: self._env = __import__('settings.{}'.format(self.env), fromlist=['']) except ModuleNotFoundError: self._env = object() self._loaded = [] def load(self, filename, fmt='json'): filename = os.path.abspath(filename) if fmt == 'json': with open(filename) as f: self._loaded.append((filename, json.load(f))) def unload(self, filename): filename = os.path.abspath(filename) self._loaded = [(f, v) for f, v in self._loaded if f != filename] def __getattr__(self, item): result = SettingManager._sentry for _, values in self._loaded: if item in values: result = values[item] result = os.getenv(item, result) if result is SettingManager._sentry: result = getattr(self._env, item, getattr(self._default, item, SettingManager._sentry)) if result is SettingManager._sentry: raise AttributeError return result def __contains__(self, item): try: self.__getattr__(item) return True except AttributeError: return False def get(self, item, default=_NONE): try: return self.__getattr__(item) except AttributeError: if default is not _NONE: return default raise AttributeError def __iter__(self): chained = itertools.chain(getattr(self._default, '__dict__', dict()).keys(), getattr(self._env, '__dict__', dict()).keys()) for _, values in self._loaded: chained = itertools.chain(chained, values.keys()) return iter(filter(lambda x: not x.startswith('_'), set(chained))) sys.modules[__name__] = SettingManager()	import os import sys import itertools import json _NONE = object() class SettingManager: _sentry = object() def __init__(self): self.env = os.getenv('ENV', 'prd') try: self._default = __import__('settings.default', fromlist=['']) except ModuleNotFoundError: self._default = object() try: self._env = __import__('settings.{}'.format(self.env), fromlist=['']) except ModuleNotFoundError: self._env = object() self._loaded = [] def load(self, filename, fmt='json'): filename = os.path.abspath(filename) if fmt == 'json': with open(filename) as f: self._loaded.append((filename, json.load(f))) def unload(self, filename): filename = os.path.abspath(filename) self._loaded = [(f, v) for f, v in self._loaded if f != filename] def __getattr__(self, item): result = SettingManager._sentry for _, values in self._loaded: if item in values: result = values[item] result = os.getenv(item, result) if result is SettingManager._sentry: result = getattr(self._env, item, getattr(self._default, item, SettingManager._sentry)) if result is SettingManager._sentry: raise AttributeError return result def __contains__(self, item): try: self.__getattr__(item) return True except AttributeError: return False def get(self, item, default=_NONE): try: return self.__getattr__(item) except AttributeError: if default is not _NONE: return default raise AttributeError def __iter__(self): chained = itertools.chain(getattr(self._default, '__dict__', dict()).keys(), getattr(self._env, '__dict__', dict()).keys()) for _, values in self._loaded: chained = itertools.chain(chained, values.keys()) return iter(filter(lambda x: not x.startswith('_'), set(chained))) sys.modules[__name__] = SettingManager()	none	1		2.350849	2
roblox/partials/partialgroup.py	speer-kinjo/ro.py	28	10269	""" This file contains partial objects related to Roblox groups. """ from __future__ import annotations from typing import TYPE_CHECKING from ..bases.basegroup import BaseGroup from ..bases.baseuser import BaseUser if TYPE_CHECKING: from ..client import Client class AssetPartialGroup(BaseGroup): """ Represents a partial group in the context of a Roblox asset. Intended to parse the `data[0]["creator"]` data from https://games.roblox.com/v1/games. Attributes: _client: The Client object, which is passed to all objects this Client generates. id: The group's name. creator: The group's owner. name: The group's name. """ def __init__(self, client: Client, data: dict): """ Arguments: client: The Client. data: The data from the endpoint. """ self._client: Client = client self.creator: BaseUser = BaseUser(client=client, user_id=data["Id"]) self.id: int = data["CreatorTargetId"] self.name: str = data["Name"] super().__init__(client, self.id) def __repr__(self): return f"<{self.__class__.__name__} id={self.id} name={self.name!r}>" class UniversePartialGroup(BaseGroup): """ Represents a partial group in the context of a Roblox universe. Attributes: _data: The data we get back from the endpoint. _client: The client object, which is passed to all objects this client generates. id: Id of the group name: Name of the group """ def __init__(self, client: Client, data: dict): """ Arguments: client: The ClientSharedObject. data: The data from the endpoint. """ self._client: Client = client self.id = data["id"] self.name: str = data["name"] super().__init__(client, self.id) def __repr__(self): return f"<{self.__class__.__name__} id={self.id} name={self.name!r}>"	""" This file contains partial objects related to Roblox groups. """ from __future__ import annotations from typing import TYPE_CHECKING from ..bases.basegroup import BaseGroup from ..bases.baseuser import BaseUser if TYPE_CHECKING: from ..client import Client class AssetPartialGroup(BaseGroup): """ Represents a partial group in the context of a Roblox asset. Intended to parse the `data[0]["creator"]` data from https://games.roblox.com/v1/games. Attributes: _client: The Client object, which is passed to all objects this Client generates. id: The group's name. creator: The group's owner. name: The group's name. """ def __init__(self, client: Client, data: dict): """ Arguments: client: The Client. data: The data from the endpoint. """ self._client: Client = client self.creator: BaseUser = BaseUser(client=client, user_id=data["Id"]) self.id: int = data["CreatorTargetId"] self.name: str = data["Name"] super().__init__(client, self.id) def __repr__(self): return f"<{self.__class__.__name__} id={self.id} name={self.name!r}>" class UniversePartialGroup(BaseGroup): """ Represents a partial group in the context of a Roblox universe. Attributes: _data: The data we get back from the endpoint. _client: The client object, which is passed to all objects this client generates. id: Id of the group name: Name of the group """ def __init__(self, client: Client, data: dict): """ Arguments: client: The ClientSharedObject. data: The data from the endpoint. """ self._client: Client = client self.id = data["id"] self.name: str = data["name"] super().__init__(client, self.id) def __repr__(self): return f"<{self.__class__.__name__} id={self.id} name={self.name!r}>"	en	0.809606	This file contains partial objects related to Roblox groups. Represents a partial group in the context of a Roblox asset. Intended to parse the `data[0]["creator"]` data from https://games.roblox.com/v1/games. Attributes: _client: The Client object, which is passed to all objects this Client generates. id: The group's name. creator: The group's owner. name: The group's name. Arguments: client: The Client. data: The data from the endpoint. Represents a partial group in the context of a Roblox universe. Attributes: _data: The data we get back from the endpoint. _client: The client object, which is passed to all objects this client generates. id: Id of the group name: Name of the group Arguments: client: The ClientSharedObject. data: The data from the endpoint.	2.923253	3
services/UserService.py	erginbalta/FarmChain	1	10270	<filename>services/UserService.py import mysql.connector import socket from contextlib import closing import json import random packetType= ["INF","TRN","USR"] database = mysql.connector.connect( host="localhost", user="root", port="3307", passwd="<PASSWORD>", database="farmchain" ) def userIdCreator(): data = [] numericId = 0 id = "" with open("/datas/userInformation.json",'r') as f: user = json.load(f) numericId = len(user) + 1 id = str(packetType[2])+str(numericId) return id def transactionIdCreator(): idKey = packetType[1] numericId = random.randint(10000,99999) id = idKey+str(numericId) return id def getUserConnectionInfo(): hst = socket.gethostname() usrHost = socket.gethostbyname(hst) usrPort = findFreePort() return [usrHost,usrPort] def findFreePort(): with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s: s.bind(('', 0)) s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) return s.getsockname()[1] def checkOnlineMiners(): mycursor = database.cursor() sql = "select * from miners where status = 1;" mycursor.execute(sql) result = mycursor.fetchall() return result def minerInfo(): result = checkOnlineMiners() info = result[0] host = result[1] port = result[2] return [host,port] def userInfoPacket(password,name,surname,company,status): info = getUserConnectionInfo() userId = userIdCreator() name = str(name).lower() surname = str(surname).lower() company = str(company).lower() status = str(status).lower() packet = [packetType[0],[userId,password,name,surname,company,status],info[0],info[1]] return packet def transactionPacketCreator(productId,productName,productNumber,fromPlace,toPlace,date): info = getUserConnectionInfo() transactionId = transactionIdCreator() productName = str(productName).lower() fromPlace = str(fromPlace).lower() toPlace = str(toPlace).lower() packet = [packetType[1],[transactionId,productId,productName,productNumber,fromPlace,toPlace,date],info[0],info[1]] return packet	<filename>services/UserService.py import mysql.connector import socket from contextlib import closing import json import random packetType= ["INF","TRN","USR"] database = mysql.connector.connect( host="localhost", user="root", port="3307", passwd="<PASSWORD>", database="farmchain" ) def userIdCreator(): data = [] numericId = 0 id = "" with open("/datas/userInformation.json",'r') as f: user = json.load(f) numericId = len(user) + 1 id = str(packetType[2])+str(numericId) return id def transactionIdCreator(): idKey = packetType[1] numericId = random.randint(10000,99999) id = idKey+str(numericId) return id def getUserConnectionInfo(): hst = socket.gethostname() usrHost = socket.gethostbyname(hst) usrPort = findFreePort() return [usrHost,usrPort] def findFreePort(): with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s: s.bind(('', 0)) s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) return s.getsockname()[1] def checkOnlineMiners(): mycursor = database.cursor() sql = "select * from miners where status = 1;" mycursor.execute(sql) result = mycursor.fetchall() return result def minerInfo(): result = checkOnlineMiners() info = result[0] host = result[1] port = result[2] return [host,port] def userInfoPacket(password,name,surname,company,status): info = getUserConnectionInfo() userId = userIdCreator() name = str(name).lower() surname = str(surname).lower() company = str(company).lower() status = str(status).lower() packet = [packetType[0],[userId,password,name,surname,company,status],info[0],info[1]] return packet def transactionPacketCreator(productId,productName,productNumber,fromPlace,toPlace,date): info = getUserConnectionInfo() transactionId = transactionIdCreator() productName = str(productName).lower() fromPlace = str(fromPlace).lower() toPlace = str(toPlace).lower() packet = [packetType[1],[transactionId,productId,productName,productNumber,fromPlace,toPlace,date],info[0],info[1]] return packet	none	1		2.468183	2
tests/blackbox/access_settings/test_bb_access_settings.py	csanders-git/waflz	1	10271	#!/usr/bin/python '''Test WAF Access settings''' #TODO: make so waflz_server only runs once and then can post to it # ------------------------------------------------------------------------------ # Imports # ------------------------------------------------------------------------------ import pytest import subprocess import os import sys import json from pprint import pprint import time import requests # ------------------------------------------------------------------------------ # Constants # ------------------------------------------------------------------------------ G_TEST_HOST = 'http://127.0.0.1:12345/' # ------------------------------------------------------------------------------ # globals # ------------------------------------------------------------------------------ g_server_pid = -1 # ------------------------------------------------------------------------------ # # ------------------------------------------------------------------------------ def run_command(command): p = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout, stderr = p.communicate() return (p.returncode, stdout, stderr) # ------------------------------------------------------------------------------ #setup_func # ------------------------------------------------------------------------------ @pytest.fixture() def setup_func(): global g_server_pid l_cwd = os.getcwd() l_file_path = os.path.dirname(os.path.abspath(__file__)) l_ruleset_path = os.path.realpath(os.path.join(l_file_path, '../../data/waf/ruleset')) l_geoip2city_path = os.path.realpath(os.path.join(l_file_path, '../../data/waf/db/GeoLite2-City.mmdb')); l_geoip2ISP_path = os.path.realpath(os.path.join(l_file_path, '../../data/waf/db/GeoLite2-ASN.mmdb')); l_profile_path = os.path.realpath(os.path.join(l_file_path, 'test_bb_access_settings.waf.prof.json')) l_waflz_server_path = os.path.abspath(os.path.join(l_file_path, '../../../build/util/waflz_server/waflz_server')) l_subproc = subprocess.Popen([l_waflz_server_path, '-f', l_profile_path, '-r', l_ruleset_path, '-g', l_geoip2city_path, '-s', l_geoip2ISP_path]) time.sleep(1) g_server_pid = l_subproc.pid time.sleep(1) print 'setup g_server_pid: %d'%(g_server_pid) time.sleep(1) # ------------------------------------------------------------------------------ #teardown_func # ------------------------------------------------------------------------------ def teardown_func(): global g_server_pid time.sleep(.5) print 'teardown g_server_pid: %d'%(g_server_pid) if g_server_pid != -1: l_code, l_out, l_err = run_command('kill -9 %d'%(g_server_pid)) time.sleep(.5) # ------------------------------------------------------------------------------ # test_bb_modsecurity_ec_access_settings_ignore_args # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_01_block_not_in_ignore_args(setup_func): #"ignore_query_args": ["ignore", "this", "crap"] l_uri = G_TEST_HOST + '?' + 'arg1&arg2&arg3&arg4&arg5' l_headers = {"host": "myhost.com"} l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) > 0 print json.dumps(l_r_json,indent=4) assert l_r_json['rule_intercept_status'] == 403 #assert 'modsecurity_crs_23_request_limits.conf' in l_r_json['sub_event'][0]['rule_file'] # ensure 403 because exceeded max_num_args assert 'Too many arguments in' in l_r_json['rule_msg'] # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_02_bypass_in_ignore_args # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_02_bypass_in_ignore_args(): #Test that passing ignore args lets it bypass #Max arg limit it 4, we pass 7 l_uri = G_TEST_HOST + '?' + 'arg1&arg2&arg3&arg4&ignore&this&crap' l_headers = {"host": "myhost.com"} l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) == 0 # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_03_block_headers_not_in_ignore_header_list # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_03_block_headers_not_in_ignore_header_list(): #ignore_header": ["(?i)(benign-header)", "super-whatever-header", "^D."] l_uri = G_TEST_HOST l_headers = {"host": "myhost.com", "kooky-Header" : "function () { doing this is kinda dumb" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() print l_r_json #We got an event assert len(l_r_json) > 0 # detect a bash shellshock assert 'Bash shellshock attack detected' in l_r_json['sub_event'][0]['rule_msg'] assert 'REQUEST_HEADERS' in l_r_json['sub_event'][0]['matched_var']['name'] assert 'ZnVuY3Rpb24gKCkgeyBkb2luZyB0aGlzIGlzIGtpbmRhIGR1bWI=' in l_r_json['sub_event'][0]['matched_var']['value'] # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_04_bypass_headers_in_ignore_header_list # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_04_bypass_headers_in_ignore_header_list(): #Test ignore headers are ignored l_uri = G_TEST_HOST l_headers = {"host": "myhost.com", "Benign-Header" : "function () { doing this is kinda dumb", "super-whatever-header" : "function () { doing this is kinda dumb" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) == 0 # ------------------------------------------------------------------------------- # test_bb_modsec_ec_access_settings_05_bypass_headers_in_ignore_header_list_regex # ------------------------------------------------------------------------------- def test_bb_modsec_ec_access_settings_05_bypass_headers_in_ignore_header_list_regex(): ######################################## # Test regex "^D." ######################################## l_uri = G_TEST_HOST #anything that starts with D should be ignored l_headers = {"host": "myhost.com", "Doopdoop" : "function () { doing this is kinda dumb", "Duper-duper-deader" : "function () { doing this is kinda dumb" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) == 0 # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_06_block_cookie_not_in_ignore_cookie_list # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_06_block_cookie_not_in_ignore_cookie_list(): #"ignore_cookie": ["(?i)(sketchy_origin)", "(?i)(yousocrazy)"] l_uri = G_TEST_HOST l_headers = {"host": "myhost.com", "Cookie": "blahblah=function () { asdf asdf asdf" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) > 0 # detect a bash shellshock assert 'Bash shellshock attack detected' in l_r_json['sub_event'][0]['rule_msg'] assert 'REQUEST_HEADERS' in l_r_json['sub_event'][0]['matched_var']['name'] # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_07_bypass_cookie_in_ignore_cookie_list # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_07_bypass_cookie_in_ignore_cookie_list(): #"ignore_cookie": ["(?i)(sketchy_origin)", "(?i)(yousocrazy)"] l_uri = G_TEST_HOST l_headers = {"host" : "myhost.com", "Cookie" : "SkeTchy_Origin=function () { asdf asdf asdf" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() #We get no event assert len(l_r_json) == 0 l_uri = G_TEST_HOST l_headers = {"host" : "myhost.com", "Cookie" : "SkeTchy_Origin=function () { asdf asdf asdf" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) == 0 # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_08_ignore_cookie_in_ignore_cookie_list # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_08_bypass_cookie_in_ignore_cookie_list_regex(): ######################################## # Test regex "^[0-9_].*$" ######################################## l_uri = G_TEST_HOST l_headers = {"host" : "myhost.com", "Cookie" : "0_123_ADB__bloop=function () { asdf asdf asdf" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) == 0 # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_09_block_disallowed_http_method # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_09_block_disallowed_http_method(): l_uri = G_TEST_HOST l_headers = {"host" : "myhost.com" } l_r = requests.put(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) > 0 assert 'Method is not allowed by policy' in l_r_json['rule_msg'] teardown_func()	#!/usr/bin/python '''Test WAF Access settings''' #TODO: make so waflz_server only runs once and then can post to it # ------------------------------------------------------------------------------ # Imports # ------------------------------------------------------------------------------ import pytest import subprocess import os import sys import json from pprint import pprint import time import requests # ------------------------------------------------------------------------------ # Constants # ------------------------------------------------------------------------------ G_TEST_HOST = 'http://127.0.0.1:12345/' # ------------------------------------------------------------------------------ # globals # ------------------------------------------------------------------------------ g_server_pid = -1 # ------------------------------------------------------------------------------ # # ------------------------------------------------------------------------------ def run_command(command): p = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout, stderr = p.communicate() return (p.returncode, stdout, stderr) # ------------------------------------------------------------------------------ #setup_func # ------------------------------------------------------------------------------ @pytest.fixture() def setup_func(): global g_server_pid l_cwd = os.getcwd() l_file_path = os.path.dirname(os.path.abspath(__file__)) l_ruleset_path = os.path.realpath(os.path.join(l_file_path, '../../data/waf/ruleset')) l_geoip2city_path = os.path.realpath(os.path.join(l_file_path, '../../data/waf/db/GeoLite2-City.mmdb')); l_geoip2ISP_path = os.path.realpath(os.path.join(l_file_path, '../../data/waf/db/GeoLite2-ASN.mmdb')); l_profile_path = os.path.realpath(os.path.join(l_file_path, 'test_bb_access_settings.waf.prof.json')) l_waflz_server_path = os.path.abspath(os.path.join(l_file_path, '../../../build/util/waflz_server/waflz_server')) l_subproc = subprocess.Popen([l_waflz_server_path, '-f', l_profile_path, '-r', l_ruleset_path, '-g', l_geoip2city_path, '-s', l_geoip2ISP_path]) time.sleep(1) g_server_pid = l_subproc.pid time.sleep(1) print 'setup g_server_pid: %d'%(g_server_pid) time.sleep(1) # ------------------------------------------------------------------------------ #teardown_func # ------------------------------------------------------------------------------ def teardown_func(): global g_server_pid time.sleep(.5) print 'teardown g_server_pid: %d'%(g_server_pid) if g_server_pid != -1: l_code, l_out, l_err = run_command('kill -9 %d'%(g_server_pid)) time.sleep(.5) # ------------------------------------------------------------------------------ # test_bb_modsecurity_ec_access_settings_ignore_args # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_01_block_not_in_ignore_args(setup_func): #"ignore_query_args": ["ignore", "this", "crap"] l_uri = G_TEST_HOST + '?' + 'arg1&arg2&arg3&arg4&arg5' l_headers = {"host": "myhost.com"} l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) > 0 print json.dumps(l_r_json,indent=4) assert l_r_json['rule_intercept_status'] == 403 #assert 'modsecurity_crs_23_request_limits.conf' in l_r_json['sub_event'][0]['rule_file'] # ensure 403 because exceeded max_num_args assert 'Too many arguments in' in l_r_json['rule_msg'] # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_02_bypass_in_ignore_args # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_02_bypass_in_ignore_args(): #Test that passing ignore args lets it bypass #Max arg limit it 4, we pass 7 l_uri = G_TEST_HOST + '?' + 'arg1&arg2&arg3&arg4&ignore&this&crap' l_headers = {"host": "myhost.com"} l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) == 0 # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_03_block_headers_not_in_ignore_header_list # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_03_block_headers_not_in_ignore_header_list(): #ignore_header": ["(?i)(benign-header)", "super-whatever-header", "^D."] l_uri = G_TEST_HOST l_headers = {"host": "myhost.com", "kooky-Header" : "function () { doing this is kinda dumb" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() print l_r_json #We got an event assert len(l_r_json) > 0 # detect a bash shellshock assert 'Bash shellshock attack detected' in l_r_json['sub_event'][0]['rule_msg'] assert 'REQUEST_HEADERS' in l_r_json['sub_event'][0]['matched_var']['name'] assert 'ZnVuY3Rpb24gKCkgeyBkb2luZyB0aGlzIGlzIGtpbmRhIGR1bWI=' in l_r_json['sub_event'][0]['matched_var']['value'] # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_04_bypass_headers_in_ignore_header_list # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_04_bypass_headers_in_ignore_header_list(): #Test ignore headers are ignored l_uri = G_TEST_HOST l_headers = {"host": "myhost.com", "Benign-Header" : "function () { doing this is kinda dumb", "super-whatever-header" : "function () { doing this is kinda dumb" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) == 0 # ------------------------------------------------------------------------------- # test_bb_modsec_ec_access_settings_05_bypass_headers_in_ignore_header_list_regex # ------------------------------------------------------------------------------- def test_bb_modsec_ec_access_settings_05_bypass_headers_in_ignore_header_list_regex(): ######################################## # Test regex "^D." ######################################## l_uri = G_TEST_HOST #anything that starts with D should be ignored l_headers = {"host": "myhost.com", "Doopdoop" : "function () { doing this is kinda dumb", "Duper-duper-deader" : "function () { doing this is kinda dumb" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) == 0 # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_06_block_cookie_not_in_ignore_cookie_list # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_06_block_cookie_not_in_ignore_cookie_list(): #"ignore_cookie": ["(?i)(sketchy_origin)", "(?i)(yousocrazy)"] l_uri = G_TEST_HOST l_headers = {"host": "myhost.com", "Cookie": "blahblah=function () { asdf asdf asdf" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) > 0 # detect a bash shellshock assert 'Bash shellshock attack detected' in l_r_json['sub_event'][0]['rule_msg'] assert 'REQUEST_HEADERS' in l_r_json['sub_event'][0]['matched_var']['name'] # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_07_bypass_cookie_in_ignore_cookie_list # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_07_bypass_cookie_in_ignore_cookie_list(): #"ignore_cookie": ["(?i)(sketchy_origin)", "(?i)(yousocrazy)"] l_uri = G_TEST_HOST l_headers = {"host" : "myhost.com", "Cookie" : "SkeTchy_Origin=function () { asdf asdf asdf" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() #We get no event assert len(l_r_json) == 0 l_uri = G_TEST_HOST l_headers = {"host" : "myhost.com", "Cookie" : "SkeTchy_Origin=function () { asdf asdf asdf" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) == 0 # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_08_ignore_cookie_in_ignore_cookie_list # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_08_bypass_cookie_in_ignore_cookie_list_regex(): ######################################## # Test regex "^[0-9_].*$" ######################################## l_uri = G_TEST_HOST l_headers = {"host" : "myhost.com", "Cookie" : "0_123_ADB__bloop=function () { asdf asdf asdf" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) == 0 # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_09_block_disallowed_http_method # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_09_block_disallowed_http_method(): l_uri = G_TEST_HOST l_headers = {"host" : "myhost.com" } l_r = requests.put(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) > 0 assert 'Method is not allowed by policy' in l_r_json['rule_msg'] teardown_func()	en	0.232524	#!/usr/bin/python Test WAF Access settings #TODO: make so waflz_server only runs once and then can post to it # ------------------------------------------------------------------------------ # Imports # ------------------------------------------------------------------------------ # ------------------------------------------------------------------------------ # Constants # ------------------------------------------------------------------------------ # ------------------------------------------------------------------------------ # globals # ------------------------------------------------------------------------------ # ------------------------------------------------------------------------------ # # ------------------------------------------------------------------------------ # ------------------------------------------------------------------------------ #setup_func # ------------------------------------------------------------------------------ # ------------------------------------------------------------------------------ #teardown_func # ------------------------------------------------------------------------------ # ------------------------------------------------------------------------------ # test_bb_modsecurity_ec_access_settings_ignore_args # ------------------------------------------------------------------------------ #"ignore_query_args": ["ignore", "this", "crap"] #assert 'modsecurity_crs_23_request_limits.conf' in l_r_json['sub_event'][0]['rule_file'] # ensure 403 because exceeded max_num_args # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_02_bypass_in_ignore_args # ------------------------------------------------------------------------------ #Test that passing ignore args lets it bypass #Max arg limit it 4, we pass 7 # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_03_block_headers_not_in_ignore_header_list # ------------------------------------------------------------------------------ #ignore_header": ["(?i)(benign-header)", "super-whatever-header", "^D."] #We got an event # detect a bash shellshock # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_04_bypass_headers_in_ignore_header_list # ------------------------------------------------------------------------------ #Test ignore headers are ignored # ------------------------------------------------------------------------------- # test_bb_modsec_ec_access_settings_05_bypass_headers_in_ignore_header_list_regex # ------------------------------------------------------------------------------- ######################################## # Test regex "^D." ######################################## #anything that starts with D should be ignored # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_06_block_cookie_not_in_ignore_cookie_list # ------------------------------------------------------------------------------ #"ignore_cookie": ["(?i)(sketchy_origin)", "(?i)(yousocrazy)"] # detect a bash shellshock # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_07_bypass_cookie_in_ignore_cookie_list # ------------------------------------------------------------------------------ #"ignore_cookie": ["(?i)(sketchy_origin)", "(?i)(yousocrazy)"] #We get no event # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_08_ignore_cookie_in_ignore_cookie_list # ------------------------------------------------------------------------------ ######################################## # Test regex "^[0-9_].*$" ######################################## # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_09_block_disallowed_http_method # ------------------------------------------------------------------------------	1.7865	2
cosmosis/runtime/analytics.py	ktanidis2/Modified_CosmoSIS_for_galaxy_number_count_angular_power_spectra	1	10272	<gh_stars>1-10 #coding: utf-8 from __future__ import print_function from builtins import zip from builtins import object from cosmosis import output as output_module import numpy as np import sys import os class Analytics(object): def __init__(self, params, pool=None): self.params = params self.pool = pool self.total_steps = 0 nparam = len(params) self.means = np.zeros(nparam) self.m2 = np.zeros(nparam) self.cov_times_n = np.zeros((nparam,nparam)) def add_traces(self, traces): if traces.shape[1] != len(self.params): raise RuntimeError("The number of traces added to Analytics " "does not match the number of varied " "parameters!") num = float(self.total_steps) for x in traces: num += 1.0 delta = x - self.means old_means = self.means.copy() self.means += delta/num self.m2 += delta(x - self.means) self.cov_times_n += np.outer(x-self.means, x-old_means) self.total_steps += traces.shape[0] def trace_means(self): if self.pool: return np.array(self.pool.gather(self.means)).T else: return self.means def trace_variances(self): if self.total_steps > 1: local_variance = self.m2 / float(self.total_steps-1) if self.pool: return np.array(self.pool.gather(local_variance)).T else: return local_variance return None def gelman_rubin(self, quiet=True): # takes current traces and returns if self.pool is None or not self.pool.size > 1: raise RuntimeError("Gelman-Rubin statistic is only " "valid for multiple chains.") if self.total_steps == 0: raise RuntimeError("Gelman-Rubin statistic not " "defined for 0-length chains.") # gather trace statistics to master process means = self.trace_means() variances = self.trace_variances() if self.pool.is_master(): B_over_n = np.var(means, ddof=1, axis=1) B = B_over_n self.total_steps W = np.mean(variances, axis=1) V = ((1. - 1./self.total_steps) * W + (1. + 1./self.pool.size) * B_over_n) # TODO: check for 0-values in W Rhat = np.sqrt(V/W) else: Rhat = None Rhat = self.pool.bcast(Rhat) if not quiet and self.pool.is_master(): print() print("Gelman-Rubin:") for (p,R) in zip(self.params, Rhat): print(" ", p, " ", R) print("Worst = ", Rhat.max()) print() return Rhat	#coding: utf-8 from __future__ import print_function from builtins import zip from builtins import object from cosmosis import output as output_module import numpy as np import sys import os class Analytics(object): def __init__(self, params, pool=None): self.params = params self.pool = pool self.total_steps = 0 nparam = len(params) self.means = np.zeros(nparam) self.m2 = np.zeros(nparam) self.cov_times_n = np.zeros((nparam,nparam)) def add_traces(self, traces): if traces.shape[1] != len(self.params): raise RuntimeError("The number of traces added to Analytics " "does not match the number of varied " "parameters!") num = float(self.total_steps) for x in traces: num += 1.0 delta = x - self.means old_means = self.means.copy() self.means += delta/num self.m2 += delta(x - self.means) self.cov_times_n += np.outer(x-self.means, x-old_means) self.total_steps += traces.shape[0] def trace_means(self): if self.pool: return np.array(self.pool.gather(self.means)).T else: return self.means def trace_variances(self): if self.total_steps > 1: local_variance = self.m2 / float(self.total_steps-1) if self.pool: return np.array(self.pool.gather(local_variance)).T else: return local_variance return None def gelman_rubin(self, quiet=True): # takes current traces and returns if self.pool is None or not self.pool.size > 1: raise RuntimeError("Gelman-Rubin statistic is only " "valid for multiple chains.") if self.total_steps == 0: raise RuntimeError("Gelman-Rubin statistic not " "defined for 0-length chains.") # gather trace statistics to master process means = self.trace_means() variances = self.trace_variances() if self.pool.is_master(): B_over_n = np.var(means, ddof=1, axis=1) B = B_over_n self.total_steps W = np.mean(variances, axis=1) V = ((1. - 1./self.total_steps) * W + (1. + 1./self.pool.size) * B_over_n) # TODO: check for 0-values in W Rhat = np.sqrt(V/W) else: Rhat = None Rhat = self.pool.bcast(Rhat) if not quiet and self.pool.is_master(): print() print("Gelman-Rubin:") for (p,R) in zip(self.params, Rhat): print(" ", p, " ", R) print("Worst = ", Rhat.max()) print() return Rhat	en	0.748833	#coding: utf-8 # takes current traces and returns # gather trace statistics to master process # TODO: check for 0-values in W	2.507523	3
sdcc2elf.py	Vector35/llil_transpiler	14	10273	<filename>sdcc2elf.py #!/usr/bin/env python # # convert SDCC .rel files to 32-bit ELF relocatable # # resulting file is simple: # # ------------------------ # ELF header # ------------------------ # .text section # .shstrtab section # .strtab section # .symtab section # ------------------------ # NULL elf32_shdr # .text elf32_shdr # .shstrtab elf32_shdr # .symtab elf32_shdr # .strtab elf32_shdr # ------------------------ import os import re import sys from struct import pack #------------------------------------------------------------------------------ # ELF helpers #------------------------------------------------------------------------------ (PF_X, PF_W, PF_R) = (1,2,4) (SHT_NULL, SHT_PROGBITS, SHT_STRTAB) = (0,1,3) sz_ehdr = 0x34 sz_shdr = 0x28 def align(fp, to=4, pad=b'\x00'): while fp.tell() % to: fp.write(pad) #------------------------------------------------------------------------------ # read .map file for symbols #------------------------------------------------------------------------------ fpath_map = sys.argv[2] assert fpath_map.endswith('.map') with open(fpath_map) as fp: lines = fp.readlines() (_CODE_ADDR, _CODE_SZ) = (None, None) (i_code, i_header) = (None, None) for (i, line) in enumerate(lines): if line.startswith('_CODE'): m = re.match(r'^_CODE\s+([A-F0-9]{8})\s+([A-F0-9]{8})', line) (addr, size) = map(lambda x: int(x, 16), m.group(1,2)) if not i_code: i_code = i _CODE_ADDR = addr _CODE_SZ = size else: if addr != _CODE_ADDR: raise Exception('conflicting code segment addresses') if size != _CODE_SZ: raise Exception('conflicting code segment sizes') if line.startswith('_HEADER0'): i_header = i break assert i_code and i_header and i_code < i_header syms = [] for line in lines[i_code:i_header]: m = re.search(r'([A-F0-9]{8})\s+(_\w+)', line) if m: (addr, symname) = m.group(1, 2) print('found %s: %s' % (addr, symname)) syms.append((symname, int(addr, 16))); assert syms print('_CODE [%08X, %08X)' % (_CODE_ADDR, _CODE_ADDR+_CODE_SZ)) print('_CODE symbols from') for (name, addr) in syms: print('%08X: %s' % (addr, name)) #------------------------------------------------------------------------------ # read .ihx file #------------------------------------------------------------------------------ fpath_ihx = sys.argv[1] assert fpath_ihx.endswith('.ihx') code_area = [b'\x00'] * (_CODE_ADDR + _CODE_SZ) with open(fpath_ihx) as fp: for line in fp.readlines(): m = re.match(r'^:(..)(....)00(.)(..)', line) if m: (count, addr, data, csum) = m.group(1,2,3,4) count = int(count,16) assert count == len(data)/2 addr = int(addr,16) if not (addr >= _CODE_ADDR and addr < (_CODE_ADDR + _CODE_SZ)): continue print('%08X: ' % addr, end='') for i in range(count): byte_str = data[2i]+data[2i+1] print('%s ' % byte_str, end='') code_area[addr + i] = pack('B', int(byte_str, 16)) print('') continue m = re.match(r'^:00000001FF', line) if m: break raise Exception('got unexpected IHX line: %s' % line) assert code_area #print(code_area) #------------------------------------------------------------------------------ # write ELF #------------------------------------------------------------------------------ # process symbols, build string table syms = sorted(syms, key=lambda name_addr: name_addr[1]) func2size = {} func2stroffs = {} strtab = b'\x00' for i in range(len(syms)): (name, addr) = syms[i] if i == len(syms)-1: func2size[name] = len(code_area) - addr else: func2size[name] = syms[i+1][1] - addr func2stroffs[name] = len(strtab) strtab = strtab + name.encode('utf-8') + b'\x00' print('%04X: %s size %X' % (addr, name, func2size[name])) fp = open('tests.elf', 'wb') # elf32_hdr (placeholder, we'll come back to fill in offsets) print('elf32_hdr @ %X' % fp.tell()) fp.write(b'\x00' sz_ehdr) # .text section contents o_text = fp.tell() print('placing .text @ %X' % o_text) for byte in code_area: fp.write(byte) sz_text = fp.tell() - o_text # .shstrtab section contents scn_shstrtab = b'\x00.text\x00.shstrtab\x00.symtab\x00.strtab\x00' align(fp) o_shstrtab = fp.tell() print('placing .shstrtab @ %X' % o_shstrtab) fp.write(scn_shstrtab) sz_shstrtab = fp.tell() - o_shstrtab # .symtab section contents align(fp) o_symtab = fp.tell() print('placing .symtab @ %X' % o_symtab) for (name, addr) in syms: st_name = func2stroffs[name] st_value = addr st_size = func2size[name] st_info = 0x12 # bind:1(GLOBAL) type:2(FUNC) st_other = 0 st_shndx = 0x1 # section header index: 0'th: NULL 1'th: .text Elf32_Sym = pack('<IIIBBH', st_name, st_value, st_size, st_info, st_other, st_shndx) fp.write(Elf32_Sym) sz_symtab = fp.tell() - o_symtab # .strtab section contents align(fp) o_strtab = fp.tell() print('placing .strtab @ %X' % o_strtab) fp.write(strtab) sz_strtab = fp.tell() - o_strtab # null section header (index 0) align(fp) o_shdr_null = fp.tell() print('placing shdr NULL @ %X' % o_shdr_null) fp.write(b'\x00' * sz_shdr) # .text section header (index 1) o_shdr_text = fp.tell() print('placing shdr .text @ %X' % fp.tell()) sh_name = scn_shstrtab.index(b'.text') sh_type = 1 # SHT_PROGBITS sh_flags = 6 # ALLOC\|EXECINSTR sh_addr = 0 sh_offset = o_text sh_size = sz_text sh_link = 0 sh_info = 0 sh_addralign = 4 sh_entsize = 0 tmp = pack('<IIIIIIIIII', \ sh_name, sh_type, sh_flags, sh_addr, sh_offset, sh_size, sh_link, sh_info, \ sh_addralign, sh_entsize) fp.write(tmp) # .shstrtab section header (index 2) o_shdr_shstrtab = fp.tell() print('placing shdr .shstrtab @ %X' % fp.tell()) sh_name = scn_shstrtab.index(b'.shstrtab') sh_type = 3 #SHT_STRTAB sh_flags = 0 sh_addr = 0 sh_offset = o_shstrtab sh_size = sz_shstrtab sh_link = 0 sh_info = 0 sh_addralign = 1 sh_entsize = 0 tmp = pack('<IIIIIIIIII', \ sh_name, sh_type, sh_flags, sh_addr, sh_offset, sh_size, sh_link, sh_info, \ sh_addralign, sh_entsize) fp.write(tmp) # .symtab section header (index 3) o_shdr_symtab = fp.tell() print('placing shdr .symtab @ %X' % fp.tell()) sh_name = scn_shstrtab.index(b'.symtab') sh_type = 2 #SHT_SYMTAB sh_flags = 0 sh_addr = 0 sh_offset = o_symtab sh_size = sz_symtab sh_link = 4 # link to scn #4 (find strings in .strtab) sh_info = 0 sh_addralign = 4 sh_entsize = 0 tmp = pack('<IIIIIIIIII', \ sh_name, sh_type, sh_flags, sh_addr, sh_offset, sh_size, sh_link, sh_info, \ sh_addralign, sh_entsize) fp.write(tmp) # .strtab section header (index 4) o_shdr_strtab = fp.tell() print('placing shdr .strtab @ %X' % fp.tell()) sh_name = scn_shstrtab.index(b'.strtab') sh_type = 3 #SHT_STRTAB sh_flags = 0 sh_addr = 0 sh_offset = o_strtab sh_size = sz_strtab sh_link = 0 sh_info = 0 sh_addralign = 1 sh_entsize = 0 tmp = pack('<IIIIIIIIII', \ sh_name, sh_type, sh_flags, sh_addr, sh_offset, sh_size, sh_link, sh_info, \ sh_addralign, sh_entsize) fp.write(tmp) # seek back, write real elf header hdr = b'\x7FELF' hdr += b'\x01' # e_ident[EI_CLASS] 32-bit hdr += b'\x01' # e_ident[EI_DATA] LSB (little-end) hdr += b'\x01\x00\x00' # version, osabi, abiversion hdr += b'\x00'*7 assert len(hdr) == 16 hdr += pack('<H', 1) # e_type = ET_REL hdr += pack('<H', 220) # e_machine = EM_Z80 hdr += pack('<I', 1) # e_version = EV_CURRENT hdr += pack('<I', 0) # e_entry hdr += pack('<I', 0) # e_phoff hdr += pack('<I', o_shdr_null) # e_shoff hdr += pack('<I', 0) # e_flags hdr += pack('<H', sz_ehdr) # e_ehsize hdr += pack('<H', 0) # e_phentsize hdr += pack('<H', 0) # e_phnum hdr += pack('<H', sz_shdr) # e_shentsize hdr += pack('<H', 5) # e_shnum hdr += pack('<H', 2) # e_shstrndx = index of .shstrtab assert len(hdr) == sz_ehdr fp.seek(0, os.SEEK_SET) fp.write(hdr) # done! fp.close()	<filename>sdcc2elf.py #!/usr/bin/env python # # convert SDCC .rel files to 32-bit ELF relocatable # # resulting file is simple: # # ------------------------ # ELF header # ------------------------ # .text section # .shstrtab section # .strtab section # .symtab section # ------------------------ # NULL elf32_shdr # .text elf32_shdr # .shstrtab elf32_shdr # .symtab elf32_shdr # .strtab elf32_shdr # ------------------------ import os import re import sys from struct import pack #------------------------------------------------------------------------------ # ELF helpers #------------------------------------------------------------------------------ (PF_X, PF_W, PF_R) = (1,2,4) (SHT_NULL, SHT_PROGBITS, SHT_STRTAB) = (0,1,3) sz_ehdr = 0x34 sz_shdr = 0x28 def align(fp, to=4, pad=b'\x00'): while fp.tell() % to: fp.write(pad) #------------------------------------------------------------------------------ # read .map file for symbols #------------------------------------------------------------------------------ fpath_map = sys.argv[2] assert fpath_map.endswith('.map') with open(fpath_map) as fp: lines = fp.readlines() (_CODE_ADDR, _CODE_SZ) = (None, None) (i_code, i_header) = (None, None) for (i, line) in enumerate(lines): if line.startswith('_CODE'): m = re.match(r'^_CODE\s+([A-F0-9]{8})\s+([A-F0-9]{8})', line) (addr, size) = map(lambda x: int(x, 16), m.group(1,2)) if not i_code: i_code = i _CODE_ADDR = addr _CODE_SZ = size else: if addr != _CODE_ADDR: raise Exception('conflicting code segment addresses') if size != _CODE_SZ: raise Exception('conflicting code segment sizes') if line.startswith('_HEADER0'): i_header = i break assert i_code and i_header and i_code < i_header syms = [] for line in lines[i_code:i_header]: m = re.search(r'([A-F0-9]{8})\s+(_\w+)', line) if m: (addr, symname) = m.group(1, 2) print('found %s: %s' % (addr, symname)) syms.append((symname, int(addr, 16))); assert syms print('_CODE [%08X, %08X)' % (_CODE_ADDR, _CODE_ADDR+_CODE_SZ)) print('_CODE symbols from') for (name, addr) in syms: print('%08X: %s' % (addr, name)) #------------------------------------------------------------------------------ # read .ihx file #------------------------------------------------------------------------------ fpath_ihx = sys.argv[1] assert fpath_ihx.endswith('.ihx') code_area = [b'\x00'] * (_CODE_ADDR + _CODE_SZ) with open(fpath_ihx) as fp: for line in fp.readlines(): m = re.match(r'^:(..)(....)00(.)(..)', line) if m: (count, addr, data, csum) = m.group(1,2,3,4) count = int(count,16) assert count == len(data)/2 addr = int(addr,16) if not (addr >= _CODE_ADDR and addr < (_CODE_ADDR + _CODE_SZ)): continue print('%08X: ' % addr, end='') for i in range(count): byte_str = data[2i]+data[2i+1] print('%s ' % byte_str, end='') code_area[addr + i] = pack('B', int(byte_str, 16)) print('') continue m = re.match(r'^:00000001FF', line) if m: break raise Exception('got unexpected IHX line: %s' % line) assert code_area #print(code_area) #------------------------------------------------------------------------------ # write ELF #------------------------------------------------------------------------------ # process symbols, build string table syms = sorted(syms, key=lambda name_addr: name_addr[1]) func2size = {} func2stroffs = {} strtab = b'\x00' for i in range(len(syms)): (name, addr) = syms[i] if i == len(syms)-1: func2size[name] = len(code_area) - addr else: func2size[name] = syms[i+1][1] - addr func2stroffs[name] = len(strtab) strtab = strtab + name.encode('utf-8') + b'\x00' print('%04X: %s size %X' % (addr, name, func2size[name])) fp = open('tests.elf', 'wb') # elf32_hdr (placeholder, we'll come back to fill in offsets) print('elf32_hdr @ %X' % fp.tell()) fp.write(b'\x00' sz_ehdr) # .text section contents o_text = fp.tell() print('placing .text @ %X' % o_text) for byte in code_area: fp.write(byte) sz_text = fp.tell() - o_text # .shstrtab section contents scn_shstrtab = b'\x00.text\x00.shstrtab\x00.symtab\x00.strtab\x00' align(fp) o_shstrtab = fp.tell() print('placing .shstrtab @ %X' % o_shstrtab) fp.write(scn_shstrtab) sz_shstrtab = fp.tell() - o_shstrtab # .symtab section contents align(fp) o_symtab = fp.tell() print('placing .symtab @ %X' % o_symtab) for (name, addr) in syms: st_name = func2stroffs[name] st_value = addr st_size = func2size[name] st_info = 0x12 # bind:1(GLOBAL) type:2(FUNC) st_other = 0 st_shndx = 0x1 # section header index: 0'th: NULL 1'th: .text Elf32_Sym = pack('<IIIBBH', st_name, st_value, st_size, st_info, st_other, st_shndx) fp.write(Elf32_Sym) sz_symtab = fp.tell() - o_symtab # .strtab section contents align(fp) o_strtab = fp.tell() print('placing .strtab @ %X' % o_strtab) fp.write(strtab) sz_strtab = fp.tell() - o_strtab # null section header (index 0) align(fp) o_shdr_null = fp.tell() print('placing shdr NULL @ %X' % o_shdr_null) fp.write(b'\x00' * sz_shdr) # .text section header (index 1) o_shdr_text = fp.tell() print('placing shdr .text @ %X' % fp.tell()) sh_name = scn_shstrtab.index(b'.text') sh_type = 1 # SHT_PROGBITS sh_flags = 6 # ALLOC\|EXECINSTR sh_addr = 0 sh_offset = o_text sh_size = sz_text sh_link = 0 sh_info = 0 sh_addralign = 4 sh_entsize = 0 tmp = pack('<IIIIIIIIII', \ sh_name, sh_type, sh_flags, sh_addr, sh_offset, sh_size, sh_link, sh_info, \ sh_addralign, sh_entsize) fp.write(tmp) # .shstrtab section header (index 2) o_shdr_shstrtab = fp.tell() print('placing shdr .shstrtab @ %X' % fp.tell()) sh_name = scn_shstrtab.index(b'.shstrtab') sh_type = 3 #SHT_STRTAB sh_flags = 0 sh_addr = 0 sh_offset = o_shstrtab sh_size = sz_shstrtab sh_link = 0 sh_info = 0 sh_addralign = 1 sh_entsize = 0 tmp = pack('<IIIIIIIIII', \ sh_name, sh_type, sh_flags, sh_addr, sh_offset, sh_size, sh_link, sh_info, \ sh_addralign, sh_entsize) fp.write(tmp) # .symtab section header (index 3) o_shdr_symtab = fp.tell() print('placing shdr .symtab @ %X' % fp.tell()) sh_name = scn_shstrtab.index(b'.symtab') sh_type = 2 #SHT_SYMTAB sh_flags = 0 sh_addr = 0 sh_offset = o_symtab sh_size = sz_symtab sh_link = 4 # link to scn #4 (find strings in .strtab) sh_info = 0 sh_addralign = 4 sh_entsize = 0 tmp = pack('<IIIIIIIIII', \ sh_name, sh_type, sh_flags, sh_addr, sh_offset, sh_size, sh_link, sh_info, \ sh_addralign, sh_entsize) fp.write(tmp) # .strtab section header (index 4) o_shdr_strtab = fp.tell() print('placing shdr .strtab @ %X' % fp.tell()) sh_name = scn_shstrtab.index(b'.strtab') sh_type = 3 #SHT_STRTAB sh_flags = 0 sh_addr = 0 sh_offset = o_strtab sh_size = sz_strtab sh_link = 0 sh_info = 0 sh_addralign = 1 sh_entsize = 0 tmp = pack('<IIIIIIIIII', \ sh_name, sh_type, sh_flags, sh_addr, sh_offset, sh_size, sh_link, sh_info, \ sh_addralign, sh_entsize) fp.write(tmp) # seek back, write real elf header hdr = b'\x7FELF' hdr += b'\x01' # e_ident[EI_CLASS] 32-bit hdr += b'\x01' # e_ident[EI_DATA] LSB (little-end) hdr += b'\x01\x00\x00' # version, osabi, abiversion hdr += b'\x00'*7 assert len(hdr) == 16 hdr += pack('<H', 1) # e_type = ET_REL hdr += pack('<H', 220) # e_machine = EM_Z80 hdr += pack('<I', 1) # e_version = EV_CURRENT hdr += pack('<I', 0) # e_entry hdr += pack('<I', 0) # e_phoff hdr += pack('<I', o_shdr_null) # e_shoff hdr += pack('<I', 0) # e_flags hdr += pack('<H', sz_ehdr) # e_ehsize hdr += pack('<H', 0) # e_phentsize hdr += pack('<H', 0) # e_phnum hdr += pack('<H', sz_shdr) # e_shentsize hdr += pack('<H', 5) # e_shnum hdr += pack('<H', 2) # e_shstrndx = index of .shstrtab assert len(hdr) == sz_ehdr fp.seek(0, os.SEEK_SET) fp.write(hdr) # done! fp.close()	en	0.244853	#!/usr/bin/env python # # convert SDCC .rel files to 32-bit ELF relocatable # # resulting file is simple: # # ------------------------ # ELF header # ------------------------ # .text section # .shstrtab section # .strtab section # .symtab section # ------------------------ # NULL elf32_shdr # .text elf32_shdr # .shstrtab elf32_shdr # .symtab elf32_shdr # .strtab elf32_shdr # ------------------------ #------------------------------------------------------------------------------ # ELF helpers #------------------------------------------------------------------------------ #------------------------------------------------------------------------------ # read .map file for symbols #------------------------------------------------------------------------------ #------------------------------------------------------------------------------ # read .ihx file #------------------------------------------------------------------------------ #print(code_area) #------------------------------------------------------------------------------ # write ELF #------------------------------------------------------------------------------ # process symbols, build string table # elf32_hdr (placeholder, we'll come back to fill in offsets) # .text section contents # .shstrtab section contents # .symtab section contents # bind:1(GLOBAL) type:2(FUNC) # section header index: 0'th: NULL 1'th: .text # .strtab section contents # null section header (index 0) # .text section header (index 1) # SHT_PROGBITS # ALLOC\|EXECINSTR # .shstrtab section header (index 2) #SHT_STRTAB # .symtab section header (index 3) #SHT_SYMTAB # link to scn #4 (find strings in .strtab) # .strtab section header (index 4) #SHT_STRTAB # seek back, write real elf header # e_ident[EI_CLASS] 32-bit # e_ident[EI_DATA] LSB (little-end) # version, osabi, abiversion # e_type = ET_REL # e_machine = EM_Z80 # e_version = EV_CURRENT # e_entry # e_phoff # e_shoff # e_flags # e_ehsize # e_phentsize # e_phnum # e_shentsize # e_shnum # e_shstrndx = index of .shstrtab # done!	2.546184	3
eval.py	nikinsta/deep-siamese-text-similarity-on-python-3	0	10274	#! /usr/bin/env python import tensorflow as tf import numpy as np import os import time import datetime from tensorflow.contrib import learn from input_helpers import InputHelper # Parameters # ================================================== # Eval Parameters tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)") tf.flags.DEFINE_string("checkpoint_dir", "", "Checkpoint directory from training run") tf.flags.DEFINE_string("eval_filepath", "match_valid.tsv", "Evaluate on this data (Default: None)") tf.flags.DEFINE_string("vocab_filepath", "runs/1479874609/checkpoints/vocab", "Load training time vocabulary (Default: None)") tf.flags.DEFINE_string("model", "runs/1479874609/checkpoints/model-32000", "Load trained model checkpoint (Default: None)") # Misc Parameters tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement") tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices") FLAGS = tf.flags.FLAGS FLAGS._parse_flags() print("\nParameters:") for attr, value in sorted(FLAGS.__flags.items()): print("{}={}".format(attr.upper(), value)) print("") if FLAGS.eval_filepath==None or FLAGS.vocab_filepath==None or FLAGS.model==None : print("Eval or Vocab filepaths are empty.") exit() # load data and map id-transform based on training time vocabulary inpH = InputHelper() x1_test,x2_test,y_test = inpH.getTestDataSet(FLAGS.eval_filepath, FLAGS.vocab_filepath, 30) print("\nEvaluating...\n") # Evaluation # ================================================== checkpoint_file = FLAGS.model print(checkpoint_file) graph = tf.Graph() with graph.as_default(): session_conf = tf.ConfigProto( allow_soft_placement=FLAGS.allow_soft_placement, log_device_placement=FLAGS.log_device_placement) sess = tf.Session(config=session_conf) with sess.as_default(): # Load the saved meta graph and restore variables saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file)) sess.run(tf.initialize_all_variables()) saver.restore(sess, checkpoint_file) # Get the placeholders from the graph by name input_x1 = graph.get_operation_by_name("input_x1").outputs[0] input_x2 = graph.get_operation_by_name("input_x2").outputs[0] input_y = graph.get_operation_by_name("input_y").outputs[0] dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0] # Tensors we want to evaluate predictions = graph.get_operation_by_name("output/distance").outputs[0] accuracy = graph.get_operation_by_name("accuracy/accuracy").outputs[0] sim = graph.get_operation_by_name("accuracy/temp_sim").outputs[0] #emb = graph.get_operation_by_name("embedding/W").outputs[0] #embedded_chars = tf.nn.embedding_lookup(emb,input_x) # Generate batches for one epoch batches = inpH.batch_iter(list(zip(x1_test,x2_test,y_test)), 2FLAGS.batch_size, 1, shuffle=False) # Collect the predictions here all_predictions = [] all_d=[] for db in batches: x1_dev_b,x2_dev_b,y_dev_b = zip(db) batch_predictions, batch_acc, sim = sess.run([predictions,accuracy,sim], {input_x1: x1_dev_b, input_x2: x2_dev_b, input_y:y_dev_b, dropout_keep_prob: 1.0}) all_predictions = np.concatenate([all_predictions, batch_predictions]) print(batch_predictions) all_d = np.concatenate([all_d, sim]) print("DEV acc {}".format(batch_acc)) for ex in all_predictions: print(ex) correct_predictions = float(np.mean(all_d == y_test)) print("Accuracy: {:g}".format(correct_predictions))	#! /usr/bin/env python import tensorflow as tf import numpy as np import os import time import datetime from tensorflow.contrib import learn from input_helpers import InputHelper # Parameters # ================================================== # Eval Parameters tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)") tf.flags.DEFINE_string("checkpoint_dir", "", "Checkpoint directory from training run") tf.flags.DEFINE_string("eval_filepath", "match_valid.tsv", "Evaluate on this data (Default: None)") tf.flags.DEFINE_string("vocab_filepath", "runs/1479874609/checkpoints/vocab", "Load training time vocabulary (Default: None)") tf.flags.DEFINE_string("model", "runs/1479874609/checkpoints/model-32000", "Load trained model checkpoint (Default: None)") # Misc Parameters tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement") tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices") FLAGS = tf.flags.FLAGS FLAGS._parse_flags() print("\nParameters:") for attr, value in sorted(FLAGS.__flags.items()): print("{}={}".format(attr.upper(), value)) print("") if FLAGS.eval_filepath==None or FLAGS.vocab_filepath==None or FLAGS.model==None : print("Eval or Vocab filepaths are empty.") exit() # load data and map id-transform based on training time vocabulary inpH = InputHelper() x1_test,x2_test,y_test = inpH.getTestDataSet(FLAGS.eval_filepath, FLAGS.vocab_filepath, 30) print("\nEvaluating...\n") # Evaluation # ================================================== checkpoint_file = FLAGS.model print(checkpoint_file) graph = tf.Graph() with graph.as_default(): session_conf = tf.ConfigProto( allow_soft_placement=FLAGS.allow_soft_placement, log_device_placement=FLAGS.log_device_placement) sess = tf.Session(config=session_conf) with sess.as_default(): # Load the saved meta graph and restore variables saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file)) sess.run(tf.initialize_all_variables()) saver.restore(sess, checkpoint_file) # Get the placeholders from the graph by name input_x1 = graph.get_operation_by_name("input_x1").outputs[0] input_x2 = graph.get_operation_by_name("input_x2").outputs[0] input_y = graph.get_operation_by_name("input_y").outputs[0] dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0] # Tensors we want to evaluate predictions = graph.get_operation_by_name("output/distance").outputs[0] accuracy = graph.get_operation_by_name("accuracy/accuracy").outputs[0] sim = graph.get_operation_by_name("accuracy/temp_sim").outputs[0] #emb = graph.get_operation_by_name("embedding/W").outputs[0] #embedded_chars = tf.nn.embedding_lookup(emb,input_x) # Generate batches for one epoch batches = inpH.batch_iter(list(zip(x1_test,x2_test,y_test)), 2FLAGS.batch_size, 1, shuffle=False) # Collect the predictions here all_predictions = [] all_d=[] for db in batches: x1_dev_b,x2_dev_b,y_dev_b = zip(db) batch_predictions, batch_acc, sim = sess.run([predictions,accuracy,sim], {input_x1: x1_dev_b, input_x2: x2_dev_b, input_y:y_dev_b, dropout_keep_prob: 1.0}) all_predictions = np.concatenate([all_predictions, batch_predictions]) print(batch_predictions) all_d = np.concatenate([all_d, sim]) print("DEV acc {}".format(batch_acc)) for ex in all_predictions: print(ex) correct_predictions = float(np.mean(all_d == y_test)) print("Accuracy: {:g}".format(correct_predictions))	en	0.594187	#! /usr/bin/env python # Parameters # ================================================== # Eval Parameters # Misc Parameters # load data and map id-transform based on training time vocabulary # Evaluation # ================================================== # Load the saved meta graph and restore variables # Get the placeholders from the graph by name # Tensors we want to evaluate #emb = graph.get_operation_by_name("embedding/W").outputs[0] #embedded_chars = tf.nn.embedding_lookup(emb,input_x) # Generate batches for one epoch # Collect the predictions here	2.301202	2
accounts/views.py	nikhiljohn10/django-auth	0	10275	<gh_stars>0 from django.urls import reverse from django.conf import settings from django.contrib import messages from django.shortcuts import render, redirect from django.core.mail import send_mail from django.contrib.auth import login, logout, views, authenticate from django.views.generic.edit import CreateView from django.contrib.sessions.models import Session from django.contrib.auth.decorators import login_required, permission_required from accounts.tools import activater, mailer from accounts.forms import SignUpForm, LoginForm from accounts.models import User @login_required @permission_required("is_staff", login_url='/dashboard/') def gmail(request): request.session['oauth_state'] = mailer.auth_state return redirect(mailer.auth_uri) @login_required @permission_required("is_staff", login_url='/dashboard/') def gmail_verify(request): code = request.GET.get('code','') state = request.GET.get('state','') if code and state == request.session['oauth_state']: mailer.verify(code) return redirect('dash:gmail') class UserLogin(views.LoginView): template_name = 'auth/login.html' authentication_form = LoginForm def form_valid(self, form): user = form.get_user() login(self.request, user) if not self.request.POST.get('remember_me', None): self.request.session.set_expiry(0) messages.info(self.request, f"You are now logged in as {user}") return redirect(self.get_success_url()) class SignUpView(CreateView): form_class = SignUpForm template_name = 'auth/signup.html' def form_valid(self, form): if mailer.activated: user = form.save() mailer.send_mail( "Django Verification Code", "Hi "+str(user)+",\nClick this link to activate: " + reverse('auth:verify_email', args=( user, activater.make_token(user))), [user.email]) login(self.request, user) else: messages.error(self.request, "Gmail is not activate. Contact site administrator.") return redirect('auth:signup') return redirect('core:home') def user_manage_permission(user, username): if not user.is_staff: if user.username == username: return True else: if user.username != username: return True return False @login_required @permission_required("is_staff", login_url='/dashboard/') def user_force_logout(request, username): user = User.objects.get(username=username) sessions = [s.delete() for s in Session.objects.all() if s.get_decoded().get('_auth_user_id') == str(user.id)] print(sessions) return redirect('dash:users') def user_verify_email(request, username, token): user = User.objects.get(username=username) if activater.check_token(user, token): print(user, "is verified") user.email_verified = True user.save() return redirect('dash:users') @login_required def user_disable(request, username): if user_manage_permission(request.user, username): user = User.objects.get(username=username) user.is_active = False user.save() messages.error(request, 'Profile successfully disabled.') else: messages.error( request, 'You are not allowed to perform this operation.') if request.user.is_staff: return redirect('dash:users') else: return redirect('dash:profile') @login_required def user_enable(request, username): if user_manage_permission(request.user, username): user = User.objects.get(username=username) user.is_active = True user.save() messages.success(request, 'Profile successfully enabled.') else: messages.error( request, 'You are not allowed to perform this operation.') if request.user.is_staff: return redirect('dash:users') else: return redirect('dash:profile') @login_required def user_delete(request, username): if user_manage_permission(request.user, username): user = User.objects.get(username=username) user.delete() messages.error(request, 'Profile successfully deleted.') else: messages.error( request, 'You are not allowed to perform this operation.') if request.user.is_staff: return redirect('dash:users') else: return redirect('dash:profile') user_login = UserLogin.as_view() user_signup = SignUpView.as_view() user_logout = views.LogoutView.as_view()	from django.urls import reverse from django.conf import settings from django.contrib import messages from django.shortcuts import render, redirect from django.core.mail import send_mail from django.contrib.auth import login, logout, views, authenticate from django.views.generic.edit import CreateView from django.contrib.sessions.models import Session from django.contrib.auth.decorators import login_required, permission_required from accounts.tools import activater, mailer from accounts.forms import SignUpForm, LoginForm from accounts.models import User @login_required @permission_required("is_staff", login_url='/dashboard/') def gmail(request): request.session['oauth_state'] = mailer.auth_state return redirect(mailer.auth_uri) @login_required @permission_required("is_staff", login_url='/dashboard/') def gmail_verify(request): code = request.GET.get('code','') state = request.GET.get('state','') if code and state == request.session['oauth_state']: mailer.verify(code) return redirect('dash:gmail') class UserLogin(views.LoginView): template_name = 'auth/login.html' authentication_form = LoginForm def form_valid(self, form): user = form.get_user() login(self.request, user) if not self.request.POST.get('remember_me', None): self.request.session.set_expiry(0) messages.info(self.request, f"You are now logged in as {user}") return redirect(self.get_success_url()) class SignUpView(CreateView): form_class = SignUpForm template_name = 'auth/signup.html' def form_valid(self, form): if mailer.activated: user = form.save() mailer.send_mail( "Django Verification Code", "Hi "+str(user)+",\nClick this link to activate: " + reverse('auth:verify_email', args=( user, activater.make_token(user))), [user.email]) login(self.request, user) else: messages.error(self.request, "Gmail is not activate. Contact site administrator.") return redirect('auth:signup') return redirect('core:home') def user_manage_permission(user, username): if not user.is_staff: if user.username == username: return True else: if user.username != username: return True return False @login_required @permission_required("is_staff", login_url='/dashboard/') def user_force_logout(request, username): user = User.objects.get(username=username) sessions = [s.delete() for s in Session.objects.all() if s.get_decoded().get('_auth_user_id') == str(user.id)] print(sessions) return redirect('dash:users') def user_verify_email(request, username, token): user = User.objects.get(username=username) if activater.check_token(user, token): print(user, "is verified") user.email_verified = True user.save() return redirect('dash:users') @login_required def user_disable(request, username): if user_manage_permission(request.user, username): user = User.objects.get(username=username) user.is_active = False user.save() messages.error(request, 'Profile successfully disabled.') else: messages.error( request, 'You are not allowed to perform this operation.') if request.user.is_staff: return redirect('dash:users') else: return redirect('dash:profile') @login_required def user_enable(request, username): if user_manage_permission(request.user, username): user = User.objects.get(username=username) user.is_active = True user.save() messages.success(request, 'Profile successfully enabled.') else: messages.error( request, 'You are not allowed to perform this operation.') if request.user.is_staff: return redirect('dash:users') else: return redirect('dash:profile') @login_required def user_delete(request, username): if user_manage_permission(request.user, username): user = User.objects.get(username=username) user.delete() messages.error(request, 'Profile successfully deleted.') else: messages.error( request, 'You are not allowed to perform this operation.') if request.user.is_staff: return redirect('dash:users') else: return redirect('dash:profile') user_login = UserLogin.as_view() user_signup = SignUpView.as_view() user_logout = views.LogoutView.as_view()	none	1		2.163409	2
python/testData/intentions/PyAnnotateVariableTypeIntentionTest/annotationTupleType.py	truthiswill/intellij-community	2	10276	v<caret>ar = (1, 'foo', None)	v<caret>ar = (1, 'foo', None)	none	1		1.302547	1
bot/venv/lib/python3.7/site-packages/scipy/version.py	manaccac/sc2_bot	76	10277	<gh_stars>10-100 # THIS FILE IS GENERATED FROM SCIPY SETUP.PY short_version = '1.5.4' version = '1.5.4' full_version = '1.5.4' git_revision = '19acfed431060aafaa963f7e530c95e70cd4b85c' release = True if not release: version = full_version	# THIS FILE IS GENERATED FROM SCIPY SETUP.PY short_version = '1.5.4' version = '1.5.4' full_version = '1.5.4' git_revision = '19acfed431060aafaa963f7e530c95e70cd4b85c' release = True if not release: version = full_version	en	0.387835	# THIS FILE IS GENERATED FROM SCIPY SETUP.PY	0.960018	1
emrichen/input/__init__.py	jbek7/emrichen	0	10278	<reponame>jbek7/emrichen from typing import TextIO, Union from .json import load_json from .yaml import load_yaml PARSERS = { 'yaml': load_yaml, 'json': load_json, } def parse(data: Union[TextIO, str], format: str): if format in PARSERS: return PARSERS[format](data) else: raise ValueError('No parser for format {format}'.format(format=format))	from typing import TextIO, Union from .json import load_json from .yaml import load_yaml PARSERS = { 'yaml': load_yaml, 'json': load_json, } def parse(data: Union[TextIO, str], format: str): if format in PARSERS: return PARSERS[format](data) else: raise ValueError('No parser for format {format}'.format(format=format))	none	1		2.85197	3
sdk/python/pulumi_aws_native/workspaces/get_workspace.py	pulumi/pulumi-aws-native	29	10279	# coding=utf-8 # * WARNING: this file was generated by the Pulumi SDK Generator. * # * 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 from . import outputs __all__ = [ 'GetWorkspaceResult', 'AwaitableGetWorkspaceResult', 'get_workspace', 'get_workspace_output', ] @pulumi.output_type class GetWorkspaceResult: def __init__(__self__, bundle_id=None, directory_id=None, id=None, root_volume_encryption_enabled=None, tags=None, user_volume_encryption_enabled=None, volume_encryption_key=None, workspace_properties=None): if bundle_id and not isinstance(bundle_id, str): raise TypeError("Expected argument 'bundle_id' to be a str") pulumi.set(__self__, "bundle_id", bundle_id) if directory_id and not isinstance(directory_id, str): raise TypeError("Expected argument 'directory_id' to be a str") pulumi.set(__self__, "directory_id", directory_id) if id and not isinstance(id, str): raise TypeError("Expected argument 'id' to be a str") pulumi.set(__self__, "id", id) if root_volume_encryption_enabled and not isinstance(root_volume_encryption_enabled, bool): raise TypeError("Expected argument 'root_volume_encryption_enabled' to be a bool") pulumi.set(__self__, "root_volume_encryption_enabled", root_volume_encryption_enabled) if tags and not isinstance(tags, list): raise TypeError("Expected argument 'tags' to be a list") pulumi.set(__self__, "tags", tags) if user_volume_encryption_enabled and not isinstance(user_volume_encryption_enabled, bool): raise TypeError("Expected argument 'user_volume_encryption_enabled' to be a bool") pulumi.set(__self__, "user_volume_encryption_enabled", user_volume_encryption_enabled) if volume_encryption_key and not isinstance(volume_encryption_key, str): raise TypeError("Expected argument 'volume_encryption_key' to be a str") pulumi.set(__self__, "volume_encryption_key", volume_encryption_key) if workspace_properties and not isinstance(workspace_properties, dict): raise TypeError("Expected argument 'workspace_properties' to be a dict") pulumi.set(__self__, "workspace_properties", workspace_properties) @property @pulumi.getter(name="bundleId") def bundle_id(self) -> Optional[str]: return pulumi.get(self, "bundle_id") @property @pulumi.getter(name="directoryId") def directory_id(self) -> Optional[str]: return pulumi.get(self, "directory_id") @property @pulumi.getter def id(self) -> Optional[str]: return pulumi.get(self, "id") @property @pulumi.getter(name="rootVolumeEncryptionEnabled") def root_volume_encryption_enabled(self) -> Optional[bool]: return pulumi.get(self, "root_volume_encryption_enabled") @property @pulumi.getter def tags(self) -> Optional[Sequence['outputs.WorkspaceTag']]: return pulumi.get(self, "tags") @property @pulumi.getter(name="userVolumeEncryptionEnabled") def user_volume_encryption_enabled(self) -> Optional[bool]: return pulumi.get(self, "user_volume_encryption_enabled") @property @pulumi.getter(name="volumeEncryptionKey") def volume_encryption_key(self) -> Optional[str]: return pulumi.get(self, "volume_encryption_key") @property @pulumi.getter(name="workspaceProperties") def workspace_properties(self) -> Optional['outputs.WorkspaceProperties']: return pulumi.get(self, "workspace_properties") class AwaitableGetWorkspaceResult(GetWorkspaceResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetWorkspaceResult( bundle_id=self.bundle_id, directory_id=self.directory_id, id=self.id, root_volume_encryption_enabled=self.root_volume_encryption_enabled, tags=self.tags, user_volume_encryption_enabled=self.user_volume_encryption_enabled, volume_encryption_key=self.volume_encryption_key, workspace_properties=self.workspace_properties) def get_workspace(id: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetWorkspaceResult: """ Resource Type definition for AWS::WorkSpaces::Workspace """ __args__ = dict() __args__['id'] = id if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('aws-native:workspaces:getWorkspace', __args__, opts=opts, typ=GetWorkspaceResult).value return AwaitableGetWorkspaceResult( bundle_id=__ret__.bundle_id, directory_id=__ret__.directory_id, id=__ret__.id, root_volume_encryption_enabled=__ret__.root_volume_encryption_enabled, tags=__ret__.tags, user_volume_encryption_enabled=__ret__.user_volume_encryption_enabled, volume_encryption_key=__ret__.volume_encryption_key, workspace_properties=__ret__.workspace_properties) @_utilities.lift_output_func(get_workspace) def get_workspace_output(id: Optional[pulumi.Input[str]] = None, opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetWorkspaceResult]: """ Resource Type definition for AWS::WorkSpaces::Workspace """ ...	# coding=utf-8 # * WARNING: this file was generated by the Pulumi SDK Generator. * # * 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 from . import outputs __all__ = [ 'GetWorkspaceResult', 'AwaitableGetWorkspaceResult', 'get_workspace', 'get_workspace_output', ] @pulumi.output_type class GetWorkspaceResult: def __init__(__self__, bundle_id=None, directory_id=None, id=None, root_volume_encryption_enabled=None, tags=None, user_volume_encryption_enabled=None, volume_encryption_key=None, workspace_properties=None): if bundle_id and not isinstance(bundle_id, str): raise TypeError("Expected argument 'bundle_id' to be a str") pulumi.set(__self__, "bundle_id", bundle_id) if directory_id and not isinstance(directory_id, str): raise TypeError("Expected argument 'directory_id' to be a str") pulumi.set(__self__, "directory_id", directory_id) if id and not isinstance(id, str): raise TypeError("Expected argument 'id' to be a str") pulumi.set(__self__, "id", id) if root_volume_encryption_enabled and not isinstance(root_volume_encryption_enabled, bool): raise TypeError("Expected argument 'root_volume_encryption_enabled' to be a bool") pulumi.set(__self__, "root_volume_encryption_enabled", root_volume_encryption_enabled) if tags and not isinstance(tags, list): raise TypeError("Expected argument 'tags' to be a list") pulumi.set(__self__, "tags", tags) if user_volume_encryption_enabled and not isinstance(user_volume_encryption_enabled, bool): raise TypeError("Expected argument 'user_volume_encryption_enabled' to be a bool") pulumi.set(__self__, "user_volume_encryption_enabled", user_volume_encryption_enabled) if volume_encryption_key and not isinstance(volume_encryption_key, str): raise TypeError("Expected argument 'volume_encryption_key' to be a str") pulumi.set(__self__, "volume_encryption_key", volume_encryption_key) if workspace_properties and not isinstance(workspace_properties, dict): raise TypeError("Expected argument 'workspace_properties' to be a dict") pulumi.set(__self__, "workspace_properties", workspace_properties) @property @pulumi.getter(name="bundleId") def bundle_id(self) -> Optional[str]: return pulumi.get(self, "bundle_id") @property @pulumi.getter(name="directoryId") def directory_id(self) -> Optional[str]: return pulumi.get(self, "directory_id") @property @pulumi.getter def id(self) -> Optional[str]: return pulumi.get(self, "id") @property @pulumi.getter(name="rootVolumeEncryptionEnabled") def root_volume_encryption_enabled(self) -> Optional[bool]: return pulumi.get(self, "root_volume_encryption_enabled") @property @pulumi.getter def tags(self) -> Optional[Sequence['outputs.WorkspaceTag']]: return pulumi.get(self, "tags") @property @pulumi.getter(name="userVolumeEncryptionEnabled") def user_volume_encryption_enabled(self) -> Optional[bool]: return pulumi.get(self, "user_volume_encryption_enabled") @property @pulumi.getter(name="volumeEncryptionKey") def volume_encryption_key(self) -> Optional[str]: return pulumi.get(self, "volume_encryption_key") @property @pulumi.getter(name="workspaceProperties") def workspace_properties(self) -> Optional['outputs.WorkspaceProperties']: return pulumi.get(self, "workspace_properties") class AwaitableGetWorkspaceResult(GetWorkspaceResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetWorkspaceResult( bundle_id=self.bundle_id, directory_id=self.directory_id, id=self.id, root_volume_encryption_enabled=self.root_volume_encryption_enabled, tags=self.tags, user_volume_encryption_enabled=self.user_volume_encryption_enabled, volume_encryption_key=self.volume_encryption_key, workspace_properties=self.workspace_properties) def get_workspace(id: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetWorkspaceResult: """ Resource Type definition for AWS::WorkSpaces::Workspace """ __args__ = dict() __args__['id'] = id if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('aws-native:workspaces:getWorkspace', __args__, opts=opts, typ=GetWorkspaceResult).value return AwaitableGetWorkspaceResult( bundle_id=__ret__.bundle_id, directory_id=__ret__.directory_id, id=__ret__.id, root_volume_encryption_enabled=__ret__.root_volume_encryption_enabled, tags=__ret__.tags, user_volume_encryption_enabled=__ret__.user_volume_encryption_enabled, volume_encryption_key=__ret__.volume_encryption_key, workspace_properties=__ret__.workspace_properties) @_utilities.lift_output_func(get_workspace) def get_workspace_output(id: Optional[pulumi.Input[str]] = None, opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetWorkspaceResult]: """ Resource Type definition for AWS::WorkSpaces::Workspace """ ...	en	0.897218	# coding=utf-8 # * WARNING: this file was generated by the Pulumi SDK Generator. * # * Do not edit by hand unless you're certain you know what you are doing! * # pylint: disable=using-constant-test Resource Type definition for AWS::WorkSpaces::Workspace Resource Type definition for AWS::WorkSpaces::Workspace	1.825763	2
testtools/__init__.py	afy2103/spambayes-9-10-Frozen	0	10280	__author__ = 'AlexYang'	__author__ = 'AlexYang'	none	1		0.949948	1
tests/test_distance.py	mkclairhong/quail	1	10281	<reponame>mkclairhong/quail # -- coding: utf-8 -- from quail.distance import * import numpy as np import pytest from scipy.spatial.distance import cdist def test_match(): a = 'A' b = 'B' assert np.equal(match(a, b), 1) def test_euclidean_list(): a = [0, 1, 0] b = [0, 1, 0] assert np.equal(euclidean(a, b), 0) def test_euclidean_array(): a = np.array([0, 1, 0]) b = np.array([0, 1, 0]) assert np.equal(euclidean(a, b), 0) def test_correlation_list(): a = [0, 1, 0] b = [0, 1, 0] assert np.equal(correlation(a, b), 1) def test_correlation_array(): a = np.array([0, 1, 0]) b = np.array([0, 1, 0]) assert np.equal(correlation(a, b), 1)	# -- coding: utf-8 -- from quail.distance import * import numpy as np import pytest from scipy.spatial.distance import cdist def test_match(): a = 'A' b = 'B' assert np.equal(match(a, b), 1) def test_euclidean_list(): a = [0, 1, 0] b = [0, 1, 0] assert np.equal(euclidean(a, b), 0) def test_euclidean_array(): a = np.array([0, 1, 0]) b = np.array([0, 1, 0]) assert np.equal(euclidean(a, b), 0) def test_correlation_list(): a = [0, 1, 0] b = [0, 1, 0] assert np.equal(correlation(a, b), 1) def test_correlation_array(): a = np.array([0, 1, 0]) b = np.array([0, 1, 0]) assert np.equal(correlation(a, b), 1)	en	0.769321	# -- coding: utf-8 --	2.58452	3
utils/manisfestManager.py	ovitrac/pizza3	1	10282	#!/usr/bin/env python ############################################################################### # # # manifestManager.py # # # # Work with online data manifests (creating / syncing / validating) # # # # Copyright (C) <NAME> # # # ############################################################################### # # # This program is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 3 of the License, or # # (at your option) any later version. # # # # This program is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with this program. If not, see <http://www.gnu.org/licenses/>. # # # ############################################################################### __author__ = "<NAME>" __copyright__ = "Copyright 2014" __credits__ = ["<NAME>"] __license__ = "GPLv3" __maintainer__ = "<NAME>" __email__ = "<EMAIL>" __version__ = "0.35" ############################################################################### ############################################################################### ############################################################################### ############################################################################### __MANIFEST__ = ".dmanifest" ############################################################################### ############################################################################### ############################################################################### ############################################################################### # system includes import os import hashlib import urllib.request, urllib.error, urllib.parse import urllib.request, urllib.parse, urllib.error import shutil import errno # local includes from fileEntity import FileEntity as FE ############################################################################### ############################################################################### ############################################################################### ############################################################################### class ManifestManager(object): """Use this interface for storing and managing file and paths""" def __init__(self, manType=None, timeout=30): self.timeout = timeout self.myExtensions = [".py",".sh"] self.files = [] if manType is not None: self.type = manType else: self.type = "generic" def createManifest(self, path, manifestName=None): """inventory all files in path and create a manifest file""" if manifestName is None: manifestName = __MANIFEST__ # make the root file entity root_path = os.path.abspath(path) root_fe = FE('root', ".", None, "-", 0) self.files.append(root_fe) # now make all the ones below parents = [root_fe] dirs, files = self.listdir(path)[:2] self.walk(parents, root_path, '', dirs, files, skipFile=manifestName) with open(os.path.join(path, manifestName), 'w') as man_fh: # print the header man_fh.write("#\t::: %s ::: \tPizza3 manifest version %s\n\n" % (self.type, __version__)) for f in self.files: if f.parent is not None: man_fh.write("%s\n" % f) def diffManifests(self, localManifestLocation, sourceManifestLocation, localManifestName=None, sourceManifestName=None, printDiffs=False): """check for any differences between two manifests if remote is true then sourceManifestLocation is a URL returns a list of files that need to be updated """ if localManifestName is None: localManifestName = __MANIFEST__ if sourceManifestName is None: sourceManifestName = __MANIFEST__ # get the "type" of the local manifest l_type = "generic" with open(os.path.join(localManifestLocation, localManifestName)) as l_man: for line in l_man: if line[0] == "#": l_type = self.getManType(line) break # load the source manifest s_type = "generic" source_man = {} source = "" # first we assume it is remote try: s_man = urllib.request.urlopen(sourceManifestLocation + "/" + sourceManifestName, None, self.timeout) source = sourceManifestLocation + "/" except ValueError: # then it is probably a file s_man = open(os.path.join(sourceManifestLocation, sourceManifestName)) source = os.path.join(sourceManifestLocation) + os.path.sep except urllib.error.URLError: # problems connecting to server, perhaps user is behind a proxy or firewall print("Error: failed to connect to server.") return (None, None, None, None, None) first_line = True for line in s_man: if first_line: first_line = False if line[0] == "#": # get the type of the manifest s_type = self.getManType(line) if s_type != l_type: print("Error: type of source manifest (%s) does not match type of local manifest (%s)" % (s_type, l_type)) return (None, None, None, None, None) else: # no type specified print("Error: type of source manifest is not specified. Is this a valid manifest file?") return (None, None, None, None, None) self.type = l_type if line[0] != "#": fields = line.rstrip().split("\t") # set the dict up as {path => [hash, size, seenLocal] source_man[fields[0]] = [fields[1], fields[2], False] # keep lists of modifications deleted = [] addedDirs = [] addedFiles = [] modified = [] with open(os.path.join(localManifestLocation, localManifestName)) as l_man: for line in l_man: if line[0] != "#": fields = line.rstrip().split("\t") try: if source_man[fields[0]][0] != fields[1]: # hashes don't match modified.append(fields[0]) # seen this file source_man[fields[0]][2] = True except KeyError: # this file has been deleted from the source manifest deleted.append(fields[0]) # check for new files for f in list(source_man.keys()): if source_man[f][2] == False: if source_man[f][0] == '-': addedDirs.append(f) else: addedFiles.append(f) if printDiffs: new_size = 0 modified_size = 0 for f in addedFiles: new_size += int(source_man[f][1]) for f in modified: modified_size += int(source_man[f][1]) if len(addedFiles) > 0: print("#------------------------------------------------------") print("# Source contains %d new file(s) (%s)" % (len(addedFiles), self.formatData(new_size))) for f in addedFiles: print("\t".join([self.formatData(int(source_man[f][1])), f])) if len(addedDirs) > 0: print("#------------------------------------------------------") print("# Source contains %d new folders(s)" % (len(addedDirs))) for f in addedDirs: print(f) if len(modified) > 0: print("#------------------------------------------------------") print("# Source contains %d modified file(s) (%s)" % (len(modified), self.formatData(modified_size))) for f in modified: print(f) if len(deleted) > 0: print("#------------------------------------------------------") print("# %d files have been deleted in the source:" % len(deleted)) for f in deleted: print(f) else: return (source, [(a, source_man[a]) for a in addedFiles], [(a, source_man[a]) for a in addedDirs], deleted, [(m, source_man[m]) for m in modified]) def updateManifest(self, localManifestLocation, sourceManifestLocation, localManifestName=None, sourceManifestName=None, prompt=True): """Update local files based on remote changes""" # get the diffs source, added_files, added_dirs, deleted, modified = self.diffManifests(localManifestLocation, sourceManifestLocation, localManifestName, sourceManifestName) # bail if the diff failed if source is None: return False # no changes by default do_down = False if prompt: total_size = 0 for f in added_files: total_size += int(f[1][1]) for f in modified: total_size += int(f[1][1]) if total_size != 0: print("***************************************************************") print("%d new file(s) to be downloaded from source" % len(added_files)) print("%d existing file(s) to be updated" % len(modified)) print("%s will need to be downloaded" % self.formatData(total_size)) do_down = self.promptUserDownload() if not do_down: print("Download aborted") update_manifest = False if do_down: update_manifest = True for add in added_dirs: # make the dirs first full_path = os.path.abspath(os.path.join(localManifestLocation, add[0])) self.makeSurePathExists(full_path) for add in added_files: full_path = os.path.abspath(os.path.join(localManifestLocation, add[0])) urllib.request.urlretrieve(source+add[0], full_path) for modify in modified: full_path = os.path.abspath(os.path.join(localManifestLocation, modify[0])) urllib.request.urlretrieve(source+modify[0], full_path) if update_manifest: print("(re) creating manifest file (please be patient)") self.createManifest(localManifestLocation, manifestName=localManifestName) return True def getManType(self, line): """Work out the manifest type from the first line of the file""" return line.rstrip().split("##")[1] def formatData(self, amount): """Pretty print file sizes""" if amount < 10241024: return "%d B" % amount elif amount < 102410241024: return "%0.2f MB" % (float(amount)/(1024.1024.)) elif amount < 1024102410241024: return "%0.2f GB" % (float(amount)/(1024.1024.1024.)) elif amount < 10241024102410241024: return "%0.2f TB" % (float(amount)/(1024.1024.1024.1024.)) #----------------------------------------------------------------------------- # FS utilities def makeSurePathExists(self, path): try: os.makedirs(path) except OSError as exception: if exception.errno != errno.EEXIST: raise def promptUserDownload(self): """Check that the user is OK with making changes""" input_not_ok = True minimal=False valid_responses = {'Y':True,'N':False} vrs = ",".join([x.lower() for x in list(valid_responses.keys())]) while(input_not_ok): if(minimal): option = input("Download? ("+vrs+") : ").upper() else: option = input("Confirm you want to download this data\n" \ "Changes WILL* be permanent\n" \ "Continue? ("+vrs+") : ").upper() if(option in valid_responses): print("****************************************************************") return valid_responses[option] else: print("ERROR: unrecognised choice '"+option+"'") minimal = True def walk(self, parents, full_path, rel_path, dirs, files, skipFile=__MANIFEST__): """recursive walk through directory tree""" # first do files here for f in files: if (f != skipFile) and os.path.splitext(f)[1] in self.myExtensions: path = os.path.join(full_path, f) self.files.append(FE(f, rel_path, parents[-1], self.hashfile(path), os.path.getsize(path) ) ) for d in dirs: # the walk will go into these dirs first tmp_fe = FE(d, rel_path, parents[-1], "-", 0) self.files.append(tmp_fe) parents.append(tmp_fe) new_full_path = os.path.join(full_path, d) new_rel_path = os.path.join(rel_path, d) new_dirs, new_files = self.listdir(new_full_path)[:2] self.walk(parents, new_full_path, new_rel_path, new_dirs, new_files) parents.pop() def listdir(self, path): """List dirs, files etc in path (one dir deep)""" dirs, files, links = [], [], [] for name in os.listdir(path): path_name = os.path.join(path, name) if os.path.isdir(path_name): dirs.append(name) elif os.path.isfile(path_name): files.append(name) elif os.path.islink(path_name): links.append(name) return dirs, files, links def hashfile(self, fileName, blocksize=65536): """Hash a file and return the digest""" hasher = hashlib.sha256() with open(fileName,"rb") as fh: buf = fh.read(blocksize) while len(buf) > 0: hasher.update(buf.strip()) buf = fh.read(blocksize) return hasher.hexdigest() return "?" ############################################################################### ############################################################################### ############################################################################### ############################################################################### # %% DEBUG # =================================================== # main() # =================================================== # for debugging purposes (code called as a script) # the code is called from here # =================================================== if __name__ == '__main__': man = ManifestManager() man.createManifest("/home/olivi/billy/python",manifestName="Pizza3.manifest")	#!/usr/bin/env python ############################################################################### # # # manifestManager.py # # # # Work with online data manifests (creating / syncing / validating) # # # # Copyright (C) <NAME> # # # ############################################################################### # # # This program is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 3 of the License, or # # (at your option) any later version. # # # # This program is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with this program. If not, see <http://www.gnu.org/licenses/>. # # # ############################################################################### __author__ = "<NAME>" __copyright__ = "Copyright 2014" __credits__ = ["<NAME>"] __license__ = "GPLv3" __maintainer__ = "<NAME>" __email__ = "<EMAIL>" __version__ = "0.35" ############################################################################### ############################################################################### ############################################################################### ############################################################################### __MANIFEST__ = ".dmanifest" ############################################################################### ############################################################################### ############################################################################### ############################################################################### # system includes import os import hashlib import urllib.request, urllib.error, urllib.parse import urllib.request, urllib.parse, urllib.error import shutil import errno # local includes from fileEntity import FileEntity as FE ############################################################################### ############################################################################### ############################################################################### ############################################################################### class ManifestManager(object): """Use this interface for storing and managing file and paths""" def __init__(self, manType=None, timeout=30): self.timeout = timeout self.myExtensions = [".py",".sh"] self.files = [] if manType is not None: self.type = manType else: self.type = "generic" def createManifest(self, path, manifestName=None): """inventory all files in path and create a manifest file""" if manifestName is None: manifestName = __MANIFEST__ # make the root file entity root_path = os.path.abspath(path) root_fe = FE('root', ".", None, "-", 0) self.files.append(root_fe) # now make all the ones below parents = [root_fe] dirs, files = self.listdir(path)[:2] self.walk(parents, root_path, '', dirs, files, skipFile=manifestName) with open(os.path.join(path, manifestName), 'w') as man_fh: # print the header man_fh.write("#\t::: %s ::: \tPizza3 manifest version %s\n\n" % (self.type, __version__)) for f in self.files: if f.parent is not None: man_fh.write("%s\n" % f) def diffManifests(self, localManifestLocation, sourceManifestLocation, localManifestName=None, sourceManifestName=None, printDiffs=False): """check for any differences between two manifests if remote is true then sourceManifestLocation is a URL returns a list of files that need to be updated """ if localManifestName is None: localManifestName = __MANIFEST__ if sourceManifestName is None: sourceManifestName = __MANIFEST__ # get the "type" of the local manifest l_type = "generic" with open(os.path.join(localManifestLocation, localManifestName)) as l_man: for line in l_man: if line[0] == "#": l_type = self.getManType(line) break # load the source manifest s_type = "generic" source_man = {} source = "" # first we assume it is remote try: s_man = urllib.request.urlopen(sourceManifestLocation + "/" + sourceManifestName, None, self.timeout) source = sourceManifestLocation + "/" except ValueError: # then it is probably a file s_man = open(os.path.join(sourceManifestLocation, sourceManifestName)) source = os.path.join(sourceManifestLocation) + os.path.sep except urllib.error.URLError: # problems connecting to server, perhaps user is behind a proxy or firewall print("Error: failed to connect to server.") return (None, None, None, None, None) first_line = True for line in s_man: if first_line: first_line = False if line[0] == "#": # get the type of the manifest s_type = self.getManType(line) if s_type != l_type: print("Error: type of source manifest (%s) does not match type of local manifest (%s)" % (s_type, l_type)) return (None, None, None, None, None) else: # no type specified print("Error: type of source manifest is not specified. Is this a valid manifest file?") return (None, None, None, None, None) self.type = l_type if line[0] != "#": fields = line.rstrip().split("\t") # set the dict up as {path => [hash, size, seenLocal] source_man[fields[0]] = [fields[1], fields[2], False] # keep lists of modifications deleted = [] addedDirs = [] addedFiles = [] modified = [] with open(os.path.join(localManifestLocation, localManifestName)) as l_man: for line in l_man: if line[0] != "#": fields = line.rstrip().split("\t") try: if source_man[fields[0]][0] != fields[1]: # hashes don't match modified.append(fields[0]) # seen this file source_man[fields[0]][2] = True except KeyError: # this file has been deleted from the source manifest deleted.append(fields[0]) # check for new files for f in list(source_man.keys()): if source_man[f][2] == False: if source_man[f][0] == '-': addedDirs.append(f) else: addedFiles.append(f) if printDiffs: new_size = 0 modified_size = 0 for f in addedFiles: new_size += int(source_man[f][1]) for f in modified: modified_size += int(source_man[f][1]) if len(addedFiles) > 0: print("#------------------------------------------------------") print("# Source contains %d new file(s) (%s)" % (len(addedFiles), self.formatData(new_size))) for f in addedFiles: print("\t".join([self.formatData(int(source_man[f][1])), f])) if len(addedDirs) > 0: print("#------------------------------------------------------") print("# Source contains %d new folders(s)" % (len(addedDirs))) for f in addedDirs: print(f) if len(modified) > 0: print("#------------------------------------------------------") print("# Source contains %d modified file(s) (%s)" % (len(modified), self.formatData(modified_size))) for f in modified: print(f) if len(deleted) > 0: print("#------------------------------------------------------") print("# %d files have been deleted in the source:" % len(deleted)) for f in deleted: print(f) else: return (source, [(a, source_man[a]) for a in addedFiles], [(a, source_man[a]) for a in addedDirs], deleted, [(m, source_man[m]) for m in modified]) def updateManifest(self, localManifestLocation, sourceManifestLocation, localManifestName=None, sourceManifestName=None, prompt=True): """Update local files based on remote changes""" # get the diffs source, added_files, added_dirs, deleted, modified = self.diffManifests(localManifestLocation, sourceManifestLocation, localManifestName, sourceManifestName) # bail if the diff failed if source is None: return False # no changes by default do_down = False if prompt: total_size = 0 for f in added_files: total_size += int(f[1][1]) for f in modified: total_size += int(f[1][1]) if total_size != 0: print("***************************************************************") print("%d new file(s) to be downloaded from source" % len(added_files)) print("%d existing file(s) to be updated" % len(modified)) print("%s will need to be downloaded" % self.formatData(total_size)) do_down = self.promptUserDownload() if not do_down: print("Download aborted") update_manifest = False if do_down: update_manifest = True for add in added_dirs: # make the dirs first full_path = os.path.abspath(os.path.join(localManifestLocation, add[0])) self.makeSurePathExists(full_path) for add in added_files: full_path = os.path.abspath(os.path.join(localManifestLocation, add[0])) urllib.request.urlretrieve(source+add[0], full_path) for modify in modified: full_path = os.path.abspath(os.path.join(localManifestLocation, modify[0])) urllib.request.urlretrieve(source+modify[0], full_path) if update_manifest: print("(re) creating manifest file (please be patient)") self.createManifest(localManifestLocation, manifestName=localManifestName) return True def getManType(self, line): """Work out the manifest type from the first line of the file""" return line.rstrip().split("##")[1] def formatData(self, amount): """Pretty print file sizes""" if amount < 10241024: return "%d B" % amount elif amount < 102410241024: return "%0.2f MB" % (float(amount)/(1024.1024.)) elif amount < 1024102410241024: return "%0.2f GB" % (float(amount)/(1024.1024.1024.)) elif amount < 10241024102410241024: return "%0.2f TB" % (float(amount)/(1024.1024.1024.1024.)) #----------------------------------------------------------------------------- # FS utilities def makeSurePathExists(self, path): try: os.makedirs(path) except OSError as exception: if exception.errno != errno.EEXIST: raise def promptUserDownload(self): """Check that the user is OK with making changes""" input_not_ok = True minimal=False valid_responses = {'Y':True,'N':False} vrs = ",".join([x.lower() for x in list(valid_responses.keys())]) while(input_not_ok): if(minimal): option = input("Download? ("+vrs+") : ").upper() else: option = input("Confirm you want to download this data\n" \ "Changes WILL* be permanent\n" \ "Continue? ("+vrs+") : ").upper() if(option in valid_responses): print("****************************************************************") return valid_responses[option] else: print("ERROR: unrecognised choice '"+option+"'") minimal = True def walk(self, parents, full_path, rel_path, dirs, files, skipFile=__MANIFEST__): """recursive walk through directory tree""" # first do files here for f in files: if (f != skipFile) and os.path.splitext(f)[1] in self.myExtensions: path = os.path.join(full_path, f) self.files.append(FE(f, rel_path, parents[-1], self.hashfile(path), os.path.getsize(path) ) ) for d in dirs: # the walk will go into these dirs first tmp_fe = FE(d, rel_path, parents[-1], "-", 0) self.files.append(tmp_fe) parents.append(tmp_fe) new_full_path = os.path.join(full_path, d) new_rel_path = os.path.join(rel_path, d) new_dirs, new_files = self.listdir(new_full_path)[:2] self.walk(parents, new_full_path, new_rel_path, new_dirs, new_files) parents.pop() def listdir(self, path): """List dirs, files etc in path (one dir deep)""" dirs, files, links = [], [], [] for name in os.listdir(path): path_name = os.path.join(path, name) if os.path.isdir(path_name): dirs.append(name) elif os.path.isfile(path_name): files.append(name) elif os.path.islink(path_name): links.append(name) return dirs, files, links def hashfile(self, fileName, blocksize=65536): """Hash a file and return the digest""" hasher = hashlib.sha256() with open(fileName,"rb") as fh: buf = fh.read(blocksize) while len(buf) > 0: hasher.update(buf.strip()) buf = fh.read(blocksize) return hasher.hexdigest() return "?" ############################################################################### ############################################################################### ############################################################################### ############################################################################### # %% DEBUG # =================================================== # main() # =================================================== # for debugging purposes (code called as a script) # the code is called from here # =================================================== if __name__ == '__main__': man = ManifestManager() man.createManifest("/home/olivi/billy/python",manifestName="Pizza3.manifest")	de	0.35631	#!/usr/bin/env python ############################################################################### # # # manifestManager.py # # # # Work with online data manifests (creating / syncing / validating) # # # # Copyright (C) <NAME> # # # ############################################################################### # # # This program is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 3 of the License, or # # (at your option) any later version. # # # # This program is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with this program. If not, see <http://www.gnu.org/licenses/>. # # # ############################################################################### ############################################################################### ############################################################################### ############################################################################### ############################################################################### ############################################################################### ############################################################################### ############################################################################### ############################################################################### # system includes # local includes ############################################################################### ############################################################################### ############################################################################### ############################################################################### Use this interface for storing and managing file and paths inventory all files in path and create a manifest file # make the root file entity # now make all the ones below # print the header check for any differences between two manifests if remote is true then sourceManifestLocation is a URL returns a list of files that need to be updated # get the "type" of the local manifest # load the source manifest # first we assume it is remote # then it is probably a file # problems connecting to server, perhaps user is behind a proxy or firewall # get the type of the manifest # no type specified # set the dict up as {path => [hash, size, seenLocal] # keep lists of modifications # hashes don't match # seen this file # this file has been deleted from the source manifest # check for new files Update local files based on remote changes # get the diffs # bail if the diff failed # no changes by default # make the dirs first Work out the manifest type from the first line of the file #")[1] Pretty print file sizes #----------------------------------------------------------------------------- # FS utilities Check that the user is OK with making changes recursive walk through directory tree # first do files here # the walk will go into these dirs first List dirs, files etc in path (one dir deep) Hash a file and return the digest ############################################################################### ############################################################################### ############################################################################### ############################################################################### # %% DEBUG # =================================================== # main() # =================================================== # for debugging purposes (code called as a script) # the code is called from here # ===================================================	1.474562	1
temperature.py	rhwlr/TEST_PRELIM_SKILLS_EXAM	0	10283	class Temperature: def __init__(self, kelvin=None, celsius=None, fahrenheit=None): values = [x for x in [kelvin, celsius, fahrenheit] if x] if len(values) < 1: raise ValueError('Need argument') if len(values) > 1: raise ValueError('Only one argument') if celsius is not None: self.kelvin = celsius + 273.15 elif fahrenheit is not None: self.kelvin = (fahrenheit - 32) * 5 / 9 + 273.15 else: self.kelvin = kelvin if self.kelvin < 0: raise ValueError('Temperature in Kelvin cannot be negative') def __str__(self): return f'Temperature = {self.kelvin} Kelvins'	class Temperature: def __init__(self, kelvin=None, celsius=None, fahrenheit=None): values = [x for x in [kelvin, celsius, fahrenheit] if x] if len(values) < 1: raise ValueError('Need argument') if len(values) > 1: raise ValueError('Only one argument') if celsius is not None: self.kelvin = celsius + 273.15 elif fahrenheit is not None: self.kelvin = (fahrenheit - 32) * 5 / 9 + 273.15 else: self.kelvin = kelvin if self.kelvin < 0: raise ValueError('Temperature in Kelvin cannot be negative') def __str__(self): return f'Temperature = {self.kelvin} Kelvins'	none	1		3.667491	4
af/shovel/test_canning.py	mimi89999/pipeline	0	10284	<gh_stars>0 #!/usr/bin/env python2.7 import unittest import canning class TestNop(unittest.TestCase): def test_nop(self): canning.NopTeeFd.write("asdf") class TestSlice(unittest.TestCase): REPORT = "20130505T065614Z-VN-AS24173-dns_consistency-no_report_id-0.1.0-probe.yaml" @staticmethod def rpt(year): assert year < 10000 return "{:04d}1231T065614Z-VN-AS24173-dns_consistency-no_report_id-0.1.0-probe.yaml".format( year ) def test_empty(self): asis, tarfiles = canning.pack_bucket(tuple()) self.assertFalse(asis) self.assertFalse(tarfiles) def test_badname(self): self.assertRaises(RuntimeError, canning.pack_bucket, [("foo", 42)]) self.assertRaises( RuntimeError, canning.pack_bucket, [("2013-05-05/" + self.REPORT, 42)] ) def test_single(self): for sz in [0, 1, 65 * 1048576]: asis, tarfiles = canning.pack_bucket([(self.REPORT, sz)]) self.assertEqual(asis, [self.REPORT]) self.assertFalse(tarfiles) def test_packing(self): asis, tarfiles = canning.pack_bucket( [(self.rpt(0), 42), (self.rpt(1), 64), (self.rpt(2), 64 * 1048576)] ) self.assertEqual(asis, [self.rpt(2)]) self.assertEqual(tarfiles, {"dns_consistency.0.tar": map(self.rpt, (0, 1))}) def test_stupid(self): # FIXME: is it really good behaviour?... asis, tarfiles = canning.pack_bucket( [(self.rpt(0), 42), (self.rpt(1), 64 * 1048576 - 1), (self.rpt(2), 64)] ) self.assertEqual(asis, map(self.rpt, (0, 1, 2))) self.assertEqual(tarfiles, {}) if __name__ == "__main__": unittest.main()	#!/usr/bin/env python2.7 import unittest import canning class TestNop(unittest.TestCase): def test_nop(self): canning.NopTeeFd.write("asdf") class TestSlice(unittest.TestCase): REPORT = "20130505T065614Z-VN-AS24173-dns_consistency-no_report_id-0.1.0-probe.yaml" @staticmethod def rpt(year): assert year < 10000 return "{:04d}1231T065614Z-VN-AS24173-dns_consistency-no_report_id-0.1.0-probe.yaml".format( year ) def test_empty(self): asis, tarfiles = canning.pack_bucket(tuple()) self.assertFalse(asis) self.assertFalse(tarfiles) def test_badname(self): self.assertRaises(RuntimeError, canning.pack_bucket, [("foo", 42)]) self.assertRaises( RuntimeError, canning.pack_bucket, [("2013-05-05/" + self.REPORT, 42)] ) def test_single(self): for sz in [0, 1, 65 * 1048576]: asis, tarfiles = canning.pack_bucket([(self.REPORT, sz)]) self.assertEqual(asis, [self.REPORT]) self.assertFalse(tarfiles) def test_packing(self): asis, tarfiles = canning.pack_bucket( [(self.rpt(0), 42), (self.rpt(1), 64), (self.rpt(2), 64 * 1048576)] ) self.assertEqual(asis, [self.rpt(2)]) self.assertEqual(tarfiles, {"dns_consistency.0.tar": map(self.rpt, (0, 1))}) def test_stupid(self): # FIXME: is it really good behaviour?... asis, tarfiles = canning.pack_bucket( [(self.rpt(0), 42), (self.rpt(1), 64 * 1048576 - 1), (self.rpt(2), 64)] ) self.assertEqual(asis, map(self.rpt, (0, 1, 2))) self.assertEqual(tarfiles, {}) if __name__ == "__main__": unittest.main()	en	0.713974	#!/usr/bin/env python2.7 # FIXME: is it really good behaviour?...	2.742623	3
Exercicios/ex061.py	jlsmirandela/Curso_Python	0	10285	<reponame>jlsmirandela/Curso_Python print('-+-' 10) print(' <NAME> PA') print('+-+' 10) c = 1 ter = int(input('Insira o primeiro termo - ')) rz = int(input('Insira a razão - ')) while c <= 10: print(ter, ' → ', end=' ') ter += rz c += 1 print('FIM')	print('-+-' 10) print(' <NAME> PA') print('+-+' 10) c = 1 ter = int(input('Insira o primeiro termo - ')) rz = int(input('Insira a razão - ')) while c <= 10: print(ter, ' → ', end=' ') ter += rz c += 1 print('FIM')	none	1		3.760039	4
gpytorch/models/approximate_gp.py	phumm/gpytorch	1	10286	<reponame>phumm/gpytorch #!/usr/bin/env python3 from .gp import GP from .pyro import _PyroMixin # This will only contain functions if Pyro is installed class ApproximateGP(GP, _PyroMixin): def __init__(self, variational_strategy): super().__init__() self.variational_strategy = variational_strategy def forward(self, x): """ As in the exact GP setting, the user-defined forward method should return the GP prior mean and covariance evaluated at input locations x. """ raise NotImplementedError def pyro_guide(self, input, beta=1.0, name_prefix=""): """ (For Pyro integration only). The component of a `pyro.guide` that corresponds to drawing samples from the latent GP function. Args: :attr:`input` (:obj:`torch.Tensor`) The inputs :math:`\mathbf X`. :attr:`beta` (float, default=1.) How much to scale the :math:`\text{KL} [ q(\mathbf f) \Vert p(\mathbf f) ]` term by. :attr:`name_prefix` (str, default="") A name prefix to prepend to pyro sample sites. """ return super().pyro_guide(input, beta=beta, name_prefix=name_prefix) def pyro_model(self, input, beta=1.0, name_prefix=""): r""" (For Pyro integration only). The component of a `pyro.model` that corresponds to drawing samples from the latent GP function. Args: :attr:`input` (:obj:`torch.Tensor`) The inputs :math:`\mathbf X`. :attr:`beta` (float, default=1.) How much to scale the :math:`\text{KL} [ q(\mathbf f) \Vert p(\mathbf f) ]` term by. :attr:`name_prefix` (str, default="") A name prefix to prepend to pyro sample sites. Returns: :obj:`torch.Tensor` samples from :math:`q(\mathbf f)` """ return super().pyro_model(input, beta=beta, name_prefix=name_prefix) def __call__(self, inputs, prior=False, **kwargs): if inputs.dim() == 1: inputs = inputs.unsqueeze(-1) return self.variational_strategy(inputs, prior=prior)	#!/usr/bin/env python3 from .gp import GP from .pyro import _PyroMixin # This will only contain functions if Pyro is installed class ApproximateGP(GP, _PyroMixin): def __init__(self, variational_strategy): super().__init__() self.variational_strategy = variational_strategy def forward(self, x): """ As in the exact GP setting, the user-defined forward method should return the GP prior mean and covariance evaluated at input locations x. """ raise NotImplementedError def pyro_guide(self, input, beta=1.0, name_prefix=""): """ (For Pyro integration only). The component of a `pyro.guide` that corresponds to drawing samples from the latent GP function. Args: :attr:`input` (:obj:`torch.Tensor`) The inputs :math:`\mathbf X`. :attr:`beta` (float, default=1.) How much to scale the :math:`\text{KL} [ q(\mathbf f) \Vert p(\mathbf f) ]` term by. :attr:`name_prefix` (str, default="") A name prefix to prepend to pyro sample sites. """ return super().pyro_guide(input, beta=beta, name_prefix=name_prefix) def pyro_model(self, input, beta=1.0, name_prefix=""): r""" (For Pyro integration only). The component of a `pyro.model` that corresponds to drawing samples from the latent GP function. Args: :attr:`input` (:obj:`torch.Tensor`) The inputs :math:`\mathbf X`. :attr:`beta` (float, default=1.) How much to scale the :math:`\text{KL} [ q(\mathbf f) \Vert p(\mathbf f) ]` term by. :attr:`name_prefix` (str, default="") A name prefix to prepend to pyro sample sites. Returns: :obj:`torch.Tensor` samples from :math:`q(\mathbf f)` """ return super().pyro_model(input, beta=beta, name_prefix=name_prefix) def __call__(self, inputs, prior=False, **kwargs): if inputs.dim() == 1: inputs = inputs.unsqueeze(-1) return self.variational_strategy(inputs, prior=prior)	en	0.645391	#!/usr/bin/env python3 # This will only contain functions if Pyro is installed As in the exact GP setting, the user-defined forward method should return the GP prior mean and covariance evaluated at input locations x. (For Pyro integration only). The component of a `pyro.guide` that corresponds to drawing samples from the latent GP function. Args: :attr:`input` (:obj:`torch.Tensor`) The inputs :math:`\mathbf X`. :attr:`beta` (float, default=1.) How much to scale the :math:`\text{KL} [ q(\mathbf f) \Vert p(\mathbf f) ]` term by. :attr:`name_prefix` (str, default="") A name prefix to prepend to pyro sample sites. (For Pyro integration only). The component of a `pyro.model` that corresponds to drawing samples from the latent GP function. Args: :attr:`input` (:obj:`torch.Tensor`) The inputs :math:`\mathbf X`. :attr:`beta` (float, default=1.) How much to scale the :math:`\text{KL} [ q(\mathbf f) \Vert p(\mathbf f) ]` term by. :attr:`name_prefix` (str, default="") A name prefix to prepend to pyro sample sites. Returns: :obj:`torch.Tensor` samples from :math:`q(\mathbf f)`	2.455583	2
