In [1]:

    

from corpus_characterizer import generator_chunker
import numpy as np
import os
# from sklearn.metrics import mean_squared_error,mean_absolute_error, median_absolute_error, mean_squared_log_error, explained_variance_score, r2_score
from sklearn.metrics import mean_squared_error, mean_absolute_error, median_absolute_error, explained_variance_score, \
    r2_score
import pandas as pd
from scipy.stats import describe, kurtosistest, skewtest, normaltest
from Conv1D_LSTM_Ensemble import pair_generator_1dconv_lstm_bagged
from Conv1D_ActivationSearch_BigLoop import pair_generator_1dconv_lstm #NOT BAGGED
from AuxRegressor import create_testing_set, create_training_set
import matplotlib.pyplot as plt 

# @@@@@@@@@@@@@@ RELATIVE PATHS @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
Base_Path = "./"
image_path = "./images/"
train_path = "./train/"
test_path = "./test/"
analysis_path = "./analysis/"
chunker_path = analysis_path + "chunker/"
preds_path = analysis_path + "preds/"

#create the data-pair filenames (using zip), use the helper methods
train_set_filenames = create_training_set()
test_set_filenames = create_testing_set()
print(train_set_filenames)
print(test_set_filenames)


    

    




Using Theano backend.






    




after shuffling: [('sequence_2b_484_5_fv1c.npy', 'sequence_2b_484_5_label_fv1c.npy'), ('sequence_1l_413_4_fv1c.npy', 'sequence_1l_413_4_label_fv1c.npy'), ('sequence_1d_161_2_fv1c.npy', 'sequence_1d_161_2_label_fv1c.npy'), ('sequence_2c_551_2_fv1c.npy', 'sequence_2c_551_2_label_fv1c.npy'), ('sequence_1l_411_2_fv1c.npy', 'sequence_1l_411_2_label_fv1c.npy'), ('sequence_3a_561_2_fv1c.npy', 'sequence_3a_561_2_label_fv1c.npy'), ('sequence_3a_690_12_fv1c.npy', 'sequence_3a_690_12_label_fv1c.npy'), ('sequence_1c_105_6_fv1c.npy', 'sequence_1c_105_6_label_fv1c.npy'), ('sequence_1d_197_8_fv1c.npy', 'sequence_1d_197_8_label_fv1c.npy'), ('sequence_3a_715_17_fv1c.npy', 'sequence_3a_715_17_label_fv1c.npy'), ('sequence_1b_15_6_fv1c.npy', 'sequence_1b_15_6_label_fv1c.npy'), ('sequence_2b_485_6_fv1c.npy', 'sequence_2b_485_6_label_fv1c.npy'), ('sequence_3a_600_2_fv1c.npy', 'sequence_3a_600_2_label_fv1c.npy'), ('sequence_3a_664_6_fv1c.npy', 'sequence_3a_664_6_label_fv1c.npy'), ('sequence_3a_646_8_fv1c.npy', 'sequence_3a_646_8_label_fv1c.npy'), ('sequence_1k_337_8_fv1c.npy', 'sequence_1k_337_8_label_fv1c.npy'), ('sequence_1d_200_11_fv1c.npy', 'sequence_1d_200_11_label_fv1c.npy'), ('sequence_1b_34_5_fv1c.npy', 'sequence_1b_34_5_label_fv1c.npy'), ('sequence_3a_624_6_fv1c.npy', 