tests/test_ping.py	d-wysocki/flask-resty	86	10287	import pytest from flask_resty import Api from flask_resty.testing import assert_response # ----------------------------------------------------------------------------- @pytest.fixture(autouse=True) def routes(app): api = Api(app, "/api") api.add_ping("/ping") # ----------------------------------------------------------------------------- def test_ping(base_client): response = base_client.get("/ping") assert_response(response, 200) assert response.get_data(as_text=True) == ""	import pytest from flask_resty import Api from flask_resty.testing import assert_response # ----------------------------------------------------------------------------- @pytest.fixture(autouse=True) def routes(app): api = Api(app, "/api") api.add_ping("/ping") # ----------------------------------------------------------------------------- def test_ping(base_client): response = base_client.get("/ping") assert_response(response, 200) assert response.get_data(as_text=True) == ""	en	0.12172	# ----------------------------------------------------------------------------- # -----------------------------------------------------------------------------	2.447855	2
tests/test_vetters.py	pllim/exovetter	0	10288	<filename>tests/test_vetters.py from numpy.testing import assert_allclose from astropy.io import ascii from astropy import units as u import lightkurve as lk from exovetter import const as exo_const from exovetter import vetters from exovetter.tce import Tce from astropy.utils.data import get_pkg_data_filename def get_wasp18_tce(): tce = Tce(period=0.94124 * u.day, epoch=58374.669883 * u.day, epoch_offset=-2400000.5 * u.day, depth=0.00990112 * exo_const.frac_amp, duration=0.08932 * u.day, event_name='WASP-18 b', target_name='WASP-18', snr=50) return tce def get_wasp18_lightcurve(): lc_file = get_pkg_data_filename("data/wasp18b_flat_lightcurve.csv") lc_table = ascii.read(lc_file, data_start=1) lc = lk.LightCurve(time=lc_table['col2'], flux=lc_table['col3'], flux_err=lc_table['col4'], time_format="btjd") return lc def test_vetters(): tce = get_wasp18_tce() lc = get_wasp18_lightcurve() metrics = dict() vetter_list = [vetters.Lpp(), vetters.OddEven(), vetters.TransitPhaseCoverage()] for v in vetter_list: vetter = v _ = vetter.run(tce, lc) metrics.update(vetter.__dict__) assert_allclose(metrics['norm_lpp'], 7.93119, rtol=1e-3) assert_allclose(metrics['tp_cover'], 1.0, rtol=1e-5) assert_allclose(metrics['odd_depth'][0], 0.99, rtol=1e-1)	<filename>tests/test_vetters.py from numpy.testing import assert_allclose from astropy.io import ascii from astropy import units as u import lightkurve as lk from exovetter import const as exo_const from exovetter import vetters from exovetter.tce import Tce from astropy.utils.data import get_pkg_data_filename def get_wasp18_tce(): tce = Tce(period=0.94124 * u.day, epoch=58374.669883 * u.day, epoch_offset=-2400000.5 * u.day, depth=0.00990112 * exo_const.frac_amp, duration=0.08932 * u.day, event_name='WASP-18 b', target_name='WASP-18', snr=50) return tce def get_wasp18_lightcurve(): lc_file = get_pkg_data_filename("data/wasp18b_flat_lightcurve.csv") lc_table = ascii.read(lc_file, data_start=1) lc = lk.LightCurve(time=lc_table['col2'], flux=lc_table['col3'], flux_err=lc_table['col4'], time_format="btjd") return lc def test_vetters(): tce = get_wasp18_tce() lc = get_wasp18_lightcurve() metrics = dict() vetter_list = [vetters.Lpp(), vetters.OddEven(), vetters.TransitPhaseCoverage()] for v in vetter_list: vetter = v _ = vetter.run(tce, lc) metrics.update(vetter.__dict__) assert_allclose(metrics['norm_lpp'], 7.93119, rtol=1e-3) assert_allclose(metrics['tp_cover'], 1.0, rtol=1e-5) assert_allclose(metrics['odd_depth'][0], 0.99, rtol=1e-1)	none	1		2.232303	2
pyqtgraph/dockarea/DockDrop.py	hishizuka/pyqtgraph	2,762	10289	<reponame>hishizuka/pyqtgraph # -- coding: utf-8 -- from ..Qt import QtCore, QtGui class DockDrop(object): """Provides dock-dropping methods""" def __init__(self, allowedAreas=None): object.__init__(self) if allowedAreas is None: allowedAreas = ['center', 'right', 'left', 'top', 'bottom'] self.allowedAreas = set(allowedAreas) self.setAcceptDrops(True) self.dropArea = None self.overlay = DropAreaOverlay(self) self.overlay.raise_() def resizeOverlay(self, size): self.overlay.resize(size) def raiseOverlay(self): self.overlay.raise_() def dragEnterEvent(self, ev): src = ev.source() if hasattr(src, 'implements') and src.implements('dock'): #print "drag enter accept" ev.accept() else: #print "drag enter ignore" ev.ignore() def dragMoveEvent(self, ev): #print "drag move" # QDragMoveEvent inherits QDropEvent which provides posF() # PyQt6 provides only position() posF = ev.posF() if hasattr(ev, 'posF') else ev.position() ld = posF.x() rd = self.width() - ld td = posF.y() bd = self.height() - td mn = min(ld, rd, td, bd) if mn > 30: self.dropArea = "center" elif (ld == mn or td == mn) and mn > self.height()/3.: self.dropArea = "center" elif (rd == mn or ld == mn) and mn > self.width()/3.: self.dropArea = "center" elif rd == mn: self.dropArea = "right" elif ld == mn: self.dropArea = "left" elif td == mn: self.dropArea = "top" elif bd == mn: self.dropArea = "bottom" if ev.source() is self and self.dropArea == 'center': #print " no self-center" self.dropArea = None ev.ignore() elif self.dropArea not in self.allowedAreas: #print " not allowed" self.dropArea = None ev.ignore() else: #print " ok" ev.accept() self.overlay.setDropArea(self.dropArea) def dragLeaveEvent(self, ev): self.dropArea = None self.overlay.setDropArea(self.dropArea) def dropEvent(self, ev): area = self.dropArea if area is None: return if area == 'center': area = 'above' self.area.moveDock(ev.source(), area, self) self.dropArea = None self.overlay.setDropArea(self.dropArea) class DropAreaOverlay(QtGui.QWidget): """Overlay widget that draws drop areas during a drag-drop operation""" def __init__(self, parent): QtGui.QWidget.__init__(self, parent) self.dropArea = None self.hide() self.setAttribute(QtCore.Qt.WidgetAttribute.WA_TransparentForMouseEvents) def setDropArea(self, area): self.dropArea = area if area is None: self.hide() else: ## Resize overlay to just the region where drop area should be displayed. ## This works around a Qt bug--can't display transparent widgets over QGLWidget prgn = self.parent().rect() rgn = QtCore.QRect(prgn) w = min(30, prgn.width()/3.) h = min(30, prgn.height()/3.) if self.dropArea == 'left': rgn.setWidth(w) elif self.dropArea == 'right': rgn.setLeft(rgn.left() + prgn.width() - w) elif self.dropArea == 'top': rgn.setHeight(h) elif self.dropArea == 'bottom': rgn.setTop(rgn.top() + prgn.height() - h) elif self.dropArea == 'center': rgn.adjust(w, h, -w, -h) self.setGeometry(rgn) self.show() self.update() def paintEvent(self, ev): if self.dropArea is None: return p = QtGui.QPainter(self) rgn = self.rect() p.setBrush(QtGui.QBrush(QtGui.QColor(100, 100, 255, 50))) p.setPen(QtGui.QPen(QtGui.QColor(50, 50, 150), 3)) p.drawRect(rgn)	# -- coding: utf-8 -- from ..Qt import QtCore, QtGui class DockDrop(object): """Provides dock-dropping methods""" def __init__(self, allowedAreas=None): object.__init__(self) if allowedAreas is None: allowedAreas = ['center', 'right', 'left', 'top', 'bottom'] self.allowedAreas = set(allowedAreas) self.setAcceptDrops(True) self.dropArea = None self.overlay = DropAreaOverlay(self) self.overlay.raise_() def resizeOverlay(self, size): self.overlay.resize(size) def raiseOverlay(self): self.overlay.raise_() def dragEnterEvent(self, ev): src = ev.source() if hasattr(src, 'implements') and src.implements('dock'): #print "drag enter accept" ev.accept() else: #print "drag enter ignore" ev.ignore() def dragMoveEvent(self, ev): #print "drag move" # QDragMoveEvent inherits QDropEvent which provides posF() # PyQt6 provides only position() posF = ev.posF() if hasattr(ev, 'posF') else ev.position() ld = posF.x() rd = self.width() - ld td = posF.y() bd = self.height() - td mn = min(ld, rd, td, bd) if mn > 30: self.dropArea = "center" elif (ld == mn or td == mn) and mn > self.height()/3.: self.dropArea = "center" elif (rd == mn or ld == mn) and mn > self.width()/3.: self.dropArea = "center" elif rd == mn: self.dropArea = "right" elif ld == mn: self.dropArea = "left" elif td == mn: self.dropArea = "top" elif bd == mn: self.dropArea = "bottom" if ev.source() is self and self.dropArea == 'center': #print " no self-center" self.dropArea = None ev.ignore() elif self.dropArea not in self.allowedAreas: #print " not allowed" self.dropArea = None ev.ignore() else: #print " ok" ev.accept() self.overlay.setDropArea(self.dropArea) def dragLeaveEvent(self, ev): self.dropArea = None self.overlay.setDropArea(self.dropArea) def dropEvent(self, ev): area = self.dropArea if area is None: return if area == 'center': area = 'above' self.area.moveDock(ev.source(), area, self) self.dropArea = None self.overlay.setDropArea(self.dropArea) class DropAreaOverlay(QtGui.QWidget): """Overlay widget that draws drop areas during a drag-drop operation""" def __init__(self, parent): QtGui.QWidget.__init__(self, parent) self.dropArea = None self.hide() self.setAttribute(QtCore.Qt.WidgetAttribute.WA_TransparentForMouseEvents) def setDropArea(self, area): self.dropArea = area if area is None: self.hide() else: ## Resize overlay to just the region where drop area should be displayed. ## This works around a Qt bug--can't display transparent widgets over QGLWidget prgn = self.parent().rect() rgn = QtCore.QRect(prgn) w = min(30, prgn.width()/3.) h = min(30, prgn.height()/3.) if self.dropArea == 'left': rgn.setWidth(w) elif self.dropArea == 'right': rgn.setLeft(rgn.left() + prgn.width() - w) elif self.dropArea == 'top': rgn.setHeight(h) elif self.dropArea == 'bottom': rgn.setTop(rgn.top() + prgn.height() - h) elif self.dropArea == 'center': rgn.adjust(w, h, -w, -h) self.setGeometry(rgn) self.show() self.update() def paintEvent(self, ev): if self.dropArea is None: return p = QtGui.QPainter(self) rgn = self.rect() p.setBrush(QtGui.QBrush(QtGui.QColor(100, 100, 255, 50))) p.setPen(QtGui.QPen(QtGui.QColor(50, 50, 150), 3)) p.drawRect(rgn)	en	0.723234	# -- coding: utf-8 -- Provides dock-dropping methods #print "drag enter accept" #print "drag enter ignore" #print "drag move" # QDragMoveEvent inherits QDropEvent which provides posF() # PyQt6 provides only position() #print " no self-center" #print " not allowed" #print " ok" Overlay widget that draws drop areas during a drag-drop operation ## Resize overlay to just the region where drop area should be displayed. ## This works around a Qt bug--can't display transparent widgets over QGLWidget	2.803189	3
data/download.py	pyaf/google-ai-open-images-object-detection-track	0	10290	import os from subprocess import call files = ['000002b66c9c498e.jpg', '000002b97e5471a0.jpg', '000002c707c9895e.jpg', '0000048549557964.jpg', '000004f4400f6ec5.jpg', '0000071d71a0a6f6.jpg', '000013ba71c12506.jpg', '000018acd19b4ad3.jpg', '00001bc2c4027449.jpg', '00001bcc92282a38.jpg', '0000201cd362f303.jpg', '000020780ccee28d.jpg', '000023aa04ab09ed.jpg', '0000253ea4ecbf19.jpg', '000025ea48cab6fc.jpg', '0000271195f2c007.jpg', '0000286a5c6a3eb5.jpg', '00002b368e91b947.jpg', '00002f4ff380c64c.jpg', '0000313e5dccf13b.jpg', '000032046c3f8371.jpg', '00003223e04e2e66.jpg', '0000333f08ced1cd.jpg'] for file in files: if not os.path.exists('train/' + file + '.jpg'): spath = "gs://open-images-dataset/train/%s " % file call(["gsutil", "cp", spath, 'train/']) print(file, 'done', 'count:') else: print(file, 'already downloaded')	import os from subprocess import call files = ['000002b66c9c498e.jpg', '000002b97e5471a0.jpg', '000002c707c9895e.jpg', '0000048549557964.jpg', '000004f4400f6ec5.jpg', '0000071d71a0a6f6.jpg', '000013ba71c12506.jpg', '000018acd19b4ad3.jpg', '00001bc2c4027449.jpg', '00001bcc92282a38.jpg', '0000201cd362f303.jpg', '000020780ccee28d.jpg', '000023aa04ab09ed.jpg', '0000253ea4ecbf19.jpg', '000025ea48cab6fc.jpg', '0000271195f2c007.jpg', '0000286a5c6a3eb5.jpg', '00002b368e91b947.jpg', '00002f4ff380c64c.jpg', '0000313e5dccf13b.jpg', '000032046c3f8371.jpg', '00003223e04e2e66.jpg', '0000333f08ced1cd.jpg'] for file in files: if not os.path.exists('train/' + file + '.jpg'): spath = "gs://open-images-dataset/train/%s " % file call(["gsutil", "cp", spath, 'train/']) print(file, 'done', 'count:') else: print(file, 'already downloaded')	none	1		2.214116	2
cisco-ios-xr/ydk/models/cisco_ios_xr/SNMP_FRAMEWORK_MIB.py	bopopescu/ACI	0	10291	""" SNMP_FRAMEWORK_MIB """ from collections import OrderedDict from ydk.types import Entity, EntityPath, Identity, Enum, YType, YLeaf, YLeafList, YList, LeafDataList, Bits, Empty, Decimal64 from ydk.filters import YFilter from ydk.errors import YError, YModelError from ydk.errors.error_handler import handle_type_error as _handle_type_error class SnmpSecurityLevel(Enum): """ SnmpSecurityLevel (Enum Class) .. data:: noAuthNoPriv = 1 .. data:: authNoPriv = 2 .. data:: authPriv = 3 """ noAuthNoPriv = Enum.YLeaf(1, "noAuthNoPriv") authNoPriv = Enum.YLeaf(2, "authNoPriv") authPriv = Enum.YLeaf(3, "authPriv") class SNMPFRAMEWORKMIB(Entity): """ .. attribute:: snmpengine type\: :py:class:`Snmpengine <ydk.models.cisco_ios_xr.SNMP_FRAMEWORK_MIB.SNMPFRAMEWORKMIB.Snmpengine>` """ _prefix = 'SNMP_FRAMEWORK_MIB' _revision = '2002-10-14' def __init__(self): super(SNMPFRAMEWORKMIB, self).__init__() self._top_entity = None self.yang_name = "SNMP-FRAMEWORK-MIB" self.yang_parent_name = "SNMP-FRAMEWORK-MIB" self.is_top_level_class = True self.has_list_ancestor = False self.ylist_key_names = [] self._child_container_classes = OrderedDict([("snmpEngine", ("snmpengine", SNMPFRAMEWORKMIB.Snmpengine))]) self._child_list_classes = OrderedDict([]) self._leafs = OrderedDict() self.snmpengine = SNMPFRAMEWORKMIB.Snmpengine() self.snmpengine.parent = self self._children_name_map["snmpengine"] = "snmpEngine" self._children_yang_names.add("snmpEngine") self._segment_path = lambda: "SNMP-FRAMEWORK-MIB:SNMP-FRAMEWORK-MIB" class Snmpengine(Entity): """ .. attribute:: snmpengineid type\: str pattern: (([0\-9a\-fA\-F]){2}(\:([0\-9a\-fA\-F]){2})\)? .. attribute:: snmpengineboots type\: int range:* 1..2147483647 .. attribute:: snmpenginetime type\: int range: 0..2147483647 .. attribute:: snmpenginemaxmessagesize type\: int range: 484..2147483647 """ _prefix = 'SNMP_FRAMEWORK_MIB' _revision = '2002-10-14' def __init__(self): super(SNMPFRAMEWORKMIB.Snmpengine, self).__init__() self.yang_name = "snmpEngine" self.yang_parent_name = "SNMP-FRAMEWORK-MIB" self.is_top_level_class = False self.has_list_ancestor = False self.ylist_key_names = [] self._child_container_classes = OrderedDict([]) self._child_list_classes = OrderedDict([]) self._leafs = OrderedDict([ ('snmpengineid', YLeaf(YType.str, 'snmpEngineID')), ('snmpengineboots', YLeaf(YType.int32, 'snmpEngineBoots')), ('snmpenginetime', YLeaf(YType.int32, 'snmpEngineTime')), ('snmpenginemaxmessagesize', YLeaf(YType.int32, 'snmpEngineMaxMessageSize')), ]) self.snmpengineid = None self.snmpengineboots = None self.snmpenginetime = None self.snmpenginemaxmessagesize = None self._segment_path = lambda: "snmpEngine" self._absolute_path = lambda: "SNMP-FRAMEWORK-MIB:SNMP-FRAMEWORK-MIB/%s" % self._segment_path() def __setattr__(self, name, value): self._perform_setattr(SNMPFRAMEWORKMIB.Snmpengine, ['snmpengineid', 'snmpengineboots', 'snmpenginetime', 'snmpenginemaxmessagesize'], name, value) def clone_ptr(self): self._top_entity = SNMPFRAMEWORKMIB() return self._top_entity	""" SNMP_FRAMEWORK_MIB """ from collections import OrderedDict from ydk.types import Entity, EntityPath, Identity, Enum, YType, YLeaf, YLeafList, YList, LeafDataList, Bits, Empty, Decimal64 from ydk.filters import YFilter from ydk.errors import YError, YModelError from ydk.errors.error_handler import handle_type_error as _handle_type_error class SnmpSecurityLevel(Enum): """ SnmpSecurityLevel (Enum Class) .. data:: noAuthNoPriv = 1 .. data:: authNoPriv = 2 .. data:: authPriv = 3 """ noAuthNoPriv = Enum.YLeaf(1, "noAuthNoPriv") authNoPriv = Enum.YLeaf(2, "authNoPriv") authPriv = Enum.YLeaf(3, "authPriv") class SNMPFRAMEWORKMIB(Entity): """ .. attribute:: snmpengine type\: :py:class:`Snmpengine <ydk.models.cisco_ios_xr.SNMP_FRAMEWORK_MIB.SNMPFRAMEWORKMIB.Snmpengine>` """ _prefix = 'SNMP_FRAMEWORK_MIB' _revision = '2002-10-14' def __init__(self): super(SNMPFRAMEWORKMIB, self).__init__() self._top_entity = None self.yang_name = "SNMP-FRAMEWORK-MIB" self.yang_parent_name = "SNMP-FRAMEWORK-MIB" self.is_top_level_class = True self.has_list_ancestor = False self.ylist_key_names = [] self._child_container_classes = OrderedDict([("snmpEngine", ("snmpengine", SNMPFRAMEWORKMIB.Snmpengine))]) self._child_list_classes = OrderedDict([]) self._leafs = OrderedDict() self.snmpengine = SNMPFRAMEWORKMIB.Snmpengine() self.snmpengine.parent = self self._children_name_map["snmpengine"] = "snmpEngine" self._children_yang_names.add("snmpEngine") self._segment_path = lambda: "SNMP-FRAMEWORK-MIB:SNMP-FRAMEWORK-MIB" class Snmpengine(Entity): """ .. attribute:: snmpengineid type\: str pattern: (([0\-9a\-fA\-F]){2}(\:([0\-9a\-fA\-F]){2})\)? .. attribute:: snmpengineboots type\: int range:* 1..2147483647 .. attribute:: snmpenginetime type\: int range: 0..2147483647 .. attribute:: snmpenginemaxmessagesize type\: int range: 484..2147483647 """ _prefix = 'SNMP_FRAMEWORK_MIB' _revision = '2002-10-14' def __init__(self): super(SNMPFRAMEWORKMIB.Snmpengine, self).__init__() self.yang_name = "snmpEngine" self.yang_parent_name = "SNMP-FRAMEWORK-MIB" self.is_top_level_class = False self.has_list_ancestor = False self.ylist_key_names = [] self._child_container_classes = OrderedDict([]) self._child_list_classes = OrderedDict([]) self._leafs = OrderedDict([ ('snmpengineid', YLeaf(YType.str, 'snmpEngineID')), ('snmpengineboots', YLeaf(YType.int32, 'snmpEngineBoots')), ('snmpenginetime', YLeaf(YType.int32, 'snmpEngineTime')), ('snmpenginemaxmessagesize', YLeaf(YType.int32, 'snmpEngineMaxMessageSize')), ]) self.snmpengineid = None self.snmpengineboots = None self.snmpenginetime = None self.snmpenginemaxmessagesize = None self._segment_path = lambda: "snmpEngine" self._absolute_path = lambda: "SNMP-FRAMEWORK-MIB:SNMP-FRAMEWORK-MIB/%s" % self._segment_path() def __setattr__(self, name, value): self._perform_setattr(SNMPFRAMEWORKMIB.Snmpengine, ['snmpengineid', 'snmpengineboots', 'snmpenginetime', 'snmpenginemaxmessagesize'], name, value) def clone_ptr(self): self._top_entity = SNMPFRAMEWORKMIB() return self._top_entity	en	0.23202	SNMP_FRAMEWORK_MIB SnmpSecurityLevel (Enum Class) .. data:: noAuthNoPriv = 1 .. data:: authNoPriv = 2 .. data:: authPriv = 3 .. attribute:: snmpengine type\: :py:class:`Snmpengine <ydk.models.cisco_ios_xr.SNMP_FRAMEWORK_MIB.SNMPFRAMEWORKMIB.Snmpengine>` .. attribute:: snmpengineid type\: str pattern: (([0\-9a\-fA\-F]){2}(\:([0\-9a\-fA\-F]){2})\)? .. attribute:: snmpengineboots type\: int range:* 1..2147483647 .. attribute:: snmpenginetime type\: int range: 0..2147483647 .. attribute:: snmpenginemaxmessagesize type\: int range: 484..2147483647	2.099786	2
handlers/redirects.py	Bainky/Ventify	6	10292	from aiogram.utils.markdown import hide_link from aiogram.types import CallbackQuery from loader import dp from utils import ( get_object, get_attributes_of_object ) from keyboards import ( anime_choose_safe_category, anime_sfw_categories, anime_nsfw_categories, animals_categories, menu_with_categories, control_buttons ) @dp.callback_query_handler(text="menu") async def call_menu_with_categories(call: CallbackQuery): """ Function for sending a menu, with a selection of safe content """ await call.answer() # Editing the message await call.message.edit_text( text=( "<b>🔗 Select a category to get a picture.</b>" ), reply_markup=menu_with_categories() ) @dp.callback_query_handler(text="anime") async def call_anime_categories(call: CallbackQuery): """ Redirect to select anime actions """ await call.answer() # Editing the message await call.message.edit_text( text=( "<b>⚜️ Choose what content you want to see.</b>" ), reply_markup=anime_choose_safe_category() ) @dp.callback_query_handler(text=["sfw", "nsfw"]) async def call_nsfw_categories(call: CallbackQuery): """ Redirect to anime content """ data = call.data.upper() message = call.message # Send answer await call.answer() if data == "SFW": kb = anime_sfw_categories() else: kb = anime_nsfw_categories() # Editing the message await message.edit_text( text=( f"<b>🍿 You are in the {data} category.</b>" ), reply_markup=kb ) @dp.callback_query_handler(text="animals") async def call_anime_categories(call: CallbackQuery): """ Redirect to animals content """ await call.answer() # Editing the message await call.message.edit_text( text=( "<b>🦄 You are in the category with animals.</b>" ), reply_markup=animals_categories() ) @dp.callback_query_handler() async def call_get_photography(call: CallbackQuery): """ Function for sending photos """ message = call.message data = call.data # Get json document api = get_attributes_of_object() if data == "generate_new": data = message.text.split("#")[1] obj = api[data]["object"] atr = api[data]["attribute"] mark = api[data]["entity"] if mark == "anime": mark = api[data]["safe"] if mark == "memes": mark = "menu" # We get a link to the preview photo link = await get_object(obj, atr) await call.answer() # Editing the message await message.edit_text( text=( f"{hide_link(link)} #{data}" ), reply_markup=control_buttons(mark) )	from aiogram.utils.markdown import hide_link from aiogram.types import CallbackQuery from loader import dp from utils import ( get_object, get_attributes_of_object ) from keyboards import ( anime_choose_safe_category, anime_sfw_categories, anime_nsfw_categories, animals_categories, menu_with_categories, control_buttons ) @dp.callback_query_handler(text="menu") async def call_menu_with_categories(call: CallbackQuery): """ Function for sending a menu, with a selection of safe content """ await call.answer() # Editing the message await call.message.edit_text( text=( "<b>🔗 Select a category to get a picture.</b>" ), reply_markup=menu_with_categories() ) @dp.callback_query_handler(text="anime") async def call_anime_categories(call: CallbackQuery): """ Redirect to select anime actions """ await call.answer() # Editing the message await call.message.edit_text( text=( "<b>⚜️ Choose what content you want to see.</b>" ), reply_markup=anime_choose_safe_category() ) @dp.callback_query_handler(text=["sfw", "nsfw"]) async def call_nsfw_categories(call: CallbackQuery): """ Redirect to anime content """ data = call.data.upper() message = call.message # Send answer await call.answer() if data == "SFW": kb = anime_sfw_categories() else: kb = anime_nsfw_categories() # Editing the message await message.edit_text( text=( f"<b>🍿 You are in the {data} category.</b>" ), reply_markup=kb ) @dp.callback_query_handler(text="animals") async def call_anime_categories(call: CallbackQuery): """ Redirect to animals content """ await call.answer() # Editing the message await call.message.edit_text( text=( "<b>🦄 You are in the category with animals.</b>" ), reply_markup=animals_categories() ) @dp.callback_query_handler() async def call_get_photography(call: CallbackQuery): """ Function for sending photos """ message = call.message data = call.data # Get json document api = get_attributes_of_object() if data == "generate_new": data = message.text.split("#")[1] obj = api[data]["object"] atr = api[data]["attribute"] mark = api[data]["entity"] if mark == "anime": mark = api[data]["safe"] if mark == "memes": mark = "menu" # We get a link to the preview photo link = await get_object(obj, atr) await call.answer() # Editing the message await message.edit_text( text=( f"{hide_link(link)} #{data}" ), reply_markup=control_buttons(mark) )	en	0.634356	Function for sending a menu, with a selection of safe content # Editing the message Redirect to select anime actions # Editing the message Redirect to anime content # Send answer # Editing the message Redirect to animals content # Editing the message Function for sending photos # Get json document # We get a link to the preview photo # Editing the message #{data}"	2.767742	3
1stRound/Medium/322-Coin Change/DP.py	ericchen12377/Leetcode-Algorithm-Python	2	10293	class Solution: def coinChange(self, coins: List[int], amount: int) -> int: M = float('inf') # dynamic programming dp = [0] + [M] * amount for i in range(1, amount+1): dp[i] = 1 + min([dp[i-c] for c in coins if i >= c] or [M]) return dp[-1] if dp[-1] < M else -1	class Solution: def coinChange(self, coins: List[int], amount: int) -> int: M = float('inf') # dynamic programming dp = [0] + [M] * amount for i in range(1, amount+1): dp[i] = 1 + min([dp[i-c] for c in coins if i >= c] or [M]) return dp[-1] if dp[-1] < M else -1	en	0.760267	# dynamic programming	3.276278	3
segmentation_test/Scripts/medpy_graphcut_voxel.py	rominashirazi/SpineSegmentation	0	10294	#!c:\users\hooma\documents\github\spinesegmentation\segmentation_test\scripts\python.exe """ Execute a graph cut on a voxel image based on some foreground and background markers. Copyright (C) 2013 <NAME> This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. """ # build-in modules from argparse import RawTextHelpFormatter import argparse import logging import os # third-party modules import scipy # path changes # own modules from medpy.core import ArgumentError, Logger from medpy.io import load, save, header from medpy import graphcut from medpy.graphcut.wrapper import split_marker # information __author__ = "<NAME>" __version__ = "r0.3.1, 2012-03-23" __email__ = "<EMAIL>" __status__ = "Release" __description__ = """ Perform a binary graph cut using Boykov's max-flow/min-cut algorithm. This implementation does only compute a boundary term and does not use any regional term. The desired boundary term can be selected via the --boundary argument. Depending on the selected term, an additional image has to be supplied as badditional. In the case of the difference of means, it is the original image. Furthermore the algorithm requires a binary image with foreground markers and a binary image with background markers. Additionally a filename for the created binary mask marking foreground and background has to be supplied. Note that the input images must be of the same dimensionality, otherwise an exception is thrown. Note to take into account the input images orientation. Note that the quality of the resulting segmentations depends also on the quality of the supplied markers. Copyright (C) 2013 <NAME> This program comes with ABSOLUTELY NO WARRANTY; This is free software, and you are welcome to redistribute it under certain conditions; see the LICENSE file or <http://www.gnu.org/licenses/> for details. """ # code def main(): # parse cmd arguments parser = getParser() parser.parse_args() args = getArguments(parser) # prepare logger logger = Logger.getInstance() if args.debug: logger.setLevel(logging.DEBUG) elif args.verbose: logger.setLevel(logging.INFO) # check if output image exists if not args.force: if os.path.exists(args.output): logger.warning('The output image {} already exists. Exiting.'.format(args.output)) exit(-1) # select boundary term ['diff_linear', 'diff_exp', 'diff_div', 'diff_pow', 'max_linear', 'max_exp', 'max_div', 'max_pow'] if 'diff_linear' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_difference_linear logger.info('Selected boundary term: linear difference of intensities') elif 'diff_exp' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_difference_exponential logger.info('Selected boundary term: exponential difference of intensities') elif 'diff_div' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_difference_division logger.info('Selected boundary term: divided difference of intensities') elif 'diff_pow' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_difference_power logger.info('Selected boundary term: power based / raised difference of intensities') elif 'max_linear' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_maximum_linear logger.info('Selected boundary term: linear maximum of intensities') elif 'max_exp' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_maximum_exponential logger.info('Selected boundary term: exponential maximum of intensities') elif 'max_div' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_maximum_division logger.info('Selected boundary term: divided maximum of intensities') elif 'max_pow' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_maximum_power logger.info('Selected boundary term: power based / raised maximum of intensities') # load input images badditional_image_data, reference_header = load(args.badditional) markers_image_data, _ = load(args.markers) # split marker image into fg and bg images fgmarkers_image_data, bgmarkers_image_data = split_marker(markers_image_data) # check if all images dimensions are the same if not (badditional_image_data.shape == fgmarkers_image_data.shape == bgmarkers_image_data.shape): logger.critical('Not all of the supplied images are of the same shape.') raise ArgumentError('Not all of the supplied images are of the same shape.') # extract spacing if required if args.spacing: spacing = header.get_pixel_spacing(reference_header) logger.info('Taking spacing of {} into account.'.format(spacing)) else: spacing = False # generate graph logger.info('Preparing BK_MFMC C++ graph...') gcgraph = graphcut.graph_from_voxels(fgmarkers_image_data, bgmarkers_image_data, boundary_term = boundary_term, boundary_term_args = (badditional_image_data, args.sigma, spacing)) # execute min-cut logger.info('Executing min-cut...') maxflow = gcgraph.maxflow() logger.debug('Maxflow is {}'.format(maxflow)) # reshape results to form a valid mask logger.info('Applying results...') result_image_data = scipy.zeros(bgmarkers_image_data.size, dtype=scipy.bool_) for idx in range(len(result_image_data)): result_image_data[idx] = 0 if gcgraph.termtype.SINK == gcgraph.what_segment(idx) else 1 result_image_data = result_image_data.reshape(bgmarkers_image_data.shape) # save resulting mask save(result_image_data.astype(scipy.bool_), args.output, reference_header, args.force) logger.info('Successfully terminated.') def getArguments(parser): "Provides additional validation of the arguments collected by argparse." return parser.parse_args() def getParser(): "Creates and returns the argparse parser object." parser = argparse.ArgumentParser(description=__description__, formatter_class=RawTextHelpFormatter) parser.add_argument('sigma', type=float, help='The sigma required for the boundary terms.') parser.add_argument('badditional', help='The additional image required by the boundary term. See there for details.') parser.add_argument('markers', help='Image containing the foreground (=1) and background (=2) markers.') parser.add_argument('output', help='The output image containing the segmentation.') parser.add_argument('--boundary', default='diff_exp', help='The boundary term to use. Note that the ones prefixed with diff_ require the original image, while the ones prefixed with max_ require the gradient image.', choices=['diff_linear', 'diff_exp', 'diff_div', 'diff_pow', 'max_linear', 'max_exp', 'max_div', 'max_pow']) parser.add_argument('-s', dest='spacing', action='store_true', help='Set this flag to take the pixel spacing of the image into account. The spacing data will be extracted from the baddtional image.') parser.add_argument('-f', dest='force', action='store_true', help='Set this flag to silently override files that exist.') parser.add_argument('-v', dest='verbose', action='store_true', help='Display more information.') parser.add_argument('-d', dest='debug', action='store_true', help='Display debug information.') return parser if __name__ == "__main__": main()	#!c:\users\hooma\documents\github\spinesegmentation\segmentation_test\scripts\python.exe """ Execute a graph cut on a voxel image based on some foreground and background markers. Copyright (C) 2013 <NAME> This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. """ # build-in modules from argparse import RawTextHelpFormatter import argparse import logging import os # third-party modules import scipy # path changes # own modules from medpy.core import ArgumentError, Logger from medpy.io import load, save, header from medpy import graphcut from medpy.graphcut.wrapper import split_marker # information __author__ = "<NAME>" __version__ = "r0.3.1, 2012-03-23" __email__ = "<EMAIL>" __status__ = "Release" __description__ = """ Perform a binary graph cut using Boykov's max-flow/min-cut algorithm. This implementation does only compute a boundary term and does not use any regional term. The desired boundary term can be selected via the --boundary argument. Depending on the selected term, an additional image has to be supplied as badditional. In the case of the difference of means, it is the original image. Furthermore the algorithm requires a binary image with foreground markers and a binary image with background markers. Additionally a filename for the created binary mask marking foreground and background has to be supplied. Note that the input images must be of the same dimensionality, otherwise an exception is thrown. Note to take into account the input images orientation. Note that the quality of the resulting segmentations depends also on the quality of the supplied markers. Copyright (C) 2013 <NAME> This program comes with ABSOLUTELY NO WARRANTY; This is free software, and you are welcome to redistribute it under certain conditions; see the LICENSE file or <http://www.gnu.org/licenses/> for details. """ # code def main(): # parse cmd arguments parser = getParser() parser.parse_args() args = getArguments(parser) # prepare logger logger = Logger.getInstance() if args.debug: logger.setLevel(logging.DEBUG) elif args.verbose: logger.setLevel(logging.INFO) # check if output image exists if not args.force: if os.path.exists(args.output): logger.warning('The output image {} already exists. Exiting.'.format(args.output)) exit(-1) # select boundary term ['diff_linear', 'diff_exp', 'diff_div', 'diff_pow', 'max_linear', 'max_exp', 'max_div', 'max_pow'] if 'diff_linear' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_difference_linear logger.info('Selected boundary term: linear difference of intensities') elif 'diff_exp' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_difference_exponential logger.info('Selected boundary term: exponential difference of intensities') elif 'diff_div' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_difference_division logger.info('Selected boundary term: divided difference of intensities') elif 'diff_pow' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_difference_power logger.info('Selected boundary term: power based / raised difference of intensities') elif 'max_linear' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_maximum_linear logger.info('Selected boundary term: linear maximum of intensities') elif 'max_exp' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_maximum_exponential logger.info('Selected boundary term: exponential maximum of intensities') elif 'max_div' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_maximum_division logger.info('Selected boundary term: divided maximum of intensities') elif 'max_pow' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_maximum_power logger.info('Selected boundary term: power based / raised maximum of intensities') # load input images badditional_image_data, reference_header = load(args.badditional) markers_image_data, _ = load(args.markers) # split marker image into fg and bg images fgmarkers_image_data, bgmarkers_image_data = split_marker(markers_image_data) # check if all images dimensions are the same if not (badditional_image_data.shape == fgmarkers_image_data.shape == bgmarkers_image_data.shape): logger.critical('Not all of the supplied images are of the same shape.') raise ArgumentError('Not all of the supplied images are of the same shape.') # extract spacing if required if args.spacing: spacing = header.get_pixel_spacing(reference_header) logger.info('Taking spacing of {} into account.'.format(spacing)) else: spacing = False # generate graph logger.info('Preparing BK_MFMC C++ graph...') gcgraph = graphcut.graph_from_voxels(fgmarkers_image_data, bgmarkers_image_data, boundary_term = boundary_term, boundary_term_args = (badditional_image_data, args.sigma, spacing)) # execute min-cut logger.info('Executing min-cut...') maxflow = gcgraph.maxflow() logger.debug('Maxflow is {}'.format(maxflow)) # reshape results to form a valid mask logger.info('Applying results...') result_image_data = scipy.zeros(bgmarkers_image_data.size, dtype=scipy.bool_) for idx in range(len(result_image_data)): result_image_data[idx] = 0 if gcgraph.termtype.SINK == gcgraph.what_segment(idx) else 1 result_image_data = result_image_data.reshape(bgmarkers_image_data.shape) # save resulting mask save(result_image_data.astype(scipy.bool_), args.output, reference_header, args.force) logger.info('Successfully terminated.') def getArguments(parser): "Provides additional validation of the arguments collected by argparse." return parser.parse_args() def getParser(): "Creates and returns the argparse parser object." parser = argparse.ArgumentParser(description=__description__, formatter_class=RawTextHelpFormatter) parser.add_argument('sigma', type=float, help='The sigma required for the boundary terms.') parser.add_argument('badditional', help='The additional image required by the boundary term. See there for details.') parser.add_argument('markers', help='Image containing the foreground (=1) and background (=2) markers.') parser.add_argument('output', help='The output image containing the segmentation.') parser.add_argument('--boundary', default='diff_exp', help='The boundary term to use. Note that the ones prefixed with diff_ require the original image, while the ones prefixed with max_ require the gradient image.', choices=['diff_linear', 'diff_exp', 'diff_div', 'diff_pow', 'max_linear', 'max_exp', 'max_div', 'max_pow']) parser.add_argument('-s', dest='spacing', action='store_true', help='Set this flag to take the pixel spacing of the image into account. The spacing data will be extracted from the baddtional image.') parser.add_argument('-f', dest='force', action='store_true', help='Set this flag to silently override files that exist.') parser.add_argument('-v', dest='verbose', action='store_true', help='Display more information.') parser.add_argument('-d', dest='debug', action='store_true', help='Display debug information.') return parser if __name__ == "__main__": main()	en	0.843025	#!c:\users\hooma\documents\github\spinesegmentation\segmentation_test\scripts\python.exe Execute a graph cut on a voxel image based on some foreground and background markers. Copyright (C) 2013 <NAME> This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. # build-in modules # third-party modules # path changes # own modules # information Perform a binary graph cut using Boykov's max-flow/min-cut algorithm. This implementation does only compute a boundary term and does not use any regional term. The desired boundary term can be selected via the --boundary argument. Depending on the selected term, an additional image has to be supplied as badditional. In the case of the difference of means, it is the original image. Furthermore the algorithm requires a binary image with foreground markers and a binary image with background markers. Additionally a filename for the created binary mask marking foreground and background has to be supplied. Note that the input images must be of the same dimensionality, otherwise an exception is thrown. Note to take into account the input images orientation. Note that the quality of the resulting segmentations depends also on the quality of the supplied markers. Copyright (C) 2013 <NAME> This program comes with ABSOLUTELY NO WARRANTY; This is free software, and you are welcome to redistribute it under certain conditions; see the LICENSE file or <http://www.gnu.org/licenses/> for details. # code # parse cmd arguments # prepare logger # check if output image exists # select boundary term # load input images # split marker image into fg and bg images # check if all images dimensions are the same # extract spacing if required # generate graph # execute min-cut # reshape results to form a valid mask # save resulting mask	3.006441	3
_notes/book/conf.py	AstroMatt/astronaut-training-en	1	10295	author = '<NAME>' email = '<EMAIL>' project = 'Astronaut Training Program' description = 'Astronaut Training Program' extensions = [ 'sphinx.ext.todo', 'sphinx.ext.imgmath', ] todo_emit_warnings = False todo_include_todos = True exclude_patterns = [] # ----------------------------------------------------------------------------- # Standard book config # ----------------------------------------------------------------------------- import os import re import subprocess import sys from datetime import datetime needs_sphinx = '2.2' mathjax_path = 'https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-MML-AM_CHTML' mathjax_config = { 'extensions': ['tex2jax.js'], 'jax': ['input/TeX', 'output/HTML-CSS'], } html_theme = 'sphinx_rtd_theme' exclude_patterns = exclude_patterns + [ '.', 'venv', 'virtualenv', '_extensions', '_img', '_slides', '_static', '_themes', '_tmp', '/_template.rst', '/contrib/', '/solution/', '/solutions/', '**.ipynb_checkpoints', 'README.rst', 'TODO.rst', ] numfig_format = { 'section': 'Sect. %s.', 'figure': 'Fig. %s.', 'table': 'Tab. %s.', 'code-block': 'Code Listing %s.', } language = 'en' source_directory = '.' master_doc = 'index' highlight_language = 'python3' pygments_style = 'borland' numfig = True templates_path = ['_templates'] source_suffix = ['.rst'] imgmath_image_format = 'svg' today_fmt = '%Y-%m-%d' project_slug = re.sub(r'[\W]+', '', project) sha1 = subprocess.Popen('git log -1 --format="%h"', stdout=subprocess.PIPE, shell=True).stdout.read().decode().replace('\n', '') now = datetime.now() year = now.year today = now.strftime('%Y-%m-%d') version = f'#{sha1}, {today}' release = f'#{sha1}, {today}' copyright = f'{year}, {author} <{email}>' extensions_dir = os.path.join(os.path.dirname(__file__), '', '_extensions') sys.path.append(extensions_dir) htmlhelp_basename = project html_theme_path = ['_themes'] html_static_path = ['_static'] html_favicon = '_static/favicon.png' html_sidebars = {'sidebar': ['localtoc.html', 'sourcelink.html', 'searchbox.html']} html_show_sphinx = False html_context = { 'css_files': [ '_static/theme-overrides.css', ], } latex_documents = [(master_doc, f'{project_slug}.tex', project, author, 'manual')] latex_elements = { 'papersize': 'a4paper', 'pointsize': '10pt', 'figure_align': 'htbp', # Fix for: LaTeX Backend Fails with Citations In Figure Captions 'preamble': r""" \usepackage{etoolbox} \AtBeginEnvironment{figure}{\renewcommand{\phantomsection}{}} """ } epub_title = project epub_author = author epub_publisher = author epub_copyright = copyright epub_exclude_files = ['search.html'] man_pages = [ (master_doc, project_slug, project, [author], 1) ] texinfo_documents = [ (master_doc, project_slug, project, author, project, '', 'Miscellaneous'), ]	author = '<NAME>' email = '<EMAIL>' project = 'Astronaut Training Program' description = 'Astronaut Training Program' extensions = [ 'sphinx.ext.todo', 'sphinx.ext.imgmath', ] todo_emit_warnings = False todo_include_todos = True exclude_patterns = [] # ----------------------------------------------------------------------------- # Standard book config # ----------------------------------------------------------------------------- import os import re import subprocess import sys from datetime import datetime needs_sphinx = '2.2' mathjax_path = 'https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-MML-AM_CHTML' mathjax_config = { 'extensions': ['tex2jax.js'], 'jax': ['input/TeX', 'output/HTML-CSS'], } html_theme = 'sphinx_rtd_theme' exclude_patterns = exclude_patterns + [ '.', 'venv', 'virtualenv', '_extensions', '_img', '_slides', '_static', '_themes', '_tmp', '/_template.rst', '/contrib/', '/solution/', '/solutions/', '**.ipynb_checkpoints', 'README.rst', 'TODO.rst', ] numfig_format = { 'section': 'Sect. %s.', 'figure': 'Fig. %s.', 'table': 'Tab. %s.', 'code-block': 'Code Listing %s.', } language = 'en' source_directory = '.' master_doc = 'index' highlight_language = 'python3' pygments_style = 'borland' numfig = True templates_path = ['_templates'] source_suffix = ['.rst'] imgmath_image_format = 'svg' today_fmt = '%Y-%m-%d' project_slug = re.sub(r'[\W]+', '', project) sha1 = subprocess.Popen('git log -1 --format="%h"', stdout=subprocess.PIPE, shell=True).stdout.read().decode().replace('\n', '') now = datetime.now() year = now.year today = now.strftime('%Y-%m-%d') version = f'#{sha1}, {today}' release = f'#{sha1}, {today}' copyright = f'{year}, {author} <{email}>' extensions_dir = os.path.join(os.path.dirname(__file__), '', '_extensions') sys.path.append(extensions_dir) htmlhelp_basename = project html_theme_path = ['_themes'] html_static_path = ['_static'] html_favicon = '_static/favicon.png' html_sidebars = {'sidebar': ['localtoc.html', 'sourcelink.html', 'searchbox.html']} html_show_sphinx = False html_context = { 'css_files': [ '_static/theme-overrides.css', ], } latex_documents = [(master_doc, f'{project_slug}.tex', project, author, 'manual')] latex_elements = { 'papersize': 'a4paper', 'pointsize': '10pt', 'figure_align': 'htbp', # Fix for: LaTeX Backend Fails with Citations In Figure Captions 'preamble': r""" \usepackage{etoolbox} \AtBeginEnvironment{figure}{\renewcommand{\phantomsection}{}} """ } epub_title = project epub_author = author epub_publisher = author epub_copyright = copyright epub_exclude_files = ['search.html'] man_pages = [ (master_doc, project_slug, project, [author], 1) ] texinfo_documents = [ (master_doc, project_slug, project, author, project, '', 'Miscellaneous'), ]	en	0.307774	# ----------------------------------------------------------------------------- # Standard book config # ----------------------------------------------------------------------------- # Fix for: LaTeX Backend Fails with Citations In Figure Captions \usepackage{etoolbox} \AtBeginEnvironment{figure}{\renewcommand{\phantomsection}{}}	1.463351	1
tutorial_application/forms.py	yamasakih/django_rdkit_tutorial	2	10296	from django_rdkit import models from django.forms.models import ModelForm from .models import Compound class SubstructureSearchForm(ModelForm): class Meta: model = Compound fields = ('molecule', )	from django_rdkit import models from django.forms.models import ModelForm from .models import Compound class SubstructureSearchForm(ModelForm): class Meta: model = Compound fields = ('molecule', )	none	1		1.472171	1
data_structures/trees/tree.py	onyonkaclifford/data-structures-and-algorithms	0	10297	from abc import ABC, abstractmethod from typing import Any, Generator, Iterable, List, Union class Empty(Exception): pass class Tree(ABC): """A tree is a hierarchical collection of nodes containing items, with each node having a unique parent and zero, one or many children items. The topmost element in a non-empty tree, the root, has no parent. Tree vocabularies include, but are not limited to: 1. Root - the topmost element in a non-empty tree, it has no parent 2. Leaf - a node with zero children 3. Siblings - nodes that share a parent node 4. Edge - a pair of nodes such the one is the parent of the other 5. Path - a collection of nodes such that any pair of adjacent nodes have a parent/child relationship 6. Height - number of edges between a node and it's furthest leaf 7. Depth - number of edges between a node and the root 8. Level - number of nodes in the path between a node and the root, inclusive of both the node itself and the root 9. Ordered tree - a tree with a meaningful organisation among its nodes such that its nodes can be arranged in a linear manner from first to last """ class _Node: def __init__(self, key, value, parent=None, children: Union[List, None] = None): self.key = key self.value = value self.parent = parent self.children = children if children is not None else [] class _Position: """A representation of the position of a node within a tree""" def __init__(self, belongs_to, node): self.__variables = {"belongs_to": belongs_to} self.__node = node def is_owned_by(self, owner): """Check whether position belongs to the tree, owner. Time complexity: O(1). :param owner: object to check whether it's the owner of this position :returns: True of the position is owned by the object passed, else False """ return owner is self.__variables["belongs_to"] def manipulate_variables(self, owner, method: str, params): """Manipulate member variables of this position. Methods of the owner list are the only ones that can call this method. Time complexity: O(1). :param owner: tree object that owns this position :param method: method name of tree object that will manipulate the member variables of this position :param params: extra optional parameters to pass to the method :returns: the return value of the tree method whose name is passed """ if not self.is_owned_by(owner): raise ValueError("Position doesn't belong to the passed owner") return getattr(owner, method)(self.__variables, params) def manipulate_node(self, owner, method: str, params): """Manipulate the node held by this position. Methods of the owner list are the only ones that can call this method. Time complexity: O(1). :param owner: tree object that owns this position :param method: method name of tree object that will manipulate the node contained in this position :param params: extra optional parameters to pass to the method :returns: the return value of the tree method whose name is passed """ if not self.is_owned_by(owner): raise ValueError("Position doesn't belong to the passed owner") return getattr(owner, method)(self.__node, params) def get_data(self): """Return the data stored by the node held by this position. Time complexity: O(1). :returns: data stored in node contained in this position """ return self.__node.key, self.__node.value def __init__(self): self._root: Union[Tree._Node, None] = None self._length = 0 self.__generator: Union[Generator, None] = None def __len__(self) -> int: """Return total number of items in tree :return: count of items in tree """ return self._length def __repr__(self) -> str: """Return a string representation of the tree :return: the string representation of the tree """ def helper(current_position): children = self.get_children(current_position) num_of_children = len(children) last_child_idx = num_of_children - 1 data_dict["string_data"] += f"{current_position.get_data()[0]}" for i, j in enumerate(children): data_dict["string_data"] += "(" if i == 0 else ", " helper(j) data_dict["string_data"] += ")" if i == last_child_idx else "" if self.is_empty(): return "" data_dict = {"string_data": ""} helper(Tree._Position(self, self._root)) return data_dict["string_data"] def __iter__(self) -> Iterable: """Return a tree iterable :return: tree iterable """ return self def __next__(self) -> _Position: """Return next position of tree iterator, implemented based on level-order traversal :return: next position :raises StopIteration: when the cursor denoting the current position surpasses the last position of the tree """ if self.__generator is None: self.__generator = self.traverse_tree_level_order() try: next_position = next(self.__generator) except StopIteration as e: self.__generator = None raise e return next_position @staticmethod def _validate_node(node): """Helper function to check if the node passed is a tree node. Returns the node passed if the validation passes, else raises a TypeError. Time complexity: O(1). :param node: node to validate :returns: the node passed if it passes validation :raises TypeError: if the validation fails """ if not isinstance(node, Tree._Node): raise TypeError("Not a tree node") return node @staticmethod def _invalidate_position(variables): """Helper function to set the belongs_to key of a dictionary to None. Used to revoke the ownership of a position by this tree. Time complexity: O(1). :returns: the dictionary passed, with the belongs_to key set to None """ variables["belongs_to"] = None return variables def is_empty(self) -> bool: """Return True if tree is empty, else False. Time complexity: O(1). :returns: True if tree is empty, else False """ return self._root is None def is_root(self, position: _Position) -> bool: """Check if the passed position contains the root node. Time complexity: O(1). :returns: True if the passed position holds the root node, else False """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") node = position.manipulate_node(self, "_validate_node") return node.parent is None def is_leaf(self, position: _Position) -> bool: """Check if the passed position contains a leaf. Time complexity: O(1). :returns: True if the passed position holds a leaf node, else False """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") return len(self.get_children(position)) == 0 def get_root(self) -> Union[_Position, None]: """Return the root position. Time complexity: O(1). :returns: the root position """ if self.is_empty(): return None else: return Tree._Position(self, self._root) def get_parent(self, position: _Position) -> Union[_Position, None]: """Return the parent of the given position. Time complexity: O(1). :param position: position containing the node whose parent is being sought :returns: the position of parent of the node contained in the passed position. None if the position passed contains the root node. """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") node = position.manipulate_node(self, "_validate_node") if self.is_root(Tree._Position(self, node)): return None else: return Tree._Position(self, node.parent) def get_children(self, position: _Position) -> Union[List[_Position], None]: """Return the children of the given position. Time complexity: O(1). :param position: position containing the node whose children are being sought :returns: the positions of the children of the node contained in the passed position. None if the position has no children. """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") node = position.manipulate_node(self, "_validate_node") children = node.children if children is None: return None else: return [Tree._Position(self, i) for i in children if i is not None] def get_siblings(self, position: _Position) -> Union[List[_Position], None]: """Return the siblings of the given position. Time complexity: O(1). :param position: position containing the node whose children are being sought :returns: the positions of the siblings of the node contained in the passed position """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") node = position.manipulate_node(self, "_validate_node") parent = node.parent if parent is None: return [] return [Tree._Position(self, i) for i in parent.children if i is not node] def get_height_of_node(self, position: _Position) -> int: """Return the number of edges between a node and the farthest leaf among its descendants. Time complexity: O(n). :param position: position containing the node whose height is being sought :returns: the number of edges between a node and the farthest leaf among its descendants """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") if self.is_leaf(position): return 0 return 1 + max(self.get_height_of_node(p) for p in self.get_children(position)) def get_height_of_tree(self) -> int: """Return the number of edges between the root node and the farthest leaf. Time complexity: O(n). :returns: the number of edges between the root node and the farthest leaf """ if self.is_empty(): raise Empty("Tree is empty") return self.get_height_of_node(Tree._Position(self, self._root)) def get_depth_of_node(self, position: _Position) -> int: """Return the number of edges between a node and the root. Time complexity: O(n). :param position: position containing the node whose depth is being sought :returns: the number of edges between a node and the root """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") if self.is_root(position): return 0 return 1 + self.get_depth_of_node(self.get_parent(position)) def get_depth_of_tree(self) -> int: """Return the number of edges between the farthest leaf and the root. Time complexity: O(n). :returns: the number of edges between the farthest leaf and the root """ return self.get_height_of_tree() def get_level_of_node(self, position: _Position) -> int: """Return the number of nodes between a node and the root, inclusive of itself. Time complexity: O(n). :param position: position containing the node whose level is being sought :returns: the number of nodes between a node and the root, inclusive of itself """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") return 1 + self.get_depth_of_node(position) def traverse_subtree_pre_order(self, position: _Position) -> Generator: """Pre-order traverse subtree whose root is the passed position and return a generator of the positions it contains :param position: position containing the node that's the root of the subtree to be traversed :returns: a generator of the positions """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") yield position for i in self.get_children(position): for j in self.traverse_subtree_pre_order(i): yield j def traverse_tree_pre_order(self) -> Generator: """Pre-order traverse tree and return a generator of the positions it contains :returns: a generator of the positions """ position = self.get_root() if position is not None: for i in self.traverse_subtree_pre_order(position): yield i def traverse_subtree_post_order(self, position: _Position) -> Generator: """Post-order traverse subtree whose root is the passed position and return a generator of the positions it contains :param position: position containing the node that's the root of the subtree to be traversed :returns: a generator of the positions """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") for i in self.get_children(position): for j in self.traverse_subtree_post_order(i): yield j yield position def traverse_tree_post_order(self) -> Generator: """Post-order traverse tree and return a generator of the positions it contains :returns: a generator of the positions """ position = self.get_root() if position is not None: for i in self.traverse_subtree_post_order(position): yield i def traverse_subtree_level_order(self, position: _Position) -> Generator: """Level-by-level traverse subtree whose root is the passed position and return a generator of the positions it contains :param position: position containing the node that's the root of the subtree to be traversed :returns: a generator of the positions """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") def helper(root_node, level): if root_node is not None: if level == 1: yield Tree._Position(self, root_node) elif level > 1: for child in root_node.children: for k in helper(child, level - 1): yield k node = position.manipulate_node(self, "_validate_node") number_of_levels = self.get_height_of_node(position) + 1 for i in range(1, number_of_levels + 1): for j in helper(node, i): yield j def traverse_tree_level_order(self) -> Generator: """Level-by-level traverse tree and return a generator of the positions it contains :returns: a generator of the positions """ position = self.get_root() if position is not None: for i in self.traverse_subtree_level_order(position): yield i def delete(self, position: _Position) -> None: """Delete a value from the tree :param position: position containing the node to be removed from the tree """ self._length -= 1 if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") def insert_node(node_to_insert, is_node_left_child, parent_node): if node_to_insert is not None: node_to_insert.parent = parent_node if is_node_left_child is not None: if is_node_left_child: parent_node.children[0] = node_to_insert else: parent_node.children[1] = node_to_insert def delete_node(node_to_delete, is_root): parent = node_to_delete.parent left = node_to_delete.children[0] right = node_to_delete.children[1] is_left_child = None if parent is None else node_to_delete.key < parent.key if left is None: insert_node(right, is_left_child, parent) if is_root: self._root = right else: current_node = left right_child = current_node.children[1] if right_child is None: current_node.children[1] = right insert_node(current_node, is_left_child, parent) if is_root: self._root = current_node else: new_node = Tree._Node( right_child.key, right_child.value, children=[current_node, right], ) insert_node(new_node, is_left_child, parent) if is_root: self._root = new_node delete_node(right_child, False) node = position.manipulate_node(self, "_validate_node") is_root_node = self.is_root(position) _ = position.manipulate_variables(self, "_invalidate_position") delete_node(node, is_root_node) @abstractmethod def insert(self, key: Any, value: Any) -> None: """Insert a value into the tree :param key: unique identifier of the item to be added to the tree :param value: item to be added to the tree """ self._length += 1	from abc import ABC, abstractmethod from typing import Any, Generator, Iterable, List, Union class Empty(Exception): pass class Tree(ABC): """A tree is a hierarchical collection of nodes containing items, with each node having a unique parent and zero, one or many children items. The topmost element in a non-empty tree, the root, has no parent. Tree vocabularies include, but are not limited to: 1. Root - the topmost element in a non-empty tree, it has no parent 2. Leaf - a node with zero children 3. Siblings - nodes that share a parent node 4. Edge - a pair of nodes such the one is the parent of the other 5. Path - a collection of nodes such that any pair of adjacent nodes have a parent/child relationship 6. Height - number of edges between a node and it's furthest leaf 7. Depth - number of edges between a node and the root 8. Level - number of nodes in the path between a node and the root, inclusive of both the node itself and the root 9. Ordered tree - a tree with a meaningful organisation among its nodes such that its nodes can be arranged in a linear manner from first to last """ class _Node: def __init__(self, key, value, parent=None, children: Union[List, None] = None): self.key = key self.value = value self.parent = parent self.children = children if children is not None else [] class _Position: """A representation of the position of a node within a tree""" def __init__(self, belongs_to, node): self.__variables = {"belongs_to": belongs_to} self.__node = node def is_owned_by(self, owner): """Check whether position belongs to the tree, owner. Time complexity: O(1). :param owner: object to check whether it's the owner of this position :returns: True of the position is owned by the object passed, else False """ return owner is self.__variables["belongs_to"] def manipulate_variables(self, owner, method: str, params): """Manipulate member variables of this position. Methods of the owner list are the only ones that can call this method. Time complexity: O(1). :param owner: tree object that owns this position :param method: method name of tree object that will manipulate the member variables of this position :param params: extra optional parameters to pass to the method :returns: the return value of the tree method whose name is passed """ if not self.is_owned_by(owner): raise ValueError("Position doesn't belong to the passed owner") return getattr(owner, method)(self.__variables, params) def manipulate_node(self, owner, method: str, params): """Manipulate the node held by this position. Methods of the owner list are the only ones that can call this method. Time complexity: O(1). :param owner: tree object that owns this position :param method: method name of tree object that will manipulate the node contained in this position :param params: extra optional parameters to pass to the method :returns: the return value of the tree method whose name is passed """ if not self.is_owned_by(owner): raise ValueError("Position doesn't belong to the passed owner") return getattr(owner, method)(self.__node, params) def get_data(self): """Return the data stored by the node held by this position. Time complexity: O(1). :returns: data stored in node contained in this position """ return self.__node.key, self.__node.value def __init__(self): self._root: Union[Tree._Node, None] = None self._length = 0 self.__generator: Union[Generator, None] = None def __len__(self) -> int: """Return total number of items in tree :return: count of items in tree """ return self._length def __repr__(self) -> str: """Return a string representation of the tree :return: the string representation of the tree """ def helper(current_position): children = self.get_children(current_position) num_of_children = len(children) last_child_idx = num_of_children - 1 data_dict["string_data"] += f"{current_position.get_data()[0]}" for i, j in enumerate(children): data_dict["string_data"] += "(" if i == 0 else ", " helper(j) data_dict["string_data"] += ")" if i == last_child_idx else "" if self.is_empty(): return "" data_dict = {"string_data": ""} helper(Tree._Position(self, self._root)) return data_dict["string_data"] def __iter__(self) -> Iterable: """Return a tree iterable :return: tree iterable """ return self def __next__(self) -> _Position: """Return next position of tree iterator, implemented based on level-order traversal :return: next position :raises StopIteration: when the cursor denoting the current position surpasses the last position of the tree """ if self.__generator is None: self.__generator = self.traverse_tree_level_order() try: next_position = next(self.__generator) except StopIteration as e: self.__generator = None raise e return next_position @staticmethod def _validate_node(node): """Helper function to check if the node passed is a tree node. Returns the node passed if the validation passes, else raises a TypeError. Time complexity: O(1). :param node: node to validate :returns: the node passed if it passes validation :raises TypeError: if the validation fails """ if not isinstance(node, Tree._Node): raise TypeError("Not a tree node") return node @staticmethod def _invalidate_position(variables): """Helper function to set the belongs_to key of a dictionary to None. Used to revoke the ownership of a position by this tree. Time complexity: O(1). :returns: the dictionary passed, with the belongs_to key set to None """ variables["belongs_to"] = None return variables def is_empty(self) -> bool: """Return True if tree is empty, else False. Time complexity: O(1). :returns: True if tree is empty, else False """ return self._root is None def is_root(self, position: _Position) -> bool: """Check if the passed position contains the root node. Time complexity: O(1). :returns: True if the passed position holds the root node, else False """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") node = position.manipulate_node(self, "_validate_node") return node.parent is None def is_leaf(self, position: _Position) -> bool: """Check if the passed position contains a leaf. Time complexity: O(1). :returns: True if the passed position holds a leaf node, else False """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") return len(self.get_children(position)) == 0 def get_root(self) -> Union[_Position, None]: """Return the root position. Time complexity: O(1). :returns: the root position """ if self.is_empty(): return None else: return Tree._Position(self, self._root) def get_parent(self, position: _Position) -> Union[_Position, None]: """Return the parent of the given position. Time complexity: O(1). :param position: position containing the node whose parent is being sought :returns: the position of parent of the node contained in the passed position. None if the position passed contains the root node. """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") node = position.manipulate_node(self, "_validate_node") if self.is_root(Tree._Position(self, node)): return None else: return Tree._Position(self, node.parent) def get_children(self, position: _Position) -> Union[List[_Position], None]: """Return the children of the given position. Time complexity: O(1). :param position: position containing the node whose children are being sought :returns: the positions of the children of the node contained in the passed position. None if the position has no children. """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") node = position.manipulate_node(self, "_validate_node") children = node.children if children is None: return None else: return [Tree._Position(self, i) for i in children if i is not None] def get_siblings(self, position: _Position) -> Union[List[_Position], None]: """Return the siblings of the given position. Time complexity: O(1). :param position: position containing the node whose children are being sought :returns: the positions of the siblings of the node contained in the passed position """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") node = position.manipulate_node(self, "_validate_node") parent = node.parent if parent is None: return [] return [Tree._Position(self, i) for i in parent.children if i is not node] def get_height_of_node(self, position: _Position) -> int: """Return the number of edges between a node and the farthest leaf among its descendants. Time complexity: O(n). :param position: position containing the node whose height is being sought :returns: the number of edges between a node and the farthest leaf among its descendants """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") if self.is_leaf(position): return 0 return 1 + max(self.get_height_of_node(p) for p in self.get_children(position)) def get_height_of_tree(self) -> int: """Return the number of edges between the root node and the farthest leaf. Time complexity: O(n). :returns: the number of edges between the root node and the farthest leaf """ if self.is_empty(): raise Empty("Tree is empty") return self.get_height_of_node(Tree._Position(self, self._root)) def get_depth_of_node(self, position: _Position) -> int: """Return the number of edges between a node and the root. Time complexity: O(n). :param position: position containing the node whose depth is being sought :returns: the number of edges between a node and the root """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") if self.is_root(position): return 0 return 1 + self.get_depth_of_node(self.get_parent(position)) def get_depth_of_tree(self) -> int: """Return the number of edges between the farthest leaf and the root. Time complexity: O(n). :returns: the number of edges between the farthest leaf and the root """ return self.get_height_of_tree() def get_level_of_node(self, position: _Position) -> int: """Return the number of nodes between a node and the root, inclusive of itself. Time complexity: O(n). :param position: position containing the node whose level is being sought :returns: the number of nodes between a node and the root, inclusive of itself """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") return 1 + self.get_depth_of_node(position) def traverse_subtree_pre_order(self, position: _Position) -> Generator: """Pre-order traverse subtree whose root is the passed position and return a generator of the positions it contains :param position: position containing the node that's the root of the subtree to be traversed :returns: a generator of the positions """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") yield position for i in self.get_children(position): for j in self.traverse_subtree_pre_order(i): yield j def traverse_tree_pre_order(self) -> Generator: """Pre-order traverse tree and return a generator of the positions it contains :returns: a generator of the positions """ position = self.get_root() if position is not None: for i in self.traverse_subtree_pre_order(position): yield i def traverse_subtree_post_order(self, position: _Position) -> Generator: """Post-order traverse subtree whose root is the passed position and return a generator of the positions it contains :param position: position containing the node that's the root of the subtree to be traversed :returns: a generator of the positions """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") for i in self.get_children(position): for j in self.traverse_subtree_post_order(i): yield j yield position def traverse_tree_post_order(self) -> Generator: """Post-order traverse tree and return a generator of the positions it contains :returns: a generator of the positions """ position = self.get_root() if position is not None: for i in self.traverse_subtree_post_order(position): yield i def traverse_subtree_level_order(self, position: _Position) -> Generator: """Level-by-level traverse subtree whose root is the passed position and return a generator of the positions it contains :param position: position containing the node that's the root of the subtree to be traversed :returns: a generator of the positions """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") def helper(root_node, level): if root_node is not None: if level == 1: yield Tree._Position(self, root_node) elif level > 1: for child in root_node.children: for k in helper(child, level - 1): yield k node = position.manipulate_node(self, "_validate_node") number_of_levels = self.get_height_of_node(position) + 1 for i in range(1, number_of_levels + 1): for j in helper(node, i): yield j def traverse_tree_level_order(self) -> Generator: """Level-by-level traverse tree and return a generator of the positions it contains :returns: a generator of the positions """ position = self.get_root() if position is not None: for i in self.traverse_subtree_level_order(position): yield i def delete(self, position: _Position) -> None: """Delete a value from the tree :param position: position containing the node to be removed from the tree """ self._length -= 1 if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") def insert_node(node_to_insert, is_node_left_child, parent_node): if node_to_insert is not None: node_to_insert.parent = parent_node if is_node_left_child is not None: if is_node_left_child: parent_node.children[0] = node_to_insert else: parent_node.children[1] = node_to_insert def delete_node(node_to_delete, is_root): parent = node_to_delete.parent left = node_to_delete.children[0] right = node_to_delete.children[1] is_left_child = None if parent is None else node_to_delete.key < parent.key if left is None: insert_node(right, is_left_child, parent) if is_root: self._root = right else: current_node = left right_child = current_node.children[1] if right_child is None: current_node.children[1] = right insert_node(current_node, is_left_child, parent) if is_root: self._root = current_node else: new_node = Tree._Node( right_child.key, right_child.value, children=[current_node, right], ) insert_node(new_node, is_left_child, parent) if is_root: self._root = new_node delete_node(right_child, False) node = position.manipulate_node(self, "_validate_node") is_root_node = self.is_root(position) _ = position.manipulate_variables(self, "_invalidate_position") delete_node(node, is_root_node) @abstractmethod def insert(self, key: Any, value: Any) -> None: """Insert a value into the tree :param key: unique identifier of the item to be added to the tree :param value: item to be added to the tree """ self._length += 1	en	0.846048	A tree is a hierarchical collection of nodes containing items, with each node having a unique parent and zero, one or many children items. The topmost element in a non-empty tree, the root, has no parent. Tree vocabularies include, but are not limited to: 1. Root - the topmost element in a non-empty tree, it has no parent 2. Leaf - a node with zero children 3. Siblings - nodes that share a parent node 4. Edge - a pair of nodes such the one is the parent of the other 5. Path - a collection of nodes such that any pair of adjacent nodes have a parent/child relationship 6. Height - number of edges between a node and it's furthest leaf 7. Depth - number of edges between a node and the root 8. Level - number of nodes in the path between a node and the root, inclusive of both the node itself and the root 9. Ordered tree - a tree with a meaningful organisation among its nodes such that its nodes can be arranged in a linear manner from first to last A representation of the position of a node within a tree Check whether position belongs to the tree, owner. Time complexity: O(1). :param owner: object to check whether it's the owner of this position :returns: True of the position is owned by the object passed, else False Manipulate member variables of this position. Methods of the owner list are the only ones that can call this method. Time complexity: O(1). :param owner: tree object that owns this position :param method: method name of tree object that will manipulate the member variables of this position :param params: extra optional parameters to pass to the method :returns: the return value of the tree method whose name is passed Manipulate the node held by this position. Methods of the owner list are the only ones that can call this method. Time complexity: O(1). :param owner: tree object that owns this position :param method: method name of tree object that will manipulate the node contained in this position :param params: extra optional parameters to pass to the method :returns: the return value of the tree method whose name is passed Return the data stored by the node held by this position. Time complexity: O(1). :returns: data stored in node contained in this position Return total number of items in tree :return: count of items in tree Return a string representation of the tree :return: the string representation of the tree Return a tree iterable :return: tree iterable Return next position of tree iterator, implemented based on level-order traversal :return: next position :raises StopIteration: when the cursor denoting the current position surpasses the last position of the tree Helper function to check if the node passed is a tree node. Returns the node passed if the validation passes, else raises a TypeError. Time complexity: O(1). :param node: node to validate :returns: the node passed if it passes validation :raises TypeError: if the validation fails Helper function to set the belongs_to key of a dictionary to None. Used to revoke the ownership of a position by this tree. Time complexity: O(1). :returns: the dictionary passed, with the belongs_to key set to None Return True if tree is empty, else False. Time complexity: O(1). :returns: True if tree is empty, else False Check if the passed position contains the root node. Time complexity: O(1). :returns: True if the passed position holds the root node, else False Check if the passed position contains a leaf. Time complexity: O(1). :returns: True if the passed position holds a leaf node, else False Return the root position. Time complexity: O(1). :returns: the root position Return the parent of the given position. Time complexity: O(1). :param position: position containing the node whose parent is being sought :returns: the position of parent of the node contained in the passed position. None if the position passed contains the root node. Return the children of the given position. Time complexity: O(1). :param position: position containing the node whose children are being sought :returns: the positions of the children of the node contained in the passed position. None if the position has no children. Return the siblings of the given position. Time complexity: O(1). :param position: position containing the node whose children are being sought :returns: the positions of the siblings of the node contained in the passed position Return the number of edges between a node and the farthest leaf among its descendants. Time complexity: O(n). :param position: position containing the node whose height is being sought :returns: the number of edges between a node and the farthest leaf among its descendants Return the number of edges between the root node and the farthest leaf. Time complexity: O(n). :returns: the number of edges between the root node and the farthest leaf Return the number of edges between a node and the root. Time complexity: O(n). :param position: position containing the node whose depth is being sought :returns: the number of edges between a node and the root Return the number of edges between the farthest leaf and the root. Time complexity: O(n). :returns: the number of edges between the farthest leaf and the root Return the number of nodes between a node and the root, inclusive of itself. Time complexity: O(n). :param position: position containing the node whose level is being sought :returns: the number of nodes between a node and the root, inclusive of itself Pre-order traverse subtree whose root is the passed position and return a generator of the positions it contains :param position: position containing the node that's the root of the subtree to be traversed :returns: a generator of the positions Pre-order traverse tree and return a generator of the positions it contains :returns: a generator of the positions Post-order traverse subtree whose root is the passed position and return a generator of the positions it contains :param position: position containing the node that's the root of the subtree to be traversed :returns: a generator of the positions Post-order traverse tree and return a generator of the positions it contains :returns: a generator of the positions Level-by-level traverse subtree whose root is the passed position and return a generator of the positions it contains :param position: position containing the node that's the root of the subtree to be traversed :returns: a generator of the positions Level-by-level traverse tree and return a generator of the positions it contains :returns: a generator of the positions Delete a value from the tree :param position: position containing the node to be removed from the tree Insert a value into the tree :param key: unique identifier of the item to be added to the tree :param value: item to be added to the tree	4.014211	4
nodes/audio.py	sddhrthrt/COVFEFE	0	10298	<filename>nodes/audio.py from abc import ABC, abstractmethod import os import logging from nodes.helper import FileOutputNode from utils import file_utils from utils import signal_processing as sp from utils.shell_run import shell_run from config import OPENSMILE_HOME class Mp3ToWav(FileOutputNode): def run(self, mp3_file): self.log(logging.INFO, "Starting %s" % (mp3_file)) if not mp3_file.endswith(".mp3"): self.log(logging.ERROR,"Failed %s. Not mp3 file" % (mp3_file)) return wav_file = self.derive_new_file_path(mp3_file, "wav") if file_utils.should_run(mp3_file, wav_file): res = shell_run(["lame", "--decode", mp3_file, wav_file]) if res != 0: self.log(logging.ERROR,"Failed %s -> %s with lame error code %i" % (mp3_file, wav_file, res)) return self.log(logging.INFO, "Done %s -> %s" % (mp3_file, wav_file)) self.emit(wav_file) class ResampleWav(FileOutputNode): def setup(self, new_sr): self.new_sr = new_sr def run(self, wav_file): self.log(logging.INFO, "Starting %s" % (wav_file)) if not wav_file.endswith(".wav"): self.log(logging.ERROR,"Failed %s. Not wav file" % (wav_file)) return new_wav_file = self.derive_new_file_path(wav_file, "wav") if file_utils.should_run(wav_file, new_wav_file): res = shell_run(["sox", wav_file, "--rate", str(self.new_sr), new_wav_file]) if res != 0: self.log(logging.ERROR,"Failed %s -> %s with lame error code %i" % (wav_file, new_wav_file, res)) return self.log(logging.INFO, "Done %s -> %s" % (wav_file, new_wav_file)) self.emit(new_wav_file) class ShellCommand(FileOutputNode): """ Take as input a format string representing a shell command that can accept an in_file and out_file. For example "someCommand -i {in_file} -o {out_file}" ext: Extension of the output file, ex. "wav", "csv" """ def setup(self, command, ext): self.command = command self.ext = ext def run(self, in_file): self.log(logging.INFO, "Starting %s" % (in_file)) out_file = self.derive_new_file_path(in_file, self.ext) if file_utils.should_run(in_file, out_file): cmd = self.command.format(in_file=in_file, out_file=out_file) res = shell_run(cmd.split(" ")) if res != 0: self.log(logging.ERROR,"Failed %s -> %s with error code %i. cmd: %s" % (in_file, out_file, res, cmd)) return self.log(logging.INFO, "Done %s -> %s" % (in_file, out_file)) self.emit(out_file) class OpenSmileRunner(FileOutputNode): """ conf_file: Either absolute path to an opensmile conf file or the name of a config file in opensmile's config folder out_flag: Flag to use for the output file. extra_flags: A string of extra flags to pass to SMILExtract. out_ext: Extension of the output file """ def setup(self, conf_file, out_flag="-csvoutput", extra_flags="-nologfile -noconsoleoutput -appendcsv 0", out_ext="csv"): self.conf_file = file_utils.locate_file(conf_file, [os.path.join(OPENSMILE_HOME, "config")]) self.extra_flags = extra_flags.split(" ") self.out_flag = out_flag self.out_ext = out_ext self.opensmile_exec = file_utils.locate_file("SMILExtract", [OPENSMILE_HOME, os.path.join(OPENSMILE_HOME, "bin")], use_path=True) def run(self, in_file): self.log(logging.INFO, "Starting %s" % (in_file)) out_file = self.derive_new_file_path(in_file, self.out_ext) if file_utils.should_run(in_file, out_file): cmd = [self.opensmile_exec, "-C", self.conf_file, "-I", in_file, self.out_flag, out_file] + self.extra_flags res = shell_run(cmd) if res != 0: self.log(logging.ERROR,"Failed %s -> %s with SmileExtract error code %i. cmd: %s" % (in_file, out_file, res, " ".join(cmd))) return self.log(logging.INFO, "Done %s -> %s" % (in_file, out_file)) self.emit([out_file]) class IS10_Paraling(OpenSmileRunner): def get_conf_name(self): return "IS10_paraling.conf" def get_command(self, wav_file, out_file): return [self.os_exec, "-C", self.conf_file, "-I", wav_file, "-csvoutput", out_file, "-nologfile", "-noconsoleoutput", "-appendcsv", "0"] class IS10_Paraling_lld(OpenSmileRunner): def get_conf_name(self): return "IS10_paraling.conf" def get_command(self, wav_file, out_file): return [self.os_exec, "-C", self.conf_file, "-I", wav_file, "-lldcsvoutput", out_file, "-nologfile", "-noconsoleoutput", "-appendcsv", "0"] class SplitSegments(FileOutputNode): """ segment_mapping_fn is a pointer to a function that takes as input a file and sample rate and returns a list of all the segments in that file in the format [(start1, end1, segname1), (start2, end2, segname2), ...] where start and end are in given in samples. Each tuple in the list can also have a 4th item, which can be any string. This string will get saved in segname.txt This is useful for isolating events of interest in audio files. For example, if the segment mapping function returns a list of where all speech occurs in the input audio, this will isolate all occurrences of speech into individual files. The 4th item may contain the annotation of what was said in the segment. """ def setup(self, segment_mapping_fn): self.segment_mapping_fn = segment_mapping_fn def run(self, in_file): self.log(logging.INFO, "Starting %s" % (in_file)) if not in_file.endswith(".wav"): self.log(logging.ERROR, "Failed %s. Not wav file" % (in_file)) return sr, original_data = sp.read_wave(in_file, first_channel=True) segments = self.segment_mapping_fn(in_file, sr) for segment in segments: if len(segment) == 3: start, end, seg_name = segment extra_info = None elif len(segment) == 4: start, end, seg_name, extra_info = segment else: self.log(logging.ERROR, "Failed %s. Segment length must be 3 or 4" % (in_file)) return seg_path = os.path.join(self.out_dir, "%s.wav" % seg_name) sp.write_wav(seg_path, sr, original_data[start:end]) extra_path = None if extra_info: extra_path = os.path.join(self.out_dir, "%s.txt" % seg_name) with open(extra_path, "w") as f: f.write(extra_info) self.emit([seg_path, extra_path])	<filename>nodes/audio.py from abc import ABC, abstractmethod import os import logging from nodes.helper import FileOutputNode from utils import file_utils from utils import signal_processing as sp from utils.shell_run import shell_run from config import OPENSMILE_HOME class Mp3ToWav(FileOutputNode): def run(self, mp3_file): self.log(logging.INFO, "Starting %s" % (mp3_file)) if not mp3_file.endswith(".mp3"): self.log(logging.ERROR,"Failed %s. Not mp3 file" % (mp3_file)) return wav_file = self.derive_new_file_path(mp3_file, "wav") if file_utils.should_run(mp3_file, wav_file): res = shell_run(["lame", "--decode", mp3_file, wav_file]) if res != 0: self.log(logging.ERROR,"Failed %s -> %s with lame error code %i" % (mp3_file, wav_file, res)) return self.log(logging.INFO, "Done %s -> %s" % (mp3_file, wav_file)) self.emit(wav_file) class ResampleWav(FileOutputNode): def setup(self, new_sr): self.new_sr = new_sr def run(self, wav_file): self.log(logging.INFO, "Starting %s" % (wav_file)) if not wav_file.endswith(".wav"): self.log(logging.ERROR,"Failed %s. Not wav file" % (wav_file)) return new_wav_file = self.derive_new_file_path(wav_file, "wav") if file_utils.should_run(wav_file, new_wav_file): res = shell_run(["sox", wav_file, "--rate", str(self.new_sr), new_wav_file]) if res != 0: self.log(logging.ERROR,"Failed %s -> %s with lame error code %i" % (wav_file, new_wav_file, res)) return self.log(logging.INFO, "Done %s -> %s" % (wav_file, new_wav_file)) self.emit(new_wav_file) class ShellCommand(FileOutputNode): """ Take as input a format string representing a shell command that can accept an in_file and out_file. For example "someCommand -i {in_file} -o {out_file}" ext: Extension of the output file, ex. "wav", "csv" """ def setup(self, command, ext): self.command = command self.ext = ext def run(self, in_file): self.log(logging.INFO, "Starting %s" % (in_file)) out_file = self.derive_new_file_path(in_file, self.ext) if file_utils.should_run(in_file, out_file): cmd = self.command.format(in_file=in_file, out_file=out_file) res = shell_run(cmd.split(" ")) if res != 0: self.log(logging.ERROR,"Failed %s -> %s with error code %i. cmd: %s" % (in_file, out_file, res, cmd)) return self.log(logging.INFO, "Done %s -> %s" % (in_file, out_file)) self.emit(out_file) class OpenSmileRunner(FileOutputNode): """ conf_file: Either absolute path to an opensmile conf file or the name of a config file in opensmile's config folder out_flag: Flag to use for the output file. extra_flags: A string of extra flags to pass to SMILExtract. out_ext: Extension of the output file """ def setup(self, conf_file, out_flag="-csvoutput", extra_flags="-nologfile -noconsoleoutput -appendcsv 0", out_ext="csv"): self.conf_file = file_utils.locate_file(conf_file, [os.path.join(OPENSMILE_HOME, "config")]) self.extra_flags = extra_flags.split(" ") self.out_flag = out_flag self.out_ext = out_ext self.opensmile_exec = file_utils.locate_file("SMILExtract", [OPENSMILE_HOME, os.path.join(OPENSMILE_HOME, "bin")], use_path=True) def run(self, in_file): self.log(logging.INFO, "Starting %s" % (in_file)) out_file = self.derive_new_file_path(in_file, self.out_ext) if file_utils.should_run(in_file, out_file): cmd = [self.opensmile_exec, "-C", self.conf_file, "-I", in_file, self.out_flag, out_file] + self.extra_flags res = shell_run(cmd) if res != 0: self.log(logging.ERROR,"Failed %s -> %s with SmileExtract error code %i. cmd: %s" % (in_file, out_file, res, " ".join(cmd))) return self.log(logging.INFO, "Done %s -> %s" % (in_file, out_file)) self.emit([out_file]) class IS10_Paraling(OpenSmileRunner): def get_conf_name(self): return "IS10_paraling.conf" def get_command(self, wav_file, out_file): return [self.os_exec, "-C", self.conf_file, "-I", wav_file, "-csvoutput", out_file, "-nologfile", "-noconsoleoutput", "-appendcsv", "0"] class IS10_Paraling_lld(OpenSmileRunner): def get_conf_name(self): return "IS10_paraling.conf" def get_command(self, wav_file, out_file): return [self.os_exec, "-C", self.conf_file, "-I", wav_file, "-lldcsvoutput", out_file, "-nologfile", "-noconsoleoutput", "-appendcsv", "0"] class SplitSegments(FileOutputNode): """ segment_mapping_fn is a pointer to a function that takes as input a file and sample rate and returns a list of all the segments in that file in the format [(start1, end1, segname1), (start2, end2, segname2), ...] where start and end are in given in samples. Each tuple in the list can also have a 4th item, which can be any string. This string will get saved in segname.txt This is useful for isolating events of interest in audio files. For example, if the segment mapping function returns a list of where all speech occurs in the input audio, this will isolate all occurrences of speech into individual files. The 4th item may contain the annotation of what was said in the segment. """ def setup(self, segment_mapping_fn): self.segment_mapping_fn = segment_mapping_fn def run(self, in_file): self.log(logging.INFO, "Starting %s" % (in_file)) if not in_file.endswith(".wav"): self.log(logging.ERROR, "Failed %s. Not wav file" % (in_file)) return sr, original_data = sp.read_wave(in_file, first_channel=True) segments = self.segment_mapping_fn(in_file, sr) for segment in segments: if len(segment) == 3: start, end, seg_name = segment extra_info = None elif len(segment) == 4: start, end, seg_name, extra_info = segment else: self.log(logging.ERROR, "Failed %s. Segment length must be 3 or 4" % (in_file)) return seg_path = os.path.join(self.out_dir, "%s.wav" % seg_name) sp.write_wav(seg_path, sr, original_data[start:end]) extra_path = None if extra_info: extra_path = os.path.join(self.out_dir, "%s.txt" % seg_name) with open(extra_path, "w") as f: f.write(extra_info) self.emit([seg_path, extra_path])	en	0.87316	Take as input a format string representing a shell command that can accept an in_file and out_file. For example "someCommand -i {in_file} -o {out_file}" ext: Extension of the output file, ex. "wav", "csv" conf_file: Either absolute path to an opensmile conf file or the name of a config file in opensmile's config folder out_flag: Flag to use for the output file. extra_flags: A string of extra flags to pass to SMILExtract. out_ext: Extension of the output file segment_mapping_fn is a pointer to a function that takes as input a file and sample rate and returns a list of all the segments in that file in the format [(start1, end1, segname1), (start2, end2, segname2), ...] where start and end are in given in samples. Each tuple in the list can also have a 4th item, which can be any string. This string will get saved in segname.txt This is useful for isolating events of interest in audio files. For example, if the segment mapping function returns a list of where all speech occurs in the input audio, this will isolate all occurrences of speech into individual files. The 4th item may contain the annotation of what was said in the segment.	2.441041	2
texar/torch/modules/pretrained/gpt2.py	VegB/VLN-Transformer	19	10299	<reponame>VegB/VLN-Transformer # Copyright 2019 The Texar Authors. All Rights Reserved. # # 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. """ Utils of GPT2 Modules. """ import json import os import warnings from abc import ABC from typing import Any, Dict import torch from texar.torch.modules.pretrained.pretrained_base import PretrainedMixin __all__ = [ "PretrainedGPT2Mixin", ] _GPT2_PATH = "https://storage.googleapis.com/gpt-2/models/" _CHECKPOINT_FILES = [ "checkpoint", "encoder.json", "hparams.json", "vocab.bpe", "model.ckpt.data-00000-of-00001", "model.ckpt.index", "model.ckpt.meta"] class PretrainedGPT2Mixin(PretrainedMixin, ABC): r"""A mixin class to support loading pre-trained checkpoints for modules that implement the GPT2 model. The GPT2 model was proposed in `Language Models are Unsupervised Multitask Learners`_ by `Radford et al.` from OpenAI. It is a unidirectional Transformer model pre-trained using the vanilla language modeling objective on a large corpus. The available GPT2 models are as follows: * ``gpt2-small``: Small version of GPT-2, 124M parameters. * ``gpt2-medium``: Medium version of GPT-2, 355M parameters. * ``gpt2-large``: Large version of GPT-2, 774M parameters. We provide the following GPT2 classes: * :class:`~texar.torch.modules.GPT2Encoder` for text encoding. * :class:`~texar.torch.modules.GPT2Decoder` for text generation and decoding. * :class:`~texar.torch.modules.GPT2Classifier` for text classification and sequence tagging. .. _`Language Models are Unsupervised Multitask Learners`: https://openai.com/blog/better-language-models/ """ _MODEL_NAME = "GPT2" _MODEL2URL = { 'gpt2-small': [_GPT2_PATH + f"124M/{file}" for file in _CHECKPOINT_FILES], 'gpt2-medium': [_GPT2_PATH + f"355M/{file}" for file in _CHECKPOINT_FILES], 'gpt2-large': [_GPT2_PATH + f"774M/{file}" for file in _CHECKPOINT_FILES], } _IS_DECODE = False # Raise warning for the deprecated pre-trained model names class MyDict(dict): def __contains__(self, key): if key == '117M': warnings.warn("Pre-trained model name '117M' is deprecated, " "use 'gpt2-small' instead.", UserWarning) return True elif key == '345M': warnings.warn("Pre-trained model name '345M' is deprecated, " "use 'gpt2-medium' instead.", UserWarning) return True else: return super().__contains__(key) _DEPRECATED_MODEL2URL = { '117M': [_GPT2_PATH + f"124M/{file}" for file in _CHECKPOINT_FILES], '345M': [_GPT2_PATH + f"355M/{file}" for file in _CHECKPOINT_FILES], } _MODEL2URL.update(_DEPRECATED_MODEL2URL) _MODEL2URL = MyDict(_MODEL2URL) # type: ignore def _transform_config(self, pretrained_model_name: str, # type: ignore cache_dir: str) -> Dict[str, Any]: info = list(os.walk(cache_dir)) root, _, files = info[0] config_path = None for file in files: if file.endswith('hparams.json'): config_path = os.path.join(root, file) if config_path is None: raise ValueError(f"Cannot find the config file in {cache_dir}") with open(config_path) as f: config_gpt = json.loads(f.read()) hidden_dim = config_gpt["n_embd"] configs = { "vocab_size": config_gpt["n_vocab"], "context_size": config_gpt["n_ctx"], "embedding_size": config_gpt["n_embd"], "embed": { "dim": hidden_dim, }, "position_size": config_gpt["n_ctx"], "position_embed": { "dim": hidden_dim } } module_name = 'decoder' if self._IS_DECODE else 'encoder' configs.update({module_name: { "dim": hidden_dim, "num_blocks": config_gpt["n_layer"], "embedding_dropout": 0, "residual_dropout": 0, "multihead_attention": { "use_bias": True, "num_units": hidden_dim, "num_heads": config_gpt["n_head"], "output_dim": hidden_dim, }, "initializer": { "type": "variance_scaling_initializer", "kwargs": { "factor": 1.0, "mode": "FAN_AVG", "uniform": True, }, }, "poswise_feedforward": { "layers": [ { "type": "Linear", "kwargs": { "in_features": hidden_dim, "out_features": hidden_dim * 4, "bias": True, } }, { "type": "GPTGELU", "kwargs": {} }, { "type": "Linear", "kwargs": { "in_features": hidden_dim * 4, "out_features": hidden_dim, "bias": True, } } ], "name": "ffn", }, }}) if self._IS_DECODE: configs[module_name].update({'use_gpt_config': True}) else: configs[module_name].update({'use_bert_config': False}) return configs def _init_from_checkpoint(self, pretrained_model_name: str, cache_dir: str, load_output_layer: bool = True, *kwargs): r"""Initialize model parameters from weights stored in the pre-trained checkpoint. Args: pretrained_model_name (str): Name of the pre-trained model. cache_dir (str): Path to the cache directory. load_output_layer (bool): If `False`, will not load weights of the output layer. Set this argument to `False` when loading weights into a GPT2 encoder. Defaults to `True`. """ try: import numpy as np import tensorflow as tf except ImportError: print("Loading TensorFlow models in PyTorch requires installing " "TensorFlow. Please see https://www.tensorflow.org/install/ " "for installation instructions.") raise module_name = 'decoder' if self._IS_DECODE else 'encoder' global_tensor_map = { "model/wte": "word_embedder.embedding", "model/wpe": "position_embedder.embedding", "model/ln_f/b": module_name + ".final_layer_norm.bias", "model/ln_f/g": module_name + ".final_layer_norm.weight", } layer_tensor_map = { "ln_1/b": module_name + ".self_attn_layer_norm.{}.bias", "ln_1/g": module_name + ".self_attn_layer_norm.{}.weight", "ln_2/b": module_name + ".poswise_layer_norm.{}.bias", "ln_2/g": module_name + ".poswise_layer_norm.{}.weight", "mlp/c_fc/b": module_name + ".poswise_networks.{}._layers.0.bias", "mlp/c_proj/b": module_name + ".poswise_networks.{}._layers.2.bias", "attn/c_proj/b": module_name + ".self_attns.{}.O_dense.bias", } layer_transpose_map = { "mlp/c_fc/w": module_name + ".poswise_networks.{}._layers.0.weight", "mlp/c_proj/w": module_name + ".poswise_networks.{}._layers.2." "weight", "attn/c_proj/w": module_name + ".self_attns.{}.O_dense.weight", } tf_path = os.path.abspath(os.path.join(cache_dir, 'model.ckpt')) # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, _ in init_vars: array = tf.train.load_variable(tf_path, name) names.append(name) arrays.append(array.squeeze()) tensor_names = [] for name, _ in self.named_parameters(): tensor_names.append(name) for name, array in zip(names, arrays): if name in global_tensor_map: v_name = global_tensor_map[name] if name == "model/wte": pointer = self._name_to_variable(v_name) assert pointer.shape == array.shape pointer.data = torch.from_numpy(array) if load_output_layer: output_pointer = self._name_to_variable( "decoder._output_layer.weight") assert output_pointer.shape == array.shape output_pointer.data = torch.from_numpy(array) elif name == "model/wpe": pointer = self._name_to_variable(v_name) assert pointer.shape == array.shape pointer.data = torch.from_numpy(array) else: pointer = self._name_to_variable(v_name) assert pointer.shape == array.shape pointer.data = torch.from_numpy(array) else: name_tmp = name.split("/") layer_no = name_tmp[1][1:] name = "/".join(name_tmp[2:]) if name in layer_tensor_map: v_name = layer_tensor_map[name].format(layer_no) pointer = self._name_to_variable(v_name) assert pointer.shape == array.shape pointer.data = torch.from_numpy(array) elif name in layer_transpose_map: v_name = layer_transpose_map[name].format(layer_no) pointer = self._name_to_variable(v_name) array_t = np.transpose(array) assert pointer.shape == array_t.shape pointer.data = torch.from_numpy(array_t) elif name == "attn/c_attn/w": index_d = array.shape[-1] // 3 Q_w = np.transpose(array[:, :index_d]) K_w = np.transpose(array[:, index_d: 2 index_d]) V_w = np.transpose(array[:, 2 * index_d:]) q_weight = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.Q_dense.weight") k_weight = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.K_dense.weight") v_weight = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.V_dense.weight") assert q_weight.shape == Q_w.shape assert k_weight.shape == K_w.shape assert v_weight.shape == V_w.shape q_weight.data = torch.from_numpy(Q_w) k_weight.data = torch.from_numpy(K_w) v_weight.data = torch.from_numpy(V_w) elif name == "attn/c_attn/b": d = array.shape[0] Q_b = array[: d // 3] K_b = array[d // 3: 2 * d // 3] V_b = array[2 * d // 3:] q_bias = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.Q_dense.bias") k_bias = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.K_dense.bias") v_bias = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.V_dense.bias") assert q_bias.shape == Q_b.shape assert k_bias.shape == K_b.shape assert v_bias.shape == V_b.shape q_bias.data = torch.from_numpy(Q_b) k_bias.data = torch.from_numpy(K_b) v_bias.data = torch.from_numpy(V_b) else: print("Name error", name) raise Exception	# Copyright 2019 The Texar Authors. All Rights Reserved. # # 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. """ Utils of GPT2 Modules. """ import json import os import warnings from abc import ABC from typing import Any, Dict import torch from texar.torch.modules.pretrained.pretrained_base import PretrainedMixin __all__ = [ "PretrainedGPT2Mixin", ] _GPT2_PATH = "https://storage.googleapis.com/gpt-2/models/" _CHECKPOINT_FILES = [ "checkpoint", "encoder.json", "hparams.json", "vocab.bpe", "model.ckpt.data-00000-of-00001", "model.ckpt.index", "model.ckpt.meta"] class PretrainedGPT2Mixin(PretrainedMixin, ABC): r"""A mixin class to support loading pre-trained checkpoints for modules that implement the GPT2 model. The GPT2 model was proposed in `Language Models are Unsupervised Multitask Learners`_ by `Radford et al.` from OpenAI. It is a unidirectional Transformer model pre-trained using the vanilla language modeling objective on a large corpus. The available GPT2 models are as follows: * ``gpt2-small``: Small version of GPT-2, 124M parameters. * ``gpt2-medium``: Medium version of GPT-2, 355M parameters. * ``gpt2-large``: Large version of GPT-2, 774M parameters. We provide the following GPT2 classes: * :class:`~texar.torch.modules.GPT2Encoder` for text encoding. * :class:`~texar.torch.modules.GPT2Decoder` for text generation and decoding. * :class:`~texar.torch.modules.GPT2Classifier` for text classification and sequence tagging. .. _`Language Models are Unsupervised Multitask Learners`: https://openai.com/blog/better-language-models/ """ _MODEL_NAME = "GPT2" _MODEL2URL = { 'gpt2-small': [_GPT2_PATH + f"124M/{file}" for file in _CHECKPOINT_FILES], 'gpt2-medium': [_GPT2_PATH + f"355M/{file}" for file in _CHECKPOINT_FILES], 'gpt2-large': [_GPT2_PATH + f"774M/{file}" for file in _CHECKPOINT_FILES], } _IS_DECODE = False # Raise warning for the deprecated pre-trained model names class MyDict(dict): def __contains__(self, key): if key == '117M': warnings.warn("Pre-trained model name '117M' is deprecated, " "use 'gpt2-small' instead.", UserWarning) return True elif key == '345M': warnings.warn("Pre-trained model name '345M' is deprecated, " "use 'gpt2-medium' instead.", UserWarning) return True else: return super().__contains__(key) _DEPRECATED_MODEL2URL = { '117M': [_GPT2_PATH + f"124M/{file}" for file in _CHECKPOINT_FILES], '345M': [_GPT2_PATH + f"355M/{file}" for file in _CHECKPOINT_FILES], } _MODEL2URL.update(_DEPRECATED_MODEL2URL) _MODEL2URL = MyDict(_MODEL2URL) # type: ignore def _transform_config(self, pretrained_model_name: str, # type: ignore cache_dir: str) -> Dict[str, Any]: info = list(os.walk(cache_dir)) root, _, files = info[0] config_path = None for file in files: if file.endswith('hparams.json'): config_path = os.path.join(root, file) if config_path is None: raise ValueError(f"Cannot find the config file in {cache_dir}") with open(config_path) as f: config_gpt = json.loads(f.read()) hidden_dim = config_gpt["n_embd"] configs = { "vocab_size": config_gpt["n_vocab"], "context_size": config_gpt["n_ctx"], "embedding_size": config_gpt["n_embd"], "embed": { "dim": hidden_dim, }, "position_size": config_gpt["n_ctx"], "position_embed": { "dim": hidden_dim } } module_name = 'decoder' if self._IS_DECODE else 'encoder' configs.update({module_name: { "dim": hidden_dim, "num_blocks": config_gpt["n_layer"], "embedding_dropout": 0, "residual_dropout": 0, "multihead_attention": { "use_bias": True, "num_units": hidden_dim, "num_heads": config_gpt["n_head"], "output_dim": hidden_dim, }, "initializer": { "type": "variance_scaling_initializer", "kwargs": { "factor": 1.0, "mode": "FAN_AVG", "uniform": True, }, }, "poswise_feedforward": { "layers": [ { "type": "Linear", "kwargs": { "in_features": hidden_dim, "out_features": hidden_dim * 4, "bias": True, } }, { "type": "GPTGELU", "kwargs": {} }, { "type": "Linear", "kwargs": { "in_features": hidden_dim * 4, "out_features": hidden_dim, "bias": True, } } ], "name": "ffn", }, }}) if self._IS_DECODE: configs[module_name].update({'use_gpt_config': True}) else: configs[module_name].update({'use_bert_config': False}) return configs def _init_from_checkpoint(self, pretrained_model_name: str, cache_dir: str, load_output_layer: bool = True, *kwargs): r"""Initialize model parameters from weights stored in the pre-trained checkpoint. Args: pretrained_model_name (str): Name of the pre-trained model. cache_dir (str): Path to the cache directory. load_output_layer (bool): If `False`, will not load weights of the output layer. Set this argument to `False` when loading weights into a GPT2 encoder. Defaults to `True`. """ try: import numpy as np import tensorflow as tf except ImportError: print("Loading TensorFlow models in PyTorch requires installing " "TensorFlow. Please see https://www.tensorflow.org/install/ " "for installation instructions.") raise module_name = 'decoder' if self._IS_DECODE else 'encoder' global_tensor_map = { "model/wte": "word_embedder.embedding", "model/wpe": "position_embedder.embedding", "model/ln_f/b": module_name + ".final_layer_norm.bias", "model/ln_f/g": module_name + ".final_layer_norm.weight", } layer_tensor_map = { "ln_1/b": module_name + ".self_attn_layer_norm.{}.bias", "ln_1/g": module_name + ".self_attn_layer_norm.{}.weight", "ln_2/b": module_name + ".poswise_layer_norm.{}.bias", "ln_2/g": module_name + ".poswise_layer_norm.{}.weight", "mlp/c_fc/b": module_name + ".poswise_networks.{}._layers.0.bias", "mlp/c_proj/b": module_name + ".poswise_networks.{}._layers.2.bias", "attn/c_proj/b": module_name + ".self_attns.{}.O_dense.bias", } layer_transpose_map = { "mlp/c_fc/w": module_name + ".poswise_networks.{}._layers.0.weight", "mlp/c_proj/w": module_name + ".poswise_networks.{}._layers.2." "weight", "attn/c_proj/w": module_name + ".self_attns.{}.O_dense.weight", } tf_path = os.path.abspath(os.path.join(cache_dir, 'model.ckpt')) # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, _ in init_vars: array = tf.train.load_variable(tf_path, name) names.append(name) arrays.append(array.squeeze()) tensor_names = [] for name, _ in self.named_parameters(): tensor_names.append(name) for name, array in zip(names, arrays): if name in global_tensor_map: v_name = global_tensor_map[name] if name == "model/wte": pointer = self._name_to_variable(v_name) assert pointer.shape == array.shape pointer.data = torch.from_numpy(array) if load_output_layer: output_pointer = self._name_to_variable( "decoder._output_layer.weight") assert output_pointer.shape == array.shape output_pointer.data = torch.from_numpy(array) elif name == "model/wpe": pointer = self._name_to_variable(v_name) assert pointer.shape == array.shape pointer.data = torch.from_numpy(array) else: pointer = self._name_to_variable(v_name) assert pointer.shape == array.shape pointer.data = torch.from_numpy(array) else: name_tmp = name.split("/") layer_no = name_tmp[1][1:] name = "/".join(name_tmp[2:]) if name in layer_tensor_map: v_name = layer_tensor_map[name].format(layer_no) pointer = self._name_to_variable(v_name) assert pointer.shape == array.shape pointer.data = torch.from_numpy(array) elif name in layer_transpose_map: v_name = layer_transpose_map[name].format(layer_no) pointer = self._name_to_variable(v_name) array_t = np.transpose(array) assert pointer.shape == array_t.shape pointer.data = torch.from_numpy(array_t) elif name == "attn/c_attn/w": index_d = array.shape[-1] // 3 Q_w = np.transpose(array[:, :index_d]) K_w = np.transpose(array[:, index_d: 2 index_d]) V_w = np.transpose(array[:, 2 * index_d:]) q_weight = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.Q_dense.weight") k_weight = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.K_dense.weight") v_weight = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.V_dense.weight") assert q_weight.shape == Q_w.shape assert k_weight.shape == K_w.shape assert v_weight.shape == V_w.shape q_weight.data = torch.from_numpy(Q_w) k_weight.data = torch.from_numpy(K_w) v_weight.data = torch.from_numpy(V_w) elif name == "attn/c_attn/b": d = array.shape[0] Q_b = array[: d // 3] K_b = array[d // 3: 2 * d // 3] V_b = array[2 * d // 3:] q_bias = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.Q_dense.bias") k_bias = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.K_dense.bias") v_bias = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.V_dense.bias") assert q_bias.shape == Q_b.shape assert k_bias.shape == K_b.shape assert v_bias.shape == V_b.shape q_bias.data = torch.from_numpy(Q_b) k_bias.data = torch.from_numpy(K_b) v_bias.data = torch.from_numpy(V_b) else: print("Name error", name) raise Exception	en	0.765635	# Copyright 2019 The Texar Authors. All Rights Reserved. # # 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. Utils of GPT2 Modules. A mixin class to support loading pre-trained checkpoints for modules that implement the GPT2 model. The GPT2 model was proposed in `Language Models are Unsupervised Multitask Learners`_ by `Radford et al.` from OpenAI. It is a unidirectional Transformer model pre-trained using the vanilla language modeling objective on a large corpus. The available GPT2 models are as follows: * ``gpt2-small``: Small version of GPT-2, 124M parameters. * ``gpt2-medium``: Medium version of GPT-2, 355M parameters. * ``gpt2-large``: Large version of GPT-2, 774M parameters. We provide the following GPT2 classes: * :class:`~texar.torch.modules.GPT2Encoder` for text encoding. * :class:`~texar.torch.modules.GPT2Decoder` for text generation and decoding. * :class:`~texar.torch.modules.GPT2Classifier` for text classification and sequence tagging. .. _`Language Models are Unsupervised Multitask Learners`: https://openai.com/blog/better-language-models/ # Raise warning for the deprecated pre-trained model names # type: ignore # type: ignore Initialize model parameters from weights stored in the pre-trained checkpoint. Args: pretrained_model_name (str): Name of the pre-trained model. cache_dir (str): Path to the cache directory. load_output_layer (bool): If `False`, will not load weights of the output layer. Set this argument to `False` when loading weights into a GPT2 encoder. Defaults to `True`. # Load weights from TF model	1.772229	2

benchmarks/SimResults/combinations_spec_mylocality/oldstuff/cmp_soplexmcfcalculixgcc/power.py

TugberkArkose/MLScheduler

0

10200

<filename>benchmarks/SimResults/combinations_spec_mylocality/oldstuff/cmp_soplexmcfcalculixgcc/power.py power = {'BUSES': {'Area': 1.33155, 'Bus/Area': 1.33155, 'Bus/Gate Leakage': 0.00662954, 'Bus/Peak Dynamic': 0.0, 'Bus/Runtime Dynamic': 0.0, 'Bus/Subthreshold Leakage': 0.0691322, 'Bus/Subthreshold Leakage with power gating': 0.0259246, 'Gate Leakage': 0.00662954, 'Peak Dynamic': 0.0, 'Runtime Dynamic': 0.0, 'Subthreshold Leakage': 0.0691322, 'Subthreshold Leakage with power gating': 0.0259246}, 'Core': [{'Area': 32.6082, 'Execution Unit/Area': 8.2042, 'Execution Unit/Complex ALUs/Area': 0.235435, 'Execution Unit/Complex ALUs/Gate Leakage': 0.0132646, 'Execution Unit/Complex ALUs/Peak Dynamic': 0.181181, 'Execution Unit/Complex ALUs/Runtime Dynamic': 0.344996, 'Execution Unit/Complex ALUs/Subthreshold Leakage': 0.20111, 'Execution Unit/Complex ALUs/Subthreshold Leakage with power gating': 0.0754163, 'Execution Unit/Floating Point Units/Area': 4.6585, 'Execution Unit/Floating Point Units/Gate Leakage': 0.0656156, 'Execution Unit/Floating Point Units/Peak Dynamic': 0.977935, 'Execution Unit/Floating Point Units/Runtime Dynamic': 0.304033, 'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829, 'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061, 'Execution Unit/Gate Leakage': 0.122718, 'Execution Unit/Instruction Scheduler/Area': 2.17927, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.328073, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.00115349, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.20978, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.486054, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.017004, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00962066, 'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00730101, 'Execution Unit/Instruction Scheduler/Instruction Window/Area': 1.00996, 'Execution Unit/Instruction Scheduler/Instruction Window/Gate Leakage': 0.00529112, 'Execution Unit/Instruction Scheduler/Instruction Window/Peak Dynamic': 2.07911, 'Execution Unit/Instruction Scheduler/Instruction Window/Runtime Dynamic': 0.841669, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage': 0.0800117, 'Execution Unit/Instruction Scheduler/Instruction Window/Subthreshold Leakage with power gating': 0.0455351, 'Execution Unit/Instruction Scheduler/Peak Dynamic': 4.84781, 'Execution Unit/Instruction Scheduler/ROB/Area': 0.841232, 'Execution Unit/Instruction Scheduler/ROB/Gate Leakage': 0.000856399, 'Execution Unit/Instruction Scheduler/ROB/Peak Dynamic': 1.55892, 'Execution Unit/Instruction Scheduler/ROB/Runtime Dynamic': 0.482721, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage': 0.0178624, 'Execution Unit/Instruction Scheduler/ROB/Subthreshold Leakage with power gating': 0.00897339, 'Execution Unit/Instruction Scheduler/Runtime Dynamic': 1.81044, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage': 0.114878, 'Execution Unit/Instruction Scheduler/Subthreshold Leakage with power gating': 0.0641291, 'Execution Unit/Integer ALUs/Area': 0.47087, 'Execution Unit/Integer ALUs/Gate Leakage': 0.0265291, 'Execution Unit/Integer ALUs/Peak Dynamic': 0.330514, 'Execution Unit/Integer ALUs/Runtime Dynamic': 0.101344, 'Execution Unit/Integer ALUs/Subthreshold Leakage': 0.40222, 'Execution Unit/Integer ALUs/Subthreshold Leakage with power gating': 0.150833, 'Execution Unit/Peak Dynamic': 7.28395, 'Execution Unit/Register Files/Area': 0.570804, 'Execution Unit/Register Files/Floating Point RF/Area': 0.208131, 'Execution Unit/Register Files/Floating Point RF/Gate Leakage': 0.000232788, 'Execution Unit/Register Files/Floating Point RF/Peak Dynamic': 0.184753, 'Execution Unit/Register Files/Floating Point 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'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682, 'Instruction Fetch Unit/Instruction Cache/Area': 3.14635, 'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931, 'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 1.94496, 'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.0958958, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386, 'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799, 'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493, 'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404, 'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.103853, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943, 'Instruction Fetch Unit/Instruction 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'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468, 'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.0166828, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0308571, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885, 'Renaming Unit/Free List/Area': 0.0340654, 'Renaming Unit/Free List/Gate Leakage': 2.5481e-05, 'Renaming Unit/Free List/Peak Dynamic': 0.0306032, 'Renaming Unit/Free List/Runtime Dynamic': 0.00482915, 'Renaming Unit/Free List/Subthreshold Leakage': 0.000370144, 'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000201064, 'Renaming Unit/Gate Leakage': 0.00708398, 'Renaming Unit/Int Front End RAT/Area': 0.0941223, 'Renaming Unit/Int Front End RAT/Gate Leakage': 0.000283242, 'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.731965, 'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.0520126, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00435488, 'Renaming Unit/Int Front End RAT/Subthreshold 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'Execution Unit/Floating Point Units/Subthreshold Leakage': 0.994829, 'Execution Unit/Floating Point Units/Subthreshold Leakage with power gating': 0.373061, 'Execution Unit/Gate Leakage': 0.120359, 'Execution Unit/Instruction Scheduler/Area': 1.66526, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Area': 0.275653, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Gate Leakage': 0.000977433, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Peak Dynamic': 1.04181, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Runtime Dynamic': 0.106185, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage': 0.0143453, 'Execution Unit/Instruction Scheduler/FP Instruction Window/Subthreshold Leakage with power gating': 0.00810519, 'Execution Unit/Instruction Scheduler/Gate Leakage': 0.00568913, 'Execution Unit/Instruction Scheduler/Instruction Window/Area': 0.805223, 'Execution Unit/Instruction Scheduler/Instruction Window/Gate 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Dynamic': 0.0749788, 'Execution Unit/Results Broadcast Bus/Runtime Dynamic': 0.202833, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage': 0.081478, 'Execution Unit/Results Broadcast Bus/Subthreshold Leakage with power gating': 0.0305543, 'Execution Unit/Runtime Dynamic': 1.21756, 'Execution Unit/Subthreshold Leakage': 1.79543, 'Execution Unit/Subthreshold Leakage with power gating': 0.688821, 'Gate Leakage': 0.368936, 'Instruction Fetch Unit/Area': 5.85939, 'Instruction Fetch Unit/Branch Predictor/Area': 0.138516, 'Instruction Fetch Unit/Branch Predictor/Chooser/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Chooser/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Chooser/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Chooser/Runtime Dynamic': 0.000625326, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Chooser/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/Gate Leakage': 0.000757657, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Area': 0.0435221, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Gate Leakage': 0.000278362, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Peak Dynamic': 0.0168831, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Runtime Dynamic': 0.000625326, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage': 0.00759719, 'Instruction Fetch Unit/Branch Predictor/Global Predictor/Subthreshold Leakage with power gating': 0.0039236, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Area': 0.0257064, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Gate Leakage': 0.000154548, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Peak Dynamic': 0.0142575, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Runtime Dynamic': 0.000550159, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage': 0.00384344, 'Instruction Fetch Unit/Branch Predictor/L1_Local Predictor/Subthreshold Leakage with power gating': 0.00198631, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Area': 0.0151917, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Gate Leakage': 8.00196e-05, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Peak Dynamic': 0.00527447, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Runtime Dynamic': 0.000215984, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage': 0.00181347, 'Instruction Fetch Unit/Branch Predictor/L2_Local Predictor/Subthreshold Leakage with power gating': 0.000957045, 'Instruction Fetch Unit/Branch Predictor/Peak Dynamic': 0.0597838, 'Instruction Fetch Unit/Branch Predictor/RAS/Area': 0.0105732, 'Instruction Fetch Unit/Branch Predictor/RAS/Gate Leakage': 4.63858e-05, 'Instruction Fetch Unit/Branch Predictor/RAS/Peak Dynamic': 0.0117602, 'Instruction Fetch Unit/Branch Predictor/RAS/Runtime Dynamic': 0.000473173, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage': 0.000932505, 'Instruction Fetch Unit/Branch Predictor/RAS/Subthreshold Leakage with power gating': 0.000494733, 'Instruction Fetch Unit/Branch Predictor/Runtime Dynamic': 0.00227399, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage': 0.0199703, 'Instruction Fetch Unit/Branch Predictor/Subthreshold Leakage with power gating': 0.0103282, 'Instruction Fetch Unit/Branch Target Buffer/Area': 0.64954, 'Instruction Fetch Unit/Branch Target Buffer/Gate Leakage': 0.00272758, 'Instruction Fetch Unit/Branch Target Buffer/Peak Dynamic': 0.177867, 'Instruction Fetch Unit/Branch Target Buffer/Runtime Dynamic': 0.00579905, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage': 0.0811682, 'Instruction Fetch Unit/Branch Target Buffer/Subthreshold Leakage with power gating': 0.0435357, 'Instruction Fetch Unit/Gate Leakage': 0.0589979, 'Instruction Fetch Unit/Instruction Buffer/Area': 0.0226323, 'Instruction Fetch Unit/Instruction Buffer/Gate Leakage': 6.83558e-05, 'Instruction Fetch Unit/Instruction Buffer/Peak Dynamic': 0.606827, 'Instruction Fetch Unit/Instruction Buffer/Runtime Dynamic': 0.0316652, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage': 0.00151885, 'Instruction Fetch Unit/Instruction Buffer/Subthreshold Leakage with power gating': 0.000701682, 'Instruction Fetch Unit/Instruction Cache/Area': 3.14635, 'Instruction Fetch Unit/Instruction Cache/Gate Leakage': 0.029931, 'Instruction Fetch Unit/Instruction Cache/Peak Dynamic': 2.01418, 'Instruction Fetch Unit/Instruction Cache/Runtime Dynamic': 0.0689457, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage': 0.367022, 'Instruction Fetch Unit/Instruction Cache/Subthreshold Leakage with power gating': 0.180386, 'Instruction Fetch Unit/Instruction Decoder/Area': 1.85799, 'Instruction Fetch Unit/Instruction Decoder/Gate Leakage': 0.0222493, 'Instruction Fetch Unit/Instruction Decoder/Peak Dynamic': 1.37404, 'Instruction Fetch Unit/Instruction Decoder/Runtime Dynamic': 0.107549, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage': 0.442943, 'Instruction Fetch Unit/Instruction Decoder/Subthreshold Leakage with power gating': 0.166104, 'Instruction Fetch Unit/Peak Dynamic': 4.33045, 'Instruction Fetch Unit/Runtime Dynamic': 0.216233, 'Instruction Fetch Unit/Subthreshold Leakage': 0.932286, 'Instruction Fetch Unit/Subthreshold Leakage with power gating': 0.40843, 'L2/Area': 4.53318, 'L2/Gate Leakage': 0.015464, 'L2/Peak Dynamic': 0.0418086, 'L2/Runtime Dynamic': 0.00989266, 'L2/Subthreshold Leakage': 0.834142, 'L2/Subthreshold Leakage with power gating': 0.401066, 'Load Store Unit/Area': 8.80901, 'Load Store Unit/Data Cache/Area': 6.84535, 'Load Store Unit/Data Cache/Gate Leakage': 0.0279261, 'Load Store Unit/Data Cache/Peak Dynamic': 2.36015, 'Load Store Unit/Data Cache/Runtime Dynamic': 0.554162, 'Load Store Unit/Data Cache/Subthreshold Leakage': 0.527675, 'Load Store Unit/Data Cache/Subthreshold Leakage with power gating': 0.25085, 'Load Store Unit/Gate Leakage': 0.0350888, 'Load Store Unit/LoadQ/Area': 0.0836782, 'Load Store Unit/LoadQ/Gate Leakage': 0.00059896, 'Load Store Unit/LoadQ/Peak Dynamic': 0.0363327, 'Load Store Unit/LoadQ/Runtime Dynamic': 0.0363327, 'Load Store Unit/LoadQ/Subthreshold Leakage': 0.00941961, 'Load Store Unit/LoadQ/Subthreshold Leakage with power gating': 0.00536918, 'Load Store Unit/Peak Dynamic': 2.53172, 'Load Store Unit/Runtime Dynamic': 0.769675, 'Load Store Unit/StoreQ/Area': 0.322079, 'Load Store Unit/StoreQ/Gate Leakage': 0.00329971, 'Load Store Unit/StoreQ/Peak Dynamic': 0.0895903, 'Load Store Unit/StoreQ/Runtime Dynamic': 0.17918, 'Load Store Unit/StoreQ/Subthreshold Leakage': 0.0345621, 'Load Store Unit/StoreQ/Subthreshold Leakage with power gating': 0.0197004, 'Load Store Unit/Subthreshold Leakage': 0.591321, 'Load Store Unit/Subthreshold Leakage with power gating': 0.283293, 'Memory Management Unit/Area': 0.4339, 'Memory Management Unit/Dtlb/Area': 0.0879726, 'Memory Management Unit/Dtlb/Gate Leakage': 0.00088729, 'Memory Management Unit/Dtlb/Peak Dynamic': 0.0317959, 'Memory Management Unit/Dtlb/Runtime Dynamic': 0.0324228, 'Memory Management Unit/Dtlb/Subthreshold Leakage': 0.0155699, 'Memory Management Unit/Dtlb/Subthreshold Leakage with power gating': 0.00887485, 'Memory Management Unit/Gate Leakage': 0.00808595, 'Memory Management Unit/Itlb/Area': 0.301552, 'Memory Management Unit/Itlb/Gate Leakage': 0.00393464, 'Memory Management Unit/Itlb/Peak Dynamic': 0.125234, 'Memory Management Unit/Itlb/Runtime Dynamic': 0.0113054, 'Memory Management Unit/Itlb/Subthreshold Leakage': 0.0413758, 'Memory Management Unit/Itlb/Subthreshold Leakage with power gating': 0.0235842, 'Memory Management Unit/Peak Dynamic': 0.335963, 'Memory Management Unit/Runtime Dynamic': 0.0437282, 'Memory Management Unit/Subthreshold Leakage': 0.0766103, 'Memory Management Unit/Subthreshold Leakage with power gating': 0.0398333, 'Peak Dynamic': 14.9434, 'Renaming Unit/Area': 0.303608, 'Renaming Unit/FP Front End RAT/Area': 0.131045, 'Renaming Unit/FP Front End RAT/Gate Leakage': 0.00351123, 'Renaming Unit/FP Front End RAT/Peak Dynamic': 2.51468, 'Renaming Unit/FP Front End RAT/Runtime Dynamic': 0.0181291, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage': 0.0308571, 'Renaming Unit/FP Front End RAT/Subthreshold Leakage with power gating': 0.0175885, 'Renaming Unit/Free List/Area': 0.0340654, 'Renaming Unit/Free List/Gate Leakage': 2.5481e-05, 'Renaming Unit/Free List/Peak Dynamic': 0.0306032, 'Renaming Unit/Free List/Runtime Dynamic': 0.0050114, 'Renaming Unit/Free List/Subthreshold Leakage': 0.000370144, 'Renaming Unit/Free List/Subthreshold Leakage with power gating': 0.000201064, 'Renaming Unit/Gate Leakage': 0.00708398, 'Renaming Unit/Int Front End RAT/Area': 0.0941223, 'Renaming Unit/Int Front End RAT/Gate Leakage': 0.000283242, 'Renaming Unit/Int Front End RAT/Peak Dynamic': 0.731965, 'Renaming Unit/Int Front End RAT/Runtime Dynamic': 0.0551057, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage': 0.00435488, 'Renaming Unit/Int Front End RAT/Subthreshold Leakage with power gating': 0.00248228, 'Renaming Unit/Peak Dynamic': 3.58947, 'Renaming Unit/Runtime Dynamic': 0.0782462, 'Renaming Unit/Subthreshold Leakage': 0.0552466, 'Renaming Unit/Subthreshold Leakage with power gating': 0.0276461, 'Runtime Dynamic': 2.33534, 'Subthreshold Leakage': 6.16288, 'Subthreshold Leakage with power gating': 2.55328}], 'DRAM': {'Area': 0, 'Gate Leakage': 0, 'Peak Dynamic': 3.868411224021876, 'Runtime Dynamic': 3.868411224021876, 'Subthreshold Leakage': 4.252, 'Subthreshold Leakage with power gating': 4.252}, 'L3': [{'Area': 61.9075, 'Gate Leakage': 0.0484137, 'Peak Dynamic': 0.371973, 'Runtime Dynamic': 0.183113, 'Subthreshold Leakage': 6.80085, 'Subthreshold Leakage with power gating': 3.32364}], 'Processor': {'Area': 191.908, 'Gate Leakage': 1.53485, 'Peak Dynamic': 75.1614, 'Peak Power': 108.274, 'Runtime Dynamic': 16.5813, 'Subthreshold Leakage': 31.5774, 'Subthreshold Leakage with power gating': 13.9484, 'Total Cores/Area': 128.669, 'Total Cores/Gate Leakage': 1.4798, 'Total Cores/Peak Dynamic': 74.7894, 'Total Cores/Runtime Dynamic': 16.3982, 'Total Cores/Subthreshold Leakage': 24.7074, 'Total Cores/Subthreshold Leakage with power gating': 10.2429, 'Total L3s/Area': 61.9075, 'Total L3s/Gate Leakage': 0.0484137, 'Total L3s/Peak Dynamic': 0.371973, 'Total L3s/Runtime Dynamic': 0.183113, 'Total L3s/Subthreshold Leakage': 6.80085, 'Total L3s/Subthreshold Leakage with power gating': 3.32364, 'Total Leakage': 33.1122, 'Total NoCs/Area': 1.33155, 'Total NoCs/Gate Leakage': 0.00662954, 'Total NoCs/Peak Dynamic': 0.0, 'Total NoCs/Runtime Dynamic': 0.0, 'Total NoCs/Subthreshold Leakage': 0.0691322, 'Total NoCs/Subthreshold Leakage with power gating': 0.0259246}}

none

1

1.286766

1

packages/gtmcore/gtmcore/environment/conda.py

gigabackup/gigantum-client

60

10201

from typing import List, Dict import json from gtmcore.http import ConcurrentRequestManager, ConcurrentRequest from gtmcore.environment.packagemanager import PackageManager, PackageResult, PackageMetadata from gtmcore.container import container_for_context from gtmcore.labbook import LabBook from gtmcore.logging import LMLogger logger = LMLogger.get_logger() class CondaPackageManagerBase(PackageManager): """Class to implement the conda package manager """ def __init__(self): # String to be set in child classes indicating which python version you are checking. Typically should be either # python 3.6* or python 2.7* self.python_depends_str = None # String of the name of the conda environment (e.g. py36 or py27, as created via container build) self.python_env = None # Note, currently we hard code channel config. Future changes to support the user specifying channels # will modify this behavior self.channel_priority = ['conda-forge', 'anaconda'] self.request_mgr = ConcurrentRequestManager() def list_versions(self, package_name: str, labbook: LabBook, username: str) -> List[str]: """Method to list all available versions of a package based on the package name Args: package_name: Name of the package to query labbook: Subject LabBook username: username of current user Returns: list(str): Version strings """ # Check for package in channels, picking out version by priority request_list = list() for channel in self.channel_priority: request_list.append(ConcurrentRequest(f"https://api.anaconda.org/package/{channel}/{package_name}", headers={'Accept': 'application/json'})) responses = self.request_mgr.resolve_many(request_list) versions = None for response in responses: if response.status_code != 200: continue versions = response.json.get('versions') break if not versions: raise ValueError(f"Package {package_name} not found in channels {' ,'.join(self.channel_priority)}.") versions.reverse() return versions def list_installed_packages(self, labbook: LabBook, username: str) -> List[Dict[str, str]]: """Method to get a list of all packages that are currently installed Note, this will return results for the computer/container in which it is executed. To get the properties of a LabBook container, a docker exec command would be needed from the Gigantum application container. return format is a list of dicts with the format (name: <package name>, version: <version string>) Returns: list """ project_container = container_for_context(username, labbook=labbook) result = project_container.run_container("conda list --no-pip --json", wait_for_output=True) if result: data = json.loads(result) if data: return [{"name": x['name'], 'version': x['version']} for x in data] else: return [] def validate_packages(self, package_list: List[Dict[str, str]], labbook: LabBook, username: str) \ -> List[PackageResult]: """Method to validate a list of packages, and if needed fill in any missing versions Should check both the provided package name and version. If the version is omitted, it should be generated from the latest version. Args: package_list(list): A list of dictionaries of packages to validate labbook(str): The labbook instance username(str): The username for the logged in user Returns: namedtuple: namedtuple indicating if the package and version are valid """ result = list() # Check for package in channels, picking out version by priority request_list = list() for pkg in package_list: for channel in self.channel_priority: request_list.append(ConcurrentRequest(f"https://api.anaconda.org/package/{channel}/{pkg['package']}", headers={'Accept': 'application/json'})) responses = self.request_mgr.resolve_many(request_list) # Repack into groups by package responses_per_package = list(zip(*(iter(responses),) * len(self.channel_priority))) for package, responses in zip(package_list, responses_per_package): versions = None latest_version = None for response in responses: if response.status_code != 200: continue versions = response.json.get('versions') latest_version = response.json.get('latest_version') break if not versions: # Package is not found result.append(PackageResult(package=package['package'], version=package.get('version'), error=True)) continue if package.get('version'): # Package has been set, so validate it if package.get('version') in versions: # Both package name and version are valid result.append(PackageResult(package=package['package'], version=package.get('version'), error=False)) else: # The package version is not in the list, so invalid result.append(PackageResult(package=package['package'], version=package.get('version'), error=True)) else: # You need to look up the latest version since not included result.append(PackageResult(package=package['package'], version=str(latest_version), error=False)) return result def get_packages_metadata(self, package_list: List[str], labbook: LabBook, username: str) -> List[PackageMetadata]: """Method to get package metadata Args: package_list: List of package names labbook(str): The labbook instance username(str): The username for the logged in user Returns: list """ def _extract_metadata(data): """Extraction method to pull out the docs URL and description""" latest_val = data.get('latest_version') description_val = data.get('summary').strip() docs_val = data.get('doc_url') if not docs_val: docs_val = data.get('html_url') return latest_val, description_val, docs_val # Check for package in channels, picking out version by priority request_list = list() for pkg in package_list: for channel in self.channel_priority: request_list.append(ConcurrentRequest(f"https://api.anaconda.org/package/{channel}/{pkg}", headers={'Accept': 'application/json'}, extraction_function=_extract_metadata)) responses = self.request_mgr.resolve_many(request_list) # Repack into groups by package responses_per_package = list(zip(*(iter(responses),) * len(self.channel_priority))) result = list() for package, responses in zip(package_list, responses_per_package): data = None for response in responses: if response.status_code == 200: data = response.extracted_json break if data: latest_version, description, docs_url = data result.append(PackageMetadata(package_manager="conda", package=package, latest_version=latest_version, description=description, docs_url=docs_url)) else: result.append(PackageMetadata(package_manager="conda", package=package, latest_version=None, description=None, docs_url=None)) return result def generate_docker_install_snippet(self, packages: List[Dict[str, str]], single_line: bool = False) -> List[str]: """Method to generate a docker snippet to install 1 or more packages Note: Because conda be so slow to solve environments with conda-forge included, always single line it. Args: packages(list(dict)): A list of package names and versions to install single_line(bool): If true, collapse Returns: list """ package_strings = [f"{x['name']}={x['version']}" for x in packages] if single_line: return [f"RUN conda install -yq {' '.join(package_strings)}"] else: return [f"RUN conda install -yq {' '.join(package_strings)}"] class Conda3PackageManager(CondaPackageManagerBase): """Class to implement the conda3 package manager """ def __init__(self): super().__init__() self.python_depends_str = 'python 3.6*' self.python_env = 'py36' class Conda2PackageManager(CondaPackageManagerBase): """Class to implement the conda2 package manager """ def __init__(self): super().__init__() self.python_depends_str = 'python 2.7*' self.python_env = 'py27'

en

0.821075

Class to implement the conda package manager # String to be set in child classes indicating which python version you are checking. Typically should be either # python 3.6* or python 2.7* # String of the name of the conda environment (e.g. py36 or py27, as created via container build) # Note, currently we hard code channel config. Future changes to support the user specifying channels # will modify this behavior Method to list all available versions of a package based on the package name Args: package_name: Name of the package to query labbook: Subject LabBook username: username of current user Returns: list(str): Version strings # Check for package in channels, picking out version by priority Method to get a list of all packages that are currently installed Note, this will return results for the computer/container in which it is executed. To get the properties of a LabBook container, a docker exec command would be needed from the Gigantum application container. return format is a list of dicts with the format (name: <package name>, version: <version string>) Returns: list Method to validate a list of packages, and if needed fill in any missing versions Should check both the provided package name and version. If the version is omitted, it should be generated from the latest version. Args: package_list(list): A list of dictionaries of packages to validate labbook(str): The labbook instance username(str): The username for the logged in user Returns: namedtuple: namedtuple indicating if the package and version are valid # Check for package in channels, picking out version by priority # Repack into groups by package # Package is not found # Package has been set, so validate it # Both package name and version are valid # The package version is not in the list, so invalid # You need to look up the latest version since not included Method to get package metadata Args: package_list: List of package names labbook(str): The labbook instance username(str): The username for the logged in user Returns: list Extraction method to pull out the docs URL and description # Check for package in channels, picking out version by priority # Repack into groups by package Method to generate a docker snippet to install 1 or more packages Note: Because conda be so slow to solve environments with conda-forge included, always single line it. Args: packages(list(dict)): A list of package names and versions to install single_line(bool): If true, collapse Returns: list Class to implement the conda3 package manager Class to implement the conda2 package manager

2.353314

2

netchos/io/io_mpl_to_px.py

brainets/netchos

11

10202

<gh_stars>10-100 """Conversion of Matplotlib / Seaborn inputs to plotly.""" import os.path as op from pkg_resources import resource_filename import json def mpl_to_px_inputs(inputs, plt_types=None): """Convert typical matplotlib inputs to plotly to simplify API. Parameters ---------- inputs : dict Dictionary of inputs plt_types : string or list or None Sub select some plotting types (e.g heatmap, line etc.). If None, all types are used Returns ------- outputs : dict Dictionary of converted inputs """ # load reference table file = op.join(op.dirname(__file__), "io_mpl_to_px.json") with open(file, 'r') as f: table = json.load(f) # go through the desired plotting types for conversion if plt_types is None: plt_types = list(table.keys()) if isinstance(plt_types, str): plt_types = [plt_types] ref = {} for plt_type in plt_types: ref.update(table[plt_type]) # convert inputs outputs = {} for k, v in inputs.items(): if k in ref.keys(): k = ref[k] outputs[k] = v return outputs

"""Conversion of Matplotlib / Seaborn inputs to plotly.""" import os.path as op from pkg_resources import resource_filename import json def mpl_to_px_inputs(inputs, plt_types=None): """Convert typical matplotlib inputs to plotly to simplify API. Parameters ---------- inputs : dict Dictionary of inputs plt_types : string or list or None Sub select some plotting types (e.g heatmap, line etc.). If None, all types are used Returns ------- outputs : dict Dictionary of converted inputs """ # load reference table file = op.join(op.dirname(__file__), "io_mpl_to_px.json") with open(file, 'r') as f: table = json.load(f) # go through the desired plotting types for conversion if plt_types is None: plt_types = list(table.keys()) if isinstance(plt_types, str): plt_types = [plt_types] ref = {} for plt_type in plt_types: ref.update(table[plt_type]) # convert inputs outputs = {} for k, v in inputs.items(): if k in ref.keys(): k = ref[k] outputs[k] = v return outputs

en

0.415941

Conversion of Matplotlib / Seaborn inputs to plotly. Convert typical matplotlib inputs to plotly to simplify API. Parameters ---------- inputs : dict Dictionary of inputs plt_types : string or list or None Sub select some plotting types (e.g heatmap, line etc.). If None, all types are used Returns ------- outputs : dict Dictionary of converted inputs # load reference table # go through the desired plotting types for conversion # convert inputs

3.279682

3

fizzbuzz_for_02.py

toastyxen/FizzBuzz

0

10203

"""Fizzbuzz for loop variant 3""" for x in range(1, 101): OUTPUT = "" if x % 3 == 0: OUTPUT += "Fizz" if x % 5 == 0: OUTPUT += "Buzz" print(OUTPUT or x)

en

0.564812

Fizzbuzz for loop variant 3

3.897786

4

cnn/struct/layer/parse_tensor_module.py

hslee1539/GIS_GANs

0

10204

from tensor.main_module import Tensor import numpy as np def getTensor(value): if type(value) is np.ndarray: return Tensor.numpy2Tensor(value) elif type(value) is Tensor: return value else: raise Exception

none

1

2.842778

3

openstack_dashboard/dashboards/admin/volume_types/qos_specs/forms.py

hemantsonawane95/horizon-apelby

0

10205

<reponame>hemantsonawane95/horizon-apelby<filename>openstack_dashboard/dashboards/admin/volume_types/qos_specs/forms.py # 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 re from django.urls import reverse from django.utils.translation import gettext_lazy as _ from horizon import exceptions from horizon import forms from horizon import messages from openstack_dashboard import api KEY_NAME_REGEX = re.compile(r"^[a-zA-Z0-9-_:. /]+$", re.UNICODE) KEY_ERROR_MESSAGES = { 'invalid': _("The key must match the following the regex: " "'^[a-zA-Z0-9-_:. /]'")} class CreateKeyValuePair(forms.SelfHandlingForm): # this if for creating a spec key-value pair for an existing QOS Spec key = forms.RegexField(max_length=255, label=_("Key"), regex=KEY_NAME_REGEX, error_messages=KEY_ERROR_MESSAGES) value = forms.CharField(max_length=255, label=_("Value")) def handle(self, request, data): qos_spec_id = self.initial['qos_spec_id'] try: # first retrieve current value of specs specs = api.cinder.qos_spec_get(request, qos_spec_id) # now add new key-value pair to list of specs specs.specs[data['key']] = data['value'] api.cinder.qos_spec_set_keys(request, qos_spec_id, specs.specs) msg = _('Created spec "%s".') % data['key'] messages.success(request, msg) return True except Exception: redirect = reverse("horizon:admin:volume_types:index") exceptions.handle(request, _("Unable to create spec."), redirect=redirect) class EditKeyValuePair(forms.SelfHandlingForm): value = forms.CharField(max_length=255, label=_("Value")) # update the backend with the new qos spec value def handle(self, request, data): key = self.initial['key'] qos_spec_id = self.initial['qos_spec_id'] # build up new 'specs' object with all previous values plus new value try: # first retrieve current value of specs specs = api.cinder.qos_spec_get_keys(request, qos_spec_id, raw=True) specs.specs[key] = data['value'] api.cinder.qos_spec_set_keys(request, qos_spec_id, specs.specs) msg = _('Saved spec "%s".') % key messages.success(request, msg) return True except Exception: redirect = reverse("horizon:admin:volume_types:index") exceptions.handle(request, _("Unable to edit spec."), redirect=redirect)

# 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 re from django.urls import reverse from django.utils.translation import gettext_lazy as _ from horizon import exceptions from horizon import forms from horizon import messages from openstack_dashboard import api KEY_NAME_REGEX = re.compile(r"^[a-zA-Z0-9-_:. /]+$", re.UNICODE) KEY_ERROR_MESSAGES = { 'invalid': _("The key must match the following the regex: " "'^[a-zA-Z0-9-_:. /]'")} class CreateKeyValuePair(forms.SelfHandlingForm): # this if for creating a spec key-value pair for an existing QOS Spec key = forms.RegexField(max_length=255, label=_("Key"), regex=KEY_NAME_REGEX, error_messages=KEY_ERROR_MESSAGES) value = forms.CharField(max_length=255, label=_("Value")) def handle(self, request, data): qos_spec_id = self.initial['qos_spec_id'] try: # first retrieve current value of specs specs = api.cinder.qos_spec_get(request, qos_spec_id) # now add new key-value pair to list of specs specs.specs[data['key']] = data['value'] api.cinder.qos_spec_set_keys(request, qos_spec_id, specs.specs) msg = _('Created spec "%s".') % data['key'] messages.success(request, msg) return True except Exception: redirect = reverse("horizon:admin:volume_types:index") exceptions.handle(request, _("Unable to create spec."), redirect=redirect) class EditKeyValuePair(forms.SelfHandlingForm): value = forms.CharField(max_length=255, label=_("Value")) # update the backend with the new qos spec value def handle(self, request, data): key = self.initial['key'] qos_spec_id = self.initial['qos_spec_id'] # build up new 'specs' object with all previous values plus new value try: # first retrieve current value of specs specs = api.cinder.qos_spec_get_keys(request, qos_spec_id, raw=True) specs.specs[key] = data['value'] api.cinder.qos_spec_set_keys(request, qos_spec_id, specs.specs) msg = _('Saved spec "%s".') % key messages.success(request, msg) return True except Exception: redirect = reverse("horizon:admin:volume_types:index") exceptions.handle(request, _("Unable to edit spec."), redirect=redirect)

en

0.789195

# 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. # this if for creating a spec key-value pair for an existing QOS Spec # first retrieve current value of specs # now add new key-value pair to list of specs # update the backend with the new qos spec value # build up new 'specs' object with all previous values plus new value # first retrieve current value of specs

1.806878

2

data_structure/const_tree.py

alipay/StructuredLM_RTDT

42

10206

<reponame>alipay/StructuredLM_RTDT # coding=utf-8 # Copyright (c) 2021 <NAME> import sys LABEL_SEP = '@' INDENT_STRING1 = '│ ' INDENT_STRING2 = '├──' EMPTY_TOKEN = '___EMPTY___' def print_tree(const_tree, indent=0, out=sys.stdout): for i in range(indent - 1): out.write(INDENT_STRING1) if indent > 0: out.write(INDENT_STRING2) out.write(const_tree.tag) if not isinstance(const_tree.children[0], ConstTree): out.write(f' {const_tree.children[0].string}\n') else: out.write('\n') for child in const_tree.children: print_tree(child, indent + 1, out) def _make_tree(string, make_leaf_fn, make_internal_fn): tokens = string.replace('(', ' ( ').replace(')', ' ) ').split() index, stack = 0, [] lexicons = [] root = None while index < len(tokens): token = tokens[index] index += 1 if token == ')': if not stack: raise ConstTreeParserError('redundant ")" at token ' + str(index)) node = stack.pop() if not stack: root = node else: stack[-1].children.append(node) elif token == '(': tag = tokens[index] index += 1 stack.append(make_internal_fn(tag)) else: if not stack: raise ConnectionError('??? at pos ' + str(index)) new_token = [] while token != ')': if not token != '(': raise Exception('bracket error') new_token.append(token) token = tokens[index] index += 1 # is lexicon leaf_node = make_leaf_fn('_'.join(new_token)) lexicons.append(leaf_node) postag_node = stack.pop() postag_node.children.append(leaf_node) if not stack: root = postag_node else: stack[-1].children.append(postag_node) if not root or stack: raise ConstTreeParserError('missing ")".') return root, lexicons class ConstTreeParserError(Exception): pass class Lexicon: __slots__ = ('string', 'span', 'parent') def __init__(self, string, span=None): self.string = string self.span = span def __str__(self): return f'<Lexicon {self.string}>' def __repr__(self): return str(self) def __eq__(self, other): return self.string == other.string def __hash__(self): return hash(self.string) + 2 @property def tag(self): return self.string def to_string(self, quote_lexicon): if quote_lexicon: return f'"{self.string}"' return self.string class ConstTree: __slots__ = ('children', 'tag', 'span', 'index', 'parent', 'attrs') ROOT_LABEL = 'ROOT' def __init__(self, tag, children=None, span=None): self.tag = tag self.children = children if children is not None else [] self.span = span self.index = None def __str__(self): child_string = ' + '.join(child.tag for child in self.children) return f'{self.span} {self.tag} => {child_string}' def __repr__(self): return str(self) def __getitem__(self, index): if isinstance(index, int): return self.children[index] if isinstance(index, str): for child in self.children: if isinstance(child, ConstTree) and child.tag == index.upper(): return child raise KeyError def to_string(self, quote_lexicon=False): child_string = ' '.join(child.to_string(quote_lexicon) for child in self.children) return f'({self.tag} {child_string})' @staticmethod def from_string(string): """ Construct ConstTree from parenthesis representation. :param string: string of parenthesis representation :return: ConstTree root and all leaf Lexicons """ tree, lexicons = _make_tree(string, Lexicon, ConstTree) for index, lexicon in enumerate(lexicons): lexicon.span = index, index + 1 tree.populate_spans_internal() return tree, lexicons def traverse_postorder(self): for child in self.children: if isinstance(child, ConstTree): yield from child.traverse_postorder() yield self def traverse_postorder_with_lexicons(self): for child in self.children: if isinstance(child, ConstTree): yield from child.traverse_postorder_with_lexicons() else: yield child yield self def generate_preterminals(self): for child in self.children: if isinstance(child, ConstTree): yield from child.generate_preterminals() for child in self.children: if isinstance(child, Lexicon): yield self def generate_lexicons(self): for child in self.children: if isinstance(child, ConstTree): yield from child.generate_lexicons() for child in self.children: if isinstance(child, Lexicon): yield child def is_binary_tree(self): if isinstance(self.children[0], Lexicon): return True return len(self.children <= 2) and all(child.is_binary_tree() for child in self.children) def condensed_unary_chain(self, include_preterminal=True, remove_root=None): if self.tag == remove_root: assert len(self.children) == 1 return self.children[0].condensed_unary_chain(include_preterminal=include_preterminal) if len(self.children) > 1: return ConstTree(self.tag, children=list(child.condensed_unary_chain() for child in self.children), span=self.span) if isinstance(self.children[0], Lexicon): return ConstTree((self.tag if include_preterminal else EMPTY_TOKEN), children=list(self.children), span=self.span) assert isinstance(self.children[0], ConstTree) node = self new_tag = self.tag while len(node.children) == 1 and isinstance(node.children[0], ConstTree): node = node.children[0] if include_preterminal or isinstance(node.children[0], ConstTree): new_tag += LABEL_SEP + node.tag if len(node.children) == 1: children = list(node.children) else: children = list(child.condensed_unary_chain() for child in node.children) return ConstTree(new_tag, children=children, span=self.span) def expanded_unary_chain(self, add_root=None): if isinstance(self.children[0], Lexicon): children = list(self.children) else: children = list(child.expanded_unary_chain() for child in self.children) tags = self.tag.split(LABEL_SEP) for tag in reversed(tags): children = [ConstTree(tag, children=children, span=self.span)] root = children[0] if add_root: root = ConstTree(add_root, children=[root]) return root def calculate_span(self): self.span = self.children[0].span[0], self.children[-1].span[1] def populate_spans_internal(self): for child in self.children: if isinstance(child, ConstTree): child.populate_spans_internal() self.calculate_span() def add_postorder_index(self): for index, node in enumerate(self.traverse_postorder()): node.index = index def add_parents(self, parent=None): self.parent = parent for child in self.children: if isinstance(child, ConstTree): child.add_parents(self) def is_ancestor_of(self, other): other = other.parent while other is not None and other is not self: other = other.parent return other is not None def generate_path_to_root(self, include_self=False): node = self if not include_self: node = self.parent while node is not None: yield node node = node.parent def lowest_common_ancestor(self, other): path = list(other.generate_path_to_root()) for node in self.generate_path_to_root(): try: return path[path.index(node)] except ValueError: pass def remove_nodes(self, filter): _children = [] for c in self.children: if isinstance(c, ConstTree): if filter(c): pass else: filtered_node = c.remove_nodes(filter) _children.append(filtered_node) else: _children.append(c) return ConstTree(self.tag, _children)

# coding=utf-8 # Copyright (c) 2021 <NAME> import sys LABEL_SEP = '@' INDENT_STRING1 = '│ ' INDENT_STRING2 = '├──' EMPTY_TOKEN = '___EMPTY___' def print_tree(const_tree, indent=0, out=sys.stdout): for i in range(indent - 1): out.write(INDENT_STRING1) if indent > 0: out.write(INDENT_STRING2) out.write(const_tree.tag) if not isinstance(const_tree.children[0], ConstTree): out.write(f' {const_tree.children[0].string}\n') else: out.write('\n') for child in const_tree.children: print_tree(child, indent + 1, out) def _make_tree(string, make_leaf_fn, make_internal_fn): tokens = string.replace('(', ' ( ').replace(')', ' ) ').split() index, stack = 0, [] lexicons = [] root = None while index < len(tokens): token = tokens[index] index += 1 if token == ')': if not stack: raise ConstTreeParserError('redundant ")" at token ' + str(index)) node = stack.pop() if not stack: root = node else: stack[-1].children.append(node) elif token == '(': tag = tokens[index] index += 1 stack.append(make_internal_fn(tag)) else: if not stack: raise ConnectionError('??? at pos ' + str(index)) new_token = [] while token != ')': if not token != '(': raise Exception('bracket error') new_token.append(token) token = tokens[index] index += 1 # is lexicon leaf_node = make_leaf_fn('_'.join(new_token)) lexicons.append(leaf_node) postag_node = stack.pop() postag_node.children.append(leaf_node) if not stack: root = postag_node else: stack[-1].children.append(postag_node) if not root or stack: raise ConstTreeParserError('missing ")".') return root, lexicons class ConstTreeParserError(Exception): pass class Lexicon: __slots__ = ('string', 'span', 'parent') def __init__(self, string, span=None): self.string = string self.span = span def __str__(self): return f'<Lexicon {self.string}>' def __repr__(self): return str(self) def __eq__(self, other): return self.string == other.string def __hash__(self): return hash(self.string) + 2 @property def tag(self): return self.string def to_string(self, quote_lexicon): if quote_lexicon: return f'"{self.string}"' return self.string class ConstTree: __slots__ = ('children', 'tag', 'span', 'index', 'parent', 'attrs') ROOT_LABEL = 'ROOT' def __init__(self, tag, children=None, span=None): self.tag = tag self.children = children if children is not None else [] self.span = span self.index = None def __str__(self): child_string = ' + '.join(child.tag for child in self.children) return f'{self.span} {self.tag} => {child_string}' def __repr__(self): return str(self) def __getitem__(self, index): if isinstance(index, int): return self.children[index] if isinstance(index, str): for child in self.children: if isinstance(child, ConstTree) and child.tag == index.upper(): return child raise KeyError def to_string(self, quote_lexicon=False): child_string = ' '.join(child.to_string(quote_lexicon) for child in self.children) return f'({self.tag} {child_string})' @staticmethod def from_string(string): """ Construct ConstTree from parenthesis representation. :param string: string of parenthesis representation :return: ConstTree root and all leaf Lexicons """ tree, lexicons = _make_tree(string, Lexicon, ConstTree) for index, lexicon in enumerate(lexicons): lexicon.span = index, index + 1 tree.populate_spans_internal() return tree, lexicons def traverse_postorder(self): for child in self.children: if isinstance(child, ConstTree): yield from child.traverse_postorder() yield self def traverse_postorder_with_lexicons(self): for child in self.children: if isinstance(child, ConstTree): yield from child.traverse_postorder_with_lexicons() else: yield child yield self def generate_preterminals(self): for child in self.children: if isinstance(child, ConstTree): yield from child.generate_preterminals() for child in self.children: if isinstance(child, Lexicon): yield self def generate_lexicons(self): for child in self.children: if isinstance(child, ConstTree): yield from child.generate_lexicons() for child in self.children: if isinstance(child, Lexicon): yield child def is_binary_tree(self): if isinstance(self.children[0], Lexicon): return True return len(self.children <= 2) and all(child.is_binary_tree() for child in self.children) def condensed_unary_chain(self, include_preterminal=True, remove_root=None): if self.tag == remove_root: assert len(self.children) == 1 return self.children[0].condensed_unary_chain(include_preterminal=include_preterminal) if len(self.children) > 1: return ConstTree(self.tag, children=list(child.condensed_unary_chain() for child in self.children), span=self.span) if isinstance(self.children[0], Lexicon): return ConstTree((self.tag if include_preterminal else EMPTY_TOKEN), children=list(self.children), span=self.span) assert isinstance(self.children[0], ConstTree) node = self new_tag = self.tag while len(node.children) == 1 and isinstance(node.children[0], ConstTree): node = node.children[0] if include_preterminal or isinstance(node.children[0], ConstTree): new_tag += LABEL_SEP + node.tag if len(node.children) == 1: children = list(node.children) else: children = list(child.condensed_unary_chain() for child in node.children) return ConstTree(new_tag, children=children, span=self.span) def expanded_unary_chain(self, add_root=None): if isinstance(self.children[0], Lexicon): children = list(self.children) else: children = list(child.expanded_unary_chain() for child in self.children) tags = self.tag.split(LABEL_SEP) for tag in reversed(tags): children = [ConstTree(tag, children=children, span=self.span)] root = children[0] if add_root: root = ConstTree(add_root, children=[root]) return root def calculate_span(self): self.span = self.children[0].span[0], self.children[-1].span[1] def populate_spans_internal(self): for child in self.children: if isinstance(child, ConstTree): child.populate_spans_internal() self.calculate_span() def add_postorder_index(self): for index, node in enumerate(self.traverse_postorder()): node.index = index def add_parents(self, parent=None): self.parent = parent for child in self.children: if isinstance(child, ConstTree): child.add_parents(self) def is_ancestor_of(self, other): other = other.parent while other is not None and other is not self: other = other.parent return other is not None def generate_path_to_root(self, include_self=False): node = self if not include_self: node = self.parent while node is not None: yield node node = node.parent def lowest_common_ancestor(self, other): path = list(other.generate_path_to_root()) for node in self.generate_path_to_root(): try: return path[path.index(node)] except ValueError: pass def remove_nodes(self, filter): _children = [] for c in self.children: if isinstance(c, ConstTree): if filter(c): pass else: filtered_node = c.remove_nodes(filter) _children.append(filtered_node) else: _children.append(c) return ConstTree(self.tag, _children)

en

0.743059

# coding=utf-8 # Copyright (c) 2021 <NAME> # is lexicon Construct ConstTree from parenthesis representation. :param string: string of parenthesis representation :return: ConstTree root and all leaf Lexicons

3.503178

4

tests/test_minimize.py

The-Ludwig/iminuit

0

10207

<filename>tests/test_minimize.py import pytest from iminuit import minimize import numpy as np from numpy.testing import assert_allclose, assert_equal opt = pytest.importorskip("scipy.optimize") def func(x, *args): c = args[0] if args else 1 return c + x[0] ** 2 + (x[1] - 1) ** 2 + (x[2] - 2) ** 2 def grad(x, *args): return 2 * (x - (0, 1, 2)) def test_simple(): result = minimize(func, (1, 1, 1)) assert_allclose(result.x, (0, 1, 2), atol=1e-8) assert_allclose(result.fun, 1) assert result.nfev > 0 assert result.njev == 0 def test_gradient(): result = minimize(func, (1, 1, 1), jac=grad) assert_allclose(result.x, (0, 1, 2), atol=1e-8) assert_allclose(result.fun, 1) assert result.nfev > 0 assert result.njev > 0 def test_args(): result = minimize(func, np.ones(3), args=(5,)) assert_allclose(result.x, (0, 1, 2), atol=1e-8) assert_allclose(result.fun, 5) assert result.nfev > 0 assert result.njev == 0 def test_callback(): trace = [] result = minimize(func, np.ones(3), callback=lambda x: trace.append(x.copy())) assert_allclose(result.x, (0, 1, 2), atol=1e-8) assert_allclose(result.fun, 1) assert result.nfev == len(trace) assert_allclose(trace[0], np.ones(3), atol=1e-2) assert_allclose(trace[-1], result.x, atol=1e-2) def test_tol(): ref = np.ones(2) def rosen(par): x, y = par return (1 - x) ** 2 + 100 * (y - x ** 2) ** 2 r1 = minimize(rosen, (0, 0), tol=1) r2 = minimize(rosen, (0, 0), tol=1e-6) assert max(np.abs(r2.x - ref)) < max(np.abs(r1.x - ref)) def test_disp(capsys): minimize(lambda x: x ** 2, 0) assert capsys.readouterr()[0] == "" minimize(lambda x: x ** 2, 0, options={"disp": True}) assert capsys.readouterr()[0] != "" def test_hessinv(): r = minimize(func, (1, 1, 1)) href = np.zeros((3, 3)) for i in range(3): href[i, i] = 0.5 assert_allclose(r.hess_inv, href, atol=1e-8) def test_unsupported(): with pytest.raises(ValueError): minimize(func, (1, 1, 1), constraints=[]) with pytest.raises(ValueError): minimize(func, (1, 1, 1), jac=True) def test_call_limit(): ref = minimize(func, (1, 1, 1)) with pytest.warns(UserWarning): r1 = minimize(func, (1, 1, 1), options={"maxiter": 1}) assert r1.nfev < ref.nfev assert not r1.success assert "Call limit" in r1.message with pytest.warns(DeprecationWarning): r2 = minimize(func, (1, 1, 1), options={"maxfev": 1}) assert not r2.success assert r2.nfev == r1.nfev r3 = minimize(func, (1, 1, 1), options={"maxfun": 1}) assert not r3.success assert r3.nfev == r1.nfev def test_eps(): ref = minimize(func, (1, 1, 1)) r = minimize(func, (1, 1, 1), options={"eps": 1e-10}) assert np.any(ref.x != r.x) assert_allclose(r.x, ref.x, atol=1e-9) def test_bad_function(): class Fcn: n = 0 def __call__(self, x): self.n += 1 return x ** 2 + 1e-4 * (self.n % 3) r = minimize(Fcn(), [1], options={"maxfun": 100000000}) assert not r.success assert "Estimated distance to minimum too large" in r.message def test_bounds(): r1 = minimize(func, (1.5, 1.7, 1.5), bounds=opt.Bounds((1, 1.5, 1), (2, 2, 2))) assert r1.success assert_allclose(r1.x, (1, 1.5, 2), atol=1e-2) r2 = minimize(func, (1.5, 1.7, 1.5), bounds=((1, 2), (1.5, 2), (1, 2))) assert r2.success assert_equal(r1.x, r2.x) def test_method_warn(): with pytest.raises(ValueError): minimize(func, (1.5, 1.7, 1.5), method="foo") def test_hess_warn(): with pytest.warns(UserWarning): minimize(func, (1.5, 1.7, 1.5), hess=True) def test_unreliable_uncertainties(): r = minimize(func, (1.5, 1.7, 1.5), options={"stra": 0}) assert ( r.message == "Optimization terminated successfully, but uncertainties are unrealiable." ) def test_simplex(): r = minimize(func, (1.5, 1.7, 1.5), method="simplex", tol=1e-4) assert r.success assert_allclose(r.x, (0, 1, 2), atol=2e-3)

none

1

2.427253

2

murtanto/parsing.py

amandatv20/botfb

1

10208

<filename>murtanto/parsing.py # coded by: salism3 # 23 - 05 - 2020 23:18 (<NAME>) from bs4 import BeautifulSoup as parser from . import sorting import re def to_bs4(html): return parser(html, "html.parser") def refsrc(html): return True if re.search(r'http.+\Wrefsrc', html) else False def parsing_href(html, href, one = False, bs4_class = False): data = to_bs4(html) if one: data = data.find("a", href = lambda x: x and href in x) if not bs4_class and data != None: data = sorting.to_mbasic(data["href"]) else: data = data.find_all("a", href = lambda x: x and href in x) if not bs4_class: data = [sorting.to_mbasic(x["href"]) for x in data] return data def parsing_href_regex(html, pattern, one = False, bs4_class = False): data = to_bs4(html) if one: data = data.find("a", href = lambda x: x and re.search(pattern, x)) if not bs4_class and data != None: data = sorting.to_mbasic(data["href"]) else: data = data.find_all("a", href = lambda x: x and re.search(pattern, x)) if not bs4_class: data = [sorting.to_mbasic(x["href"]) for x in data] return data def getMyName(html): data = to_bs4(html).find("title").text return data def getName(html): data = to_bs4(html).find("title").text return data def getMyId(html): data = to_bs4(html).find("a", href = lambda x:"/allactivity" in x)["href"] data = re.search(r"/\d+/?", data).group().replace("/", "") return data def getHiddenInput(html, post_action): rv = {} data = to_bs4(html).find("form", action = lambda x: post_action in x) data = data.find_all("input", {"type":"hidden", "name":True, "value":True}) for x in data: rv[x["name"]] = x["value"] return rv def friendRequestParser(html): confirm = parsing_href(html, "?confirm=") reject = parsing_href(html, "?delete=") rv = list(zip(confirm, reject)) next = parsing_href(html, "?ppk=", one = True) return {"items":rv, "next":next} def listFriendParser(html): data = parsing_href(html, "fref=fr_tab", bs4_class = True) nama = [x.text for x in data] id_ = [re.search(r"\w[\w.]+", x["href"].replace("/", "").replace("profile.php?id=", "")).group() for x in data] img = [x["src"] for x in to_bs4(html).find_all("img", alt = lambda x: x and "profile picture" in x)] if "/allactivity?" in html: del img[0] next = parsing_href(html, "unit_cursor=", one = True) return {"items":list(zip(nama, id_, img)), "next":next, "html":html}

en

0.44789

# coded by: salism3 # 23 - 05 - 2020 23:18 (<NAME>)

2.850233

3

test/test_watchdog_status.py

ike709/tgs4-api-pyclient

0

10209

# coding: utf-8 """ TGS API A production scale tool for BYOND server management # noqa: E501 OpenAPI spec version: 9.0.0 Generated by: https://github.com/swagger-api/swagger-codegen.git """ from __future__ import absolute_import import unittest import swagger_client from swagger_client.models.watchdog_status import WatchdogStatus # noqa: E501 from swagger_client.rest import ApiException class TestWatchdogStatus(unittest.TestCase): """WatchdogStatus unit test stubs""" def setUp(self): pass def tearDown(self): pass def testWatchdogStatus(self): """Test WatchdogStatus""" # FIXME: construct object with mandatory attributes with example values # model = swagger_client.models.watchdog_status.WatchdogStatus() # noqa: E501 pass if __name__ == '__main__': unittest.main()

en

0.55272

# coding: utf-8 TGS API A production scale tool for BYOND server management # noqa: E501 OpenAPI spec version: 9.0.0 Generated by: https://github.com/swagger-api/swagger-codegen.git # noqa: E501 WatchdogStatus unit test stubs Test WatchdogStatus # FIXME: construct object with mandatory attributes with example values # model = swagger_client.models.watchdog_status.WatchdogStatus() # noqa: E501

1.742767

2

setup.py

joesan/housing-classification-example

0

10210

from setuptools import find_packages, setup setup( name='src', packages=find_packages(), version='0.1.0', description='Python codebase for the housing classification ML problem', author='Joesan', license='', )

none

1

1.049739

1

tests/test_models/test_backbones/test_sr_backbones/test_edvr_net.py

wangruohui/mmediting

45

10211

# Copyright (c) OpenMMLab. All rights reserved. import pytest import torch from mmedit.models.backbones.sr_backbones.edvr_net import (EDVRNet, PCDAlignment, TSAFusion) def test_pcd_alignment(): """Test PCDAlignment.""" # cpu pcd_alignment = PCDAlignment(mid_channels=4, deform_groups=2) input_list = [] for i in range(3, 0, -1): input_list.append(torch.rand(1, 4, 2**i, 2**i)) pcd_alignment = pcd_alignment input_list = [v for v in input_list] output = pcd_alignment(input_list, input_list) assert output.shape == (1, 4, 8, 8) with pytest.raises(AssertionError): pcd_alignment(input_list[0:2], input_list) # gpu if torch.cuda.is_available(): pcd_alignment = PCDAlignment(mid_channels=4, deform_groups=2) input_list = [] for i in range(3, 0, -1): input_list.append(torch.rand(1, 4, 2**i, 2**i)) pcd_alignment = pcd_alignment.cuda() input_list = [v.cuda() for v in input_list] output = pcd_alignment(input_list, input_list) assert output.shape == (1, 4, 8, 8) with pytest.raises(AssertionError): pcd_alignment(input_list[0:2], input_list) def test_tsa_fusion(): """Test TSAFusion.""" # cpu tsa_fusion = TSAFusion(mid_channels=4, num_frames=5, center_frame_idx=2) input_tensor = torch.rand(1, 5, 4, 8, 8) output = tsa_fusion(input_tensor) assert output.shape == (1, 4, 8, 8) # gpu if torch.cuda.is_available(): tsa_fusion = tsa_fusion.cuda() input_tensor = input_tensor.cuda() output = tsa_fusion(input_tensor) assert output.shape == (1, 4, 8, 8) def test_edvrnet(): """Test EDVRNet.""" # cpu # with tsa edvrnet = EDVRNet( 3, 3, mid_channels=8, num_frames=5, deform_groups=2, num_blocks_extraction=1, num_blocks_reconstruction=1, center_frame_idx=2, with_tsa=True) input_tensor = torch.rand(1, 5, 3, 8, 8) edvrnet.init_weights(pretrained=None) output = edvrnet(input_tensor) assert output.shape == (1, 3, 32, 32) # without tsa edvrnet = EDVRNet( 3, 3, mid_channels=8, num_frames=5, deform_groups=2, num_blocks_extraction=1, num_blocks_reconstruction=1, center_frame_idx=2, with_tsa=False) output = edvrnet(input_tensor) assert output.shape == (1, 3, 32, 32) with pytest.raises(AssertionError): # The height and width of inputs should be a multiple of 4 input_tensor = torch.rand(1, 5, 3, 3, 3) edvrnet(input_tensor) with pytest.raises(TypeError): # pretrained should be str or None edvrnet.init_weights(pretrained=[1]) # gpu if torch.cuda.is_available(): # with tsa edvrnet = EDVRNet( 3, 3, mid_channels=8, num_frames=5, deform_groups=2, num_blocks_extraction=1, num_blocks_reconstruction=1, center_frame_idx=2, with_tsa=True).cuda() input_tensor = torch.rand(1, 5, 3, 8, 8).cuda() edvrnet.init_weights(pretrained=None) output = edvrnet(input_tensor) assert output.shape == (1, 3, 32, 32) # without tsa edvrnet = EDVRNet( 3, 3, mid_channels=8, num_frames=5, deform_groups=2, num_blocks_extraction=1, num_blocks_reconstruction=1, center_frame_idx=2, with_tsa=False).cuda() output = edvrnet(input_tensor) assert output.shape == (1, 3, 32, 32) with pytest.raises(AssertionError): # The height and width of inputs should be a multiple of 4 input_tensor = torch.rand(1, 5, 3, 3, 3).cuda() edvrnet(input_tensor) with pytest.raises(TypeError): # pretrained should be str or None edvrnet.init_weights(pretrained=[1])

en

0.78364

# Copyright (c) OpenMMLab. All rights reserved. Test PCDAlignment. # cpu # gpu Test TSAFusion. # cpu # gpu Test EDVRNet. # cpu # with tsa # without tsa # The height and width of inputs should be a multiple of 4 # pretrained should be str or None # gpu # with tsa # without tsa # The height and width of inputs should be a multiple of 4 # pretrained should be str or None

2.108957

2

mat2py/core/datastoreio.py

mat2py/mat2py

0

10212

# type: ignore __all__ = [ "readDatastoreImage", "datastore", ] def readDatastoreImage(*args): raise NotImplementedError("readDatastoreImage") def datastore(*args): raise NotImplementedError("datastore")

it

0.190853

# type: ignore

1.800924

2

enjoliver-api/tests/test_generate_groups.py

netturpin/enjoliver

11

10213

import os from shutil import rmtree from tempfile import mkdtemp from unittest import TestCase from enjoliver import generator class GenerateGroupTestCase(TestCase): api_uri = None test_matchbox_path = None test_resources_path = None tests_path = None @classmethod def setUpClass(cls): cls.tests_path = mkdtemp(dir='/tmp') cls.test_matchbox_path = os.path.join(cls.tests_path, 'test_matchbox') cls.test_resources_path = os.path.join(cls.tests_path, 'test_resources') os.mkdir(cls.test_matchbox_path) os.mkdir(cls.test_resources_path) os.mkdir(os.path.join(cls.test_matchbox_path, 'groups')) cls.api_uri = "http://127.0.0.1:5000" @classmethod def tearDownClass(cls): rmtree(cls.tests_path) class TestGenerateGroups(GenerateGroupTestCase): @classmethod def setUpClass(cls): super().setUpClass() cls.gen = generator.GenerateGroup( api_uri=cls.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="TestGenerateProfiles", matchbox_path=cls.test_matchbox_path ) cls.gen.profiles_path = cls.test_resources_path def test_instantiate_generate_group_with_incorrect_parameters(self): with self.assertRaises(TypeError): generator.GenerateGroup() def test_instantiate_generate_group_with_non_existing_matchbox_path(self): with self.assertRaises(OSError): generator.GenerateGroup( api_uri='foobar', _id='foo', name='foo-bar', profile='foo-bar-baz', matchbox_path='/foo/bar' ) def test_instantiate_generate_group(self): sandbox = mkdtemp(dir='/tmp') os.mkdir(os.path.join(sandbox, 'groups')) generator.GenerateGroup( api_uri='foobar', _id='foo', name='foo-bar', profile='foo-bar-baz', matchbox_path=sandbox ) rmtree(sandbox) def test_00_uri(self): ip = self.gen.api_uri self.assertIsNotNone(ip) def test_01_metadata(self): expect = {'etcd_initial_cluster': '', 'api_uri': '%s' % self.gen.api_uri, 'ssh_authorized_keys': []} self.gen._metadata() self.assertEqual(expect['api_uri'], self.gen._target_data["metadata"]["api_uri"]) def test_990_generate(self): expect = { 'profile': 'etcd-proxy.yaml', 'metadata': { 'api_uri': '%s' % self.gen.api_uri, 'ssh_authorized_keys': [] }, 'id': 'etcd-proxy', 'name': 'etcd-proxy' } new = generator.GenerateGroup( api_uri=self.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="etcd-proxy.yaml", matchbox_path=self.test_matchbox_path ) result = new.generate() self.assertEqual(expect["profile"], result["profile"]) self.assertEqual(expect["id"], result["id"]) self.assertEqual(expect["name"], result["name"]) self.assertEqual(expect["metadata"]["api_uri"], result["metadata"]["api_uri"]) def test_991_dump(self): _id = "etcd-test-%s" % self.test_991_dump.__name__ new = generator.GenerateGroup( api_uri=self.api_uri, _id=_id, name="etcd-test", profile="etcd-test.yaml", matchbox_path=self.test_matchbox_path ) self.assertTrue(new.dump()) self.assertTrue(os.path.isfile("%s/groups/%s.json" % (self.test_matchbox_path, _id))) self.assertFalse(new.dump()) self.assertTrue(os.path.isfile("%s/groups/%s.json" % (self.test_matchbox_path, _id))) new = generator.GenerateGroup( api_uri=self.api_uri, _id=_id, name="etcd-test", profile="etcd-test.yaml", matchbox_path=self.test_matchbox_path, selector={"one": "selector"} ) self.assertTrue(new.dump()) self.assertTrue(os.path.isfile("%s/groups/%s.json" % (self.test_matchbox_path, _id))) os.remove("%s/groups/%s.json" % (self.test_matchbox_path, _id)) class TestGenerateGroupsSelectorLower(GenerateGroupTestCase): @classmethod def setUpClass(cls): super().setUpClass() os.environ["MATCHBOX_URI"] = "http://127.0.0.1:8080" os.environ["API_URI"] = "http://127.0.0.1:5000" cls.gen = generator.GenerateGroup( api_uri=cls.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="TestGenerateProfiles", selector={"mac": "08:00:27:37:28:2e"}, matchbox_path=cls.test_matchbox_path ) def test_00_api_uri(self): ip = self.gen.api_uri self.assertIsNotNone(ip) def test_01_metadata(self): expect = { 'api_uri': "%s" % self.gen.api_uri, 'ssh_authorized_keys': [] } self.gen._metadata() self.gen._target_data["metadata"]['ssh_authorized_keys'] = [] self.assertEqual(expect, self.gen._target_data["metadata"]) def test_02_selector(self): expect = {'mac': '08:00:27:37:28:2e'} self.gen._selector() self.assertEqual(expect, self.gen._target_data["selector"]) def test_990_generate(self): expect = { 'profile': 'etcd-proxy.yaml', 'metadata': { 'api_uri': self.gen.api_uri, 'selector': {'mac': '08:00:27:37:28:2e'}, 'ssh_authorized_keys': [] }, 'id': 'etcd-proxy', 'name': 'etcd-proxy', 'selector': {'mac': '08:00:27:37:28:2e'} } new = generator.GenerateGroup( api_uri=self.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="etcd-proxy.yaml", selector={"mac": "08:00:27:37:28:2e"}, matchbox_path=self.test_matchbox_path) result = new.generate() result["metadata"]['ssh_authorized_keys'] = [] self.assertEqual(expect, result) def test_991_dump(self): _id = "etcd-test-%s" % self.test_991_dump.__name__ new = generator.GenerateGroup( api_uri=self.api_uri, _id="%s" % _id, name="etcd-test", profile="etcd-test.yaml", matchbox_path=self.test_matchbox_path, selector={"mac": "08:00:27:37:28:2e"} ) self.assertTrue(new.dump()) self.assertTrue(os.path.isfile("%s/groups/%s.json" % (self.test_matchbox_path, _id))) os.remove("%s/groups/%s.json" % (self.test_matchbox_path, _id)) class TestGenerateGroupsSelectorUpper(GenerateGroupTestCase): @classmethod def setUpClass(cls): super().setUpClass() os.environ["MATCHBOX_URI"] = "http://127.0.0.1:8080" os.environ["API_URI"] = "http://127.0.0.1:5000" cls.gen = generator.GenerateGroup( api_uri=cls.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="TestGenerateProfiles", selector={"mac": "08:00:27:37:28:2E"}, matchbox_path=cls.test_matchbox_path ) def test_00_ip_address(self): ip = self.gen.api_uri self.assertIsNotNone(ip) def test_01_metadata(self): expect = { 'api_uri': "%s" % self.gen.api_uri, 'ssh_authorized_keys': [] } self.gen._metadata() self.gen._target_data["metadata"]['ssh_authorized_keys'] = [] self.assertEqual(expect, self.gen._target_data["metadata"]) def test_02_selector(self): expect = {'mac': '08:00:27:37:28:2e'} self.gen._selector() self.assertEqual(expect, self.gen._target_data["selector"]) def test_990_generate(self): expect = { 'profile': 'etcd-proxy.yaml', 'metadata': { 'api_uri': "%s" % self.gen.api_uri, 'selector': {'mac': '08:00:27:37:28:2e'}, 'ssh_authorized_keys': [] }, 'id': 'etcd-proxy', 'name': 'etcd-proxy', 'selector': {'mac': '08:00:27:37:28:2e'} } new = generator.GenerateGroup( api_uri=self.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="etcd-proxy.yaml", selector={"mac": "08:00:27:37:28:2e"}, matchbox_path=self.test_matchbox_path ) result = new.generate() result["metadata"]['ssh_authorized_keys'] = [] self.assertEqual(expect, result) def test_991_dump(self): _id = "etcd-test-%s" % self.test_991_dump.__name__ new = generator.GenerateGroup( api_uri=self.api_uri, _id="%s" % _id, name="etcd-test", profile="etcd-test.yaml", matchbox_path=self.test_matchbox_path, selector={"mac": "08:00:27:37:28:2e"} ) new.dump() self.assertTrue(os.path.isfile("%s/groups/%s.json" % (self.test_matchbox_path, _id))) os.remove("%s/groups/%s.json" % (self.test_matchbox_path, _id)) class TestGenerateGroupsExtraMetadata(GenerateGroupTestCase): @classmethod def setUpClass(cls): super().setUpClass() os.environ["MATCHBOX_URI"] = "http://127.0.0.1:8080" os.environ["API_URI"] = "http://127.0.0.1:5000" cls.gen = generator.GenerateGroup( api_uri=cls.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="TestGenerateProfiles", selector={"mac": "08:00:27:37:28:2E"}, metadata={"etcd_initial_cluster": "static0=http://192.168.1.1:2379", "api_seed": "http://192.168.1.2:5000"}, matchbox_path=cls.test_matchbox_path ) def test_00_api_uri(self): ip = self.gen.api_uri self.assertIsNotNone(ip) def test_01_metadata(self): expect = {'etcd_initial_cluster': 'static0=http://192.168.1.1:2379', 'api_uri': "%s" % self.gen.api_uri, 'api_seed': 'http://192.168.1.2:5000', 'ssh_authorized_keys': []} self.gen._metadata() self.gen._target_data["metadata"]['ssh_authorized_keys'] = [] self.assertEqual(expect, self.gen._target_data["metadata"]) def test_02_selector(self): expect = {'mac': '08:00:27:37:28:2e'} self.gen._selector() self.assertEqual(expect, self.gen._target_data["selector"]) def test_990_generate(self): expect = { 'profile': 'etcd-proxy.yaml', 'metadata': { 'api_uri': "%s" % self.gen.api_uri, 'selector': {'mac': '08:00:27:37:28:2e'}, 'ssh_authorized_keys': [] }, 'id': 'etcd-proxy', 'name': 'etcd-proxy', 'selector': {'mac': '08:00:27:37:28:2e'} } new = generator.GenerateGroup( api_uri=self.api_uri, _id="etcd-proxy", name="etcd-proxy", profile="etcd-proxy.yaml", selector={"mac": "08:00:27:37:28:2e"}, matchbox_path=self.test_matchbox_path ) result = new.generate() result["metadata"]["ssh_authorized_keys"] = [] self.assertEqual(expect, result) def test_991_dump(self): _id = "etcd-test-%s" % self.test_991_dump.__name__ new = generator.GenerateGroup( api_uri=self.api_uri, _id="%s" % _id, name="etcd-test", profile="etcd-test.yaml", matchbox_path=self.test_matchbox_path, selector={"mac": "08:00:27:37:28:2e"} ) self.assertTrue(new.dump()) self.assertTrue(os.path.isfile("%s/groups/%s.json" % (self.test_matchbox_path, _id))) os.remove("%s/groups/%s.json" % (self.test_matchbox_path, _id)) self.assertTrue(new.dump()) for i in range(10): self.assertFalse(new.dump()) new.api_uri = "http://google.com" self.assertTrue(new.dump()) self.assertFalse(new.dump())

none

1

2.376044

2

HackerRank/Calendar Module/solution.py

nikku1234/Code-Practise

9

10214

# Enter your code here. Read input from STDIN. Print output to STDOUT import calendar mm,dd,yyyy = map(int,input().split()) day = ["MONDAY","TUESDAY","WEDNESDAY","THURSDAY","FRIDAY","SATURDAY","SUNDAY"] val = int (calendar.weekday(yyyy,mm,dd)) print(day[val])

en

0.824269

# Enter your code here. Read input from STDIN. Print output to STDOUT

3.817077

4

scale/trigger/models.py

stevevarner/scale

0

10215

"""Defines the models for trigger rules and events""" from __future__ import unicode_literals import django.contrib.postgres.fields from django.db import models, transaction from django.utils.timezone import now class TriggerEventManager(models.Manager): """Provides additional methods for handling trigger events """ def create_trigger_event(self, trigger_type, rule, description, occurred): """Creates a new trigger event and returns the event model. The given rule model, if not None, must have already been saved in the database (it must have an ID). The returned trigger event model will be saved in the database. :param trigger_type: The type of the trigger that occurred :type trigger_type: str :param rule: The rule that triggered the event, possibly None :type rule: :class:`trigger.models.TriggerRule` :param description: The JSON description of the event as a dict :type description: dict :param occurred: When the event occurred :type occurred: :class:`datetime.datetime` :returns: The new trigger event :rtype: :class:`trigger.models.TriggerEvent` """ if trigger_type is None: raise Exception('Trigger event must have a type') if description is None: raise Exception('Trigger event must have a JSON description') if occurred is None: raise Exception('Trigger event must have a timestamp') event = TriggerEvent() event.type = trigger_type event.rule = rule event.description = description event.occurred = occurred event.save() return event class TriggerEvent(models.Model): """Represents an event where a trigger occurred :keyword type: The type of the trigger that occurred :type type: :class:`django.db.models.CharField` :keyword rule: The rule that triggered this event, possibly None (some events are not triggered by rules) :type rule: :class:`django.db.models.ForeignKey` :keyword description: JSON description of the event. This will contain fields specific to the type of the trigger that occurred. :type description: :class:`django.contrib.postgres.fields.JSONField` :keyword occurred: When the event occurred :type occurred: :class:`django.db.models.DateTimeField` """ type = models.CharField(db_index=True, max_length=50) rule = models.ForeignKey('trigger.TriggerRule', blank=True, null=True, on_delete=models.PROTECT) description = django.contrib.postgres.fields.JSONField(default=dict) occurred = models.DateTimeField(db_index=True) objects = TriggerEventManager() class Meta(object): """meta information for the db""" db_table = 'trigger_event' class TriggerRuleManager(models.Manager): """Provides additional methods for handling trigger rules """ @transaction.atomic def archive_trigger_rule(self, trigger_rule_id): """Archives the trigger rule (will no longer be active) with the given ID :param trigger_rule_id: The ID of the trigger rule to archive :type trigger_rule_id: int """ rule = TriggerRule.objects.select_for_update().get(pk=trigger_rule_id) rule.is_active = False rule.archived = now() rule.save() def create_trigger_rule(self, trigger_type, configuration, name='', is_active=True): """Creates a new trigger rule and returns the rule model. The returned trigger rule model will be saved in the database. :param trigger_type: The type of this trigger rule :type trigger_type: str :param configuration: The rule configuration :type configuration: :class:`trigger.configuration.TriggerRuleConfiguration` :param name: An optional name for the trigger :type name: str :param is_active: Whether or not the trigger should be active :type is_active: bool :returns: The new trigger rule :rtype: :class:`trigger.models.TriggerRule` :raises trigger.configuration.exceptions.InvalidTriggerRule: If the configuration is invalid """ if not trigger_type: raise Exception('Trigger rule must have a type') if not configuration: raise Exception('Trigger rule must have a configuration') configuration.validate() rule = TriggerRule() rule.type = trigger_type rule.name = name rule.is_active = is_active rule.configuration = configuration.get_dict() rule.save() return rule def get_by_natural_key(self, name): """Django method to retrieve a trigger rule for the given natural key. NOTE: All trigger rule names are NOT unique. This is implemented to allow the loading of defined system trigger rules which do have unique names. :param name: The name of the trigger rule :type name: str :returns: The trigger rule defined by the natural key :rtype: :class:`error.models.Error` """ return self.get(name=name) class TriggerRule(models.Model): """Represents a rule that, when triggered, creates a trigger event :keyword type: The type of the trigger for the rule :type type: :class:`django.db.models.CharField` :keyword name: The identifying name of the trigger rule used by clients for queries :type name: :class:`django.db.models.CharField` :keyword configuration: JSON configuration for the rule. This will contain fields specific to the type of the trigger. :type configuration: :class:`django.contrib.postgres.fields.JSONField` :keyword is_active: Whether the rule is still active (false once rule is archived) :type is_active: :class:`django.db.models.BooleanField` :keyword created: When the rule was created :type created: :class:`django.db.models.DateTimeField` :keyword archived: When the rule was archived (no longer active) :type archived: :class:`django.db.models.DateTimeField` :keyword last_modified: When the rule was last modified :type last_modified: :class:`django.db.models.DateTimeField` """ type = models.CharField(max_length=50, db_index=True) name = models.CharField(blank=True, max_length=50) configuration = django.contrib.postgres.fields.JSONField(default=dict) is_active = models.BooleanField(default=True, db_index=True) created = models.DateTimeField(auto_now_add=True) archived = models.DateTimeField(blank=True, null=True) last_modified = models.DateTimeField(auto_now=True) objects = TriggerRuleManager() def get_configuration(self): """Returns the configuration for this trigger rule :returns: The configuration for this trigger rule :rtype: :class:`trigger.configuration.trigger_rule.TriggerRuleConfiguration` :raises :class:`trigger.configuration.exceptions.InvalidTriggerType`: If the trigger type is invalid """ from trigger.handler import get_trigger_rule_handler handler = get_trigger_rule_handler(self.type) return handler.create_configuration(self.configuration) def natural_key(self): """Django method to define the natural key for a trigger rule as the name :returns: A tuple representing the natural key :rtype: tuple(str,) """ return (self.name,) class Meta(object): """meta information for the db""" db_table = 'trigger_rule'

en

0.757686

Defines the models for trigger rules and events Provides additional methods for handling trigger events Creates a new trigger event and returns the event model. The given rule model, if not None, must have already been saved in the database (it must have an ID). The returned trigger event model will be saved in the database. :param trigger_type: The type of the trigger that occurred :type trigger_type: str :param rule: The rule that triggered the event, possibly None :type rule: :class:`trigger.models.TriggerRule` :param description: The JSON description of the event as a dict :type description: dict :param occurred: When the event occurred :type occurred: :class:`datetime.datetime` :returns: The new trigger event :rtype: :class:`trigger.models.TriggerEvent` Represents an event where a trigger occurred :keyword type: The type of the trigger that occurred :type type: :class:`django.db.models.CharField` :keyword rule: The rule that triggered this event, possibly None (some events are not triggered by rules) :type rule: :class:`django.db.models.ForeignKey` :keyword description: JSON description of the event. This will contain fields specific to the type of the trigger that occurred. :type description: :class:`django.contrib.postgres.fields.JSONField` :keyword occurred: When the event occurred :type occurred: :class:`django.db.models.DateTimeField` meta information for the db Provides additional methods for handling trigger rules Archives the trigger rule (will no longer be active) with the given ID :param trigger_rule_id: The ID of the trigger rule to archive :type trigger_rule_id: int Creates a new trigger rule and returns the rule model. The returned trigger rule model will be saved in the database. :param trigger_type: The type of this trigger rule :type trigger_type: str :param configuration: The rule configuration :type configuration: :class:`trigger.configuration.TriggerRuleConfiguration` :param name: An optional name for the trigger :type name: str :param is_active: Whether or not the trigger should be active :type is_active: bool :returns: The new trigger rule :rtype: :class:`trigger.models.TriggerRule` :raises trigger.configuration.exceptions.InvalidTriggerRule: If the configuration is invalid Django method to retrieve a trigger rule for the given natural key. NOTE: All trigger rule names are NOT unique. This is implemented to allow the loading of defined system trigger rules which do have unique names. :param name: The name of the trigger rule :type name: str :returns: The trigger rule defined by the natural key :rtype: :class:`error.models.Error` Represents a rule that, when triggered, creates a trigger event :keyword type: The type of the trigger for the rule :type type: :class:`django.db.models.CharField` :keyword name: The identifying name of the trigger rule used by clients for queries :type name: :class:`django.db.models.CharField` :keyword configuration: JSON configuration for the rule. This will contain fields specific to the type of the trigger. :type configuration: :class:`django.contrib.postgres.fields.JSONField` :keyword is_active: Whether the rule is still active (false once rule is archived) :type is_active: :class:`django.db.models.BooleanField` :keyword created: When the rule was created :type created: :class:`django.db.models.DateTimeField` :keyword archived: When the rule was archived (no longer active) :type archived: :class:`django.db.models.DateTimeField` :keyword last_modified: When the rule was last modified :type last_modified: :class:`django.db.models.DateTimeField` Returns the configuration for this trigger rule :returns: The configuration for this trigger rule :rtype: :class:`trigger.configuration.trigger_rule.TriggerRuleConfiguration` :raises :class:`trigger.configuration.exceptions.InvalidTriggerType`: If the trigger type is invalid Django method to define the natural key for a trigger rule as the name :returns: A tuple representing the natural key :rtype: tuple(str,) meta information for the db

2.763077

3

leetcode/0506_relative_ranks.py

chaosWsF/Python-Practice

0

10216

""" Given scores of N athletes, find their relative ranks and the people with the top three highest scores, who will be awarded medals: "Gold Medal", "Silver Medal" and "Bronze Medal". Example 1: Input: [5, 4, 3, 2, 1] Output: ["Gold Medal", "Silver Medal", "Bronze Medal", "4", "5"] Explanation: The first three athletes got the top three highest scores, so they got "Gold Medal", "Silver Medal" and "Bronze Medal". For the left two athletes, you just need to output their relative ranks according to their scores. Note: N is a positive integer and won't exceed 10,000. All the scores of athletes are guaranteed to be unique. """ class Solution: def findRelativeRanks(self, nums): scores_rank = sorted(nums, reverse=True) d = {} for i, score in enumerate(scores_rank): if i == 0: d[score] = 'Gold Medal' elif i == 1: d[score] = 'Silver Medal' elif i == 2: d[score] = 'Bronze Medal' else: d[score] = str(i + 1) return [d[x] for x in nums]

en

0.931818

Given scores of N athletes, find their relative ranks and the people with the top three highest scores, who will be awarded medals: "Gold Medal", "Silver Medal" and "Bronze Medal". Example 1: Input: [5, 4, 3, 2, 1] Output: ["Gold Medal", "Silver Medal", "Bronze Medal", "4", "5"] Explanation: The first three athletes got the top three highest scores, so they got "Gold Medal", "Silver Medal" and "Bronze Medal". For the left two athletes, you just need to output their relative ranks according to their scores. Note: N is a positive integer and won't exceed 10,000. All the scores of athletes are guaranteed to be unique.

4.113877

4

barriers/models/history/assessments/economic_impact.py

felix781/market-access-python-frontend

1

10217

from ..base import BaseHistoryItem, GenericHistoryItem from ..utils import PolymorphicBase class ArchivedHistoryItem(BaseHistoryItem): field = "archived" field_name = "Valuation assessment: Archived" def get_value(self, value): if value is True: return "Archived" elif value is False: return "Unarchived" class ExplanationHistoryItem(BaseHistoryItem): field = "explanation" field_name = "Valuation assessment: Explanation" class ImpactHistoryItem(BaseHistoryItem): field = "impact" field_name = "Valuation assessment: Impact" def get_value(self, value): if value: return value.get("name") class EconomicImpactAssessmentHistoryItem(PolymorphicBase): model = "economic_impact_assessment" key = "field" subclasses = ( ArchivedHistoryItem, ExplanationHistoryItem, ImpactHistoryItem, ) default_subclass = GenericHistoryItem class_lookup = {}

none

1

2.689965

3

link_prob_show.py

Rheinwalt/spatial-effects-networks

3

10218

<filename>link_prob_show.py import sys import numpy as np from sern import * ids, lon, lat = np.loadtxt('nodes', unpack = True) links = np.loadtxt('links', dtype = 'int') A, b = AdjacencyMatrix(ids, links) lon, lat = lon[b], lat[b] n = A.shape[0] # LinkProbability expects A as triu A = A[np.triu_indices(n, 1)] # play around with the scale, maybe you don't need log binning? D, x = IntegerDistances(lat, lon, scale = 50) p = LinkProbability(A, D) from matplotlib import pyplot as pl pl.plot(p, 'bo') pl.ylabel('Link probability given distance') pl.xlabel('Bin number') pl.savefig('link_prob_bin.png') pl.close('all') pl.semilogx(x, p, 'bo') pl.ylabel('Link probability given distance') pl.xlabel('Distance [km]') pl.savefig('link_prob_distance.png')

en

0.989835

# LinkProbability expects A as triu # play around with the scale, maybe you don't need log binning?

2.420012

2

controller/components/app.py

isabella232/flight-lab

15

10219

# Copyright 2018 Flight Lab authors. # # 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 # # https://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. """Library for components related to running apps.""" import subprocess import threading from components import base from protos import controller_pb2 from utils import app class AppComponent(base.Component): """Component to run command-line based app on any platform. This component can start app, restart app upon crash, and stop app. Events: "status_changed": when status of the app is changed. Args: app_component: instance of this class. """ def __init__(self, proto, *args, **kwargs): """Initializes the component. Args: proto: flightlab.App proto defining app details and options. """ super(AppComponent, self).__init__(proto, *args, **kwargs) self._app = app.Application( name=self.name, bin_path=self.settings.executable_path, arguments=(list(self.settings.arguments) if self.settings.arguments else []), working_dir=self.settings.working_dir, restart_on_crash=(self.settings.restart_on_crash if self.settings.restart_on_crash else False), env=(self.settings.env if self.settings.env else None)) self._app.on('started', self._on_app_started) self._app.on('stopped', self._on_app_stopped) self._monitor = threading.Timer(1, self._check_status) self._monitor.start() def close(self): if self._monitor: self._monitor.cancel() self._monitor = None self._app.stop() def _check_status(self): if self._app.has_running_instance(): component_status = controller_pb2.Component.ON app_status = controller_pb2.App.RUNNING else: component_status = controller_pb2.Component.OFF app_status = controller_pb2.App.NOT_RUNNING if (self.proto.status != component_status or self.settings.status != app_status): self.proto.status = component_status self.settings.status = app_status self.emit('status_changed', self) def _start(self): self.logger.info('[App - {0}] Starting...'.format(self.name)) self._app.start() def _stop(self): self.logger.info('[App - {0}] Stopping...'.format(self.name)) self._app.stop() def _restart(self): self._stop() self._start() def _on_app_started(self, app): self.logger.info('[App - {0}] Started.'.format(self.name)) self.settings.status = controller_pb2.App.RUNNING self.proto.status = controller_pb2.Component.ON self.emit('status_changed', self) def _on_app_stopped(self, app): self.logger.info('[App - {0}] Stopped.'.format(self.name)) self.settings.status = controller_pb2.App.NOT_RUNNING self.proto.status = controller_pb2.Component.OFF self.emit('status_changed', self) class CommandLineComponent(base.Component): """Component to run command-line based apps on any platform.""" def _start(self): for cmd in self.settings.when_on: self.logger.info('[{0}] Running: {1}'.format(self.name, cmd)) ret = subprocess.call(cmd) self.logger.info('[{0}] Done (return code={1})'.format(self.name, ret)) def _stop(self): for cmd in self.settings.when_off: self.logger.info('[{0}] Running: {1}'.format(self.name, cmd)) ret = subprocess.call(cmd) self.logger.info('[{0}] Done (return code={1})'.format(self.name, ret))

en

0.859134

# Copyright 2018 Flight Lab authors. # # 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 # # https://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. Library for components related to running apps. Component to run command-line based app on any platform. This component can start app, restart app upon crash, and stop app. Events: "status_changed": when status of the app is changed. Args: app_component: instance of this class. Initializes the component. Args: proto: flightlab.App proto defining app details and options. Component to run command-line based apps on any platform.

2.215003

2

botorch/acquisition/__init__.py

jmren168/botorch

1

10220

#!/usr/bin/env python3 # Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved from .acquisition import AcquisitionFunction from .analytic import ( AnalyticAcquisitionFunction, ConstrainedExpectedImprovement, ExpectedImprovement, NoisyExpectedImprovement, PosteriorMean, ProbabilityOfImprovement, UpperConfidenceBound, ) from .fixed_feature import FixedFeatureAcquisitionFunction from .monte_carlo import ( MCAcquisitionFunction, qExpectedImprovement, qNoisyExpectedImprovement, qProbabilityOfImprovement, qSimpleRegret, qUpperConfidenceBound, ) from .objective import ( ConstrainedMCObjective, GenericMCObjective, IdentityMCObjective, LinearMCObjective, MCAcquisitionObjective, ScalarizedObjective, ) from .utils import get_acquisition_function __all__ = [ "AcquisitionFunction", "AnalyticAcquisitionFunction", "ConstrainedExpectedImprovement", "ExpectedImprovement", "FixedFeatureAcquisitionFunction", "NoisyExpectedImprovement", "PosteriorMean", "ProbabilityOfImprovement", "UpperConfidenceBound", "qExpectedImprovement", "qNoisyExpectedImprovement", "qProbabilityOfImprovement", "qSimpleRegret", "qUpperConfidenceBound", "ConstrainedMCObjective", "GenericMCObjective", "IdentityMCObjective", "LinearMCObjective", "MCAcquisitionFunction", "MCAcquisitionObjective", "ScalarizedObjective", "get_acquisition_function", ]

en

0.797894

1.340029

1

examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_alternating_legs_env_randomizer.py

felipeek/bullet3

9,136

10221

"""Randomize the minitaur_gym_alternating_leg_env when reset() is called. The randomization include swing_offset, extension_offset of all legs that mimics bent legs, desired_pitch from user input, battery voltage and motor damping. """ import os, inspect currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe()))) parentdir = os.path.dirname(os.path.dirname(currentdir)) parentdir = os.path.dirname(os.path.dirname(parentdir)) os.sys.path.insert(0, parentdir) import numpy as np import tf.compat.v1 as tf from pybullet_envs.minitaur.envs import env_randomizer_base # Absolute range. NUM_LEGS = 4 BATTERY_VOLTAGE_RANGE = (14.8, 16.8) MOTOR_VISCOUS_DAMPING_RANGE = (0, 0.01) class MinitaurAlternatingLegsEnvRandomizer(env_randomizer_base.EnvRandomizerBase): """A randomizer that changes the minitaur_gym_alternating_leg_env.""" def __init__(self, perturb_swing_bound=0.1, perturb_extension_bound=0.1, perturb_desired_pitch_bound=0.01): super(MinitaurAlternatingLegsEnvRandomizer, self).__init__() self.perturb_swing_bound = perturb_swing_bound self.perturb_extension_bound = perturb_extension_bound self.perturb_desired_pitch_bound = perturb_desired_pitch_bound def randomize_env(self, env): perturb_magnitude = np.random.uniform(low=-self.perturb_swing_bound, high=self.perturb_swing_bound, size=NUM_LEGS) env.set_swing_offset(perturb_magnitude) tf.logging.info("swing_offset: {}".format(perturb_magnitude)) perturb_magnitude = np.random.uniform(low=-self.perturb_extension_bound, high=self.perturb_extension_bound, size=NUM_LEGS) env.set_extension_offset(perturb_magnitude) tf.logging.info("extension_offset: {}".format(perturb_magnitude)) perturb_magnitude = np.random.uniform(low=-self.perturb_desired_pitch_bound, high=self.perturb_desired_pitch_bound) env.set_desired_pitch(perturb_magnitude) tf.logging.info("desired_pitch: {}".format(perturb_magnitude)) randomized_battery_voltage = np.random.uniform(BATTERY_VOLTAGE_RANGE[0], BATTERY_VOLTAGE_RANGE[1]) env.minitaur.SetBatteryVoltage(randomized_battery_voltage) tf.logging.info("battery_voltage: {}".format(randomized_battery_voltage)) randomized_motor_damping = np.random.uniform(MOTOR_VISCOUS_DAMPING_RANGE[0], MOTOR_VISCOUS_DAMPING_RANGE[1]) env.minitaur.SetMotorViscousDamping(randomized_motor_damping) tf.logging.info("motor_damping: {}".format(randomized_motor_damping))

en

0.691932

Randomize the minitaur_gym_alternating_leg_env when reset() is called. The randomization include swing_offset, extension_offset of all legs that mimics bent legs, desired_pitch from user input, battery voltage and motor damping. # Absolute range. A randomizer that changes the minitaur_gym_alternating_leg_env.