'sequence_3a_624_6_label_fv1c.npy'), ('sequence_1d_151_22_fv1c.npy', 'sequence_1d_151_22_label_fv1c.npy'), ('sequence_2b_476_7_fv1c.npy', 'sequence_2b_476_7_label_fv1c.npy'), ('sequence_1d_212_23_fv1c.npy', 'sequence_1d_212_23_label_fv1c.npy'), ('sequence_3a_618_20_fv1c.npy', 'sequence_3a_618_20_label_fv1c.npy'), ('sequence_1c_116_7_fv1c.npy', 'sequence_1c_116_7_label_fv1c.npy'), ('sequence_2b_457_8_fv1c.npy', 'sequence_2b_457_8_label_fv1c.npy'), ('sequence_2c_511_2_fv1c.npy', 'sequence_2c_511_2_label_fv1c.npy'), ('sequence_1d_163_4_fv1c.npy', 'sequence_1d_163_4_label_fv1c.npy'), ('sequence_1b_54_5_fv1c.npy', 'sequence_1b_54_5_label_fv1c.npy'), ('sequence_1d_257_8_fv1c.npy', 'sequence_1d_257_8_label_fv1c.npy'), ('sequence_1d_144_15_fv1c.npy', 'sequence_1d_144_15_label_fv1c.npy'), ('sequence_1d_239_20_fv1c.npy', 'sequence_1d_239_20_label_fv1c.npy'), ('sequence_1d_206_17_fv1c.npy', 'sequence_1d_206_17_label_fv1c.npy'), ('sequence_1d_130_1_fv1c.npy', 'sequence_1d_130_1_label_fv1c.npy'), ('sequence_2b_482_3_fv1c.npy', 'sequence_2b_482_3_label_fv1c.npy'), ('sequence_1k_312_3_fv1c.npy', 'sequence_1k_312_3_label_fv1c.npy'), ('sequence_3a_570_11_fv1c.npy', 'sequence_3a_570_11_label_fv1c.npy'), ('sequence_1d_232_13_fv1c.npy', 'sequence_1d_232_13_label_fv1c.npy'), ('sequence_1l_394_5_fv1c.npy', 'sequence_1l_394_5_label_fv1c.npy'), ('sequence_1d_262_13_fv1c.npy', 'sequence_1d_262_13_label_fv1c.npy'), ('sequence_1b_6_7_fv1c.npy', 'sequence_1b_6_7_label_fv1c.npy'), ('sequence_1l_404_5_fv1c.npy', 'sequence_1l_404_5_label_fv1c.npy'), ('sequence_3a_703_5_fv1c.npy', 'sequence_3a_703_5_label_fv1c.npy'), ('sequence_1c_71_2_fv1c.npy', 'sequence_1c_71_2_label_fv1c.npy'), ('sequence_1d_137_8_fv1c.npy', 'sequence_1d_137_8_label_fv1c.npy'), ('sequence_2c_536_7_fv1c.npy', 'sequence_2c_536_7_label_fv1c.npy'), ('sequence_1d_185_26_fv1c.npy', 'sequence_1d_185_26_label_fv1c.npy'), ('sequence_1b_30_1_fv1c.npy', 'sequence_1b_30_1_label_fv1c.npy'), ('sequence_2b_463_4_fv1c.npy', 'sequence_2b_463_4_label_fv1c.npy'), ('sequence_1c_66_7_fv1c.npy', 'sequence_1c_66_7_label_fv1c.npy'), ('sequence_2b_467_8_fv1c.npy', 'sequence_2b_467_8_label_fv1c.npy'), ('sequence_1d_270_21_fv1c.npy', 'sequence_1d_270_21_label_fv1c.npy'), ('sequence_1c_61_2_fv1c.npy', 'sequence_1c_61_2_label_fv1c.npy'), ('sequence_1d_131_2_fv1c.npy', 'sequence_1d_131_2_label_fv1c.npy'), ('sequence_2b_473_4_fv1c.npy', 'sequence_2b_473_4_label_fv1c.npy'), ('sequence_1d_234_15_fv1c.npy', 'sequence_1d_234_15_label_fv1c.npy'), ('sequence_1c_72_3_fv1c.npy', 'sequence_1c_72_3_label_fv1c.npy'), ('sequence_1k_321_2_fv1c.npy', 