2.704479

3

pygsti/modelmembers/states/tensorprodstate.py

pyGSTi-Developers/pyGSTi

73

10222

""" The TensorProductState class and supporting functionality. """ #*************************************************************************************************** # Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS). # Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights # in this software. # 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 or in the LICENSE file in the root pyGSTi directory. #*************************************************************************************************** import functools as _functools import itertools as _itertools import numpy as _np from pygsti.modelmembers.states.state import State as _State from pygsti.modelmembers import modelmember as _modelmember, term as _term from pygsti.baseobjs import statespace as _statespace from pygsti.tools import listtools as _lt from pygsti.tools import matrixtools as _mt class TensorProductState(_State): """ A state vector that is a tensor-product of other state vectors. Parameters ---------- factors : list of States a list of the component states to take the tensor product of. state_space : StateSpace, optional The state space for this operation. """ def __init__(self, factors, state_space): assert(len(factors) > 0), "Must have at least one factor!" self.factors = factors # do *not* copy - needs to reference common objects evotype = self.factors[0]._evotype rep = evotype.create_tensorproduct_state_rep([f._rep for f in factors], state_space) _State.__init__(self, rep, evotype) self.init_gpindices() # initialize our gpindices based on sub-members self._update_rep() # initializes rep data #Note: no to_memoized_dict needed, as ModelMember version does all we need. @classmethod def _from_memoized_dict(cls, mm_dict, serial_memo): state_space = _statespace.StateSpace.from_nice_serialization(mm_dict['state_space']) factors = [serial_memo[i] for i in mm_dict['submembers']] return cls(factors, state_space) def submembers(self): """ Get the ModelMember-derived objects contained in this one. Returns ------- list """ return self.factors # factor POVM object def _update_rep(self): self._rep.reps_have_changed() @property def parameter_labels(self): """ An array of labels (usually strings) describing this model member's parameters. """ vl = _np.empty(self.num_params, dtype=object) for factor_state, factor_local_inds in zip(self.factors, self._submember_rpindices): vl[factor_local_inds] = factor_state.parameter_labels return vl def to_dense(self, on_space='minimal', scratch=None): """ Return this state vector as a (dense) numpy array. The memory in `scratch` maybe used when it is not-None. Parameters ---------- on_space : {'minimal', 'Hilbert', 'HilbertSchmidt'} The space that the returned dense operation acts upon. For unitary matrices and bra/ket vectors, use `'Hilbert'`. For superoperator matrices and super-bra/super-ket vectors use `'HilbertSchmidt'`. `'minimal'` means that `'Hilbert'` is used if possible given this operator's evolution type, and otherwise `'HilbertSchmidt'` is used. scratch : numpy.ndarray, optional scratch space available for use. Returns ------- numpy.ndarray """ return self._rep.to_dense(on_space) def taylor_order_terms(self, order, max_polynomial_vars=100, return_coeff_polys=False): """ Get the `order`-th order Taylor-expansion terms of this state vector. This function either constructs or returns a cached list of the terms at the given order. Each term is "rank-1", meaning that it is a state preparation followed by or POVM effect preceded by actions on a density matrix `rho` of the form: `rho -> A rho B` The coefficients of these terms are typically polynomials of the State's parameters, where the polynomial's variable indices index the *global* parameters of the State's parent (usually a :class:`Model`) , not the State's local parameter array (i.e. that returned from `to_vector`). Parameters ---------- order : int The order of terms to get. max_polynomial_vars : int, optional maximum number of variables the created polynomials can have. return_coeff_polys : bool Whether a parallel list of locally-indexed (using variable indices corresponding to *this* object's parameters rather than its parent's) polynomial coefficients should be returned as well. Returns ------- terms : list A list of :class:`RankOneTerm` objects. coefficients : list Only present when `return_coeff_polys == True`. A list of *compact* polynomial objects, meaning that each element is a `(vtape,ctape)` 2-tuple formed by concatenating together the output of :method:`Polynomial.compact`. """ terms = [] fnq = [int(round(_np.log2(f.dim))) // 2 for f in self.factors] # num of qubits per factor # assumes density matrix evolution total_nQ = sum(fnq) # total number of qubits for p in _lt.partition_into(order, len(self.factors)): factor_lists = [self.factors[i].taylor_order_terms(pi, max_polynomial_vars) for i, pi in enumerate(p)] # When possible, create COLLAPSED factor_lists so each factor has just a single # (State) pre & post op, which can be formed into the new terms' # TensorProdState ops. # - DON'T collapse stabilizer states & clifford ops - can't for POVMs collapsible = False # bool(self._evotype =="svterm") # need to use reps for collapsing now... TODO? if collapsible: factor_lists = [[t.collapse_vec() for t in fterms] for fterms in factor_lists] for factors in _itertools.product(*factor_lists): # create a term with a TensorProdState - Note we always create # "prep"-mode vectors, since even when self._prep_or_effect == "effect" these # vectors are created with factor (prep- or effect-type) States not factor POVMs # we workaround this by still allowing such "prep"-mode # TensorProdStates to be represented as effects (i.e. in torep('effect'...) works) coeff = _functools.reduce(lambda x, y: x.mult(y), [f.coeff for f in factors]) pre_rep = self._evotype.create_tensorproduct_state_rep( [f.pre_state for f in factors if (f.pre_state is not None)], self.state_space) post_rep = self._evotype.create_tensorproduct_state_rep( [f.post_state for f in factors if (f.post_state is not None)], self.state_space) term = _term.RankOnePolynomialPrepTerm.create_from(coeff, pre_rep, post_rep, self._evotype, self.state_space) if not collapsible: # then may need to add more ops. Assume factor ops are clifford gates # Embed each factors ops according to their target qubit(s) and just daisy chain them ss = _statespace.QubitSpace(total_nQ); curQ = 0 for f, nq in zip(factors, fnq): targetLabels = tuple(range(curQ, curQ + nq)); curQ += nq term._rep.pre_ops.extend([self._evotype.create_embedded_rep(ss, targetLabels, op) for op in f.pre_ops]) # embed and add ops term._rep.post_ops.extend([self._evotype.create_embedded_rep(ss, targetLabels, op) for op in f.post_ops]) # embed and add ops terms.append(term) if return_coeff_polys: def _decompose_indices(x): return tuple(_modelmember._decompose_gpindices( self.gpindices, _np.array(x, _np.int64))) poly_coeffs = [t.coeff.map_indices(_decompose_indices) for t in terms] # with *local* indices tapes = [poly.compact(complex_coeff_tape=True) for poly in poly_coeffs] if len(tapes) > 0: vtape = _np.concatenate([t[0] for t in tapes]) ctape = _np.concatenate([t[1] for t in tapes]) else: vtape = _np.empty(0, _np.int64) ctape = _np.empty(0, complex) coeffs_as_compact_polys = (vtape, ctape) #self.local_term_poly_coeffs[order] = coeffs_as_compact_polys #FUTURE? return terms, coeffs_as_compact_polys else: return terms # Cache terms in FUTURE? @property def num_params(self): """ Get the number of independent parameters which specify this state vector. Returns ------- int the number of independent parameters. """ return len(self.gpindices_as_array()) def to_vector(self): """ Get the state vector parameters as an array of values. Returns ------- numpy array The parameters as a 1D array with length num_params(). """ v = _np.empty(self.num_params, 'd') for factor_state, factor_local_inds in zip(self.factors, self._submember_rpindices): v[factor_local_inds] = factor_state.to_vector() return v def from_vector(self, v, close=False, dirty_value=True): """ Initialize the state vector using a 1D array of parameters. Parameters ---------- v : numpy array The 1D vector of state vector parameters. Length must == num_params() close : bool, optional Whether `v` is close to this state vector's current set of parameters. Under some circumstances, when this is true this call can be completed more quickly. dirty_value : bool, optional The value to set this object's "dirty flag" to before exiting this call. This is passed as an argument so it can be updated *recursively*. Leave this set to `True` unless you know what you're doing. Returns ------- None """ for factor_state, factor_local_inds in zip(self.factors, self._submember_rpindices): factor_state.from_vector(v[factor_local_inds], close, dirty_value) #Update representation, which may be a dense matrix or # just fast-kron arrays or a stabilizer state. self._update_rep() # TODO - how does this apply to state reps?? def deriv_wrt_params(self, wrt_filter=None): """ The element-wise derivative this state vector. Construct a matrix whose columns are the derivatives of the state vector with respect to a single param. Thus, each column is of length dimension and there is one column per state vector parameter. An empty 2D array in the StaticState case (num_params == 0). Parameters ---------- wrt_filter : list or numpy.ndarray List of parameter indices to take derivative with respect to. (None means to use all the this operation's parameters.) Returns ------- numpy array Array of derivatives, shape == (dimension, num_params) """ typ = self.factors[0].to_dense(on_space='minimal').dtype if len(self.factors) > 0 else 'd' #HACK to deal with fact that output of to_dense is really what is differentiated # but this may not match self.dim == self.state_space.dim, e.g. for pure state vecs. dims = [len(fct.to_dense(on_space='minimal')) for fct in self.factors] dim = int(_np.product(dims)) derivMx = _np.zeros((dim, self.num_params), typ) #Product rule to compute jacobian # loop over the spamvec/povm we differentiate wrt: for i, (fct, fct_local_inds, fct_dim) in enumerate(zip(self.factors, self._submember_rpindices, dims)): vec = fct if vec.num_params == 0: continue # no contribution deriv = vec.deriv_wrt_params(None) # TODO: use filter?? / make relative to this gate... deriv.shape = (fct_dim, vec.num_params) if i > 0: # factors before ith pre = self.factors[0].to_dense(on_space='minimal') for vecA in self.factors[1:i]: pre = _np.kron(pre, vecA.to_dense(on_space='minimal')) deriv = _np.kron(pre[:, None], deriv) # add a dummy 1-dim to 'pre' and do kron properly... if i + 1 < len(self.factors): # factors after ith post = self.factors[i + 1].to_dense(on_space='minimal') for vecA in self.factors[i + 2:]: post = _np.kron(post, vecA.to_dense(on_space='minimal')) deriv = _np.kron(deriv, post[:, None]) # add a dummy 1-dim to 'post' and do kron properly... assert(fct_local_inds is not None), \ "Error: gpindices has not been initialized for factor %d - cannot compute derivative!" % i derivMx[:, fct_local_inds] += deriv derivMx.shape = (dim, self.num_params) # necessary? if wrt_filter is None: return derivMx else: return _np.take(derivMx, wrt_filter, axis=1) def has_nonzero_hessian(self): """ Whether this state vector has a non-zero Hessian with respect to its parameters. Returns ------- bool """ return False def __str__(self): s = "Tensor product %s vector with length %d\n" % (self._prep_or_effect, self.dim) #ar = self.to_dense() #s += _mt.mx_to_string(ar, width=4, prec=2) # factors are just other States s += " x ".join([_mt.mx_to_string(fct.to_dense(on_space='minimal'), width=4, prec=2) for fct in self.factors]) return s

en

0.753729

The TensorProductState class and supporting functionality. #*************************************************************************************************** # Copyright 2015, 2019 National Technology & Engineering Solutions of Sandia, LLC (NTESS). # Under the terms of Contract DE-NA0003525 with NTESS, the U.S. Government retains certain rights # in this software. # 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 or in the LICENSE file in the root pyGSTi directory. #*************************************************************************************************** A state vector that is a tensor-product of other state vectors. Parameters ---------- factors : list of States a list of the component states to take the tensor product of. state_space : StateSpace, optional The state space for this operation. # do *not* copy - needs to reference common objects # initialize our gpindices based on sub-members # initializes rep data #Note: no to_memoized_dict needed, as ModelMember version does all we need. Get the ModelMember-derived objects contained in this one. Returns ------- list # factor POVM object An array of labels (usually strings) describing this model member's parameters. Return this state vector as a (dense) numpy array. The memory in `scratch` maybe used when it is not-None. Parameters ---------- on_space : {'minimal', 'Hilbert', 'HilbertSchmidt'} The space that the returned dense operation acts upon. For unitary matrices and bra/ket vectors, use `'Hilbert'`. For superoperator matrices and super-bra/super-ket vectors use `'HilbertSchmidt'`. `'minimal'` means that `'Hilbert'` is used if possible given this operator's evolution type, and otherwise `'HilbertSchmidt'` is used. scratch : numpy.ndarray, optional scratch space available for use. Returns ------- numpy.ndarray Get the `order`-th order Taylor-expansion terms of this state vector. This function either constructs or returns a cached list of the terms at the given order. Each term is "rank-1", meaning that it is a state preparation followed by or POVM effect preceded by actions on a density matrix `rho` of the form: `rho -> A rho B` The coefficients of these terms are typically polynomials of the State's parameters, where the polynomial's variable indices index the *global* parameters of the State's parent (usually a :class:`Model`) , not the State's local parameter array (i.e. that returned from `to_vector`). Parameters ---------- order : int The order of terms to get. max_polynomial_vars : int, optional maximum number of variables the created polynomials can have. return_coeff_polys : bool Whether a parallel list of locally-indexed (using variable indices corresponding to *this* object's parameters rather than its parent's) polynomial coefficients should be returned as well. Returns ------- terms : list A list of :class:`RankOneTerm` objects. coefficients : list Only present when `return_coeff_polys == True`. A list of *compact* polynomial objects, meaning that each element is a `(vtape,ctape)` 2-tuple formed by concatenating together the output of :method:`Polynomial.compact`. # num of qubits per factor # assumes density matrix evolution # total number of qubits # When possible, create COLLAPSED factor_lists so each factor has just a single # (State) pre & post op, which can be formed into the new terms' # TensorProdState ops. # - DON'T collapse stabilizer states & clifford ops - can't for POVMs # bool(self._evotype =="svterm") # need to use reps for collapsing now... TODO? # create a term with a TensorProdState - Note we always create # "prep"-mode vectors, since even when self._prep_or_effect == "effect" these # vectors are created with factor (prep- or effect-type) States not factor POVMs # we workaround this by still allowing such "prep"-mode # TensorProdStates to be represented as effects (i.e. in torep('effect'...) works) # then may need to add more ops. Assume factor ops are clifford gates # Embed each factors ops according to their target qubit(s) and just daisy chain them # embed and add ops # embed and add ops # with *local* indices #self.local_term_poly_coeffs[order] = coeffs_as_compact_polys #FUTURE? # Cache terms in FUTURE? Get the number of independent parameters which specify this state vector. Returns ------- int the number of independent parameters. Get the state vector parameters as an array of values. Returns ------- numpy array The parameters as a 1D array with length num_params(). Initialize the state vector using a 1D array of parameters. Parameters ---------- v : numpy array The 1D vector of state vector parameters. Length must == num_params() close : bool, optional Whether `v` is close to this state vector's current set of parameters. Under some circumstances, when this is true this call can be completed more quickly. dirty_value : bool, optional The value to set this object's "dirty flag" to before exiting this call. This is passed as an argument so it can be updated *recursively*. Leave this set to `True` unless you know what you're doing. Returns ------- None #Update representation, which may be a dense matrix or # just fast-kron arrays or a stabilizer state. # TODO - how does this apply to state reps?? The element-wise derivative this state vector. Construct a matrix whose columns are the derivatives of the state vector with respect to a single param. Thus, each column is of length dimension and there is one column per state vector parameter. An empty 2D array in the StaticState case (num_params == 0). Parameters ---------- wrt_filter : list or numpy.ndarray List of parameter indices to take derivative with respect to. (None means to use all the this operation's parameters.) Returns ------- numpy array Array of derivatives, shape == (dimension, num_params) #HACK to deal with fact that output of to_dense is really what is differentiated # but this may not match self.dim == self.state_space.dim, e.g. for pure state vecs. #Product rule to compute jacobian # loop over the spamvec/povm we differentiate wrt: # no contribution # TODO: use filter?? / make relative to this gate... # factors before ith # add a dummy 1-dim to 'pre' and do kron properly... # factors after ith # add a dummy 1-dim to 'post' and do kron properly... # necessary? Whether this state vector has a non-zero Hessian with respect to its parameters. Returns ------- bool #ar = self.to_dense() #s += _mt.mx_to_string(ar, width=4, prec=2) # factors are just other States

1.80285

2

edivorce/apps/core/views/graphql.py

gerritvdm/eDivorce

6

10223

<filename>edivorce/apps/core/views/graphql.py import graphene import graphene_django from django.http import HttpResponseForbidden from graphene_django.views import GraphQLView from graphql import GraphQLError from edivorce.apps.core.models import Document class PrivateGraphQLView(GraphQLView): def dispatch(self, request, *args, **kwargs): if not request.user.is_authenticated: return HttpResponseForbidden() return super().dispatch(request, *args, **kwargs) class DocumentType(graphene_django.DjangoObjectType): file_url = graphene.String(source='get_file_url') content_type = graphene.String(source='get_content_type') class Meta: model = Document exclude = ('id', 'file') class Query(graphene.ObjectType): documents = graphene.List(DocumentType, doc_type=graphene.String(required=True), party_code=graphene.Int(required=True)) def resolve_documents(self, info, **kwargs): if info.context.user.is_anonymous: raise GraphQLError('Unauthorized') q = Document.objects.filter(bceid_user=info.context.user, **kwargs) for doc in q: if not doc.file_exists(): q.delete() return Document.objects.none() return q class DocumentInput(graphene.InputObjectType): filename = graphene.String(required=True) size = graphene.Int(required=True) width = graphene.Int() height = graphene.Int() rotation = graphene.Int() class DocumentMetaDataInput(graphene.InputObjectType): files = graphene.List(DocumentInput, required=True) doc_type = graphene.String(required=True) party_code = graphene.Int(required=True) class UpdateMetadata(graphene.Mutation): class Arguments: input = DocumentMetaDataInput(required=True) documents = graphene.List(DocumentType) def mutate(self, info, **kwargs): input_ = kwargs['input'] documents = Document.objects.filter(bceid_user=info.context.user, doc_type=input_['doc_type'], party_code=input_['party_code']) unique_files = [dict(s) for s in set(frozenset(d.items()) for d in input_['files'])] if documents.count() != len(input_['files']) or documents.count() != len(unique_files): raise GraphQLError("Invalid input: there must be the same number of files") for i, file in enumerate(input_['files']): try: doc = documents.get(filename=file['filename'], size=file['size']) doc.sort_order = i + 1 doc.width = file.get('width', doc.width) doc.height = file.get('height', doc.height) doc.rotation = file.get('rotation', doc.rotation) if doc.rotation not in [0, 90, 180, 270]: raise GraphQLError(f"Invalid rotation {doc.rotation}, must be 0, 90, 180, 270") doc.save() except Document.DoesNotExist: raise GraphQLError(f"Couldn't find document '{file['filename']}' with size '{file['size']}'") return UpdateMetadata(documents=documents.all()) class Mutations(graphene.ObjectType): update_metadata = UpdateMetadata.Field() graphql_schema = graphene.Schema(query=Query, mutation=Mutations)

none

1

2.255126

2

amazing/maze.py

danieloconell/maze-solver

0

10224

from .exceptions import MazeNotSolved, AlgorithmNotFound from .dijkstra import Dijkstra from .astar import Astar from functools import wraps import warnings from daedalus import Maze as _maze from PIL import Image warnings.simplefilter("once", UserWarning) class Maze: """ Create a maze and solve it. Available algorithms: dijkstra astar (WIP) Steps: 1. Create maze using the daedalus library. 2. Convert maze to graph. 3. Solve maze with algorithm. """ WHITE = (0, 0, 0) BLACK = (255, 255, 255) RED = (255, 0, 0) def __init__(self, width, height, algorithm="dijkstra"): """Set algorithm to be used when solving. Args: algorithm (str) to be used when solving maze width (int) of maze in pixels height (int) of maze in pixels """ self.algorithm = algorithm if not width % 2 or not height % 2: warnings.warn( "Using even width or height, use even numbers for optimal images" ) self._create_maze(width, height) self._create_graph() self.width = width self.height = height def _create_maze(self, width, height): """Make maze to be solved and add border to maze. Args: width (int) of maze height (int) of maze """ # create maze self.maze = _maze(width, height) self.maze.create_perfect() # define maze variables self.entrance = self.maze.entrance self.exit = self.maze.exit # add index to maze self.maze = { row_i: {item_i: item for item_i, item in enumerate(row)} for row_i, row in enumerate(self.maze) } def _create_graph(self): """Remove unnecessary states from maze and convert maze to graph to be solved.""" self.graph = {} # convert to graph for column in self.maze.keys(): for row in self.maze[column].keys(): item = self.maze[column][row] if item != 1: neighbours = [] try: if self.maze[column][row - 1] != 1: neighbours.append(["left", (column, row - 1)]) except KeyError: None try: if self.maze[column][row + 1] != 1: neighbours.append(["right", (column, row + 1)]) except KeyError: None try: if self.maze[column - 1][row] != 1: neighbours.append(["above", (column - 1, row)]) except KeyError: None try: if self.maze[column + 1][row] != 1: neighbours.append(["below", (column + 1, row)]) except KeyError: None self.graph[(column, row)] = {x[:][1]: 1 for x in neighbours} # TODO: remove unnecessary states def _maze_maker(file_name): def real_decorator(func): @wraps(func) def wrapper(self, *args, **kwargs): data = [] for row_i, row in enumerate(list(self.maze)): for item_i, item in enumerate(self.maze[row].values()): func(self, data, item, row_i=row_i, item_i=item_i) # save maze image = Image.new("RGB", (self.width, self.height)) image.putdata(data) image.save(file_name) return wrapper return real_decorator @_maze_maker("maze.png") def save(self, data, item, row_i=None, item_i=None): """Save maze locally as an image.""" # invert maze because maze is incorrect if item: data.append(self.WHITE) else: data.append(self.BLACK) def solve(self): """ Solve maze using specified algorithm. Returns: shortest path as a queue from start to finish of maze """ if self.algorithm == "astar": algorithm = Astar() elif self.algorithm == "dijkstra": algorithm = Dijkstra() else: raise AlgorithmNotFound( f"Invalid algorithm: {self.algorithm}. See help({type(self).__name__}) for available algorithms." ) # add nodes to graph for node in self.graph: algorithm.add_node(node, self.graph[node]) # pydaedalus stores y then x value which need to be reversed self.entrance = tuple(reversed(self.entrance)) self.exit = tuple(reversed(self.exit)) self.path = algorithm.shortest_path(self.entrance, self.exit) @_maze_maker("solution.png") def save_solution(self, data, item, row_i=None, item_i=None): """Save maze image and the shortest path.""" if not hasattr(self, "path"): raise MazeNotSolved( f"Maze must be solved to save solution. Run {type(self).__name__}.solve() first." ) if (row_i, item_i) in self.path: data.append(self.RED) elif item: data.append(self.WHITE) else: data.append(self.BLACK) def __str__(self): """Just cause it looks nice.""" string = [] for row in self.maze: string.append(["█" if item else " " for item in self.maze[row].values()]) return "\n".join(["".join(line) for line in string]) def __repr__(self): """Easier on the eyes.""" return f"Maze(algorithm='{self.algorithm}', width={self.width}, height={self.height})"

en

0.809515

Create a maze and solve it. Available algorithms: dijkstra astar (WIP) Steps: 1. Create maze using the daedalus library. 2. Convert maze to graph. 3. Solve maze with algorithm. Set algorithm to be used when solving. Args: algorithm (str) to be used when solving maze width (int) of maze in pixels height (int) of maze in pixels Make maze to be solved and add border to maze. Args: width (int) of maze height (int) of maze # create maze # define maze variables # add index to maze Remove unnecessary states from maze and convert maze to graph to be solved. # convert to graph # TODO: remove unnecessary states # save maze Save maze locally as an image. # invert maze because maze is incorrect Solve maze using specified algorithm. Returns: shortest path as a queue from start to finish of maze # add nodes to graph # pydaedalus stores y then x value which need to be reversed Save maze image and the shortest path. Just cause it looks nice. Easier on the eyes.

3.357956

3

config.py

FarbodFarhangfar/midi_player_python

0

10225

<reponame>FarbodFarhangfar/midi_player_python import os def get_note_dic(): _note_dic = {'C': 0, 'C#': 1, 'Db': 1, 'D': 2, 'D#': 3, 'Eb': 3, 'E': 4, 'F': 5, 'F#': 6, 'Gb': 6, 'G': 7, 'G#': 8, 'Ab': 8, 'A': 9, 'A#': 10, 'Bb': 10, 'B': 11} return _note_dic def get_value_list(): values = {"16": 16, "8": 8, "4": 4, "2": 2, "1": 1, "0.5": 0.5, "1/2": 0.5, "0.25": 0.25, "1/4": 0.25, "0.125": 0.125, "1/8": 0.125, "0.0625": 0.0625, "1/16": 0.0625, "0.03125": 0.03125, "1/32": 0.03125} return values def instruments(inst): instruments_dict = { # Piano 'Acoustic Grand Piano': '1', 'Bright Acoustic Piano': '2', 'Electric Grand Piano': '3', 'Honky-tonk Piano': '4', 'Electric Piano 1': '5', 'Electric Piano 2': '6', 'Harpsichord': '7', 'Clavi': '8', # Chromatic Percussion 'Celesta': '9', 'Glockenspiel': '10', 'Music Box': '11', 'Vibraphone': '12', 'Marimba': '13', 'Xylophone': '14', 'Tubular Bells': '15', 'Dulcimer': '16', # Organ 'Drawbar Organ': '17', 'Percussive Organ': '18', 'Rock Organ': '19', 'Church Organ': '20', 'Reed Organ': '21', 'Accordion': '22', 'Harmonica': '23', 'Tango Accordion': '24', # Guitar 'Acoustic Guitar (nylon)': '25', 'Acoustic Guitar (steel)': '26', 'Electric Guitar (jazz)': '27', 'Electric Guitar (clean)': '28', 'Electric Guitar (muted)': '29', 'Overdriven Guitar': '30', 'Distortion Guitar': '31', 'Guitar Harmonics': '32', # Bass 'Acoustic Bass': '33', 'Electric Bass (finger)': '34', 'Electric Bass (pick)': '35', 'Fretless Bass': '36', 'Slap Bass 1': '37', 'Slap Bass 2': '38', 'Synth Bass 1': '39', 'Synth Bass 2': '40', # Strings 'Violin': '41', 'Viola': '42', 'Cello': '43', 'Contrabass': '44', 'Tremolo Strings': '45', 'Pizzicato Strings': '46', 'Orchestral Harp': '47', 'Timpani': '48', # Ensemble 'String Ensemble 1': '49', 'String Ensemble 2': '50', 'Synth Strings 1': '51', 'Synth Strings 2': '52', 'Choir Aahs': '53', 'Voice Oohs': '54', 'Synth Choir': '55', 'Orchestra Hit': '56', # Brass 'Trumpet': '57', 'Trombone': '58', 'Tuba': '59', 'Muted Trumpet': '60', 'French Horn': '61', 'Brass Section': '62', 'Synth Brass 1': '63', 'Synth Brass 2': '64', # Reed 'Soprano Sax': '65', 'Alto Sax': '66', 'Tenor Sax': '67', 'Baritone Sax': '68', 'Oboe': '69', 'English Horn': '70', 'Bassoon': '71', 'Clarinet': '72', # Pipe 'Piccolo': '73', 'Flute': '74', 'Recorder': '75', 'Pan Flute': '76', 'Blown bottle': '77', 'Shakuhachi': '78', 'Whistle': '79', 'Ocarina': '80', # Synth Lead 'Lead 1 (square)': '81', 'Lead 2 (sawtooth)': '82', 'Lead 3 (calliope)': '83', 'Lead 4 (chiff)': '84', 'Lead 5 (charang)': '85', 'Lead 6 (voice)': '86', 'Lead 7 (fifths)': '87', 'Lead 8 (bass + lead)': '88', # Synth Pad 'Pad 1 (new age)': '89', 'Pad 2 (warm)': '90', 'Pad 3 (polysynth)': '91', 'Pad 4 (choir)': '92', 'Pad 5 (bowed)': '93', 'Pad 6 (metallic)': '94', 'Pad 7 (halo)': '95', 'Pad 8 (sweep)': '96', # Synth Effects 'FX 1 (rain)': '97', 'FX 2 (soundtrack)': '98', 'FX 3 (crystal)': '99', 'FX 4 (atmosphere)': '100', 'FX 5 (brightness)': '101', 'FX 6 (goblins)': '102', 'FX 7 (echoes)': '103', 'FX 8 (sci-fi)': '104', # Ethnic 'Sitar': '105', 'Banjo': '106', 'Shamisen': '107', 'Koto': '108', 'Kalimba': '109', 'Bagpipe': '110', 'Fiddle': '111', 'Shanai': '112', # Percussive 'Tinkle Bell': '113', 'Agogo': '114', 'Steel Drums': '115', 'Woodblock': '116', 'Taiko Drum': '117', 'Melodic Tom': '118', 'Synth Drum': '119', 'Reverse Cymbal': '120', # Sound effects 'Guitar Fret Noise': '121', 'Breath Noise': '122', 'Seashore': '123', 'Bird Tweet': '124', 'Telephone Ring': '125', 'Helicopter': '126', 'Applause': '127'} return instruments_dict

import os def get_note_dic(): _note_dic = {'C': 0, 'C#': 1, 'Db': 1, 'D': 2, 'D#': 3, 'Eb': 3, 'E': 4, 'F': 5, 'F#': 6, 'Gb': 6, 'G': 7, 'G#': 8, 'Ab': 8, 'A': 9, 'A#': 10, 'Bb': 10, 'B': 11} return _note_dic def get_value_list(): values = {"16": 16, "8": 8, "4": 4, "2": 2, "1": 1, "0.5": 0.5, "1/2": 0.5, "0.25": 0.25, "1/4": 0.25, "0.125": 0.125, "1/8": 0.125, "0.0625": 0.0625, "1/16": 0.0625, "0.03125": 0.03125, "1/32": 0.03125} return values def instruments(inst): instruments_dict = { # Piano 'Acoustic Grand Piano': '1', 'Bright Acoustic Piano': '2', 'Electric Grand Piano': '3', 'Honky-tonk Piano': '4', 'Electric Piano 1': '5', 'Electric Piano 2': '6', 'Harpsichord': '7', 'Clavi': '8', # Chromatic Percussion 'Celesta': '9', 'Glockenspiel': '10', 'Music Box': '11', 'Vibraphone': '12', 'Marimba': '13', 'Xylophone': '14', 'Tubular Bells': '15', 'Dulcimer': '16', # Organ 'Drawbar Organ': '17', 'Percussive Organ': '18', 'Rock Organ': '19', 'Church Organ': '20', 'Reed Organ': '21', 'Accordion': '22', 'Harmonica': '23', 'Tango Accordion': '24', # Guitar 'Acoustic Guitar (nylon)': '25', 'Acoustic Guitar (steel)': '26', 'Electric Guitar (jazz)': '27', 'Electric Guitar (clean)': '28', 'Electric Guitar (muted)': '29', 'Overdriven Guitar': '30', 'Distortion Guitar': '31', 'Guitar Harmonics': '32', # Bass 'Acoustic Bass': '33', 'Electric Bass (finger)': '34', 'Electric Bass (pick)': '35', 'Fretless Bass': '36', 'Slap Bass 1': '37', 'Slap Bass 2': '38', 'Synth Bass 1': '39', 'Synth Bass 2': '40', # Strings 'Violin': '41', 'Viola': '42', 'Cello': '43', 'Contrabass': '44', 'Tremolo Strings': '45', 'Pizzicato Strings': '46', 'Orchestral Harp': '47', 'Timpani': '48', # Ensemble 'String Ensemble 1': '49', 'String Ensemble 2': '50', 'Synth Strings 1': '51', 'Synth Strings 2': '52', 'Choir Aahs': '53', 'Voice Oohs': '54', 'Synth Choir': '55', 'Orchestra Hit': '56', # Brass 'Trumpet': '57', 'Trombone': '58', 'Tuba': '59', 'Muted Trumpet': '60', 'French Horn': '61', 'Brass Section': '62', 'Synth Brass 1': '63', 'Synth Brass 2': '64', # Reed 'Soprano Sax': '65', 'Alto Sax': '66', 'Tenor Sax': '67', 'Baritone Sax': '68', 'Oboe': '69', 'English Horn': '70', 'Bassoon': '71', 'Clarinet': '72', # Pipe 'Piccolo': '73', 'Flute': '74', 'Recorder': '75', 'Pan Flute': '76', 'Blown bottle': '77', 'Shakuhachi': '78', 'Whistle': '79', 'Ocarina': '80', # Synth Lead 'Lead 1 (square)': '81', 'Lead 2 (sawtooth)': '82', 'Lead 3 (calliope)': '83', 'Lead 4 (chiff)': '84', 'Lead 5 (charang)': '85', 'Lead 6 (voice)': '86', 'Lead 7 (fifths)': '87', 'Lead 8 (bass + lead)': '88', # Synth Pad 'Pad 1 (new age)': '89', 'Pad 2 (warm)': '90', 'Pad 3 (polysynth)': '91', 'Pad 4 (choir)': '92', 'Pad 5 (bowed)': '93', 'Pad 6 (metallic)': '94', 'Pad 7 (halo)': '95', 'Pad 8 (sweep)': '96', # Synth Effects 'FX 1 (rain)': '97', 'FX 2 (soundtrack)': '98', 'FX 3 (crystal)': '99', 'FX 4 (atmosphere)': '100', 'FX 5 (brightness)': '101', 'FX 6 (goblins)': '102', 'FX 7 (echoes)': '103', 'FX 8 (sci-fi)': '104', # Ethnic 'Sitar': '105', 'Banjo': '106', 'Shamisen': '107', 'Koto': '108', 'Kalimba': '109', 'Bagpipe': '110', 'Fiddle': '111', 'Shanai': '112', # Percussive 'Tinkle Bell': '113', 'Agogo': '114', 'Steel Drums': '115', 'Woodblock': '116', 'Taiko Drum': '117', 'Melodic Tom': '118', 'Synth Drum': '119', 'Reverse Cymbal': '120', # Sound effects 'Guitar Fret Noise': '121', 'Breath Noise': '122', 'Seashore': '123', 'Bird Tweet': '124', 'Telephone Ring': '125', 'Helicopter': '126', 'Applause': '127'} return instruments_dict

en

0.497871

#': 1, 'Db': 1, 'D': 2, 'D#': 3, 'Eb': 3, 'E': 4, 'F': 5, 'F#': 6, #': 8, 'Ab': 8, 'A': 9, 'A#': 10, 'Bb': 10, 'B': 11} # Piano # Chromatic Percussion # Organ # Guitar # Bass # Strings # Ensemble # Brass # Reed # Pipe # Synth Lead # Synth Pad # Synth Effects # Ethnic # Percussive # Sound effects

2.8247

3

roles/openshift_health_checker/library/ocutil.py

shgriffi/openshift-ansible

164

10226

#!/usr/bin/python """Interface to OpenShift oc command""" import os import shlex import shutil import subprocess from ansible.module_utils.basic import AnsibleModule ADDITIONAL_PATH_LOOKUPS = ['/usr/local/bin', os.path.expanduser('~/bin')] def locate_oc_binary(): """Find and return oc binary file""" # https://github.com/openshift/openshift-ansible/issues/3410 # oc can be in /usr/local/bin in some cases, but that may not # be in $PATH due to ansible/sudo paths = os.environ.get("PATH", os.defpath).split(os.pathsep) + ADDITIONAL_PATH_LOOKUPS oc_binary = 'oc' # Use shutil.which if it is available, otherwise fallback to a naive path search try: which_result = shutil.which(oc_binary, path=os.pathsep.join(paths)) if which_result is not None: oc_binary = which_result except AttributeError: for path in paths: if os.path.exists(os.path.join(path, oc_binary)): oc_binary = os.path.join(path, oc_binary) break return oc_binary def main(): """Module that executes commands on a remote OpenShift cluster""" module = AnsibleModule( argument_spec=dict( namespace=dict(type="str", required=False), config_file=dict(type="str", required=True), cmd=dict(type="str", required=True), extra_args=dict(type="list", default=[]), ), ) cmd = [locate_oc_binary(), '--config', module.params["config_file"]] if module.params["namespace"]: cmd += ['-n', module.params["namespace"]] cmd += shlex.split(module.params["cmd"]) + module.params["extra_args"] failed = True try: cmd_result = subprocess.check_output(list(cmd), stderr=subprocess.STDOUT) failed = False except subprocess.CalledProcessError as exc: cmd_result = '[rc {}] {}\n{}'.format(exc.returncode, ' '.join(exc.cmd), exc.output) except OSError as exc: # we get this when 'oc' is not there cmd_result = str(exc) module.exit_json( changed=False, failed=failed, result=cmd_result, ) if __name__ == '__main__': main()

en

0.793541

#!/usr/bin/python Interface to OpenShift oc command Find and return oc binary file # https://github.com/openshift/openshift-ansible/issues/3410 # oc can be in /usr/local/bin in some cases, but that may not # be in $PATH due to ansible/sudo # Use shutil.which if it is available, otherwise fallback to a naive path search Module that executes commands on a remote OpenShift cluster # we get this when 'oc' is not there

2.471472

2

code/network/__init__.py

michalochman/complex-networks

0

10227

import fractions class Network(object): def __init__(self, network): self.network = network def degree(self, link_type, key): return len(self.network.get(link_type).get(key)) def average_degree(self, link_type): degree = 0 for link in self.network.get(link_type).itervalues(): degree += len(link) return float(degree) / float(len(self.network.get(link_type))) def nn_degree(self, link_type, link_n_type, key): degree = self.degree(link_type, key) nn_degree = 0 for n_key in self.network.get(link_type, key): nn_degree += self.degree(link_n_type, n_key) return '%d/%d' % (nn_degree, degree) def jaccard_index(self, set_a, set_b): n = len(set_a & set_b) return float(n)/float(len(set_a) + len(set_b) - n) def jaccard_similarity(self, link_type, key_a, key_b, return_string=False): key_a = int(key_a) key_b = int(key_b) set_a = set(self.network.get(link_type).get(key_a).values()) set_b = set(self.network.get(link_type).get(key_b).values()) if return_string: intersection = len(set_a & set_b) union = len(set_a | set_b) gcd = fractions.gcd(intersection, union) return '%d/%d' % (intersection/gcd, union/gcd) return self.jaccard_index(set_a, set_b) def collaborative_similarity(self, link_type, link_n_type, key, return_string=False): degree = self.degree(link_type, key) if degree <= 1: return 0 similarity_sum = 0 for n_key_1 in self.network.get(link_type).get(key).itervalues(): for n_key_2 in self.network.get(link_type).get(key).itervalues(): if n_key_1 == n_key_2: continue similarity_sum += self.jaccard_similarity(link_n_type, n_key_1, n_key_2) if return_string: precision = 1e3 new_similarity_sum = round(similarity_sum * degree*(degree-1) * precision) gcd = fractions.gcd(new_similarity_sum, degree*(degree-1) * precision) new_similarity_sum /= gcd return '%d/%d' % (new_similarity_sum, degree*(degree-1)*round(new_similarity_sum/similarity_sum)) return similarity_sum / (degree*(degree-1)) def average_jaccard_similarity(self, link_type, link_n_type, return_string=False): nodes = 0 similarity_sum = 0 for key_links in self.network.get(link_type).itervalues(): for n_key_1 in key_links.itervalues(): for n_key_2 in key_links.itervalues(): if n_key_1 == n_key_2: continue nodes += 1 similarity_sum += self.jaccard_similarity(link_n_type, n_key_1, n_key_2) if nodes == 0: return 0 if return_string: precision = 1e3 new_similarity_sum = round(similarity_sum * nodes * precision) gcd = fractions.gcd(new_similarity_sum, nodes * precision) new_similarity_sum /= gcd return '%d/%d' % (new_similarity_sum, nodes*round(new_similarity_sum/similarity_sum)) return similarity_sum / nodes def network_collaborative_similarity(self, link_type, link_n_type, return_string=False): nodes = 0 similarity_sum = 0 for key, key_links in self.network.get(link_type).iteritems(): if self.degree(link_type, key) <= 1: continue nodes += 1 collaborative_similarity = self.collaborative_similarity(link_type, link_n_type, key) similarity_sum += collaborative_similarity if nodes == 0: return 0 if return_string: precision = 1e3 new_similarity_sum = round(similarity_sum * nodes * precision) gcd = fractions.gcd(new_similarity_sum, nodes * precision) new_similarity_sum /= gcd return '%d/%d' % (new_similarity_sum, nodes*(new_similarity_sum/similarity_sum)) return similarity_sum/nodes

none

1

3.337132

3

invoke_ansible.py

samvarankashyap/ansible_api_usage

0

10228

import ansible import pprint from ansible import utils from jinja2 import Environment, PackageLoader from collections import namedtuple from ansible import utils from ansible.parsing.dataloader import DataLoader from ansible.vars import VariableManager from ansible.inventory import Inventory from ansible.executor.playbook_executor import PlaybookExecutor from ansible.plugins.callback import CallbackBase from callbacks import PlaybookCallback def invoke_ansible_playbook(module_path, e_vars, playbook_path="site.yml", console=True): """ Invokes playbook """ loader = DataLoader() variable_manager = VariableManager() variable_manager.extra_vars = e_vars inventory = Inventory(loader=loader, variable_manager=variable_manager, host_list=['localhost']) passwords = {} utils.VERBOSITY = 4 Options = namedtuple('Options', ['listtags', 'listtasks', 'listhosts', 'syntax', 'connection', 'module_path', 'forks', 'remote_user', 'private_key_file', 'ssh_common_args', 'ssh_extra_args', 'sftp_extra_args', 'scp_extra_args', 'become', 'become_method', 'become_user', 'verbosity', 'check']) options = Options(listtags=False, listtasks=False, listhosts=False, syntax=False, connection='ssh', module_path=module_path, forks=100, remote_user='root', private_key_file=None, ssh_common_args=None, ssh_extra_args=None, sftp_extra_args=None, scp_extra_args=None, become=False, become_method=None, become_user='root', verbosity=utils.VERBOSITY, check=False) pbex = PlaybookExecutor(playbooks=[playbook_path], inventory=inventory, variable_manager=variable_manager, loader=loader, options=options, passwords=passwords) if not console: cb = PlaybookCallback() pbex._tqm._stdout_callback = cb return_code = pbex.run() results = cb.results else: results = pbex.run() return results

en

0.212026

Invokes playbook

2.031261

2

bin/python/csv2es.py

reid-wagner/proteomics-pipelines

2

10229

#!/usr/bin/env python3 import itertools import string from elasticsearch import Elasticsearch,helpers import sys import os from glob import glob import pandas as pd import json host = sys.argv[1] port = int(sys.argv[2]) alias = sys.argv[3] print(host) print(port) print(alias) es = Elasticsearch([{'host': host, 'port': port}]) # create our test index # Get all csv files in /root/data files = [y for x in os.walk('/root/data') for y in glob(os.path.join(x[0], '*.csv'))] count = 0 def clean_field(val): val = val.split('.') val = [i for i in val if i != ''] val = '_'.join(val) val = val.split() val = [i for i in val if i != ''] val = '_'.join(val) val = val.split('/') val = [i for i in val if i != ''] val = '_'.join(val) return val es.indices.delete(index=alias + '*', ignore=[400, 404]) indices = [] for file in files: data = pd.read_csv(file, sep=None, engine='python') index = alias + '_'.join(file.split('/')) index = clean_field(index).lower().split('_csv')[0] indices.append(index) es.indices.create(index) for col in data.columns: if col.startswith('Unnamed'): del data[col] else: data.rename(columns= { col : clean_field(col) },inplace=True ) data = data.reset_index() # Make sure there is no duplicate indexing data.rename(columns={'index':'row'},inplace =True) data['File'] = file data['_id'] = data['File'] + '.{}.'.format(str(count)) + data.reset_index()['index'].apply(str) data['_type'] = "document" data['_index'] = index records = data.to_json(orient='records') records = json.loads(records) helpers.bulk(es, records, chunk_size=100) count += 1 print(es.count(index=index)) # Create an index table in elasticsearch to locate the files indices_table = pd.DataFrame() indices_table['Index'] = pd.Series(indices) indices_table['File'] = pd.Series(files) indices_table['Alias'] = alias indices_table['_id'] = indices_table['Alias'] + '.' + indices_table['File'] indices_table['_type'] = "document" indices_table['_index'] = alias + '_indices' es.indices.create(alias + '_indices') records = indices_table.to_json(orient='records') records = json.loads(records) helpers.bulk(es, records, chunk_size=100) print(es.count(index=alias + '_indices'))

en

0.575823

#!/usr/bin/env python3 # create our test index # Get all csv files in /root/data # Make sure there is no duplicate indexing # Create an index table in elasticsearch to locate the files

2.789907

3

main/src/preparation/parsers/tree-sitter-python/examples/crlf-line-endings.py

jason424217/Artificial-Code-Gen

0

10230

<filename>main/src/preparation/parsers/tree-sitter-python/examples/crlf-line-endings.py<gh_stars>0 print a if b: if c: d e

none

1

1.835831

2

Src/main.py

DukeA/DAT02X-19-03-MachineLearning-Starcraft2

0

10231

from absl import app from mainLoop import main if __name__ == '__main__': app.run(main)

none

1

1.250541

1

bos_sarcat_scraper/__main__.py

hysds/bos_sarcat_scraper

1

10232

<gh_stars>1-10 from __future__ import absolute_import from builtins import str from builtins import input import sys import argparse from . import bosart_scrape import datetime import json def valid_date(s): try: try: date = datetime.datetime.strptime(s, "%Y-%m-%dT%H:%M:%S.%fZ") except: date = datetime.datetime.strptime(s, "%Y-%m-%dT%H:%M:%SZ") return date except ValueError: msg = "Not a valid date: '{0}'.".format(s) raise argparse.ArgumentTypeError(msg) def geojson(spatial_extent): if type(json.loads(spatial_extent)) is dict: return spatial_extent def sort_field(s_f): if s_f == "start_time" or s_f == "stop_time" or s_f == "bos_ingest": return s_f else: raise argparse.ArgumentError("The value for sortBy should be either start_time, stop_time or bos_ingest not %s."%s_f) def sort_order(order): if order == "asc" or order == "des": return order else: raise argparse.ArgumentError("The value for sort should be either asc or des not %s,"%order) def check_inputs(args): yes = "y" no = "n" if not args.fromTime and not args.fromBosIngestTime: print ("You have NOT specified any start time using --fromTime, -from or --fromBosIngestTime. \nYou are asking to find all acquisitions from the beginning of time! \nThis query will take a very long time.\nTHIS IS NOT RECOMMENDED.") resp = str(eval(input('Are you sure you want to proceed? (y/n):'))) if resp.lower() == yes.lower(): print("Okay! Please wait...") return True elif resp.lower() == no.lower(): print("Please try again with the start time specified using --fromTime, -from or --fromBosIngestTime.") exit() else: print("Please specify y/n\n") return False return True def main(): parser = argparse.ArgumentParser(description='Query BOS SarCat for acquisitions.') parser.add_argument("-from","--fromTime", help='specify the temporal start point in format , to get acquisitions starting after the given timestamp in the format yyyy-mm-ddThh:mm:ss.sssZ', type=valid_date) parser.add_argument("--fromBosIngestTime", help='provide date and time in format , to get acquisitions acquired by BOS after the given timestamp in the format yyyy-mm-ddThh:mm:ss.sssZ', type=valid_date) parser.add_argument("-to","--toTime", help='specify the temporal end point in format , to get acquisitions ending before the given timestamp in the format yyyy-mm-ddThh:mm:ss.sssZ', type=valid_date) parser.add_argument("--spatialExtent", help='specify the area of interest in GeoJSON format', type = geojson) parser.add_argument("--sortBy", help='type "start_time" , "stop_time" or "bos_ingest" to sort results by field', type = sort_field) parser.add_argument("--sort", help='type "asc" or "des" to get results in ascending or descending order of time respectively. If sortBy is specified but sort is not, then defaults to ascending', type = sort_order) args = parser.parse_args() checked = False while not checked: checked = check_inputs(args) # construct the parameter list based on user specified restrictions params = {} if args.fromTime: params["fromTime"] = args.fromTime if args.fromBosIngestTime: params["fromBosIngestTime"] = args.fromBosIngestTime if args.toTime: params["toTime"] = args.toTime if args.spatialExtent: params["spatialExtent"] = json.dumps(args.spatialExtent) if args.sortBy: params["sortBy"] = args.sortBy if args.sort: params["sort"] = args.sort print(bosart_scrape.make_api_call(parameters=params)) if __name__ == '__main__': main()

from __future__ import absolute_import from builtins import str from builtins import input import sys import argparse from . import bosart_scrape import datetime import json def valid_date(s): try: try: date = datetime.datetime.strptime(s, "%Y-%m-%dT%H:%M:%S.%fZ") except: date = datetime.datetime.strptime(s, "%Y-%m-%dT%H:%M:%SZ") return date except ValueError: msg = "Not a valid date: '{0}'.".format(s) raise argparse.ArgumentTypeError(msg) def geojson(spatial_extent): if type(json.loads(spatial_extent)) is dict: return spatial_extent def sort_field(s_f): if s_f == "start_time" or s_f == "stop_time" or s_f == "bos_ingest": return s_f else: raise argparse.ArgumentError("The value for sortBy should be either start_time, stop_time or bos_ingest not %s."%s_f) def sort_order(order): if order == "asc" or order == "des": return order else: raise argparse.ArgumentError("The value for sort should be either asc or des not %s,"%order) def check_inputs(args): yes = "y" no = "n" if not args.fromTime and not args.fromBosIngestTime: print ("You have NOT specified any start time using --fromTime, -from or --fromBosIngestTime. \nYou are asking to find all acquisitions from the beginning of time! \nThis query will take a very long time.\nTHIS IS NOT RECOMMENDED.") resp = str(eval(input('Are you sure you want to proceed? (y/n):'))) if resp.lower() == yes.lower(): print("Okay! Please wait...") return True elif resp.lower() == no.lower(): print("Please try again with the start time specified using --fromTime, -from or --fromBosIngestTime.") exit() else: print("Please specify y/n\n") return False return True def main(): parser = argparse.ArgumentParser(description='Query BOS SarCat for acquisitions.') parser.add_argument("-from","--fromTime", help='specify the temporal start point in format , to get acquisitions starting after the given timestamp in the format yyyy-mm-ddThh:mm:ss.sssZ', type=valid_date) parser.add_argument("--fromBosIngestTime", help='provide date and time in format , to get acquisitions acquired by BOS after the given timestamp in the format yyyy-mm-ddThh:mm:ss.sssZ', type=valid_date) parser.add_argument("-to","--toTime", help='specify the temporal end point in format , to get acquisitions ending before the given timestamp in the format yyyy-mm-ddThh:mm:ss.sssZ', type=valid_date) parser.add_argument("--spatialExtent", help='specify the area of interest in GeoJSON format', type = geojson) parser.add_argument("--sortBy", help='type "start_time" , "stop_time" or "bos_ingest" to sort results by field', type = sort_field) parser.add_argument("--sort", help='type "asc" or "des" to get results in ascending or descending order of time respectively. If sortBy is specified but sort is not, then defaults to ascending', type = sort_order) args = parser.parse_args() checked = False while not checked: checked = check_inputs(args) # construct the parameter list based on user specified restrictions params = {} if args.fromTime: params["fromTime"] = args.fromTime if args.fromBosIngestTime: params["fromBosIngestTime"] = args.fromBosIngestTime if args.toTime: params["toTime"] = args.toTime if args.spatialExtent: params["spatialExtent"] = json.dumps(args.spatialExtent) if args.sortBy: params["sortBy"] = args.sortBy if args.sort: params["sort"] = args.sort print(bosart_scrape.make_api_call(parameters=params)) if __name__ == '__main__': main()

en

0.404797

# construct the parameter list based on user specified restrictions

2.897735

3

vgm2electron.py

simondotm/vgm2electron

2

10233

#!/usr/bin/env python # vgm2electron.py # Tool for converting SN76489-based PSG VGM data to Acorn Electron # By <NAME> (https://github.com/simondotm/) # See https://github.com/simondotm/vgm-packer # # Copyright (c) 2019 <NAME>. All rights reserved. # # "MIT License": # 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. import functools import itertools import struct import sys import time import binascii import math import operator import os from modules.vgmparser import VgmStream class VgmElectron: OUTPUT_RAWDATA = False # output raw dumps of the data that was compressed by LZ4/Huffman VERBOSE = True # 0-3 represents approx the loudest 50% of volumes (=ON), 4-15 are the quietest 50% (=OFF) ATTENTUATION_THRESHOLD1 = 10 ATTENTUATION_THRESHOLD2 = 10 ATTENTUATION_THRESHOLD3 = 10 # define the number of octaves to transpose whole song by, in case too much bass getting lost TRANSPOSE_OCTAVES1 = 0 TRANSPOSE_OCTAVES2 = 0 TRANSPOSE_OCTAVES3 = 0 #-1 ENABLE_CHANNEL1 = True ENABLE_CHANNEL2 = True ENABLE_CHANNEL3 = True USE_TECHNIQUE = 2 def __init__(self): print("init") #---------------------------------------------------------- # Utilities #---------------------------------------------------------- # split the packed raw data into 11 separate streams # returns array of 11 bytearrays def split_raw(self, rawData, stripCommands = True): registers = [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] registers_opt = [ 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] latched_channel = -1 output_block = bytearray() output_blocks = [] for o in range(11): output_blocks.append( bytearray() ) if stripCommands: register_mask = 15 else: register_mask = 255 # unpack the raw binary data in 11 arrays of register data without any deltas between them # eg. the raw chip writes to all 11 registers every frame n = 0 Packet = True verbose = False while (Packet): packet_size = rawData[n] if verbose: print("packet_size=" + str(packet_size)) n += 1 if packet_size == 255: Packet = False else: for x in range(packet_size): d = rawData[n+x] #if verbose: # print " frame byte number=" +str(x) # print " frame byte=" +str(d) if d & 128: # latch c = (d>>5)&3 latched_channel = c if d & 16: # volume if verbose: print(" volume on channel " + str(c)) registers[c+7] = d & register_mask else: # tone if verbose: print(" tone on channel " + str(c)) registers[c*2+0] = d & register_mask else: if verbose: print(" tone data on latched channel " + str(latched_channel)) registers[latched_channel*2+1] = d # we no longer do any masking here # d & 63 # tone data only contains 6 bits of info anyway, so no need for mask if latched_channel == 3: print("ERROR CHANNEL") # emit current state of each of the 11 registers to 11 different bytearrays for x in range(11): output_blocks[x].append( registers[x] ) # next packet n += packet_size #print(output_blocks[6]) #IGNORE we no longer do this - let the decoder do it instead. if False: # make sure we only emit tone3 when it changes, or 15 for no-change # this prevents the LFSR from being reset lastTone3 = 255 for x in range(len(output_blocks[6])): t = output_blocks[6][x] if t == lastTone3: output_blocks[6][x] = 15 lastTone3 = t # print(output_blocks[6]) # Add EOF marker (0x08) to tone3 byte stream output_blocks[6].append(0x08) # 0x08 is an invalid noise tone. # return the split blocks return output_blocks # given an array of data points, serialize it to a bytearray # size is the number of bytes to be used to represent each element in the source array. def toByteArray(self, array, size = 1): r = bytearray() for v in array: if size < 2: r.append(v & 255) else: r.append(v & 255) r.append(v >> 8) return r #---------------------------------------------------------- # Process(filename) # Convert the given VGM file to an electron VGM file #---------------------------------------------------------- def process(self, src_filename, dst_filename): # load the VGM file, or alternatively interpret as a binary if src_filename.lower()[-4:] != ".vgm": print("ERROR: Not a VGM source") return vgm = VgmStream(src_filename) data_block = vgm.as_binary() data_offset = 0 # parse the header header_size = data_block[0] # header size play_rate = data_block[1] # play rate if header_size == 5 and play_rate == 50: packet_count = data_block[2] + data_block[3]*256 # packet count LO duration_mm = data_block[4] # duration mm duration_ss = data_block[5] # duration ss data_offset = header_size+1 data_offset += data_block[data_offset]+1 data_offset += data_block[data_offset]+1 print("header_size=" +str(header_size)) print("play_rate="+str(play_rate)) print("packet_count="+str(packet_count)) print("duration_mm="+str(duration_mm)) print("duration_ss="+str(duration_ss)) print("data_offset="+str(data_offset)) else: print("No header.") print("") # Trim off the header data. The rest is raw data. data_block = data_block[data_offset:] #---------------------------------------------------------- # Unpack the register data into 11 separate data streams #---------------------------------------------------------- registers = self.split_raw(data_block, True) #---------------------------------------------------------- # Begin VGM conversion to Electron #---------------------------------------------------------- # Filter out channels we do not need # Modify all volumes to full or none # Interleave sound to a single channel # output final VGM vgm_stream = bytearray() vgm_time = 0 electron_data = bytearray() # given an SN76489 tone register value, return the equivalent Electron ULA register setting def sn_to_electron(tone_value): # hack to protect against divbyzero if (tone_value == 0): tone_value = 1 hz = float(vgm.vgm_source_clock) / ( 2.0 * float(tone_value) * 16.0) print(" sn_to_electron freq " + str(hz) + "hz") # electron # Sound frequency = 1 MHz / [32 * (S + 1)] # f * 32*(S+1) = 1Mhz # 32*(S+1) = 1Mhz / f # (S+1) = 1Mhz / f*32 #print ("SN freq is " + str(hz)) ula6 = int( 1000000.0 / (hz * 32.0) ) - 1 # check we are within range if ula6 < 0: print(" WARNING: Electron freqency '" + str(ula6) + "' too high (" + str(hz) + ")") ula6 = 0 if ula6 > 255: print(" WARNING: Electron frequency '" + str(ula6) + "' too low (" + str(hz) + ")") ula6 = 255 return ula6 #-------------------------------------------------------------- # conversion settings #-------------------------------------------------------------- # convert the register data to a vgm stream sample_interval = int(44100 / vgm.metadata['rate']) # 882 # 50hz - TODO: use frame rate print("sample_interval=" + str(sample_interval)) USE_TONE3 = VgmElectron.ENABLE_CHANNEL3 # True # TODO: make these all parameters # Add channel filter option # Add mix type options # --attentuation 468 --filter 123 --transpose 00F --mix 123 --arpeggio 2 --rate 50 # Add option to clamp or transpose out of range frequencies # Make the .ula output file filename.electron.ula # Add 0x01 as a terminating byte in the output ULA MIX_RATE = 2 # modulo 2 for interleaving channels # other options # bias for channels # transpose or silence out of range notes channel_mix = 0 #-------------------------------------------------------------- # pre-process music to suit Electron capabilities #-------------------------------------------------------------- for i in range(len(registers[0])): print("Frame " + str(i)) #-------------------------------------------------------------- # step 1- map volumes to 1-bit precision #-------------------------------------------------------------- # 11 registers per frame # Tone 0 HL Tone 1 HL Tone 2 HL Tone 3 Vol 0123 for r in range(11): if r > 6: register_data = registers[r][i] # apply the threshold for each channel threshold = VgmElectron.ATTENTUATION_THRESHOLD1 if r == 8: threshold = VgmElectron.ATTENTUATION_THRESHOLD2 if r == 9: threshold = VgmElectron.ATTENTUATION_THRESHOLD3 # if its a volume, map to loudest volume or no volume (using logarithmic scale) if register_data < threshold: register_data = 0 # full volume else: register_data = 15 # zero volume if r == 7 and VgmElectron.ENABLE_CHANNEL1 == False: register_data = 15 # zero volume if r == 8 and VgmElectron.ENABLE_CHANNEL2 == False: register_data = 15 # zero volume if r == 9 and VgmElectron.ENABLE_CHANNEL3 == False: register_data = 15 # zero volume registers[r][i] = register_data #-------------------------------------------------------------- # step 2 - transpose to fit frequency range #-------------------------------------------------------------- # final step - bring tone1 into the frequency range of the electron # if the frequency goes below the range of the ULA capabilities, add an octave def retune(octaves, l,h,v): #if (octaves == 0): # print(" No transpose performed, octaves set to 0") # return print( " tonehi=" + str(registers[h][i]) + ", tonelo=" + str(registers[l][i])) tone_value = (registers[h][i] << 4) + registers[l][i] if tone_value > 0: tone_freq = float(vgm.vgm_source_clock) / ( 2.0 * float(tone_value) * 16.0) print(" Retune, Channel " + str(int(l/2)) + " tone=" + str(tone_value) + ", freq=" + str(tone_freq)) # electron baseline is 122Hz not 244Hz as the AUG states. baseline_freq = 1000000.0 / (32.0*256.0) target_freq = tone_freq retuned = 0 transpose = abs(octaves) while retuned != transpose: # target_freq < baseline_freq: if (octaves < 0): target_freq /= 2.0 else: target_freq *= 2.0 retuned += 1 # if cant reach baseline freq, transpose once, then silence if still too low :( if target_freq < baseline_freq: print(" WARNING: Freq too low - Added " + str(1) + " octave(s) - from " + str(target_freq) + " to " + str(target_freq*2.0) + "Hz") # better to just clamp low frequencies at the bottom, and risk tuning issues rather than transposition jumps target_freq = baseline_freq #*= 2.0 retuned = 1 if target_freq < baseline_freq: registers[v][i] = 15 print(" Tone " + str(i) + " silenced because frequency too low - " + str(target_freq)) #target_freq *= 2.0 #retuned += 1 if retuned: #print(" WARNING: Freq too low - Added " + str(retuned) + " octave(s) - from " + str(tone_freq) + " to " + str(target_freq) + "Hz") tone_value = int( round( float(vgm.vgm_source_clock) / (2.0 * target_freq * 16.0 ) ) ) registers[h][i] = tone_value >> 4 registers[l][i] = tone_value & 15 # transpose #if TRANSPOSE_OCTAVES > 0: print(" Transposing ") retune(VgmElectron.TRANSPOSE_OCTAVES1, 0,1,7) retune(VgmElectron.TRANSPOSE_OCTAVES2, 2,3,8) retune(VgmElectron.TRANSPOSE_OCTAVES3, 4,5,9) #-------------------------------------------------------------- # Step 3 - mix the 2 primary channels down to 1 channel #-------------------------------------------------------------- # map channel 2 to channel 1 # noise channel is completely ignored ENABLE_DOWNMIX = True if ENABLE_DOWNMIX: print(" Downmix channels ") #print("Frame " + str(i)) vol1 = registers[7][i] vol2 = registers[8][i] vol3 = registers[9][i] tone1_active = vol1 != 15 tone2_active = vol2 != 15 tone3_active = vol3 != 15 tone_active = tone1_active or tone2_active or tone3_active if tone_active: print(" Tone active, mixing") output_tone = 1 if self.USE_TECHNIQUE == 2: c1f = (registers[1][i] << 4) + registers[0][i] c2f = (registers[3][i] << 4) + registers[2][i] c3f = (registers[5][i] << 4) + registers[4][i] active_channels = [ False, False, False ] if tone1_active: active_channels[0] = True print("Channel 1 is active volume") if tone2_active: active_channels[1] = True print("Channel 2 is active volume") if tone3_active: active_channels[2] = True print("Channel 3 is active volume") # any channels playing the same frequency are filtered out if tone1_active and tone2_active and c2f == c1f: active_channels[1] = False print("Channel 2 is same freq as Channel 1, filtered") if tone1_active and tone3_active and c3f == c1f: active_channels[2] = False print("Channel 3 is same freq as Channel 1, filtered") if tone2_active and tone3_active and c2f == c3f: active_channels[2] = False print("Channel 3 is same freq as Channel 2, filtered") channel_count = 0 if active_channels[0]: channel_count += 1 if active_channels[1]: channel_count += 1 if active_channels[2]: channel_count += 1 print("channel_count=" + str(channel_count)) output_mix = [] if active_channels[0]: output_mix.append(1) if active_channels[1]: output_mix.append(2) if active_channels[2]: output_mix.append(3) mix = (i % channel_count) output_tone = output_mix[mix] if self.USE_TECHNIQUE == 1: # interleaving of channels 1+2 is done on odd/even frames for a consistent effect mix = (i % MIX_RATE) == 0 #(i & 1) == 0 # random is no good, thought it might average out but it sounds , well random #mix = random.random() < 0.5 # test code to see if modulo 3 any good, it wasn't if False: if channel_mix == 0 and vol1 != 0: channel_mix = (channel_mix + 1) % 3 if channel_mix == 1 and vol2 != 0: channel_mix = (channel_mix + 1) % 3 if channel_mix == 1 and vol3 != 0: channel_mix = (channel_mix + 1) % 3 output_tone = (channel_mix % 3) + 1 print("output tone=" + str(output_tone)) channel_mix = (channel_mix + 1) % 3 if True: # detect if channel 1 needs priority this frame # - its volume is on, and the alternative frame mix flag is good c1p = vol1 == 0 and mix # don't give channel 2 priority if tone is the same and channel1 is playing c1f = (registers[1][i] << 4) + registers[0][i] c2f = (registers[3][i] << 4) + registers[2][i] sametone = (c1f == c2f/2) or (c1f == c2f * 2) or (c1f == c2f) sametone = sametone and (vol1 == vol2) and (vol1 == 0) if vol1 == 0 and sametone: #diff < 100: #registers[0][i] == registers[2][i] and registers[1][i] == registers[2][i] and vol1 == 0: c1p = True print(" NOTE: channel 1 & channel 2 have same tone") # replace channel 1 data with channel 2 data # if, channel2 is active, but c1 doesn't have priority this frame if vol2 == 0 and not c1p:# and vol1 != 0: output_tone = 2 # if no volume on tone1, we can look at channel 3 too if USE_TONE3: #if registers[7][i] == 15: if vol1 == 15 and vol2 == 15 and vol3 == 0 and not mix:# and not c1p and output_tone != 2: print("tone3 active") output_tone = 3 # pick which tone to output if output_tone == 1: # do nothing, because tone1 register frequency already setup output_tone = 1 elif output_tone == 2: # replace tone 1 frequency with tone 2 frequency registers[0][i] = registers[2][i] registers[1][i] = registers[3][i] registers[7][i] = registers[8][i] elif output_tone == 3: # replace tone 1 frequency with tone 3 frequency registers[0][i] = registers[4][i] registers[1][i] = registers[5][i] registers[7][i] = registers[9][i] else: print("UNHANDLED CASE - output_tone not set") # output ULA data final_volume = registers[7][i] ula_tone = 0 # zero is highest freq. so inaudible, so thats how we handle volume if final_volume == 0: final_tone1 = (registers[1][i] << 4) + registers[0][i] ula_tone = sn_to_electron(final_tone1) electron_data.append( ula_tone ) # write to output ULA file ula_file = open(dst_filename + ".ula.bin", 'wb') ula_file.write(electron_data) ula_file.close() #-------------------------------------------------------------- # Final stage - output to vgm #-------------------------------------------------------------- # Tone1----- Tone2----- Tone3----- Tone4 Vol1 Vol2 Vol3 Vol4 control = [ 0x80, 0x00, 0xa0, 0x00, 0xc0, 0x00, 0xe0, 0x90, 0xb0, 0xd0, 0xf0 ] #filter = [ 0,1,2,3,7,8 ] #filter = [ 2,3,8 ] #filter = [ 0,1,2,3,4,5,6,7,8,9,10 ] filter = [ 0,1,2,3,4,5,7,8,9 ] if ENABLE_DOWNMIX: filter = [ 0,1,7 ] last_tone3 = 255 for i in range(len(registers[0])): # 11 registers per frame # Tone 0 HL Tone 1 HL Tone 2 HL Tone 3 Vol 0123 for r in range(11): register_data = registers[r][i] # dont update noise register unless different update = True if r == 6: if register_data == last_tone3: update = False else: last_tone3 = register_data if not r in filter: update = False if update: register_data |= control[r] vgm_stream.extend( struct.pack('B', 0x50) ) # COMMAND vgm_stream.extend( struct.pack('B', register_data) ) # DATA # next frame if sample_interval == 882: # wait 50 vgm_stream.extend( struct.pack('B', 0x63) ) elif sample_interval == 735: # wait 60 vgm_stream.extend( struct.pack('B', 0x62) ) else: vgm_stream.extend( struct.pack('B', 0x61) ) vgm_stream.extend( struct.pack('B', int(sample_interval % 256)) ) vgm_stream.extend( struct.pack('B', int(sample_interval / 256)) ) # END command vgm_stream.extend( struct.pack('B', 0x66) ) vgm.write_vgm(vgm_stream, dst_filename) #output = bytearray() # write the electron vgm file #open(dst_filename, "wb").write( output ) #------------------------------------------------------------------------ # Main() #------------------------------------------------------------------------ import argparse # Determine if running as a script if __name__ == '__main__': print("Vgm2Electron.py : VGM music converter for Acorn Electron") print("Written in 2019 by <NAME>, https://github.com/simondotm/vgm-packer") print("") epilog_string = "" parser = argparse.ArgumentParser( formatter_class=argparse.RawDescriptionHelpFormatter, epilog=epilog_string) parser.add_argument("input", help="VGM source file (must be single SN76489 PSG format) [input]") parser.add_argument("-o", "--output", metavar="<output>", help="write VGC file <output> (default is '[input].vgc')") parser.add_argument("-v", "--verbose", help="Enable verbose mode", action="store_true") parser.add_argument("-a", "--attenuation", default="444", metavar="<nnn>", help="Set attenuation threshold for each channel, 3 character string where each character is 0-F and 0 is loudest, 4 is 50%, F is quietest, default: 444") parser.add_argument("-t", "--transpose", default="000", metavar="<nnn>", help="Set octaves to transpose for each channel, where 1 is +1 octave and F is -1 octave.") parser.add_argument("-c", "--channels", default="123", metavar="[1][2][3]", help="Set which channels will be included in the conversion, default 123, which means all 3 channels") parser.add_argument("-q", "--technique", default=2, metavar="<n>", help="Set which downmix technique to use 1 or 2.") args = parser.parse_args() src = args.input dst = args.output if dst == None: dst = os.path.splitext(src)[0] + ".electron.vgm" # attenuation options attenuation = args.attenuation if (len(attenuation) != 3): print("ERROR: attenuation must be 3 values eg. '444'") sys.exit() #print("attenuation=" + attenuation) VgmElectron.ATTENTUATION_THRESHOLD1 = int(attenuation[0],16) VgmElectron.ATTENTUATION_THRESHOLD2 = int(attenuation[1],16) VgmElectron.ATTENTUATION_THRESHOLD3 = int(attenuation[2],16) # transpose options transpose = args.transpose if (len(transpose) != 3): print("ERROR: transpose must be 3 values eg. '000'") sys.exit() #print("transpose=" + transpose) # 0 1 2 3 4 5 6 7 8 9 a b c d e f ttable = [0,1,2,3,4,5,6,7,-8,-7,-6,-5,-4,-3,-2,-1] VgmElectron.TRANSPOSE_OCTAVES1 = ttable[ int(transpose[0],16) ] VgmElectron.TRANSPOSE_OCTAVES2 = ttable[ int(transpose[1],16) ] VgmElectron.TRANSPOSE_OCTAVES3 = ttable[ int(transpose[2],16) ] # channel options print(args.channels) VgmElectron.ENABLE_CHANNEL1 = args.channels.find("1") >= 0 VgmElectron.ENABLE_CHANNEL2 = args.channels.find("2") >= 0 VgmElectron.ENABLE_CHANNEL3 = args.channels.find("3") >= 0 print("Channel 1: Enabled=" + str(VgmElectron.ENABLE_CHANNEL1) + ", Transpose=" + str(VgmElectron.TRANSPOSE_OCTAVES1) + ", Attenuation="+str(VgmElectron.ATTENTUATION_THRESHOLD1)) print("Channel 2: Enabled=" + str(VgmElectron.ENABLE_CHANNEL2) + ", Transpose=" + str(VgmElectron.TRANSPOSE_OCTAVES2) + ", Attenuation="+str(VgmElectron.ATTENTUATION_THRESHOLD2)) print("Channel 3: Enabled=" + str(VgmElectron.ENABLE_CHANNEL3) + ", Transpose=" + str(VgmElectron.TRANSPOSE_OCTAVES3) + ", Attenuation="+str(VgmElectron.ATTENTUATION_THRESHOLD3)) # technique VgmElectron.USE_TECHNIQUE = int(args.technique) print("Using technique " + str(VgmElectron.USE_TECHNIQUE)) # check for missing files if not os.path.isfile(src): print("ERROR: File '" + src + "' not found") sys.exit() packer = VgmElectron() packer.VERBOSE = args.verbose packer.process(src, dst)

en

0.590557

#!/usr/bin/env python # vgm2electron.py # Tool for converting SN76489-based PSG VGM data to Acorn Electron # By <NAME> (https://github.com/simondotm/) # See https://github.com/simondotm/vgm-packer # # Copyright (c) 2019 <NAME>. All rights reserved. # # "MIT License": # 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. # output raw dumps of the data that was compressed by LZ4/Huffman # 0-3 represents approx the loudest 50% of volumes (=ON), 4-15 are the quietest 50% (=OFF) # define the number of octaves to transpose whole song by, in case too much bass getting lost #-1 #---------------------------------------------------------- # Utilities #---------------------------------------------------------- # split the packed raw data into 11 separate streams # returns array of 11 bytearrays # unpack the raw binary data in 11 arrays of register data without any deltas between them # eg. the raw chip writes to all 11 registers every frame #if verbose: # print " frame byte number=" +str(x) # print " frame byte=" +str(d) # latch # volume # tone # we no longer do any masking here # d & 63 # tone data only contains 6 bits of info anyway, so no need for mask # emit current state of each of the 11 registers to 11 different bytearrays # next packet #print(output_blocks[6]) #IGNORE we no longer do this - let the decoder do it instead. # make sure we only emit tone3 when it changes, or 15 for no-change # this prevents the LFSR from being reset # print(output_blocks[6]) # Add EOF marker (0x08) to tone3 byte stream # 0x08 is an invalid noise tone. # return the split blocks # given an array of data points, serialize it to a bytearray # size is the number of bytes to be used to represent each element in the source array. #---------------------------------------------------------- # Process(filename) # Convert the given VGM file to an electron VGM file #---------------------------------------------------------- # load the VGM file, or alternatively interpret as a binary # parse the header # header size # play rate # packet count LO # duration mm # duration ss # Trim off the header data. The rest is raw data. #---------------------------------------------------------- # Unpack the register data into 11 separate data streams #---------------------------------------------------------- #---------------------------------------------------------- # Begin VGM conversion to Electron #---------------------------------------------------------- # Filter out channels we do not need # Modify all volumes to full or none # Interleave sound to a single channel # output final VGM # given an SN76489 tone register value, return the equivalent Electron ULA register setting # hack to protect against divbyzero # electron # Sound frequency = 1 MHz / [32 * (S + 1)] # f * 32*(S+1) = 1Mhz # 32*(S+1) = 1Mhz / f # (S+1) = 1Mhz / f*32 #print ("SN freq is " + str(hz)) # check we are within range #-------------------------------------------------------------- # conversion settings #-------------------------------------------------------------- # convert the register data to a vgm stream # 882 # 50hz - TODO: use frame rate # True # TODO: make these all parameters # Add channel filter option # Add mix type options # --attentuation 468 --filter 123 --transpose 00F --mix 123 --arpeggio 2 --rate 50 # Add option to clamp or transpose out of range frequencies # Make the .ula output file filename.electron.ula # Add 0x01 as a terminating byte in the output ULA # modulo 2 for interleaving channels # other options # bias for channels # transpose or silence out of range notes #-------------------------------------------------------------- # pre-process music to suit Electron capabilities #-------------------------------------------------------------- #-------------------------------------------------------------- # step 1- map volumes to 1-bit precision #-------------------------------------------------------------- # 11 registers per frame # Tone 0 HL Tone 1 HL Tone 2 HL Tone 3 Vol 0123 # apply the threshold for each channel # if its a volume, map to loudest volume or no volume (using logarithmic scale) # full volume # zero volume # zero volume # zero volume # zero volume #-------------------------------------------------------------- # step 2 - transpose to fit frequency range #-------------------------------------------------------------- # final step - bring tone1 into the frequency range of the electron # if the frequency goes below the range of the ULA capabilities, add an octave #if (octaves == 0): # print(" No transpose performed, octaves set to 0") # return # electron baseline is 122Hz not 244Hz as the AUG states. # target_freq < baseline_freq: # if cant reach baseline freq, transpose once, then silence if still too low :( # better to just clamp low frequencies at the bottom, and risk tuning issues rather than transposition jumps #*= 2.0 #target_freq *= 2.0 #retuned += 1 #print(" WARNING: Freq too low - Added " + str(retuned) + " octave(s) - from " + str(tone_freq) + " to " + str(target_freq) + "Hz") # transpose #if TRANSPOSE_OCTAVES > 0: #-------------------------------------------------------------- # Step 3 - mix the 2 primary channels down to 1 channel #-------------------------------------------------------------- # map channel 2 to channel 1 # noise channel is completely ignored #print("Frame " + str(i)) # any channels playing the same frequency are filtered out # interleaving of channels 1+2 is done on odd/even frames for a consistent effect #(i & 1) == 0 # random is no good, thought it might average out but it sounds , well random #mix = random.random() < 0.5 # test code to see if modulo 3 any good, it wasn't # detect if channel 1 needs priority this frame # - its volume is on, and the alternative frame mix flag is good # don't give channel 2 priority if tone is the same and channel1 is playing #diff < 100: #registers[0][i] == registers[2][i] and registers[1][i] == registers[2][i] and vol1 == 0: # replace channel 1 data with channel 2 data # if, channel2 is active, but c1 doesn't have priority this frame # and vol1 != 0: # if no volume on tone1, we can look at channel 3 too #if registers[7][i] == 15: # and not c1p and output_tone != 2: # pick which tone to output # do nothing, because tone1 register frequency already setup # replace tone 1 frequency with tone 2 frequency # replace tone 1 frequency with tone 3 frequency # output ULA data # zero is highest freq. so inaudible, so thats how we handle volume # write to output ULA file #-------------------------------------------------------------- # Final stage - output to vgm #-------------------------------------------------------------- # Tone1----- Tone2----- Tone3----- Tone4 Vol1 Vol2 Vol3 Vol4 #filter = [ 0,1,2,3,7,8 ] #filter = [ 2,3,8 ] #filter = [ 0,1,2,3,4,5,6,7,8,9,10 ] # 11 registers per frame # Tone 0 HL Tone 1 HL Tone 2 HL Tone 3 Vol 0123 # dont update noise register unless different # COMMAND # DATA # next frame # wait 50 # wait 60 # END command #output = bytearray() # write the electron vgm file #open(dst_filename, "wb").write( output ) #------------------------------------------------------------------------ # Main() #------------------------------------------------------------------------ # Determine if running as a script # attenuation options #print("attenuation=" + attenuation) # transpose options #print("transpose=" + transpose) # 0 1 2 3 4 5 6 7 8 9 a b c d e f # channel options # technique # check for missing files

1.670812

2

twitter_sent.py

rthorst/TwitterSentiment

6

10234

<gh_stars>1-10 import webapp2 import tweepy import json import csv import os import statistics import bokeh from bokeh.io import show, output_file from bokeh.plotting import figure from bokeh.models import HoverTool, ColumnDataSource from bokeh.embed import components, json_item from bokeh.resources import INLINE from bokeh.models.glyphs import Line, Text import numpy as np import random import operator from collections import Counter from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer """ ---AUTHOR: --- <NAME> <EMAIL> ---LICENSE: --- MIT License. ---ABOUT: --- Application to get the sentiment of recent tweets based on a keyword. Example: keyword -> "taco bell" retrieve 300 recent tweets mentioning taco bell. get average sentiment. plot distribution of tweets and sentiment. plot most informative words for this application. This script runs based on google app server. Expects Python 2.7 Depenencies need to be included in the lib/ directory (pip install -t lib [PACKAGE_NAME]) The main work is done by the MainPage class. The get() method runs the main pipeline of code and returns HTML as a string. Working online version: https://twittersentiment-247018.appspot.com/ """ def get_tweets(keyword, max_tweets=200): """ Given a keyword as a string (e.g. "data science"), get recent tweets matching that string up to # max_tweets. Return a list of tweets, represented as strings. """ # API keys. consumer_key = "kNOG1klRMMUYbsjMuY5TKl4lE" consumer_secret = "ieghv6WI1qseYly43A0Ra1MPksEw1i5Onma0txfEu5aHantD2v" access_key = "<KEY>" access_secret = "<KEY>" # Initialize tweepy API object and authorize using API key. auth = tweepy.OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_key, access_secret) api = tweepy.API(auth) """ Get tweets.""" alltweets = [] for status in tweepy.Cursor( api.search, q=keyword + " -RT", # the -RT flag excludes retweets. count=1000, result_type="recent", include_entities=True, monitor_rate_limit=True, wait_on_rate_limit=True, lang="en", ).items(): # get text of the tweet, encoding as utf-8. text = str(status.text.encode("utf-8")) # add to the data structure, alltweets, holding the tweets. alltweets.append(text) # if we've reached max_tweets, break. if len(alltweets) >= max_tweets: break return alltweets class VaderSentimentModel: """ Calculate sentiment using a mostly lexicon-based approach that is optimized for social media. Approach is social media aware, for example emoticons are part of the lexicon and tokenization is twitter-sensitive. There are also some basic rules, e.g. it's sensitive to negations. """ def __init__(self): # Initialize a vader_analyzer object which does the work of sentiment analysis. self.vader_analyzer = SentimentIntensityAnalyzer() pass def classify_sentiment(self, tweet): # Classify sentiment of a single tweet. # Input tweet: as string. # Return sentiment score : # range -1 (very negaitve) to +1 (very positive). # score is calculated as p(positive) - p(negative) # normalizing to range from -1 to 1. # calculate sentiment in a dictionary. key is polarity ("pos", "neg", "neut") and value is probability. sentiment_dict = self.vader_analyzer.polarity_scores(tweet) # retrieve the compound sentiment score, which is p(pos) - p(neg), but normalized to range from {-1, 1} score = sentiment_dict["compound"] # compound is the combined score scaled to {-1, 1} return score def plot_tweets(tweets, sentiment_scores): """ Create a histogram-style barplot of tweets and their sentiment. Return a bokeh plot object, expressed as a tuple of (resources, script, div). Where : resources: some CSS, etc. that goes in the head of the webpage for styling the plot. script: javascript for the plot to function. expressed as string. div: html div container for the plot. expressed as string. """ # Sort tweets from negative to positive. # This step is not strictly necessary, but makes it easier to see the overall shape of the data. sorted_indices = np.argsort(sentiment_scores) sentiment_scores = np.array(sentiment_scores)[sorted_indices] tweets = np.array(tweets)[sorted_indices] # Express the data as a bokeh data source object. source = ColumnDataSource(data={ "text": tweets, "sentiment": sentiment_scores, "x": np.arange(len(tweets)), }) """ Create plot. """ # Create plot object. width = 0.9 p = figure(x_axis_label="Tweet", y_axis_label="Sentiment (0 = Neutral)") p.vbar(source=source, x="x", top="sentiment", width=width) # Add hover tool, allowing mouseover to view text and sentiment. hover = HoverTool( tooltips=[ ("text", "@text"), ("sentiment", "@sentiment") ], formatters={ "text": "printf", "sentiment": "printf" }, mode="vline" ) p.add_tools(hover) """ Format plot. """ # axis font size p.xaxis.axis_label_text_font_size = "15pt" p.yaxis.axis_label_text_font_size = "15pt" # remove tick marks from axes p.xaxis.major_tick_line_color = None p.xaxis.minor_tick_line_color = None p.yaxis.major_tick_line_color = None p.yaxis.minor_tick_line_color = None # adjust plot width, height scale = 1.5 p.plot_height = int(250 * scale) p.plot_width = int(450 * scale) # remove toolbar (e.g. move, resize, etc) from right of plot. p.toolbar.logo = None p.toolbar_location = None # remove gridlines p.xgrid.visible = False p.ygrid.visible = False # remove x axis tick labels (done by setting label fontsize to 0 pt) p.xaxis.major_label_text_font_size = '0pt' """ Export plot """ # Create resources string, which is CSS, etc. that goes in the head of resources = INLINE.render() # Get javascript (script) and HTML div (div) for the plot. script, div = components(p) return (resources, script, div) def plot_reason(tweets, sentiment_scores): """ Plot the top words that lead us to the classification as positive or negative. Return: script : javascript for the plot, expressed as string. div : html container for the plot, expressed as string. NOTE: requires the shared resources attribute from plot_tweets() in the HTML header. """ """ Calculate the sentiment of each individual token in the tweets. """ # list tokens, keeping only unique tokens (e.g. remove repeated words). all_toks = [] for tweet in tweets: toks = tweet.lower().split() all_toks.extend(toks) all_toks = [tok for tok in set(all_toks)] # remove duplicates. # calculate sentiment of each token. sm = VaderSentimentModel() toks_sentiment = [sm.classify_sentiment(tok) for tok in all_toks] """ sort tokens by sentiment. if overall valence is negative, sort negative to postitive. if overall valence is positive, sort positive to negative. thus, in any case, the earliest elements in the list are the most informative words. """ nwords = 20 # negative? sort neg -> positive. if np.mean(sentiment_scores) < 0: sorted_indices = np.argsort(toks_sentiment) # else (positive)? sort positive -> negative else: sorted_indices = np.argsort(toks_sentiment)[::-1] # toks_to_plot: shape (nwords, ) list of informative tokens. # sentiment_to_plot: shape (nwords, ) list of sentiment of these tokens. toks_to_plot = np.array(all_toks)[sorted_indices][:nwords] sentiment_to_plot = np.array(toks_sentiment)[sorted_indices][:nwords] # convert all sentiment scores to positive values. # this is for DISPLAY only, to make all plots go from left to right. # we still retain the correct tokens and sorting order. sentiment_to_plot = np.array([abs(v) for v in sentiment_to_plot]) """ Set up plot. - create data source object. - define formatting variables. """ text_offset = 0.1 source = ColumnDataSource(data={ "token": toks_to_plot, "sentiment": sentiment_to_plot, "x": np.arange(len(toks_to_plot))[::-1], "label_x": sentiment_to_plot + text_offset }) """ Make plot. """ # Create initial plot. width = 0.9 xrange = [0, max(sentiment_to_plot) + 1] p2 = figure(x_axis_label="Sentiment", y_axis_label="Word", x_range=xrange) p2.hbar(source=source, y="x", right="sentiment", height=width) """ Format plot. """ # Annotate each bar with the word being represented. glyph = Text(x="label_x", y="x", text="token") p2.add_glyph(source, glyph) # Axis labels. p2.xaxis.axis_label_text_font_size = "15pt" p2.yaxis.axis_label_text_font_size = "15pt" # Remove ticks. p2.xaxis.major_tick_line_color = None p2.xaxis.minor_tick_line_color = None p2.yaxis.major_tick_line_color = None p2.yaxis.minor_tick_line_color = None # Remove y axis tick labels. p2.yaxis.major_label_text_font_size = '0pt' # Plot width, height. scale = 1.5 p2.plot_height = int(250 * scale) p2.plot_width = int(250 * scale) # remove toolbar (e.g. move, resize, etc) from right of plot. p2.toolbar.logo = None p2.toolbar_location = None # remove gridlines p2.xgrid.visible = False p2.ygrid.visible = False # remove x axis tick labels (set font to 0pt) p2.xaxis.major_label_text_font_size = '0pt' # get bokeh component for plot 2. script2, div2 = components(p2) return (script2, div2) class MainPage(webapp2.RequestHandler): """ This class does the work of writing HTML to the google app server. Thus, we allow the get() method to incorporate: our main pipeline (getting tweets, analyzing sentiment, producing graphs) writing html """ def get(self): """ Get tweets and sentiment scores. """ # Retrieve keyword from the HTML form. If no keyword provided, use a random suggested keyword. keyword = self.request.get("keyword") if not keyword: suggested_keywords = ["alarm clocks", "the future", "miller lite", "taco bell", "yoga", "netflix", "life", "traffic", "elon musk", "beards", "world trade", "pepsi", "amazon"] indices = np.arange(len(suggested_keywords)) random.shuffle(indices) keyword = suggested_keywords[indices[0]] # Get recent tweets based on the keyword, up to 300 maximum tweets. tweets = get_tweets(keyword, max_tweets=300) # Compute the sentiment of each tweet. v = VaderSentimentModel() sentiment_scores = [v.classify_sentiment(tw) for tw in tweets] # shape (ntweets,) # Label sentiment categorically, e.g. "negative" or "positive" M_sent = np.mean(sentiment_scores) map = {1 : "positive", 0 : "negative"} valence = map[int(M_sent > 0)] """ Create plots. """ ############# # Plot #1: ############ # Plot the distribution of tweets and sentiment. # Resources is CSS code that goes in the header of the HTML. Shared across all bokeh plots. # Script1 is javascript for this plot. # Div1 is an HTML container for the plot. Goes where you want the plot to appear. resources, script1, div1 = plot_tweets(tweets=tweets, sentiment_scores=sentiment_scores) ############# # Plot #2: ############ # Plot the key words that lead us to this classification. # Script2 is javascript for this plot. # Div2 is an HTML container for this plot. Goes where you want the plot to appear. # Requires the HTML to include the shared resources, generated above, in the <HEAD> script2, div2 = plot_reason(tweets=tweets, sentiment_scores=sentiment_scores) """ Create HTML output. """ # Load HTML template. # This is a functioning webpage, with some placeholders for the keywords and plots we have created. html_p = os.path.join("html", "index.html") html = open(html_p, "r").read() # Fill in placeholders in the HTML with varibles we have created. term_to_value = { "[[!KEYWORD]]" : keyword, "[[!VALENCE]]" : valence, "[[!BOKEH_SCRIPT]]" : script1, "[[!BOKEH_SCRIPT2]]": script2, "[[!BOKEH_DIV]]" : div1, "[[!BOKEH_RESOURCES]]" : resources, "[[!BOKEH_DIV2]]" : div2 } for term, val in term_to_value.items(): html = html.replace(term, val) """ Write a response. This essentially returns HTML to the google app engine. This will render a webpage visible to the user. """ self.response.headers["Content-Type"] = "text/html" self.response.write(html) # Run application. routes = [('/', MainPage)] my_app = webapp2.WSGIApplication(routes, debug=True)

import webapp2 import tweepy import json import csv import os import statistics import bokeh from bokeh.io import show, output_file from bokeh.plotting import figure from bokeh.models import HoverTool, ColumnDataSource from bokeh.embed import components, json_item from bokeh.resources import INLINE from bokeh.models.glyphs import Line, Text import numpy as np import random import operator from collections import Counter from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer """ ---AUTHOR: --- <NAME> <EMAIL> ---LICENSE: --- MIT License. ---ABOUT: --- Application to get the sentiment of recent tweets based on a keyword. Example: keyword -> "taco bell" retrieve 300 recent tweets mentioning taco bell. get average sentiment. plot distribution of tweets and sentiment. plot most informative words for this application. This script runs based on google app server. Expects Python 2.7 Depenencies need to be included in the lib/ directory (pip install -t lib [PACKAGE_NAME]) The main work is done by the MainPage class. The get() method runs the main pipeline of code and returns HTML as a string. Working online version: https://twittersentiment-247018.appspot.com/ """ def get_tweets(keyword, max_tweets=200): """ Given a keyword as a string (e.g. "data science"), get recent tweets matching that string up to # max_tweets. Return a list of tweets, represented as strings. """ # API keys. consumer_key = "kNOG1klRMMUYbsjMuY5TKl4lE" consumer_secret = "ieghv6WI1qseYly43A0Ra1MPksEw1i5Onma0txfEu5aHantD2v" access_key = "<KEY>" access_secret = "<KEY>" # Initialize tweepy API object and authorize using API key. auth = tweepy.OAuthHandler(consumer_key, consumer_secret) auth.set_access_token(access_key, access_secret) api = tweepy.API(auth) """ Get tweets.""" alltweets = [] for status in tweepy.Cursor( api.search, q=keyword + " -RT", # the -RT flag excludes retweets. count=1000, result_type="recent", include_entities=True, monitor_rate_limit=True, wait_on_rate_limit=True, lang="en", ).items(): # get text of the tweet, encoding as utf-8. text = str(status.text.encode("utf-8")) # add to the data structure, alltweets, holding the tweets. alltweets.append(text) # if we've reached max_tweets, break. if len(alltweets) >= max_tweets: break return alltweets class VaderSentimentModel: """ Calculate sentiment using a mostly lexicon-based approach that is optimized for social media. Approach is social media aware, for example emoticons are part of the lexicon and tokenization is twitter-sensitive. There are also some basic rules, e.g. it's sensitive to negations. """ def __init__(self): # Initialize a vader_analyzer object which does the work of sentiment analysis. self.vader_analyzer = SentimentIntensityAnalyzer() pass def classify_sentiment(self, tweet): # Classify sentiment of a single tweet. # Input tweet: as string. # Return sentiment score : # range -1 (very negaitve) to +1 (very positive). # score is calculated as p(positive) - p(negative) # normalizing to range from -1 to 1. # calculate sentiment in a dictionary. key is polarity ("pos", "neg", "neut") and value is probability. sentiment_dict = self.vader_analyzer.polarity_scores(tweet) # retrieve the compound sentiment score, which is p(pos) - p(neg), but normalized to range from {-1, 1} score = sentiment_dict["compound"] # compound is the combined score scaled to {-1, 1} return score def plot_tweets(tweets, sentiment_scores): """ Create a histogram-style barplot of tweets and their sentiment. Return a bokeh plot object, expressed as a tuple of (resources, script, div). Where : resources: some CSS, etc. that goes in the head of the webpage for styling the plot. script: javascript for the plot to function. expressed as string. div: html div container for the plot. expressed as string. """ # Sort tweets from negative to positive. # This step is not strictly necessary, but makes it easier to see the overall shape of the data. sorted_indices = np.argsort(sentiment_scores) sentiment_scores = np.array(sentiment_scores)[sorted_indices] tweets = np.array(tweets)[sorted_indices] # Express the data as a bokeh data source object. source = ColumnDataSource(data={ "text": tweets, "sentiment": sentiment_scores, "x": np.arange(len(tweets)), }) """ Create plot. """ # Create plot object. width = 0.9 p = figure(x_axis_label="Tweet", y_axis_label="Sentiment (0 = Neutral)") p.vbar(source=source, x="x", top="sentiment", width=width) # Add hover tool, allowing mouseover to view text and sentiment. hover = HoverTool( tooltips=[ ("text", "@text"), ("sentiment", "@sentiment") ], formatters={ "text": "printf", "sentiment": "printf" }, mode="vline" ) p.add_tools(hover) """ Format plot. """ # axis font size p.xaxis.axis_label_text_font_size = "15pt" p.yaxis.axis_label_text_font_size = "15pt" # remove tick marks from axes p.xaxis.major_tick_line_color = None p.xaxis.minor_tick_line_color = None p.yaxis.major_tick_line_color = None p.yaxis.minor_tick_line_color = None # adjust plot width, height scale = 1.5 p.plot_height = int(250 * scale) p.plot_width = int(450 * scale) # remove toolbar (e.g. move, resize, etc) from right of plot. p.toolbar.logo = None p.toolbar_location = None # remove gridlines p.xgrid.visible = False p.ygrid.visible = False # remove x axis tick labels (done by setting label fontsize to 0 pt) p.xaxis.major_label_text_font_size = '0pt' """ Export plot """ # Create resources string, which is CSS, etc. that goes in the head of resources = INLINE.render() # Get javascript (script) and HTML div (div) for the plot. script, div = components(p) return (resources, script, div) def plot_reason(tweets, sentiment_scores): """ Plot the top words that lead us to the classification as positive or negative. Return: script : javascript for the plot, expressed as string. div : html container for the plot, expressed as string. NOTE: requires the shared resources attribute from plot_tweets() in the HTML header. """ """ Calculate the sentiment of each individual token in the tweets. """ # list tokens, keeping only unique tokens (e.g. remove repeated words). all_toks = [] for tweet in tweets: toks = tweet.lower().split() all_toks.extend(toks) all_toks = [tok for tok in set(all_toks)] # remove duplicates. # calculate sentiment of each token. sm = VaderSentimentModel() toks_sentiment = [sm.classify_sentiment(tok) for tok in all_toks] """ sort tokens by sentiment. if overall valence is negative, sort negative to postitive. if overall valence is positive, sort positive to negative. thus, in any case, the earliest elements in the list are the most informative words. """ nwords = 20 # negative? sort neg -> positive. if np.mean(sentiment_scores) < 0: sorted_indices = np.argsort(toks_sentiment) # else (positive)? sort positive -> negative else: sorted_indices = np.argsort(toks_sentiment)[::-1] # toks_to_plot: shape (nwords, ) list of informative tokens. # sentiment_to_plot: shape (nwords, ) list of sentiment of these tokens. toks_to_plot = np.array(all_toks)[sorted_indices][:nwords] sentiment_to_plot = np.array(toks_sentiment)[sorted_indices][:nwords] # convert all sentiment scores to positive values. # this is for DISPLAY only, to make all plots go from left to right. # we still retain the correct tokens and sorting order. sentiment_to_plot = np.array([abs(v) for v in sentiment_to_plot]) """ Set up plot. - create data source object. - define formatting variables. """ text_offset = 0.1 source = ColumnDataSource(data={ "token": toks_to_plot, "sentiment": sentiment_to_plot, "x": np.arange(len(toks_to_plot))[::-1], "label_x": sentiment_to_plot + text_offset }) """ Make plot. """ # Create initial plot. width = 0.9 xrange = [0, max(sentiment_to_plot) + 1] p2 = figure(x_axis_label="Sentiment", y_axis_label="Word", x_range=xrange) p2.hbar(source=source, y="x", right="sentiment", height=width) """ Format plot. """ # Annotate each bar with the word being represented. glyph = Text(x="label_x", y="x", text="token") p2.add_glyph(source, glyph) # Axis labels. p2.xaxis.axis_label_text_font_size = "15pt" p2.yaxis.axis_label_text_font_size = "15pt" # Remove ticks. p2.xaxis.major_tick_line_color = None p2.xaxis.minor_tick_line_color = None p2.yaxis.major_tick_line_color = None p2.yaxis.minor_tick_line_color = None # Remove y axis tick labels. p2.yaxis.major_label_text_font_size = '0pt' # Plot width, height. scale = 1.5 p2.plot_height = int(250 * scale) p2.plot_width = int(250 * scale) # remove toolbar (e.g. move, resize, etc) from right of plot. p2.toolbar.logo = None p2.toolbar_location = None # remove gridlines p2.xgrid.visible = False p2.ygrid.visible = False # remove x axis tick labels (set font to 0pt) p2.xaxis.major_label_text_font_size = '0pt' # get bokeh component for plot 2. script2, div2 = components(p2) return (script2, div2) class MainPage(webapp2.RequestHandler): """ This class does the work of writing HTML to the google app server. Thus, we allow the get() method to incorporate: our main pipeline (getting tweets, analyzing sentiment, producing graphs) writing html """ def get(self): """ Get tweets and sentiment scores. """ # Retrieve keyword from the HTML form. If no keyword provided, use a random suggested keyword. keyword = self.request.get("keyword") if not keyword: suggested_keywords = ["alarm clocks", "the future", "miller lite", "taco bell", "yoga", "netflix", "life", "traffic", "elon musk", "beards", "world trade", "pepsi", "amazon"] indices = np.arange(len(suggested_keywords)) random.shuffle(indices) keyword = suggested_keywords[indices[0]] # Get recent tweets based on the keyword, up to 300 maximum tweets. tweets = get_tweets(keyword, max_tweets=300) # Compute the sentiment of each tweet. v = VaderSentimentModel() sentiment_scores = [v.classify_sentiment(tw) for tw in tweets] # shape (ntweets,) # Label sentiment categorically, e.g. "negative" or "positive" M_sent = np.mean(sentiment_scores) map = {1 : "positive", 0 : "negative"} valence = map[int(M_sent > 0)] """ Create plots. """ ############# # Plot #1: ############ # Plot the distribution of tweets and sentiment. # Resources is CSS code that goes in the header of the HTML. Shared across all bokeh plots. # Script1 is javascript for this plot. # Div1 is an HTML container for the plot. Goes where you want the plot to appear. resources, script1, div1 = plot_tweets(tweets=tweets, sentiment_scores=sentiment_scores) ############# # Plot #2: ############ # Plot the key words that lead us to this classification. # Script2 is javascript for this plot. # Div2 is an HTML container for this plot. Goes where you want the plot to appear. # Requires the HTML to include the shared resources, generated above, in the <HEAD> script2, div2 = plot_reason(tweets=tweets, sentiment_scores=sentiment_scores) """ Create HTML output. """ # Load HTML template. # This is a functioning webpage, with some placeholders for the keywords and plots we have created. html_p = os.path.join("html", "index.html") html = open(html_p, "r").read() # Fill in placeholders in the HTML with varibles we have created. term_to_value = { "[[!KEYWORD]]" : keyword, "[[!VALENCE]]" : valence, "[[!BOKEH_SCRIPT]]" : script1, "[[!BOKEH_SCRIPT2]]": script2, "[[!BOKEH_DIV]]" : div1, "[[!BOKEH_RESOURCES]]" : resources, "[[!BOKEH_DIV2]]" : div2 } for term, val in term_to_value.items(): html = html.replace(term, val) """ Write a response. This essentially returns HTML to the google app engine. This will render a webpage visible to the user. """ self.response.headers["Content-Type"] = "text/html" self.response.write(html) # Run application. routes = [('/', MainPage)] my_app = webapp2.WSGIApplication(routes, debug=True)

en

0.824098

---AUTHOR: --- <NAME> <EMAIL> ---LICENSE: --- MIT License. ---ABOUT: --- Application to get the sentiment of recent tweets based on a keyword. Example: keyword -> "taco bell" retrieve 300 recent tweets mentioning taco bell. get average sentiment. plot distribution of tweets and sentiment. plot most informative words for this application. This script runs based on google app server. Expects Python 2.7 Depenencies need to be included in the lib/ directory (pip install -t lib [PACKAGE_NAME]) The main work is done by the MainPage class. The get() method runs the main pipeline of code and returns HTML as a string. Working online version: https://twittersentiment-247018.appspot.com/ Given a keyword as a string (e.g. "data science"), get recent tweets matching that string up to # max_tweets. Return a list of tweets, represented as strings. # API keys. # Initialize tweepy API object and authorize using API key. Get tweets. # the -RT flag excludes retweets. # get text of the tweet, encoding as utf-8. # add to the data structure, alltweets, holding the tweets. # if we've reached max_tweets, break. Calculate sentiment using a mostly lexicon-based approach that is optimized for social media. Approach is social media aware, for example emoticons are part of the lexicon and tokenization is twitter-sensitive. There are also some basic rules, e.g. it's sensitive to negations. # Initialize a vader_analyzer object which does the work of sentiment analysis. # Classify sentiment of a single tweet. # Input tweet: as string. # Return sentiment score : # range -1 (very negaitve) to +1 (very positive). # score is calculated as p(positive) - p(negative) # normalizing to range from -1 to 1. # calculate sentiment in a dictionary. key is polarity ("pos", "neg", "neut") and value is probability. # retrieve the compound sentiment score, which is p(pos) - p(neg), but normalized to range from {-1, 1} # compound is the combined score scaled to {-1, 1} Create a histogram-style barplot of tweets and their sentiment. Return a bokeh plot object, expressed as a tuple of (resources, script, div). Where : resources: some CSS, etc. that goes in the head of the webpage for styling the plot. script: javascript for the plot to function. expressed as string. div: html div container for the plot. expressed as string. # Sort tweets from negative to positive. # This step is not strictly necessary, but makes it easier to see the overall shape of the data. # Express the data as a bokeh data source object. Create plot. # Create plot object. # Add hover tool, allowing mouseover to view text and sentiment. Format plot. # axis font size # remove tick marks from axes # adjust plot width, height # remove toolbar (e.g. move, resize, etc) from right of plot. # remove gridlines # remove x axis tick labels (done by setting label fontsize to 0 pt) Export plot # Create resources string, which is CSS, etc. that goes in the head of # Get javascript (script) and HTML div (div) for the plot. Plot the top words that lead us to the classification as positive or negative. Return: script : javascript for the plot, expressed as string. div : html container for the plot, expressed as string. NOTE: requires the shared resources attribute from plot_tweets() in the HTML header. Calculate the sentiment of each individual token in the tweets. # list tokens, keeping only unique tokens (e.g. remove repeated words). # remove duplicates. # calculate sentiment of each token. sort tokens by sentiment. if overall valence is negative, sort negative to postitive. if overall valence is positive, sort positive to negative. thus, in any case, the earliest elements in the list are the most informative words. # negative? sort neg -> positive. # else (positive)? sort positive -> negative # toks_to_plot: shape (nwords, ) list of informative tokens. # sentiment_to_plot: shape (nwords, ) list of sentiment of these tokens. # convert all sentiment scores to positive values. # this is for DISPLAY only, to make all plots go from left to right. # we still retain the correct tokens and sorting order. Set up plot. - create data source object. - define formatting variables. Make plot. # Create initial plot. Format plot. # Annotate each bar with the word being represented. # Axis labels. # Remove ticks. # Remove y axis tick labels. # Plot width, height. # remove toolbar (e.g. move, resize, etc) from right of plot. # remove gridlines # remove x axis tick labels (set font to 0pt) # get bokeh component for plot 2. This class does the work of writing HTML to the google app server. Thus, we allow the get() method to incorporate: our main pipeline (getting tweets, analyzing sentiment, producing graphs) writing html Get tweets and sentiment scores. # Retrieve keyword from the HTML form. If no keyword provided, use a random suggested keyword. # Get recent tweets based on the keyword, up to 300 maximum tweets. # Compute the sentiment of each tweet. # shape (ntweets,) # Label sentiment categorically, e.g. "negative" or "positive" Create plots. ############# # Plot #1: ############ # Plot the distribution of tweets and sentiment. # Resources is CSS code that goes in the header of the HTML. Shared across all bokeh plots. # Script1 is javascript for this plot. # Div1 is an HTML container for the plot. Goes where you want the plot to appear. ############# # Plot #2: ############ # Plot the key words that lead us to this classification. # Script2 is javascript for this plot. # Div2 is an HTML container for this plot. Goes where you want the plot to appear. # Requires the HTML to include the shared resources, generated above, in the <HEAD> Create HTML output. # Load HTML template. # This is a functioning webpage, with some placeholders for the keywords and plots we have created. # Fill in placeholders in the HTML with varibles we have created. Write a response. This essentially returns HTML to the google app engine. This will render a webpage visible to the user. # Run application.

2.989811

3

tests/testcgatools.py

ereide/pyga-camcal

5

10235

<reponame>ereide/pyga-camcal<gh_stars>1-10 import unittest import clifford as cl from clifford import g3c from numpy import pi, e import numpy as np from scipy.sparse.linalg.matfuncs import _sinch as sinch from clifford import MultiVector from pygacal.common.cgatools import ( Sandwich, Dilator, Translator, Reflector, inversion, Rotor, Transversor, I3, I5, VectorEquality, Distance, ga_log, ga_exp, MVEqual, Meet, extractBivectorParameters_complicated, ga_exp_complicated, one) from pygacal.geometry import createRandomBivector, createRandomVector, createRandomPoints from pygacal.geometry.lines import createLine from pygacal.geometry.planes import createPlane layout = g3c.layout locals().update(g3c.blades) ep, en, up, down, homo, E0, ninf, no = (g3c.stuff["ep"], g3c.stuff["en"], g3c.stuff["up"], g3c.stuff["down"], g3c.stuff["homo"], g3c.stuff["E0"], g3c.stuff["einf"], -g3c.stuff["eo"]) np.random.seed(2512) def AssertMVEqual(actual, expected, rtol = 1e-5, atol = 1e-6, verbose = False): assert(MVEqual(actual, expected, rtol, atol, verbose)) def AssertMVUnEqual(actual, expected, rtol = 1e-5, atol = 1e-6, verbose = False): assert(not MVEqual(actual, expected, rtol, atol, verbose)) class TestCGAOperators(unittest.TestCase): def testDilator(self): x = 2*e1 + 3* e2 + 4*e3 X = up(x) assert(down(Sandwich(X, Dilator(0.1))) == x * 0.1) def testTranslation(self): x = 2*e1 + 3* e2 + 4*e3 X = up(x) a = 2 * e1 + e3 assert(down(Sandwich(X, Translator(a))) == x + a) def testRotation(self): x = 2*e1 + 3* e2 + 4*e3 X = up(x) actual = down(Sandwich(X, Rotor(e12, pi/2))) expected = (-3.0)*e1 + 2.0*e2 + 4.0 * e3 assert(actual == expected) def testInversion(self): x = 2*e1 + 3* e2 + 4*e3 X = up(x) assert(down(inversion(X)) * x == 1) def testDistance(self): a = e1 b = e2 A, B = up(a), up(b) assert(Distance(A, B) == np.sqrt(2)) def testMeet(self): A, B, C, D = createRandomPoints(N = 4, scale = 50) L = createLine(A, B) L2 = createLine(A, C) P1 = createPlane(A, B, C) P2 = createPlane(A, B, D) L_actual = Meet(P1, P2) assert(MVEqual(L, L_actual)) #Plane to line Q = (ninf ^ A).normal() P3 = A ^ C ^ D ^ ninf Q_actual = Meet(P3, L).normal() #How do we define order/direction? assert(MVEqual(Q, Q_actual)) def testAssertEqual(self): verbose = False a = createRandomBivector() b = a + 0.01 a2 = b - 0.01 c = a + 1 d = c - a AssertMVEqual(a, a2, verbose = verbose) AssertMVUnEqual(a, b, verbose = verbose) AssertMVEqual(d, 1, verbose = verbose) def testLogarithm(self): verbose = False if verbose: print("\nTest Logarithms and exponents") phi = 0.5 #Rotation amount P = (e12 + 2*e23 + 3*e13).normal() #Rotation Plane P_n = P*I3 t = 2.73 * e1 + 3.14*e2 #Translation vector t_nor = (P_n | t) * P_n #Decomposition into normal component t_par = t - t_nor #Decomposition into paralel component assert(t_par + t_nor == t) if verbose: print("P = ", P) print("phi = ", phi) print("t = ", t) print("t_nor = ", t_nor) print("t_par = ", t_par) print("") assert(P|t_nor == 0) #Normal to P assert(P^t_nor != 0) #Normal to P assert(P|t_par != 0) #Parallel to P assert(P^t_par == 0) #Parallel to P assert(P*t != 0) #Non zero product R_expected = (np.cos(phi) + (np.sin(phi) * P))*(1 + (t_nor*ninf)) + np.sinc(phi/np.pi)*t_par * ninf B_expected = phi * P + t*ninf R_exponential = np.exp(B_expected) R_actual = ga_exp(B_expected, verbose = verbose) B_new = ga_log(R_expected, verbose = verbose) R_ga = ga_exp(B_new) if verbose: print("R_old ", R_expected) print("R_expected ", R_actual) print("R_exponential", R_exponential) print("R_ga ", R_ga) print("B_new ", B_new) print("B_expected ", B_expected) #Rotor properties AssertMVEqual(R_expected * ~R_expected, 1, verbose = verbose) AssertMVEqual(R_ga * ~R_ga, 1, verbose = verbose) #Equalities AssertMVEqual(R_actual, R_expected, verbose = verbose) AssertMVEqual(R_exponential, R_expected, verbose = verbose) AssertMVEqual(B_new, B_expected, verbose = verbose) AssertMVEqual(R_ga, R_expected, verbose = verbose) N = 100 #Random bivectors to test this as well for i in range(N): B = createRandomBivector() AssertMVEqual(B, ga_log(ga_exp(B, verbose = verbose), verbose = verbose), verbose = verbose) def testComplicatedLogarithm(self): verbose = True if verbose: print("\nTest Complicated Logarithms and exponents") phi = 0.2 #Rotation amount P = (e12 + 2*e23 + 3*e13).normal() #Rotation Plane P_n = P*I3 #t = 0 t = 2.73 * e1 + 3.14*e2 #Translation vector t_nor = (P_n | t) * P_n #Decomposition into normal component t_par = t - t_nor #Decomposition into paralel component omega = 0.1 assert(t_par + t_nor == t) if verbose: print("P = ", P) print("phi = ", phi) print("t = ", t) print("t_nor = ", t_nor) print("t_par = ", t_par) print("omega = ", omega) print("") """ assert(P|t_nor == 0) #Normal to P assert(P^t_nor != 0) #Normal to P assert(P|t_par != 0) #Parallel to P assert(P^t_par == 0) #Parallel to P assert(P*t != 0) #Non zero product assert(t_par|t_nor == 0) #Non zero product """ B_expected = (phi * P) + (t*ninf) + (omega * E0) k = (omega * omega + phi * phi) R_expected = (np.cos(phi) + np.sin(phi) * P)*(np.cosh(omega) + np.sinh(omega) * E0 + sinch(omega) * t_nor*ninf) if (k > 0): R_expected += 1/k* ( (-omega * np.sin(phi) * np.cosh(omega) + phi * np.cos(phi) * np.sinh(omega)) * P + ( omega * np.cos(phi) * np.sinh(omega) + phi * np.sin(phi) * np.cosh(omega))) * t_par * ninf else: R_expected += t_par * ninf phi_test, P_test, t_nor_test, t_par_test, omega_test = extractBivectorParameters_complicated(B_expected) B_actual = phi_test * P_test + (t_nor_test + t_par_test)*ninf + omega_test * E0 #Testing some basic properties of the extraction AssertMVEqual(phi*(P * ~P), phi*one, verbose = False) AssertMVEqual(phi*P, phi*P_test, verbose = False) R_exponential = np.exp(B_expected) R_actual = ga_exp_complicated(B_expected, verbose = verbose) #B_new = ga_log(R_expected, verbose = verbose) #R_ga = ga_exp(B_new) if verbose: print("R_expected ", R_expected) print("R_actual ", R_actual) print("R_exponential ", R_exponential) #print("R_ga ", R_ga) #print("B_new ", B_new) print("B_expected ", B_expected) print() #BivectorExtraction AssertMVEqual(B_actual, B_expected, verbose = verbose) AssertMVEqual(R_expected * ~R_expected, one, verbose = verbose) #Rotor properties AssertMVEqual(R_actual * ~R_actual, one, verbose = verbose) #Only an approximation AssertMVEqual(R_exponential * ~R_exponential, one, verbose = verbose) #AssertMVEqual(R_expected * ~R_expected, 1, verbose = verbose) #AssertMVEqual(R_ga * ~R_ga, 1, verbose = verbose) #Equalities #AssertMVEqual(R_actual, R_expected, verbose = verbose) AssertMVEqual(R_exponential, R_actual, rtol = 1e-2, atol = 1e-3, verbose = verbose) #AssertMVEqual(B_new, B_expected, verbose = verbose) #AssertMVEqual(R_ga, R_expected, verbose = verbose) #N = 100 #Random bivectors to test this as well #for i in range(N): # B = createRandomBivector() # AssertMVEqual(B, ga_log(ga_exp(B, verbose = verbose), verbose = verbose), verbose = verbose) if __name__ == "__main__": unittest.main()

import unittest import clifford as cl from clifford import g3c from numpy import pi, e import numpy as np from scipy.sparse.linalg.matfuncs import _sinch as sinch from clifford import MultiVector from pygacal.common.cgatools import ( Sandwich, Dilator, Translator, Reflector, inversion, Rotor, Transversor, I3, I5, VectorEquality, Distance, ga_log, ga_exp, MVEqual, Meet, extractBivectorParameters_complicated, ga_exp_complicated, one) from pygacal.geometry import createRandomBivector, createRandomVector, createRandomPoints from pygacal.geometry.lines import createLine from pygacal.geometry.planes import createPlane layout = g3c.layout locals().update(g3c.blades) ep, en, up, down, homo, E0, ninf, no = (g3c.stuff["ep"], g3c.stuff["en"], g3c.stuff["up"], g3c.stuff["down"], g3c.stuff["homo"], g3c.stuff["E0"], g3c.stuff["einf"], -g3c.stuff["eo"]) np.random.seed(2512) def AssertMVEqual(actual, expected, rtol = 1e-5, atol = 1e-6, verbose = False): assert(MVEqual(actual, expected, rtol, atol, verbose)) def AssertMVUnEqual(actual, expected, rtol = 1e-5, atol = 1e-6, verbose = False): assert(not MVEqual(actual, expected, rtol, atol, verbose)) class TestCGAOperators(unittest.TestCase): def testDilator(self): x = 2*e1 + 3* e2 + 4*e3 X = up(x) assert(down(Sandwich(X, Dilator(0.1))) == x * 0.1) def testTranslation(self): x = 2*e1 + 3* e2 + 4*e3 X = up(x) a = 2 * e1 + e3 assert(down(Sandwich(X, Translator(a))) == x + a) def testRotation(self): x = 2*e1 + 3* e2 + 4*e3 X = up(x) actual = down(Sandwich(X, Rotor(e12, pi/2))) expected = (-3.0)*e1 + 2.0*e2 + 4.0 * e3 assert(actual == expected) def testInversion(self): x = 2*e1 + 3* e2 + 4*e3 X = up(x) assert(down(inversion(X)) * x == 1) def testDistance(self): a = e1 b = e2 A, B = up(a), up(b) assert(Distance(A, B) == np.sqrt(2)) def testMeet(self): A, B, C, D = createRandomPoints(N = 4, scale = 50) L = createLine(A, B) L2 = createLine(A, C) P1 = createPlane(A, B, C) P2 = createPlane(A, B, D) L_actual = Meet(P1, P2) assert(MVEqual(L, L_actual)) #Plane to line Q = (ninf ^ A).normal() P3 = A ^ C ^ D ^ ninf Q_actual = Meet(P3, L).normal() #How do we define order/direction? assert(MVEqual(Q, Q_actual)) def testAssertEqual(self): verbose = False a = createRandomBivector() b = a + 0.01 a2 = b - 0.01 c = a + 1 d = c - a AssertMVEqual(a, a2, verbose = verbose) AssertMVUnEqual(a, b, verbose = verbose) AssertMVEqual(d, 1, verbose = verbose) def testLogarithm(self): verbose = False if verbose: print("\nTest Logarithms and exponents") phi = 0.5 #Rotation amount P = (e12 + 2*e23 + 3*e13).normal() #Rotation Plane P_n = P*I3 t = 2.73 * e1 + 3.14*e2 #Translation vector t_nor = (P_n | t) * P_n #Decomposition into normal component t_par = t - t_nor #Decomposition into paralel component assert(t_par + t_nor == t) if verbose: print("P = ", P) print("phi = ", phi) print("t = ", t) print("t_nor = ", t_nor) print("t_par = ", t_par) print("") assert(P|t_nor == 0) #Normal to P assert(P^t_nor != 0) #Normal to P assert(P|t_par != 0) #Parallel to P assert(P^t_par == 0) #Parallel to P assert(P*t != 0) #Non zero product R_expected = (np.cos(phi) + (np.sin(phi) * P))*(1 + (t_nor*ninf)) + np.sinc(phi/np.pi)*t_par * ninf B_expected = phi * P + t*ninf R_exponential = np.exp(B_expected) R_actual = ga_exp(B_expected, verbose = verbose) B_new = ga_log(R_expected, verbose = verbose) R_ga = ga_exp(B_new) if verbose: print("R_old ", R_expected) print("R_expected ", R_actual) print("R_exponential", R_exponential) print("R_ga ", R_ga) print("B_new ", B_new) print("B_expected ", B_expected) #Rotor properties AssertMVEqual(R_expected * ~R_expected, 1, verbose = verbose) AssertMVEqual(R_ga * ~R_ga, 1, verbose = verbose) #Equalities AssertMVEqual(R_actual, R_expected, verbose = verbose) AssertMVEqual(R_exponential, R_expected, verbose = verbose) AssertMVEqual(B_new, B_expected, verbose = verbose) AssertMVEqual(R_ga, R_expected, verbose = verbose) N = 100 #Random bivectors to test this as well for i in range(N): B = createRandomBivector() AssertMVEqual(B, ga_log(ga_exp(B, verbose = verbose), verbose = verbose), verbose = verbose) def testComplicatedLogarithm(self): verbose = True if verbose: print("\nTest Complicated Logarithms and exponents") phi = 0.2 #Rotation amount P = (e12 + 2*e23 + 3*e13).normal() #Rotation Plane P_n = P*I3 #t = 0 t = 2.73 * e1 + 3.14*e2 #Translation vector t_nor = (P_n | t) * P_n #Decomposition into normal component t_par = t - t_nor #Decomposition into paralel component omega = 0.1 assert(t_par + t_nor == t) if verbose: print("P = ", P) print("phi = ", phi) print("t = ", t) print("t_nor = ", t_nor) print("t_par = ", t_par) print("omega = ", omega) print("") """ assert(P|t_nor == 0) #Normal to P assert(P^t_nor != 0) #Normal to P assert(P|t_par != 0) #Parallel to P assert(P^t_par == 0) #Parallel to P assert(P*t != 0) #Non zero product assert(t_par|t_nor == 0) #Non zero product """ B_expected = (phi * P) + (t*ninf) + (omega * E0) k = (omega * omega + phi * phi) R_expected = (np.cos(phi) + np.sin(phi) * P)*(np.cosh(omega) + np.sinh(omega) * E0 + sinch(omega) * t_nor*ninf) if (k > 0): R_expected += 1/k* ( (-omega * np.sin(phi) * np.cosh(omega) + phi * np.cos(phi) * np.sinh(omega)) * P + ( omega * np.cos(phi) * np.sinh(omega) + phi * np.sin(phi) * np.cosh(omega))) * t_par * ninf else: R_expected += t_par * ninf phi_test, P_test, t_nor_test, t_par_test, omega_test = extractBivectorParameters_complicated(B_expected) B_actual = phi_test * P_test + (t_nor_test + t_par_test)*ninf + omega_test * E0 #Testing some basic properties of the extraction AssertMVEqual(phi*(P * ~P), phi*one, verbose = False) AssertMVEqual(phi*P, phi*P_test, verbose = False) R_exponential = np.exp(B_expected) R_actual = ga_exp_complicated(B_expected, verbose = verbose) #B_new = ga_log(R_expected, verbose = verbose) #R_ga = ga_exp(B_new) if verbose: print("R_expected ", R_expected) print("R_actual ", R_actual) print("R_exponential ", R_exponential) #print("R_ga ", R_ga) #print("B_new ", B_new) print("B_expected ", B_expected) print() #BivectorExtraction AssertMVEqual(B_actual, B_expected, verbose = verbose) AssertMVEqual(R_expected * ~R_expected, one, verbose = verbose) #Rotor properties AssertMVEqual(R_actual * ~R_actual, one, verbose = verbose) #Only an approximation AssertMVEqual(R_exponential * ~R_exponential, one, verbose = verbose) #AssertMVEqual(R_expected * ~R_expected, 1, verbose = verbose) #AssertMVEqual(R_ga * ~R_ga, 1, verbose = verbose) #Equalities #AssertMVEqual(R_actual, R_expected, verbose = verbose) AssertMVEqual(R_exponential, R_actual, rtol = 1e-2, atol = 1e-3, verbose = verbose) #AssertMVEqual(B_new, B_expected, verbose = verbose) #AssertMVEqual(R_ga, R_expected, verbose = verbose) #N = 100 #Random bivectors to test this as well #for i in range(N): # B = createRandomBivector() # AssertMVEqual(B, ga_log(ga_exp(B, verbose = verbose), verbose = verbose), verbose = verbose) if __name__ == "__main__": unittest.main()

en

0.775235

#Plane to line #How do we define order/direction? #Rotation amount #Rotation Plane #Translation vector #Decomposition into normal component #Decomposition into paralel component #Normal to P #Normal to P #Parallel to P #Parallel to P #Non zero product #Rotor properties #Equalities #Random bivectors to test this as well #Rotation amount #Rotation Plane #t = 0 #Translation vector #Decomposition into normal component #Decomposition into paralel component assert(P|t_nor == 0) #Normal to P assert(P^t_nor != 0) #Normal to P assert(P|t_par != 0) #Parallel to P assert(P^t_par == 0) #Parallel to P assert(P*t != 0) #Non zero product assert(t_par|t_nor == 0) #Non zero product #Testing some basic properties of the extraction #B_new = ga_log(R_expected, verbose = verbose) #R_ga = ga_exp(B_new) #print("R_ga ", R_ga) #print("B_new ", B_new) #BivectorExtraction #Rotor properties #Only an approximation #AssertMVEqual(R_expected * ~R_expected, 1, verbose = verbose) #AssertMVEqual(R_ga * ~R_ga, 1, verbose = verbose) #Equalities #AssertMVEqual(R_actual, R_expected, verbose = verbose) #AssertMVEqual(B_new, B_expected, verbose = verbose) #AssertMVEqual(R_ga, R_expected, verbose = verbose) #N = 100 #Random bivectors to test this as well #for i in range(N): # B = createRandomBivector() # AssertMVEqual(B, ga_log(ga_exp(B, verbose = verbose), verbose = verbose), verbose = verbose)

2.070098

2

router/posts.py

DiegoLing33/prestij.xyz-api

0

10236

# ██╗░░░░░██╗███╗░░██╗░██████╗░░░░██████╗░██╗░░░░░░█████╗░░█████╗░██╗░░██╗ # ██║░░░░░██║████╗░██║██╔════╝░░░░██╔══██╗██║░░░░░██╔══██╗██╔══██╗██║░██╔╝ # ██║░░░░░██║██╔██╗██║██║░░██╗░░░░██████╦╝██║░░░░░███████║██║░░╚═╝█████═╝░ # ██║░░░░░██║██║╚████║██║░░╚██╗░░░██╔══██╗██║░░░░░██╔══██║██║░░██╗██╔═██╗░ # ███████╗██║██║░╚███║╚██████╔╝░░░██████╦╝███████╗██║░░██║╚█████╔╝██║░╚██╗ # ╚══════╝╚═╝╚═╝░░╚══╝░╚═════╝░░░░╚═════╝░╚══════╝╚═╝░░╚═╝░╚════╝░╚═╝░░╚═╝ # # Developed by <NAME> (C) Ling • Black 2020 # @site http://ling.black # ██╗░░░░░██╗███╗░░██╗░██████╗░░░░██████╗░██╗░░░░░░█████╗░░█████╗░██╗░░██╗ # ██║░░░░░██║████╗░██║██╔════╝░░░░██╔══██╗██║░░░░░██╔══██╗██╔══██╗██║░██╔╝ # ██║░░░░░██║██╔██╗██║██║░░██╗░░░░██████╦╝██║░░░░░███████║██║░░╚═╝█████═╝░ # ██║░░░░░██║██║╚████║██║░░╚██╗░░░██╔══██╗██║░░░░░██╔══██║██║░░██╗██╔═██╗░ # ███████╗██║██║░╚███║╚██████╔╝░░░██████╦╝███████╗██║░░██║╚█████╔╝██║░╚██╗ # ╚══════╝╚═╝╚═╝░░╚══╝░╚═════╝░░░░╚═════╝░╚══════╝╚═╝░░╚═╝░╚════╝░╚═╝░░╚═╝ # # Developed by <NAME> (C) Ling • Black 2020 # @site http://ling.black from typing import List from fastapi import APIRouter, Depends, HTTPException from pydantic import BaseModel from core.response import RequestLimit from database import get_db, DatabaseUtils from database.wow.models import PostModel, PostCommentsModel from wow.interface.entity import PostCategory, Post, PostCategoryCreate, PostCreate, PostLikeCreate, PostCommentCreate from wow.utils.posts import PostsUtils from wow.utils.users import BlizzardUsersUtils router = APIRouter() class TokenArgs(BaseModel): token: str class TokenPostIdArgs(BaseModel): token: str post_id: int class CommentIdAndToken(TokenArgs): comment_id: int class PostAPIList(BaseModel): items: List[Post] count: int class PostAPIListResponse(BaseModel): response: PostAPIList request: RequestLimit # ----------------------------------- # CATEGORIES # ----------------------------------- @router.post( "/categories", response_model=PostCategory, summary='Adds the category' ) def add_category(body: PostCategoryCreate): """ Adds the category :param body: :return: """ blizzard_id = BlizzardUsersUtils.id__safe(body.token) return PostsUtils.add_category(user_id=blizzard_id, url=body.url, title=body.title) @router.get( "/categories", response_model=List[PostCategory], summary='Returns the categories' ) def get_categories(): """ Returns the categories list :return: """ return PostsUtils.get_categories() # ----------------------------------- # POSTS # ----------------------------------- @router.get( "/", response_model=PostAPIListResponse, summary='Returns all the posts' ) def get_posts_all(limit: int = 100, offset: int = 0): return PostsUtils.get_posts_limit( limit=limit, offset=offset ) @router.get( "/category/{category_url}", response_model=PostAPIListResponse, summary='Returns the posts in category' ) def get_posts_all(category_url: int, limit: int = 100, offset: int = 0): """ Returns all the posts by category :param category_url: :param limit: :param offset: :return: """ return PostsUtils.get_posts_by_category_limit( category_id=category_url, limit=limit, offset=offset ) @router.get( "/user/{blizzard_id}", response_model=PostAPIListResponse, summary='Returns the posts by users' ) def get_posts_all(blizzard_id: int, limit: int = 100, offset: int = 0): """ Returns all the posts by category :param blizzard_id: :param limit: :param offset: :return: """ return PostsUtils.get_posts_by_blizzard_id( blizzard_id=blizzard_id, limit=limit, offset=offset ) @router.post( "/like", summary='Likes the post', tags=['Лайки'] ) def like_post(body: PostLikeCreate): blizzard_id = BlizzardUsersUtils.id__safe(body.token) return PostsUtils.add_like( user_id=blizzard_id, post_id=body.post_id, ) @router.post( "/unlike", summary='Unlikes the post', tags=['Лайки'] ) def like_post(body: PostLikeCreate): blizzard_id = BlizzardUsersUtils.id__safe(body.token) return PostsUtils.remove_like( user_id=blizzard_id, post_id=body.post_id, ) @router.post( "/comment", summary='Adds the comment', tags=['Комментарии'] ) def like_post(body: PostCommentCreate): blizzard_id = BlizzardUsersUtils.id__safe(body.token) return PostsUtils.add_comment( user_id=blizzard_id, post_id=body.post_id, reply_id=body.reply_id, text=body.text, ) @router.delete( "/comment", summary='Removes the comment', tags=['Комментарии'] ) def removes_post(body: CommentIdAndToken, db=Depends(get_db)): blizzard_id = BlizzardUsersUtils.id__safe(body.token) com = db.query(PostCommentsModel).filter(PostCommentsModel.id == body.comment_id).filter( PostCommentsModel.user_id == blizzard_id) if com.count() > 0: com.delete() db.commit() return True return False @router.post( "/", response_model=Post, summary='Adds the post' ) def add_post(body: PostCreate): """ Adds the post item :param body: :return: """ blizzard_id = BlizzardUsersUtils.id__safe(body.token) return PostsUtils.add_post( user_id=blizzard_id, category_id=body.category_id, title=body.title, content=body.content, tags=body.tags, image=body.image ) @router.delete( "/{post_id}", summary='Deletes the post' ) def delete_post(post_id: int, body: TokenArgs, db=Depends(get_db)): blizzard_id = BlizzardUsersUtils.id__safe(body.token) q = db.query(PostModel).filter(PostModel.id == post_id).filter(PostModel.user_id == blizzard_id) if q.count() == 0: raise HTTPException(status_code=404, detail='Post is undefined') return DatabaseUtils.remove_query(db, q) @router.post( "/{post_id}", summary='Edits the post' ) def edit_post(post_id: int, body: PostCreate, db=Depends(get_db)): blizzard_id = BlizzardUsersUtils.id__safe(body.token) q = db.query(PostModel).filter(PostModel.id == post_id).filter(PostModel.user_id == blizzard_id) if q.count() == 0: raise HTTPException(status_code=404, detail='Post is undefined') q.update({ 'title': body.title, 'content': body.content, 'category_id': body.category_id, 'image': body.image, 'tags': body.tags, }) db.commit() return True @router.get( "/{post_id}", response_model=Post, summary='Returns the post' ) def get_post(post_id: int, db=Depends(get_db)): return db.query(PostModel).filter(PostModel.id == post_id).first()

en

0.085405

# ██╗░░░░░██╗███╗░░██╗░██████╗░░░░██████╗░██╗░░░░░░█████╗░░█████╗░██╗░░██╗ # ██║░░░░░██║████╗░██║██╔════╝░░░░██╔══██╗██║░░░░░██╔══██╗██╔══██╗██║░██╔╝ # ██║░░░░░██║██╔██╗██║██║░░██╗░░░░██████╦╝██║░░░░░███████║██║░░╚═╝█████═╝░ # ██║░░░░░██║██║╚████║██║░░╚██╗░░░██╔══██╗██║░░░░░██╔══██║██║░░██╗██╔═██╗░ # ███████╗██║██║░╚███║╚██████╔╝░░░██████╦╝███████╗██║░░██║╚█████╔╝██║░╚██╗ # ╚══════╝╚═╝╚═╝░░╚══╝░╚═════╝░░░░╚═════╝░╚══════╝╚═╝░░╚═╝░╚════╝░╚═╝░░╚═╝ # # Developed by <NAME> (C) Ling • Black 2020 # @site http://ling.black # ██╗░░░░░██╗███╗░░██╗░██████╗░░░░██████╗░██╗░░░░░░█████╗░░█████╗░██╗░░██╗ # ██║░░░░░██║████╗░██║██╔════╝░░░░██╔══██╗██║░░░░░██╔══██╗██╔══██╗██║░██╔╝ # ██║░░░░░██║██╔██╗██║██║░░██╗░░░░██████╦╝██║░░░░░███████║██║░░╚═╝█████═╝░ # ██║░░░░░██║██║╚████║██║░░╚██╗░░░██╔══██╗██║░░░░░██╔══██║██║░░██╗██╔═██╗░ # ███████╗██║██║░╚███║╚██████╔╝░░░██████╦╝███████╗██║░░██║╚█████╔╝██║░╚██╗ # ╚══════╝╚═╝╚═╝░░╚══╝░╚═════╝░░░░╚═════╝░╚══════╝╚═╝░░╚═╝░╚════╝░╚═╝░░╚═╝ # # Developed by <NAME> (C) Ling • Black 2020 # @site http://ling.black # ----------------------------------- # CATEGORIES # ----------------------------------- Adds the category :param body: :return: Returns the categories list :return: # ----------------------------------- # POSTS # ----------------------------------- Returns all the posts by category :param category_url: :param limit: :param offset: :return: Returns all the posts by category :param blizzard_id: :param limit: :param offset: :return: Adds the post item :param body: :return:

2.165511

2

toontown/catalog/CatalogChatBalloon.py

CrankySupertoon01/Toontown-2

1

10237

<filename>toontown/catalog/CatalogChatBalloon.py from pandac.PandaModules import * class CatalogChatBalloon: TEXT_SHIFT = (0.1, -0.05, 1.1) TEXT_SHIFT_REVERSED = -0.05 TEXT_SHIFT_PROP = 0.08 NATIVE_WIDTH = 10.0 MIN_WIDTH = 2.5 MIN_HEIGHT = 1 BUBBLE_PADDING = 0.3 BUBBLE_PADDING_PROP = 0.05 BUTTON_SCALE = 6 BUTTON_SHIFT = (-0.2, 0, 0.6) FRAME_SHIFT = (0.2, 1.4) def __init__(self, model): self.model = model def generate(self, text, font, textColor=(0,0,0,1), balloonColor=(1,1,1,1), wordWrap = 10.0, button=None, reversed=False): root = NodePath('balloon') # Add balloon geometry: balloon = self.model.copyTo(root) top = balloon.find('**/top') middle = balloon.find('**/middle') bottom = balloon.find('**/bottom') balloon.setColor(balloonColor) if balloonColor[3] < 1.0: balloon.setTransparency(1) # Render the text into a TextNode, using the font: t = root.attachNewNode(TextNode('text')) t.node().setFont(font) t.node().setWordwrap(wordWrap) t.node().setText(text) t.node().setTextColor(textColor) width, height = t.node().getWidth(), t.node().getHeight() # Turn off depth write for the text: The place in the depth buffer is # held by the chat bubble anyway, and the text renders after the bubble # so there's no risk of the bubble overwriting the text's pixels. t.setAttrib(DepthWriteAttrib.make(0)) t.setPos(self.TEXT_SHIFT) t.setX(t, self.TEXT_SHIFT_PROP*width) t.setZ(t, height) if reversed: # The nametag code wants the text on the left side of the axis, # rather than on the right side. Therefore, we move the text to the # opposite side: t.setX(self.TEXT_SHIFT_REVERSED - self.TEXT_SHIFT_PROP*width - width) # Give the chat bubble a button, if one is requested: if button: np = button.copyTo(root) np.setPos(t, width, 0, -height) np.setPos(np, self.BUTTON_SHIFT) np.setScale(self.BUTTON_SCALE) # Set a minimum width and height for short or empty messages if width < self.MIN_WIDTH: width = self.MIN_WIDTH if reversed: t.setX(t, -width/2.0) else: t.setX(t, width/2.0) t.node().setAlign(TextNode.ACenter) if height < self.MIN_HEIGHT: height = self.MIN_HEIGHT t.setX(t, height/2.0) t.node().setAlign(TextNode.ACenter) # Set the balloon's size: width *= 1+self.BUBBLE_PADDING_PROP width += self.BUBBLE_PADDING balloon.setSx(width/self.NATIVE_WIDTH) if reversed: balloon.setSx(-balloon.getSx()) balloon.setTwoSided(True) # Render the backface of the balloon middle.setSz(height) top.setZ(top, height-1) # Calculate the frame occupied by the balloon: left, bottom = self.FRAME_SHIFT if reversed: left = -left - width frame = (left, left+width, bottom, bottom+height+1) return root, frame

en

0.842173

# Add balloon geometry: # Render the text into a TextNode, using the font: # Turn off depth write for the text: The place in the depth buffer is # held by the chat bubble anyway, and the text renders after the bubble # so there's no risk of the bubble overwriting the text's pixels. # The nametag code wants the text on the left side of the axis, # rather than on the right side. Therefore, we move the text to the # opposite side: # Give the chat bubble a button, if one is requested: # Set a minimum width and height for short or empty messages # Set the balloon's size: # Render the backface of the balloon # Calculate the frame occupied by the balloon:

2.802248

3

TTS/vocoder/tf/utils/io.py

mightmay/Mien-TTS

0

10238

import datetime import pickle import tensorflow as tf def save_checkpoint(model, current_step, epoch, output_path, **kwargs): """ Save TF Vocoder model """ state = { 'model': model.weights, 'step': current_step, 'epoch': epoch, 'date': datetime.date.today().strftime("%B %d, %Y"), } state.update(kwargs) pickle.dump(state, open(output_path, 'wb')) def load_checkpoint(model, checkpoint_path): """ Load TF Vocoder model """ checkpoint = pickle.load(open(checkpoint_path, 'rb')) chkp_var_dict = {var.name: var.numpy() for var in checkpoint['model']} tf_vars = model.weights for tf_var in tf_vars: layer_name = tf_var.name chkp_var_value = chkp_var_dict[layer_name] tf.keras.backend.set_value(tf_var, chkp_var_value) return model

en

0.399366

Save TF Vocoder model Load TF Vocoder model

2.468851

2

tests/test_path_choice.py

jataware/flee

3

10239

<reponame>jataware/flee from flee import flee """ Generation 1 code. Incorporates only distance, travel always takes one day. """ def test_path_choice(): print("Testing basic data handling and simulation kernel.") flee.SimulationSettings.MinMoveSpeed = 5000.0 flee.SimulationSettings.MaxMoveSpeed = 5000.0 flee.SimulationSettings.MaxWalkSpeed = 5000.0 e = flee.Ecosystem() l1 = e.addLocation(name="A", movechance=1.0) _ = e.addLocation(name="B", movechance=1.0) _ = e.addLocation(name="C1", movechance=1.0) _ = e.addLocation(name="C2", movechance=1.0) _ = e.addLocation(name="D1", movechance=1.0) _ = e.addLocation(name="D2", movechance=1.0) _ = e.addLocation(name="D3", movechance=1.0) # l2 = e.addLocation(name="B", movechance=1.0) # l3 = e.addLocation(name="C1", movechance=1.0) # l4 = e.addLocation(name="C2", movechance=1.0) # l5 = e.addLocation(name="D1", movechance=1.0) # l6 = e.addLocation(name="D2", movechance=1.0) # l7 = e.addLocation(name="D3", movechance=1.0) e.linkUp(endpoint1="A", endpoint2="B", distance=10.0) e.linkUp(endpoint1="A", endpoint2="C1", distance=10.0) e.linkUp(endpoint1="A", endpoint2="D1", distance=10.0) e.linkUp(endpoint1="C1", endpoint2="C2", distance=10.0) e.linkUp(endpoint1="D1", endpoint2="D2", distance=10.0) e.linkUp(endpoint1="D2", endpoint2="D3", distance=10.0) e.addAgent(location=l1) print("Test successful!") if __name__ == "__main__": test_path_choice()

from flee import flee """ Generation 1 code. Incorporates only distance, travel always takes one day. """ def test_path_choice(): print("Testing basic data handling and simulation kernel.") flee.SimulationSettings.MinMoveSpeed = 5000.0 flee.SimulationSettings.MaxMoveSpeed = 5000.0 flee.SimulationSettings.MaxWalkSpeed = 5000.0 e = flee.Ecosystem() l1 = e.addLocation(name="A", movechance=1.0) _ = e.addLocation(name="B", movechance=1.0) _ = e.addLocation(name="C1", movechance=1.0) _ = e.addLocation(name="C2", movechance=1.0) _ = e.addLocation(name="D1", movechance=1.0) _ = e.addLocation(name="D2", movechance=1.0) _ = e.addLocation(name="D3", movechance=1.0) # l2 = e.addLocation(name="B", movechance=1.0) # l3 = e.addLocation(name="C1", movechance=1.0) # l4 = e.addLocation(name="C2", movechance=1.0) # l5 = e.addLocation(name="D1", movechance=1.0) # l6 = e.addLocation(name="D2", movechance=1.0) # l7 = e.addLocation(name="D3", movechance=1.0) e.linkUp(endpoint1="A", endpoint2="B", distance=10.0) e.linkUp(endpoint1="A", endpoint2="C1", distance=10.0) e.linkUp(endpoint1="A", endpoint2="D1", distance=10.0) e.linkUp(endpoint1="C1", endpoint2="C2", distance=10.0) e.linkUp(endpoint1="D1", endpoint2="D2", distance=10.0) e.linkUp(endpoint1="D2", endpoint2="D3", distance=10.0) e.addAgent(location=l1) print("Test successful!") if __name__ == "__main__": test_path_choice()

en

0.281603

Generation 1 code. Incorporates only distance, travel always takes one day. # l2 = e.addLocation(name="B", movechance=1.0) # l3 = e.addLocation(name="C1", movechance=1.0) # l4 = e.addLocation(name="C2", movechance=1.0) # l5 = e.addLocation(name="D1", movechance=1.0) # l6 = e.addLocation(name="D2", movechance=1.0) # l7 = e.addLocation(name="D3", movechance=1.0)

2.96455

3

archive/old_plots/plot_supplemental_divergence_correlations.py

garudlab/mother_infant

2

10240

<reponame>garudlab/mother_infant<filename>archive/old_plots/plot_supplemental_divergence_correlations.py import matplotlib matplotlib.use('Agg') import config import parse_midas_data import parse_HMP_data import os.path import pylab import sys import numpy import diversity_utils import gene_diversity_utils import calculate_substitution_rates import stats_utils import matplotlib.colors as colors import matplotlib.cm as cmx from math import log10,ceil import matplotlib as mpl import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec from numpy.random import randint from scipy.cluster.hierarchy import dendrogram, linkage from scipy.cluster.hierarchy import cophenet from scipy.cluster.hierarchy import fcluster from scipy.stats import gaussian_kde mpl.rcParams['font.size'] = 6 mpl.rcParams['lines.linewidth'] = 0.5 mpl.rcParams['legend.frameon'] = False mpl.rcParams['legend.fontsize'] = 'small' ################################################################################ # # Standard header to read in argument information # ################################################################################ import argparse parser = argparse.ArgumentParser() parser.add_argument("--debug", help="Loads only a subset of SNPs for speed", action="store_true") parser.add_argument("--chunk-size", type=int, help="max number of records to load", default=1000000000) args = parser.parse_args() debug = args.debug chunk_size = args.chunk_size ################################################################################ good_species_list = ['Bacteroides_vulgatus_57955', 'Bacteroides_uniformis_57318', 'Alistipes_putredinis_61533'] #################################################### # # Set up Figure (3 panels, arranged in 1x3 grid) # #################################################### pylab.figure(1,figsize=(7,1.5)) fig = pylab.gcf() # make three panels panels outer_grid = gridspec.GridSpec(1,3,width_ratios=[1,1,1],wspace=0.1) ####### # # SNP divergence vs Gene divergence in B. vulgatus # ####### gene_axis = plt.Subplot(fig, outer_grid[0]) fig.add_subplot(gene_axis) gene_axis.set_ylabel('SNP divergence\n %s' % (good_species_list[0])) gene_axis.set_xlabel('Gene divergence\n %s' % (good_species_list[0])) gene_axis.set_ylim([1e-06,1e-01]) #gene_axis.set_xlim([1e-02,1]) gene_axis.spines['top'].set_visible(False) gene_axis.spines['right'].set_visible(False) gene_axis.get_xaxis().tick_bottom() gene_axis.get_yaxis().tick_left() ####### # # SNP divergence (B vulgatus) vs SNP divergence (A putredinis) # ####### species_axis_1 = plt.Subplot(fig, outer_grid[1]) fig.add_subplot(species_axis_1) species_axis_1.set_xlabel('SNP divergence\n %s' % (good_species_list[1])) species_axis_1.set_ylim([1e-06,1e-01]) species_axis_1.set_xlim([1e-06,1e-01]) species_axis_1.spines['top'].set_visible(False) species_axis_1.spines['right'].set_visible(False) species_axis_1.get_xaxis().tick_bottom() species_axis_1.get_yaxis().tick_left() ####### # # SNP divergence (B vulgatus) vs SNP divergence (A putredinis) # ####### species_axis_2 = plt.Subplot(fig, outer_grid[2]) fig.add_subplot(species_axis_2) species_axis_2.set_xlabel('SNP divergence\n %s' % (good_species_list[2])) species_axis_2.set_ylim([1e-06,1e-01]) species_axis_2.set_xlim([1e-06,1e-01]) species_axis_2.spines['top'].set_visible(False) species_axis_2.spines['right'].set_visible(False) species_axis_2.get_xaxis().tick_bottom() species_axis_2.get_yaxis().tick_left() ######## # # Now do calculation and plot figures # ######## sys.stderr.write("Loading sample metadata...\n") subject_sample_map = parse_HMP_data.parse_subject_sample_map() sample_order_map = parse_HMP_data.parse_sample_order_map() sys.stderr.write("Done!\n") snp_divergence_map = {species_name: {} for species_name in good_species_list} gene_divergence_map = {species_name: {} for species_name in good_species_list} for species_name in good_species_list: sys.stderr.write("Loading haploid samples...\n") snp_samples = diversity_utils.calculate_haploid_samples(species_name, debug=debug) sys.stderr.write("Calculating unique samples...\n") # Only consider one sample per person snp_samples = snp_samples[parse_midas_data.calculate_unique_samples(subject_sample_map, sample_list=snp_samples)] sys.stderr.write("Loading pre-computed substitution rates for %s...\n" % species_name) substitution_rate_map = calculate_substitution_rates.load_substitution_rate_map(species_name) sys.stderr.write("Calculating snp matrix...\n") dummy_samples, snp_difference_matrix, snp_opportunity_matrix = calculate_substitution_rates.calculate_matrices_from_substitution_rate_map(substitution_rate_map, 'core', allowed_samples=snp_samples) snp_samples = dummy_samples sys.stderr.write("Done!\n") sys.stderr.write("Calculating gene matrix...\n") gene_samples, gene_difference_matrix, gene_opportunity_matrix = calculate_substitution_rates.calculate_matrices_from_substitution_rate_map(substitution_rate_map, 'genes', allowed_samples=snp_samples) snp_samples = gene_samples sys.stderr.write("Done!\n") # Focus on the subset of samples that have sufficient gene depth and snp depth desired_samples = gene_samples # Figure out which pairs of indices in desired_samples belong to diff subjects desired_same_sample_idxs, desired_same_subject_idxs, desired_diff_subject_idxs = parse_midas_data.calculate_subject_pairs( subject_sample_map, desired_samples) # Turn these into indices for snp and gene matrices snp_sample_idx_map = parse_midas_data.calculate_sample_idx_map(desired_samples, snp_samples) gene_sample_idx_map = parse_midas_data.calculate_sample_idx_map(desired_samples, gene_samples) same_subject_snp_idxs = parse_midas_data.apply_sample_index_map_to_indices(snp_sample_idx_map, desired_same_subject_idxs) same_subject_gene_idxs = parse_midas_data.apply_sample_index_map_to_indices(gene_sample_idx_map, desired_same_subject_idxs) diff_subject_snp_idxs = parse_midas_data.apply_sample_index_map_to_indices(snp_sample_idx_map, desired_diff_subject_idxs) diff_subject_gene_idxs = parse_midas_data.apply_sample_index_map_to_indices(gene_sample_idx_map, desired_diff_subject_idxs) for sample_pair_idx in xrange(0,len(diff_subject_snp_idxs[0])): snp_i = diff_subject_snp_idxs[0][sample_pair_idx] snp_j = diff_subject_snp_idxs[1][sample_pair_idx] gene_i = diff_subject_gene_idxs[0][sample_pair_idx] gene_j = diff_subject_gene_idxs[1][sample_pair_idx] sample_i = desired_samples[gene_i] sample_j = desired_samples[gene_j] # This will serve as a key in snp_divergence_map sample_pair = frozenset([sample_i,sample_j]) # Focus on pairs of samples with sufficient coverage if snp_opportunity_matrix[snp_i,snp_j]>0: snp_d = snp_difference_matrix[snp_i,snp_j]*1.0/snp_opportunity_matrix[snp_i,snp_j] snp_divergence_map[species_name][sample_pair] = snp_d if gene_opportunity_matrix[gene_i, gene_j]>0: gene_d = gene_difference_matrix[gene_i, gene_j]*1.0/gene_opportunity_matrix[gene_i, gene_j] gene_divergence_map[species_name][sample_pair] = gene_d ################# # # Plot figures! # ################# # First calculate SNP vs gene divergence in B. vulgatus species_name = good_species_list[0] snp_divergences = [] gene_divergences = [] # Loop over sample pairs that are in both snp_divergence_map and gene_divergence_map for sample_pair in (set(snp_divergence_map[species_name].keys()) & set(gene_divergence_map[species_name].keys()) ): snp_divergences.append( snp_divergence_map[species_name][sample_pair] ) gene_divergences.append( gene_divergence_map[species_name][sample_pair] ) snp_divergences = numpy.array(snp_divergences) gene_divergences = numpy.array(gene_divergences) # Null expectation (medians line up) median_ratio = numpy.median(snp_divergences)/numpy.median(gene_divergences) gene_axis.loglog([1e-02,1],[1e-02*median_ratio,1*median_ratio],'k-',linewidth=0.25) gene_axis.loglog(gene_divergences, snp_divergences, 'r.', markersize=2,alpha=0.5,markeredgewidth=0, rasterized=True) # Then SNP divergence between two species species_1 = good_species_list[0] species_2 = good_species_list[1] snp_divergences_1 = [] snp_divergences_2 = [] # Loop over sample pairs that are in both snp_divergence_map and gene_divergence_map for sample_pair in (set(snp_divergence_map[species_1].keys()) & set(snp_divergence_map[species_2].keys()) ): snp_divergences_1.append( snp_divergence_map[species_1][sample_pair] ) snp_divergences_2.append( snp_divergence_map[species_2][sample_pair] ) snp_divergences_1 = numpy.array(snp_divergences_1) snp_divergences_2 = numpy.array(snp_divergences_2) # Null expectation (medians line up) median_ratio = numpy.median(snp_divergences_1)/numpy.median(snp_divergences_2) species_axis_1.loglog([1e-06,1e-01],[1e-06*median_ratio,1e-01*median_ratio],'k-',linewidth=0.25) # Observed values species_axis_1.loglog(snp_divergences_2, snp_divergences_1, 'r.', markersize=2,alpha=0.5,markeredgewidth=0, rasterized=True) # Then SNP divergence between other two species species_1 = good_species_list[0] species_2 = good_species_list[2] snp_divergences_1 = [] snp_divergences_2 = [] # Loop over sample pairs that are in both snp_divergence_map and gene_divergence_map for sample_pair in (set(snp_divergence_map[species_1].keys()) & set(snp_divergence_map[species_2].keys()) ): snp_divergences_1.append( snp_divergence_map[species_1][sample_pair] ) snp_divergences_2.append( snp_divergence_map[species_2][sample_pair] ) snp_divergences_1 = numpy.array(snp_divergences_1) snp_divergences_2 = numpy.array(snp_divergences_2) # Null expectation (medians line up) median_ratio = numpy.median(snp_divergences_1)/numpy.median(snp_divergences_2) species_axis_2.loglog([1e-06,1e-01],[1e-06*median_ratio,1e-01*median_ratio],'k-',linewidth=0.25) species_axis_2.loglog(snp_divergences_2, snp_divergences_1, 'r.', markersize=2,alpha=0.5,markeredgewidth=0,rasterized=True) # Since y-axes are shared, do not duplicate ticklables species_axis_1.set_yticklabels([]) species_axis_2.set_yticklabels([]) sys.stderr.write("Saving figure...\t") fig.savefig('%s/supplemental_divergence_correlations.pdf' % (parse_midas_data.analysis_directory),bbox_inches='tight',dpi=600) sys.stderr.write("Done!\n")

import matplotlib matplotlib.use('Agg') import config import parse_midas_data import parse_HMP_data import os.path import pylab import sys import numpy import diversity_utils import gene_diversity_utils import calculate_substitution_rates import stats_utils import matplotlib.colors as colors import matplotlib.cm as cmx from math import log10,ceil import matplotlib as mpl import matplotlib.pyplot as plt import matplotlib.gridspec as gridspec from numpy.random import randint from scipy.cluster.hierarchy import dendrogram, linkage from scipy.cluster.hierarchy import cophenet from scipy.cluster.hierarchy import fcluster from scipy.stats import gaussian_kde mpl.rcParams['font.size'] = 6 mpl.rcParams['lines.linewidth'] = 0.5 mpl.rcParams['legend.frameon'] = False mpl.rcParams['legend.fontsize'] = 'small' ################################################################################ # # Standard header to read in argument information # ################################################################################ import argparse parser = argparse.ArgumentParser() parser.add_argument("--debug", help="Loads only a subset of SNPs for speed", action="store_true") parser.add_argument("--chunk-size", type=int, help="max number of records to load", default=1000000000) args = parser.parse_args() debug = args.debug chunk_size = args.chunk_size ################################################################################ good_species_list = ['Bacteroides_vulgatus_57955', 'Bacteroides_uniformis_57318', 'Alistipes_putredinis_61533'] #################################################### # # Set up Figure (3 panels, arranged in 1x3 grid) # #################################################### pylab.figure(1,figsize=(7,1.5)) fig = pylab.gcf() # make three panels panels outer_grid = gridspec.GridSpec(1,3,width_ratios=[1,1,1],wspace=0.1) ####### # # SNP divergence vs Gene divergence in B. vulgatus # ####### gene_axis = plt.Subplot(fig, outer_grid[0]) fig.add_subplot(gene_axis) gene_axis.set_ylabel('SNP divergence\n %s' % (good_species_list[0])) gene_axis.set_xlabel('Gene divergence\n %s' % (good_species_list[0])) gene_axis.set_ylim([1e-06,1e-01]) #gene_axis.set_xlim([1e-02,1]) gene_axis.spines['top'].set_visible(False) gene_axis.spines['right'].set_visible(False) gene_axis.get_xaxis().tick_bottom() gene_axis.get_yaxis().tick_left() ####### # # SNP divergence (B vulgatus) vs SNP divergence (A putredinis) # ####### species_axis_1 = plt.Subplot(fig, outer_grid[1]) fig.add_subplot(species_axis_1) species_axis_1.set_xlabel('SNP divergence\n %s' % (good_species_list[1])) species_axis_1.set_ylim([1e-06,1e-01]) species_axis_1.set_xlim([1e-06,1e-01]) species_axis_1.spines['top'].set_visible(False) species_axis_1.spines['right'].set_visible(False) species_axis_1.get_xaxis().tick_bottom() species_axis_1.get_yaxis().tick_left() ####### # # SNP divergence (B vulgatus) vs SNP divergence (A putredinis) # ####### species_axis_2 = plt.Subplot(fig, outer_grid[2]) fig.add_subplot(species_axis_2) species_axis_2.set_xlabel('SNP divergence\n %s' % (good_species_list[2])) species_axis_2.set_ylim([1e-06,1e-01]) species_axis_2.set_xlim([1e-06,1e-01]) species_axis_2.spines['top'].set_visible(False) species_axis_2.spines['right'].set_visible(False) species_axis_2.get_xaxis().tick_bottom() species_axis_2.get_yaxis().tick_left() ######## # # Now do calculation and plot figures # ######## sys.stderr.write("Loading sample metadata...\n") subject_sample_map = parse_HMP_data.parse_subject_sample_map() sample_order_map = parse_HMP_data.parse_sample_order_map() sys.stderr.write("Done!\n") snp_divergence_map = {species_name: {} for species_name in good_species_list} gene_divergence_map = {species_name: {} for species_name in good_species_list} for species_name in good_species_list: sys.stderr.write("Loading haploid samples...\n") snp_samples = diversity_utils.calculate_haploid_samples(species_name, debug=debug) sys.stderr.write("Calculating unique samples...\n") # Only consider one sample per person snp_samples = snp_samples[parse_midas_data.calculate_unique_samples(subject_sample_map, sample_list=snp_samples)] sys.stderr.write("Loading pre-computed substitution rates for %s...\n" % species_name) substitution_rate_map = calculate_substitution_rates.load_substitution_rate_map(species_name) sys.stderr.write("Calculating snp matrix...\n") dummy_samples, snp_difference_matrix, snp_opportunity_matrix = calculate_substitution_rates.calculate_matrices_from_substitution_rate_map(substitution_rate_map, 'core', allowed_samples=snp_samples) snp_samples = dummy_samples sys.stderr.write("Done!\n") sys.stderr.write("Calculating gene matrix...\n") gene_samples, gene_difference_matrix, gene_opportunity_matrix = calculate_substitution_rates.calculate_matrices_from_substitution_rate_map(substitution_rate_map, 'genes', allowed_samples=snp_samples) snp_samples = gene_samples sys.stderr.write("Done!\n") # Focus on the subset of samples that have sufficient gene depth and snp depth desired_samples = gene_samples # Figure out which pairs of indices in desired_samples belong to diff subjects desired_same_sample_idxs, desired_same_subject_idxs, desired_diff_subject_idxs = parse_midas_data.calculate_subject_pairs( subject_sample_map, desired_samples) # Turn these into indices for snp and gene matrices snp_sample_idx_map = parse_midas_data.calculate_sample_idx_map(desired_samples, snp_samples) gene_sample_idx_map = parse_midas_data.calculate_sample_idx_map(desired_samples, gene_samples) same_subject_snp_idxs = parse_midas_data.apply_sample_index_map_to_indices(snp_sample_idx_map, desired_same_subject_idxs) same_subject_gene_idxs = parse_midas_data.apply_sample_index_map_to_indices(gene_sample_idx_map, desired_same_subject_idxs) diff_subject_snp_idxs = parse_midas_data.apply_sample_index_map_to_indices(snp_sample_idx_map, desired_diff_subject_idxs) diff_subject_gene_idxs = parse_midas_data.apply_sample_index_map_to_indices(gene_sample_idx_map, desired_diff_subject_idxs) for sample_pair_idx in xrange(0,len(diff_subject_snp_idxs[0])): snp_i = diff_subject_snp_idxs[0][sample_pair_idx] snp_j = diff_subject_snp_idxs[1][sample_pair_idx] gene_i = diff_subject_gene_idxs[0][sample_pair_idx] gene_j = diff_subject_gene_idxs[1][sample_pair_idx] sample_i = desired_samples[gene_i] sample_j = desired_samples[gene_j] # This will serve as a key in snp_divergence_map sample_pair = frozenset([sample_i,sample_j]) # Focus on pairs of samples with sufficient coverage if snp_opportunity_matrix[snp_i,snp_j]>0: snp_d = snp_difference_matrix[snp_i,snp_j]*1.0/snp_opportunity_matrix[snp_i,snp_j] snp_divergence_map[species_name][sample_pair] = snp_d if gene_opportunity_matrix[gene_i, gene_j]>0: gene_d = gene_difference_matrix[gene_i, gene_j]*1.0/gene_opportunity_matrix[gene_i, gene_j] gene_divergence_map[species_name][sample_pair] = gene_d ################# # # Plot figures! # ################# # First calculate SNP vs gene divergence in B. vulgatus species_name = good_species_list[0] snp_divergences = [] gene_divergences = [] # Loop over sample pairs that are in both snp_divergence_map and gene_divergence_map for sample_pair in (set(snp_divergence_map[species_name].keys()) & set(gene_divergence_map[species_name].keys()) ): snp_divergences.append( snp_divergence_map[species_name][sample_pair] ) gene_divergences.append( gene_divergence_map[species_name][sample_pair] ) snp_divergences = numpy.array(snp_divergences) gene_divergences = numpy.array(gene_divergences) # Null expectation (medians line up) median_ratio = numpy.median(snp_divergences)/numpy.median(gene_divergences) gene_axis.loglog([1e-02,1],[1e-02*median_ratio,1*median_ratio],'k-',linewidth=0.25) gene_axis.loglog(gene_divergences, snp_divergences, 'r.', markersize=2,alpha=0.5,markeredgewidth=0, rasterized=True) # Then SNP divergence between two species species_1 = good_species_list[0] species_2 = good_species_list[1] snp_divergences_1 = [] snp_divergences_2 = [] # Loop over sample pairs that are in both snp_divergence_map and gene_divergence_map for sample_pair in (set(snp_divergence_map[species_1].keys()) & set(snp_divergence_map[species_2].keys()) ): snp_divergences_1.append( snp_divergence_map[species_1][sample_pair] ) snp_divergences_2.append( snp_divergence_map[species_2][sample_pair] ) snp_divergences_1 = numpy.array(snp_divergences_1) snp_divergences_2 = numpy.array(snp_divergences_2) # Null expectation (medians line up) median_ratio = numpy.median(snp_divergences_1)/numpy.median(snp_divergences_2) species_axis_1.loglog([1e-06,1e-01],[1e-06*median_ratio,1e-01*median_ratio],'k-',linewidth=0.25) # Observed values species_axis_1.loglog(snp_divergences_2, snp_divergences_1, 'r.', markersize=2,alpha=0.5,markeredgewidth=0, rasterized=True) # Then SNP divergence between other two species species_1 = good_species_list[0] species_2 = good_species_list[2] snp_divergences_1 = [] snp_divergences_2 = [] # Loop over sample pairs that are in both snp_divergence_map and gene_divergence_map for sample_pair in (set(snp_divergence_map[species_1].keys()) & set(snp_divergence_map[species_2].keys()) ): snp_divergences_1.append( snp_divergence_map[species_1][sample_pair] ) snp_divergences_2.append( snp_divergence_map[species_2][sample_pair] ) snp_divergences_1 = numpy.array(snp_divergences_1) snp_divergences_2 = numpy.array(snp_divergences_2) # Null expectation (medians line up) median_ratio = numpy.median(snp_divergences_1)/numpy.median(snp_divergences_2) species_axis_2.loglog([1e-06,1e-01],[1e-06*median_ratio,1e-01*median_ratio],'k-',linewidth=0.25) species_axis_2.loglog(snp_divergences_2, snp_divergences_1, 'r.', markersize=2,alpha=0.5,markeredgewidth=0,rasterized=True) # Since y-axes are shared, do not duplicate ticklables species_axis_1.set_yticklabels([]) species_axis_2.set_yticklabels([]) sys.stderr.write("Saving figure...\t") fig.savefig('%s/supplemental_divergence_correlations.pdf' % (parse_midas_data.analysis_directory),bbox_inches='tight',dpi=600) sys.stderr.write("Done!\n")

en

0.498721

################################################################################ # # Standard header to read in argument information # ################################################################################ ################################################################################ #################################################### # # Set up Figure (3 panels, arranged in 1x3 grid) # #################################################### # make three panels panels ####### # # SNP divergence vs Gene divergence in B. vulgatus # ####### #gene_axis.set_xlim([1e-02,1]) ####### # # SNP divergence (B vulgatus) vs SNP divergence (A putredinis) # ####### ####### # # SNP divergence (B vulgatus) vs SNP divergence (A putredinis) # ####### ######## # # Now do calculation and plot figures # ######## # Only consider one sample per person # Focus on the subset of samples that have sufficient gene depth and snp depth # Figure out which pairs of indices in desired_samples belong to diff subjects # Turn these into indices for snp and gene matrices # This will serve as a key in snp_divergence_map # Focus on pairs of samples with sufficient coverage ################# # # Plot figures! # ################# # First calculate SNP vs gene divergence in B. vulgatus # Loop over sample pairs that are in both snp_divergence_map and gene_divergence_map # Null expectation (medians line up) # Then SNP divergence between two species # Loop over sample pairs that are in both snp_divergence_map and gene_divergence_map # Null expectation (medians line up) # Observed values # Then SNP divergence between other two species # Loop over sample pairs that are in both snp_divergence_map and gene_divergence_map # Null expectation (medians line up) # Since y-axes are shared, do not duplicate ticklables

2.018415

2

multivis/plotFeatures.py

brettChapman/cimcb_vis

1

10241

import sys import copy import matplotlib import matplotlib.pyplot as plt import seaborn as sns from collections import Counter from .utils import * import numpy as np import pandas as pd class plotFeatures: usage = """Produces different feature plots given a data table and peak table. Initial_Parameters ---------- peaktable : Pandas dataframe containing peak data. Must contain 'Name' and 'Label'. datatable : Pandas dataframe containing matrix of values to plot (N samples x N features). Columns/features must be same as 'Name' from Peak Table. Methods ------- set_params : Set parameters - plot_type: The type of plot. Either "point", "violin", "box", "swarm", "violin-swarm" or "box-swarm" (default: 'point') column_numbers: The number of columns to display in the plots (default: 4) log_data: Perform a log ('natural', base 2 or base 10) on all data (default: (True, 2)) scale_data: Scale the data ('standard' (centers to the mean and scales to unit variance), 'minmax' (scales between 0 and 1), 'maxabs' (scales to the absolute maximum value), 'robust' (centers to the median and scales to between 25th and 75th quantile range) (default: (True, 'minmax')) impute_data: Impute any missing values using KNN impute with a set number of nearest neighbours (default: (True, 3)) style: Set the matplotlib style (see https://matplotlib.org/stable/tutorials/introductory/customizing.html) (default: 'seaborn-white') transparent: Setting to 'True' will make the background transparent (default: False) figSize: The figure size as a tuple (width,height) (default: (15,10)) fontSize: The font size for all text (default: 12) colour_palette: The colour palette to use for the plot (default: None) y_axis_label: The label to customise the y axis (default: None) x_axis_rotation: Rotate the x axis labels this number of degrees (default: 0) group_column_name: The group column name used in the datatable (e.g. 'Class') (default: None) point_estimator: The statistical function to use for the point plot. Either "mean" or "median" (default: 'mean') point_ci: The bootstrapped confidence interval for the point plot. Can also be standard deviation ("sd") (default: 95) violin_distribution_type: The representation of the distribution of data points within the violin plot. Either "quartile", "box", "point", "stick" or None (default: 'box') violin_width_scale: The method used to scale the width of the violin plot. Either "area", "count" or "width" (default: "width") box_iqr: The proportion past the lower and upper quartiles to extend the plot whiskers for the box plot. Points outside this range will be identified as outliers (default: 1.5) saveImage: Setting to 'True' will save the image to file (default: True) imageFileName: The image file name to save to (default: [plot_type]_features.png') dpi: The number of Dots Per Inch (DPI) for the image (default: 200) help : Print this help text plot : Generates feature plots """ def __init__(self, peaktable, datatable): peaktable = self.__checkPeakTable(self.__checkData(peaktable)) datatable = self.__checkData(datatable) # Slice the meta-data, and select only peaks from the peaktable for processing, and add the meta-data back meta = datatable.T[~datatable.T.index.isin(peaktable['Name'])].T.reset_index(drop=True) dat = datatable[peaktable['Name']].reset_index() datatable = pd.concat([meta, dat], axis=1).set_index(['index']) datatable.index.name = None self.__peaktable = peaktable # Search for duplicate labels and amend with a suffix, to avoid issues when relabelling the datatable labels = copy.deepcopy(list(peaktable['Label'])) label_counts = {k: v for k, v in Counter(labels).items() if v > 1} for i in reversed(range(len(labels))): item = str(labels[i]) if item in label_counts and label_counts[item]: labels[i] += "_" + str(label_counts[item]) label_counts[item] -= 1 #Label datatable with peak labels instead of names for ease of feature plotting col_label_dict = dict(zip(list(peaktable['Name']), labels)) datatable.rename(columns=col_label_dict, inplace=True) self.__peak_labels = labels self.__datatable = datatable self.set_params() def help(self): print(plotFeatures.usage) def set_params(self, plot_type='point', column_numbers=4, log_data=(True, 2), scale_data=(True, 'minmax'), impute_data=(True, 3), style='seaborn-white', transparent=False, figSize = (15, 10), fontSize = 12, colour_palette=None, y_axis_label=None, x_axis_rotation=0, group_column_name=None, point_estimator='mean', point_ci=95, violin_distribution_type='box', violin_width_scale='width', box_iqr=1.5, saveImage=True, imageFileName='_features.png', dpi = 200): plot_type, column_numbers, log_data, scale_data, impute_data, style, transparent, figSize, fontSize, colour_palette, y_axis_label, x_axis_rotation, group_column_name, point_estimator, point_ci, violin_distribution_type, violin_width_scale, box_iqr, saveImage, imageFileName, dpi = self.__paramCheck(plot_type, column_numbers, log_data, scale_data, impute_data, style, transparent, figSize, fontSize, colour_palette, y_axis_label, x_axis_rotation, group_column_name, point_estimator, point_ci, violin_distribution_type, violin_width_scale, box_iqr, saveImage, imageFileName, dpi) self.__plot_type = plot_type; self.__column_numbers = column_numbers; self.__log_data = log_data; self.__scale_data = scale_data; self.__impute_data = impute_data; self.__style = style; self.__transparent = transparent; self.__figSize = figSize; self.__fontSize = fontSize; self.__colour_palette = colour_palette; self.__y_axis_label = y_axis_label; self.__x_axis_rotation = x_axis_rotation; self.__group_column_name = group_column_name; self.__point_estimator = point_estimator; self.__point_ci = point_ci; self.__violin_distribution_type = violin_distribution_type; self.__violin_width_scale = violin_width_scale; self.__box_iqr = box_iqr; self.__saveImage = saveImage; self.__imageFileName = imageFileName; self.__dpi = dpi; def plot(self): datatable = copy.deepcopy(self.__datatable) labels = self.__peak_labels plot_type = self.__plot_type group_column_name = self.__group_column_name column_numbers = self.__column_numbers colour_palette = self.__colour_palette point_ci = self.__point_ci point_estimator = self.__point_estimator log_data = self.__log_data scale_data = self.__scale_data impute_data = self.__impute_data x_axis_rotation = self.__x_axis_rotation y_axis_label = self.__y_axis_label violin_distribution_type = self.__violin_distribution_type violin_width_scale = self.__violin_width_scale box_iqr = self.__box_iqr imageFileName = self.__imageFileName saveImage = self.__saveImage fontSize = self.__fontSize style = self.__style transparent = self.__transparent dpi = self.__dpi figSize = self.__figSize meta = datatable.T[~datatable.T.index.isin(labels)].T.reset_index(drop=True) X = datatable[labels].reset_index(drop=True) (log_bool, log_base) = log_data; if log_bool: if isinstance(log_base, str) and log_base.lower() == 'natural': X = X.applymap(np.log); elif log_base == 2: X = X.applymap(np.log2); elif log_base == 10: X = X.applymap(np.log10); else: print("Error: The chosen log type is invalid.") sys.exit() (scale_bool, scale_type) = scale_data if scale_bool: if isinstance(scale_type, str) and scale_type.lower() == 'standard': X = scaler(X, type=scale_type.lower()).reset_index(drop=True) elif isinstance(scale_type, str) and scale_type.lower() == 'minmax': X = scaler(X, type=scale_type.lower()).reset_index(drop=True) elif isinstance(scale_type, str) and scale_type.lower() == 'maxabs': X = scaler(X, type=scale_type.lower()).reset_index(drop=True) elif isinstance(scale_type, str) and scale_type.lower() == 'robust': X = scaler(X, type=scale_type.lower()).reset_index(drop=True) else: print("Error: The chosen scale type is invalid.") sys.exit() (impute_bool, k) = impute_data; if impute_bool: X = imputeData(X, k=k).reset_index(drop=True) if not isinstance(X, pd.DataFrame): X = pd.DataFrame(X, columns=labels) # Add the meta data back in with the logged, scaled, or imputed data datatable = pd.concat([meta, X], axis=1).reset_index(drop=True) with plt.style.context(style): fig, axes = plt.subplots(nrows=int(np.ceil(float(len(labels) / column_numbers))), ncols=column_numbers, sharey=True, figsize=figSize) if plot_type == 'point': for peak_index, peak in enumerate(labels): if point_estimator.lower() == 'mean': point_estimator = 'Mean' ax = sns.pointplot(data=datatable, x=group_column_name, y=peak, estimator=np.nanmean, capsize=0.1, ci=point_ci, palette=colour_palette, ax=axes.flat[peak_index]) elif point_estimator.lower() == 'median': point_estimator = 'Median' ax = sns.pointplot(data=datatable, x=group_column_name, y=peak, estimator=np.nanmedian, capsize=0.1, ci=point_ci, palette=colour_palette, ax=axes.flat[peak_index]) else: print("Error: Invalid point plot estimator type.") sys.exit() ax.tick_params(labelrotation=x_axis_rotation, labelsize=fontSize) if log_bool: if scale_data: if isinstance(point_ci, str): if point_ci == 'sd': ax.set_title(peak + ' within SD', fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) {} Peak Area within SD'.format(log_base, scale_type, point_estimator), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: ax.set_title(peak + ' with {}% CI'.format(point_ci), fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) {} Peak Area & {}% CI'.format(log_base, scale_type, point_estimator, point_ci), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if isinstance(point_ci, str): if point_ci == 'sd': ax.set_title(peak + ' within SD', fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('Log({}) {} Peak Area within SD'.format(log_base, point_estimator), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: ax.set_title(peak + ' with {}% CI'.format(point_ci), fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('Log({}) {} Peak Area & {}% CI'.format(log_base, point_estimator, point_ci), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if scale_data: if isinstance(point_ci, str): if point_ci == 'sd': ax.set_title(peak + ' within SD', fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('Scaled ({}) {} Peak Area within SD'.format(scale_type, point_estimator), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: ax.set_title(peak + ' with {}% CI'.format(point_ci), fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('Scaled ({}) {} Peak Area & {}% CI'.format(scale_type, point_estimator, point_ci), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if isinstance(point_ci, str): if point_ci == 'sd': ax.set_title(peak + ' within SD', fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('{} Peak Area within SD'.format(point_estimator), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: ax.set_title(peak + ' with {}% CI'.format(point_ci), fontsize=fontSize) ax.set_xlabel('') if y_axis_label is None: ax.set_ylabel('{} Peak Area & {}% CI'.format(point_estimator, point_ci), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) elif plot_type.lower() == 'violin': for peak_index, peak in enumerate(labels): ax = sns.violinplot(data=datatable, x=group_column_name, y=peak, linewidth=1, inner=violin_distribution_type, scale=violin_width_scale, palette=colour_palette, ax=axes.flat[peak_index]) ax.tick_params(labelrotation=x_axis_rotation, labelsize=fontSize) ax.set_title(peak, fontsize=fontSize) ax.set_xlabel('') if log_bool: if scale_data: if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) Peak Area'.format(log_base, scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Log({}) Peak Area'.format(log_base), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if scale_data: if y_axis_label is None: ax.set_ylabel('Scaled ({}) Peak Area'.format(scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Peak Area', fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) elif plot_type.lower() == 'box': for peak_index, peak in enumerate(labels): ax = sns.boxplot(data=datatable, x=group_column_name, y=peak, palette=colour_palette, whis=box_iqr, ax=axes.flat[peak_index]) ax.tick_params(labelrotation=x_axis_rotation, labelsize=fontSize) ax.set_title(peak, fontsize=fontSize) ax.set_xlabel('') if log_bool: if scale_data: if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) Peak Area'.format(log_base, scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Log({}) Peak Area'.format(log_base), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if scale_data: if y_axis_label is None: ax.set_ylabel('Scaled ({}) Peak Area'.format(scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Peak Area', fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) elif plot_type.lower() == 'swarm': for peak_index, peak in enumerate(labels): ax = sns.swarmplot(data=datatable, x=group_column_name, y=peak, size=10, palette=colour_palette, ax=axes.flat[peak_index]) ax.tick_params(labelrotation=x_axis_rotation, labelsize=fontSize) ax.set_title(peak, fontsize=fontSize) ax.set_xlabel('') if log_bool: if scale_data: if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) Peak Area'.format(log_base, scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Log({}) Peak Area'.format(log_base), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if scale_data: if y_axis_label is None: ax.set_ylabel('Scaled ({}) Peak Area'.format(scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Peak Area', fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) elif plot_type.lower() == 'violin-swarm': for peak_index, peak in enumerate(labels): ax = sns.violinplot(data=datatable, x=group_column_name, y=peak, linewidth=1, inner=None, scale=violin_width_scale, palette=colour_palette, ax=axes.flat[peak_index]) ax = sns.swarmplot(data=datatable, x=group_column_name, y=peak, color="white", edgecolor="gray", ax=axes.flat[peak_index]) ax.tick_params(labelrotation=x_axis_rotation, labelsize=fontSize) ax.set_title(peak, fontsize=fontSize) ax.set_xlabel('') if log_bool: if scale_data: if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) Peak Area'.format(log_base, scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Log({}) Peak Area'.format(log_base), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if scale_data: if y_axis_label is None: ax.set_ylabel('Scaled ({}) Peak Area'.format(scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Peak Area', fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) elif plot_type.lower() == 'box-swarm': for peak_index, peak in enumerate(labels): ax = sns.boxplot(data=datatable, x=group_column_name, y=peak, palette=colour_palette, whis=np.inf, ax=axes.flat[peak_index]) ax = sns.swarmplot(data=datatable, x=group_column_name, y=peak, color="0.2", ax=axes.flat[peak_index]) ax.tick_params(labelrotation=x_axis_rotation, labelsize=fontSize) ax.set_title(peak, fontsize=fontSize) ax.set_xlabel('') if log_bool: if scale_data: if y_axis_label is None: ax.set_ylabel('Log({}) scaled ({}) Peak Area'.format(log_base, scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Log({}) Peak Area'.format(log_base), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if scale_data: if y_axis_label is None: ax.set_ylabel('Scaled ({}) Peak Area'.format(scale_type), fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) else: if y_axis_label is None: ax.set_ylabel('Peak Area', fontsize=fontSize) else: ax.set_ylabel(y_axis_label, fontsize=fontSize) fig.tight_layout(h_pad=5, w_pad=2) if saveImage: plt.savefig(plot_type + 'Plot' + imageFileName, dpi=dpi, transparent=transparent) plt.show() def __paramCheck(self, plot_type, column_numbers, log_data, scale_data, impute_data, style, transparent, figSize, fontSize, colour_palette, y_axis_label, x_axis_rotation, group_column_name, point_estimator, point_ci, violin_distribution_type, violin_width_scale, box_iqr, saveImage, imageFileName, dpi): cmap_list = list(matplotlib.cm.cmaps_listed) + list(matplotlib.cm.datad) cmap_list_r = [cmap + '_r' for cmap in cmap_list] cmap_list = cmap_list + cmap_list_r plot_types = ['point', 'violin', 'box', 'swarm', 'violin-swarm', 'box-swarm'] estimator_types = ['mean', 'median'] datatable = self.__datatable if plot_type.lower() not in plot_types: print("Error: Plot type is not valid. Choose one of the following: {}.".format(', '.join(plot_types))) sys.exit() if not isinstance(column_numbers, int): print("Error: Column numbers is not valid. Choose a integer value.") sys.exit() if not isinstance(log_data, tuple): print("Error: Log data type if not a tuple. Please ensure the value is a tuple (e.g. (True, 2).") sys.exit() else: (log_bool, log_base) = log_data if not isinstance(log_bool, bool): print("Error: Log data first tuple item is not a boolean value. Choose either \"True\" or \"False\".") sys.exit() base_types = ['natural', 2, 10] if isinstance(log_base, str): log_base = log_base.lower() if log_base not in base_types: print("Error: Log data second tuple item is not valid. Choose one of {}.".format(', '.join(base_types))) sys.exit() if not isinstance(scale_data, tuple): print("Error: Scale data type if not a tuple. Please ensure the value is a tuple (e.g. (True, 'standard').") sys.exit() else: (scale_bool, scale_type) = scale_data if not isinstance(scale_bool, bool): print("Error: Scale data first tuple item is not a boolean value. Choose either \"True\" or \"False\".") sys.exit() scale_types = ['standard', 'minmax', 'maxabs', 'robust'] if isinstance(scale_type, str): scale_type = scale_type.lower() if scale_type not in scale_types: print("Error: Scale data second tuple item is not valid. Choose one of {}.".format(', '.join(scale_types))) sys.exit() if not isinstance(impute_data, tuple): print("Error: Impute data type if not a tuple. Please ensure the value is a tuple (e.g. (True, 3).") sys.exit() else: (impute_bool, k) = impute_data if not isinstance(impute_bool, bool): print("Error: Impute data first tuple item is not a boolean value. Choose either \"True\" or \"False\".") sys.exit() if not isinstance(k, float): if not isinstance(k, int): print("Error: Impute data second tuple item, the nearest neighbours k value, is not valid. Choose a float or integer value.") sys.exit() if not isinstance(style, str): print("Error: Seaborn style is not valid. Choose a string value.") sys.exit() else: styleList = list(plt.style.available) if style not in styleList: print("Error: Chosen style is not valid. Choose one of the following: {}.".format(', '.join(styleList))) sys.exit() if not isinstance(transparent, bool): print("Error: The transparent value is not valid. Choose either \"True\" or \"False\".") sys.exit() if not isinstance(figSize, tuple): print("Error: Figure size is not valid. Choose a tuple of length 2.") sys.exit() else: for length in figSize: if not isinstance(length, float): if not isinstance(length, int): print("Error: Figure size value is not valid. Choose a float or integer value.") sys.exit() if not isinstance(fontSize, float): if not isinstance(fontSize, int): print("Error: Font size is not valid. Choose a float or integer value.") sys.exit() if colour_palette is not None: if not isinstance(colour_palette, str): print("Error: The colour palette is not valid. Choose a string value.") sys.exit() else: if colour_palette not in cmap_list: print("Error: The colour palette is not valid. Choose one of the following: {}.".format(', '.join(cmap_list))) sys.exit() if y_axis_label is not None: if isinstance(y_axis_label, str): print("Error: The y axis label is not valid. Choose a string value.") sys.exit() if not isinstance(x_axis_rotation, float): if not isinstance(x_axis_rotation, int): print("Error: The x axis rotation value is not valid. Choose a float or integer value.") sys.exit() if ((x_axis_rotation < 0) or (x_axis_rotation > 360)): print("Error: The x axis rotation value is not valid. Choose a value >=0 or <= 360.") sys.exit() if group_column_name is not None: if not isinstance(group_column_name, str): print("Error: Group column name is not valid. Choose a string value.") sys.exit() else: if group_column_name not in list(datatable.columns): print("Error: Group column name not valid. Choose one of {}.".format(', '.join(list(datatable.columns)))) sys.exit() if point_estimator.lower() not in estimator_types: print("Error: The chosen point plot estimator is invalid. Choose one of \"{}\".".format('\" or \"'.join(estimator_types))) sys.exit() if isinstance(point_ci, str): if point_ci != 'sd': print("Error: The string value for point plot ci is invalid. Choose a float, integer or 'sd' value for standard deviation.") sys.exit() else: if not isinstance(point_ci, float): if not isinstance(point_ci, int): print("Error: The value for point plot ci is invalid. Choose a float, integer or 'sd' value for standard deviation.") sys.exit() violin_distribution_types = ['quartile', 'box', 'point', 'stick', None] violin_width_scale_types = ['area', 'count', 'width'] if plot_type.lower() == "violin": if violin_distribution_type not in violin_distribution_types: print("Error: Violin distribution type not valid. Choose one of the following: {}.".format(', '.join(violin_distribution_types))) sys.exit() if violin_width_scale not in violin_width_scale_types: print("Error: Violin width scale type not valid. Choose one of the following: {}.".format(', '.join(violin_width_scale_types))) sys.exit() if plot_type.lower == "box": if not isinstance(box_iqr, float): if not isinstance(box_iqr, int): print( "Error: The box plot interquartile range extension beyond whiskers is not valid. Choose a float or integer value.") sys.exit() if not isinstance(saveImage, bool): print("Error: Save image is not valid. Choose either \"True\" or \"False\".") sys.exit() if not isinstance(imageFileName, str): print("Error: Image file name is not valid. Choose a string value.") sys.exit() if not isinstance(dpi, float): if not isinstance(dpi, int): print("Error: Dpi is not valid. Choose a float or integer value.") sys.exit() return plot_type, column_numbers, log_data, scale_data, impute_data, style, transparent, figSize, fontSize, colour_palette, y_axis_label, x_axis_rotation, group_column_name, point_estimator, point_ci, violin_distribution_type, violin_width_scale, box_iqr, saveImage, imageFileName, dpi def __checkData(self, df): if not isinstance(df, pd.DataFrame): print("Error: A dataframe was not entered. Please check your data.") return df def __checkPeakTable(self, PeakTable): if "Name" not in PeakTable.columns: print("Error: \"Name\" column not in Peak Table. Please check your data.") sys.exit() if "Label" not in PeakTable.columns: print("Error: \"Label\" column not in Peak Table. Please check your data.") sys.exit() # Do not assume the peaks/nodes have been indexed correctly. Remove any index columns and reindex. column_list = [column.lower() for column in PeakTable.columns] if 'idx' in column_list: index = column_list.index('idx') column_name = PeakTable.columns[index] PeakTable = PeakTable.drop(columns=[column_name]) if 'index' in column_list: index = column_list.index('index') column_name = PeakTable.columns[index] PeakTable = PeakTable.drop(columns=[column_name]) PeakTable = PeakTable.reset_index(drop=True) PeakTable.index.name = 'Idx' PeakTable = PeakTable.reset_index() return PeakTable

en

0.600224

Produces different feature plots given a data table and peak table. Initial_Parameters ---------- peaktable : Pandas dataframe containing peak data. Must contain 'Name' and 'Label'. datatable : Pandas dataframe containing matrix of values to plot (N samples x N features). Columns/features must be same as 'Name' from Peak Table. Methods ------- set_params : Set parameters - plot_type: The type of plot. Either "point", "violin", "box", "swarm", "violin-swarm" or "box-swarm" (default: 'point') column_numbers: The number of columns to display in the plots (default: 4) log_data: Perform a log ('natural', base 2 or base 10) on all data (default: (True, 2)) scale_data: Scale the data ('standard' (centers to the mean and scales to unit variance), 'minmax' (scales between 0 and 1), 'maxabs' (scales to the absolute maximum value), 'robust' (centers to the median and scales to between 25th and 75th quantile range) (default: (True, 'minmax')) impute_data: Impute any missing values using KNN impute with a set number of nearest neighbours (default: (True, 3)) style: Set the matplotlib style (see https://matplotlib.org/stable/tutorials/introductory/customizing.html) (default: 'seaborn-white') transparent: Setting to 'True' will make the background transparent (default: False) figSize: The figure size as a tuple (width,height) (default: (15,10)) fontSize: The font size for all text (default: 12) colour_palette: The colour palette to use for the plot (default: None) y_axis_label: The label to customise the y axis (default: None) x_axis_rotation: Rotate the x axis labels this number of degrees (default: 0) group_column_name: The group column name used in the datatable (e.g. 'Class') (default: None) point_estimator: The statistical function to use for the point plot. Either "mean" or "median" (default: 'mean') point_ci: The bootstrapped confidence interval for the point plot. Can also be standard deviation ("sd") (default: 95) violin_distribution_type: The representation of the distribution of data points within the violin plot. Either "quartile", "box", "point", "stick" or None (default: 'box') violin_width_scale: The method used to scale the width of the violin plot. Either "area", "count" or "width" (default: "width") box_iqr: The proportion past the lower and upper quartiles to extend the plot whiskers for the box plot. Points outside this range will be identified as outliers (default: 1.5) saveImage: Setting to 'True' will save the image to file (default: True) imageFileName: The image file name to save to (default: [plot_type]_features.png') dpi: The number of Dots Per Inch (DPI) for the image (default: 200) help : Print this help text plot : Generates feature plots # Slice the meta-data, and select only peaks from the peaktable for processing, and add the meta-data back # Search for duplicate labels and amend with a suffix, to avoid issues when relabelling the datatable #Label datatable with peak labels instead of names for ease of feature plotting # Add the meta data back in with the logged, scaled, or imputed data # Do not assume the peaks/nodes have been indexed correctly. Remove any index columns and reindex.

3.178132

3

core/data/DataWriter.py

berendkleinhaneveld/Registrationshop

25

10242

<reponame>berendkleinhaneveld/Registrationshop<gh_stars>10-100 """ DataWriter.py """ from DataController import DataController from DataReader import DataReader from vtk import vtkMetaImageWriter from vtk import vtkXMLImageDataWriter class DataWriter(DataController): """ DataWriter writes an image data object to disk using the provided format. """ def __init__(self): super(DataWriter, self).__init__() self.supportedExtensions = [DataReader.TypeMHD, DataReader.TypeVTI, DataReader.TypeMHA] def WriteToFile(self, imageData, exportFileName, fileType): if fileType == DataReader.TypeMHD: if not exportFileName.endswith(".mhd"): exportFileName = exportFileName + ".mhd" writer = vtkMetaImageWriter() writer.SetFileName(exportFileName) writer.SetInputData(imageData) writer.Write() elif fileType == DataReader.TypeVTI: writer = vtkXMLImageDataWriter() writer.SetFileName(exportFileName) writer.SetInputData(imageData) writer.Write() elif fileType == DataReader.TypeMHA: writer = vtkMetaImageWriter() writer.SetFileName(exportFileName) writer.SetInputData(imageData) writer.Write() else: raise NotImplementedError("No writing support for type " + str(fileType))

""" DataWriter.py """ from DataController import DataController from DataReader import DataReader from vtk import vtkMetaImageWriter from vtk import vtkXMLImageDataWriter class DataWriter(DataController): """ DataWriter writes an image data object to disk using the provided format. """ def __init__(self): super(DataWriter, self).__init__() self.supportedExtensions = [DataReader.TypeMHD, DataReader.TypeVTI, DataReader.TypeMHA] def WriteToFile(self, imageData, exportFileName, fileType): if fileType == DataReader.TypeMHD: if not exportFileName.endswith(".mhd"): exportFileName = exportFileName + ".mhd" writer = vtkMetaImageWriter() writer.SetFileName(exportFileName) writer.SetInputData(imageData) writer.Write() elif fileType == DataReader.TypeVTI: writer = vtkXMLImageDataWriter() writer.SetFileName(exportFileName) writer.SetInputData(imageData) writer.Write() elif fileType == DataReader.TypeMHA: writer = vtkMetaImageWriter() writer.SetFileName(exportFileName) writer.SetInputData(imageData) writer.Write() else: raise NotImplementedError("No writing support for type " + str(fileType))

en

0.563685

DataWriter.py DataWriter writes an image data object to disk using the provided format.