'sequence_1k_321_2_label_fv1c.npy'), ('sequence_1d_147_18_fv1c.npy', 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('sequence_1k_357_8_fv1c.npy', 'sequence_1k_357_8_label_fv1c.npy'), ('sequence_1d_266_17_fv1c.npy', 'sequence_1d_266_17_label_fv1c.npy'), ('sequence_1k_341_2_fv1c.npy', 'sequence_1k_341_2_label_fv1c.npy'), ('sequence_1l_397_8_fv1c.npy', 'sequence_1l_397_8_label_fv1c.npy'), ('sequence_3a_582_4_fv1c.npy', 'sequence_3a_582_4_label_fv1c.npy'), ('sequence_1d_227_8_fv1c.npy', 'sequence_1d_227_8_label_fv1c.npy'), ('sequence_1d_190_1_fv1c.npy', 'sequence_1d_190_1_label_fv1c.npy'), ('sequence_1b_43_4_fv1c.npy', 'sequence_1b_43_4_label_fv1c.npy'), ('sequence_1d_218_29_fv1c.npy', 'sequence_1d_218_29_label_fv1c.npy'), ('sequence_2c_555_6_fv1c.npy', 'sequence_2c_555_6_label_fv1c.npy'), ('sequence_1d_184_25_fv1c.npy', 'sequence_1d_184_25_label_fv1c.npy'), ('sequence_3a_603_5_fv1c.npy', 'sequence_3a_603_5_label_fv1c.npy'), ('sequence_1b_35_6_fv1c.npy', 'sequence_1b_35_6_label_fv1c.npy'), ('sequence_1l_386_7_fv1c.npy', 'sequence_1l_386_7_label_fv1c.npy'), ('sequence_2b_455_6_fv1c.npy', 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'sequence_1d_240_21_label_fv1c.npy'), ('sequence_1d_133_4_fv1c.npy', 'sequence_1d_133_4_label_fv1c.npy'), ('sequence_1d_217_28_fv1c.npy', 'sequence_1d_217_28_label_fv1c.npy'), ('sequence_3a_652_14_fv1c.npy', 'sequence_3a_652_14_label_fv1c.npy'), ('sequence_2b_443_4_fv1c.npy', 'sequence_2b_443_4_label_fv1c.npy'), ('sequence_3a_595_17_fv1c.npy', 'sequence_3a_595_17_label_fv1c.npy'), ('sequence_3a_605_7_fv1c.npy', 'sequence_3a_605_7_label_fv1c.npy'), ('sequence_3a_573_14_fv1c.npy', 'sequence_3a_573_14_label_fv1c.npy'), ('sequence_1k_355_6_fv1c.npy', 'sequence_1k_355_6_label_fv1c.npy'), ('sequence_1d_272_23_fv1c.npy', 'sequence_1d_272_23_label_fv1c.npy'), ('sequence_3a_577_18_fv1c.npy', 'sequence_3a_577_18_label_fv1c.npy'), ('sequence_1d_166_7_fv1c.npy', 'sequence_1d_166_7_label_fv1c.npy'), ('sequence_3a_602_4_fv1c.npy', 'sequence_3a_602_4_label_fv1c.npy'), ('sequence_1b_56_7_fv1c.npy', 'sequence_1b_56_7_label_fv1c.npy'), ('sequence_1d_122_3_fv1c.npy', 'sequence_1d_122_3_label_fv1c.npy'), ('sequence_2b_466_7_fv1c.npy', 'sequence_2b_466_7_label_fv1c.npy'), ('sequence_3a_693_15_fv1c.npy', 'sequence_3a_693_15_label_fv1c.npy'), ('sequence_3a_666_8_fv1c.npy', 'sequence_3a_666_8_label_fv1c.npy'), ('sequence_2c_534_5_fv1c.npy', 'sequence_2c_534_5_label_fv1c.npy'), ('sequence_1l_375_6_fv1c.npy', 'sequence_1l_375_6_label_fv1c.npy'), ('sequence_2b_447_8_fv1c.npy', 