2.945427

3

parkings/models/permit.py

klemmari1/parkkihubi

12

10243

from itertools import chain from django.conf import settings from django.contrib.gis.db import models as gis_models from django.db import models, router, transaction from django.utils import timezone from django.utils.translation import gettext_lazy as _ from ..fields import CleaningJsonField from ..validators import DictListValidator, TextField, TimestampField from .constants import GK25FIN_SRID from .enforcement_domain import EnforcementDomain from .mixins import TimestampedModelMixin from .parking import Parking class PermitArea(TimestampedModelMixin): name = models.CharField(max_length=40, verbose_name=_('name')) domain = models.ForeignKey( EnforcementDomain, on_delete=models.PROTECT, related_name='permit_areas') identifier = models.CharField(max_length=10, verbose_name=_('identifier')) geom = gis_models.MultiPolygonField( srid=GK25FIN_SRID, verbose_name=_('geometry')) permitted_user = models.ForeignKey( settings.AUTH_USER_MODEL, on_delete=models.PROTECT, verbose_name=_("permitted_user")) class Meta: unique_together = [('domain', 'identifier')] ordering = ('identifier',) def __str__(self): return '{}/{}: {}'.format(self.domain.code, self.identifier, self.name) class PermitSeriesQuerySet(models.QuerySet): def active(self): return self.filter(active=True) def latest_active(self): return self.active().order_by('-modified_at').first() def prunable(self, time_limit=None): limit = time_limit or ( timezone.now() - settings.PARKKIHUBI_PERMITS_PRUNABLE_AFTER) return self.filter(created_at__lt=limit, active=False) class PermitSeries(TimestampedModelMixin, models.Model): active = models.BooleanField(default=False) owner = models.ForeignKey( settings.AUTH_USER_MODEL, on_delete=models.PROTECT, verbose_name=_("owner")) objects = PermitSeriesQuerySet.as_manager() class Meta: ordering = ('created_at', 'id') verbose_name = _("permit series") verbose_name_plural = _("permit series") @classmethod def delete_prunable_series(cls, time_limit=None): prunable = cls.objects.prunable(time_limit) Permit.objects.filter(series__in=prunable).delete() prunable.delete() def __str__(self): return str(self.id) class PermitQuerySet(models.QuerySet): def active(self): return self.filter(series__active=True) def by_time(self, timestamp): lookup_items = PermitLookupItem.objects.by_time(timestamp) return self.filter(lookup_items__in=lookup_items).distinct() def by_subject(self, registration_number): lookup_items = PermitLookupItem.objects.by_subject(registration_number) return self.filter(lookup_items__in=lookup_items).distinct() def by_area(self, area): lookup_items = PermitLookupItem.objects.by_area(area) return self.filter(lookup_items__in=lookup_items).distinct() def bulk_create(self, permits, *args, **kwargs): for permit in permits: assert isinstance(permit, Permit) permit.full_clean() with transaction.atomic(using=self.db, savepoint=False): created_permits = super().bulk_create(permits, *args, **kwargs) PermitLookupItem.objects.using(self.db).bulk_create( chain(*(x._make_lookup_items() for x in created_permits))) return created_permits class Permit(TimestampedModelMixin, models.Model): domain = models.ForeignKey( EnforcementDomain, on_delete=models.PROTECT, related_name='permits') series = models.ForeignKey(PermitSeries, on_delete=models.PROTECT) external_id = models.CharField(max_length=50, null=True, blank=True) subjects = CleaningJsonField(blank=True, validators=[DictListValidator({ 'start_time': TimestampField(), 'end_time': TimestampField(), 'registration_number': TextField(max_length=20), })]) areas = CleaningJsonField(blank=True, validators=[DictListValidator({ 'start_time': TimestampField(), 'end_time': TimestampField(), 'area': TextField(max_length=10), })]) objects = PermitQuerySet.as_manager() class Meta: unique_together = [('series', 'external_id')] indexes = [ models.Index(fields=['series', 'id']), ] ordering = ('series', 'id') def __str__(self): return 'Permit {id} ({series}{active}/{external_id} {dom})'.format( id=self.id, dom=self.domain.code, series=self.series, active='*' if self.series.active else '', external_id=self.external_id) def save(self, using=None, *args, **kwargs): self.full_clean() using = using or router.db_for_write(type(self), instance=self) with transaction.atomic(using=using, savepoint=False): super(Permit, self).save(using=using, *args, **kwargs) self.lookup_items.all().using(using).delete() new_lookup_items = self._make_lookup_items() PermitLookupItem.objects.using(using).bulk_create(new_lookup_items) def _make_lookup_items(self): for area in self.areas: for subject in self.subjects: max_start_time = max(subject['start_time'], area['start_time']) min_end_time = min(subject['end_time'], area['end_time']) if max_start_time >= min_end_time: continue yield PermitLookupItem( permit=self, registration_number=Parking.normalize_reg_num( subject['registration_number']), area=PermitArea.objects.get(identifier=area['area'], domain=self.domain), start_time=max_start_time, end_time=min_end_time ) class PermitLookupItemQuerySet(models.QuerySet): def active(self): return self.filter(permit__series__active=True) def by_time(self, timestamp): return self.filter(start_time__lte=timestamp, end_time__gte=timestamp) def by_subject(self, registration_number): normalized_reg_num = Parking.normalize_reg_num(registration_number) return self.filter(registration_number=normalized_reg_num) def by_area(self, area): return self.filter(area=area) class PermitLookupItem(models.Model): permit = models.ForeignKey( Permit, related_name="lookup_items", on_delete=models.CASCADE) registration_number = models.CharField(max_length=20) area = models.ForeignKey(PermitArea, on_delete=models.PROTECT, default=None, null=True, blank=True) start_time = models.DateTimeField() end_time = models.DateTimeField() objects = PermitLookupItemQuerySet.as_manager() class Meta: indexes = [ models.Index(fields=[ 'registration_number', 'start_time', 'end_time', 'area', 'permit']), ] ordering = ('registration_number', 'start_time', 'end_time') def __str__(self): return ( '{start_time:%Y-%m-%d %H:%M} -- {end_time:%Y-%m-%d %H:%M} / ' '{registration_number} / {area}' ).format( start_time=self.start_time, end_time=self.end_time, registration_number=self.registration_number, area=self.area.identifier)

none

1

1.983396

2

poi_mining/biz/LSA/logEntropy.py

yummydeli/machine_learning

1

10244

<filename>poi_mining/biz/LSA/logEntropy.py #!/usr/bin/env python # encoding:utf-8 # ############################################################################## # The MIT License (MIT) # # Copyright (c) [2015] [baidu.com] # # 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. # ############################################################################## """ 生成LogEntropy矩阵并筛选出合适的词汇 """ import glob import collections import pandas from sklearn.feature_extraction.text import CountVectorizer import math class LogEntropy(object): """计算logentropy，得到类别关键字""" def __init__(self): self.fnames = glob.glob('data/segs/names.*') def extract_segs(self): """分词文件中获取分词结果""" idx = [] words = [] for f in self.fnames: lines = [] for i, line in enumerate(open(f)): if i % 2 == 1: non_int = '\t'.join([e for e in line.decode('GBK').rstrip('\n').split('\t') \ if not e.isdigit()]) lines.append(non_int) words.append('\t'.join(lines)) idx.append(f.split('.')[1][1:]) return words, idx def mk_document_term_matrix(self): """生成TDM矩阵""" words, idx = self.extract_segs() countvec = CountVectorizer() dtm = pandas.DataFrame(countvec.fit_transform(words).toarray(), columns=countvec.get_feature_names(), index=idx) """ canting faguo riben zhongwen 1001 1 0 0 1 991 1 0 1 0 203 1 1 0 0 """ return dtm def global_weighting(self, dtm): """ 1 - Entropy(words) / log(N) """ # normalized entropy for word pdtm = (dtm / dtm.sum(axis=0)) ndocs = pdtm.shape[0] gw = 1 + (pdtm.applymap(lambda x: x * math.log(x) if x != 0 else 0).sum() / math.log(ndocs)) """ canting 2.220446e-16 faguo 1.000000e+00 riben 1.000000e+00 zhongwen 1.000000e+00 """ return gw def local_weighting(self, dtm): """ math.log(freq + 1)""" lw = dtm.applymap(lambda freq: math.log(freq + 1)) """ canting faguo riben zhongwen 1001 0.693147 0.000000 0.000000 0.693147 991 0.693147 0.000000 0.693147 0.000000 203 0.693147 0.693147 0.000000 0.000000 """ return lw def logEntropyWeighting(self): """计算最终的logentropy得分""" dtm = self.mk_document_term_matrix() """ canting faguo riben zhongwen 1001 1.539096e-16 0.000000 0.000000 0.693147 991 1.539096e-16 0.000000 0.693147 0.000000 203 1.539096e-16 0.693147 0.000000 0.000000 """ logEntro = (self.global_weighting(dtm.copy()) * self.local_weighting(dtm)).applymap( lambda x: 0 if x < 0.001 else x ) logEntro.T.to_csv('data/keyWords.cates', sep='\t', encoding='UTF-8') if __name__ == '__main__': lsaEntropy = LogEntropy() lsaEntropy.logEntropyWeighting()

en

0.504559

#!/usr/bin/env python # encoding:utf-8 # ############################################################################## # The MIT License (MIT) # # Copyright (c) [2015] [baidu.com] # # 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. # ############################################################################## 生成LogEntropy矩阵并筛选出合适的词汇计算logentropy，得到类别关键字分词文件中获取分词结果生成TDM矩阵 canting faguo riben zhongwen 1001 1 0 0 1 991 1 0 1 0 203 1 1 0 0 1 - Entropy(words) / log(N) # normalized entropy for word canting 2.220446e-16 faguo 1.000000e+00 riben 1.000000e+00 zhongwen 1.000000e+00 math.log(freq + 1) canting faguo riben zhongwen 1001 0.693147 0.000000 0.000000 0.693147 991 0.693147 0.000000 0.693147 0.000000 203 0.693147 0.693147 0.000000 0.000000 计算最终的logentropy得分 canting faguo riben zhongwen 1001 1.539096e-16 0.000000 0.000000 0.693147 991 1.539096e-16 0.000000 0.693147 0.000000 203 1.539096e-16 0.693147 0.000000 0.000000

1.218488

1

Python/swap_numbers.py

saurabhcommand/Hello-world

1,428

10245

a = 5 b = 7 a,b = b,a print a print b

none

1

2.308451

2

algorithms/tests/test_unionfind.py

tommyod/PythonAlgorithms

1

10246

#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Tests for the union find data structure. """ try: from ..unionfind import UnionFind except ValueError: pass def test_unionfind_basics(): """ Test the basic properties of unionfind. """ u = UnionFind([1, 2, 3]) assert u.in_same_set(1, 2) is False assert u.in_same_set(2, 3) is False u.union(1, 3) assert u.in_same_set(1, 2) is False assert u.in_same_set(3, 1) assert u.get_root(1) == u.get_root(3) def test_unionfind_adding_elements(): """ Test adding operations, mostly syntactic sugar. """ u = UnionFind([1, 2]) u.add(['a', 'b']) assert 1 in u assert 'a' in u def test_unionfind_example(): """ Test on a slightly more invovled example. """ u = UnionFind([1, 2, 3, 4, 5]) u.union(1, 3) u.union(2, 4) assert u.in_same_set(1, 3) assert u.in_same_set(4, 2) assert not u.in_same_set(2, 5) assert not u.in_same_set(2, 1) assert not u.in_same_set(1, 4) u.union(5, 1) assert u.in_same_set(3, 5) def test_unionfind_several(): """ Test that we can take union of more than two elements. """ u = UnionFind([1, 2, 3, 4, 5, 6, 7, 8]) u.union([1, 2, 3]) u.union([4, 5, 6]) u.union([7, 8]) assert u.in_same_set(1, 3) assert u.in_same_set(6, 4) assert u.in_same_set(7, 8) assert not u.in_same_set(2, 5) assert not u.in_same_set(4, 8) def test_unionfind_compression(): """ Test path compression and the union by rank. """ # Test the ranking elements = list(range(100)) u = UnionFind(elements) for i in range(len(elements) - 1): u.union(elements[i], elements[i + 1]) assert max(u._rank.values()) == 1 # Test path compression parent_nodes = list(u._parent.values()) assert all(parent == parent_nodes[0] for parent in parent_nodes) if __name__ == "__main__": import pytest # --durations=10 <- May be used to show potentially slow tests pytest.main(args=['.', '--doctest-modules', '-v'])

en

0.808516

#!/usr/bin/env python3 # -*- coding: utf-8 -*- Tests for the union find data structure. Test the basic properties of unionfind. Test adding operations, mostly syntactic sugar. Test on a slightly more invovled example. Test that we can take union of more than two elements. Test path compression and the union by rank. # Test the ranking # Test path compression # --durations=10 <- May be used to show potentially slow tests

3.456439

3

a3/ga.py

mishless/LearningSystems

1

10247

<filename>a3/ga.py # Genetic Algorithm for solving the Traveling Salesman problem # Authors: <NAME>, <NAME> # Includes import configparser import math import matplotlib.pyplot as plt import numpy import random import sys from operator import itemgetter #Global variables(yay!) # Configuration variables(read from config.txt) mutation_rate = 0; population_size = 0; elitism_rate = 0; tournament_rate = 0; max_iterations = 0; input_file_name = ""; parent_rate = 0; # General global variables cities = {}; number_of_cities = 0; parent_number = 0; tournament_size = 0; elite_number = 0; crossover_number = 0; def read_config(): global mutation_rate; global elitism_rate; global tournament_rate; global population_size; global input_file_name; global max_iterations; global parent_rate; global parent_number; global tournament_size; global elite_number; global crossover_number; config = configparser.ConfigParser(); config.read("config.txt"); mutation_rate = float(config['general']['mutation_rate']); population_size = int(config['general']['population_size']); elitism_rate = float(config['general']['elitism_rate']); tournament_rate = float(config['general']['tournament_rate']); max_iterations = int(config['general']['max_iterations']); parent_rate = float(config['general']['parent_rate']); input_file_name = config['general']['input_file_name']; parent_number = int(population_size * parent_rate); elite_number = int(population_size * elitism_rate); tournament_size = int(population_size * tournament_rate); crossover_number = population_size - elite_number; def print_config(): print("***** CONFIGURATION *****"); print_var("Population size", population_size); print_var("Elitism rate", elitism_rate); print_var("Tournament rate", tournament_rate); print_var("Mutation rate", mutation_rate); print_var("Parent rate", parent_rate); print_var("Iteration number", max_iterations); print(""); print_var("Tournament size", tournament_size); print_var("Parent number", parent_number); print_var("Elite number", elite_number); print_var("Crossover number", crossover_number); print(""); def read_input_file(): global number_of_cities; file = open(input_file_name, "r"); file_lines = file.readlines(); file.close(); for file_line in file_lines: temp = file_line.split(); cities[int(temp[0])] = {'x' : float(temp[1]), 'y' : float(temp[2])}; number_of_cities = len(cities); def get_distance(city1, city2): return math.sqrt( ((city1['x']-city2['x'])**2) + ((city1['y']-city2['y'])**2)); def print_cities(): print("***** CITIES *****"); for key, city in cities.items(): print("#" + "%2s" % str(key) + ": (" + "%6s" % str(city['x']) + ', ' + "%6s" % str(city['y']) + ')'); print(""); def print_var(name, var): print(name + ":" + " "*(17-len(name)) + str(var)); def init(): read_config(); read_input_file(); print_config(); def create_random_individual(): individual = []; # We must begin at first city individual.append(1); # Create list of city indexes indexes = list(range(2,number_of_cities+1)); while len(indexes) > 0: picked_index = random.choice(indexes); indexes.remove(picked_index); individual.append(picked_index); # We must end at first city individual.append(1); return individual; def print_population(population, name): print("***** POPULATION: " + name + " *****"); print("Population size = " + str(len(population))); i = 0; for individual in population: print("IND #" + str(i) + ": " + str(individual)); i += 1; def print_population_2(population, name): print("***** POPULATION: " + name + " *****"); print("Population size = " + str(len(population))); i = 0; for individual in population: print("IND #" + str(i) + " distance = " + str(evaluate_individual(individual))); i += 1; print(""); def print_population_3(population, name): print("***** POPULATION: " + name + " *****"); print("Population size = " + str(len(population))); for individual in population: print(str(individual) + ": distance = " + str(evaluate_individual(individual))); print(""); def create_random_population(population_size): population = []; for i in range(0, population_size): population.append(create_random_individual()); return population; def evaluate_individual(individual): distance_traveled = 0; for i in range(0, len(individual)-1): distance_traveled = (distance_traveled + get_distance(cities[individual[i]], cities[individual[i+1]])); return distance_traveled; def evaluate_population(population): evaluations = []; for individual in population: evaluations.append((evaluate_individual(individual), individual)); return evaluations; def select_tournament_pool(data): tournament_pool = []; indexes = list(range(0, len(data))); for i in range(0, tournament_size): chosen_index = random.choice(indexes); tournament_pool.append(data[chosen_index]); indexes.remove(chosen_index); return tournament_pool; def best_solution(pool): best_individual = {'eval' : sys.float_info.max}; for individual in pool: if individual['eval'] < best_individual['eval']: best_individual = individual; return best_individual; def run_tournament(pool): return best_solution(pool); def merge_popul_and_eval(population, evaluations): data = []; for i in range(0, len(population)): data.append({'ind' : population[i], 'eval' : evaluations[i]}); return data; def select_parent_pool(population, evaluations): parent_pool = []; data = merge_popul_and_eval(population, evaluations); for i in range(0, parent_number): tournament_pool = select_tournament_pool(data); parent = run_tournament(tournament_pool); parent_pool.append(parent['ind']); data.remove(parent); return parent_pool; def is_individual_valid(individual): if(len(individual) != (number_of_cities+1)): print("INVALID " + str(individual)); return False; if(individual[0] != 1): print("INVALID " + str(individual)); return False; if(individual[-1] != 1): print("INVALID " + str(individual)); return False; for city in individual: if city == 1: if individual.count(city) != 2: print("INVALID " + str(individual)); return False; else: if individual.count(city) != 1: print("INVALID " + str(individual)); return False; return True; def is_population_valid(population): for individual in population: if is_individual_valid(individual) == False: return False; return True; def create_child(parent1, parent2): l = len(parent1); x = random.randint(1, l-1); y = random.randint(x, l-1); child = []; extract = parent1[x:y]; """print_var("P1", parent1); print_var("P2", parent2); print_var("x", x); print_var("y", y); print_var("Extract", extract);""" i = 0; for j in range(0, x): while(parent2[i] in extract): i += 1; child.append(parent2[i]); i += 1; child.extend(extract); for j in range(y, l): while(parent2[i] in extract): i += 1; child.append(parent2[i]); i += 1; return child; def generate_children(parent_pool, child_num): children = []; for i in range(0, child_num): parent1 = random.choice(parent_pool); parent_pool.remove(parent1); parent2 = random.choice(parent_pool); parent_pool.append(parent1); new_child = create_child(parent1, parent2); children.append(new_child); return children; def generate_elites(population, evaluations, number): data = merge_popul_and_eval(population, evaluations); elites = []; for i in range(0, number): best = best_solution(data); elites.append(best['ind']); data.remove(best); return elites; def mutate_individual(individual): i = random.randint(1, len(individual)-2); j = i; while j == i: j = random.randint(1, len(individual)-2); individual[i], individual[j] = individual[j], individual[i]; def mutate_population(population): for individual in population: if random.random() < mutation_rate: mutate_individual(individual); def test_stuff(): """ p1 = "abcdefg"; p2 = "1234567"; for i in range(0,10): print(create_child(p1,p2)); ind = [1,2,3,4,5,6]; print("Before", ind); mutate_individual(ind); print("After", ind); exit();""" def perform_GA(): best_solutions = []; best_individuals = []; best_solution = None; #print("***** ALGORITHM START *****"); population = create_random_population(population_size); iteration_counter = 1; while True: #print("Running iteration " + str(iteration_counter) + ":"); evaluations = evaluate_population(population); best_solution = min(evaluations, key=lambda evaluation:evaluation[0]) best_solutions.append(best_solution[0]); best_individuals.append(best_solution[1]); evaluations = [evaluation[0] for evaluation in evaluations] if iteration_counter == max_iterations: break; parent_pool = select_parent_pool(population, evaluations); children = generate_children(parent_pool, crossover_number); mutate_population(children); elites = generate_elites(population, evaluations, elite_number); # Prepare population for the next iteration population = children + elites; iteration_counter += 1; if is_population_valid(population) == False: break; return (best_solutions, best_individuals); def do_what_needs_to_be_done(): results = []; bests = []; print("***** ALGORITHM START *****"); sys.stdout.flush() for i in range(0, 10): print("Starting cycle " + str(i+1)); results.append(perform_GA()); bests.append((results[i][0][-1], results[i][1][-1])); best_ind = bests.index(min(bests, key=lambda best:best[0])); print(str(best_ind)); print("***** RESULTS *****"); print("Best result is " + str(bests[best_ind][0])); print("Best result is " + str(bests[best_ind][1])); plt.plot(results[best_ind][0]); plt.show(); #main init(); do_what_needs_to_be_done()

en

0.516051

# Genetic Algorithm for solving the Traveling Salesman problem # Authors: <NAME>, <NAME> # Includes #Global variables(yay!) # Configuration variables(read from config.txt) # General global variables # We must begin at first city # Create list of city indexes # We must end at first city #" + str(i) + ": " + str(individual)); #" + str(i) + " distance = " + print_var("P1", parent1); print_var("P2", parent2); print_var("x", x); print_var("y", y); print_var("Extract", extract); p1 = "abcdefg"; p2 = "1234567"; for i in range(0,10): print(create_child(p1,p2)); ind = [1,2,3,4,5,6]; print("Before", ind); mutate_individual(ind); print("After", ind); exit(); #print("***** ALGORITHM START *****"); #print("Running iteration " + str(iteration_counter) + ":"); # Prepare population for the next iteration #main

2.929989

3

products/migrations/0010_remove_product_updated_at.py

UB-ES-2021-A1/wannasell-backend

0

10248

# Generated by Django 3.2.8 on 2021-11-25 17:50 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('products', '0009_auto_20211125_1846'), ] operations = [ migrations.RemoveField( model_name='product', name='updated_at', ), ]

en

0.882256

# Generated by Django 3.2.8 on 2021-11-25 17:50

1.295391

1

ResumeAnalyser/apps.py

samyakj2307/recruitai_resume_backend

0

10249

<gh_stars>0 from django.apps import AppConfig class ResumeanalyserConfig(AppConfig): default_auto_field = 'django.db.models.BigAutoField' name = 'ResumeAnalyser'

from django.apps import AppConfig class ResumeanalyserConfig(AppConfig): default_auto_field = 'django.db.models.BigAutoField' name = 'ResumeAnalyser'

none

1

1.263755

1

plugins/core/player_manager_plugin/__init__.py

StarryPy/StarryPy-Historic

38

10250

<filename>plugins/core/player_manager_plugin/__init__.py<gh_stars>10-100 from plugins.core.player_manager_plugin.plugin import PlayerManagerPlugin from plugins.core.player_manager_plugin.manager import ( Banned, UserLevels, permissions, PlayerManager )

none

1

1.208188

1

src/config/svc-monitor/svc_monitor/services/loadbalancer/drivers/ha_proxy/custom_attributes/haproxy_validator.py

jnpr-pranav/contrail-controller

37

10251

from builtins import str from builtins import range from builtins import object import logging import inspect import os class CustomAttr(object): """This type handles non-flat data-types like int, str, bool. """ def __init__(self, key, value): self._value = value self._key = key def validate(self): pass def post_validation(self): pass class CustomAttrTlsContainer(CustomAttr): def __init__(self, key, value): super(CustomAttrTlsContainer, self).__init__(key, value) def validate(self): return True def post_validation(self): return self._value def validate_custom_attributes(custom_attributes_dict, section, custom_attributes): section_dict = {} if custom_attributes and section in custom_attributes_dict: for key, value in list(custom_attributes.items()): if key in custom_attributes_dict[section]: #Sanitize the value try: type_attr = custom_attributes_dict[section][key]['type'] limits = custom_attributes_dict[section][key]['limits'] if type_attr == 'int': value = int(value) if value in range(limits[0], limits[1]): section_dict.update({key:value}) else: logging.info("Skipping key: %s, value: %s due to" \ "validation failure" % (key, value)) elif type_attr == 'str': if len(value) in range(limits[0], limits[1]): section_dict.update({key:value}) else: logging.info("Skipping key: %s, value: %s due to" \ "validation failure" % (key, value)) elif type_attr == 'bool': if value in limits: if value == 'True': value = '' elif value == 'False': value = 'no ' section_dict.update({key:value}) else: logging.info("Skipping key: %s, value: %s due to" \ "validation failure" % (key, value)) elif inspect.isclass(eval(type_attr)): new_custom_attr = eval(type_attr)(key, value) if new_custom_attr.validate(): value = new_custom_attr.post_validation() section_dict.update({key:value}) else: logging.info("Skipping key: %s, value: %s due to" \ "validation failure" % (key, value)) except Exception as e: logging.error(str(e)) continue return section_dict

en

0.725463

This type handles non-flat data-types like int, str, bool. #Sanitize the value

2.978925

3

python/janitor/typecache.py

monkeyman79/janitor

2

10252

import gdb class TypeCache(object): def __init__(self): self.cache = {} self.intptr_type = False def clear(self): self.cache = {} self.intptr_type = False def get_type(self, typename): if typename in self.cache: return self.cache[typename] try: gdb_type = gdb.lookup_type(typename) self.cache[typename] = gdb_type return gdb_type except: pass try: proto = gdb.parse_and_eval("(%s*)0" % typename) gdb_type = proto.type.target() self.cache[typename] = gdb_type return gdb_type except: pass return None def get_intptr_type(self): if self.intptr_type != False: return self.intptr_type ptr_type = self.get_type("void*") if ptr_type == None: self.intptr_type = None return None ulong_type = self.get_type("unsigned long") if ulong_type == None: self.intptr_type = None return None if ulong_type.sizeof >= ptr_type.sizeof: self.intptr_type = ulong_type return ulong_type ullong_type = self.get_type("unsigned long long") self.intptr_type = ullong_type return ullong_type cache = TypeCache()

none

1

2.447621

2

key_phrase.py

Santara/autoSLR

1

10253

import os import sys directory = sys.argv[1] outfile = open("key_phrases.csv","w") files = {} for filename in os.listdir(directory): text=[] with open(os.path.join(directory, filename)) as f: text=[l.strip() for l in f if len(l.strip())>2] data='' for t in text: if len(t.split()) > 1: data = data+'. '+t.strip() whitelist = set('abcdefghijklmnopqrstuvwxy ABCDEFGHIJKLMNOPQRSTUVWXYZ') answer = ''.join(filter(whitelist.__contains__, data)) answer=' '.join(answer.split()) import rake import operator rake_object = rake.Rake("/home/ashutosh/Sudeshna/RAKE-tutorial/data/stoplists/SmartStoplist.txt", 3,3,1) import pprint pp = pprint.PrettyPrinter() keywords = rake_object.run(answer) for entry in keywords: outfile.write("%s, %s\n" % (entry[0], str(entry[1])) ) outfile.close()

none

1

2.646277

3

tests/test_utils.py

aced-differentiate/dft-input-gen

1

10254

"""Unit tests for helper utilities in :mod:`dftinputgen.utils`.""" import os import pytest from ase import io as ase_io from dftinputgen.utils import get_elem_symbol from dftinputgen.utils import read_crystal_structure from dftinputgen.utils import get_kpoint_grid_from_spacing from dftinputgen.utils import DftInputGeneratorUtilsError test_base_dir = os.path.dirname(__file__) feo_conv_file = os.path.join(test_base_dir, "qe", "files", "feo_conv.vasp") feo_conv = ase_io.read(feo_conv_file) def test_get_elem_symbol(): assert get_elem_symbol("Fe-34") == "Fe" assert get_elem_symbol("3RGe-34") == "Ge" with pytest.raises(DftInputGeneratorUtilsError): get_elem_symbol("G23") def test_read_crystal_structure(): # str with path to crystal structure file is OK cs = read_crystal_structure(feo_conv_file) assert cs == feo_conv # any other type of input should throw an error with pytest.raises(TypeError): read_crystal_structure(feo_conv) def test_kpoint_grid_from_spacing(): assert get_kpoint_grid_from_spacing(feo_conv, 0.2) == pytest.approx( [7, 7, 7] )

en

0.726543

Unit tests for helper utilities in :mod:`dftinputgen.utils`. # str with path to crystal structure file is OK # any other type of input should throw an error

2.515499

3

core/models.py

nforesperance/Django-Channels-ChatApp

2

10255

from django.contrib.auth.models import User from django.db.models import (Model, TextField, DateTimeField, ForeignKey, CASCADE) from asgiref.sync import async_to_sync from channels.layers import get_channel_layer from django.db import models import json class MessageModel(Model): """ This class represents a chat message. It has a owner (user), timestamp and the message body. """ user = ForeignKey(User, on_delete=CASCADE, verbose_name='user', related_name='from_user', db_index=True) recipient = ForeignKey(User, on_delete=CASCADE, verbose_name='recipient', related_name='to_user', db_index=True) timestamp = DateTimeField('timestamp', auto_now_add=True, editable=False, db_index=True) body = TextField('body') def __str__(self): return str(self.id) def characters(self): """ Toy function to count body characters. :return: body's char number """ return len(self.body) def notify_ws_clients(self): """ Inform client there is a new message. """ notification = { 'type': 'chat_message', 'message': '{}'.format(self.id) } channel_layer = get_channel_layer() print("user.id {}".format(self.user.id)) print("user.id {}".format(self.recipient.id)) async_to_sync(channel_layer.group_send)("{}".format(self.user.id), notification) async_to_sync(channel_layer.group_send)("{}".format(self.recipient.id), notification) def save(self, *args, **kwargs): """ Trims white spaces, saves the message and notifies the recipient via WS if the message is new. """ new = self.id self.body = self.body.strip() # Trimming whitespaces from the body super(MessageModel, self).save(*args, **kwargs) if new is None: self.notify_ws_clients() # Meta class Meta: app_label = 'core' verbose_name = 'message' verbose_name_plural = 'messages' ordering = ('-timestamp',) class Group(models.Model): name = models.CharField(max_length = 20) members = models.TextField() messages = models.TextField () def set_members(self,user_id_list): self.members = json.dumps(user_id_list) def get_members(self): return json.loads(self.members) def add(self,user_id): current_list = self.get_members() if user_id in current_list: print("user is already in the group") else: new_list = current_list.append(user_id) self.set_members(new_list) def remove(self,user_id): current_list = self.get_members() if user_id in current_list: new_list = current_list.remove(user_id) self.set_members(new_list) else: print("User is not a member of theis group") def has(self,user_id): current_list = self.get_members() return(user_id in current_list) # Set of functions for dealing with group messages def set_messages(self,message_id_list): self.messages = json.dumps(message_id_list) def get_messages(self): return json.loads(self.messages) def add_message(self,message_id): current_list = self.get_messages() new_list = current_list.append(message_id) self.set_messages(new_list) def delete_message(self,message_id): current_list = self.get_messages() if message_id in current_list: new_list = current_list.remove(message_id) self.set_messages(new_list) def save(self, *args, **kwargs): if self.pk is None or self.members is None or self.members == '': self.set_members([]) if self.pk is None or self.messages is None or self.messages == '': self.set_messages([]) super(Group, self).save(*args, **kwargs) def __str__(self): return self.name+" ID: "+str(self.id) # Meta class Meta: app_label = 'core' verbose_name = 'Group' verbose_name_plural = 'Groups' ordering = ('name',) class GroupMessage(Model): """ This class represents a chat message. It has a owner (user), timestamp and the message body. """ sender = ForeignKey(User, on_delete=CASCADE, verbose_name='sender', related_name='from_sender', db_index=True) group = ForeignKey(Group, on_delete=CASCADE, verbose_name='group', related_name='to_group', db_index=True) time = DateTimeField('time', auto_now_add=True, editable=False, db_index=True) body = TextField('body') def __str__(self): return str(self.id) def characters(self): """ Toy function to count body characters. :return: body's char number """ return len(self.body) def notify_ws_clients(self): """ Inform client there is a new message. """ notification = { 'type': 'group_message', 'group': '{}'.format(self.id) } channel_layer = get_channel_layer() group_id = "group"+str(self.group.id) print("group.id {}".format(group_id)) async_to_sync(channel_layer.group_send)(group_id, notification) def save(self, *args, **kwargs): """ Trims white spaces, saves the message and notifies the recipient via WS if the message is new. """ new = self.id self.body = self.body.strip() # Trimming whitespaces from the body super(GroupMessage, self).save(*args, **kwargs) if new is None: self.notify_ws_clients() # Meta class Meta: app_label = 'core' verbose_name = 'group message' verbose_name_plural = 'group messags' ordering = ('-time',)

en

0.771246

This class represents a chat message. It has a owner (user), timestamp and the message body. Toy function to count body characters. :return: body's char number Inform client there is a new message. Trims white spaces, saves the message and notifies the recipient via WS if the message is new. # Trimming whitespaces from the body # Meta # Set of functions for dealing with group messages # Meta This class represents a chat message. It has a owner (user), timestamp and the message body. Toy function to count body characters. :return: body's char number Inform client there is a new message. Trims white spaces, saves the message and notifies the recipient via WS if the message is new. # Trimming whitespaces from the body # Meta

2.333341

2

backup/model.py

jsikyoon/ASNP-RMR

8

10256

<reponame>jsikyoon/ASNP-RMR import tensorflow as tf import numpy as np # utility methods def batch_mlp(input, output_sizes, variable_scope): """Apply MLP to the final axis of a 3D tensor (reusing already defined MLPs). Args: input: input tensor of shape [B,n,d_in]. output_sizes: An iterable containing the output sizes of the MLP as defined in `basic.Linear`. variable_scope: String giving the name of the variable scope. If this is set to be the same as a previously defined MLP, then the weights are reused. Returns: tensor of shape [B,n,d_out] where d_out=output_sizes[-1] """ # Get the shapes of the input and reshape to parallelise across observations batch_size, _, filter_size = input.shape.as_list() output = tf.reshape(input, (-1, filter_size)) output.set_shape((None, filter_size)) # Pass through MLP with tf.variable_scope(variable_scope, reuse=tf.AUTO_REUSE): for i, size in enumerate(output_sizes[:-1]): output = tf.nn.relu( tf.layers.dense(output, size, name="layer_{}".format(i))) # Last layer without a ReLu output = tf.layers.dense( output, output_sizes[-1], name="layer_{}".format(i + 1)) # Bring back into original shape output = tf.reshape(output, (batch_size, -1, output_sizes[-1])) return output class DeterministicEncoder(object): """The Deterministic Encoder.""" def __init__(self, output_sizes, attention): """(A)NP deterministic encoder. Args: output_sizes: An iterable containing the output sizes of the encoding MLP. attention: The attention module. """ self._output_sizes = output_sizes self._attention = attention def __call__(self, context_x, context_y, target_x): """Encodes the inputs into one representation. Args: context_x: Tensor of shape [B,observations,d_x]. For this 1D regression task this corresponds to the x-values. context_y: Tensor of shape [B,observations,d_y]. For this 1D regression task this corresponds to the y-values. target_x: Tensor of shape [B,target_observations,d_x]. For this 1D regression task this corresponds to the x-values. Returns: The encoded representation. Tensor of shape [B,target_observations,d] """ # Concatenate x and y along the filter axes encoder_input = tf.concat([context_x, context_y], axis=-1) # Pass final axis through MLP hidden = batch_mlp(encoder_input, self._output_sizes, "deterministic_encoder") # Apply attention with tf.variable_scope("deterministic_encoder", reuse=tf.AUTO_REUSE): hidden = self._attention(context_x, target_x, hidden) return hidden class LatentEncoder(object): """The Latent Encoder.""" def __init__(self, output_sizes, num_latents): """(A)NP latent encoder. Args: output_sizes: An iterable containing the output sizes of the encoding MLP. num_latents: The latent dimensionality. """ self._output_sizes = output_sizes self._num_latents = num_latents def __call__(self, x, y): """Encodes the inputs into one representation. Args: x: Tensor of shape [B,observations,d_x]. For this 1D regression task this corresponds to the x-values. y: Tensor of shape [B,observations,d_y]. For this 1D regression task this corresponds to the y-values. Returns: A normal distribution over tensors of shape [B, num_latents] """ # Concatenate x and y along the filter axes encoder_input = tf.concat([x, y], axis=-1) # Pass final axis through MLP hidden = batch_mlp(encoder_input, self._output_sizes, "latent_encoder") # Aggregator: take the mean over all points hidden = tf.reduce_mean(hidden, axis=1) # Have further MLP layers that map to the parameters of the Gaussian latent with tf.variable_scope("latent_encoder", reuse=tf.AUTO_REUSE): # First apply intermediate relu layer hidden = tf.nn.relu( tf.layers.dense(hidden, (self._output_sizes[-1] + self._num_latents)/2, name="penultimate_layer")) # Then apply further linear layers to output latent mu and log sigma mu = tf.layers.dense(hidden, self._num_latents, name="mean_layer") log_sigma = tf.layers.dense(hidden, self._num_latents, name="std_layer") # Compute sigma sigma = 0.1 + 0.9 * tf.sigmoid(log_sigma) return tf.contrib.distributions.Normal(loc=mu, scale=sigma) class Decoder(object): """The Decoder.""" def __init__(self, output_sizes): """(A)NP decoder. Args: output_sizes: An iterable containing the output sizes of the decoder MLP as defined in `basic.Linear`. """ self._output_sizes = output_sizes def __call__(self, representation, target_x): """Decodes the individual targets. Args: representation: The representation of the context for target predictions. Tensor of shape [B,target_observations,?]. target_x: The x locations for the target query. Tensor of shape [B,target_observations,d_x]. Returns: dist: A multivariate Gaussian over the target points. A distribution over tensors of shape [B,target_observations,d_y]. mu: The mean of the multivariate Gaussian. Tensor of shape [B,target_observations,d_x]. sigma: The standard deviation of the multivariate Gaussian. Tensor of shape [B,target_observations,d_x]. """ # concatenate target_x and representation hidden = tf.concat([representation, target_x], axis=-1) # Pass final axis through MLP hidden = batch_mlp(hidden, self._output_sizes, "decoder") # Get the mean an the variance mu, log_sigma = tf.split(hidden, 2, axis=-1) # Bound the variance sigma = 0.1 + 0.9 * tf.nn.softplus(log_sigma) # Get the distribution dist = tf.contrib.distributions.MultivariateNormalDiag( loc=mu, scale_diag=sigma) return dist, mu, sigma class LatentModel(object): """The (A)NP model.""" def __init__(self, latent_encoder_output_sizes, num_latents, decoder_output_sizes, use_deterministic_path=True, deterministic_encoder_output_sizes=None, attention=None): """Initialises the model. Args: latent_encoder_output_sizes: An iterable containing the sizes of hidden layers of the latent encoder. num_latents: The latent dimensionality. decoder_output_sizes: An iterable containing the sizes of hidden layers of the decoder. The last element should correspond to d_y * 2 (it encodes both mean and variance concatenated) use_deterministic_path: a boolean that indicates whether the deterministic encoder is used or not. deterministic_encoder_output_sizes: An iterable containing the sizes of hidden layers of the deterministic encoder. The last one is the size of the deterministic representation r. attention: The attention module used in the deterministic encoder. Only relevant when use_deterministic_path=True. """ self._latent_encoder = LatentEncoder(latent_encoder_output_sizes, num_latents) self._decoder = Decoder(decoder_output_sizes) self._use_deterministic_path = use_deterministic_path if use_deterministic_path: self._deterministic_encoder = DeterministicEncoder( deterministic_encoder_output_sizes, attention) def __call__(self, query, num_targets, target_y=None): """Returns the predicted mean and variance at the target points. Args: query: Array containing ((context_x, context_y), target_x) where: context_x: Tensor of shape [B,num_contexts,d_x]. Contains the x values of the context points. context_y: Tensor of shape [B,num_contexts,d_y]. Contains the y values of the context points. target_x: Tensor of shape [B,num_targets,d_x]. Contains the x values of the target points. num_targets: Number of target points. target_y: The ground truth y values of the target y. Tensor of shape [B,num_targets,d_y]. Returns: log_p: The log_probability of the target_y given the predicted distribution. Tensor of shape [B,num_targets]. mu: The mean of the predicted distribution. Tensor of shape [B,num_targets,d_y]. sigma: The variance of the predicted distribution. Tensor of shape [B,num_targets,d_y]. """ (context_x, context_y), target_x = query # Pass query through the encoder and the decoder prior = self._latent_encoder(context_x, context_y) # For training, when target_y is available, use targets for latent encoder. # Note that targets contain contexts by design. if target_y is None: latent_rep = prior.sample() # For testing, when target_y unavailable, use contexts for latent encoder. else: posterior = self._latent_encoder(target_x, target_y) latent_rep = posterior.sample() latent_rep = tf.tile(tf.expand_dims(latent_rep, axis=1), [1, num_targets, 1]) if self._use_deterministic_path: deterministic_rep = self._deterministic_encoder(context_x, context_y, target_x) representation = tf.concat([deterministic_rep, latent_rep], axis=-1) else: representation = latent_rep dist, mu, sigma = self._decoder(representation, target_x) # If we want to calculate the log_prob for training we will make use of the # target_y. At test time the target_y is not available so we return None. if target_y is not None: log_p = dist.log_prob(target_y) posterior = self._latent_encoder(target_x, target_y) kl = tf.reduce_sum( tf.contrib.distributions.kl_divergence(posterior, prior), axis=-1, keepdims=True) kl = tf.tile(kl, [1, num_targets]) loss = - tf.reduce_mean(log_p - kl / tf.cast(num_targets, tf.float32)) else: log_p = None kl = None loss = None return mu, sigma, log_p, kl, loss def uniform_attention(q, v): """Uniform attention. Equivalent to np. Args: q: queries. tensor of shape [B,m,d_k]. v: values. tensor of shape [B,n,d_v]. Returns: tensor of shape [B,m,d_v]. """ total_points = tf.shape(q)[1] rep = tf.reduce_mean(v, axis=1, keepdims=True) # [B,1,d_v] rep = tf.tile(rep, [1, total_points, 1]) return rep def laplace_attention(q, k, v, scale, normalise): """Computes laplace exponential attention. Args: q: queries. tensor of shape [B,m,d_k]. k: keys. tensor of shape [B,n,d_k]. v: values. tensor of shape [B,n,d_v]. scale: float that scales the L1 distance. normalise: Boolean that determines whether weights sum to 1. Returns: tensor of shape [B,m,d_v]. """ k = tf.expand_dims(k, axis=1) # [B,1,n,d_k] q = tf.expand_dims(q, axis=2) # [B,m,1,d_k] unnorm_weights = - tf.abs((k - q) / scale) # [B,m,n,d_k] unnorm_weights = tf.reduce_sum(unnorm_weights, axis=-1) # [B,m,n] if normalise: weight_fn = tf.nn.softmax else: weight_fn = lambda x: 1 + tf.tanh(x) weights = weight_fn(unnorm_weights) # [B,m,n] rep = tf.einsum('bik,bkj->bij', weights, v) # [B,m,d_v] return rep def dot_product_attention(q, k, v, normalise): """Computes dot product attention. Args: q: queries. tensor of shape [B,m,d_k]. k: keys. tensor of shape [B,n,d_k]. v: values. tensor of shape [B,n,d_v]. normalise: Boolean that determines whether weights sum to 1. Returns: tensor of shape [B,m,d_v]. """ d_k = tf.shape(q)[-1] scale = tf.sqrt(tf.cast(d_k, tf.float32)) unnorm_weights = tf.einsum('bjk,bik->bij', k, q) / scale # [B,m,n] if normalise: weight_fn = tf.nn.softmax else: weight_fn = tf.sigmoid weights = weight_fn(unnorm_weights) # [B,m,n] rep = tf.einsum('bik,bkj->bij', weights, v) # [B,m,d_v] return rep def multihead_attention(q, k, v, num_heads=8): """Computes multi-head attention. Args: q: queries. tensor of shape [B,m,d_k]. k: keys. tensor of shape [B,n,d_k]. v: values. tensor of shape [B,n,d_v]. num_heads: number of heads. Should divide d_v. Returns: tensor of shape [B,m,d_v]. """ d_k = q.get_shape().as_list()[-1] d_v = v.get_shape().as_list()[-1] head_size = d_v / num_heads key_initializer = tf.random_normal_initializer(stddev=d_k**-0.5) value_initializer = tf.random_normal_initializer(stddev=d_v**-0.5) rep = tf.constant(0.0) for h in range(num_heads): o = dot_product_attention( tf.layers.Conv1D(head_size, 1, kernel_initializer=key_initializer, name='wq%d' % h, use_bias=False, padding='VALID')(q), tf.layers.Conv1D(head_size, 1, kernel_initializer=key_initializer, name='wk%d' % h, use_bias=False, padding='VALID')(k), tf.layers.Conv1D(head_size, 1, kernel_initializer=key_initializer, name='wv%d' % h, use_bias=False, padding='VALID')(v), normalise=True) rep += tf.layers.Conv1D(d_v, 1, kernel_initializer=value_initializer, name='wo%d' % h, use_bias=False, padding='VALID')(o) return rep class Attention(object): """The Attention module.""" def __init__(self, rep, output_sizes, att_type, scale=1., normalise=True, num_heads=8): """Create attention module. Takes in context inputs, target inputs and representations of each context input/output pair to output an aggregated representation of the context data. Args: rep: transformation to apply to contexts before computing attention. One of: ['identity','mlp']. output_sizes: list of number of hidden units per layer of mlp. Used only if rep == 'mlp'. att_type: type of attention. One of the following: ['uniform','laplace','dot_product','multihead'] scale: scale of attention. normalise: Boolean determining whether to: 1. apply softmax to weights so that they sum to 1 across context pts or 2. apply custom transformation to have weights in [0,1]. num_heads: number of heads for multihead. """ self._rep = rep self._output_sizes = output_sizes self._type = att_type self._scale = scale self._normalise = normalise if self._type == 'multihead': self._num_heads = num_heads def __call__(self, x1, x2, r): """Apply attention to create aggregated representation of r. Args: x1: tensor of shape [B,n1,d_x]. x2: tensor of shape [B,n2,d_x]. r: tensor of shape [B,n1,d]. Returns: tensor of shape [B,n2,d] Raises: NameError: The argument for rep/type was invalid. """ if self._rep == 'identity': k, q = (x1, x2) elif self._rep == 'mlp': # Pass through MLP k = batch_mlp(x1, self._output_sizes, "attention") q = batch_mlp(x2, self._output_sizes, "attention") else: raise NameError("'rep' not among ['identity','mlp']") if self._type == 'uniform': rep = uniform_attention(q, r) elif self._type == 'laplace': rep = laplace_attention(q, k, r, self._scale, self._normalise) elif self._type == 'dot_product': rep = dot_product_attention(q, k, r, self._normalise) elif self._type == 'multihead': rep = multihead_attention(q, k, r, self._num_heads) else: raise NameError(("'att_type' not among ['uniform','laplace','dot_product'" ",'multihead']")) return rep

import tensorflow as tf import numpy as np # utility methods def batch_mlp(input, output_sizes, variable_scope): """Apply MLP to the final axis of a 3D tensor (reusing already defined MLPs). Args: input: input tensor of shape [B,n,d_in]. output_sizes: An iterable containing the output sizes of the MLP as defined in `basic.Linear`. variable_scope: String giving the name of the variable scope. If this is set to be the same as a previously defined MLP, then the weights are reused. Returns: tensor of shape [B,n,d_out] where d_out=output_sizes[-1] """ # Get the shapes of the input and reshape to parallelise across observations batch_size, _, filter_size = input.shape.as_list() output = tf.reshape(input, (-1, filter_size)) output.set_shape((None, filter_size)) # Pass through MLP with tf.variable_scope(variable_scope, reuse=tf.AUTO_REUSE): for i, size in enumerate(output_sizes[:-1]): output = tf.nn.relu( tf.layers.dense(output, size, name="layer_{}".format(i))) # Last layer without a ReLu output = tf.layers.dense( output, output_sizes[-1], name="layer_{}".format(i + 1)) # Bring back into original shape output = tf.reshape(output, (batch_size, -1, output_sizes[-1])) return output class DeterministicEncoder(object): """The Deterministic Encoder.""" def __init__(self, output_sizes, attention): """(A)NP deterministic encoder. Args: output_sizes: An iterable containing the output sizes of the encoding MLP. attention: The attention module. """ self._output_sizes = output_sizes self._attention = attention def __call__(self, context_x, context_y, target_x): """Encodes the inputs into one representation. Args: context_x: Tensor of shape [B,observations,d_x]. For this 1D regression task this corresponds to the x-values. context_y: Tensor of shape [B,observations,d_y]. For this 1D regression task this corresponds to the y-values. target_x: Tensor of shape [B,target_observations,d_x]. For this 1D regression task this corresponds to the x-values. Returns: The encoded representation. Tensor of shape [B,target_observations,d] """ # Concatenate x and y along the filter axes encoder_input = tf.concat([context_x, context_y], axis=-1) # Pass final axis through MLP hidden = batch_mlp(encoder_input, self._output_sizes, "deterministic_encoder") # Apply attention with tf.variable_scope("deterministic_encoder", reuse=tf.AUTO_REUSE): hidden = self._attention(context_x, target_x, hidden) return hidden class LatentEncoder(object): """The Latent Encoder.""" def __init__(self, output_sizes, num_latents): """(A)NP latent encoder. Args: output_sizes: An iterable containing the output sizes of the encoding MLP. num_latents: The latent dimensionality. """ self._output_sizes = output_sizes self._num_latents = num_latents def __call__(self, x, y): """Encodes the inputs into one representation. Args: x: Tensor of shape [B,observations,d_x]. For this 1D regression task this corresponds to the x-values. y: Tensor of shape [B,observations,d_y]. For this 1D regression task this corresponds to the y-values. Returns: A normal distribution over tensors of shape [B, num_latents] """ # Concatenate x and y along the filter axes encoder_input = tf.concat([x, y], axis=-1) # Pass final axis through MLP hidden = batch_mlp(encoder_input, self._output_sizes, "latent_encoder") # Aggregator: take the mean over all points hidden = tf.reduce_mean(hidden, axis=1) # Have further MLP layers that map to the parameters of the Gaussian latent with tf.variable_scope("latent_encoder", reuse=tf.AUTO_REUSE): # First apply intermediate relu layer hidden = tf.nn.relu( tf.layers.dense(hidden, (self._output_sizes[-1] + self._num_latents)/2, name="penultimate_layer")) # Then apply further linear layers to output latent mu and log sigma mu = tf.layers.dense(hidden, self._num_latents, name="mean_layer") log_sigma = tf.layers.dense(hidden, self._num_latents, name="std_layer") # Compute sigma sigma = 0.1 + 0.9 * tf.sigmoid(log_sigma) return tf.contrib.distributions.Normal(loc=mu, scale=sigma) class Decoder(object): """The Decoder.""" def __init__(self, output_sizes): """(A)NP decoder. Args: output_sizes: An iterable containing the output sizes of the decoder MLP as defined in `basic.Linear`. """ self._output_sizes = output_sizes def __call__(self, representation, target_x): """Decodes the individual targets. Args: representation: The representation of the context for target predictions. Tensor of shape [B,target_observations,?]. target_x: The x locations for the target query. Tensor of shape [B,target_observations,d_x]. Returns: dist: A multivariate Gaussian over the target points. A distribution over tensors of shape [B,target_observations,d_y]. mu: The mean of the multivariate Gaussian. Tensor of shape [B,target_observations,d_x]. sigma: The standard deviation of the multivariate Gaussian. Tensor of shape [B,target_observations,d_x]. """ # concatenate target_x and representation hidden = tf.concat([representation, target_x], axis=-1) # Pass final axis through MLP hidden = batch_mlp(hidden, self._output_sizes, "decoder") # Get the mean an the variance mu, log_sigma = tf.split(hidden, 2, axis=-1) # Bound the variance sigma = 0.1 + 0.9 * tf.nn.softplus(log_sigma) # Get the distribution dist = tf.contrib.distributions.MultivariateNormalDiag( loc=mu, scale_diag=sigma) return dist, mu, sigma class LatentModel(object): """The (A)NP model.""" def __init__(self, latent_encoder_output_sizes, num_latents, decoder_output_sizes, use_deterministic_path=True, deterministic_encoder_output_sizes=None, attention=None): """Initialises the model. Args: latent_encoder_output_sizes: An iterable containing the sizes of hidden layers of the latent encoder. num_latents: The latent dimensionality. decoder_output_sizes: An iterable containing the sizes of hidden layers of the decoder. The last element should correspond to d_y * 2 (it encodes both mean and variance concatenated) use_deterministic_path: a boolean that indicates whether the deterministic encoder is used or not. deterministic_encoder_output_sizes: An iterable containing the sizes of hidden layers of the deterministic encoder. The last one is the size of the deterministic representation r. attention: The attention module used in the deterministic encoder. Only relevant when use_deterministic_path=True. """ self._latent_encoder = LatentEncoder(latent_encoder_output_sizes, num_latents) self._decoder = Decoder(decoder_output_sizes) self._use_deterministic_path = use_deterministic_path if use_deterministic_path: self._deterministic_encoder = DeterministicEncoder( deterministic_encoder_output_sizes, attention) def __call__(self, query, num_targets, target_y=None): """Returns the predicted mean and variance at the target points. Args: query: Array containing ((context_x, context_y), target_x) where: context_x: Tensor of shape [B,num_contexts,d_x]. Contains the x values of the context points. context_y: Tensor of shape [B,num_contexts,d_y]. Contains the y values of the context points. target_x: Tensor of shape [B,num_targets,d_x]. Contains the x values of the target points. num_targets: Number of target points. target_y: The ground truth y values of the target y. Tensor of shape [B,num_targets,d_y]. Returns: log_p: The log_probability of the target_y given the predicted distribution. Tensor of shape [B,num_targets]. mu: The mean of the predicted distribution. Tensor of shape [B,num_targets,d_y]. sigma: The variance of the predicted distribution. Tensor of shape [B,num_targets,d_y]. """ (context_x, context_y), target_x = query # Pass query through the encoder and the decoder prior = self._latent_encoder(context_x, context_y) # For training, when target_y is available, use targets for latent encoder. # Note that targets contain contexts by design. if target_y is None: latent_rep = prior.sample() # For testing, when target_y unavailable, use contexts for latent encoder. else: posterior = self._latent_encoder(target_x, target_y) latent_rep = posterior.sample() latent_rep = tf.tile(tf.expand_dims(latent_rep, axis=1), [1, num_targets, 1]) if self._use_deterministic_path: deterministic_rep = self._deterministic_encoder(context_x, context_y, target_x) representation = tf.concat([deterministic_rep, latent_rep], axis=-1) else: representation = latent_rep dist, mu, sigma = self._decoder(representation, target_x) # If we want to calculate the log_prob for training we will make use of the # target_y. At test time the target_y is not available so we return None. if target_y is not None: log_p = dist.log_prob(target_y) posterior = self._latent_encoder(target_x, target_y) kl = tf.reduce_sum( tf.contrib.distributions.kl_divergence(posterior, prior), axis=-1, keepdims=True) kl = tf.tile(kl, [1, num_targets]) loss = - tf.reduce_mean(log_p - kl / tf.cast(num_targets, tf.float32)) else: log_p = None kl = None loss = None return mu, sigma, log_p, kl, loss def uniform_attention(q, v): """Uniform attention. Equivalent to np. Args: q: queries. tensor of shape [B,m,d_k]. v: values. tensor of shape [B,n,d_v]. Returns: tensor of shape [B,m,d_v]. """ total_points = tf.shape(q)[1] rep = tf.reduce_mean(v, axis=1, keepdims=True) # [B,1,d_v] rep = tf.tile(rep, [1, total_points, 1]) return rep def laplace_attention(q, k, v, scale, normalise): """Computes laplace exponential attention. Args: q: queries. tensor of shape [B,m,d_k]. k: keys. tensor of shape [B,n,d_k]. v: values. tensor of shape [B,n,d_v]. scale: float that scales the L1 distance. normalise: Boolean that determines whether weights sum to 1. Returns: tensor of shape [B,m,d_v]. """ k = tf.expand_dims(k, axis=1) # [B,1,n,d_k] q = tf.expand_dims(q, axis=2) # [B,m,1,d_k] unnorm_weights = - tf.abs((k - q) / scale) # [B,m,n,d_k] unnorm_weights = tf.reduce_sum(unnorm_weights, axis=-1) # [B,m,n] if normalise: weight_fn = tf.nn.softmax else: weight_fn = lambda x: 1 + tf.tanh(x) weights = weight_fn(unnorm_weights) # [B,m,n] rep = tf.einsum('bik,bkj->bij', weights, v) # [B,m,d_v] return rep def dot_product_attention(q, k, v, normalise): """Computes dot product attention. Args: q: queries. tensor of shape [B,m,d_k]. k: keys. tensor of shape [B,n,d_k]. v: values. tensor of shape [B,n,d_v]. normalise: Boolean that determines whether weights sum to 1. Returns: tensor of shape [B,m,d_v]. """ d_k = tf.shape(q)[-1] scale = tf.sqrt(tf.cast(d_k, tf.float32)) unnorm_weights = tf.einsum('bjk,bik->bij', k, q) / scale # [B,m,n] if normalise: weight_fn = tf.nn.softmax else: weight_fn = tf.sigmoid weights = weight_fn(unnorm_weights) # [B,m,n] rep = tf.einsum('bik,bkj->bij', weights, v) # [B,m,d_v] return rep def multihead_attention(q, k, v, num_heads=8): """Computes multi-head attention. Args: q: queries. tensor of shape [B,m,d_k]. k: keys. tensor of shape [B,n,d_k]. v: values. tensor of shape [B,n,d_v]. num_heads: number of heads. Should divide d_v. Returns: tensor of shape [B,m,d_v]. """ d_k = q.get_shape().as_list()[-1] d_v = v.get_shape().as_list()[-1] head_size = d_v / num_heads key_initializer = tf.random_normal_initializer(stddev=d_k**-0.5) value_initializer = tf.random_normal_initializer(stddev=d_v**-0.5) rep = tf.constant(0.0) for h in range(num_heads): o = dot_product_attention( tf.layers.Conv1D(head_size, 1, kernel_initializer=key_initializer, name='wq%d' % h, use_bias=False, padding='VALID')(q), tf.layers.Conv1D(head_size, 1, kernel_initializer=key_initializer, name='wk%d' % h, use_bias=False, padding='VALID')(k), tf.layers.Conv1D(head_size, 1, kernel_initializer=key_initializer, name='wv%d' % h, use_bias=False, padding='VALID')(v), normalise=True) rep += tf.layers.Conv1D(d_v, 1, kernel_initializer=value_initializer, name='wo%d' % h, use_bias=False, padding='VALID')(o) return rep class Attention(object): """The Attention module.""" def __init__(self, rep, output_sizes, att_type, scale=1., normalise=True, num_heads=8): """Create attention module. Takes in context inputs, target inputs and representations of each context input/output pair to output an aggregated representation of the context data. Args: rep: transformation to apply to contexts before computing attention. One of: ['identity','mlp']. output_sizes: list of number of hidden units per layer of mlp. Used only if rep == 'mlp'. att_type: type of attention. One of the following: ['uniform','laplace','dot_product','multihead'] scale: scale of attention. normalise: Boolean determining whether to: 1. apply softmax to weights so that they sum to 1 across context pts or 2. apply custom transformation to have weights in [0,1]. num_heads: number of heads for multihead. """ self._rep = rep self._output_sizes = output_sizes self._type = att_type self._scale = scale self._normalise = normalise if self._type == 'multihead': self._num_heads = num_heads def __call__(self, x1, x2, r): """Apply attention to create aggregated representation of r. Args: x1: tensor of shape [B,n1,d_x]. x2: tensor of shape [B,n2,d_x]. r: tensor of shape [B,n1,d]. Returns: tensor of shape [B,n2,d] Raises: NameError: The argument for rep/type was invalid. """ if self._rep == 'identity': k, q = (x1, x2) elif self._rep == 'mlp': # Pass through MLP k = batch_mlp(x1, self._output_sizes, "attention") q = batch_mlp(x2, self._output_sizes, "attention") else: raise NameError("'rep' not among ['identity','mlp']") if self._type == 'uniform': rep = uniform_attention(q, r) elif self._type == 'laplace': rep = laplace_attention(q, k, r, self._scale, self._normalise) elif self._type == 'dot_product': rep = dot_product_attention(q, k, r, self._normalise) elif self._type == 'multihead': rep = multihead_attention(q, k, r, self._num_heads) else: raise NameError(("'att_type' not among ['uniform','laplace','dot_product'" ",'multihead']")) return rep

en

0.72758

# utility methods Apply MLP to the final axis of a 3D tensor (reusing already defined MLPs). Args: input: input tensor of shape [B,n,d_in]. output_sizes: An iterable containing the output sizes of the MLP as defined in `basic.Linear`. variable_scope: String giving the name of the variable scope. If this is set to be the same as a previously defined MLP, then the weights are reused. Returns: tensor of shape [B,n,d_out] where d_out=output_sizes[-1] # Get the shapes of the input and reshape to parallelise across observations # Pass through MLP # Last layer without a ReLu # Bring back into original shape The Deterministic Encoder. (A)NP deterministic encoder. Args: output_sizes: An iterable containing the output sizes of the encoding MLP. attention: The attention module. Encodes the inputs into one representation. Args: context_x: Tensor of shape [B,observations,d_x]. For this 1D regression task this corresponds to the x-values. context_y: Tensor of shape [B,observations,d_y]. For this 1D regression task this corresponds to the y-values. target_x: Tensor of shape [B,target_observations,d_x]. For this 1D regression task this corresponds to the x-values. Returns: The encoded representation. Tensor of shape [B,target_observations,d] # Concatenate x and y along the filter axes # Pass final axis through MLP # Apply attention The Latent Encoder. (A)NP latent encoder. Args: output_sizes: An iterable containing the output sizes of the encoding MLP. num_latents: The latent dimensionality. Encodes the inputs into one representation. Args: x: Tensor of shape [B,observations,d_x]. For this 1D regression task this corresponds to the x-values. y: Tensor of shape [B,observations,d_y]. For this 1D regression task this corresponds to the y-values. Returns: A normal distribution over tensors of shape [B, num_latents] # Concatenate x and y along the filter axes # Pass final axis through MLP # Aggregator: take the mean over all points # Have further MLP layers that map to the parameters of the Gaussian latent # First apply intermediate relu layer # Then apply further linear layers to output latent mu and log sigma # Compute sigma The Decoder. (A)NP decoder. Args: output_sizes: An iterable containing the output sizes of the decoder MLP as defined in `basic.Linear`. Decodes the individual targets. Args: representation: The representation of the context for target predictions. Tensor of shape [B,target_observations,?]. target_x: The x locations for the target query. Tensor of shape [B,target_observations,d_x]. Returns: dist: A multivariate Gaussian over the target points. A distribution over tensors of shape [B,target_observations,d_y]. mu: The mean of the multivariate Gaussian. Tensor of shape [B,target_observations,d_x]. sigma: The standard deviation of the multivariate Gaussian. Tensor of shape [B,target_observations,d_x]. # concatenate target_x and representation # Pass final axis through MLP # Get the mean an the variance # Bound the variance # Get the distribution The (A)NP model. Initialises the model. Args: latent_encoder_output_sizes: An iterable containing the sizes of hidden layers of the latent encoder. num_latents: The latent dimensionality. decoder_output_sizes: An iterable containing the sizes of hidden layers of the decoder. The last element should correspond to d_y * 2 (it encodes both mean and variance concatenated) use_deterministic_path: a boolean that indicates whether the deterministic encoder is used or not. deterministic_encoder_output_sizes: An iterable containing the sizes of hidden layers of the deterministic encoder. The last one is the size of the deterministic representation r. attention: The attention module used in the deterministic encoder. Only relevant when use_deterministic_path=True. Returns the predicted mean and variance at the target points. Args: query: Array containing ((context_x, context_y), target_x) where: context_x: Tensor of shape [B,num_contexts,d_x]. Contains the x values of the context points. context_y: Tensor of shape [B,num_contexts,d_y]. Contains the y values of the context points. target_x: Tensor of shape [B,num_targets,d_x]. Contains the x values of the target points. num_targets: Number of target points. target_y: The ground truth y values of the target y. Tensor of shape [B,num_targets,d_y]. Returns: log_p: The log_probability of the target_y given the predicted distribution. Tensor of shape [B,num_targets]. mu: The mean of the predicted distribution. Tensor of shape [B,num_targets,d_y]. sigma: The variance of the predicted distribution. Tensor of shape [B,num_targets,d_y]. # Pass query through the encoder and the decoder # For training, when target_y is available, use targets for latent encoder. # Note that targets contain contexts by design. # For testing, when target_y unavailable, use contexts for latent encoder. # If we want to calculate the log_prob for training we will make use of the # target_y. At test time the target_y is not available so we return None. Uniform attention. Equivalent to np. Args: q: queries. tensor of shape [B,m,d_k]. v: values. tensor of shape [B,n,d_v]. Returns: tensor of shape [B,m,d_v]. # [B,1,d_v] Computes laplace exponential attention. Args: q: queries. tensor of shape [B,m,d_k]. k: keys. tensor of shape [B,n,d_k]. v: values. tensor of shape [B,n,d_v]. scale: float that scales the L1 distance. normalise: Boolean that determines whether weights sum to 1. Returns: tensor of shape [B,m,d_v]. # [B,1,n,d_k] # [B,m,1,d_k] # [B,m,n,d_k] # [B,m,n] # [B,m,n] # [B,m,d_v] Computes dot product attention. Args: q: queries. tensor of shape [B,m,d_k]. k: keys. tensor of shape [B,n,d_k]. v: values. tensor of shape [B,n,d_v]. normalise: Boolean that determines whether weights sum to 1. Returns: tensor of shape [B,m,d_v]. # [B,m,n] # [B,m,n] # [B,m,d_v] Computes multi-head attention. Args: q: queries. tensor of shape [B,m,d_k]. k: keys. tensor of shape [B,n,d_k]. v: values. tensor of shape [B,n,d_v]. num_heads: number of heads. Should divide d_v. Returns: tensor of shape [B,m,d_v]. The Attention module. Create attention module. Takes in context inputs, target inputs and representations of each context input/output pair to output an aggregated representation of the context data. Args: rep: transformation to apply to contexts before computing attention. One of: ['identity','mlp']. output_sizes: list of number of hidden units per layer of mlp. Used only if rep == 'mlp'. att_type: type of attention. One of the following: ['uniform','laplace','dot_product','multihead'] scale: scale of attention. normalise: Boolean determining whether to: 1. apply softmax to weights so that they sum to 1 across context pts or 2. apply custom transformation to have weights in [0,1]. num_heads: number of heads for multihead. Apply attention to create aggregated representation of r. Args: x1: tensor of shape [B,n1,d_x]. x2: tensor of shape [B,n2,d_x]. r: tensor of shape [B,n1,d]. Returns: tensor of shape [B,n2,d] Raises: NameError: The argument for rep/type was invalid. # Pass through MLP

3.012563

3

minotaur/_minotaur.py

giannitedesco/minotaur

172

10257

<filename>minotaur/_minotaur.py from typing import Dict, Tuple, Optional from pathlib import Path import asyncio from ._mask import Mask from ._event import Event from ._base import InotifyBase __all__ = ('Minotaur',) class Notification: __slots__ = ( '_path', '_type', '_isdir', '_unmount', '_qoverflow', ) def __init__(self, path: Path, type: Mask, isdir: bool, unmount: bool, qoverflow: bool = False): self._path = path self._type = type self._isdir = bool(isdir) self._unmount = bool(unmount) self._qoverflow = bool(qoverflow) @property def isdir(self) -> bool: return self._isdir @property def unmount(self) -> bool: return self._unmount @property def qoverflow(self) -> bool: return self._qoverflow @property def path(self) -> Path: return self._path def __repr__(self) -> str: t = self._isdir and 'dir' or 'file' return f'{type(self).__name__}({self._type.name} {t} {self._path})' @classmethod def create(cls, path: Path, mask: Mask) -> 'Notification': return cls(path, mask & Mask.EVENT_TYPE, bool(mask & Mask.ISDIR), bool(mask & Mask.UNMOUNT), bool(mask & Mask.Q_OVERFLOW)) class Minotaur(InotifyBase): """ Fancy interface for Inotify which does questionable things like: 1. Resolve watch-descriptors back to paths (which races with renames of original paths and can't be used safely, but other inotify packages provide this feature, so here it is for your delectation). 2. Link rename_from/rename_to events together. This feature would be useful but isn't yet actually implemented. Working on it... """ __slots__ = ( '_wdmap', '_cmap', ) _wdmap: Dict[int, Path] _cmap: Dict[Tuple[int, int], Event] def __init__(self, blocking: bool = True, cloexec: bool = True, loop: Optional[asyncio.AbstractEventLoop] = None, ) -> None: super().__init__(blocking, cloexec, loop) self._wdmap = {} self._cmap = {} def add_watch(self, p: Path, mask: Mask) -> int: try: wd = super().add_watch(p, mask) except Exception: raise else: self._wdmap[wd] = p.resolve() return wd def rm_watch(self, wd: int) -> int: try: return super().rm_watch(wd) except Exception: raise else: del self._wdmap[wd] def _resolve_path(self, wd: int, name: Path) -> Path: try: base_dir = self._wdmap[wd] except KeyError: path = name else: path = base_dir / name return path def __next__(self) -> Notification: evt = super()._next_event() if evt is None: raise StopIteration # TODO: Link rename_from/rename_to together if we have them path = self._resolve_path(evt.wd, evt.name) return Notification.create(path, evt.mask) async def __anext__(self) -> Notification: evt = await super()._next_event_async() if evt is None: raise StopIteration path = self._resolve_path(evt.wd, evt.name) return Notification.create(path, evt.mask)

en

0.932626

Fancy interface for Inotify which does questionable things like: 1. Resolve watch-descriptors back to paths (which races with renames of original paths and can't be used safely, but other inotify packages provide this feature, so here it is for your delectation). 2. Link rename_from/rename_to events together. This feature would be useful but isn't yet actually implemented. Working on it... # TODO: Link rename_from/rename_to together if we have them

2.328925

2

pyclustering/container/examples/__init__.py

JosephChataignon/pyclustering

1,013

10258

<reponame>JosephChataignon/pyclustering """! @brief Collection of examples devoted to containers. @authors <NAME> (<EMAIL>) @date 2014-2020 @copyright BSD-3-Clause """

"""! @brief Collection of examples devoted to containers. @authors <NAME> (<EMAIL>) @date 2014-2020 @copyright BSD-3-Clause """

en

0.532854

! @brief Collection of examples devoted to containers. @authors <NAME> (<EMAIL>) @date 2014-2020 @copyright BSD-3-Clause

1.233931

1

novelty-detection/train_wood_vgg19.py

matherm/python-data-science

1

10259

<reponame>matherm/python-data-science import argparse import sys import torch import numpy as np import torch.nn as nn from torch.utils.data import DataLoader from torchvision.datasets import MNIST from torchvision.datasets import CIFAR10 import torchvision.transforms as transforms import matplotlib.pyplot as plt parser = argparse.ArgumentParser(description='PyTorch Novelty Detection') # TRAINING PARAMS parser.add_argument('--epochs', type=int, default=100, metavar='', help='Amount of epochs for training (default: 100)') parser.add_argument('--batch_size', type=int, default=1000, metavar='', help='Batch size for SGD (default: 100)') parser.add_argument('--lrate', type=float, default=0.0001, metavar="", help="Learning rate (default: 0.001") parser.add_argument('--with_cuda', action='store_true', dest='use_cuda', help="Shall cuda be used (default: False)") parser.add_argument('--model', type=int, default=0, help="Which model to train (0=KLminimizer, 1=Euclidean-Minimizer) (default: 0)") parser.add_argument('--plots', action='store_true', dest='plots', help="Shall matplotlib be used (default: False)") parser.add_argument('--grid', action='store_true', dest='grid', help="Grid search (default: False)") argv = parser.parse_args() sys.argv = [sys.argv[0]] from ummon import * from negvarbound import * from model import * from helpers import Evaluator import helpers torch.manual_seed(4) if __name__ == '__main__': # WOOD transform = transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), VGG19Features("pool4"), helpers.flatten_transform]) wood_data = ImagePatches("/ext/data/Wood-0035.png", mode='rgb', train=True, stride_y=14, stride_x=14, window_size=28, transform=transform) wood_data_test = AnomalyImagePatches("/ext/data/Wood-0035.png", mode='rgb', train=True, stride_y=14, stride_x=14, window_size=28, transform=transform, propability=1.0, anomaly=SquareAnomaly(size=8, color=255)) wood_data = [wood_data[i][0].data for i in range(len(wood_data))] wood_data = torch.stack(wood_data).numpy() / 10 wood_data_test = [wood_data_test[i][0].data for i in range(len(wood_data_test))] wood_data_test = torch.stack(wood_data_test).numpy() / 10 # Novelty data_novelty = wood_data_test # Train data_train = wood_data # Val data_val = data_train ###################################################### # NORMAL DISTRIBUTION ###################################################### # Model model = ModelNormal(input_features = data_train.shape[1], hidden_layer=20, latent_features=20) torch.manual_seed(4) # LOSS criterion = KLLoss(model=model, size_average=False) # INSTANTIATE OPTIMIZER optimizer = torch.optim.SGD(model.parameters(), lr=argv.lrate, weight_decay=1) #Evaluator evaluator = Evaluator(model, data_train, data_val, data_novelty) # Activate matplotlib argv.plots = True with Logger(loglevel=10, log_batch_interval=601) as lg: # CREATE A TRAINER my_trainer = UnsupervisedTrainer(lg, model, criterion, optimizer, trainingstate = Trainingstate(), model_filename="KL_MIN", use_cuda= argv.use_cuda, profile = False, convergence_eps = 1e-5) # START TRAINING my_trainer.fit(dataloader_training=(wood_data, 20), epochs=200) evaluator.evaluate_model(argv) ###################################################### # LOGNORMAL ###################################################### # Model model = ModelLogNormal(input_features = data_train.shape[1], hidden_layer=20, latent_features=20) torch.manual_seed(4) # LOSS criterion = KLLoss_lognormal(model=model, size_average=False) # INSTANTIATE OPTIMIZER optimizer = torch.optim.SGD(model.parameters(), lr=argv.lrate, weight_decay=1) #Evaluator evaluator = Evaluator(model, data_train, data_val, data_novelty) # Activate matplotlib argv.plots = True with Logger(loglevel=10, log_batch_interval=601) as lg: # CREATE A TRAINER my_trainer = UnsupervisedTrainer(lg, model, criterion, optimizer, trainingstate = Trainingstate(), model_filename="KL_MIN", use_cuda= argv.use_cuda, profile = False, convergence_eps = 1e-5) # START TRAINING my_trainer.fit(dataloader_training=(data_train, 20), epochs=argv.epochs) evaluator.evaluate_model(argv) ###################################################### # LAPLACE ###################################################### # Model model = ModelLaplace(input_features = data_train.shape[1], hidden_layer=20, latent_features=20) torch.manual_seed(4) # LOSS criterion = KLLoss_laplace(model=model, size_average=False, mean=2, scale=0.5) # INSTANTIATE OPTIMIZER optimizer = torch.optim.SGD(model.parameters(), lr=0.000001, weight_decay=1) #Evaluator evaluator = Evaluator(model, data_train, data_val, data_novelty) # Activate matplotlib argv.plots = True with Logger(loglevel=10, log_batch_interval=601) as lg: # CREATE A TRAINER my_trainer = UnsupervisedTrainer(lg, model, criterion, optimizer, trainingstate = Trainingstate(), model_filename="KL_MIN", use_cuda= argv.use_cuda, profile = False, convergence_eps = 1e-1) # START TRAINING my_trainer.fit(dataloader_training=(data_train, 20), epochs=300) evaluator.evaluate_model(argv) # {'AUROC LAT (TRAIN)': 0.8743801652892562, # 'AUROC LAT (VAL)': 0.8661157024793389, # 'AUROC REC (TRAIN)': 0.86900826446281, # 'AUROC REC (VAL)': 0.8528925619834712} ###################################################### # LAPLACE WITH R-SHIFT ###################################################### class CombinedLoss(nn.Module): def __init__(self, model, *args, **kwargs): super(CombinedLoss, self).__init__() self.model = model self.r_shift = KLLoss_shift_r(model=model, size_average=False) self.kl_loss = KLLoss_laplace(model=model, size_average=False, mean=10, scale=0.3) def forward(self, inpt, outpt): self.r_shift() return self.kl_loss(inpt,outpt) # Model model = ModelLaplace(input_features = data_train.shape[1], hidden_layer=20, latent_features=20) torch.manual_seed(4) # LOSS criterion = CombinedLoss(model) # INSTANTIATE OPTIMIZER optimizer = torch.optim.SGD(model.parameters(), lr=argv.lrate, weight_decay=1) #Evaluator evaluator = Evaluator(model, data_train, data_val, data_novelty) # Activate matplotlib argv.plots = True with Logger(loglevel=10, log_batch_interval=601) as lg: # CREATE A TRAINER my_trainer = UnsupervisedTrainer(lg, model, criterion, optimizer, trainingstate = Trainingstate(), model_filename="KL_MIN", use_cuda= argv.use_cuda, profile = False, convergence_eps = 1e-3) # START TRAINING my_trainer.fit(dataloader_training=(data_train, 20), epochs=200) evaluator.evaluate_model(argv) # {'AUROC LAT (TRAIN)': 0.8590909090909091, # 'AUROC LAT (VAL)': 0.8752066115702479, # 'AUROC REC (TRAIN)': 0.8677685950413224, # 'AUROC REC (VAL)': 0.8619834710743801}

import argparse import sys import torch import numpy as np import torch.nn as nn from torch.utils.data import DataLoader from torchvision.datasets import MNIST from torchvision.datasets import CIFAR10 import torchvision.transforms as transforms import matplotlib.pyplot as plt parser = argparse.ArgumentParser(description='PyTorch Novelty Detection') # TRAINING PARAMS parser.add_argument('--epochs', type=int, default=100, metavar='', help='Amount of epochs for training (default: 100)') parser.add_argument('--batch_size', type=int, default=1000, metavar='', help='Batch size for SGD (default: 100)') parser.add_argument('--lrate', type=float, default=0.0001, metavar="", help="Learning rate (default: 0.001") parser.add_argument('--with_cuda', action='store_true', dest='use_cuda', help="Shall cuda be used (default: False)") parser.add_argument('--model', type=int, default=0, help="Which model to train (0=KLminimizer, 1=Euclidean-Minimizer) (default: 0)") parser.add_argument('--plots', action='store_true', dest='plots', help="Shall matplotlib be used (default: False)") parser.add_argument('--grid', action='store_true', dest='grid', help="Grid search (default: False)") argv = parser.parse_args() sys.argv = [sys.argv[0]] from ummon import * from negvarbound import * from model import * from helpers import Evaluator import helpers torch.manual_seed(4) if __name__ == '__main__': # WOOD transform = transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]), VGG19Features("pool4"), helpers.flatten_transform]) wood_data = ImagePatches("/ext/data/Wood-0035.png", mode='rgb', train=True, stride_y=14, stride_x=14, window_size=28, transform=transform) wood_data_test = AnomalyImagePatches("/ext/data/Wood-0035.png", mode='rgb', train=True, stride_y=14, stride_x=14, window_size=28, transform=transform, propability=1.0, anomaly=SquareAnomaly(size=8, color=255)) wood_data = [wood_data[i][0].data for i in range(len(wood_data))] wood_data = torch.stack(wood_data).numpy() / 10 wood_data_test = [wood_data_test[i][0].data for i in range(len(wood_data_test))] wood_data_test = torch.stack(wood_data_test).numpy() / 10 # Novelty data_novelty = wood_data_test # Train data_train = wood_data # Val data_val = data_train ###################################################### # NORMAL DISTRIBUTION ###################################################### # Model model = ModelNormal(input_features = data_train.shape[1], hidden_layer=20, latent_features=20) torch.manual_seed(4) # LOSS criterion = KLLoss(model=model, size_average=False) # INSTANTIATE OPTIMIZER optimizer = torch.optim.SGD(model.parameters(), lr=argv.lrate, weight_decay=1) #Evaluator evaluator = Evaluator(model, data_train, data_val, data_novelty) # Activate matplotlib argv.plots = True with Logger(loglevel=10, log_batch_interval=601) as lg: # CREATE A TRAINER my_trainer = UnsupervisedTrainer(lg, model, criterion, optimizer, trainingstate = Trainingstate(), model_filename="KL_MIN", use_cuda= argv.use_cuda, profile = False, convergence_eps = 1e-5) # START TRAINING my_trainer.fit(dataloader_training=(wood_data, 20), epochs=200) evaluator.evaluate_model(argv) ###################################################### # LOGNORMAL ###################################################### # Model model = ModelLogNormal(input_features = data_train.shape[1], hidden_layer=20, latent_features=20) torch.manual_seed(4) # LOSS criterion = KLLoss_lognormal(model=model, size_average=False) # INSTANTIATE OPTIMIZER optimizer = torch.optim.SGD(model.parameters(), lr=argv.lrate, weight_decay=1) #Evaluator evaluator = Evaluator(model, data_train, data_val, data_novelty) # Activate matplotlib argv.plots = True with Logger(loglevel=10, log_batch_interval=601) as lg: # CREATE A TRAINER my_trainer = UnsupervisedTrainer(lg, model, criterion, optimizer, trainingstate = Trainingstate(), model_filename="KL_MIN", use_cuda= argv.use_cuda, profile = False, convergence_eps = 1e-5) # START TRAINING my_trainer.fit(dataloader_training=(data_train, 20), epochs=argv.epochs) evaluator.evaluate_model(argv) ###################################################### # LAPLACE ###################################################### # Model model = ModelLaplace(input_features = data_train.shape[1], hidden_layer=20, latent_features=20) torch.manual_seed(4) # LOSS criterion = KLLoss_laplace(model=model, size_average=False, mean=2, scale=0.5) # INSTANTIATE OPTIMIZER optimizer = torch.optim.SGD(model.parameters(), lr=0.000001, weight_decay=1) #Evaluator evaluator = Evaluator(model, data_train, data_val, data_novelty) # Activate matplotlib argv.plots = True with Logger(loglevel=10, log_batch_interval=601) as lg: # CREATE A TRAINER my_trainer = UnsupervisedTrainer(lg, model, criterion, optimizer, trainingstate = Trainingstate(), model_filename="KL_MIN", use_cuda= argv.use_cuda, profile = False, convergence_eps = 1e-1) # START TRAINING my_trainer.fit(dataloader_training=(data_train, 20), epochs=300) evaluator.evaluate_model(argv) # {'AUROC LAT (TRAIN)': 0.8743801652892562, # 'AUROC LAT (VAL)': 0.8661157024793389, # 'AUROC REC (TRAIN)': 0.86900826446281, # 'AUROC REC (VAL)': 0.8528925619834712} ###################################################### # LAPLACE WITH R-SHIFT ###################################################### class CombinedLoss(nn.Module): def __init__(self, model, *args, **kwargs): super(CombinedLoss, self).__init__() self.model = model self.r_shift = KLLoss_shift_r(model=model, size_average=False) self.kl_loss = KLLoss_laplace(model=model, size_average=False, mean=10, scale=0.3) def forward(self, inpt, outpt): self.r_shift() return self.kl_loss(inpt,outpt) # Model model = ModelLaplace(input_features = data_train.shape[1], hidden_layer=20, latent_features=20) torch.manual_seed(4) # LOSS criterion = CombinedLoss(model) # INSTANTIATE OPTIMIZER optimizer = torch.optim.SGD(model.parameters(), lr=argv.lrate, weight_decay=1) #Evaluator evaluator = Evaluator(model, data_train, data_val, data_novelty) # Activate matplotlib argv.plots = True with Logger(loglevel=10, log_batch_interval=601) as lg: # CREATE A TRAINER my_trainer = UnsupervisedTrainer(lg, model, criterion, optimizer, trainingstate = Trainingstate(), model_filename="KL_MIN", use_cuda= argv.use_cuda, profile = False, convergence_eps = 1e-3) # START TRAINING my_trainer.fit(dataloader_training=(data_train, 20), epochs=200) evaluator.evaluate_model(argv) # {'AUROC LAT (TRAIN)': 0.8590909090909091, # 'AUROC LAT (VAL)': 0.8752066115702479, # 'AUROC REC (TRAIN)': 0.8677685950413224, # 'AUROC REC (VAL)': 0.8619834710743801}

de

0.33531

# TRAINING PARAMS # WOOD # Novelty # Train # Val ###################################################### # NORMAL DISTRIBUTION ###################################################### # Model # LOSS # INSTANTIATE OPTIMIZER #Evaluator # Activate matplotlib # CREATE A TRAINER # START TRAINING ###################################################### # LOGNORMAL ###################################################### # Model # LOSS # INSTANTIATE OPTIMIZER #Evaluator # Activate matplotlib # CREATE A TRAINER # START TRAINING ###################################################### # LAPLACE ###################################################### # Model # LOSS # INSTANTIATE OPTIMIZER #Evaluator # Activate matplotlib # CREATE A TRAINER # START TRAINING # {'AUROC LAT (TRAIN)': 0.8743801652892562, # 'AUROC LAT (VAL)': 0.8661157024793389, # 'AUROC REC (TRAIN)': 0.86900826446281, # 'AUROC REC (VAL)': 0.8528925619834712} ###################################################### # LAPLACE WITH R-SHIFT ###################################################### # Model # LOSS # INSTANTIATE OPTIMIZER #Evaluator # Activate matplotlib # CREATE A TRAINER # START TRAINING # {'AUROC LAT (TRAIN)': 0.8590909090909091, # 'AUROC LAT (VAL)': 0.8752066115702479, # 'AUROC REC (TRAIN)': 0.8677685950413224, # 'AUROC REC (VAL)': 0.8619834710743801}

2.441559

2

sample_architectures/cnn.py

hvarS/PyTorch-Refer

0

10260

# -*- coding: utf-8 -*- """CNN.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1Tq6HUya2PrC0SmyOIFo2c_eVtguRED2q """ import torch import torch.nn as nn import torch.optim as optim import torch.nn.functional as F from torch.utils.data import DataLoader import torchvision.datasets as datasets import torchvision.transforms as transforms class CNN(nn.Module): def __init__(self,in_channels = 1,num_classes = 10): super(CNN,self).__init__() self.conv1 = nn.Conv2d(in_channels= in_channels,out_channels = 8,kernel_size =(3,3),stride = (1,1),padding = (1,1)) self.pool1 = nn.MaxPool2d(kernel_size=(2,2),stride=(2,2)) self.conv2 = nn.Conv2d(in_channels= 8,out_channels = 16,kernel_size =(3,3),stride = (1,1),padding = (1,1)) self.pool2 = nn.MaxPool2d(kernel_size=(2,2),stride=(2,2)) self.fc1 = nn.Linear(16*7*7,num_classes) def forward(self,x): x = F.relu(self.conv1(x)) x = self.pool1(x) x = F.relu(self.conv2(x)) x = self.pool2(x) x = x.reshape(x.shape[0],-1) x = self.fc1(x) return x model = CNN(1,10) x = torch.randn((64,1,28,28)) print(model(x).shape) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device in_channels = 1 num_classes = 10 learning_rate = 0.001 batch_size = 64 num_epochs = 4 train_dataset = datasets.MNIST(root = "dataset/",train = True,transform = transforms.ToTensor(),download = True) train_loader = DataLoader(dataset=train_dataset,batch_size=64,shuffle=True) test_dataset = train_dataset = datasets.MNIST(root = "dataset/",train = False,transform = transforms.ToTensor(),download = True) test_loader = DataLoader(dataset = test_dataset,batch_size = batch_size,shuffle = True) model = CNN(1,10).to(device = device) criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(),lr = learning_rate) for epoch in range(num_epochs): for batch_idx,(data,targets) in enumerate(train_loader): #get data to cuda if possible data = data.cuda() targets = targets.cuda() scores = model(data) loss = criterion(scores,targets) #backward optimizer.zero_grad() loss.backward() #gradient_descent or adam-step optimizer.step() # Check the accuracy for the training step def check_accuracy(loader,model): if loader.dataset.train: print("Checking accuracy on training data") else: print("Checking accuracy on test data") num_correct = 0 num_samples = 0 model.eval() with torch.no_grad(): for x,y in loader: x = x.cuda() y = y.cuda() scores = model(x) _,predictions = scores.max(1) num_correct += (predictions == y).sum() num_samples += predictions.size(0) print(f' Got {num_correct}/{num_samples} with accuracy ={float(num_correct)/float(num_samples)*100:.2f} ') model.train() check_accuracy(train_loader,model) check_accuracy(test_loader,model)

en

0.798453

# -*- coding: utf-8 -*- CNN.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1Tq6HUya2PrC0SmyOIFo2c_eVtguRED2q #get data to cuda if possible #backward #gradient_descent or adam-step # Check the accuracy for the training step

3.112416

3

aispace/layers/callbacks/qa_evaluators.py

SmileGoat/AiSpace

32

10261

<reponame>SmileGoat/AiSpace # -*- coding: utf-8 -*- # @Time : 2020-07-30 15:06 # @Author : yingyuankai # @Email : <EMAIL> # @File : qa_evaluators.py import os import logging import numpy as np import tensorflow as tf import json from pprint import pprint from collections import defaultdict from aispace.utils.eval_utils import calc_em_score, calc_f1_score from aispace.utils.io_utils import save_json from aispace.utils.print_utils import print_boxed from aispace.utils.metrics_utils import ConfusionMatrix __all__ = [ 'EvaluatorForQaSimple', 'EvaluatorForQaWithImpossible' ] logger = logging.getLogger(__name__) class EvaluatorForQaSimple(tf.keras.callbacks.Callback): """ start_top_log_prob and end_top_log_prob's shape is [batch, k] ref: https://keras.io/examples/nlp/text_extraction_with_bert/ """ def __init__(self, validation_dataset, validation_steps, test_dataset, test_steps, report_dir, max_answer_length=64, n_best_size=5): self.validation_dataset = validation_dataset self.validation_steps = validation_steps self.test_dataset = test_dataset self.test_steps = test_steps self.max_answer_length = max_answer_length self.n_best_size = n_best_size self.report_dir = report_dir def on_epoch_end(self, epoch, logs=None): new_logs = self.eval_process(self.validation_dataset, self.validation_steps) logs = logs or {} logs.update(new_logs) print(f"Epoch: {epoch + 1}, val_f1_score: {logs['val_f1_score']:.4f}, val_em_score: {logs['val_em_score']:.4f}, " f"val_f1_em_avg_score: {logs['val_f1_em_avg_score']:.4f}") def on_train_end(self, logs=None): logger.info("Start Evaluate.") if not os.path.exists(self.report_dir): os.makedirs(self.report_dir) new_logs = self.eval_process(self.test_dataset, self.test_steps) save_json(os.path.join(self.report_dir, 'performance.json'), new_logs) print_boxed(f"Question Answer Evaluation") pprint(new_logs) logger.info(f"Save question answer reports in {self.report_dir}") def eval_process(self, dataset, n_steps=None): f1 = 0 em = 0 total_count = 0 skip_count = 0 start_top_res, end_top_res, unique_id_res = self.model.predict(dataset, steps=n_steps) start_top_log_prob, start_top_index = start_top_res[:, :, 0], start_top_res[:, :, 1].astype(np.int) # [b, k] end_top_log_prob, end_top_index = end_top_res[:, :, 0], end_top_res[:, :, 1].astype(np.int) # [b, k] unique_id_res = unique_id_res.astype(np.int) # predict results results = {} for i in range(end_top_index.shape[0]): unique_id = unique_id_res[i][0] itm = { 'unique_id': unique_id, 'start_top_log_prob': start_top_log_prob[i], 'start_top_index': start_top_index[i], 'end_top_log_prob': end_top_log_prob[i], 'end_top_index': end_top_index[i], } results[unique_id] = itm # raw inputs start_n_top, end_n_top = start_top_index.shape[-1], end_top_index.shape[-1] qas_id_to_examples = defaultdict(list) unique_id_to_examples = {} for idx, (inputs, outputs) in enumerate(dataset): if n_steps is not None and idx >= n_steps: break unique_ids = inputs['unique_id'].numpy().astype(np.int).tolist() offsets = inputs['offset'].numpy().astype(np.int).tolist() qas_ids = inputs['qas_id'].numpy().astype(str).tolist() doc_token2char_raw_start_indexs = inputs['doc_token2char_raw_start_index'].numpy().astype(str).tolist() doc_token2char_raw_end_indexs = inputs['doc_token2char_raw_end_index'].numpy().astype(str).tolist() doc_token2doc_indexs = inputs['doc_token2doc_index'].numpy().astype(str).tolist() all_answers = inputs['all_answers'].numpy().astype(str).tolist() answer_texts = inputs['answer_text'].numpy().tolist() context_texts = inputs['context_text'].numpy().tolist() question_texts = inputs['question_text'].numpy().tolist() is_impossibles = inputs['is_impossible'].numpy().tolist() p_masks = inputs['p_mask'].numpy().astype(np.int).tolist() for t in range(len(unique_ids)): itm = { 'unique_id': unique_ids[t], 'qas_id': qas_ids[t], 'question_text': question_texts[t].decode("utf8"), 'context_text': context_texts[t].decode("utf8"), 'answer_text': answer_texts[t].decode("utf8"), 'all_answers': json.loads(all_answers[t]), 'doc_token2char_raw_start_index': json.loads(doc_token2char_raw_start_indexs[t]), 'doc_token2char_raw_end_index': json.loads(doc_token2char_raw_end_indexs[t]), 'doc_token2doc_index': json.loads(doc_token2doc_indexs[t]), 'is_impossible': is_impossibles[t], 'p_mask': p_masks[t], 'offset': offsets[t] } unique_id_to_examples[unique_ids[t]] = itm qas_id_to_examples[qas_ids[t]].append(itm) for qas_id, examples in qas_id_to_examples.items(): example_all_predicts = [] answers = set() for example in examples: cur_unique_id = example['unique_id'] if cur_unique_id not in results: continue if example['is_impossible'] == 1: continue # if example['answer_text'] not in answers: # answers.append(example['answer_text']) answers |= set(example['all_answers']) cur_result = results.get(cur_unique_id) cur_start_top_log_prob = cur_result['start_top_log_prob'] cur_start_top_index = cur_result['start_top_index'] cur_end_top_log_prob = cur_result['end_top_log_prob'] cur_end_top_index = cur_result['end_top_index'] cur_p_mask = example['p_mask'] for i in range(start_n_top): start_prob = cur_start_top_log_prob[i] start_index = cur_start_top_index[i] if not cur_p_mask[start_index]: continue for j in range(end_n_top): end_prob = cur_end_top_log_prob[j] end_index = cur_end_top_index[j] if not cur_p_mask[end_index]: continue answer_length = end_index - start_index + 1 if end_index < start_index or answer_length > self.max_answer_length: continue itm = { 'unique_id': cur_unique_id, 'start_prob': start_prob, 'start_index': start_index, 'end_prob': end_prob, 'end_index': end_index, 'predict_score': np.log(start_prob) + np.log(end_prob) } example_all_predicts.append(itm) if len(answers) != 0: total_count += 1 else: skip_count += 1 continue example_all_predicts.sort(key=lambda s: s['predict_score'], reverse=True) example_top_predicts = [] is_visited = set() for example_predict in example_all_predicts: if len(example_top_predicts) >= self.n_best_size: break example_feature = unique_id_to_examples[example_predict['unique_id']] if example_predict['start_index'] - example_feature['offset'] < 0 or example_predict['end_index'] - example_feature['offset'] < 0: predict_text = "" else: predict_start = example_feature['doc_token2char_raw_start_index'][ example_predict['start_index'] - example_feature['offset']] predict_end = example_feature['doc_token2char_raw_end_index'][ example_predict['end_index'] - example_feature['offset']] predict_text = example_feature['context_text'][predict_start: predict_end + 1].strip() if predict_text in is_visited: continue is_visited.add(predict_text) itm = { 'predict_text': predict_text, 'start_prob': example_predict['start_prob'], 'end_prob': example_predict['end_prob'], 'predict_score': example_predict['predict_score'] } example_top_predicts.append(itm) if len(example_top_predicts) == 0: example_top_predicts.append( { 'predict_text': "", 'start_prob': 0., 'end_prob': 0., 'predict_score': 0. } ) example_best_predict = example_top_predicts[0] cur_f1 = calc_f1_score(list(answers), example_best_predict['predict_text']) cur_em = calc_em_score(list(answers), example_best_predict['predict_text']) f1 += cur_f1 em += cur_em # debug if cur_f1 == 0 or cur_em == 0: example_output = {} example_output.update(example_best_predict) example_output['question'] = examples[0]['question_text'] example_output['answer'] = answers example_output['f1'] = cur_f1 example_output['em'] = cur_em print(example_output) # total_count = len(qas_id_to_examples) f1_score = f1 / total_count em_score = em / total_count logs = {} logs['skip_count'] = skip_count logs['total'] = total_count logs['val_f1_score'] = f1_score logs['val_em_score'] = em_score logs['val_f1_em_avg_score'] = (em_score + f1_score) / 2. return logs class EvaluatorForQaWithImpossible(tf.keras.callbacks.Callback): """ start_top_log_prob and end_top_log_prob's shape is [batch, k, k] ref: https://keras.io/examples/nlp/text_extraction_with_bert/ """ def __init__(self, validation_dataset, validation_steps, test_dataset, test_steps, report_dir, max_answer_length=64, n_best_size=5, is_impossible_threshold=0.5, weights=[1., 1., 1.]): self.validation_dataset = validation_dataset self.validation_steps = validation_steps self.test_dataset = test_dataset self.test_steps = test_steps self.max_answer_length = max_answer_length self.n_best_size = n_best_size self.report_dir = report_dir self.is_impossible_threshold = is_impossible_threshold self.weights = weights def on_epoch_end(self, epoch, logs=None): new_logs = self.eval_process(self.validation_dataset, self.validation_steps) logs = logs or {} logs.update(new_logs) print(f"\nEpoch: {epoch + 1}, val_f1_score: {logs['val_f1_score']:.4f}, " f"val_em_score: {logs['val_em_score']:.4f}, " f"val_f1_em_avg_score: {logs['val_f1_em_avg_score']:.4f}," f" val_f1_for_impossible: {logs['val_f1_for_impossible']:.4f}," f" val_f1_avg_score: {logs['val_f1_avg_score']:.4f},") def on_train_end(self, logs=None): logger.info("Start Evaluate.") if not os.path.exists(self.report_dir): os.makedirs(self.report_dir) new_logs = self.eval_process(self.test_dataset, self.test_steps) save_json(os.path.join(self.report_dir, 'performance.json'), new_logs) print_boxed(f"Question Answer Evaluation") pprint(new_logs) logger.info(f"Save question answer reports in {self.report_dir}") def eval_process(self, dataset, n_steps=None): f1 = 0 em = 0 total_count = 0 skip_count = 0 start_top_res, end_top_res, answer_prob, unique_id_res = self.model.predict(dataset, steps=n_steps) start_top_log_prob, start_top_index = start_top_res[:, :, 0], start_top_res[:, :, 1].astype(np.int) # [b, k] end_top_log_prob, end_top_index = end_top_res[:, :, :, 0], end_top_res[:, :, :, 1].astype(np.int) # [b, k, k] unique_id_res = unique_id_res.astype(np.int) # predict results results = {} for i in range(end_top_index.shape[0]): unique_id = unique_id_res[i][0] itm = { 'unique_id': unique_id, 'start_top_log_prob': start_top_log_prob[i], 'start_top_index': start_top_index[i], 'end_top_log_prob': end_top_log_prob[i], 'end_top_index': end_top_index[i], 'is_impossible_prob': answer_prob[i][0] } results[unique_id] = itm # raw inputs start_n_top, end_n_top = end_top_index.shape[1:] qas_id_to_examples = defaultdict(list) unique_id_to_examples = {} for idx, (inputs, outputs) in enumerate(dataset): if n_steps is not None and idx >= n_steps: break unique_ids = inputs['unique_id'].numpy().astype(np.int).tolist() offsets = inputs['offset'].numpy().astype(np.int).tolist() qas_ids = inputs['qas_id'].numpy().astype(str).tolist() doc_token2char_raw_start_indexs = inputs['doc_token2char_raw_start_index'].numpy().astype(str).tolist() doc_token2char_raw_end_indexs = inputs['doc_token2char_raw_end_index'].numpy().astype(str).tolist() doc_token2doc_indexs = inputs['doc_token2doc_index'].numpy().astype(str).tolist() all_answers = inputs['all_answers'].numpy().astype(str).tolist() answer_texts = inputs['answer_text'].numpy().tolist() context_texts = inputs['context_text'].numpy().tolist() question_texts = inputs['question_text'].numpy().tolist() is_impossibles = inputs['is_impossible'].numpy().tolist() p_masks = inputs['p_mask'].numpy().astype(np.int).tolist() for t in range(len(unique_ids)): itm = { 'unique_id': unique_ids[t], 'qas_id': qas_ids[t], 'question_text': question_texts[t].decode("utf8"), 'context_text': context_texts[t].decode("utf8"), 'answer_text': answer_texts[t].decode("utf8"), 'all_answers': json.loads(all_answers[t]), 'doc_token2char_raw_start_index': json.loads(doc_token2char_raw_start_indexs[t]), 'doc_token2char_raw_end_index': json.loads(doc_token2char_raw_end_indexs[t]), 'doc_token2doc_index': json.loads(doc_token2doc_indexs[t]), 'is_impossible': is_impossibles[t], 'p_mask': p_masks[t], 'offset': offsets[t] } unique_id_to_examples[unique_ids[t]] = itm qas_id_to_examples[qas_ids[t]].append(itm) ground_truth_for_impossible, predictions_for_impossible = [], [] for qas_id, examples in qas_id_to_examples.items(): example_all_predicts = [] answers = set() for example in examples: cur_unique_id = example['unique_id'] if cur_unique_id not in results: continue # if example['answer_text'] not in answers: # answers.append(example['answer_text']) answers |= set(example['all_answers']) cur_result = results.get(cur_unique_id) cur_start_top_log_prob = cur_result['start_top_log_prob'] cur_start_top_index = cur_result['start_top_index'] cur_end_top_log_prob = cur_result['end_top_log_prob'] cur_end_top_index = cur_result['end_top_index'] ground_truth_for_impossible.append(example['is_impossible']) predictions_for_impossible.append(int(cur_result['is_impossible_prob'] >= self.is_impossible_threshold)) if example['is_impossible'] == 1: continue cur_p_mask = example['p_mask'] for i in range(start_n_top): start_prob = cur_start_top_log_prob[i] start_index = cur_start_top_index[i] if not cur_p_mask[start_index]: continue for j in range(end_n_top): end_prob = cur_end_top_log_prob[i, j] end_index = cur_end_top_index[i, j] if not cur_p_mask[end_index]: continue answer_length = end_index - start_index + 1 if end_index < start_index or answer_length > self.max_answer_length: continue itm = { 'unique_id': cur_unique_id, 'start_prob': start_prob, 'start_index': start_index, 'end_prob': end_prob, 'end_index': end_index, 'predict_score': np.log(end_prob) } example_all_predicts.append(itm) if len(answers) != 0 and "" not in answers: total_count += 1 else: skip_count += 1 continue example_all_predicts.sort(key=lambda s: s['predict_score'], reverse=True) example_top_predicts = [] is_visited = set() for example_predict in example_all_predicts: if len(example_top_predicts) >= self.n_best_size: break example_feature = unique_id_to_examples[example_predict['unique_id']] if example_predict['start_index'] - example_feature['offset'] < 0 or example_predict['end_index'] - example_feature['offset'] < 0: predict_text = "" else: predict_start = example_feature['doc_token2char_raw_start_index'][ example_predict['start_index'] - example_feature['offset']] predict_end = example_feature['doc_token2char_raw_end_index'][ example_predict['end_index'] - example_feature['offset']] predict_text = example_feature['context_text'][predict_start: predict_end + 1].strip() if predict_text in is_visited: continue is_visited.add(predict_text) itm = { 'predict_text': predict_text, 'start_prob': example_predict['start_prob'], 'end_prob': example_predict['end_prob'], 'predict_score': example_predict['predict_score'] } example_top_predicts.append(itm) if len(example_top_predicts) == 0: example_top_predicts.append( { 'predict_text': "", 'start_prob': 0., 'end_prob': 0., 'predict_score': 0. } ) example_best_predict = example_top_predicts[0] cur_f1 = calc_f1_score(list(answers), example_best_predict['predict_text']) cur_em = calc_em_score(list(answers), example_best_predict['predict_text']) f1 += cur_f1 em += cur_em # debug if cur_f1 == 0 or cur_em == 0: example_output = {} example_output.update(example_best_predict) example_output['question'] = examples[0]['question_text'] example_output['answer'] = answers example_output['f1'] = cur_f1 example_output['em'] = cur_em print(example_output) # total_count = len(qas_id_to_examples) f1_score = f1 / total_count em_score = em / total_count cm = ConfusionMatrix(ground_truth_for_impossible, predictions_for_impossible) logs = {} logs['skip_count'] = skip_count logs['total'] = total_count logs['val_f1_score'] = f1_score logs['val_em_score'] = em_score logs['val_f1_em_avg_score'] = (em_score * self.weights[0] + f1_score * self.weights[1]) / sum(self.weights[:2]) logs['val_f1_for_impossible'] = cm.avg_f1_score(average='macro') logs['val_accuracy_for_impossible'] = cm.overall_accuracy() logs['val_f1_avg_score'] = (em_score * self.weights[0] + f1_score * self.weights[1] + logs['val_f1_for_impossible'] * self.weights[2]) / sum(self.weights) return logs

# -*- coding: utf-8 -*- # @Time : 2020-07-30 15:06 # @Author : yingyuankai # @Email : <EMAIL> # @File : qa_evaluators.py import os import logging import numpy as np import tensorflow as tf import json from pprint import pprint from collections import defaultdict from aispace.utils.eval_utils import calc_em_score, calc_f1_score from aispace.utils.io_utils import save_json from aispace.utils.print_utils import print_boxed from aispace.utils.metrics_utils import ConfusionMatrix __all__ = [ 'EvaluatorForQaSimple', 'EvaluatorForQaWithImpossible' ] logger = logging.getLogger(__name__) class EvaluatorForQaSimple(tf.keras.callbacks.Callback): """ start_top_log_prob and end_top_log_prob's shape is [batch, k] ref: https://keras.io/examples/nlp/text_extraction_with_bert/ """ def __init__(self, validation_dataset, validation_steps, test_dataset, test_steps, report_dir, max_answer_length=64, n_best_size=5): self.validation_dataset = validation_dataset self.validation_steps = validation_steps self.test_dataset = test_dataset self.test_steps = test_steps self.max_answer_length = max_answer_length self.n_best_size = n_best_size self.report_dir = report_dir def on_epoch_end(self, epoch, logs=None): new_logs = self.eval_process(self.validation_dataset, self.validation_steps) logs = logs or {} logs.update(new_logs) print(f"Epoch: {epoch + 1}, val_f1_score: {logs['val_f1_score']:.4f}, val_em_score: {logs['val_em_score']:.4f}, " f"val_f1_em_avg_score: {logs['val_f1_em_avg_score']:.4f}") def on_train_end(self, logs=None): logger.info("Start Evaluate.") if not os.path.exists(self.report_dir): os.makedirs(self.report_dir) new_logs = self.eval_process(self.test_dataset, self.test_steps) save_json(os.path.join(self.report_dir, 'performance.json'), new_logs) print_boxed(f"Question Answer Evaluation") pprint(new_logs) logger.info(f"Save question answer reports in {self.report_dir}") def eval_process(self, dataset, n_steps=None): f1 = 0 em = 0 total_count = 0 skip_count = 0 start_top_res, end_top_res, unique_id_res = self.model.predict(dataset, steps=n_steps) start_top_log_prob, start_top_index = start_top_res[:, :, 0], start_top_res[:, :, 1].astype(np.int) # [b, k] end_top_log_prob, end_top_index = end_top_res[:, :, 0], end_top_res[:, :, 1].astype(np.int) # [b, k] unique_id_res = unique_id_res.astype(np.int) # predict results results = {} for i in range(end_top_index.shape[0]): unique_id = unique_id_res[i][0] itm = { 'unique_id': unique_id, 'start_top_log_prob': start_top_log_prob[i], 'start_top_index': start_top_index[i], 'end_top_log_prob': end_top_log_prob[i], 'end_top_index': end_top_index[i], } results[unique_id] = itm # raw inputs start_n_top, end_n_top = start_top_index.shape[-1], end_top_index.shape[-1] qas_id_to_examples = defaultdict(list) unique_id_to_examples = {} for idx, (inputs, outputs) in enumerate(dataset): if n_steps is not None and idx >= n_steps: break unique_ids = inputs['unique_id'].numpy().astype(np.int).tolist() offsets = inputs['offset'].numpy().astype(np.int).tolist() qas_ids = inputs['qas_id'].numpy().astype(str).tolist() doc_token2char_raw_start_indexs = inputs['doc_token2char_raw_start_index'].numpy().astype(str).tolist() doc_token2char_raw_end_indexs = inputs['doc_token2char_raw_end_index'].numpy().astype(str).tolist() doc_token2doc_indexs = inputs['doc_token2doc_index'].numpy().astype(str).tolist() all_answers = inputs['all_answers'].numpy().astype(str).tolist() answer_texts = inputs['answer_text'].numpy().tolist() context_texts = inputs['context_text'].numpy().tolist() question_texts = inputs['question_text'].numpy().tolist() is_impossibles = inputs['is_impossible'].numpy().tolist() p_masks = inputs['p_mask'].numpy().astype(np.int).tolist() for t in range(len(unique_ids)): itm = { 'unique_id': unique_ids[t], 'qas_id': qas_ids[t], 'question_text': question_texts[t].decode("utf8"), 'context_text': context_texts[t].decode("utf8"), 'answer_text': answer_texts[t].decode("utf8"), 'all_answers': json.loads(all_answers[t]), 'doc_token2char_raw_start_index': json.loads(doc_token2char_raw_start_indexs[t]), 'doc_token2char_raw_end_index': json.loads(doc_token2char_raw_end_indexs[t]), 'doc_token2doc_index': json.loads(doc_token2doc_indexs[t]), 'is_impossible': is_impossibles[t], 'p_mask': p_masks[t], 'offset': offsets[t] } unique_id_to_examples[unique_ids[t]] = itm qas_id_to_examples[qas_ids[t]].append(itm) for qas_id, examples in qas_id_to_examples.items(): example_all_predicts = [] answers = set() for example in examples: cur_unique_id = example['unique_id'] if cur_unique_id not in results: continue if example['is_impossible'] == 1: continue # if example['answer_text'] not in answers: # answers.append(example['answer_text']) answers |= set(example['all_answers']) cur_result = results.get(cur_unique_id) cur_start_top_log_prob = cur_result['start_top_log_prob'] cur_start_top_index = cur_result['start_top_index'] cur_end_top_log_prob = cur_result['end_top_log_prob'] cur_end_top_index = cur_result['end_top_index'] cur_p_mask = example['p_mask'] for i in range(start_n_top): start_prob = cur_start_top_log_prob[i] start_index = cur_start_top_index[i] if not cur_p_mask[start_index]: continue for j in range(end_n_top): end_prob = cur_end_top_log_prob[j] end_index = cur_end_top_index[j] if not cur_p_mask[end_index]: continue answer_length = end_index - start_index + 1 if end_index < start_index or answer_length > self.max_answer_length: continue itm = { 'unique_id': cur_unique_id, 'start_prob': start_prob, 'start_index': start_index, 'end_prob': end_prob, 'end_index': end_index, 'predict_score': np.log(start_prob) + np.log(end_prob) } example_all_predicts.append(itm) if len(answers) != 0: total_count += 1 else: skip_count += 1 continue example_all_predicts.sort(key=lambda s: s['predict_score'], reverse=True) example_top_predicts = [] is_visited = set() for example_predict in example_all_predicts: if len(example_top_predicts) >= self.n_best_size: break example_feature = unique_id_to_examples[example_predict['unique_id']] if example_predict['start_index'] - example_feature['offset'] < 0 or example_predict['end_index'] - example_feature['offset'] < 0: predict_text = "" else: predict_start = example_feature['doc_token2char_raw_start_index'][ example_predict['start_index'] - example_feature['offset']] predict_end = example_feature['doc_token2char_raw_end_index'][ example_predict['end_index'] - example_feature['offset']] predict_text = example_feature['context_text'][predict_start: predict_end + 1].strip() if predict_text in is_visited: continue is_visited.add(predict_text) itm = { 'predict_text': predict_text, 'start_prob': example_predict['start_prob'], 'end_prob': example_predict['end_prob'], 'predict_score': example_predict['predict_score'] } example_top_predicts.append(itm) if len(example_top_predicts) == 0: example_top_predicts.append( { 'predict_text': "", 'start_prob': 0., 'end_prob': 0., 'predict_score': 0. } ) example_best_predict = example_top_predicts[0] cur_f1 = calc_f1_score(list(answers), example_best_predict['predict_text']) cur_em = calc_em_score(list(answers), example_best_predict['predict_text']) f1 += cur_f1 em += cur_em # debug if cur_f1 == 0 or cur_em == 0: example_output = {} example_output.update(example_best_predict) example_output['question'] = examples[0]['question_text'] example_output['answer'] = answers example_output['f1'] = cur_f1 example_output['em'] = cur_em print(example_output) # total_count = len(qas_id_to_examples) f1_score = f1 / total_count em_score = em / total_count logs = {} logs['skip_count'] = skip_count logs['total'] = total_count logs['val_f1_score'] = f1_score logs['val_em_score'] = em_score logs['val_f1_em_avg_score'] = (em_score + f1_score) / 2. return logs class EvaluatorForQaWithImpossible(tf.keras.callbacks.Callback): """ start_top_log_prob and end_top_log_prob's shape is [batch, k, k] ref: https://keras.io/examples/nlp/text_extraction_with_bert/ """ def __init__(self, validation_dataset, validation_steps, test_dataset, test_steps, report_dir, max_answer_length=64, n_best_size=5, is_impossible_threshold=0.5, weights=[1., 1., 1.]): self.validation_dataset = validation_dataset self.validation_steps = validation_steps self.test_dataset = test_dataset self.test_steps = test_steps self.max_answer_length = max_answer_length self.n_best_size = n_best_size self.report_dir = report_dir self.is_impossible_threshold = is_impossible_threshold self.weights = weights def on_epoch_end(self, epoch, logs=None): new_logs = self.eval_process(self.validation_dataset, self.validation_steps) logs = logs or {} logs.update(new_logs) print(f"\nEpoch: {epoch + 1}, val_f1_score: {logs['val_f1_score']:.4f}, " f"val_em_score: {logs['val_em_score']:.4f}, " f"val_f1_em_avg_score: {logs['val_f1_em_avg_score']:.4f}," f" val_f1_for_impossible: {logs['val_f1_for_impossible']:.4f}," f" val_f1_avg_score: {logs['val_f1_avg_score']:.4f},") def on_train_end(self, logs=None): logger.info("Start Evaluate.") if not os.path.exists(self.report_dir): os.makedirs(self.report_dir) new_logs = self.eval_process(self.test_dataset, self.test_steps) save_json(os.path.join(self.report_dir, 'performance.json'), new_logs) print_boxed(f"Question Answer Evaluation") pprint(new_logs) logger.info(f"Save question answer reports in {self.report_dir}") def eval_process(self, dataset, n_steps=None): f1 = 0 em = 0 total_count = 0 skip_count = 0 start_top_res, end_top_res, answer_prob, unique_id_res = self.model.predict(dataset, steps=n_steps) start_top_log_prob, start_top_index = start_top_res[:, :, 0], start_top_res[:, :, 1].astype(np.int) # [b, k] end_top_log_prob, end_top_index = end_top_res[:, :, :, 0], end_top_res[:, :, :, 1].astype(np.int) # [b, k, k] unique_id_res = unique_id_res.astype(np.int) # predict results results = {} for i in range(end_top_index.shape[0]): unique_id = unique_id_res[i][0] itm = { 'unique_id': unique_id, 'start_top_log_prob': start_top_log_prob[i], 'start_top_index': start_top_index[i], 'end_top_log_prob': end_top_log_prob[i], 'end_top_index': end_top_index[i], 'is_impossible_prob': answer_prob[i][0] } results[unique_id] = itm # raw inputs start_n_top, end_n_top = end_top_index.shape[1:] qas_id_to_examples = defaultdict(list) unique_id_to_examples = {} for idx, (inputs, outputs) in enumerate(dataset): if n_steps is not None and idx >= n_steps: break unique_ids = inputs['unique_id'].numpy().astype(np.int).tolist() offsets = inputs['offset'].numpy().astype(np.int).tolist() qas_ids = inputs['qas_id'].numpy().astype(str).tolist() doc_token2char_raw_start_indexs = inputs['doc_token2char_raw_start_index'].numpy().astype(str).tolist() doc_token2char_raw_end_indexs = inputs['doc_token2char_raw_end_index'].numpy().astype(str).tolist() doc_token2doc_indexs = inputs['doc_token2doc_index'].numpy().astype(str).tolist() all_answers = inputs['all_answers'].numpy().astype(str).tolist() answer_texts = inputs['answer_text'].numpy().tolist() context_texts = inputs['context_text'].numpy().tolist() question_texts = inputs['question_text'].numpy().tolist() is_impossibles = inputs['is_impossible'].numpy().tolist() p_masks = inputs['p_mask'].numpy().astype(np.int).tolist() for t in range(len(unique_ids)): itm = { 'unique_id': unique_ids[t], 'qas_id': qas_ids[t], 'question_text': question_texts[t].decode("utf8"), 'context_text': context_texts[t].decode("utf8"), 'answer_text': answer_texts[t].decode("utf8"), 'all_answers': json.loads(all_answers[t]), 'doc_token2char_raw_start_index': json.loads(doc_token2char_raw_start_indexs[t]), 'doc_token2char_raw_end_index': json.loads(doc_token2char_raw_end_indexs[t]), 'doc_token2doc_index': json.loads(doc_token2doc_indexs[t]), 'is_impossible': is_impossibles[t], 'p_mask': p_masks[t], 'offset': offsets[t] } unique_id_to_examples[unique_ids[t]] = itm qas_id_to_examples[qas_ids[t]].append(itm) ground_truth_for_impossible, predictions_for_impossible = [], [] for qas_id, examples in qas_id_to_examples.items(): example_all_predicts = [] answers = set() for example in examples: cur_unique_id = example['unique_id'] if cur_unique_id not in results: continue # if example['answer_text'] not in answers: # answers.append(example['answer_text']) answers |= set(example['all_answers']) cur_result = results.get(cur_unique_id) cur_start_top_log_prob = cur_result['start_top_log_prob'] cur_start_top_index = cur_result['start_top_index'] cur_end_top_log_prob = cur_result['end_top_log_prob'] cur_end_top_index = cur_result['end_top_index'] ground_truth_for_impossible.append(example['is_impossible']) predictions_for_impossible.append(int(cur_result['is_impossible_prob'] >= self.is_impossible_threshold)) if example['is_impossible'] == 1: continue cur_p_mask = example['p_mask'] for i in range(start_n_top): start_prob = cur_start_top_log_prob[i] start_index = cur_start_top_index[i] if not cur_p_mask[start_index]: continue for j in range(end_n_top): end_prob = cur_end_top_log_prob[i, j] end_index = cur_end_top_index[i, j] if not cur_p_mask[end_index]: continue answer_length = end_index - start_index + 1 if end_index < start_index or answer_length > self.max_answer_length: continue itm = { 'unique_id': cur_unique_id, 'start_prob': start_prob, 'start_index': start_index, 'end_prob': end_prob, 'end_index': end_index, 'predict_score': np.log(end_prob) } example_all_predicts.append(itm) if len(answers) != 0 and "" not in answers: total_count += 1 else: skip_count += 1 continue example_all_predicts.sort(key=lambda s: s['predict_score'], reverse=True) example_top_predicts = [] is_visited = set() for example_predict in example_all_predicts: if len(example_top_predicts) >= self.n_best_size: break example_feature = unique_id_to_examples[example_predict['unique_id']] if example_predict['start_index'] - example_feature['offset'] < 0 or example_predict['end_index'] - example_feature['offset'] < 0: predict_text = "" else: predict_start = example_feature['doc_token2char_raw_start_index'][ example_predict['start_index'] - example_feature['offset']] predict_end = example_feature['doc_token2char_raw_end_index'][ example_predict['end_index'] - example_feature['offset']] predict_text = example_feature['context_text'][predict_start: predict_end + 1].strip() if predict_text in is_visited: continue is_visited.add(predict_text) itm = { 'predict_text': predict_text, 'start_prob': example_predict['start_prob'], 'end_prob': example_predict['end_prob'], 'predict_score': example_predict['predict_score'] } example_top_predicts.append(itm) if len(example_top_predicts) == 0: example_top_predicts.append( { 'predict_text': "", 'start_prob': 0., 'end_prob': 0., 'predict_score': 0. } ) example_best_predict = example_top_predicts[0] cur_f1 = calc_f1_score(list(answers), example_best_predict['predict_text']) cur_em = calc_em_score(list(answers), example_best_predict['predict_text']) f1 += cur_f1 em += cur_em # debug if cur_f1 == 0 or cur_em == 0: example_output = {} example_output.update(example_best_predict) example_output['question'] = examples[0]['question_text'] example_output['answer'] = answers example_output['f1'] = cur_f1 example_output['em'] = cur_em print(example_output) # total_count = len(qas_id_to_examples) f1_score = f1 / total_count em_score = em / total_count cm = ConfusionMatrix(ground_truth_for_impossible, predictions_for_impossible) logs = {} logs['skip_count'] = skip_count logs['total'] = total_count logs['val_f1_score'] = f1_score logs['val_em_score'] = em_score logs['val_f1_em_avg_score'] = (em_score * self.weights[0] + f1_score * self.weights[1]) / sum(self.weights[:2]) logs['val_f1_for_impossible'] = cm.avg_f1_score(average='macro') logs['val_accuracy_for_impossible'] = cm.overall_accuracy() logs['val_f1_avg_score'] = (em_score * self.weights[0] + f1_score * self.weights[1] + logs['val_f1_for_impossible'] * self.weights[2]) / sum(self.weights) return logs

en

0.509923

# -*- coding: utf-8 -*- # @Time : 2020-07-30 15:06 # @Author : yingyuankai # @Email : <EMAIL> # @File : qa_evaluators.py start_top_log_prob and end_top_log_prob's shape is [batch, k] ref: https://keras.io/examples/nlp/text_extraction_with_bert/ # [b, k] # [b, k] # predict results # raw inputs # if example['answer_text'] not in answers: # answers.append(example['answer_text']) # debug # total_count = len(qas_id_to_examples) start_top_log_prob and end_top_log_prob's shape is [batch, k, k] ref: https://keras.io/examples/nlp/text_extraction_with_bert/ # [b, k] # [b, k, k] # predict results # raw inputs # if example['answer_text'] not in answers: # answers.append(example['answer_text']) # debug # total_count = len(qas_id_to_examples)

2.171053

2

tests/conftest.py

junjunjunk/torchgpipe

532

10262

<reponame>junjunjunk/torchgpipe import pytest import torch @pytest.fixture(autouse=True) def manual_seed_zero(): torch.manual_seed(0) @pytest.fixture(scope='session') def cuda_sleep(): # Warm-up CUDA. torch.empty(1, device='cuda') # From test/test_cuda.py in PyTorch. start = torch.cuda.Event(enable_timing=True) end = torch.cuda.Event(enable_timing=True) start.record() torch.cuda._sleep(1000000) end.record() end.synchronize() cycles_per_ms = 1000000 / start.elapsed_time(end) def cuda_sleep(seconds): torch.cuda._sleep(int(seconds * cycles_per_ms * 1000)) return cuda_sleep def pytest_report_header(): return f'torch: {torch.__version__}'

import pytest import torch @pytest.fixture(autouse=True) def manual_seed_zero(): torch.manual_seed(0) @pytest.fixture(scope='session') def cuda_sleep(): # Warm-up CUDA. torch.empty(1, device='cuda') # From test/test_cuda.py in PyTorch. start = torch.cuda.Event(enable_timing=True) end = torch.cuda.Event(enable_timing=True) start.record() torch.cuda._sleep(1000000) end.record() end.synchronize() cycles_per_ms = 1000000 / start.elapsed_time(end) def cuda_sleep(seconds): torch.cuda._sleep(int(seconds * cycles_per_ms * 1000)) return cuda_sleep def pytest_report_header(): return f'torch: {torch.__version__}'

en

0.545309

# Warm-up CUDA. # From test/test_cuda.py in PyTorch.

2.126187

2

lib/python/treadmill/scheduler/__init__.py

drienyov/treadmill

0

10263

"""Treadmill hierarchical scheduler. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function from __future__ import unicode_literals import abc import collections import datetime import heapq import itertools import logging import operator import sys import time import enum import numpy as np import six _LOGGER = logging.getLogger(__name__) MAX_PRIORITY = 100 DEFAULT_RANK = 100 _UNPLACED_RANK = sys.maxsize DIMENSION_COUNT = None _MAX_UTILIZATION = float('inf') _GLOBAL_ORDER_BASE = time.mktime((2014, 1, 1, 0, 0, 0, 0, 0, 0)) # 21 day DEFAULT_SERVER_UPTIME = 21 * 24 * 60 * 60 # 1 day MIN_SERVER_UPTIME = 1 * 24 * 60 * 60 # 7 days DEFAULT_MAX_APP_LEASE = 7 * 24 * 60 * 60 # Default partition threshold DEFAULT_THRESHOLD = 0.9 # pylint: disable=C0302,too-many-lines def _bit_count(value): """Returns number of bits set. """ count = 0 while value: value &= value - 1 count += 1 return count def zero_capacity(): """Returns zero capacity vector. """ assert DIMENSION_COUNT is not None, 'Dimension count not set.' return np.zeros(DIMENSION_COUNT) def eps_capacity(): """Returns eps capacity vector. """ assert DIMENSION_COUNT is not None, 'Dimension count not set.' return np.array( [np.finfo(float).eps for _x in range(0, DIMENSION_COUNT)] ) def _global_order(): """Use timestamp in nanoseconds, from Jan 1st 2014, to break tie in scheduling conflicts for apps of the same priority, in a FIFO fashion. """ # Take the current EPOCH in nanosec global_order = int(time.time() * 1000000) - _GLOBAL_ORDER_BASE return global_order def utilization(demand, allocated, available): """Calculates utilization score. """ return np.max(np.subtract(demand, allocated) / available) def _all(oper, left, right): """Short circuit all for ndarray. """ return all( oper(ai, bi) for ai, bi in six.moves.zip(left, right) ) def _any(oper, left, right): """Short circuit any for ndarray. """ return any( oper(ai, bi) for ai, bi in six.moves.zip(left, right) ) def _any_eq(left, right): """Short circuit any eq for ndarray. """ return _any(operator.eq, left, right) def _any_isclose(left, right): """Short circuit any isclose for ndarray. """ return _any(np.isclose, left, right) def _any_lt(left, right): """Short circuit any lt for ndarray. """ return _any(operator.lt, left, right) def _any_le(left, right): """Short circuit any le for ndarray. """ return _any(operator.le, left, right) def _any_gt(left, right): """Short circuit any gt for ndarray. """ return _any(operator.gt, left, right) def _any_ge(left, right): """Short circuit any ge for ndarray. """ return _any(operator.ge, left, right) def _all_eq(left, right): """Short circuit all eq for ndarray. """ return _all(operator.eq, left, right) def _all_isclose(left, right): """Short circuit all isclose for ndarray. """ return _all(np.isclose, left, right) def _all_lt(left, right): """Short circuit all lt for ndarray. """ return _all(operator.lt, left, right) def _all_le(left, right): """Short circuit all le for ndarray. """ return _all(operator.le, left, right) def _all_gt(left, right): """Short circuit all gt for ndarray. """ return _all(operator.gt, left, right) def _all_ge(left, right): """Short circuit all ge for ndarray. """ return _all(operator.ge, left, right) class IdentityGroup: """Identity group. """ __slots__ = ( 'available', 'count', ) def __init__(self, count=0): self.count = count self.available = set(range(0, count)) def acquire(self): """Return next available identity or None. """ if self.available: return self.available.pop() else: return None def release(self, ident): """Mark identity as available. """ if ident < self.count: self.available.add(ident) def adjust(self, count): """Adjust identities with new count. If count is larger, add additional identities to the set. If count is lower, remove identities that are no longer valid. All apps that have invalid identities will be adjusted in the schedule cycle. """ if count >= self.count: self.available ^= set(six.moves.xrange(self.count, count)) else: self.available -= set(six.moves.xrange(count, self.count)) self.count = count class State(enum.Enum): """Enumeration of node/server states. """ # Ready to accept new applications. # TODO: Fix attribute name up = 'up' # pylint: disable=invalid-name # Applications need to be migrated. down = 'down' # Existing applications can stay, but will not accept new. frozen = 'frozen' class Affinity: """Model affinity and affinity limits. """ __slots__ = ( 'name', 'limits', 'constraints', ) def __init__(self, name, limits=None): self.name = name self.limits = collections.defaultdict(lambda: float('inf')) if limits: self.limits.update(limits) # freeze affinity shape constraints. self.constraints = tuple([self.name] + sorted(self.limits.values())) class Application: """Application object. """ __slots__ = ( 'global_order', 'name', 'demand', 'affinity', 'priority', 'allocation', 'data_retention_timeout', 'server', 'lease', 'identity', 'identity_group', 'identity_group_ref', 'schedule_once', 'evicted', 'placement_expiry', 'renew', 'unschedule', 'final_rank', 'final_util', 'constraints', ) def __init__(self, name, priority, demand, affinity, affinity_limits=None, data_retention_timeout=0, lease=0, identity_group=None, identity=None, schedule_once=False): self.global_order = _global_order() self.allocation = None self.server = None self.name = name self.affinity = Affinity(affinity, affinity_limits) self.priority = priority self.demand = np.array(demand, dtype=float) self.data_retention_timeout = data_retention_timeout self.lease = lease self.identity_group = identity_group self.identity = identity self.identity_group_ref = None self.schedule_once = schedule_once self.evicted = False self.unschedule = False self.placement_expiry = None self.renew = False def shape(self): """Return tuple of application (constraints, demand). Application shape is tuple of constraints that affect application placement. Currently this includes affinity constraints and app lease time. """ constraints = (self.affinity.constraints + (self.lease,)) if self.allocation: constraints += self.allocation.constraints return constraints, self.demand def acquire_identity(self): """Try to acquire identity if belong to the group. Returns True if successfull or if identity group is none. """ if not self.identity_group_ref: return True if self.identity is None: self.identity = self.identity_group_ref.acquire() _LOGGER.info('Acquired identity: %s: %s - %s', self.name, self.identity_group, self.identity) return self.identity is not None def release_identity(self): """Release app identity. """ if self.identity_group_ref and self.identity is not None: self.identity_group_ref.release(self.identity) self.identity = None def force_set_identity(self, identity): """Force identity of the app. """ if identity is not None: assert self.identity_group_ref self.identity = identity self.identity_group_ref.available.discard(identity) def has_identity(self): """Checks if app has identity if identity group is specified. """ return self.identity_group_ref is None or self.identity is not None @property def traits(self): """The app traits are derived from allocation. """ if self.allocation is None: return 0 else: return self.allocation.traits @six.add_metaclass(abc.ABCMeta) class Strategy: """Base class for all placement strategies. """ @abc.abstractmethod def suggested_node(self): """Suggested node that should be tried first. """ pass @abc.abstractmethod def next_node(self): """Next node to try, if previous suggestion was rejected. """ pass class SpreadStrategy(Strategy): """Spread strategy will suggest new node for each subsequent placement. """ __slots__ = ( 'current_idx', 'node', ) def __init__(self, node): self.current_idx = 0 self.node = node def suggested_node(self): """Suggest next node from the cycle. """ for _ in six.moves.xrange(0, len(self.node.children)): if self.current_idx == len(self.node.children): self.current_idx = 0 current = self.node.children[self.current_idx] self.current_idx += 1 if current: return current # Not a single non-none node. return None def next_node(self): """Suggest next node from the cycle. """ return self.suggested_node() class PackStrategy(Strategy): """Pack strategy will suggest same node until it is full. """ __slots__ = ( 'current_idx', 'node', ) def __init__(self, node): self.current_idx = 0 self.node = node def suggested_node(self): """Suggest same node as previous placement. """ for _ in six.moves.xrange(0, len(self.node.children)): if self.current_idx == len(self.node.children): self.current_idx = 0 node = self.node.children[self.current_idx] if node: return node return None def next_node(self): """Suggest next node from the cycle. """ self.current_idx += 1 return self.suggested_node() class TraitSet: """Hierarchical set of traits. """ __slots__ = ( 'self_traits', 'children_traits', 'traits', ) def __init__(self, traits=0): if not traits: traits = 0 # Private traits. assert isinstance(traits, six.integer_types) self.self_traits = traits # Union of all children traits. self.children_traits = dict() self._recalculate() def _recalculate(self): """Calculate combined set of all traits. """ self.traits = self.self_traits for trait in six.itervalues(self.children_traits): self.traits |= trait def has(self, traits): """Check if all traits are present. """ return (self.traits & traits) == traits def add(self, child, traits): """Add a child with given traits. """ # Update children traits. self.children_traits[child] = traits self._recalculate() def remove(self, child): """Remove child traits from the list. """ if child in self.children_traits: del self.children_traits[child] self._recalculate() def is_same(self, other): """Compares own traits, ignore child. """ return self.self_traits == other.self_traits class AffinityCounter: """Manages affinity count. """ __slots__ = ( 'affinity_counter', ) def __init__(self): self.affinity_counter = collections.Counter() class Node: """Abstract placement node. """ __slots__ = ( 'name', 'level', 'free_capacity', 'parent', 'children', 'children_by_name', 'traits', 'labels', 'affinity_counters', 'valid_until', '_state', '_state_since', ) def __init__(self, name, traits, level, valid_until=0): self.name = name self.level = level self.free_capacity = zero_capacity() self.parent = None self.children = list() self.children_by_name = dict() self.traits = TraitSet(traits) self.labels = set() self.affinity_counters = collections.Counter() self.valid_until = valid_until self._state = State.up self._state_since = time.time() def empty(self): """Return true if there are no children. """ return not bool(self.children_by_name) def children_iter(self): """Iterate over active children. """ for child in self.children: if child: yield child def get_state(self): """Returns tuple of (state, since). """ return self. _state, self._state_since def set_state(self, state, since): """Sets the state and time since. """ if self._state is not state: self._state_since = since self._state = state _LOGGER.debug('state: %s - (%s, %s)', self.name, self._state, self._state_since) @property def state(self): """Return current state. """ return self._state @state.setter def state(self, new_state): """Set node state and records time. """ self.set_state(new_state, time.time()) def add_child_traits(self, node): """Recursively add child traits up. """ self.traits.add(node.name, node.traits.traits) if self.parent: self.parent.remove_child_traits(self.name) self.parent.add_child_traits(self) def adjust_valid_until(self, child_valid_until): """Recursively adjust valid until time. """ if child_valid_until: self.valid_until = max(self.valid_until, child_valid_until) else: if self.empty(): self.valid_until = 0 else: self.valid_until = max([node.valid_until for node in self.children_iter()]) if self.parent: self.parent.adjust_valid_until(child_valid_until) def remove_child_traits(self, node_name): """Recursively remove child traits up. """ self.traits.remove(node_name) if self.parent: self.parent.remove_child_traits(self.name) self.parent.add_child_traits(self) def reset_children(self): """Reset children to empty list. """ for child in self.children_iter(): child.parent = None self.children = list() self.children_by_name = dict() def add_node(self, node): """Add child node, set the traits and propagate traits up. """ assert node.parent is None assert node.name not in self.children_by_name node.parent = self self.children.append(node) self.children_by_name[node.name] = node self.add_child_traits(node) self.increment_affinity(node.affinity_counters) self.add_labels(node.labels) self.adjust_valid_until(node.valid_until) def add_labels(self, labels): """Recursively add labels to self and parents. """ self.labels.update(labels) if self.parent: self.parent.add_labels(self.labels) def remove_node(self, node): """Remove child node and adjust the traits. """ assert node.name in self.children_by_name del self.children_by_name[node.name] for idx in six.moves.xrange(0, len(self.children)): if self.children[idx] == node: self.children[idx] = None self.remove_child_traits(node.name) self.decrement_affinity(node.affinity_counters) self.adjust_valid_until(None) node.parent = None return node def remove_node_by_name(self, nodename): """Removes node by name. """ assert nodename in self.children_by_name return self.remove_node(self.children_by_name[nodename]) def check_app_constraints(self, app): """Find app placement on the node. """ if app.allocation is not None: if app.allocation.label not in self.labels: _LOGGER.info('Missing label: %s on %s', app.allocation.label, self.name) return False if app.traits != 0 and not self.traits.has(app.traits): _LOGGER.info('Missing traits: %s on %s', app.traits, self.name) return False if not self.check_app_affinity_limit(app): return False if _any_gt(app.demand, self.free_capacity): _LOGGER.info('Not enough free capacity: %s', self.free_capacity) return False return True def check_app_affinity_limit(self, app): """Check app affinity limits """ count = self.affinity_counters[app.affinity.name] limit = app.affinity.limits[self.level] return count < limit def put(self, _app): """Abstract method, should never be called. """ raise Exception('Not implemented.') def size(self, label): """Returns total capacity of the children. """ if self.empty() or label not in self.labels: return eps_capacity() return np.sum([ n.size(label) for n in self.children_iter()], 0) def members(self): """Return set of all leaf node names. """ names = dict() for node in self.children_iter(): names.update(node.members()) return names def increment_affinity(self, counters): """Increment affinity counters recursively. """ self.affinity_counters.update(counters) if self.parent: self.parent.increment_affinity(counters) def decrement_affinity(self, counters): """Decrement affinity counters recursively. """ self.affinity_counters.subtract(counters) if self.parent: self.parent.decrement_affinity(counters) class Bucket(Node): """Collection of nodes/buckets. """ __slots__ = ( 'affinity_strategies', 'traits', ) _default_strategy_t = SpreadStrategy def __init__(self, name, traits=0, level=None): super(Bucket, self).__init__(name, traits, level) self.affinity_strategies = dict() self.traits = TraitSet(traits) def set_affinity_strategy(self, affinity, strategy_t): """Initilaizes placement strategy for given affinity. """ self.affinity_strategies[affinity] = strategy_t(self) def get_affinity_strategy(self, affinity): """Returns placement strategy for the affinity, defaults to spread. """ if affinity not in self.affinity_strategies: self.set_affinity_strategy(affinity, Bucket._default_strategy_t) return self.affinity_strategies[affinity] def adjust_capacity_up(self, new_capacity): """Node can only increase capacity. """ self.free_capacity = np.maximum(self.free_capacity, new_capacity) if self.parent: self.parent.adjust_capacity_up(self.free_capacity) def adjust_capacity_down(self, prev_capacity=None): """Called when capacity is decreased. """ if self.empty(): self.free_capacity = zero_capacity() if self.parent: self.parent.adjust_capacity_down() else: if prev_capacity is not None and _all_lt(prev_capacity, self.free_capacity): return free_capacity = zero_capacity() for child_node in self.children_iter(): if child_node.state is not State.up: continue free_capacity = np.maximum(free_capacity, child_node.free_capacity) # If resulting free_capacity is less the previous, we need to # adjust the parent, otherwise, nothing needs to be done. prev_capacity = self.free_capacity.copy() if _any_lt(free_capacity, self.free_capacity): self.free_capacity = free_capacity if self.parent: self.parent.adjust_capacity_down(prev_capacity) def add_node(self, node): """Adds node to the bucket. """ super(Bucket, self).add_node(node) self.adjust_capacity_up(node.free_capacity) def remove_node(self, node): """Removes node from the bucket. """ super(Bucket, self).remove_node(node) # if _any_isclose(self.free_capacity, node.free_capacity): self.adjust_capacity_down(node.free_capacity) return node def put(self, app): """Try to put app on one of the nodes that belong to the bucket. """ # Check if it is feasible to put app on some node low in the # hierarchy _LOGGER.debug('bucket.put: %s => %s', app.name, self.name) if not self.check_app_constraints(app): return False strategy = self.get_affinity_strategy(app.affinity.name) node = strategy.suggested_node() if node is None: _LOGGER.debug('All nodes in the bucket deleted.') return False nodename0 = node.name first = True while True: # End of iteration. if not first and node.name == nodename0: _LOGGER.debug('Finished iterating on: %s.', self.name) break first = False _LOGGER.debug('Trying node: %s:', node.name) if node.state is not State.up: _LOGGER.debug('Node not up: %s, %s', node.name, node.state) else: if node.put(app): return True node = strategy.next_node() return False class Server(Node): """Server object, final app placement. """ __slots__ = ( 'init_capacity', 'apps', 'up_since', 'presence_id', ) def __init__(self, name, capacity, up_since=0, valid_until=0, traits=0, label=None, presence_id=None): super(Server, self).__init__(name, traits=traits, level='server', valid_until=valid_until) self.labels = set([label]) self.init_capacity = np.array(capacity, dtype=float) self.free_capacity = self.init_capacity.copy() self.apps = dict() self.up_since = up_since self.presence_id = presence_id def __str__(self): return 'server: %s %s' % (self.name, self.init_capacity) def is_same(self, other): """Compares capacity and traits against another server. valid_until is ignored, as server comes up after reboot will have different valid_until value. """ return (self.labels == other.labels and _all_eq(self.init_capacity, other.init_capacity) and self.traits.is_same(other.traits)) def put(self, app): """Tries to put the app on the server. """ assert app.name not in self.apps _LOGGER.debug('server.put: %s => %s', app.name, self.name) if not self.check_app_lifetime(app): return False if not self.check_app_constraints(app): return False prev_capacity = self.free_capacity.copy() self.free_capacity -= app.demand self.apps[app.name] = app self.increment_affinity([app.affinity.name]) app.server = self.name if self.parent: self.parent.adjust_capacity_down(prev_capacity) if app.placement_expiry is None: app.placement_expiry = time.time() + app.lease return True def restore(self, app, placement_expiry=None): """Put app back on the server, ignore app lifetime. """ _LOGGER.debug('server.restore: %s => %s (%s)', app.name, self.name, placement_expiry) lease = app.lease # If not explicit if placement_expiry is None: placement_expiry = app.placement_expiry app.lease = 0 rc = self.put(app) app.lease = lease app.placement_expiry = placement_expiry return rc def renew(self, app): """Try to extend the placement for app lease. """ can_renew = self.check_app_lifetime(app) if can_renew: app.placement_expiry = time.time() + app.lease return can_renew def check_app_lifetime(self, app): """Check if the app lease fits until server is rebooted. """ # app with 0 lease can be placed anywhere (ignore potentially # expired servers) if not app.lease: return True return time.time() + app.lease < self.valid_until def remove(self, app_name): """Removes app from the server. """ assert app_name in self.apps app = self.apps[app_name] del self.apps[app_name] app.server = None app.evicted = True app.unschedule = False app.placement_expiry = None self.free_capacity += app.demand self.decrement_affinity([app.affinity.name]) if self.parent: self.parent.adjust_capacity_up(self.free_capacity) def remove_all(self): """Remove all apps. """ # iterate over copy of the keys, as we are removing them in the loop. for appname in list(self.apps): self.remove(appname) def size(self, label): """Return server capacity. """ if label not in self.labels: return eps_capacity() return self.init_capacity def members(self): """Return set of all leaf node names. """ return {self.name: self} def set_state(self, state, since): """Change host state. """ if self.state is state: return super(Server, self).set_state(state, since) if state == State.up: if self.parent: self.parent.adjust_capacity_up(self.free_capacity) elif state in (State.down, State.frozen): if self.parent: self.parent.adjust_capacity_down(self.free_capacity) else: raise Exception('Invalid state: ' % state) class Allocation: """Allocation manages queue of apps sharing same reserved capacity. In reality allocation is tied to grn via application proid. Applications within the allocation are organized by application priority. Allocations are ranked, and the rank is used to globally order applications from different allocations into global queue. Default allocation has rank 100. Defining allocation with lower rank will result in all it's applications to be evaluated first regardless of utilization. This is used to model "system" applications that should be always present regardless of utilization. Allocation queue can be capped with max_utilization parameter. If set, it will specify the max_utilization which will be considered for scheduling. """ __slots__ = ( 'reserved', 'rank', 'rank_adjustment', 'traits', 'label', 'max_utilization', 'apps', 'sub_allocations', 'path', 'constraints', ) def __init__(self, reserved=None, rank=None, traits=None, max_utilization=None, partition=None): self.set_reserved(reserved) self.rank = None self.rank_adjustment = 0 self.traits = 0 self.label = partition self.max_utilization = _MAX_UTILIZATION self.reserved = zero_capacity() self.set_max_utilization(max_utilization) self.set_traits(traits) self.update(reserved, rank, 0) self.apps = dict() self.sub_allocations = dict() self.path = [] # Freeze shape constraintes. self.constraints = (self.label, self.traits,) @property def name(self): """Returns full allocation name. """ return '/'.join(self.path) def set_reserved(self, reserved): """Update reserved capacity. """ if reserved is None: self.reserved = zero_capacity() elif isinstance(reserved, int): assert reserved == 0 self.reserved = zero_capacity() elif isinstance(reserved, float): assert reserved == 0.0 self.reserved = zero_capacity() elif isinstance(reserved, list): assert len(reserved) == DIMENSION_COUNT self.reserved = np.array(reserved, dtype=float) elif isinstance(reserved, np.ndarray): self.reserved = reserved else: assert 'Unsupported type: %r' % type(reserved) def update(self, reserved, rank, rank_adjustment, max_utilization=None): """Updates allocation. """ if rank is not None: self.rank = rank else: self.rank = DEFAULT_RANK if rank_adjustment is not None: self.rank_adjustment = rank_adjustment self.set_reserved(reserved) self.set_max_utilization(max_utilization) def set_max_utilization(self, max_utilization): """Sets max_utilization, accounting for default None value. """ if max_utilization is not None: self.max_utilization = max_utilization else: self.max_utilization = _MAX_UTILIZATION def set_traits(self, traits): """Set traits, account for default None value. """ if not traits: self.traits = 0 else: self.traits = traits def add(self, app): """Add application to the allocation queue. Once added, the scheduler will make an attempt to place the app on one of the cell nodes. """ # Check that there are no duplicate app names. if app.name in self.apps: _LOGGER.warning( 'Duplicate app on alllocation queue: %s', app.name ) return app.allocation = self self.apps[app.name] = app def remove(self, name): """Remove application from the allocation queue. """ if name in self.apps: self.apps[name].allocation = None del self.apps[name] def priv_utilization_queue(self): """Returns tuples for sorted by global utilization. Apps in the queue are ordered by priority, insertion order. Adding or removing maintains invariant that apps utilization monotonically increases as well. Returns local prioritization queue in a tuple where first element is utilization ratio, so that this queue is suitable for merging into global priority queue. """ def _app_key(app): """Compares apps by priority, state, global index """ return (-app.priority, 0 if app.server else 1, app.global_order, app.name) prio_queue = sorted(six.viewvalues(self.apps), key=_app_key) acc_demand = zero_capacity() available = self.reserved + np.finfo(float).eps util_before = utilization(acc_demand, self.reserved, available) for app in prio_queue: acc_demand = acc_demand + app.demand util_after = utilization(acc_demand, self.reserved, available) # Priority 0 apps are treated specially - utilization is set to # max float. # # This ensures that they are at the end of the all queues. if app.priority == 0: util_before = _MAX_UTILIZATION util_after = _MAX_UTILIZATION # All things equal, already scheduled applications have priority # over pending. pending = 0 if app.server else 1 if util_after <= self.max_utilization - 1: rank = self.rank if util_before < 0: rank -= self.rank_adjustment else: rank = _UNPLACED_RANK entry = (rank, util_before, util_after, pending, app.global_order, app) util_before = util_after yield entry def utilization_queue(self, free_capacity, visitor=None): """Returns utilization queue including the sub-allocs. All app queues from self and sub-allocs are merged in standard order, and then utilization is recalculated based on total reserved capacity of this alloc and sub-allocs combined. The function maintains invariant that any app (self or inside sub-alloc with utilization < 1 will remain with utilzation < 1. """ total_reserved = self.total_reserved() queues = [ alloc.utilization_queue(free_capacity, visitor) for alloc in six.itervalues(self.sub_allocations) ] queues.append(self.priv_utilization_queue()) acc_demand = zero_capacity() available = total_reserved + free_capacity + np.finfo(float).eps util_before = utilization(acc_demand, total_reserved, available) for item in heapq.merge(*queues): rank, _u_before, _u_after, pending, order, app = item acc_demand = acc_demand + app.demand util_after = utilization(acc_demand, total_reserved, available) if app.priority == 0: util_before = _MAX_UTILIZATION util_after = _MAX_UTILIZATION # - lower rank allocations take precedence. # - for same rank, utilization takes precedence # - False < True, so for apps with same utilization we prefer # those that already running (False == not pending) # - Global order entry = (rank, util_before, util_after, pending, order, app) if visitor: visitor(self, entry, acc_demand) util_before = util_after yield entry def total_reserved(self): """Total reserved capacity including sub-allocs. """ return six.moves.reduce( lambda acc, alloc: acc + alloc.total_reserved(), six.itervalues(self.sub_allocations), self.reserved ) def add_sub_alloc(self, name, alloc): """Add child allocation. """ self.sub_allocations[name] = alloc assert not alloc.path alloc.path = self.path + [name] alloc.label = self.label def remove_sub_alloc(self, name): """Remove chlid allocation. """ if name in self.sub_allocations: del self.sub_allocations[name] def get_sub_alloc(self, name): """Return sub allocation, create empty if it does not exist. """ if name not in self.sub_allocations: self.add_sub_alloc(name, Allocation()) return self.sub_allocations[name] def all_apps(self): """Return all apps in allocation and sub-allocations.""" all_apps = list(six.itervalues(self.apps)) for alloc in six.itervalues(self.sub_allocations): all_apps.extend(alloc.all_apps()) return all_apps class Partition: """Cell partition. """ __slots__ = ( 'allocation', 'max_server_uptime', 'max_lease', 'threshold', 'label', '_reboot_buckets', '_reboot_dates', '_reboot_last', ) def __init__(self, max_server_uptime=None, max_lease=None, threshold=None, label=None, reboot_schedule=None, now=None): self.label = label self.allocation = Allocation(partition=label) # Default - if not max_server_uptime: max_server_uptime = DEFAULT_SERVER_UPTIME if not max_lease: max_lease = DEFAULT_MAX_APP_LEASE if not threshold: threshold = DEFAULT_THRESHOLD self.max_server_uptime = max_server_uptime self.max_lease = max_lease self.threshold = threshold if not reboot_schedule: # reboot every day reboot_schedule = {day: (23, 59, 59) for day in range(7)} if not now: now = time.time() self._reboot_dates = reboot_dates( reboot_schedule, start_date=datetime.date.fromtimestamp(now) ) self._reboot_buckets = [] self._reboot_last = now self.tick(now) def _find_bucket(self, timestamp): """Try to find bucket with given timestamp. """ for bucket in self._reboot_buckets: if bucket.timestamp == timestamp: return bucket return None def add(self, server, timestamp=None): """Add server. """ bucket = None if timestamp: bucket = self._find_bucket(timestamp) # servers with larger than max lifetime should be rebooted at # the next opportunity if (self._reboot_buckets[0].timestamp > server.up_since + DEFAULT_SERVER_UPTIME): bucket = self._reboot_buckets[0] if not bucket: bucket = min(reversed(self._reboot_buckets), key=lambda b: b.cost(server)) bucket.add(server) def remove(self, server): """Remove server. """ for bucket in self._reboot_buckets: bucket.remove(server) def tick(self, now): """Do per-tick-bookkeeping. """ while self._reboot_last <= now + DEFAULT_SERVER_UPTIME: bucket = RebootBucket(next(self._reboot_dates)) self._reboot_buckets.append(bucket) self._reboot_last = bucket.timestamp while self._reboot_buckets[0].timestamp < now: self._reboot_buckets.pop(0) class PartitionDict(dict): """Dict that creates partitions on demand. We use this instead of collections.defaultdict so that we can provide the new partition with its label, to be propagated to its allocations. """ def __missing__(self, label): """Create a new partition, passing the label to its constructor. """ self[label] = Partition(label=label) return self[label] # pylint: disable=invalid-name def reboot_dates(schedule, start_date=None): """Generate list of valid reboot dates. """ date = datetime.date.today() if start_date: date = start_date while True: weekday = date.weekday() if weekday in schedule: h, m, s = schedule[weekday] yield time.mktime((date.year, date.month, date.day, h, m, s, 0, 0, 0)) date += datetime.timedelta(days=1) class RebootBucket: """Bucket of servers to be rebooted at the same time. """ __slots__ = ( 'timestamp', 'servers', ) def __init__(self, timestamp): self.timestamp = timestamp self.servers = [] def add(self, server): """Add server to this bucket. """ self.servers.append(server) server.valid_until = self.timestamp _LOGGER.info('Setting valid until on server: %s %s', server.name, server.valid_until) def remove(self, server): """Remove server from this bucket. """ try: self.servers.remove(server) except ValueError: pass def cost(self, server): """The cost of adding server to this bucket. """ if self.timestamp > server.up_since + DEFAULT_SERVER_UPTIME: return float('inf') if self.timestamp < server.up_since + MIN_SERVER_UPTIME: return float('inf') return len(self.servers) class PlacementFeasibilityTracker: """Tracks similar apps placement failures.""" def __init__(self): self.recorder = dict() def feasible(self, app): """Checks if it is feasible to satisfy demand.""" constraints, demand = app.shape() if constraints in self.recorder: # If demand is >= than recorded failure, placement is not feasible. if _all_ge(demand, self.recorder[constraints]): return False return True def adjust(self, app): """Adjust info about failed placement.""" constraints, demand = app.shape() if constraints not in self.recorder: self.recorder[constraints] = demand else: if _all_le(demand, self.recorder[constraints]): self.recorder[constraints] = demand class Cell(Bucket): """Top level node. """ __slots__ = ( 'partitions', 'next_event_at', 'apps', 'identity_groups', ) def __init__(self, name): super(Cell, self).__init__(name, traits=0, level='cell') self.partitions = PartitionDict() self.apps = dict() self.identity_groups = collections.defaultdict(IdentityGroup) self.next_event_at = np.inf def add_app(self, allocation, app): """Adds application to the scheduled list. """ assert allocation is not None if app.allocation: app.allocation.remove(app.name) allocation.add(app) self.apps[app.name] = app if app.identity_group: app.identity_group_ref = self.identity_groups[app.identity_group] def remove_app(self, appname): """Remove app from scheduled list. """ if appname not in self.apps: return app = self.apps[appname] servers = self.members() if app.server in servers: servers[app.server].remove(app.name) if app.allocation: app.allocation.remove(app.name) app.release_identity() del self.apps[appname] def configure_identity_group(self, name, count): """Add identity group to the cell. """ if name not in self.identity_groups: self.identity_groups[name] = IdentityGroup(count) else: self.identity_groups[name].adjust(count) def remove_identity_group(self, name): """Remove identity group. """ ident_group = self.identity_groups.get(name) if ident_group: in_use = False for app in six.itervalues(self.apps): if app.identity_group_ref == ident_group: ident_group.adjust(0) in_use = True break if not in_use: del self.identity_groups[name] def _fix_invalid_placements(self, queue, servers): """If app is placed on non-existent server, set server to None. """ for app in queue: if app.server and app.server not in servers: app.server = None app.evicted = True app.release_identity() def _record_rank_and_util(self, queue): """Set final rank and utilization for all apps in the queue. """ for item in queue: rank = item[0] util = item[1] app = item[-1] app.final_rank = rank app.final_util = util def _fix_invalid_identities(self, queue, servers): """Check that app identity is valid for given identity group. """ for app in queue: if app.identity is not None and app.identity_group_ref is not None: # Can happen if identity group was adjusted to lower count. if app.identity >= app.identity_group_ref.count: # Can't release identity as it is invalid. _LOGGER.info('Identity exceeds limit: %s - %s, limit %s', app.name, app.identity, app.identity_group_ref.count) app.identity = None # Invalidate any existing placement. if app.server: servers[app.server].remove(app.name) def _handle_inactive_servers(self, servers): """Migrate apps from inactive servers. """ self.next_event_at = np.inf for server in six.itervalues(servers): state, since = server.get_state() to_be_moved = [] if state == State.down: _LOGGER.debug('Server state is down: %s', server.name) for name, app in six.iteritems(server.apps): if app.data_retention_timeout is None: expires_at = 0 else: expires_at = since + app.data_retention_timeout if expires_at <= time.time(): _LOGGER.debug('Expired placement: %s', name) app.release_identity() to_be_moved.append(name) else: _LOGGER.debug('Keep placement: %s until %s', name, expires_at) self.next_event_at = min(expires_at, self.next_event_at) elif state == State.frozen: _LOGGER.debug('Server state is frozen: %s', server.name) to_be_moved = [ name for name, app in six.iteritems(server.apps) if app.unschedule ] for name in to_be_moved: server.remove(name) def _find_placements(self, queue, servers): """Run the queue and find placements. """ # TODO: refactor to get rid of warnings. # # pylint: disable=too-many-branches,too-many-statements # # At this point, if app.server is defined, it points to attached # server. evicted = dict() reversed_queue = queue[::-1] placement_tracker = PlacementFeasibilityTracker() for app in queue: _LOGGER.debug('scheduling %s', app.name) if app.final_rank == _UNPLACED_RANK: if app.server: assert app.server in servers assert app.has_identity() servers[app.server].remove(app.name) app.release_identity() continue restore = {} if app.renew: assert app.server assert app.has_identity() assert app.server in servers server = servers[app.server] if not server.renew(app): # Save information that will be used to restore placement # in case renewal fails. _LOGGER.debug('Cannot renew app %s on server %s', app.name, app.server) restore['server'] = server restore['placement_expiry'] = app.placement_expiry server.remove(app.name) # At this point app was either renewed on the same server, or # temporarily removed from server if renew failed. # # If placement will be found, renew should remain False. If # placement will not be found, renew will be set to True when # placement is restored to the server it was running. app.renew = False if app.server: assert app.server in servers assert app.has_identity() continue assert app.server is None if not app.acquire_identity(): _LOGGER.info('Unable to acquire identity: %s, %s', app.name, app.identity_group) continue # If app was evicted before, try to restore to the same node. if app in evicted: assert app.has_identity() evicted_from, app_expiry = evicted[app] del evicted[app] if evicted_from.restore(app, app_expiry): app.evicted = False continue assert app.server is None if app.schedule_once and app.evicted: continue # Check if placement is feasible. if not placement_tracker.feasible(app): _LOGGER.info( 'Placement not feasible: %s %r', app.name, app.shape() ) continue if not self.put(app): # There is not enough capacity, from the end of the queue, # evict apps, freeing capacity. for evicted_app in reversed_queue: # We reached the app we can't place if evicted_app == app: break # The app is not yet placed, skip if not evicted_app.server: continue assert evicted_app.server in servers evicted_app_server = servers[evicted_app.server] # Do not consider servers that are not up. if evicted_app_server.state is not State.up: continue evicted[evicted_app] = (evicted_app_server, evicted_app.placement_expiry) evicted_app_server.remove(evicted_app.name) # TODO: we need to check affinity limit constraints on # each level, all the way to the top. if evicted_app_server.put(app): break # Placement failed. if not app.server: # If renewal attempt failed, restore previous placement and # expiry date. if restore: restore['server'].restore(app, restore['placement_expiry']) app.renew = True else: app.release_identity() placement_tracker.adjust(app) def schedule_alloc(self, allocation, servers): """Run the scheduler for given allocation. """ begin = time.time() size = self.size(allocation.label) util_queue = list(allocation.utilization_queue(size)) self._record_rank_and_util(util_queue) queue = [item[-1] for item in util_queue] self._find_placements(queue, servers) _LOGGER.info('Scheduled %s (%d) apps in %r', allocation.label, len(queue), time.time() - begin) def schedule(self): """Run the scheduler. """ begin = time.time() all_apps = [] for label, partition in six.iteritems(self.partitions): allocation = partition.allocation all_apps.extend(allocation.all_apps()) before = [(app.name, app.server, app.placement_expiry) for app in all_apps] servers = self.members() self._fix_invalid_placements(six.viewvalues(self.apps), servers) self._handle_inactive_servers(servers) self._fix_invalid_identities(six.viewvalues(self.apps), servers) for label, partition in six.iteritems(self.partitions): allocation = partition.allocation allocation.label = label self.schedule_alloc(allocation, servers) after = [(app.server, app.placement_expiry) for app in all_apps] placement = [ tuple(itertools.chain(b, a)) for b, a in six.moves.zip(before, after) ] for appname, s_before, exp_before, s_after, exp_after in placement: if s_before != s_after: _LOGGER.info('New placement: %s - %s => %s', appname, s_before, s_after) else: if exp_before != exp_after: _LOGGER.info('Renewed: %s [%s] - %s => %s', appname, s_before, exp_before, exp_after) _LOGGER.info('Total scheduler time for %s apps: %r (sec)', len(all_apps), time.time() - begin) return placement def resolve_reboot_conflicts(self): """Adjust server exipiration time to avoid conflicts. """ pass def dumps(cell): """Serializes cell to string. """ del cell return '' def loads(data): """Loads scheduler from string. """ del data assert False, 'not implemented.'

en

0.88203

Treadmill hierarchical scheduler. # 21 day # 1 day # 7 days # Default partition threshold # pylint: disable=C0302,too-many-lines Returns number of bits set. Returns zero capacity vector. Returns eps capacity vector. Use timestamp in nanoseconds, from Jan 1st 2014, to break tie in scheduling conflicts for apps of the same priority, in a FIFO fashion. # Take the current EPOCH in nanosec Calculates utilization score. Short circuit all for ndarray. Short circuit any for ndarray. Short circuit any eq for ndarray. Short circuit any isclose for ndarray. Short circuit any lt for ndarray. Short circuit any le for ndarray. Short circuit any gt for ndarray. Short circuit any ge for ndarray. Short circuit all eq for ndarray. Short circuit all isclose for ndarray. Short circuit all lt for ndarray. Short circuit all le for ndarray. Short circuit all gt for ndarray. Short circuit all ge for ndarray. Identity group. Return next available identity or None. Mark identity as available. Adjust identities with new count. If count is larger, add additional identities to the set. If count is lower, remove identities that are no longer valid. All apps that have invalid identities will be adjusted in the schedule cycle. Enumeration of node/server states. # Ready to accept new applications. # TODO: Fix attribute name # pylint: disable=invalid-name # Applications need to be migrated. # Existing applications can stay, but will not accept new. Model affinity and affinity limits. # freeze affinity shape constraints. Application object. Return tuple of application (constraints, demand). Application shape is tuple of constraints that affect application placement. Currently this includes affinity constraints and app lease time. Try to acquire identity if belong to the group. Returns True if successfull or if identity group is none. Release app identity. Force identity of the app. Checks if app has identity if identity group is specified. The app traits are derived from allocation. Base class for all placement strategies. Suggested node that should be tried first. Next node to try, if previous suggestion was rejected. Spread strategy will suggest new node for each subsequent placement. Suggest next node from the cycle. # Not a single non-none node. Suggest next node from the cycle. Pack strategy will suggest same node until it is full. Suggest same node as previous placement. Suggest next node from the cycle. Hierarchical set of traits. # Private traits. # Union of all children traits. Calculate combined set of all traits. Check if all traits are present. Add a child with given traits. # Update children traits. Remove child traits from the list. Compares own traits, ignore child. Manages affinity count. Abstract placement node. Return true if there are no children. Iterate over active children. Returns tuple of (state, since). Sets the state and time since. Return current state. Set node state and records time. Recursively add child traits up. Recursively adjust valid until time. Recursively remove child traits up. Reset children to empty list. Add child node, set the traits and propagate traits up. Recursively add labels to self and parents. Remove child node and adjust the traits. Removes node by name. Find app placement on the node. Check app affinity limits Abstract method, should never be called. Returns total capacity of the children. Return set of all leaf node names. Increment affinity counters recursively. Decrement affinity counters recursively. Collection of nodes/buckets. Initilaizes placement strategy for given affinity. Returns placement strategy for the affinity, defaults to spread. Node can only increase capacity. Called when capacity is decreased. # If resulting free_capacity is less the previous, we need to # adjust the parent, otherwise, nothing needs to be done. Adds node to the bucket. Removes node from the bucket. # if _any_isclose(self.free_capacity, node.free_capacity): Try to put app on one of the nodes that belong to the bucket. # Check if it is feasible to put app on some node low in the # hierarchy # End of iteration. Server object, final app placement. Compares capacity and traits against another server. valid_until is ignored, as server comes up after reboot will have different valid_until value. Tries to put the app on the server. Put app back on the server, ignore app lifetime. # If not explicit Try to extend the placement for app lease. Check if the app lease fits until server is rebooted. # app with 0 lease can be placed anywhere (ignore potentially # expired servers) Removes app from the server. Remove all apps. # iterate over copy of the keys, as we are removing them in the loop. Return server capacity. Return set of all leaf node names. Change host state. Allocation manages queue of apps sharing same reserved capacity. In reality allocation is tied to grn via application proid. Applications within the allocation are organized by application priority. Allocations are ranked, and the rank is used to globally order applications from different allocations into global queue. Default allocation has rank 100. Defining allocation with lower rank will result in all it's applications to be evaluated first regardless of utilization. This is used to model "system" applications that should be always present regardless of utilization. Allocation queue can be capped with max_utilization parameter. If set, it will specify the max_utilization which will be considered for scheduling. # Freeze shape constraintes. Returns full allocation name. Update reserved capacity. Updates allocation. Sets max_utilization, accounting for default None value. Set traits, account for default None value. Add application to the allocation queue. Once added, the scheduler will make an attempt to place the app on one of the cell nodes. # Check that there are no duplicate app names. Remove application from the allocation queue. Returns tuples for sorted by global utilization. Apps in the queue are ordered by priority, insertion order. Adding or removing maintains invariant that apps utilization monotonically increases as well. Returns local prioritization queue in a tuple where first element is utilization ratio, so that this queue is suitable for merging into global priority queue. Compares apps by priority, state, global index # Priority 0 apps are treated specially - utilization is set to # max float. # # This ensures that they are at the end of the all queues. # All things equal, already scheduled applications have priority # over pending. Returns utilization queue including the sub-allocs. All app queues from self and sub-allocs are merged in standard order, and then utilization is recalculated based on total reserved capacity of this alloc and sub-allocs combined. The function maintains invariant that any app (self or inside sub-alloc with utilization < 1 will remain with utilzation < 1. # - lower rank allocations take precedence. # - for same rank, utilization takes precedence # - False < True, so for apps with same utilization we prefer # those that already running (False == not pending) # - Global order Total reserved capacity including sub-allocs. Add child allocation. Remove chlid allocation. Return sub allocation, create empty if it does not exist. Return all apps in allocation and sub-allocations. Cell partition. # Default - # reboot every day Try to find bucket with given timestamp. Add server. # servers with larger than max lifetime should be rebooted at # the next opportunity Remove server. Do per-tick-bookkeeping. Dict that creates partitions on demand. We use this instead of collections.defaultdict so that we can provide the new partition with its label, to be propagated to its allocations. Create a new partition, passing the label to its constructor. # pylint: disable=invalid-name Generate list of valid reboot dates. Bucket of servers to be rebooted at the same time. Add server to this bucket. Remove server from this bucket. The cost of adding server to this bucket. Tracks similar apps placement failures. Checks if it is feasible to satisfy demand. # If demand is >= than recorded failure, placement is not feasible. Adjust info about failed placement. Top level node. Adds application to the scheduled list. Remove app from scheduled list. Add identity group to the cell. Remove identity group. If app is placed on non-existent server, set server to None. Set final rank and utilization for all apps in the queue. Check that app identity is valid for given identity group. # Can happen if identity group was adjusted to lower count. # Can't release identity as it is invalid. # Invalidate any existing placement. Migrate apps from inactive servers. Run the queue and find placements. # TODO: refactor to get rid of warnings. # # pylint: disable=too-many-branches,too-many-statements # # At this point, if app.server is defined, it points to attached # server. # Save information that will be used to restore placement # in case renewal fails. # At this point app was either renewed on the same server, or # temporarily removed from server if renew failed. # # If placement will be found, renew should remain False. If # placement will not be found, renew will be set to True when # placement is restored to the server it was running. # If app was evicted before, try to restore to the same node. # Check if placement is feasible. # There is not enough capacity, from the end of the queue, # evict apps, freeing capacity. # We reached the app we can't place # The app is not yet placed, skip # Do not consider servers that are not up. # TODO: we need to check affinity limit constraints on # each level, all the way to the top. # Placement failed. # If renewal attempt failed, restore previous placement and # expiry date. Run the scheduler for given allocation. Run the scheduler. Adjust server exipiration time to avoid conflicts. Serializes cell to string. Loads scheduler from string.

2.319293

2

banners/bannerRan.py

gothyyy/AIDungeon

1

10264

<filename>banners/bannerRan.py import random import sys import time import json import os import warnings import numpy as np import glob, os stat_mini = 1 stat_max = 0 listBanners = [] #HOW TO USE IT: #1 copy the opening.txt #2 remove the graphic (but do keep top logo for consistency) #3 add ASCII art that is 78 or less characters in width #4 save txt file under a complete new name class bannerRan: def __init__(self): banner_number = load_banner() #insert function to get random self.banner_number = banner_number def load_banner(): global stat_max global stat_mini global listBanners hey = scanBanners() #load text and get proper numbers choose_between = r(stat_mini, stat_max) x = random.choice(listBanners) return x def r(x,y): #randmom, picks between X and Y return int(str(random.randint(x,y))) def scanBanners(): global stat_max global listBanners dir_path = os.path.dirname(os.path.realpath(__file__)) # directory of banners path #os.chdir("") i = 0 for file in glob.glob("banners/*.txt"): i+=1 listBanners.append(file) #print(str(i), file) stat_max = i x = dir_path return x

en

0.72102

#HOW TO USE IT: #1 copy the opening.txt #2 remove the graphic (but do keep top logo for consistency) #3 add ASCII art that is 78 or less characters in width #4 save txt file under a complete new name #insert function to get random #load text and get proper numbers #randmom, picks between X and Y # directory of banners path #os.chdir("") #print(str(i), file)

3.18566

3

BondMarket/app/theme_lib.py

Meith0717/BondMarket

0

10265

<reponame>Meith0717/BondMarket<filename>BondMarket/app/theme_lib.py from dataclasses import dataclass @dataclass class theme: name : str bg_color : str fg_color : str lb_color : str ttk_theme : str LIGHT = theme( name='LIGHT', bg_color=None, fg_color='black', lb_color='#f0f0f0', ttk_theme='xpnative' ) DARK = theme( name='DARK', bg_color='#424242', fg_color='white', lb_color='#424242', ttk_theme='black' )

from dataclasses import dataclass @dataclass class theme: name : str bg_color : str fg_color : str lb_color : str ttk_theme : str LIGHT = theme( name='LIGHT', bg_color=None, fg_color='black', lb_color='#f0f0f0', ttk_theme='xpnative' ) DARK = theme( name='DARK', bg_color='#424242', fg_color='white', lb_color='#424242', ttk_theme='black' )

none

1

2.749645

3

run.py

rimijoker/CA-MTL

1

10266

import os import sys import re import json import logging import torch from transformers import ( HfArgumentParser, set_seed, AutoTokenizer, AutoConfig, EvalPrediction, ) from src.model.ca_mtl import CaMtl, CaMtlArguments from src.utils.misc import MultiTaskDataArguments, Split from src.mtl_trainer import MultiTaskTrainer, MultiTaskTrainingArguments from src.data.mtl_dataset import MultiTaskDataset from src.data.task_dataset import TaskDataset logger = logging.getLogger(__name__) def setup_logging(training_args): logging.basicConfig( format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN, ) logger.warning( "Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s", training_args.local_rank, training_args.device, training_args.n_gpu, bool(training_args.local_rank != -1), training_args.fp16, ) def parse_cmd_args(): parser = HfArgumentParser( ( CaMtlArguments, MultiTaskDataArguments, MultiTaskTrainingArguments, ) ) if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): model_args, data_args, training_args = parser.parse_json_file( json_file=os.path.abspath(sys.argv[1]) ) else: ( model_args, data_args, training_args, ) = parser.parse_args_into_dataclasses() logger.info("Training/evaluation parameters %s", training_args) return model_args, data_args, training_args def create_eval_datasets(mode, data_args, tokenizer): eval_datasets = {} for task_id, task_name in enumerate(data_args.tasks): eval_datasets[task_name] = TaskDataset( task_name, task_id, data_args, tokenizer, mode=mode ) if task_name == "mnli": # Loop to handle MNLI double evaluation (matched, mis-matched) eval_datasets["mnli-mm"] = TaskDataset( "mnli-mm", task_id, data_args, tokenizer, mode=mode ) return eval_datasets def main(): model_args, data_args, training_args = parse_cmd_args() setup_logging(training_args) set_seed(training_args.seed) config = AutoConfig.from_pretrained( CaMtl.get_base_model(model_args.model_name_or_path), ) model = CaMtl.from_pretrained( CaMtl.get_base_model(model_args.model_name_or_path), model_args, data_args, config=config) model.freeze_encoder_layers(model_args) logger.info(model) tokenizer = AutoTokenizer.from_pretrained( CaMtl.get_base_model(model_args.model_name_or_path), ) logger.info("Training tasks: %s", ", ".join([t for t in data_args.tasks])) trainer = MultiTaskTrainer( tokenizer, data_args, model=model, args=training_args, train_dataset=MultiTaskDataset(data_args, tokenizer, limit_length=50) if training_args.do_train else None, eval_datasets=create_eval_datasets(Split.dev, data_args, tokenizer) if training_args.do_eval or training_args.evaluate_during_training else None, test_datasets=create_eval_datasets(Split.test, data_args, tokenizer) if training_args.do_predict else None, ) if training_args.do_train: trainer.train( model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path) else None ) if training_args.do_eval: trainer.evaluate() if training_args.do_predict: trainer.predict() def _mp_fn(index): # For xla_spawn (TPUs) main() if __name__ == "__main__": main()

en

0.717475

# Loop to handle MNLI double evaluation (matched, mis-matched) # For xla_spawn (TPUs)

1.848349

2

examples/readWebsocket.py

uadlq/PhyPiDAQ-PiOS11

0

10267

#!/usr/bin/env python3 """Read data in CSV format from websocket """ import sys import asyncio import websockets # read url from command line if len(sys.argv) >= 2: uri = sys.argv[1] else: # host url and port uri = "ws://localhost:8314" print("*==* ", sys.argv[0], " Lese Daten von url ", uri) async def read_ws(): """asynchronous read from websocket """ async with websockets.connect(uri, ping_interval=None) as websocket: # test connection await websocket.send("req_connect") answ = await websocket.recv() if answ == "ack_connect": print("** connected to websocket ", uri) # get data await websocket.send("getData") while True: inp = await websocket.recv() if inp == '\n': # empty record, end print("empty input - closing") sys.exit(0) else: print('read: %s ' % inp, end='') # run web client asyncio.get_event_loop().run_until_complete(read_ws())

en

0.68723

#!/usr/bin/env python3 Read data in CSV format from websocket # read url from command line # host url and port asynchronous read from websocket # test connection # get data # empty record, end # run web client

3.16624

3

settings/__init__.py

arcana261/python-grpc-boilerplate

0

10268

<gh_stars>0 import os import sys import itertools import json _NONE = object() class SettingManager: _sentry = object() def __init__(self): self.env = os.getenv('ENV', 'prd') try: self._default = __import__('settings.default', fromlist=['*']) except ModuleNotFoundError: self._default = object() try: self._env = __import__('settings.{}'.format(self.env), fromlist=['*']) except ModuleNotFoundError: self._env = object() self._loaded = [] def load(self, filename, fmt='json'): filename = os.path.abspath(filename) if fmt == 'json': with open(filename) as f: self._loaded.append((filename, json.load(f))) def unload(self, filename): filename = os.path.abspath(filename) self._loaded = [(f, v) for f, v in self._loaded if f != filename] def __getattr__(self, item): result = SettingManager._sentry for _, values in self._loaded: if item in values: result = values[item] result = os.getenv(item, result) if result is SettingManager._sentry: result = getattr(self._env, item, getattr(self._default, item, SettingManager._sentry)) if result is SettingManager._sentry: raise AttributeError return result def __contains__(self, item): try: self.__getattr__(item) return True except AttributeError: return False def get(self, item, default=_NONE): try: return self.__getattr__(item) except AttributeError: if default is not _NONE: return default raise AttributeError def __iter__(self): chained = itertools.chain(getattr(self._default, '__dict__', dict()).keys(), getattr(self._env, '__dict__', dict()).keys()) for _, values in self._loaded: chained = itertools.chain(chained, values.keys()) return iter(filter(lambda x: not x.startswith('_'), set(chained))) sys.modules[__name__] = SettingManager()

import os import sys import itertools import json _NONE = object() class SettingManager: _sentry = object() def __init__(self): self.env = os.getenv('ENV', 'prd') try: self._default = __import__('settings.default', fromlist=['*']) except ModuleNotFoundError: self._default = object() try: self._env = __import__('settings.{}'.format(self.env), fromlist=['*']) except ModuleNotFoundError: self._env = object() self._loaded = [] def load(self, filename, fmt='json'): filename = os.path.abspath(filename) if fmt == 'json': with open(filename) as f: self._loaded.append((filename, json.load(f))) def unload(self, filename): filename = os.path.abspath(filename) self._loaded = [(f, v) for f, v in self._loaded if f != filename] def __getattr__(self, item): result = SettingManager._sentry for _, values in self._loaded: if item in values: result = values[item] result = os.getenv(item, result) if result is SettingManager._sentry: result = getattr(self._env, item, getattr(self._default, item, SettingManager._sentry)) if result is SettingManager._sentry: raise AttributeError return result def __contains__(self, item): try: self.__getattr__(item) return True except AttributeError: return False def get(self, item, default=_NONE): try: return self.__getattr__(item) except AttributeError: if default is not _NONE: return default raise AttributeError def __iter__(self): chained = itertools.chain(getattr(self._default, '__dict__', dict()).keys(), getattr(self._env, '__dict__', dict()).keys()) for _, values in self._loaded: chained = itertools.chain(chained, values.keys()) return iter(filter(lambda x: not x.startswith('_'), set(chained))) sys.modules[__name__] = SettingManager()

none

1

2.350849

2

roblox/partials/partialgroup.py

speer-kinjo/ro.py

28

10269

""" This file contains partial objects related to Roblox groups. """ from __future__ import annotations from typing import TYPE_CHECKING from ..bases.basegroup import BaseGroup from ..bases.baseuser import BaseUser if TYPE_CHECKING: from ..client import Client class AssetPartialGroup(BaseGroup): """ Represents a partial group in the context of a Roblox asset. Intended to parse the `data[0]["creator"]` data from https://games.roblox.com/v1/games. Attributes: _client: The Client object, which is passed to all objects this Client generates. id: The group's name. creator: The group's owner. name: The group's name. """ def __init__(self, client: Client, data: dict): """ Arguments: client: The Client. data: The data from the endpoint. """ self._client: Client = client self.creator: BaseUser = BaseUser(client=client, user_id=data["Id"]) self.id: int = data["CreatorTargetId"] self.name: str = data["Name"] super().__init__(client, self.id) def __repr__(self): return f"<{self.__class__.__name__} id={self.id} name={self.name!r}>" class UniversePartialGroup(BaseGroup): """ Represents a partial group in the context of a Roblox universe. Attributes: _data: The data we get back from the endpoint. _client: The client object, which is passed to all objects this client generates. id: Id of the group name: Name of the group """ def __init__(self, client: Client, data: dict): """ Arguments: client: The ClientSharedObject. data: The data from the endpoint. """ self._client: Client = client self.id = data["id"] self.name: str = data["name"] super().__init__(client, self.id) def __repr__(self): return f"<{self.__class__.__name__} id={self.id} name={self.name!r}>"

en

0.809606

This file contains partial objects related to Roblox groups. Represents a partial group in the context of a Roblox asset. Intended to parse the `data[0]["creator"]` data from https://games.roblox.com/v1/games. Attributes: _client: The Client object, which is passed to all objects this Client generates. id: The group's name. creator: The group's owner. name: The group's name. Arguments: client: The Client. data: The data from the endpoint. Represents a partial group in the context of a Roblox universe. Attributes: _data: The data we get back from the endpoint. _client: The client object, which is passed to all objects this client generates. id: Id of the group name: Name of the group Arguments: client: The ClientSharedObject. data: The data from the endpoint.

2.923253

3

services/UserService.py

erginbalta/FarmChain

1

10270

<filename>services/UserService.py import mysql.connector import socket from contextlib import closing import json import random packetType= ["INF","TRN","USR"] database = mysql.connector.connect( host="localhost", user="root", port="3307", passwd="<PASSWORD>", database="farmchain" ) def userIdCreator(): data = [] numericId = 0 id = "" with open("/datas/userInformation.json",'r') as f: user = json.load(f) numericId = len(user) + 1 id = str(packetType[2])+str(numericId) return id def transactionIdCreator(): idKey = packetType[1] numericId = random.randint(10000,99999) id = idKey+str(numericId) return id def getUserConnectionInfo(): hst = socket.gethostname() usrHost = socket.gethostbyname(hst) usrPort = findFreePort() return [usrHost,usrPort] def findFreePort(): with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as s: s.bind(('', 0)) s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) return s.getsockname()[1] def checkOnlineMiners(): mycursor = database.cursor() sql = "select * from miners where status = 1;" mycursor.execute(sql) result = mycursor.fetchall() return result def minerInfo(): result = checkOnlineMiners() info = result[0] host = result[1] port = result[2] return [host,port] def userInfoPacket(password,name,surname,company,status): info = getUserConnectionInfo() userId = userIdCreator() name = str(name).lower() surname = str(surname).lower() company = str(company).lower() status = str(status).lower() packet = [packetType[0],[userId,password,name,surname,company,status],info[0],info[1]] return packet def transactionPacketCreator(productId,productName,productNumber,fromPlace,toPlace,date): info = getUserConnectionInfo() transactionId = transactionIdCreator() productName = str(productName).lower() fromPlace = str(fromPlace).lower() toPlace = str(toPlace).lower() packet = [packetType[1],[transactionId,productId,productName,productNumber,fromPlace,toPlace,date],info[0],info[1]] return packet

none

1

2.468183

2

tests/blackbox/access_settings/test_bb_access_settings.py

csanders-git/waflz

1

10271

#!/usr/bin/python '''Test WAF Access settings''' #TODO: make so waflz_server only runs once and then can post to it # ------------------------------------------------------------------------------ # Imports # ------------------------------------------------------------------------------ import pytest import subprocess import os import sys import json from pprint import pprint import time import requests # ------------------------------------------------------------------------------ # Constants # ------------------------------------------------------------------------------ G_TEST_HOST = 'http://127.0.0.1:12345/' # ------------------------------------------------------------------------------ # globals # ------------------------------------------------------------------------------ g_server_pid = -1 # ------------------------------------------------------------------------------ # # ------------------------------------------------------------------------------ def run_command(command): p = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) stdout, stderr = p.communicate() return (p.returncode, stdout, stderr) # ------------------------------------------------------------------------------ #setup_func # ------------------------------------------------------------------------------ @pytest.fixture() def setup_func(): global g_server_pid l_cwd = os.getcwd() l_file_path = os.path.dirname(os.path.abspath(__file__)) l_ruleset_path = os.path.realpath(os.path.join(l_file_path, '../../data/waf/ruleset')) l_geoip2city_path = os.path.realpath(os.path.join(l_file_path, '../../data/waf/db/GeoLite2-City.mmdb')); l_geoip2ISP_path = os.path.realpath(os.path.join(l_file_path, '../../data/waf/db/GeoLite2-ASN.mmdb')); l_profile_path = os.path.realpath(os.path.join(l_file_path, 'test_bb_access_settings.waf.prof.json')) l_waflz_server_path = os.path.abspath(os.path.join(l_file_path, '../../../build/util/waflz_server/waflz_server')) l_subproc = subprocess.Popen([l_waflz_server_path, '-f', l_profile_path, '-r', l_ruleset_path, '-g', l_geoip2city_path, '-s', l_geoip2ISP_path]) time.sleep(1) g_server_pid = l_subproc.pid time.sleep(1) print 'setup g_server_pid: %d'%(g_server_pid) time.sleep(1) # ------------------------------------------------------------------------------ #teardown_func # ------------------------------------------------------------------------------ def teardown_func(): global g_server_pid time.sleep(.5) print 'teardown g_server_pid: %d'%(g_server_pid) if g_server_pid != -1: l_code, l_out, l_err = run_command('kill -9 %d'%(g_server_pid)) time.sleep(.5) # ------------------------------------------------------------------------------ # test_bb_modsecurity_ec_access_settings_ignore_args # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_01_block_not_in_ignore_args(setup_func): #"ignore_query_args": ["ignore", "this", "crap"] l_uri = G_TEST_HOST + '?' + 'arg1&arg2&arg3&arg4&arg5' l_headers = {"host": "myhost.com"} l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) > 0 print json.dumps(l_r_json,indent=4) assert l_r_json['rule_intercept_status'] == 403 #assert 'modsecurity_crs_23_request_limits.conf' in l_r_json['sub_event'][0]['rule_file'] # ensure 403 because exceeded max_num_args assert 'Too many arguments in' in l_r_json['rule_msg'] # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_02_bypass_in_ignore_args # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_02_bypass_in_ignore_args(): #Test that passing ignore args lets it bypass #Max arg limit it 4, we pass 7 l_uri = G_TEST_HOST + '?' + 'arg1&arg2&arg3&arg4&ignore&this&crap' l_headers = {"host": "myhost.com"} l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) == 0 # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_03_block_headers_not_in_ignore_header_list # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_03_block_headers_not_in_ignore_header_list(): #ignore_header": ["(?i)(benign-header)", "super-whatever-header", "^D.*"] l_uri = G_TEST_HOST l_headers = {"host": "myhost.com", "kooky-Header" : "function () { doing this is kinda dumb" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() print l_r_json #We got an event assert len(l_r_json) > 0 # detect a bash shellshock assert 'Bash shellshock attack detected' in l_r_json['sub_event'][0]['rule_msg'] assert 'REQUEST_HEADERS' in l_r_json['sub_event'][0]['matched_var']['name'] assert 'ZnVuY3Rpb24gKCkgeyBkb2luZyB0aGlzIGlzIGtpbmRhIGR1bWI=' in l_r_json['sub_event'][0]['matched_var']['value'] # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_04_bypass_headers_in_ignore_header_list # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_04_bypass_headers_in_ignore_header_list(): #Test ignore headers are ignored l_uri = G_TEST_HOST l_headers = {"host": "myhost.com", "Benign-Header" : "function () { doing this is kinda dumb", "super-whatever-header" : "function () { doing this is kinda dumb" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) == 0 # ------------------------------------------------------------------------------- # test_bb_modsec_ec_access_settings_05_bypass_headers_in_ignore_header_list_regex # ------------------------------------------------------------------------------- def test_bb_modsec_ec_access_settings_05_bypass_headers_in_ignore_header_list_regex(): ######################################## # Test regex "^D.*" ######################################## l_uri = G_TEST_HOST #anything that starts with D should be ignored l_headers = {"host": "myhost.com", "Doopdoop" : "function () { doing this is kinda dumb", "Duper-duper-deader" : "function () { doing this is kinda dumb" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) == 0 # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_06_block_cookie_not_in_ignore_cookie_list # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_06_block_cookie_not_in_ignore_cookie_list(): #"ignore_cookie": ["(?i)(sketchy_origin)", "(?i)(yousocrazy)"] l_uri = G_TEST_HOST l_headers = {"host": "myhost.com", "Cookie": "blahblah=function () { asdf asdf asdf" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) > 0 # detect a bash shellshock assert 'Bash shellshock attack detected' in l_r_json['sub_event'][0]['rule_msg'] assert 'REQUEST_HEADERS' in l_r_json['sub_event'][0]['matched_var']['name'] # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_07_bypass_cookie_in_ignore_cookie_list # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_07_bypass_cookie_in_ignore_cookie_list(): #"ignore_cookie": ["(?i)(sketchy_origin)", "(?i)(yousocrazy)"] l_uri = G_TEST_HOST l_headers = {"host" : "myhost.com", "Cookie" : "SkeTchy_Origin=function () { asdf asdf asdf" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() #We get no event assert len(l_r_json) == 0 l_uri = G_TEST_HOST l_headers = {"host" : "myhost.com", "Cookie" : "SkeTchy_Origin=function () { asdf asdf asdf" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) == 0 # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_08_ignore_cookie_in_ignore_cookie_list # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_08_bypass_cookie_in_ignore_cookie_list_regex(): ######################################## # Test regex "^[0-9_].*$" ######################################## l_uri = G_TEST_HOST l_headers = {"host" : "myhost.com", "Cookie" : "0_123_ADB__bloop=function () { asdf asdf asdf" } l_r = requests.get(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) == 0 # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_09_block_disallowed_http_method # ------------------------------------------------------------------------------ def test_bb_modsec_ec_access_settings_09_block_disallowed_http_method(): l_uri = G_TEST_HOST l_headers = {"host" : "myhost.com" } l_r = requests.put(l_uri, headers=l_headers) assert l_r.status_code == 200 l_r_json = l_r.json() assert len(l_r_json) > 0 assert 'Method is not allowed by policy' in l_r_json['rule_msg'] teardown_func()

en

0.232524

#!/usr/bin/python Test WAF Access settings #TODO: make so waflz_server only runs once and then can post to it # ------------------------------------------------------------------------------ # Imports # ------------------------------------------------------------------------------ # ------------------------------------------------------------------------------ # Constants # ------------------------------------------------------------------------------ # ------------------------------------------------------------------------------ # globals # ------------------------------------------------------------------------------ # ------------------------------------------------------------------------------ # # ------------------------------------------------------------------------------ # ------------------------------------------------------------------------------ #setup_func # ------------------------------------------------------------------------------ # ------------------------------------------------------------------------------ #teardown_func # ------------------------------------------------------------------------------ # ------------------------------------------------------------------------------ # test_bb_modsecurity_ec_access_settings_ignore_args # ------------------------------------------------------------------------------ #"ignore_query_args": ["ignore", "this", "crap"] #assert 'modsecurity_crs_23_request_limits.conf' in l_r_json['sub_event'][0]['rule_file'] # ensure 403 because exceeded max_num_args # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_02_bypass_in_ignore_args # ------------------------------------------------------------------------------ #Test that passing ignore args lets it bypass #Max arg limit it 4, we pass 7 # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_03_block_headers_not_in_ignore_header_list # ------------------------------------------------------------------------------ #ignore_header": ["(?i)(benign-header)", "super-whatever-header", "^D.*"] #We got an event # detect a bash shellshock # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_04_bypass_headers_in_ignore_header_list # ------------------------------------------------------------------------------ #Test ignore headers are ignored # ------------------------------------------------------------------------------- # test_bb_modsec_ec_access_settings_05_bypass_headers_in_ignore_header_list_regex # ------------------------------------------------------------------------------- ######################################## # Test regex "^D.*" ######################################## #anything that starts with D should be ignored # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_06_block_cookie_not_in_ignore_cookie_list # ------------------------------------------------------------------------------ #"ignore_cookie": ["(?i)(sketchy_origin)", "(?i)(yousocrazy)"] # detect a bash shellshock # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_07_bypass_cookie_in_ignore_cookie_list # ------------------------------------------------------------------------------ #"ignore_cookie": ["(?i)(sketchy_origin)", "(?i)(yousocrazy)"] #We get no event # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_08_ignore_cookie_in_ignore_cookie_list # ------------------------------------------------------------------------------ ######################################## # Test regex "^[0-9_].*$" ######################################## # ------------------------------------------------------------------------------ # test_bb_modsec_ec_access_settings_09_block_disallowed_http_method # ------------------------------------------------------------------------------

1.7865

2

cosmosis/runtime/analytics.py

ktanidis2/Modified_CosmoSIS_for_galaxy_number_count_angular_power_spectra

1

10272

<gh_stars>1-10 #coding: utf-8 from __future__ import print_function from builtins import zip from builtins import object from cosmosis import output as output_module import numpy as np import sys import os class Analytics(object): def __init__(self, params, pool=None): self.params = params self.pool = pool self.total_steps = 0 nparam = len(params) self.means = np.zeros(nparam) self.m2 = np.zeros(nparam) self.cov_times_n = np.zeros((nparam,nparam)) def add_traces(self, traces): if traces.shape[1] != len(self.params): raise RuntimeError("The number of traces added to Analytics " "does not match the number of varied " "parameters!") num = float(self.total_steps) for x in traces: num += 1.0 delta = x - self.means old_means = self.means.copy() self.means += delta/num self.m2 += delta*(x - self.means) self.cov_times_n += np.outer(x-self.means, x-old_means) self.total_steps += traces.shape[0] def trace_means(self): if self.pool: return np.array(self.pool.gather(self.means)).T else: return self.means def trace_variances(self): if self.total_steps > 1: local_variance = self.m2 / float(self.total_steps-1) if self.pool: return np.array(self.pool.gather(local_variance)).T else: return local_variance return None def gelman_rubin(self, quiet=True): # takes current traces and returns if self.pool is None or not self.pool.size > 1: raise RuntimeError("Gelman-Rubin statistic is only " "valid for multiple chains.") if self.total_steps == 0: raise RuntimeError("Gelman-Rubin statistic not " "defined for 0-length chains.") # gather trace statistics to master process means = self.trace_means() variances = self.trace_variances() if self.pool.is_master(): B_over_n = np.var(means, ddof=1, axis=1) B = B_over_n * self.total_steps W = np.mean(variances, axis=1) V = ((1. - 1./self.total_steps) * W + (1. + 1./self.pool.size) * B_over_n) # TODO: check for 0-values in W Rhat = np.sqrt(V/W) else: Rhat = None Rhat = self.pool.bcast(Rhat) if not quiet and self.pool.is_master(): print() print("Gelman-Rubin:") for (p,R) in zip(self.params, Rhat): print(" ", p, " ", R) print("Worst = ", Rhat.max()) print() return Rhat

#coding: utf-8 from __future__ import print_function from builtins import zip from builtins import object from cosmosis import output as output_module import numpy as np import sys import os class Analytics(object): def __init__(self, params, pool=None): self.params = params self.pool = pool self.total_steps = 0 nparam = len(params) self.means = np.zeros(nparam) self.m2 = np.zeros(nparam) self.cov_times_n = np.zeros((nparam,nparam)) def add_traces(self, traces): if traces.shape[1] != len(self.params): raise RuntimeError("The number of traces added to Analytics " "does not match the number of varied " "parameters!") num = float(self.total_steps) for x in traces: num += 1.0 delta = x - self.means old_means = self.means.copy() self.means += delta/num self.m2 += delta*(x - self.means) self.cov_times_n += np.outer(x-self.means, x-old_means) self.total_steps += traces.shape[0] def trace_means(self): if self.pool: return np.array(self.pool.gather(self.means)).T else: return self.means def trace_variances(self): if self.total_steps > 1: local_variance = self.m2 / float(self.total_steps-1) if self.pool: return np.array(self.pool.gather(local_variance)).T else: return local_variance return None def gelman_rubin(self, quiet=True): # takes current traces and returns if self.pool is None or not self.pool.size > 1: raise RuntimeError("Gelman-Rubin statistic is only " "valid for multiple chains.") if self.total_steps == 0: raise RuntimeError("Gelman-Rubin statistic not " "defined for 0-length chains.") # gather trace statistics to master process means = self.trace_means() variances = self.trace_variances() if self.pool.is_master(): B_over_n = np.var(means, ddof=1, axis=1) B = B_over_n * self.total_steps W = np.mean(variances, axis=1) V = ((1. - 1./self.total_steps) * W + (1. + 1./self.pool.size) * B_over_n) # TODO: check for 0-values in W Rhat = np.sqrt(V/W) else: Rhat = None Rhat = self.pool.bcast(Rhat) if not quiet and self.pool.is_master(): print() print("Gelman-Rubin:") for (p,R) in zip(self.params, Rhat): print(" ", p, " ", R) print("Worst = ", Rhat.max()) print() return Rhat

en

0.748833

#coding: utf-8 # takes current traces and returns # gather trace statistics to master process # TODO: check for 0-values in W

2.507523

3

sdcc2elf.py

Vector35/llil_transpiler

14

10273

<filename>sdcc2elf.py #!/usr/bin/env python # # convert SDCC .rel files to 32-bit ELF relocatable # # resulting file is simple: # # ------------------------ # ELF header # ------------------------ # .text section # .shstrtab section # .strtab section # .symtab section # ------------------------ # NULL elf32_shdr # .text elf32_shdr # .shstrtab elf32_shdr # .symtab elf32_shdr # .strtab elf32_shdr # ------------------------ import os import re import sys from struct import pack #------------------------------------------------------------------------------ # ELF helpers #------------------------------------------------------------------------------ (PF_X, PF_W, PF_R) = (1,2,4) (SHT_NULL, SHT_PROGBITS, SHT_STRTAB) = (0,1,3) sz_ehdr = 0x34 sz_shdr = 0x28 def align(fp, to=4, pad=b'\x00'): while fp.tell() % to: fp.write(pad) #------------------------------------------------------------------------------ # read .map file for symbols #------------------------------------------------------------------------------ fpath_map = sys.argv[2] assert fpath_map.endswith('.map') with open(fpath_map) as fp: lines = fp.readlines() (_CODE_ADDR, _CODE_SZ) = (None, None) (i_code, i_header) = (None, None) for (i, line) in enumerate(lines): if line.startswith('_CODE'): m = re.match(r'^_CODE\s+([A-F0-9]{8})\s+([A-F0-9]{8})', line) (addr, size) = map(lambda x: int(x, 16), m.group(1,2)) if not i_code: i_code = i _CODE_ADDR = addr _CODE_SZ = size else: if addr != _CODE_ADDR: raise Exception('conflicting code segment addresses') if size != _CODE_SZ: raise Exception('conflicting code segment sizes') if line.startswith('_HEADER0'): i_header = i break assert i_code and i_header and i_code < i_header syms = [] for line in lines[i_code:i_header]: m = re.search(r'([A-F0-9]{8})\s+(_\w+)', line) if m: (addr, symname) = m.group(1, 2) print('found %s: %s' % (addr, symname)) syms.append((symname, int(addr, 16))); assert syms print('_CODE [%08X, %08X)' % (_CODE_ADDR, _CODE_ADDR+_CODE_SZ)) print('_CODE symbols from') for (name, addr) in syms: print('%08X: %s' % (addr, name)) #------------------------------------------------------------------------------ # read .ihx file #------------------------------------------------------------------------------ fpath_ihx = sys.argv[1] assert fpath_ihx.endswith('.ihx') code_area = [b'\x00'] * (_CODE_ADDR + _CODE_SZ) with open(fpath_ihx) as fp: for line in fp.readlines(): m = re.match(r'^:(..)(....)00(.*)(..)', line) if m: (count, addr, data, csum) = m.group(1,2,3,4) count = int(count,16) assert count == len(data)/2 addr = int(addr,16) if not (addr >= _CODE_ADDR and addr < (_CODE_ADDR + _CODE_SZ)): continue print('%08X: ' % addr, end='') for i in range(count): byte_str = data[2*i]+data[2*i+1] print('%s ' % byte_str, end='') code_area[addr + i] = pack('B', int(byte_str, 16)) print('') continue m = re.match(r'^:00000001FF', line) if m: break raise Exception('got unexpected IHX line: %s' % line) assert code_area #print(code_area) #------------------------------------------------------------------------------ # write ELF #------------------------------------------------------------------------------ # process symbols, build string table syms = sorted(syms, key=lambda name_addr: name_addr[1]) func2size = {} func2stroffs = {} strtab = b'\x00' for i in range(len(syms)): (name, addr) = syms[i] if i == len(syms)-1: func2size[name] = len(code_area) - addr else: func2size[name] = syms[i+1][1] - addr func2stroffs[name] = len(strtab) strtab = strtab + name.encode('utf-8') + b'\x00' print('%04X: %s size %X' % (addr, name, func2size[name])) fp = open('tests.elf', 'wb') # elf32_hdr (placeholder, we'll come back to fill in offsets) print('elf32_hdr @ %X' % fp.tell()) fp.write(b'\x00' * sz_ehdr) # .text section contents o_text = fp.tell() print('placing .text @ %X' % o_text) for byte in code_area: fp.write(byte) sz_text = fp.tell() - o_text # .shstrtab section contents scn_shstrtab = b'\x00.text\x00.shstrtab\x00.symtab\x00.strtab\x00' align(fp) o_shstrtab = fp.tell() print('placing .shstrtab @ %X' % o_shstrtab) fp.write(scn_shstrtab) sz_shstrtab = fp.tell() - o_shstrtab # .symtab section contents align(fp) o_symtab = fp.tell() print('placing .symtab @ %X' % o_symtab) for (name, addr) in syms: st_name = func2stroffs[name] st_value = addr st_size = func2size[name] st_info = 0x12 # bind:1(GLOBAL) type:2(FUNC) st_other = 0 st_shndx = 0x1 # section header index: 0'th: NULL 1'th: .text Elf32_Sym = pack('<IIIBBH', st_name, st_value, st_size, st_info, st_other, st_shndx) fp.write(Elf32_Sym) sz_symtab = fp.tell() - o_symtab # .strtab section contents align(fp) o_strtab = fp.tell() print('placing .strtab @ %X' % o_strtab) fp.write(strtab) sz_strtab = fp.tell() - o_strtab # null section header (index 0) align(fp) o_shdr_null = fp.tell() print('placing shdr NULL @ %X' % o_shdr_null) fp.write(b'\x00' * sz_shdr) # .text section header (index 1) o_shdr_text = fp.tell() print('placing shdr .text @ %X' % fp.tell()) sh_name = scn_shstrtab.index(b'.text') sh_type = 1 # SHT_PROGBITS sh_flags = 6 # ALLOC|EXECINSTR sh_addr = 0 sh_offset = o_text sh_size = sz_text sh_link = 0 sh_info = 0 sh_addralign = 4 sh_entsize = 0 tmp = pack('<IIIIIIIIII', \ sh_name, sh_type, sh_flags, sh_addr, sh_offset, sh_size, sh_link, sh_info, \ sh_addralign, sh_entsize) fp.write(tmp) # .shstrtab section header (index 2) o_shdr_shstrtab = fp.tell() print('placing shdr .shstrtab @ %X' % fp.tell()) sh_name = scn_shstrtab.index(b'.shstrtab') sh_type = 3 #SHT_STRTAB sh_flags = 0 sh_addr = 0 sh_offset = o_shstrtab sh_size = sz_shstrtab sh_link = 0 sh_info = 0 sh_addralign = 1 sh_entsize = 0 tmp = pack('<IIIIIIIIII', \ sh_name, sh_type, sh_flags, sh_addr, sh_offset, sh_size, sh_link, sh_info, \ sh_addralign, sh_entsize) fp.write(tmp) # .symtab section header (index 3) o_shdr_symtab = fp.tell() print('placing shdr .symtab @ %X' % fp.tell()) sh_name = scn_shstrtab.index(b'.symtab') sh_type = 2 #SHT_SYMTAB sh_flags = 0 sh_addr = 0 sh_offset = o_symtab sh_size = sz_symtab sh_link = 4 # link to scn #4 (find strings in .strtab) sh_info = 0 sh_addralign = 4 sh_entsize = 0 tmp = pack('<IIIIIIIIII', \ sh_name, sh_type, sh_flags, sh_addr, sh_offset, sh_size, sh_link, sh_info, \ sh_addralign, sh_entsize) fp.write(tmp) # .strtab section header (index 4) o_shdr_strtab = fp.tell() print('placing shdr .strtab @ %X' % fp.tell()) sh_name = scn_shstrtab.index(b'.strtab') sh_type = 3 #SHT_STRTAB sh_flags = 0 sh_addr = 0 sh_offset = o_strtab sh_size = sz_strtab sh_link = 0 sh_info = 0 sh_addralign = 1 sh_entsize = 0 tmp = pack('<IIIIIIIIII', \ sh_name, sh_type, sh_flags, sh_addr, sh_offset, sh_size, sh_link, sh_info, \ sh_addralign, sh_entsize) fp.write(tmp) # seek back, write real elf header hdr = b'\x7FELF' hdr += b'\x01' # e_ident[EI_CLASS] 32-bit hdr += b'\x01' # e_ident[EI_DATA] LSB (little-end) hdr += b'\x01\x00\x00' # version, osabi, abiversion hdr += b'\x00'*7 assert len(hdr) == 16 hdr += pack('<H', 1) # e_type = ET_REL hdr += pack('<H', 220) # e_machine = EM_Z80 hdr += pack('<I', 1) # e_version = EV_CURRENT hdr += pack('<I', 0) # e_entry hdr += pack('<I', 0) # e_phoff hdr += pack('<I', o_shdr_null) # e_shoff hdr += pack('<I', 0) # e_flags hdr += pack('<H', sz_ehdr) # e_ehsize hdr += pack('<H', 0) # e_phentsize hdr += pack('<H', 0) # e_phnum hdr += pack('<H', sz_shdr) # e_shentsize hdr += pack('<H', 5) # e_shnum hdr += pack('<H', 2) # e_shstrndx = index of .shstrtab assert len(hdr) == sz_ehdr fp.seek(0, os.SEEK_SET) fp.write(hdr) # done! fp.close()

en

0.244853

#!/usr/bin/env python # # convert SDCC .rel files to 32-bit ELF relocatable # # resulting file is simple: # # ------------------------ # ELF header # ------------------------ # .text section # .shstrtab section # .strtab section # .symtab section # ------------------------ # NULL elf32_shdr # .text elf32_shdr # .shstrtab elf32_shdr # .symtab elf32_shdr # .strtab elf32_shdr # ------------------------ #------------------------------------------------------------------------------ # ELF helpers #------------------------------------------------------------------------------ #------------------------------------------------------------------------------ # read .map file for symbols #------------------------------------------------------------------------------ #------------------------------------------------------------------------------ # read .ihx file #------------------------------------------------------------------------------ #print(code_area) #------------------------------------------------------------------------------ # write ELF #------------------------------------------------------------------------------ # process symbols, build string table # elf32_hdr (placeholder, we'll come back to fill in offsets) # .text section contents # .shstrtab section contents # .symtab section contents # bind:1(GLOBAL) type:2(FUNC) # section header index: 0'th: NULL 1'th: .text # .strtab section contents # null section header (index 0) # .text section header (index 1) # SHT_PROGBITS # ALLOC|EXECINSTR # .shstrtab section header (index 2) #SHT_STRTAB # .symtab section header (index 3) #SHT_SYMTAB # link to scn #4 (find strings in .strtab) # .strtab section header (index 4) #SHT_STRTAB # seek back, write real elf header # e_ident[EI_CLASS] 32-bit # e_ident[EI_DATA] LSB (little-end) # version, osabi, abiversion # e_type = ET_REL # e_machine = EM_Z80 # e_version = EV_CURRENT # e_entry # e_phoff # e_shoff # e_flags # e_ehsize # e_phentsize # e_phnum # e_shentsize # e_shnum # e_shstrndx = index of .shstrtab # done!

2.546184

3

eval.py

nikinsta/deep-siamese-text-similarity-on-python-3

0

10274

#! /usr/bin/env python import tensorflow as tf import numpy as np import os import time import datetime from tensorflow.contrib import learn from input_helpers import InputHelper # Parameters # ================================================== # Eval Parameters tf.flags.DEFINE_integer("batch_size", 64, "Batch Size (default: 64)") tf.flags.DEFINE_string("checkpoint_dir", "", "Checkpoint directory from training run") tf.flags.DEFINE_string("eval_filepath", "match_valid.tsv", "Evaluate on this data (Default: None)") tf.flags.DEFINE_string("vocab_filepath", "runs/1479874609/checkpoints/vocab", "Load training time vocabulary (Default: None)") tf.flags.DEFINE_string("model", "runs/1479874609/checkpoints/model-32000", "Load trained model checkpoint (Default: None)") # Misc Parameters tf.flags.DEFINE_boolean("allow_soft_placement", True, "Allow device soft device placement") tf.flags.DEFINE_boolean("log_device_placement", False, "Log placement of ops on devices") FLAGS = tf.flags.FLAGS FLAGS._parse_flags() print("\nParameters:") for attr, value in sorted(FLAGS.__flags.items()): print("{}={}".format(attr.upper(), value)) print("") if FLAGS.eval_filepath==None or FLAGS.vocab_filepath==None or FLAGS.model==None : print("Eval or Vocab filepaths are empty.") exit() # load data and map id-transform based on training time vocabulary inpH = InputHelper() x1_test,x2_test,y_test = inpH.getTestDataSet(FLAGS.eval_filepath, FLAGS.vocab_filepath, 30) print("\nEvaluating...\n") # Evaluation # ================================================== checkpoint_file = FLAGS.model print(checkpoint_file) graph = tf.Graph() with graph.as_default(): session_conf = tf.ConfigProto( allow_soft_placement=FLAGS.allow_soft_placement, log_device_placement=FLAGS.log_device_placement) sess = tf.Session(config=session_conf) with sess.as_default(): # Load the saved meta graph and restore variables saver = tf.train.import_meta_graph("{}.meta".format(checkpoint_file)) sess.run(tf.initialize_all_variables()) saver.restore(sess, checkpoint_file) # Get the placeholders from the graph by name input_x1 = graph.get_operation_by_name("input_x1").outputs[0] input_x2 = graph.get_operation_by_name("input_x2").outputs[0] input_y = graph.get_operation_by_name("input_y").outputs[0] dropout_keep_prob = graph.get_operation_by_name("dropout_keep_prob").outputs[0] # Tensors we want to evaluate predictions = graph.get_operation_by_name("output/distance").outputs[0] accuracy = graph.get_operation_by_name("accuracy/accuracy").outputs[0] sim = graph.get_operation_by_name("accuracy/temp_sim").outputs[0] #emb = graph.get_operation_by_name("embedding/W").outputs[0] #embedded_chars = tf.nn.embedding_lookup(emb,input_x) # Generate batches for one epoch batches = inpH.batch_iter(list(zip(x1_test,x2_test,y_test)), 2*FLAGS.batch_size, 1, shuffle=False) # Collect the predictions here all_predictions = [] all_d=[] for db in batches: x1_dev_b,x2_dev_b,y_dev_b = zip(*db) batch_predictions, batch_acc, sim = sess.run([predictions,accuracy,sim], {input_x1: x1_dev_b, input_x2: x2_dev_b, input_y:y_dev_b, dropout_keep_prob: 1.0}) all_predictions = np.concatenate([all_predictions, batch_predictions]) print(batch_predictions) all_d = np.concatenate([all_d, sim]) print("DEV acc {}".format(batch_acc)) for ex in all_predictions: print(ex) correct_predictions = float(np.mean(all_d == y_test)) print("Accuracy: {:g}".format(correct_predictions))

en

0.594187

#! /usr/bin/env python # Parameters # ================================================== # Eval Parameters # Misc Parameters # load data and map id-transform based on training time vocabulary # Evaluation # ================================================== # Load the saved meta graph and restore variables # Get the placeholders from the graph by name # Tensors we want to evaluate #emb = graph.get_operation_by_name("embedding/W").outputs[0] #embedded_chars = tf.nn.embedding_lookup(emb,input_x) # Generate batches for one epoch # Collect the predictions here

2.301202

2

accounts/views.py

nikhiljohn10/django-auth

0

10275

<gh_stars>0 from django.urls import reverse from django.conf import settings from django.contrib import messages from django.shortcuts import render, redirect from django.core.mail import send_mail from django.contrib.auth import login, logout, views, authenticate from django.views.generic.edit import CreateView from django.contrib.sessions.models import Session from django.contrib.auth.decorators import login_required, permission_required from accounts.tools import activater, mailer from accounts.forms import SignUpForm, LoginForm from accounts.models import User @login_required @permission_required("is_staff", login_url='/dashboard/') def gmail(request): request.session['oauth_state'] = mailer.auth_state return redirect(mailer.auth_uri) @login_required @permission_required("is_staff", login_url='/dashboard/') def gmail_verify(request): code = request.GET.get('code','') state = request.GET.get('state','') if code and state == request.session['oauth_state']: mailer.verify(code) return redirect('dash:gmail') class UserLogin(views.LoginView): template_name = 'auth/login.html' authentication_form = LoginForm def form_valid(self, form): user = form.get_user() login(self.request, user) if not self.request.POST.get('remember_me', None): self.request.session.set_expiry(0) messages.info(self.request, f"You are now logged in as {user}") return redirect(self.get_success_url()) class SignUpView(CreateView): form_class = SignUpForm template_name = 'auth/signup.html' def form_valid(self, form): if mailer.activated: user = form.save() mailer.send_mail( "Django Verification Code", "Hi "+str(user)+",\nClick this link to activate: " + reverse('auth:verify_email', args=( user, activater.make_token(user))), [user.email]) login(self.request, user) else: messages.error(self.request, "Gmail is not activate. Contact site administrator.") return redirect('auth:signup') return redirect('core:home') def user_manage_permission(user, username): if not user.is_staff: if user.username == username: return True else: if user.username != username: return True return False @login_required @permission_required("is_staff", login_url='/dashboard/') def user_force_logout(request, username): user = User.objects.get(username=username) sessions = [s.delete() for s in Session.objects.all() if s.get_decoded().get('_auth_user_id') == str(user.id)] print(sessions) return redirect('dash:users') def user_verify_email(request, username, token): user = User.objects.get(username=username) if activater.check_token(user, token): print(user, "is verified") user.email_verified = True user.save() return redirect('dash:users') @login_required def user_disable(request, username): if user_manage_permission(request.user, username): user = User.objects.get(username=username) user.is_active = False user.save() messages.error(request, 'Profile successfully disabled.') else: messages.error( request, 'You are not allowed to perform this operation.') if request.user.is_staff: return redirect('dash:users') else: return redirect('dash:profile') @login_required def user_enable(request, username): if user_manage_permission(request.user, username): user = User.objects.get(username=username) user.is_active = True user.save() messages.success(request, 'Profile successfully enabled.') else: messages.error( request, 'You are not allowed to perform this operation.') if request.user.is_staff: return redirect('dash:users') else: return redirect('dash:profile') @login_required def user_delete(request, username): if user_manage_permission(request.user, username): user = User.objects.get(username=username) user.delete() messages.error(request, 'Profile successfully deleted.') else: messages.error( request, 'You are not allowed to perform this operation.') if request.user.is_staff: return redirect('dash:users') else: return redirect('dash:profile') user_login = UserLogin.as_view() user_signup = SignUpView.as_view() user_logout = views.LogoutView.as_view()

from django.urls import reverse from django.conf import settings from django.contrib import messages from django.shortcuts import render, redirect from django.core.mail import send_mail from django.contrib.auth import login, logout, views, authenticate from django.views.generic.edit import CreateView from django.contrib.sessions.models import Session from django.contrib.auth.decorators import login_required, permission_required from accounts.tools import activater, mailer from accounts.forms import SignUpForm, LoginForm from accounts.models import User @login_required @permission_required("is_staff", login_url='/dashboard/') def gmail(request): request.session['oauth_state'] = mailer.auth_state return redirect(mailer.auth_uri) @login_required @permission_required("is_staff", login_url='/dashboard/') def gmail_verify(request): code = request.GET.get('code','') state = request.GET.get('state','') if code and state == request.session['oauth_state']: mailer.verify(code) return redirect('dash:gmail') class UserLogin(views.LoginView): template_name = 'auth/login.html' authentication_form = LoginForm def form_valid(self, form): user = form.get_user() login(self.request, user) if not self.request.POST.get('remember_me', None): self.request.session.set_expiry(0) messages.info(self.request, f"You are now logged in as {user}") return redirect(self.get_success_url()) class SignUpView(CreateView): form_class = SignUpForm template_name = 'auth/signup.html' def form_valid(self, form): if mailer.activated: user = form.save() mailer.send_mail( "Django Verification Code", "Hi "+str(user)+",\nClick this link to activate: " + reverse('auth:verify_email', args=( user, activater.make_token(user))), [user.email]) login(self.request, user) else: messages.error(self.request, "Gmail is not activate. Contact site administrator.") return redirect('auth:signup') return redirect('core:home') def user_manage_permission(user, username): if not user.is_staff: if user.username == username: return True else: if user.username != username: return True return False @login_required @permission_required("is_staff", login_url='/dashboard/') def user_force_logout(request, username): user = User.objects.get(username=username) sessions = [s.delete() for s in Session.objects.all() if s.get_decoded().get('_auth_user_id') == str(user.id)] print(sessions) return redirect('dash:users') def user_verify_email(request, username, token): user = User.objects.get(username=username) if activater.check_token(user, token): print(user, "is verified") user.email_verified = True user.save() return redirect('dash:users') @login_required def user_disable(request, username): if user_manage_permission(request.user, username): user = User.objects.get(username=username) user.is_active = False user.save() messages.error(request, 'Profile successfully disabled.') else: messages.error( request, 'You are not allowed to perform this operation.') if request.user.is_staff: return redirect('dash:users') else: return redirect('dash:profile') @login_required def user_enable(request, username): if user_manage_permission(request.user, username): user = User.objects.get(username=username) user.is_active = True user.save() messages.success(request, 'Profile successfully enabled.') else: messages.error( request, 'You are not allowed to perform this operation.') if request.user.is_staff: return redirect('dash:users') else: return redirect('dash:profile') @login_required def user_delete(request, username): if user_manage_permission(request.user, username): user = User.objects.get(username=username) user.delete() messages.error(request, 'Profile successfully deleted.') else: messages.error( request, 'You are not allowed to perform this operation.') if request.user.is_staff: return redirect('dash:users') else: return redirect('dash:profile') user_login = UserLogin.as_view() user_signup = SignUpView.as_view() user_logout = views.LogoutView.as_view()

none

1

2.163409

2

python/testData/intentions/PyAnnotateVariableTypeIntentionTest/annotationTupleType.py

truthiswill/intellij-community

2

10276

v<caret>ar = (1, 'foo', None)

none

1

1.302547

1

bot/venv/lib/python3.7/site-packages/scipy/version.py

manaccac/sc2_bot

76

10277

<gh_stars>10-100 # THIS FILE IS GENERATED FROM SCIPY SETUP.PY short_version = '1.5.4' version = '1.5.4' full_version = '1.5.4' git_revision = '19acfed431060aafaa963f7e530c95e70cd4b85c' release = True if not release: version = full_version

# THIS FILE IS GENERATED FROM SCIPY SETUP.PY short_version = '1.5.4' version = '1.5.4' full_version = '1.5.4' git_revision = '19acfed431060aafaa963f7e530c95e70cd4b85c' release = True if not release: version = full_version

en

0.387835

# THIS FILE IS GENERATED FROM SCIPY SETUP.PY

0.960018

1

emrichen/input/__init__.py

jbek7/emrichen

0

10278

<reponame>jbek7/emrichen from typing import TextIO, Union from .json import load_json from .yaml import load_yaml PARSERS = { 'yaml': load_yaml, 'json': load_json, } def parse(data: Union[TextIO, str], format: str): if format in PARSERS: return PARSERS[format](data) else: raise ValueError('No parser for format {format}'.format(format=format))

from typing import TextIO, Union from .json import load_json from .yaml import load_yaml PARSERS = { 'yaml': load_yaml, 'json': load_json, } def parse(data: Union[TextIO, str], format: str): if format in PARSERS: return PARSERS[format](data) else: raise ValueError('No parser for format {format}'.format(format=format))

none

1

2.85197

3

sdk/python/pulumi_aws_native/workspaces/get_workspace.py

pulumi/pulumi-aws-native

29

10279

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** 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 from . import outputs __all__ = [ 'GetWorkspaceResult', 'AwaitableGetWorkspaceResult', 'get_workspace', 'get_workspace_output', ] @pulumi.output_type class GetWorkspaceResult: def __init__(__self__, bundle_id=None, directory_id=None, id=None, root_volume_encryption_enabled=None, tags=None, user_volume_encryption_enabled=None, volume_encryption_key=None, workspace_properties=None): if bundle_id and not isinstance(bundle_id, str): raise TypeError("Expected argument 'bundle_id' to be a str") pulumi.set(__self__, "bundle_id", bundle_id) if directory_id and not isinstance(directory_id, str): raise TypeError("Expected argument 'directory_id' to be a str") pulumi.set(__self__, "directory_id", directory_id) if id and not isinstance(id, str): raise TypeError("Expected argument 'id' to be a str") pulumi.set(__self__, "id", id) if root_volume_encryption_enabled and not isinstance(root_volume_encryption_enabled, bool): raise TypeError("Expected argument 'root_volume_encryption_enabled' to be a bool") pulumi.set(__self__, "root_volume_encryption_enabled", root_volume_encryption_enabled) if tags and not isinstance(tags, list): raise TypeError("Expected argument 'tags' to be a list") pulumi.set(__self__, "tags", tags) if user_volume_encryption_enabled and not isinstance(user_volume_encryption_enabled, bool): raise TypeError("Expected argument 'user_volume_encryption_enabled' to be a bool") pulumi.set(__self__, "user_volume_encryption_enabled", user_volume_encryption_enabled) if volume_encryption_key and not isinstance(volume_encryption_key, str): raise TypeError("Expected argument 'volume_encryption_key' to be a str") pulumi.set(__self__, "volume_encryption_key", volume_encryption_key) if workspace_properties and not isinstance(workspace_properties, dict): raise TypeError("Expected argument 'workspace_properties' to be a dict") pulumi.set(__self__, "workspace_properties", workspace_properties) @property @pulumi.getter(name="bundleId") def bundle_id(self) -> Optional[str]: return pulumi.get(self, "bundle_id") @property @pulumi.getter(name="directoryId") def directory_id(self) -> Optional[str]: return pulumi.get(self, "directory_id") @property @pulumi.getter def id(self) -> Optional[str]: return pulumi.get(self, "id") @property @pulumi.getter(name="rootVolumeEncryptionEnabled") def root_volume_encryption_enabled(self) -> Optional[bool]: return pulumi.get(self, "root_volume_encryption_enabled") @property @pulumi.getter def tags(self) -> Optional[Sequence['outputs.WorkspaceTag']]: return pulumi.get(self, "tags") @property @pulumi.getter(name="userVolumeEncryptionEnabled") def user_volume_encryption_enabled(self) -> Optional[bool]: return pulumi.get(self, "user_volume_encryption_enabled") @property @pulumi.getter(name="volumeEncryptionKey") def volume_encryption_key(self) -> Optional[str]: return pulumi.get(self, "volume_encryption_key") @property @pulumi.getter(name="workspaceProperties") def workspace_properties(self) -> Optional['outputs.WorkspaceProperties']: return pulumi.get(self, "workspace_properties") class AwaitableGetWorkspaceResult(GetWorkspaceResult): # pylint: disable=using-constant-test def __await__(self): if False: yield self return GetWorkspaceResult( bundle_id=self.bundle_id, directory_id=self.directory_id, id=self.id, root_volume_encryption_enabled=self.root_volume_encryption_enabled, tags=self.tags, user_volume_encryption_enabled=self.user_volume_encryption_enabled, volume_encryption_key=self.volume_encryption_key, workspace_properties=self.workspace_properties) def get_workspace(id: Optional[str] = None, opts: Optional[pulumi.InvokeOptions] = None) -> AwaitableGetWorkspaceResult: """ Resource Type definition for AWS::WorkSpaces::Workspace """ __args__ = dict() __args__['id'] = id if opts is None: opts = pulumi.InvokeOptions() if opts.version is None: opts.version = _utilities.get_version() __ret__ = pulumi.runtime.invoke('aws-native:workspaces:getWorkspace', __args__, opts=opts, typ=GetWorkspaceResult).value return AwaitableGetWorkspaceResult( bundle_id=__ret__.bundle_id, directory_id=__ret__.directory_id, id=__ret__.id, root_volume_encryption_enabled=__ret__.root_volume_encryption_enabled, tags=__ret__.tags, user_volume_encryption_enabled=__ret__.user_volume_encryption_enabled, volume_encryption_key=__ret__.volume_encryption_key, workspace_properties=__ret__.workspace_properties) @_utilities.lift_output_func(get_workspace) def get_workspace_output(id: Optional[pulumi.Input[str]] = None, opts: Optional[pulumi.InvokeOptions] = None) -> pulumi.Output[GetWorkspaceResult]: """ Resource Type definition for AWS::WorkSpaces::Workspace """ ...

en

0.897218

# coding=utf-8 # *** WARNING: this file was generated by the Pulumi SDK Generator. *** # *** Do not edit by hand unless you're certain you know what you are doing! *** # pylint: disable=using-constant-test Resource Type definition for AWS::WorkSpaces::Workspace Resource Type definition for AWS::WorkSpaces::Workspace

1.825763

2

testtools/__init__.py

afy2103/spambayes-9-10-Frozen

0

10280

__author__ = 'AlexYang'

none

1

0.949948

1

tests/test_distance.py

mkclairhong/quail

1

10281

<reponame>mkclairhong/quail # -*- coding: utf-8 -*- from quail.distance import * import numpy as np import pytest from scipy.spatial.distance import cdist def test_match(): a = 'A' b = 'B' assert np.equal(match(a, b), 1) def test_euclidean_list(): a = [0, 1, 0] b = [0, 1, 0] assert np.equal(euclidean(a, b), 0) def test_euclidean_array(): a = np.array([0, 1, 0]) b = np.array([0, 1, 0]) assert np.equal(euclidean(a, b), 0) def test_correlation_list(): a = [0, 1, 0] b = [0, 1, 0] assert np.equal(correlation(a, b), 1) def test_correlation_array(): a = np.array([0, 1, 0]) b = np.array([0, 1, 0]) assert np.equal(correlation(a, b), 1)

# -*- coding: utf-8 -*- from quail.distance import * import numpy as np import pytest from scipy.spatial.distance import cdist def test_match(): a = 'A' b = 'B' assert np.equal(match(a, b), 1) def test_euclidean_list(): a = [0, 1, 0] b = [0, 1, 0] assert np.equal(euclidean(a, b), 0) def test_euclidean_array(): a = np.array([0, 1, 0]) b = np.array([0, 1, 0]) assert np.equal(euclidean(a, b), 0) def test_correlation_list(): a = [0, 1, 0] b = [0, 1, 0] assert np.equal(correlation(a, b), 1) def test_correlation_array(): a = np.array([0, 1, 0]) b = np.array([0, 1, 0]) assert np.equal(correlation(a, b), 1)

en

0.769321

# -*- coding: utf-8 -*-

2.58452

3

utils/manisfestManager.py

ovitrac/pizza3

1

10282

#!/usr/bin/env python ############################################################################### # # # manifestManager.py # # # # Work with online data manifests (creating / syncing / validating) # # # # Copyright (C) <NAME> # # # ############################################################################### # # # This program is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 3 of the License, or # # (at your option) any later version. # # # # This program is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with this program. If not, see <http://www.gnu.org/licenses/>. # # # ############################################################################### __author__ = "<NAME>" __copyright__ = "Copyright 2014" __credits__ = ["<NAME>"] __license__ = "GPLv3" __maintainer__ = "<NAME>" __email__ = "<EMAIL>" __version__ = "0.35" ############################################################################### ############################################################################### ############################################################################### ############################################################################### __MANIFEST__ = ".dmanifest" ############################################################################### ############################################################################### ############################################################################### ############################################################################### # system includes import os import hashlib import urllib.request, urllib.error, urllib.parse import urllib.request, urllib.parse, urllib.error import shutil import errno # local includes from fileEntity import FileEntity as FE ############################################################################### ############################################################################### ############################################################################### ############################################################################### class ManifestManager(object): """Use this interface for storing and managing file and paths""" def __init__(self, manType=None, timeout=30): self.timeout = timeout self.myExtensions = [".py",".sh"] self.files = [] if manType is not None: self.type = manType else: self.type = "generic" def createManifest(self, path, manifestName=None): """inventory all files in path and create a manifest file""" if manifestName is None: manifestName = __MANIFEST__ # make the root file entity root_path = os.path.abspath(path) root_fe = FE('root', ".", None, "-", 0) self.files.append(root_fe) # now make all the ones below parents = [root_fe] dirs, files = self.listdir(path)[:2] self.walk(parents, root_path, '', dirs, files, skipFile=manifestName) with open(os.path.join(path, manifestName), 'w') as man_fh: # print the header man_fh.write("#\t::: %s ::: \tPizza3 manifest version %s\n\n" % (self.type, __version__)) for f in self.files: if f.parent is not None: man_fh.write("%s\n" % f) def diffManifests(self, localManifestLocation, sourceManifestLocation, localManifestName=None, sourceManifestName=None, printDiffs=False): """check for any differences between two manifests if remote is true then sourceManifestLocation is a URL returns a list of files that need to be updated """ if localManifestName is None: localManifestName = __MANIFEST__ if sourceManifestName is None: sourceManifestName = __MANIFEST__ # get the "type" of the local manifest l_type = "generic" with open(os.path.join(localManifestLocation, localManifestName)) as l_man: for line in l_man: if line[0] == "#": l_type = self.getManType(line) break # load the source manifest s_type = "generic" source_man = {} source = "" # first we assume it is remote try: s_man = urllib.request.urlopen(sourceManifestLocation + "/" + sourceManifestName, None, self.timeout) source = sourceManifestLocation + "/" except ValueError: # then it is probably a file s_man = open(os.path.join(sourceManifestLocation, sourceManifestName)) source = os.path.join(sourceManifestLocation) + os.path.sep except urllib.error.URLError: # problems connecting to server, perhaps user is behind a proxy or firewall print("Error: failed to connect to server.") return (None, None, None, None, None) first_line = True for line in s_man: if first_line: first_line = False if line[0] == "#": # get the type of the manifest s_type = self.getManType(line) if s_type != l_type: print("Error: type of source manifest (%s) does not match type of local manifest (%s)" % (s_type, l_type)) return (None, None, None, None, None) else: # no type specified print("Error: type of source manifest is not specified. Is this a valid manifest file?") return (None, None, None, None, None) self.type = l_type if line[0] != "#": fields = line.rstrip().split("\t") # set the dict up as {path => [hash, size, seenLocal] source_man[fields[0]] = [fields[1], fields[2], False] # keep lists of modifications deleted = [] addedDirs = [] addedFiles = [] modified = [] with open(os.path.join(localManifestLocation, localManifestName)) as l_man: for line in l_man: if line[0] != "#": fields = line.rstrip().split("\t") try: if source_man[fields[0]][0] != fields[1]: # hashes don't match modified.append(fields[0]) # seen this file source_man[fields[0]][2] = True except KeyError: # this file has been deleted from the source manifest deleted.append(fields[0]) # check for new files for f in list(source_man.keys()): if source_man[f][2] == False: if source_man[f][0] == '-': addedDirs.append(f) else: addedFiles.append(f) if printDiffs: new_size = 0 modified_size = 0 for f in addedFiles: new_size += int(source_man[f][1]) for f in modified: modified_size += int(source_man[f][1]) if len(addedFiles) > 0: print("#------------------------------------------------------") print("# Source contains %d new file(s) (%s)" % (len(addedFiles), self.formatData(new_size))) for f in addedFiles: print("\t".join([self.formatData(int(source_man[f][1])), f])) if len(addedDirs) > 0: print("#------------------------------------------------------") print("# Source contains %d new folders(s)" % (len(addedDirs))) for f in addedDirs: print(f) if len(modified) > 0: print("#------------------------------------------------------") print("# Source contains %d modified file(s) (%s)" % (len(modified), self.formatData(modified_size))) for f in modified: print(f) if len(deleted) > 0: print("#------------------------------------------------------") print("# %d files have been deleted in the source:" % len(deleted)) for f in deleted: print(f) else: return (source, [(a, source_man[a]) for a in addedFiles], [(a, source_man[a]) for a in addedDirs], deleted, [(m, source_man[m]) for m in modified]) def updateManifest(self, localManifestLocation, sourceManifestLocation, localManifestName=None, sourceManifestName=None, prompt=True): """Update local files based on remote changes""" # get the diffs source, added_files, added_dirs, deleted, modified = self.diffManifests(localManifestLocation, sourceManifestLocation, localManifestName, sourceManifestName) # bail if the diff failed if source is None: return False # no changes by default do_down = False if prompt: total_size = 0 for f in added_files: total_size += int(f[1][1]) for f in modified: total_size += int(f[1][1]) if total_size != 0: print("****************************************************************") print("%d new file(s) to be downloaded from source" % len(added_files)) print("%d existing file(s) to be updated" % len(modified)) print("%s will need to be downloaded" % self.formatData(total_size)) do_down = self.promptUserDownload() if not do_down: print("Download aborted") update_manifest = False if do_down: update_manifest = True for add in added_dirs: # make the dirs first full_path = os.path.abspath(os.path.join(localManifestLocation, add[0])) self.makeSurePathExists(full_path) for add in added_files: full_path = os.path.abspath(os.path.join(localManifestLocation, add[0])) urllib.request.urlretrieve(source+add[0], full_path) for modify in modified: full_path = os.path.abspath(os.path.join(localManifestLocation, modify[0])) urllib.request.urlretrieve(source+modify[0], full_path) if update_manifest: print("(re) creating manifest file (please be patient)") self.createManifest(localManifestLocation, manifestName=localManifestName) return True def getManType(self, line): """Work out the manifest type from the first line of the file""" return line.rstrip().split("##")[1] def formatData(self, amount): """Pretty print file sizes""" if amount < 1024*1024: return "%d B" % amount elif amount < 1024*1024*1024: return "%0.2f MB" % (float(amount)/(1024.*1024.)) elif amount < 1024*1024*1024*1024: return "%0.2f GB" % (float(amount)/(1024.*1024.*1024.)) elif amount < 1024*1024*1024*1024*1024: return "%0.2f TB" % (float(amount)/(1024.*1024.*1024.*1024.)) #----------------------------------------------------------------------------- # FS utilities def makeSurePathExists(self, path): try: os.makedirs(path) except OSError as exception: if exception.errno != errno.EEXIST: raise def promptUserDownload(self): """Check that the user is OK with making changes""" input_not_ok = True minimal=False valid_responses = {'Y':True,'N':False} vrs = ",".join([x.lower() for x in list(valid_responses.keys())]) while(input_not_ok): if(minimal): option = input("Download? ("+vrs+") : ").upper() else: option = input("Confirm you want to download this data\n" \ "Changes *WILL* be permanent\n" \ "Continue? ("+vrs+") : ").upper() if(option in valid_responses): print("****************************************************************") return valid_responses[option] else: print("ERROR: unrecognised choice '"+option+"'") minimal = True def walk(self, parents, full_path, rel_path, dirs, files, skipFile=__MANIFEST__): """recursive walk through directory tree""" # first do files here for f in files: if (f != skipFile) and os.path.splitext(f)[1] in self.myExtensions: path = os.path.join(full_path, f) self.files.append(FE(f, rel_path, parents[-1], self.hashfile(path), os.path.getsize(path) ) ) for d in dirs: # the walk will go into these dirs first tmp_fe = FE(d, rel_path, parents[-1], "-", 0) self.files.append(tmp_fe) parents.append(tmp_fe) new_full_path = os.path.join(full_path, d) new_rel_path = os.path.join(rel_path, d) new_dirs, new_files = self.listdir(new_full_path)[:2] self.walk(parents, new_full_path, new_rel_path, new_dirs, new_files) parents.pop() def listdir(self, path): """List dirs, files etc in path (one dir deep)""" dirs, files, links = [], [], [] for name in os.listdir(path): path_name = os.path.join(path, name) if os.path.isdir(path_name): dirs.append(name) elif os.path.isfile(path_name): files.append(name) elif os.path.islink(path_name): links.append(name) return dirs, files, links def hashfile(self, fileName, blocksize=65536): """Hash a file and return the digest""" hasher = hashlib.sha256() with open(fileName,"rb") as fh: buf = fh.read(blocksize) while len(buf) > 0: hasher.update(buf.strip()) buf = fh.read(blocksize) return hasher.hexdigest() return "?" ############################################################################### ############################################################################### ############################################################################### ############################################################################### # %% DEBUG # =================================================== # main() # =================================================== # for debugging purposes (code called as a script) # the code is called from here # =================================================== if __name__ == '__main__': man = ManifestManager() man.createManifest("/home/olivi/billy/python",manifestName="Pizza3.manifest")

de

0.35631

#!/usr/bin/env python ############################################################################### # # # manifestManager.py # # # # Work with online data manifests (creating / syncing / validating) # # # # Copyright (C) <NAME> # # # ############################################################################### # # # This program is free software: you can redistribute it and/or modify # # it under the terms of the GNU General Public License as published by # # the Free Software Foundation, either version 3 of the License, or # # (at your option) any later version. # # # # This program is distributed in the hope that it will be useful, # # but WITHOUT ANY WARRANTY; without even the implied warranty of # # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # # GNU General Public License for more details. # # # # You should have received a copy of the GNU General Public License # # along with this program. If not, see <http://www.gnu.org/licenses/>. # # # ############################################################################### ############################################################################### ############################################################################### ############################################################################### ############################################################################### ############################################################################### ############################################################################### ############################################################################### ############################################################################### # system includes # local includes ############################################################################### ############################################################################### ############################################################################### ############################################################################### Use this interface for storing and managing file and paths inventory all files in path and create a manifest file # make the root file entity # now make all the ones below # print the header check for any differences between two manifests if remote is true then sourceManifestLocation is a URL returns a list of files that need to be updated # get the "type" of the local manifest # load the source manifest # first we assume it is remote # then it is probably a file # problems connecting to server, perhaps user is behind a proxy or firewall # get the type of the manifest # no type specified # set the dict up as {path => [hash, size, seenLocal] # keep lists of modifications # hashes don't match # seen this file # this file has been deleted from the source manifest # check for new files Update local files based on remote changes # get the diffs # bail if the diff failed # no changes by default # make the dirs first Work out the manifest type from the first line of the file #")[1] Pretty print file sizes #----------------------------------------------------------------------------- # FS utilities Check that the user is OK with making changes recursive walk through directory tree # first do files here # the walk will go into these dirs first List dirs, files etc in path (one dir deep) Hash a file and return the digest ############################################################################### ############################################################################### ############################################################################### ############################################################################### # %% DEBUG # =================================================== # main() # =================================================== # for debugging purposes (code called as a script) # the code is called from here # ===================================================

1.474562

1

temperature.py

rhwlr/TEST_PRELIM_SKILLS_EXAM

0

10283

class Temperature: def __init__(self, kelvin=None, celsius=None, fahrenheit=None): values = [x for x in [kelvin, celsius, fahrenheit] if x] if len(values) < 1: raise ValueError('Need argument') if len(values) > 1: raise ValueError('Only one argument') if celsius is not None: self.kelvin = celsius + 273.15 elif fahrenheit is not None: self.kelvin = (fahrenheit - 32) * 5 / 9 + 273.15 else: self.kelvin = kelvin if self.kelvin < 0: raise ValueError('Temperature in Kelvin cannot be negative') def __str__(self): return f'Temperature = {self.kelvin} Kelvins'

none

1

3.667491

4

af/shovel/test_canning.py

mimi89999/pipeline

0

10284

<gh_stars>0 #!/usr/bin/env python2.7 import unittest import canning class TestNop(unittest.TestCase): def test_nop(self): canning.NopTeeFd.write("asdf") class TestSlice(unittest.TestCase): REPORT = "20130505T065614Z-VN-AS24173-dns_consistency-no_report_id-0.1.0-probe.yaml" @staticmethod def rpt(year): assert year < 10000 return "{:04d}1231T065614Z-VN-AS24173-dns_consistency-no_report_id-0.1.0-probe.yaml".format( year ) def test_empty(self): asis, tarfiles = canning.pack_bucket(tuple()) self.assertFalse(asis) self.assertFalse(tarfiles) def test_badname(self): self.assertRaises(RuntimeError, canning.pack_bucket, [("foo", 42)]) self.assertRaises( RuntimeError, canning.pack_bucket, [("2013-05-05/" + self.REPORT, 42)] ) def test_single(self): for sz in [0, 1, 65 * 1048576]: asis, tarfiles = canning.pack_bucket([(self.REPORT, sz)]) self.assertEqual(asis, [self.REPORT]) self.assertFalse(tarfiles) def test_packing(self): asis, tarfiles = canning.pack_bucket( [(self.rpt(0), 42), (self.rpt(1), 64), (self.rpt(2), 64 * 1048576)] ) self.assertEqual(asis, [self.rpt(2)]) self.assertEqual(tarfiles, {"dns_consistency.0.tar": map(self.rpt, (0, 1))}) def test_stupid(self): # FIXME: is it really good behaviour?... asis, tarfiles = canning.pack_bucket( [(self.rpt(0), 42), (self.rpt(1), 64 * 1048576 - 1), (self.rpt(2), 64)] ) self.assertEqual(asis, map(self.rpt, (0, 1, 2))) self.assertEqual(tarfiles, {}) if __name__ == "__main__": unittest.main()

#!/usr/bin/env python2.7 import unittest import canning class TestNop(unittest.TestCase): def test_nop(self): canning.NopTeeFd.write("asdf") class TestSlice(unittest.TestCase): REPORT = "20130505T065614Z-VN-AS24173-dns_consistency-no_report_id-0.1.0-probe.yaml" @staticmethod def rpt(year): assert year < 10000 return "{:04d}1231T065614Z-VN-AS24173-dns_consistency-no_report_id-0.1.0-probe.yaml".format( year ) def test_empty(self): asis, tarfiles = canning.pack_bucket(tuple()) self.assertFalse(asis) self.assertFalse(tarfiles) def test_badname(self): self.assertRaises(RuntimeError, canning.pack_bucket, [("foo", 42)]) self.assertRaises( RuntimeError, canning.pack_bucket, [("2013-05-05/" + self.REPORT, 42)] ) def test_single(self): for sz in [0, 1, 65 * 1048576]: asis, tarfiles = canning.pack_bucket([(self.REPORT, sz)]) self.assertEqual(asis, [self.REPORT]) self.assertFalse(tarfiles) def test_packing(self): asis, tarfiles = canning.pack_bucket( [(self.rpt(0), 42), (self.rpt(1), 64), (self.rpt(2), 64 * 1048576)] ) self.assertEqual(asis, [self.rpt(2)]) self.assertEqual(tarfiles, {"dns_consistency.0.tar": map(self.rpt, (0, 1))}) def test_stupid(self): # FIXME: is it really good behaviour?... asis, tarfiles = canning.pack_bucket( [(self.rpt(0), 42), (self.rpt(1), 64 * 1048576 - 1), (self.rpt(2), 64)] ) self.assertEqual(asis, map(self.rpt, (0, 1, 2))) self.assertEqual(tarfiles, {}) if __name__ == "__main__": unittest.main()

en

0.713974

#!/usr/bin/env python2.7 # FIXME: is it really good behaviour?...

2.742623

3

Exercicios/ex061.py

jlsmirandela/Curso_Python

0

10285

<reponame>jlsmirandela/Curso_Python print('-+-' *10) print(' <NAME> PA') print('+-+' * 10) c = 1 ter = int(input('Insira o primeiro termo - ')) rz = int(input('Insira a razão - ')) while c <= 10: print(ter, ' → ', end=' ') ter += rz c += 1 print('FIM')

print('-+-' *10) print(' <NAME> PA') print('+-+' * 10) c = 1 ter = int(input('Insira o primeiro termo - ')) rz = int(input('Insira a razão - ')) while c <= 10: print(ter, ' → ', end=' ') ter += rz c += 1 print('FIM')

none

1

3.760039

4

gpytorch/models/approximate_gp.py

phumm/gpytorch

1

10286

<reponame>phumm/gpytorch #!/usr/bin/env python3 from .gp import GP from .pyro import _PyroMixin # This will only contain functions if Pyro is installed class ApproximateGP(GP, _PyroMixin): def __init__(self, variational_strategy): super().__init__() self.variational_strategy = variational_strategy def forward(self, x): """ As in the exact GP setting, the user-defined forward method should return the GP prior mean and covariance evaluated at input locations x. """ raise NotImplementedError def pyro_guide(self, input, beta=1.0, name_prefix=""): """ (For Pyro integration only). The component of a `pyro.guide` that corresponds to drawing samples from the latent GP function. Args: :attr:`input` (:obj:`torch.Tensor`) The inputs :math:`\mathbf X`. :attr:`beta` (float, default=1.) How much to scale the :math:`\text{KL} [ q(\mathbf f) \Vert p(\mathbf f) ]` term by. :attr:`name_prefix` (str, default="") A name prefix to prepend to pyro sample sites. """ return super().pyro_guide(input, beta=beta, name_prefix=name_prefix) def pyro_model(self, input, beta=1.0, name_prefix=""): r""" (For Pyro integration only). The component of a `pyro.model` that corresponds to drawing samples from the latent GP function. Args: :attr:`input` (:obj:`torch.Tensor`) The inputs :math:`\mathbf X`. :attr:`beta` (float, default=1.) How much to scale the :math:`\text{KL} [ q(\mathbf f) \Vert p(\mathbf f) ]` term by. :attr:`name_prefix` (str, default="") A name prefix to prepend to pyro sample sites. Returns: :obj:`torch.Tensor` samples from :math:`q(\mathbf f)` """ return super().pyro_model(input, beta=beta, name_prefix=name_prefix) def __call__(self, inputs, prior=False, **kwargs): if inputs.dim() == 1: inputs = inputs.unsqueeze(-1) return self.variational_strategy(inputs, prior=prior)

#!/usr/bin/env python3 from .gp import GP from .pyro import _PyroMixin # This will only contain functions if Pyro is installed class ApproximateGP(GP, _PyroMixin): def __init__(self, variational_strategy): super().__init__() self.variational_strategy = variational_strategy def forward(self, x): """ As in the exact GP setting, the user-defined forward method should return the GP prior mean and covariance evaluated at input locations x. """ raise NotImplementedError def pyro_guide(self, input, beta=1.0, name_prefix=""): """ (For Pyro integration only). The component of a `pyro.guide` that corresponds to drawing samples from the latent GP function. Args: :attr:`input` (:obj:`torch.Tensor`) The inputs :math:`\mathbf X`. :attr:`beta` (float, default=1.) How much to scale the :math:`\text{KL} [ q(\mathbf f) \Vert p(\mathbf f) ]` term by. :attr:`name_prefix` (str, default="") A name prefix to prepend to pyro sample sites. """ return super().pyro_guide(input, beta=beta, name_prefix=name_prefix) def pyro_model(self, input, beta=1.0, name_prefix=""): r""" (For Pyro integration only). The component of a `pyro.model` that corresponds to drawing samples from the latent GP function. Args: :attr:`input` (:obj:`torch.Tensor`) The inputs :math:`\mathbf X`. :attr:`beta` (float, default=1.) How much to scale the :math:`\text{KL} [ q(\mathbf f) \Vert p(\mathbf f) ]` term by. :attr:`name_prefix` (str, default="") A name prefix to prepend to pyro sample sites. Returns: :obj:`torch.Tensor` samples from :math:`q(\mathbf f)` """ return super().pyro_model(input, beta=beta, name_prefix=name_prefix) def __call__(self, inputs, prior=False, **kwargs): if inputs.dim() == 1: inputs = inputs.unsqueeze(-1) return self.variational_strategy(inputs, prior=prior)

en

0.645391

#!/usr/bin/env python3 # This will only contain functions if Pyro is installed As in the exact GP setting, the user-defined forward method should return the GP prior mean and covariance evaluated at input locations x. (For Pyro integration only). The component of a `pyro.guide` that corresponds to drawing samples from the latent GP function. Args: :attr:`input` (:obj:`torch.Tensor`) The inputs :math:`\mathbf X`. :attr:`beta` (float, default=1.) How much to scale the :math:`\text{KL} [ q(\mathbf f) \Vert p(\mathbf f) ]` term by. :attr:`name_prefix` (str, default="") A name prefix to prepend to pyro sample sites. (For Pyro integration only). The component of a `pyro.model` that corresponds to drawing samples from the latent GP function. Args: :attr:`input` (:obj:`torch.Tensor`) The inputs :math:`\mathbf X`. :attr:`beta` (float, default=1.) How much to scale the :math:`\text{KL} [ q(\mathbf f) \Vert p(\mathbf f) ]` term by. :attr:`name_prefix` (str, default="") A name prefix to prepend to pyro sample sites. Returns: :obj:`torch.Tensor` samples from :math:`q(\mathbf f)`

2.455583

2

tests/test_ping.py

d-wysocki/flask-resty

86

10287

import pytest from flask_resty import Api from flask_resty.testing import assert_response # ----------------------------------------------------------------------------- @pytest.fixture(autouse=True) def routes(app): api = Api(app, "/api") api.add_ping("/ping") # ----------------------------------------------------------------------------- def test_ping(base_client): response = base_client.get("/ping") assert_response(response, 200) assert response.get_data(as_text=True) == ""

en

0.12172

# ----------------------------------------------------------------------------- # -----------------------------------------------------------------------------

2.447855

2

tests/test_vetters.py

pllim/exovetter

0

10288

<filename>tests/test_vetters.py from numpy.testing import assert_allclose from astropy.io import ascii from astropy import units as u import lightkurve as lk from exovetter import const as exo_const from exovetter import vetters from exovetter.tce import Tce from astropy.utils.data import get_pkg_data_filename def get_wasp18_tce(): tce = Tce(period=0.94124 * u.day, epoch=58374.669883 * u.day, epoch_offset=-2400000.5 * u.day, depth=0.00990112 * exo_const.frac_amp, duration=0.08932 * u.day, event_name='WASP-18 b', target_name='WASP-18', snr=50) return tce def get_wasp18_lightcurve(): lc_file = get_pkg_data_filename("data/wasp18b_flat_lightcurve.csv") lc_table = ascii.read(lc_file, data_start=1) lc = lk.LightCurve(time=lc_table['col2'], flux=lc_table['col3'], flux_err=lc_table['col4'], time_format="btjd") return lc def test_vetters(): tce = get_wasp18_tce() lc = get_wasp18_lightcurve() metrics = dict() vetter_list = [vetters.Lpp(), vetters.OddEven(), vetters.TransitPhaseCoverage()] for v in vetter_list: vetter = v _ = vetter.run(tce, lc) metrics.update(vetter.__dict__) assert_allclose(metrics['norm_lpp'], 7.93119, rtol=1e-3) assert_allclose(metrics['tp_cover'], 1.0, rtol=1e-5) assert_allclose(metrics['odd_depth'][0], 0.99, rtol=1e-1)

none

1

2.232303

2

pyqtgraph/dockarea/DockDrop.py

hishizuka/pyqtgraph

2,762

10289

<reponame>hishizuka/pyqtgraph # -*- coding: utf-8 -*- from ..Qt import QtCore, QtGui class DockDrop(object): """Provides dock-dropping methods""" def __init__(self, allowedAreas=None): object.__init__(self) if allowedAreas is None: allowedAreas = ['center', 'right', 'left', 'top', 'bottom'] self.allowedAreas = set(allowedAreas) self.setAcceptDrops(True) self.dropArea = None self.overlay = DropAreaOverlay(self) self.overlay.raise_() def resizeOverlay(self, size): self.overlay.resize(size) def raiseOverlay(self): self.overlay.raise_() def dragEnterEvent(self, ev): src = ev.source() if hasattr(src, 'implements') and src.implements('dock'): #print "drag enter accept" ev.accept() else: #print "drag enter ignore" ev.ignore() def dragMoveEvent(self, ev): #print "drag move" # QDragMoveEvent inherits QDropEvent which provides posF() # PyQt6 provides only position() posF = ev.posF() if hasattr(ev, 'posF') else ev.position() ld = posF.x() rd = self.width() - ld td = posF.y() bd = self.height() - td mn = min(ld, rd, td, bd) if mn > 30: self.dropArea = "center" elif (ld == mn or td == mn) and mn > self.height()/3.: self.dropArea = "center" elif (rd == mn or ld == mn) and mn > self.width()/3.: self.dropArea = "center" elif rd == mn: self.dropArea = "right" elif ld == mn: self.dropArea = "left" elif td == mn: self.dropArea = "top" elif bd == mn: self.dropArea = "bottom" if ev.source() is self and self.dropArea == 'center': #print " no self-center" self.dropArea = None ev.ignore() elif self.dropArea not in self.allowedAreas: #print " not allowed" self.dropArea = None ev.ignore() else: #print " ok" ev.accept() self.overlay.setDropArea(self.dropArea) def dragLeaveEvent(self, ev): self.dropArea = None self.overlay.setDropArea(self.dropArea) def dropEvent(self, ev): area = self.dropArea if area is None: return if area == 'center': area = 'above' self.area.moveDock(ev.source(), area, self) self.dropArea = None self.overlay.setDropArea(self.dropArea) class DropAreaOverlay(QtGui.QWidget): """Overlay widget that draws drop areas during a drag-drop operation""" def __init__(self, parent): QtGui.QWidget.__init__(self, parent) self.dropArea = None self.hide() self.setAttribute(QtCore.Qt.WidgetAttribute.WA_TransparentForMouseEvents) def setDropArea(self, area): self.dropArea = area if area is None: self.hide() else: ## Resize overlay to just the region where drop area should be displayed. ## This works around a Qt bug--can't display transparent widgets over QGLWidget prgn = self.parent().rect() rgn = QtCore.QRect(prgn) w = min(30, prgn.width()/3.) h = min(30, prgn.height()/3.) if self.dropArea == 'left': rgn.setWidth(w) elif self.dropArea == 'right': rgn.setLeft(rgn.left() + prgn.width() - w) elif self.dropArea == 'top': rgn.setHeight(h) elif self.dropArea == 'bottom': rgn.setTop(rgn.top() + prgn.height() - h) elif self.dropArea == 'center': rgn.adjust(w, h, -w, -h) self.setGeometry(rgn) self.show() self.update() def paintEvent(self, ev): if self.dropArea is None: return p = QtGui.QPainter(self) rgn = self.rect() p.setBrush(QtGui.QBrush(QtGui.QColor(100, 100, 255, 50))) p.setPen(QtGui.QPen(QtGui.QColor(50, 50, 150), 3)) p.drawRect(rgn)

# -*- coding: utf-8 -*- from ..Qt import QtCore, QtGui class DockDrop(object): """Provides dock-dropping methods""" def __init__(self, allowedAreas=None): object.__init__(self) if allowedAreas is None: allowedAreas = ['center', 'right', 'left', 'top', 'bottom'] self.allowedAreas = set(allowedAreas) self.setAcceptDrops(True) self.dropArea = None self.overlay = DropAreaOverlay(self) self.overlay.raise_() def resizeOverlay(self, size): self.overlay.resize(size) def raiseOverlay(self): self.overlay.raise_() def dragEnterEvent(self, ev): src = ev.source() if hasattr(src, 'implements') and src.implements('dock'): #print "drag enter accept" ev.accept() else: #print "drag enter ignore" ev.ignore() def dragMoveEvent(self, ev): #print "drag move" # QDragMoveEvent inherits QDropEvent which provides posF() # PyQt6 provides only position() posF = ev.posF() if hasattr(ev, 'posF') else ev.position() ld = posF.x() rd = self.width() - ld td = posF.y() bd = self.height() - td mn = min(ld, rd, td, bd) if mn > 30: self.dropArea = "center" elif (ld == mn or td == mn) and mn > self.height()/3.: self.dropArea = "center" elif (rd == mn or ld == mn) and mn > self.width()/3.: self.dropArea = "center" elif rd == mn: self.dropArea = "right" elif ld == mn: self.dropArea = "left" elif td == mn: self.dropArea = "top" elif bd == mn: self.dropArea = "bottom" if ev.source() is self and self.dropArea == 'center': #print " no self-center" self.dropArea = None ev.ignore() elif self.dropArea not in self.allowedAreas: #print " not allowed" self.dropArea = None ev.ignore() else: #print " ok" ev.accept() self.overlay.setDropArea(self.dropArea) def dragLeaveEvent(self, ev): self.dropArea = None self.overlay.setDropArea(self.dropArea) def dropEvent(self, ev): area = self.dropArea if area is None: return if area == 'center': area = 'above' self.area.moveDock(ev.source(), area, self) self.dropArea = None self.overlay.setDropArea(self.dropArea) class DropAreaOverlay(QtGui.QWidget): """Overlay widget that draws drop areas during a drag-drop operation""" def __init__(self, parent): QtGui.QWidget.__init__(self, parent) self.dropArea = None self.hide() self.setAttribute(QtCore.Qt.WidgetAttribute.WA_TransparentForMouseEvents) def setDropArea(self, area): self.dropArea = area if area is None: self.hide() else: ## Resize overlay to just the region where drop area should be displayed. ## This works around a Qt bug--can't display transparent widgets over QGLWidget prgn = self.parent().rect() rgn = QtCore.QRect(prgn) w = min(30, prgn.width()/3.) h = min(30, prgn.height()/3.) if self.dropArea == 'left': rgn.setWidth(w) elif self.dropArea == 'right': rgn.setLeft(rgn.left() + prgn.width() - w) elif self.dropArea == 'top': rgn.setHeight(h) elif self.dropArea == 'bottom': rgn.setTop(rgn.top() + prgn.height() - h) elif self.dropArea == 'center': rgn.adjust(w, h, -w, -h) self.setGeometry(rgn) self.show() self.update() def paintEvent(self, ev): if self.dropArea is None: return p = QtGui.QPainter(self) rgn = self.rect() p.setBrush(QtGui.QBrush(QtGui.QColor(100, 100, 255, 50))) p.setPen(QtGui.QPen(QtGui.QColor(50, 50, 150), 3)) p.drawRect(rgn)

en

0.723234

# -*- coding: utf-8 -*- Provides dock-dropping methods #print "drag enter accept" #print "drag enter ignore" #print "drag move" # QDragMoveEvent inherits QDropEvent which provides posF() # PyQt6 provides only position() #print " no self-center" #print " not allowed" #print " ok" Overlay widget that draws drop areas during a drag-drop operation ## Resize overlay to just the region where drop area should be displayed. ## This works around a Qt bug--can't display transparent widgets over QGLWidget

2.803189

3

data/download.py

pyaf/google-ai-open-images-object-detection-track

0

10290

import os from subprocess import call files = ['000002b66c9c498e.jpg', '000002b97e5471a0.jpg', '000002c707c9895e.jpg', '0000048549557964.jpg', '000004f4400f6ec5.jpg', '0000071d71a0a6f6.jpg', '000013ba71c12506.jpg', '000018acd19b4ad3.jpg', '00001bc2c4027449.jpg', '00001bcc92282a38.jpg', '0000201cd362f303.jpg', '000020780ccee28d.jpg', '000023aa04ab09ed.jpg', '0000253ea4ecbf19.jpg', '000025ea48cab6fc.jpg', '0000271195f2c007.jpg', '0000286a5c6a3eb5.jpg', '00002b368e91b947.jpg', '00002f4ff380c64c.jpg', '0000313e5dccf13b.jpg', '000032046c3f8371.jpg', '00003223e04e2e66.jpg', '0000333f08ced1cd.jpg'] for file in files: if not os.path.exists('train/' + file + '.jpg'): spath = "gs://open-images-dataset/train/%s " % file call(["gsutil", "cp", spath, 'train/']) print(file, 'done', 'count:') else: print(file, 'already downloaded')

none

1

2.214116

2

cisco-ios-xr/ydk/models/cisco_ios_xr/SNMP_FRAMEWORK_MIB.py

bopopescu/ACI

0

10291

""" SNMP_FRAMEWORK_MIB """ from collections import OrderedDict from ydk.types import Entity, EntityPath, Identity, Enum, YType, YLeaf, YLeafList, YList, LeafDataList, Bits, Empty, Decimal64 from ydk.filters import YFilter from ydk.errors import YError, YModelError from ydk.errors.error_handler import handle_type_error as _handle_type_error class SnmpSecurityLevel(Enum): """ SnmpSecurityLevel (Enum Class) .. data:: noAuthNoPriv = 1 .. data:: authNoPriv = 2 .. data:: authPriv = 3 """ noAuthNoPriv = Enum.YLeaf(1, "noAuthNoPriv") authNoPriv = Enum.YLeaf(2, "authNoPriv") authPriv = Enum.YLeaf(3, "authPriv") class SNMPFRAMEWORKMIB(Entity): """ .. attribute:: snmpengine **type**\: :py:class:`Snmpengine <ydk.models.cisco_ios_xr.SNMP_FRAMEWORK_MIB.SNMPFRAMEWORKMIB.Snmpengine>` """ _prefix = 'SNMP_FRAMEWORK_MIB' _revision = '2002-10-14' def __init__(self): super(SNMPFRAMEWORKMIB, self).__init__() self._top_entity = None self.yang_name = "SNMP-FRAMEWORK-MIB" self.yang_parent_name = "SNMP-FRAMEWORK-MIB" self.is_top_level_class = True self.has_list_ancestor = False self.ylist_key_names = [] self._child_container_classes = OrderedDict([("snmpEngine", ("snmpengine", SNMPFRAMEWORKMIB.Snmpengine))]) self._child_list_classes = OrderedDict([]) self._leafs = OrderedDict() self.snmpengine = SNMPFRAMEWORKMIB.Snmpengine() self.snmpengine.parent = self self._children_name_map["snmpengine"] = "snmpEngine" self._children_yang_names.add("snmpEngine") self._segment_path = lambda: "SNMP-FRAMEWORK-MIB:SNMP-FRAMEWORK-MIB" class Snmpengine(Entity): """ .. attribute:: snmpengineid **type**\: str **pattern:** (([0\-9a\-fA\-F]){2}(\:([0\-9a\-fA\-F]){2})\*)? .. attribute:: snmpengineboots **type**\: int **range:** 1..2147483647 .. attribute:: snmpenginetime **type**\: int **range:** 0..2147483647 .. attribute:: snmpenginemaxmessagesize **type**\: int **range:** 484..2147483647 """ _prefix = 'SNMP_FRAMEWORK_MIB' _revision = '2002-10-14' def __init__(self): super(SNMPFRAMEWORKMIB.Snmpengine, self).__init__() self.yang_name = "snmpEngine" self.yang_parent_name = "SNMP-FRAMEWORK-MIB" self.is_top_level_class = False self.has_list_ancestor = False self.ylist_key_names = [] self._child_container_classes = OrderedDict([]) self._child_list_classes = OrderedDict([]) self._leafs = OrderedDict([ ('snmpengineid', YLeaf(YType.str, 'snmpEngineID')), ('snmpengineboots', YLeaf(YType.int32, 'snmpEngineBoots')), ('snmpenginetime', YLeaf(YType.int32, 'snmpEngineTime')), ('snmpenginemaxmessagesize', YLeaf(YType.int32, 'snmpEngineMaxMessageSize')), ]) self.snmpengineid = None self.snmpengineboots = None self.snmpenginetime = None self.snmpenginemaxmessagesize = None self._segment_path = lambda: "snmpEngine" self._absolute_path = lambda: "SNMP-FRAMEWORK-MIB:SNMP-FRAMEWORK-MIB/%s" % self._segment_path() def __setattr__(self, name, value): self._perform_setattr(SNMPFRAMEWORKMIB.Snmpengine, ['snmpengineid', 'snmpengineboots', 'snmpenginetime', 'snmpenginemaxmessagesize'], name, value) def clone_ptr(self): self._top_entity = SNMPFRAMEWORKMIB() return self._top_entity

en

0.23202

SNMP_FRAMEWORK_MIB SnmpSecurityLevel (Enum Class) .. data:: noAuthNoPriv = 1 .. data:: authNoPriv = 2 .. data:: authPriv = 3 .. attribute:: snmpengine **type**\: :py:class:`Snmpengine <ydk.models.cisco_ios_xr.SNMP_FRAMEWORK_MIB.SNMPFRAMEWORKMIB.Snmpengine>` .. attribute:: snmpengineid **type**\: str **pattern:** (([0\-9a\-fA\-F]){2}(\:([0\-9a\-fA\-F]){2})\*)? .. attribute:: snmpengineboots **type**\: int **range:** 1..2147483647 .. attribute:: snmpenginetime **type**\: int **range:** 0..2147483647 .. attribute:: snmpenginemaxmessagesize **type**\: int **range:** 484..2147483647

2.099786

2

handlers/redirects.py

Bainky/Ventify

6

10292

from aiogram.utils.markdown import hide_link from aiogram.types import CallbackQuery from loader import dp from utils import ( get_object, get_attributes_of_object ) from keyboards import ( anime_choose_safe_category, anime_sfw_categories, anime_nsfw_categories, animals_categories, menu_with_categories, control_buttons ) @dp.callback_query_handler(text="menu") async def call_menu_with_categories(call: CallbackQuery): """ Function for sending a menu, with a selection of safe content """ await call.answer() # Editing the message await call.message.edit_text( text=( "🔗 Select a category to get a picture." ), reply_markup=menu_with_categories() ) @dp.callback_query_handler(text="anime") async def call_anime_categories(call: CallbackQuery): """ Redirect to select anime actions """ await call.answer() # Editing the message await call.message.edit_text( text=( "⚜️ Choose what content you want to see." ), reply_markup=anime_choose_safe_category() ) @dp.callback_query_handler(text=["sfw", "nsfw"]) async def call_nsfw_categories(call: CallbackQuery): """ Redirect to anime content """ data = call.data.upper() message = call.message # Send answer await call.answer() if data == "SFW": kb = anime_sfw_categories() else: kb = anime_nsfw_categories() # Editing the message await message.edit_text( text=( f"🍿 You are in the {data} category." ), reply_markup=kb ) @dp.callback_query_handler(text="animals") async def call_anime_categories(call: CallbackQuery): """ Redirect to animals content """ await call.answer() # Editing the message await call.message.edit_text( text=( "🦄 You are in the category with animals." ), reply_markup=animals_categories() ) @dp.callback_query_handler() async def call_get_photography(call: CallbackQuery): """ Function for sending photos """ message = call.message data = call.data # Get json document api = get_attributes_of_object() if data == "generate_new": data = message.text.split("#")[1] obj = api[data]["object"] atr = api[data]["attribute"] mark = api[data]["entity"] if mark == "anime": mark = api[data]["safe"] if mark == "memes": mark = "menu" # We get a link to the preview photo link = await get_object(obj, atr) await call.answer() # Editing the message await message.edit_text( text=( f"{hide_link(link)} #{data}" ), reply_markup=control_buttons(mark) )

en

0.634356

Function for sending a menu, with a selection of safe content # Editing the message Redirect to select anime actions # Editing the message Redirect to anime content # Send answer # Editing the message Redirect to animals content # Editing the message Function for sending photos # Get json document # We get a link to the preview photo # Editing the message #{data}"

2.767742

3

1stRound/Medium/322-Coin Change/DP.py

ericchen12377/Leetcode-Algorithm-Python

2

10293

class Solution: def coinChange(self, coins: List[int], amount: int) -> int: M = float('inf') # dynamic programming dp = [0] + [M] * amount for i in range(1, amount+1): dp[i] = 1 + min([dp[i-c] for c in coins if i >= c] or [M]) return dp[-1] if dp[-1] < M else -1

en

0.760267

# dynamic programming

3.276278

3

segmentation_test/Scripts/medpy_graphcut_voxel.py

rominashirazi/SpineSegmentation

0

10294

#!c:\users\hooma\documents\github\spinesegmentation\segmentation_test\scripts\python.exe """ Execute a graph cut on a voxel image based on some foreground and background markers. Copyright (C) 2013 <NAME> This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. """ # build-in modules from argparse import RawTextHelpFormatter import argparse import logging import os # third-party modules import scipy # path changes # own modules from medpy.core import ArgumentError, Logger from medpy.io import load, save, header from medpy import graphcut from medpy.graphcut.wrapper import split_marker # information __author__ = "<NAME>" __version__ = "r0.3.1, 2012-03-23" __email__ = "<EMAIL>" __status__ = "Release" __description__ = """ Perform a binary graph cut using Boykov's max-flow/min-cut algorithm. This implementation does only compute a boundary term and does not use any regional term. The desired boundary term can be selected via the --boundary argument. Depending on the selected term, an additional image has to be supplied as badditional. In the case of the difference of means, it is the original image. Furthermore the algorithm requires a binary image with foreground markers and a binary image with background markers. Additionally a filename for the created binary mask marking foreground and background has to be supplied. Note that the input images must be of the same dimensionality, otherwise an exception is thrown. Note to take into account the input images orientation. Note that the quality of the resulting segmentations depends also on the quality of the supplied markers. Copyright (C) 2013 <NAME> This program comes with ABSOLUTELY NO WARRANTY; This is free software, and you are welcome to redistribute it under certain conditions; see the LICENSE file or <http://www.gnu.org/licenses/> for details. """ # code def main(): # parse cmd arguments parser = getParser() parser.parse_args() args = getArguments(parser) # prepare logger logger = Logger.getInstance() if args.debug: logger.setLevel(logging.DEBUG) elif args.verbose: logger.setLevel(logging.INFO) # check if output image exists if not args.force: if os.path.exists(args.output): logger.warning('The output image {} already exists. Exiting.'.format(args.output)) exit(-1) # select boundary term ['diff_linear', 'diff_exp', 'diff_div', 'diff_pow', 'max_linear', 'max_exp', 'max_div', 'max_pow'] if 'diff_linear' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_difference_linear logger.info('Selected boundary term: linear difference of intensities') elif 'diff_exp' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_difference_exponential logger.info('Selected boundary term: exponential difference of intensities') elif 'diff_div' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_difference_division logger.info('Selected boundary term: divided difference of intensities') elif 'diff_pow' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_difference_power logger.info('Selected boundary term: power based / raised difference of intensities') elif 'max_linear' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_maximum_linear logger.info('Selected boundary term: linear maximum of intensities') elif 'max_exp' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_maximum_exponential logger.info('Selected boundary term: exponential maximum of intensities') elif 'max_div' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_maximum_division logger.info('Selected boundary term: divided maximum of intensities') elif 'max_pow' == args.boundary: boundary_term = graphcut.energy_voxel.boundary_maximum_power logger.info('Selected boundary term: power based / raised maximum of intensities') # load input images badditional_image_data, reference_header = load(args.badditional) markers_image_data, _ = load(args.markers) # split marker image into fg and bg images fgmarkers_image_data, bgmarkers_image_data = split_marker(markers_image_data) # check if all images dimensions are the same if not (badditional_image_data.shape == fgmarkers_image_data.shape == bgmarkers_image_data.shape): logger.critical('Not all of the supplied images are of the same shape.') raise ArgumentError('Not all of the supplied images are of the same shape.') # extract spacing if required if args.spacing: spacing = header.get_pixel_spacing(reference_header) logger.info('Taking spacing of {} into account.'.format(spacing)) else: spacing = False # generate graph logger.info('Preparing BK_MFMC C++ graph...') gcgraph = graphcut.graph_from_voxels(fgmarkers_image_data, bgmarkers_image_data, boundary_term = boundary_term, boundary_term_args = (badditional_image_data, args.sigma, spacing)) # execute min-cut logger.info('Executing min-cut...') maxflow = gcgraph.maxflow() logger.debug('Maxflow is {}'.format(maxflow)) # reshape results to form a valid mask logger.info('Applying results...') result_image_data = scipy.zeros(bgmarkers_image_data.size, dtype=scipy.bool_) for idx in range(len(result_image_data)): result_image_data[idx] = 0 if gcgraph.termtype.SINK == gcgraph.what_segment(idx) else 1 result_image_data = result_image_data.reshape(bgmarkers_image_data.shape) # save resulting mask save(result_image_data.astype(scipy.bool_), args.output, reference_header, args.force) logger.info('Successfully terminated.') def getArguments(parser): "Provides additional validation of the arguments collected by argparse." return parser.parse_args() def getParser(): "Creates and returns the argparse parser object." parser = argparse.ArgumentParser(description=__description__, formatter_class=RawTextHelpFormatter) parser.add_argument('sigma', type=float, help='The sigma required for the boundary terms.') parser.add_argument('badditional', help='The additional image required by the boundary term. See there for details.') parser.add_argument('markers', help='Image containing the foreground (=1) and background (=2) markers.') parser.add_argument('output', help='The output image containing the segmentation.') parser.add_argument('--boundary', default='diff_exp', help='The boundary term to use. Note that the ones prefixed with diff_ require the original image, while the ones prefixed with max_ require the gradient image.', choices=['diff_linear', 'diff_exp', 'diff_div', 'diff_pow', 'max_linear', 'max_exp', 'max_div', 'max_pow']) parser.add_argument('-s', dest='spacing', action='store_true', help='Set this flag to take the pixel spacing of the image into account. The spacing data will be extracted from the baddtional image.') parser.add_argument('-f', dest='force', action='store_true', help='Set this flag to silently override files that exist.') parser.add_argument('-v', dest='verbose', action='store_true', help='Display more information.') parser.add_argument('-d', dest='debug', action='store_true', help='Display debug information.') return parser if __name__ == "__main__": main()

en

0.843025

#!c:\users\hooma\documents\github\spinesegmentation\segmentation_test\scripts\python.exe Execute a graph cut on a voxel image based on some foreground and background markers. Copyright (C) 2013 <NAME> This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <http://www.gnu.org/licenses/>. # build-in modules # third-party modules # path changes # own modules # information Perform a binary graph cut using Boykov's max-flow/min-cut algorithm. This implementation does only compute a boundary term and does not use any regional term. The desired boundary term can be selected via the --boundary argument. Depending on the selected term, an additional image has to be supplied as badditional. In the case of the difference of means, it is the original image. Furthermore the algorithm requires a binary image with foreground markers and a binary image with background markers. Additionally a filename for the created binary mask marking foreground and background has to be supplied. Note that the input images must be of the same dimensionality, otherwise an exception is thrown. Note to take into account the input images orientation. Note that the quality of the resulting segmentations depends also on the quality of the supplied markers. Copyright (C) 2013 <NAME> This program comes with ABSOLUTELY NO WARRANTY; This is free software, and you are welcome to redistribute it under certain conditions; see the LICENSE file or <http://www.gnu.org/licenses/> for details. # code # parse cmd arguments # prepare logger # check if output image exists # select boundary term # load input images # split marker image into fg and bg images # check if all images dimensions are the same # extract spacing if required # generate graph # execute min-cut # reshape results to form a valid mask # save resulting mask

3.006441

3

_notes/book/conf.py

AstroMatt/astronaut-training-en

1

10295

author = '<NAME>' email = '<EMAIL>' project = 'Astronaut Training Program' description = 'Astronaut Training Program' extensions = [ 'sphinx.ext.todo', 'sphinx.ext.imgmath', ] todo_emit_warnings = False todo_include_todos = True exclude_patterns = [] # ----------------------------------------------------------------------------- # Standard book config # ----------------------------------------------------------------------------- import os import re import subprocess import sys from datetime import datetime needs_sphinx = '2.2' mathjax_path = 'https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/latest.js?config=TeX-MML-AM_CHTML' mathjax_config = { 'extensions': ['tex2jax.js'], 'jax': ['input/TeX', 'output/HTML-CSS'], } html_theme = 'sphinx_rtd_theme' exclude_patterns = exclude_patterns + [ '.*', 'venv*', 'virtualenv*', '_extensions', '_img', '_slides', '_static', '_themes', '_tmp', '*/_template.rst', '*/contrib/*', '*/solution/*', '*/solutions/*', '**.ipynb_checkpoints', 'README.rst', 'TODO.rst', ] numfig_format = { 'section': 'Sect. %s.', 'figure': 'Fig. %s.', 'table': 'Tab. %s.', 'code-block': 'Code Listing %s.', } language = 'en' source_directory = '.' master_doc = 'index' highlight_language = 'python3' pygments_style = 'borland' numfig = True templates_path = ['_templates'] source_suffix = ['.rst'] imgmath_image_format = 'svg' today_fmt = '%Y-%m-%d' project_slug = re.sub(r'[\W]+', '', project) sha1 = subprocess.Popen('git log -1 --format="%h"', stdout=subprocess.PIPE, shell=True).stdout.read().decode().replace('\n', '') now = datetime.now() year = now.year today = now.strftime('%Y-%m-%d') version = f'#{sha1}, {today}' release = f'#{sha1}, {today}' copyright = f'{year}, {author} <{email}>' extensions_dir = os.path.join(os.path.dirname(__file__), '', '_extensions') sys.path.append(extensions_dir) htmlhelp_basename = project html_theme_path = ['_themes'] html_static_path = ['_static'] html_favicon = '_static/favicon.png' html_sidebars = {'sidebar': ['localtoc.html', 'sourcelink.html', 'searchbox.html']} html_show_sphinx = False html_context = { 'css_files': [ '_static/theme-overrides.css', ], } latex_documents = [(master_doc, f'{project_slug}.tex', project, author, 'manual')] latex_elements = { 'papersize': 'a4paper', 'pointsize': '10pt', 'figure_align': 'htbp', # Fix for: LaTeX Backend Fails with Citations In Figure Captions 'preamble': r""" \usepackage{etoolbox} \AtBeginEnvironment{figure}{\renewcommand{\phantomsection}{}} """ } epub_title = project epub_author = author epub_publisher = author epub_copyright = copyright epub_exclude_files = ['search.html'] man_pages = [ (master_doc, project_slug, project, [author], 1) ] texinfo_documents = [ (master_doc, project_slug, project, author, project, '', 'Miscellaneous'), ]

en

0.307774

# ----------------------------------------------------------------------------- # Standard book config # ----------------------------------------------------------------------------- # Fix for: LaTeX Backend Fails with Citations In Figure Captions \usepackage{etoolbox} \AtBeginEnvironment{figure}{\renewcommand{\phantomsection}{}}

1.463351

1

tutorial_application/forms.py

yamasakih/django_rdkit_tutorial

2

10296

from django_rdkit import models from django.forms.models import ModelForm from .models import Compound class SubstructureSearchForm(ModelForm): class Meta: model = Compound fields = ('molecule', )

none

1

1.472171

1

data_structures/trees/tree.py

onyonkaclifford/data-structures-and-algorithms

0

10297

from abc import ABC, abstractmethod from typing import Any, Generator, Iterable, List, Union class Empty(Exception): pass class Tree(ABC): """A tree is a hierarchical collection of nodes containing items, with each node having a unique parent and zero, one or many children items. The topmost element in a non-empty tree, the root, has no parent. Tree vocabularies include, but are not limited to: 1. Root - the topmost element in a non-empty tree, it has no parent 2. Leaf - a node with zero children 3. Siblings - nodes that share a parent node 4. Edge - a pair of nodes such the one is the parent of the other 5. Path - a collection of nodes such that any pair of adjacent nodes have a parent/child relationship 6. Height - number of edges between a node and it's furthest leaf 7. Depth - number of edges between a node and the root 8. Level - number of nodes in the path between a node and the root, inclusive of both the node itself and the root 9. Ordered tree - a tree with a meaningful organisation among its nodes such that its nodes can be arranged in a linear manner from first to last """ class _Node: def __init__(self, key, value, parent=None, children: Union[List, None] = None): self.key = key self.value = value self.parent = parent self.children = children if children is not None else [] class _Position: """A representation of the position of a node within a tree""" def __init__(self, belongs_to, node): self.__variables = {"belongs_to": belongs_to} self.__node = node def is_owned_by(self, owner): """Check whether position belongs to the tree, owner. Time complexity: O(1). :param owner: object to check whether it's the owner of this position :returns: True of the position is owned by the object passed, else False """ return owner is self.__variables["belongs_to"] def manipulate_variables(self, owner, method: str, *params): """Manipulate member variables of this position. Methods of the owner list are the only ones that can call this method. Time complexity: O(1). :param owner: tree object that owns this position :param method: method name of tree object that will manipulate the member variables of this position :param params: extra optional parameters to pass to the method :returns: the return value of the tree method whose name is passed """ if not self.is_owned_by(owner): raise ValueError("Position doesn't belong to the passed owner") return getattr(owner, method)(self.__variables, *params) def manipulate_node(self, owner, method: str, *params): """Manipulate the node held by this position. Methods of the owner list are the only ones that can call this method. Time complexity: O(1). :param owner: tree object that owns this position :param method: method name of tree object that will manipulate the node contained in this position :param params: extra optional parameters to pass to the method :returns: the return value of the tree method whose name is passed """ if not self.is_owned_by(owner): raise ValueError("Position doesn't belong to the passed owner") return getattr(owner, method)(self.__node, *params) def get_data(self): """Return the data stored by the node held by this position. Time complexity: O(1). :returns: data stored in node contained in this position """ return self.__node.key, self.__node.value def __init__(self): self._root: Union[Tree._Node, None] = None self._length = 0 self.__generator: Union[Generator, None] = None def __len__(self) -> int: """Return total number of items in tree :return: count of items in tree """ return self._length def __repr__(self) -> str: """Return a string representation of the tree :return: the string representation of the tree """ def helper(current_position): children = self.get_children(current_position) num_of_children = len(children) last_child_idx = num_of_children - 1 data_dict["string_data"] += f"{current_position.get_data()[0]}" for i, j in enumerate(children): data_dict["string_data"] += "(" if i == 0 else ", " helper(j) data_dict["string_data"] += ")" if i == last_child_idx else "" if self.is_empty(): return "" data_dict = {"string_data": ""} helper(Tree._Position(self, self._root)) return data_dict["string_data"] def __iter__(self) -> Iterable: """Return a tree iterable :return: tree iterable """ return self def __next__(self) -> _Position: """Return next position of tree iterator, implemented based on level-order traversal :return: next position :raises StopIteration: when the cursor denoting the current position surpasses the last position of the tree """ if self.__generator is None: self.__generator = self.traverse_tree_level_order() try: next_position = next(self.__generator) except StopIteration as e: self.__generator = None raise e return next_position @staticmethod def _validate_node(node): """Helper function to check if the node passed is a tree node. Returns the node passed if the validation passes, else raises a TypeError. Time complexity: O(1). :param node: node to validate :returns: the node passed if it passes validation :raises TypeError: if the validation fails """ if not isinstance(node, Tree._Node): raise TypeError("Not a tree node") return node @staticmethod def _invalidate_position(variables): """Helper function to set the belongs_to key of a dictionary to None. Used to revoke the ownership of a position by this tree. Time complexity: O(1). :returns: the dictionary passed, with the belongs_to key set to None """ variables["belongs_to"] = None return variables def is_empty(self) -> bool: """Return True if tree is empty, else False. Time complexity: O(1). :returns: True if tree is empty, else False """ return self._root is None def is_root(self, position: _Position) -> bool: """Check if the passed position contains the root node. Time complexity: O(1). :returns: True if the passed position holds the root node, else False """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") node = position.manipulate_node(self, "_validate_node") return node.parent is None def is_leaf(self, position: _Position) -> bool: """Check if the passed position contains a leaf. Time complexity: O(1). :returns: True if the passed position holds a leaf node, else False """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") return len(self.get_children(position)) == 0 def get_root(self) -> Union[_Position, None]: """Return the root position. Time complexity: O(1). :returns: the root position """ if self.is_empty(): return None else: return Tree._Position(self, self._root) def get_parent(self, position: _Position) -> Union[_Position, None]: """Return the parent of the given position. Time complexity: O(1). :param position: position containing the node whose parent is being sought :returns: the position of parent of the node contained in the passed position. None if the position passed contains the root node. """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") node = position.manipulate_node(self, "_validate_node") if self.is_root(Tree._Position(self, node)): return None else: return Tree._Position(self, node.parent) def get_children(self, position: _Position) -> Union[List[_Position], None]: """Return the children of the given position. Time complexity: O(1). :param position: position containing the node whose children are being sought :returns: the positions of the children of the node contained in the passed position. None if the position has no children. """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") node = position.manipulate_node(self, "_validate_node") children = node.children if children is None: return None else: return [Tree._Position(self, i) for i in children if i is not None] def get_siblings(self, position: _Position) -> Union[List[_Position], None]: """Return the siblings of the given position. Time complexity: O(1). :param position: position containing the node whose children are being sought :returns: the positions of the siblings of the node contained in the passed position """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") node = position.manipulate_node(self, "_validate_node") parent = node.parent if parent is None: return [] return [Tree._Position(self, i) for i in parent.children if i is not node] def get_height_of_node(self, position: _Position) -> int: """Return the number of edges between a node and the farthest leaf among its descendants. Time complexity: O(n). :param position: position containing the node whose height is being sought :returns: the number of edges between a node and the farthest leaf among its descendants """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") if self.is_leaf(position): return 0 return 1 + max(self.get_height_of_node(p) for p in self.get_children(position)) def get_height_of_tree(self) -> int: """Return the number of edges between the root node and the farthest leaf. Time complexity: O(n). :returns: the number of edges between the root node and the farthest leaf """ if self.is_empty(): raise Empty("Tree is empty") return self.get_height_of_node(Tree._Position(self, self._root)) def get_depth_of_node(self, position: _Position) -> int: """Return the number of edges between a node and the root. Time complexity: O(n). :param position: position containing the node whose depth is being sought :returns: the number of edges between a node and the root """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") if self.is_root(position): return 0 return 1 + self.get_depth_of_node(self.get_parent(position)) def get_depth_of_tree(self) -> int: """Return the number of edges between the farthest leaf and the root. Time complexity: O(n). :returns: the number of edges between the farthest leaf and the root """ return self.get_height_of_tree() def get_level_of_node(self, position: _Position) -> int: """Return the number of nodes between a node and the root, inclusive of itself. Time complexity: O(n). :param position: position containing the node whose level is being sought :returns: the number of nodes between a node and the root, inclusive of itself """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") return 1 + self.get_depth_of_node(position) def traverse_subtree_pre_order(self, position: _Position) -> Generator: """Pre-order traverse subtree whose root is the passed position and return a generator of the positions it contains :param position: position containing the node that's the root of the subtree to be traversed :returns: a generator of the positions """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") yield position for i in self.get_children(position): for j in self.traverse_subtree_pre_order(i): yield j def traverse_tree_pre_order(self) -> Generator: """Pre-order traverse tree and return a generator of the positions it contains :returns: a generator of the positions """ position = self.get_root() if position is not None: for i in self.traverse_subtree_pre_order(position): yield i def traverse_subtree_post_order(self, position: _Position) -> Generator: """Post-order traverse subtree whose root is the passed position and return a generator of the positions it contains :param position: position containing the node that's the root of the subtree to be traversed :returns: a generator of the positions """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") for i in self.get_children(position): for j in self.traverse_subtree_post_order(i): yield j yield position def traverse_tree_post_order(self) -> Generator: """Post-order traverse tree and return a generator of the positions it contains :returns: a generator of the positions """ position = self.get_root() if position is not None: for i in self.traverse_subtree_post_order(position): yield i def traverse_subtree_level_order(self, position: _Position) -> Generator: """Level-by-level traverse subtree whose root is the passed position and return a generator of the positions it contains :param position: position containing the node that's the root of the subtree to be traversed :returns: a generator of the positions """ if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") def helper(root_node, level): if root_node is not None: if level == 1: yield Tree._Position(self, root_node) elif level > 1: for child in root_node.children: for k in helper(child, level - 1): yield k node = position.manipulate_node(self, "_validate_node") number_of_levels = self.get_height_of_node(position) + 1 for i in range(1, number_of_levels + 1): for j in helper(node, i): yield j def traverse_tree_level_order(self) -> Generator: """Level-by-level traverse tree and return a generator of the positions it contains :returns: a generator of the positions """ position = self.get_root() if position is not None: for i in self.traverse_subtree_level_order(position): yield i def delete(self, position: _Position) -> None: """Delete a value from the tree :param position: position containing the node to be removed from the tree """ self._length -= 1 if not position.is_owned_by(self): raise ValueError("Position doesn't belong to this tree") def insert_node(node_to_insert, is_node_left_child, parent_node): if node_to_insert is not None: node_to_insert.parent = parent_node if is_node_left_child is not None: if is_node_left_child: parent_node.children[0] = node_to_insert else: parent_node.children[1] = node_to_insert def delete_node(node_to_delete, is_root): parent = node_to_delete.parent left = node_to_delete.children[0] right = node_to_delete.children[1] is_left_child = None if parent is None else node_to_delete.key < parent.key if left is None: insert_node(right, is_left_child, parent) if is_root: self._root = right else: current_node = left right_child = current_node.children[1] if right_child is None: current_node.children[1] = right insert_node(current_node, is_left_child, parent) if is_root: self._root = current_node else: new_node = Tree._Node( right_child.key, right_child.value, children=[current_node, right], ) insert_node(new_node, is_left_child, parent) if is_root: self._root = new_node delete_node(right_child, False) node = position.manipulate_node(self, "_validate_node") is_root_node = self.is_root(position) _ = position.manipulate_variables(self, "_invalidate_position") delete_node(node, is_root_node) @abstractmethod def insert(self, key: Any, value: Any) -> None: """Insert a value into the tree :param key: unique identifier of the item to be added to the tree :param value: item to be added to the tree """ self._length += 1

en

0.846048

A tree is a hierarchical collection of nodes containing items, with each node having a unique parent and zero, one or many children items. The topmost element in a non-empty tree, the root, has no parent. Tree vocabularies include, but are not limited to: 1. Root - the topmost element in a non-empty tree, it has no parent 2. Leaf - a node with zero children 3. Siblings - nodes that share a parent node 4. Edge - a pair of nodes such the one is the parent of the other 5. Path - a collection of nodes such that any pair of adjacent nodes have a parent/child relationship 6. Height - number of edges between a node and it's furthest leaf 7. Depth - number of edges between a node and the root 8. Level - number of nodes in the path between a node and the root, inclusive of both the node itself and the root 9. Ordered tree - a tree with a meaningful organisation among its nodes such that its nodes can be arranged in a linear manner from first to last A representation of the position of a node within a tree Check whether position belongs to the tree, owner. Time complexity: O(1). :param owner: object to check whether it's the owner of this position :returns: True of the position is owned by the object passed, else False Manipulate member variables of this position. Methods of the owner list are the only ones that can call this method. Time complexity: O(1). :param owner: tree object that owns this position :param method: method name of tree object that will manipulate the member variables of this position :param params: extra optional parameters to pass to the method :returns: the return value of the tree method whose name is passed Manipulate the node held by this position. Methods of the owner list are the only ones that can call this method. Time complexity: O(1). :param owner: tree object that owns this position :param method: method name of tree object that will manipulate the node contained in this position :param params: extra optional parameters to pass to the method :returns: the return value of the tree method whose name is passed Return the data stored by the node held by this position. Time complexity: O(1). :returns: data stored in node contained in this position Return total number of items in tree :return: count of items in tree Return a string representation of the tree :return: the string representation of the tree Return a tree iterable :return: tree iterable Return next position of tree iterator, implemented based on level-order traversal :return: next position :raises StopIteration: when the cursor denoting the current position surpasses the last position of the tree Helper function to check if the node passed is a tree node. Returns the node passed if the validation passes, else raises a TypeError. Time complexity: O(1). :param node: node to validate :returns: the node passed if it passes validation :raises TypeError: if the validation fails Helper function to set the belongs_to key of a dictionary to None. Used to revoke the ownership of a position by this tree. Time complexity: O(1). :returns: the dictionary passed, with the belongs_to key set to None Return True if tree is empty, else False. Time complexity: O(1). :returns: True if tree is empty, else False Check if the passed position contains the root node. Time complexity: O(1). :returns: True if the passed position holds the root node, else False Check if the passed position contains a leaf. Time complexity: O(1). :returns: True if the passed position holds a leaf node, else False Return the root position. Time complexity: O(1). :returns: the root position Return the parent of the given position. Time complexity: O(1). :param position: position containing the node whose parent is being sought :returns: the position of parent of the node contained in the passed position. None if the position passed contains the root node. Return the children of the given position. Time complexity: O(1). :param position: position containing the node whose children are being sought :returns: the positions of the children of the node contained in the passed position. None if the position has no children. Return the siblings of the given position. Time complexity: O(1). :param position: position containing the node whose children are being sought :returns: the positions of the siblings of the node contained in the passed position Return the number of edges between a node and the farthest leaf among its descendants. Time complexity: O(n). :param position: position containing the node whose height is being sought :returns: the number of edges between a node and the farthest leaf among its descendants Return the number of edges between the root node and the farthest leaf. Time complexity: O(n). :returns: the number of edges between the root node and the farthest leaf Return the number of edges between a node and the root. Time complexity: O(n). :param position: position containing the node whose depth is being sought :returns: the number of edges between a node and the root Return the number of edges between the farthest leaf and the root. Time complexity: O(n). :returns: the number of edges between the farthest leaf and the root Return the number of nodes between a node and the root, inclusive of itself. Time complexity: O(n). :param position: position containing the node whose level is being sought :returns: the number of nodes between a node and the root, inclusive of itself Pre-order traverse subtree whose root is the passed position and return a generator of the positions it contains :param position: position containing the node that's the root of the subtree to be traversed :returns: a generator of the positions Pre-order traverse tree and return a generator of the positions it contains :returns: a generator of the positions Post-order traverse subtree whose root is the passed position and return a generator of the positions it contains :param position: position containing the node that's the root of the subtree to be traversed :returns: a generator of the positions Post-order traverse tree and return a generator of the positions it contains :returns: a generator of the positions Level-by-level traverse subtree whose root is the passed position and return a generator of the positions it contains :param position: position containing the node that's the root of the subtree to be traversed :returns: a generator of the positions Level-by-level traverse tree and return a generator of the positions it contains :returns: a generator of the positions Delete a value from the tree :param position: position containing the node to be removed from the tree Insert a value into the tree :param key: unique identifier of the item to be added to the tree :param value: item to be added to the tree

4.014211

4

nodes/audio.py

sddhrthrt/COVFEFE

0

10298

<filename>nodes/audio.py from abc import ABC, abstractmethod import os import logging from nodes.helper import FileOutputNode from utils import file_utils from utils import signal_processing as sp from utils.shell_run import shell_run from config import OPENSMILE_HOME class Mp3ToWav(FileOutputNode): def run(self, mp3_file): self.log(logging.INFO, "Starting %s" % (mp3_file)) if not mp3_file.endswith(".mp3"): self.log(logging.ERROR,"Failed %s. Not mp3 file" % (mp3_file)) return wav_file = self.derive_new_file_path(mp3_file, "wav") if file_utils.should_run(mp3_file, wav_file): res = shell_run(["lame", "--decode", mp3_file, wav_file]) if res != 0: self.log(logging.ERROR,"Failed %s -> %s with lame error code %i" % (mp3_file, wav_file, res)) return self.log(logging.INFO, "Done %s -> %s" % (mp3_file, wav_file)) self.emit(wav_file) class ResampleWav(FileOutputNode): def setup(self, new_sr): self.new_sr = new_sr def run(self, wav_file): self.log(logging.INFO, "Starting %s" % (wav_file)) if not wav_file.endswith(".wav"): self.log(logging.ERROR,"Failed %s. Not wav file" % (wav_file)) return new_wav_file = self.derive_new_file_path(wav_file, "wav") if file_utils.should_run(wav_file, new_wav_file): res = shell_run(["sox", wav_file, "--rate", str(self.new_sr), new_wav_file]) if res != 0: self.log(logging.ERROR,"Failed %s -> %s with lame error code %i" % (wav_file, new_wav_file, res)) return self.log(logging.INFO, "Done %s -> %s" % (wav_file, new_wav_file)) self.emit(new_wav_file) class ShellCommand(FileOutputNode): """ Take as input a format string representing a shell command that can accept an in_file and out_file. For example "someCommand -i {in_file} -o {out_file}" ext: Extension of the output file, ex. "wav", "csv" """ def setup(self, command, ext): self.command = command self.ext = ext def run(self, in_file): self.log(logging.INFO, "Starting %s" % (in_file)) out_file = self.derive_new_file_path(in_file, self.ext) if file_utils.should_run(in_file, out_file): cmd = self.command.format(in_file=in_file, out_file=out_file) res = shell_run(cmd.split(" ")) if res != 0: self.log(logging.ERROR,"Failed %s -> %s with error code %i. cmd: %s" % (in_file, out_file, res, cmd)) return self.log(logging.INFO, "Done %s -> %s" % (in_file, out_file)) self.emit(out_file) class OpenSmileRunner(FileOutputNode): """ conf_file: Either absolute path to an opensmile conf file or the name of a config file in opensmile's config folder out_flag: Flag to use for the output file. extra_flags: A string of extra flags to pass to SMILExtract. out_ext: Extension of the output file """ def setup(self, conf_file, out_flag="-csvoutput", extra_flags="-nologfile -noconsoleoutput -appendcsv 0", out_ext="csv"): self.conf_file = file_utils.locate_file(conf_file, [os.path.join(OPENSMILE_HOME, "config")]) self.extra_flags = extra_flags.split(" ") self.out_flag = out_flag self.out_ext = out_ext self.opensmile_exec = file_utils.locate_file("SMILExtract", [OPENSMILE_HOME, os.path.join(OPENSMILE_HOME, "bin")], use_path=True) def run(self, in_file): self.log(logging.INFO, "Starting %s" % (in_file)) out_file = self.derive_new_file_path(in_file, self.out_ext) if file_utils.should_run(in_file, out_file): cmd = [self.opensmile_exec, "-C", self.conf_file, "-I", in_file, self.out_flag, out_file] + self.extra_flags res = shell_run(cmd) if res != 0: self.log(logging.ERROR,"Failed %s -> %s with SmileExtract error code %i. cmd: %s" % (in_file, out_file, res, " ".join(cmd))) return self.log(logging.INFO, "Done %s -> %s" % (in_file, out_file)) self.emit([out_file]) class IS10_Paraling(OpenSmileRunner): def get_conf_name(self): return "IS10_paraling.conf" def get_command(self, wav_file, out_file): return [self.os_exec, "-C", self.conf_file, "-I", wav_file, "-csvoutput", out_file, "-nologfile", "-noconsoleoutput", "-appendcsv", "0"] class IS10_Paraling_lld(OpenSmileRunner): def get_conf_name(self): return "IS10_paraling.conf" def get_command(self, wav_file, out_file): return [self.os_exec, "-C", self.conf_file, "-I", wav_file, "-lldcsvoutput", out_file, "-nologfile", "-noconsoleoutput", "-appendcsv", "0"] class SplitSegments(FileOutputNode): """ segment_mapping_fn is a pointer to a function that takes as input a file and sample rate and returns a list of all the segments in that file in the format [(start1, end1, segname1), (start2, end2, segname2), ...] where start and end are in given in samples. Each tuple in the list can also have a 4th item, which can be any string. This string will get saved in segname.txt This is useful for isolating events of interest in audio files. For example, if the segment mapping function returns a list of where all speech occurs in the input audio, this will isolate all occurrences of speech into individual files. The 4th item may contain the annotation of what was said in the segment. """ def setup(self, segment_mapping_fn): self.segment_mapping_fn = segment_mapping_fn def run(self, in_file): self.log(logging.INFO, "Starting %s" % (in_file)) if not in_file.endswith(".wav"): self.log(logging.ERROR, "Failed %s. Not wav file" % (in_file)) return sr, original_data = sp.read_wave(in_file, first_channel=True) segments = self.segment_mapping_fn(in_file, sr) for segment in segments: if len(segment) == 3: start, end, seg_name = segment extra_info = None elif len(segment) == 4: start, end, seg_name, extra_info = segment else: self.log(logging.ERROR, "Failed %s. Segment length must be 3 or 4" % (in_file)) return seg_path = os.path.join(self.out_dir, "%s.wav" % seg_name) sp.write_wav(seg_path, sr, original_data[start:end]) extra_path = None if extra_info: extra_path = os.path.join(self.out_dir, "%s.txt" % seg_name) with open(extra_path, "w") as f: f.write(extra_info) self.emit([seg_path, extra_path])

en

0.87316

Take as input a format string representing a shell command that can accept an in_file and out_file. For example "someCommand -i {in_file} -o {out_file}" ext: Extension of the output file, ex. "wav", "csv" conf_file: Either absolute path to an opensmile conf file or the name of a config file in opensmile's config folder out_flag: Flag to use for the output file. extra_flags: A string of extra flags to pass to SMILExtract. out_ext: Extension of the output file segment_mapping_fn is a pointer to a function that takes as input a file and sample rate and returns a list of all the segments in that file in the format [(start1, end1, segname1), (start2, end2, segname2), ...] where start and end are in given in samples. Each tuple in the list can also have a 4th item, which can be any string. This string will get saved in segname.txt This is useful for isolating events of interest in audio files. For example, if the segment mapping function returns a list of where all speech occurs in the input audio, this will isolate all occurrences of speech into individual files. The 4th item may contain the annotation of what was said in the segment.

2.441041

2

texar/torch/modules/pretrained/gpt2.py

VegB/VLN-Transformer

19

10299

<reponame>VegB/VLN-Transformer # Copyright 2019 The Texar Authors. All Rights Reserved. # # 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. """ Utils of GPT2 Modules. """ import json import os import warnings from abc import ABC from typing import Any, Dict import torch from texar.torch.modules.pretrained.pretrained_base import PretrainedMixin __all__ = [ "PretrainedGPT2Mixin", ] _GPT2_PATH = "https://storage.googleapis.com/gpt-2/models/" _CHECKPOINT_FILES = [ "checkpoint", "encoder.json", "hparams.json", "vocab.bpe", "model.ckpt.data-00000-of-00001", "model.ckpt.index", "model.ckpt.meta"] class PretrainedGPT2Mixin(PretrainedMixin, ABC): r"""A mixin class to support loading pre-trained checkpoints for modules that implement the GPT2 model. The GPT2 model was proposed in `Language Models are Unsupervised Multitask Learners`_ by `Radford et al.` from OpenAI. It is a unidirectional Transformer model pre-trained using the vanilla language modeling objective on a large corpus. The available GPT2 models are as follows: * ``gpt2-small``: Small version of GPT-2, 124M parameters. * ``gpt2-medium``: Medium version of GPT-2, 355M parameters. * ``gpt2-large``: Large version of GPT-2, 774M parameters. We provide the following GPT2 classes: * :class:`~texar.torch.modules.GPT2Encoder` for text encoding. * :class:`~texar.torch.modules.GPT2Decoder` for text generation and decoding. * :class:`~texar.torch.modules.GPT2Classifier` for text classification and sequence tagging. .. _`Language Models are Unsupervised Multitask Learners`: https://openai.com/blog/better-language-models/ """ _MODEL_NAME = "GPT2" _MODEL2URL = { 'gpt2-small': [_GPT2_PATH + f"124M/{file}" for file in _CHECKPOINT_FILES], 'gpt2-medium': [_GPT2_PATH + f"355M/{file}" for file in _CHECKPOINT_FILES], 'gpt2-large': [_GPT2_PATH + f"774M/{file}" for file in _CHECKPOINT_FILES], } _IS_DECODE = False # Raise warning for the deprecated pre-trained model names class MyDict(dict): def __contains__(self, key): if key == '117M': warnings.warn("Pre-trained model name '117M' is deprecated, " "use 'gpt2-small' instead.", UserWarning) return True elif key == '345M': warnings.warn("Pre-trained model name '345M' is deprecated, " "use 'gpt2-medium' instead.", UserWarning) return True else: return super().__contains__(key) _DEPRECATED_MODEL2URL = { '117M': [_GPT2_PATH + f"124M/{file}" for file in _CHECKPOINT_FILES], '345M': [_GPT2_PATH + f"355M/{file}" for file in _CHECKPOINT_FILES], } _MODEL2URL.update(_DEPRECATED_MODEL2URL) _MODEL2URL = MyDict(_MODEL2URL) # type: ignore def _transform_config(self, pretrained_model_name: str, # type: ignore cache_dir: str) -> Dict[str, Any]: info = list(os.walk(cache_dir)) root, _, files = info[0] config_path = None for file in files: if file.endswith('hparams.json'): config_path = os.path.join(root, file) if config_path is None: raise ValueError(f"Cannot find the config file in {cache_dir}") with open(config_path) as f: config_gpt = json.loads(f.read()) hidden_dim = config_gpt["n_embd"] configs = { "vocab_size": config_gpt["n_vocab"], "context_size": config_gpt["n_ctx"], "embedding_size": config_gpt["n_embd"], "embed": { "dim": hidden_dim, }, "position_size": config_gpt["n_ctx"], "position_embed": { "dim": hidden_dim } } module_name = 'decoder' if self._IS_DECODE else 'encoder' configs.update({module_name: { "dim": hidden_dim, "num_blocks": config_gpt["n_layer"], "embedding_dropout": 0, "residual_dropout": 0, "multihead_attention": { "use_bias": True, "num_units": hidden_dim, "num_heads": config_gpt["n_head"], "output_dim": hidden_dim, }, "initializer": { "type": "variance_scaling_initializer", "kwargs": { "factor": 1.0, "mode": "FAN_AVG", "uniform": True, }, }, "poswise_feedforward": { "layers": [ { "type": "Linear", "kwargs": { "in_features": hidden_dim, "out_features": hidden_dim * 4, "bias": True, } }, { "type": "GPTGELU", "kwargs": {} }, { "type": "Linear", "kwargs": { "in_features": hidden_dim * 4, "out_features": hidden_dim, "bias": True, } } ], "name": "ffn", }, }}) if self._IS_DECODE: configs[module_name].update({'use_gpt_config': True}) else: configs[module_name].update({'use_bert_config': False}) return configs def _init_from_checkpoint(self, pretrained_model_name: str, cache_dir: str, load_output_layer: bool = True, **kwargs): r"""Initialize model parameters from weights stored in the pre-trained checkpoint. Args: pretrained_model_name (str): Name of the pre-trained model. cache_dir (str): Path to the cache directory. load_output_layer (bool): If `False`, will not load weights of the output layer. Set this argument to `False` when loading weights into a GPT2 encoder. Defaults to `True`. """ try: import numpy as np import tensorflow as tf except ImportError: print("Loading TensorFlow models in PyTorch requires installing " "TensorFlow. Please see https://www.tensorflow.org/install/ " "for installation instructions.") raise module_name = 'decoder' if self._IS_DECODE else 'encoder' global_tensor_map = { "model/wte": "word_embedder.embedding", "model/wpe": "position_embedder.embedding", "model/ln_f/b": module_name + ".final_layer_norm.bias", "model/ln_f/g": module_name + ".final_layer_norm.weight", } layer_tensor_map = { "ln_1/b": module_name + ".self_attn_layer_norm.{}.bias", "ln_1/g": module_name + ".self_attn_layer_norm.{}.weight", "ln_2/b": module_name + ".poswise_layer_norm.{}.bias", "ln_2/g": module_name + ".poswise_layer_norm.{}.weight", "mlp/c_fc/b": module_name + ".poswise_networks.{}._layers.0.bias", "mlp/c_proj/b": module_name + ".poswise_networks.{}._layers.2.bias", "attn/c_proj/b": module_name + ".self_attns.{}.O_dense.bias", } layer_transpose_map = { "mlp/c_fc/w": module_name + ".poswise_networks.{}._layers.0.weight", "mlp/c_proj/w": module_name + ".poswise_networks.{}._layers.2." "weight", "attn/c_proj/w": module_name + ".self_attns.{}.O_dense.weight", } tf_path = os.path.abspath(os.path.join(cache_dir, 'model.ckpt')) # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, _ in init_vars: array = tf.train.load_variable(tf_path, name) names.append(name) arrays.append(array.squeeze()) tensor_names = [] for name, _ in self.named_parameters(): tensor_names.append(name) for name, array in zip(names, arrays): if name in global_tensor_map: v_name = global_tensor_map[name] if name == "model/wte": pointer = self._name_to_variable(v_name) assert pointer.shape == array.shape pointer.data = torch.from_numpy(array) if load_output_layer: output_pointer = self._name_to_variable( "decoder._output_layer.weight") assert output_pointer.shape == array.shape output_pointer.data = torch.from_numpy(array) elif name == "model/wpe": pointer = self._name_to_variable(v_name) assert pointer.shape == array.shape pointer.data = torch.from_numpy(array) else: pointer = self._name_to_variable(v_name) assert pointer.shape == array.shape pointer.data = torch.from_numpy(array) else: name_tmp = name.split("/") layer_no = name_tmp[1][1:] name = "/".join(name_tmp[2:]) if name in layer_tensor_map: v_name = layer_tensor_map[name].format(layer_no) pointer = self._name_to_variable(v_name) assert pointer.shape == array.shape pointer.data = torch.from_numpy(array) elif name in layer_transpose_map: v_name = layer_transpose_map[name].format(layer_no) pointer = self._name_to_variable(v_name) array_t = np.transpose(array) assert pointer.shape == array_t.shape pointer.data = torch.from_numpy(array_t) elif name == "attn/c_attn/w": index_d = array.shape[-1] // 3 Q_w = np.transpose(array[:, :index_d]) K_w = np.transpose(array[:, index_d: 2 * index_d]) V_w = np.transpose(array[:, 2 * index_d:]) q_weight = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.Q_dense.weight") k_weight = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.K_dense.weight") v_weight = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.V_dense.weight") assert q_weight.shape == Q_w.shape assert k_weight.shape == K_w.shape assert v_weight.shape == V_w.shape q_weight.data = torch.from_numpy(Q_w) k_weight.data = torch.from_numpy(K_w) v_weight.data = torch.from_numpy(V_w) elif name == "attn/c_attn/b": d = array.shape[0] Q_b = array[: d // 3] K_b = array[d // 3: 2 * d // 3] V_b = array[2 * d // 3:] q_bias = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.Q_dense.bias") k_bias = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.K_dense.bias") v_bias = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.V_dense.bias") assert q_bias.shape == Q_b.shape assert k_bias.shape == K_b.shape assert v_bias.shape == V_b.shape q_bias.data = torch.from_numpy(Q_b) k_bias.data = torch.from_numpy(K_b) v_bias.data = torch.from_numpy(V_b) else: print("Name error", name) raise Exception

# Copyright 2019 The Texar Authors. All Rights Reserved. # # 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. """ Utils of GPT2 Modules. """ import json import os import warnings from abc import ABC from typing import Any, Dict import torch from texar.torch.modules.pretrained.pretrained_base import PretrainedMixin __all__ = [ "PretrainedGPT2Mixin", ] _GPT2_PATH = "https://storage.googleapis.com/gpt-2/models/" _CHECKPOINT_FILES = [ "checkpoint", "encoder.json", "hparams.json", "vocab.bpe", "model.ckpt.data-00000-of-00001", "model.ckpt.index", "model.ckpt.meta"] class PretrainedGPT2Mixin(PretrainedMixin, ABC): r"""A mixin class to support loading pre-trained checkpoints for modules that implement the GPT2 model. The GPT2 model was proposed in `Language Models are Unsupervised Multitask Learners`_ by `Radford et al.` from OpenAI. It is a unidirectional Transformer model pre-trained using the vanilla language modeling objective on a large corpus. The available GPT2 models are as follows: * ``gpt2-small``: Small version of GPT-2, 124M parameters. * ``gpt2-medium``: Medium version of GPT-2, 355M parameters. * ``gpt2-large``: Large version of GPT-2, 774M parameters. We provide the following GPT2 classes: * :class:`~texar.torch.modules.GPT2Encoder` for text encoding. * :class:`~texar.torch.modules.GPT2Decoder` for text generation and decoding. * :class:`~texar.torch.modules.GPT2Classifier` for text classification and sequence tagging. .. _`Language Models are Unsupervised Multitask Learners`: https://openai.com/blog/better-language-models/ """ _MODEL_NAME = "GPT2" _MODEL2URL = { 'gpt2-small': [_GPT2_PATH + f"124M/{file}" for file in _CHECKPOINT_FILES], 'gpt2-medium': [_GPT2_PATH + f"355M/{file}" for file in _CHECKPOINT_FILES], 'gpt2-large': [_GPT2_PATH + f"774M/{file}" for file in _CHECKPOINT_FILES], } _IS_DECODE = False # Raise warning for the deprecated pre-trained model names class MyDict(dict): def __contains__(self, key): if key == '117M': warnings.warn("Pre-trained model name '117M' is deprecated, " "use 'gpt2-small' instead.", UserWarning) return True elif key == '345M': warnings.warn("Pre-trained model name '345M' is deprecated, " "use 'gpt2-medium' instead.", UserWarning) return True else: return super().__contains__(key) _DEPRECATED_MODEL2URL = { '117M': [_GPT2_PATH + f"124M/{file}" for file in _CHECKPOINT_FILES], '345M': [_GPT2_PATH + f"355M/{file}" for file in _CHECKPOINT_FILES], } _MODEL2URL.update(_DEPRECATED_MODEL2URL) _MODEL2URL = MyDict(_MODEL2URL) # type: ignore def _transform_config(self, pretrained_model_name: str, # type: ignore cache_dir: str) -> Dict[str, Any]: info = list(os.walk(cache_dir)) root, _, files = info[0] config_path = None for file in files: if file.endswith('hparams.json'): config_path = os.path.join(root, file) if config_path is None: raise ValueError(f"Cannot find the config file in {cache_dir}") with open(config_path) as f: config_gpt = json.loads(f.read()) hidden_dim = config_gpt["n_embd"] configs = { "vocab_size": config_gpt["n_vocab"], "context_size": config_gpt["n_ctx"], "embedding_size": config_gpt["n_embd"], "embed": { "dim": hidden_dim, }, "position_size": config_gpt["n_ctx"], "position_embed": { "dim": hidden_dim } } module_name = 'decoder' if self._IS_DECODE else 'encoder' configs.update({module_name: { "dim": hidden_dim, "num_blocks": config_gpt["n_layer"], "embedding_dropout": 0, "residual_dropout": 0, "multihead_attention": { "use_bias": True, "num_units": hidden_dim, "num_heads": config_gpt["n_head"], "output_dim": hidden_dim, }, "initializer": { "type": "variance_scaling_initializer", "kwargs": { "factor": 1.0, "mode": "FAN_AVG", "uniform": True, }, }, "poswise_feedforward": { "layers": [ { "type": "Linear", "kwargs": { "in_features": hidden_dim, "out_features": hidden_dim * 4, "bias": True, } }, { "type": "GPTGELU", "kwargs": {} }, { "type": "Linear", "kwargs": { "in_features": hidden_dim * 4, "out_features": hidden_dim, "bias": True, } } ], "name": "ffn", }, }}) if self._IS_DECODE: configs[module_name].update({'use_gpt_config': True}) else: configs[module_name].update({'use_bert_config': False}) return configs def _init_from_checkpoint(self, pretrained_model_name: str, cache_dir: str, load_output_layer: bool = True, **kwargs): r"""Initialize model parameters from weights stored in the pre-trained checkpoint. Args: pretrained_model_name (str): Name of the pre-trained model. cache_dir (str): Path to the cache directory. load_output_layer (bool): If `False`, will not load weights of the output layer. Set this argument to `False` when loading weights into a GPT2 encoder. Defaults to `True`. """ try: import numpy as np import tensorflow as tf except ImportError: print("Loading TensorFlow models in PyTorch requires installing " "TensorFlow. Please see https://www.tensorflow.org/install/ " "for installation instructions.") raise module_name = 'decoder' if self._IS_DECODE else 'encoder' global_tensor_map = { "model/wte": "word_embedder.embedding", "model/wpe": "position_embedder.embedding", "model/ln_f/b": module_name + ".final_layer_norm.bias", "model/ln_f/g": module_name + ".final_layer_norm.weight", } layer_tensor_map = { "ln_1/b": module_name + ".self_attn_layer_norm.{}.bias", "ln_1/g": module_name + ".self_attn_layer_norm.{}.weight", "ln_2/b": module_name + ".poswise_layer_norm.{}.bias", "ln_2/g": module_name + ".poswise_layer_norm.{}.weight", "mlp/c_fc/b": module_name + ".poswise_networks.{}._layers.0.bias", "mlp/c_proj/b": module_name + ".poswise_networks.{}._layers.2.bias", "attn/c_proj/b": module_name + ".self_attns.{}.O_dense.bias", } layer_transpose_map = { "mlp/c_fc/w": module_name + ".poswise_networks.{}._layers.0.weight", "mlp/c_proj/w": module_name + ".poswise_networks.{}._layers.2." "weight", "attn/c_proj/w": module_name + ".self_attns.{}.O_dense.weight", } tf_path = os.path.abspath(os.path.join(cache_dir, 'model.ckpt')) # Load weights from TF model init_vars = tf.train.list_variables(tf_path) names = [] arrays = [] for name, _ in init_vars: array = tf.train.load_variable(tf_path, name) names.append(name) arrays.append(array.squeeze()) tensor_names = [] for name, _ in self.named_parameters(): tensor_names.append(name) for name, array in zip(names, arrays): if name in global_tensor_map: v_name = global_tensor_map[name] if name == "model/wte": pointer = self._name_to_variable(v_name) assert pointer.shape == array.shape pointer.data = torch.from_numpy(array) if load_output_layer: output_pointer = self._name_to_variable( "decoder._output_layer.weight") assert output_pointer.shape == array.shape output_pointer.data = torch.from_numpy(array) elif name == "model/wpe": pointer = self._name_to_variable(v_name) assert pointer.shape == array.shape pointer.data = torch.from_numpy(array) else: pointer = self._name_to_variable(v_name) assert pointer.shape == array.shape pointer.data = torch.from_numpy(array) else: name_tmp = name.split("/") layer_no = name_tmp[1][1:] name = "/".join(name_tmp[2:]) if name in layer_tensor_map: v_name = layer_tensor_map[name].format(layer_no) pointer = self._name_to_variable(v_name) assert pointer.shape == array.shape pointer.data = torch.from_numpy(array) elif name in layer_transpose_map: v_name = layer_transpose_map[name].format(layer_no) pointer = self._name_to_variable(v_name) array_t = np.transpose(array) assert pointer.shape == array_t.shape pointer.data = torch.from_numpy(array_t) elif name == "attn/c_attn/w": index_d = array.shape[-1] // 3 Q_w = np.transpose(array[:, :index_d]) K_w = np.transpose(array[:, index_d: 2 * index_d]) V_w = np.transpose(array[:, 2 * index_d:]) q_weight = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.Q_dense.weight") k_weight = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.K_dense.weight") v_weight = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.V_dense.weight") assert q_weight.shape == Q_w.shape assert k_weight.shape == K_w.shape assert v_weight.shape == V_w.shape q_weight.data = torch.from_numpy(Q_w) k_weight.data = torch.from_numpy(K_w) v_weight.data = torch.from_numpy(V_w) elif name == "attn/c_attn/b": d = array.shape[0] Q_b = array[: d // 3] K_b = array[d // 3: 2 * d // 3] V_b = array[2 * d // 3:] q_bias = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.Q_dense.bias") k_bias = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.K_dense.bias") v_bias = self._name_to_variable( f"{module_name}.self_attns.{layer_no}.V_dense.bias") assert q_bias.shape == Q_b.shape assert k_bias.shape == K_b.shape assert v_bias.shape == V_b.shape q_bias.data = torch.from_numpy(Q_b) k_bias.data = torch.from_numpy(K_b) v_bias.data = torch.from_numpy(V_b) else: print("Name error", name) raise Exception

en

0.765635

# Copyright 2019 The Texar Authors. All Rights Reserved. # # 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. Utils of GPT2 Modules. A mixin class to support loading pre-trained checkpoints for modules that implement the GPT2 model. The GPT2 model was proposed in `Language Models are Unsupervised Multitask Learners`_ by `Radford et al.` from OpenAI. It is a unidirectional Transformer model pre-trained using the vanilla language modeling objective on a large corpus. The available GPT2 models are as follows: * ``gpt2-small``: Small version of GPT-2, 124M parameters. * ``gpt2-medium``: Medium version of GPT-2, 355M parameters. * ``gpt2-large``: Large version of GPT-2, 774M parameters. We provide the following GPT2 classes: * :class:`~texar.torch.modules.GPT2Encoder` for text encoding. * :class:`~texar.torch.modules.GPT2Decoder` for text generation and decoding. * :class:`~texar.torch.modules.GPT2Classifier` for text classification and sequence tagging. .. _`Language Models are Unsupervised Multitask Learners`: https://openai.com/blog/better-language-models/ # Raise warning for the deprecated pre-trained model names # type: ignore # type: ignore Initialize model parameters from weights stored in the pre-trained checkpoint. Args: pretrained_model_name (str): Name of the pre-trained model. cache_dir (str): Path to the cache directory. load_output_layer (bool): If `False`, will not load weights of the output layer. Set this argument to `False` when loading weights into a GPT2 encoder. Defaults to `True`. # Load weights from TF model

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