'sequence_2b_447_8_label_fv1c.npy'), ('sequence_3a_635_17_fv1c.npy', 'sequence_3a_635_17_label_fv1c.npy'), ('sequence_2b_456_7_fv1c.npy', 'sequence_2b_456_7_label_fv1c.npy'), ('sequence_3a_702_4_fv1c.npy', 'sequence_3a_702_4_label_fv1c.npy'), ('sequence_1d_203_14_fv1c.npy', 'sequence_1d_203_14_label_fv1c.npy'), ('sequence_1d_216_27_fv1c.npy', 'sequence_1d_216_27_label_fv1c.npy'), ('sequence_2b_495_6_fv1c.npy', 'sequence_2b_495_6_label_fv1c.npy'), ('sequence_2b_496_7_fv1c.npy', 'sequence_2b_496_7_label_fv1c.npy'), ('sequence_3a_626_8_fv1c.npy', 'sequence_3a_626_8_label_fv1c.npy'), ('sequence_1l_415_6_fv1c.npy', 'sequence_1l_415_6_label_fv1c.npy'), ('sequence_1b_13_4_fv1c.npy', 'sequence_1b_13_4_label_fv1c.npy'), ('sequence_1b_52_3_fv1c.npy', 'sequence_1b_52_3_label_fv1c.npy'), ('sequence_3a_630_12_fv1c.npy', 'sequence_3a_630_12_label_fv1c.npy')]
Training data read.
after shuffling: [('sequence_1b_28_9_fv1c.npy', 'sequence_1b_28_9_label_fv1c.npy'), ('sequence_1l_419_10_fv1c.npy', 'sequence_1l_419_10_label_fv1c.npy'), ('sequence_3a_628_10_fv1c.npy', 'sequence_3a_628_10_label_fv1c.npy'), ('sequence_1l_428_9_fv1c.npy', 'sequence_1l_428_9_label_fv1c.npy'), ('sequence_1b_49_10_fv1c.npy', 'sequence_1b_49_10_label_fv1c.npy'), ('sequence_2c_549_10_fv1c.npy', 'sequence_2c_549_10_label_fv1c.npy'), ('sequence_1k_308_9_fv1c.npy', 'sequence_1k_308_9_label_fv1c.npy'), ('sequence_1b_59_10_fv1c.npy', 'sequence_1b_59_10_label_fv1c.npy'), ('sequence_1k_358_9_fv1c.npy', 'sequence_1k_358_9_label_fv1c.npy'), ('sequence_3a_587_9_fv1c.npy', 'sequence_3a_587_9_label_fv1c.npy'), ('sequence_1c_79_10_fv1c.npy', 'sequence_1c_79_10_label_fv1c.npy'), ('sequence_3a_647_9_fv1c.npy', 'sequence_3a_647_9_label_fv1c.npy'), ('sequence_1b_19_10_fv1c.npy', 'sequence_1b_19_10_label_fv1c.npy'), ('sequence_1k_328_9_fv1c.npy', 'sequence_1k_328_9_label_fv1c.npy'), ('sequence_1k_288_9_fv1c.npy', 'sequence_1k_288_9_label_fv1c.npy'), ('sequence_3a_707_9_fv1c.npy', 'sequence_3a_707_9_label_fv1c.npy'), ('sequence_1l_379_10_fv1c.npy', 'sequence_1l_379_10_label_fv1c.npy'), ('sequence_1d_259_10_fv1c.npy', 'sequence_1d_259_10_label_fv1c.npy'), ('sequence_2c_558_9_fv1c.npy', 'sequence_2c_558_9_label_fv1c.npy'), ('sequence_1b_9_10_fv1c.npy', 'sequence_1b_9_10_label_fv1c.npy'), ('sequence_2b_449_10_fv1c.npy', 'sequence_2b_449_10_label_fv1c.npy'), ('sequence_2c_538_9_fv1c.npy', 'sequence_2c_538_9_label_fv1c.npy'), ('sequence_1l_368_9_fv1c.npy', 'sequence_1l_368_9_label_fv1c.npy'), ('sequence_1d_199_10_fv1c.npy', 'sequence_1d_199_10_label_fv1c.npy'), ('sequence_3a_648_10_fv1c.npy', 'sequence_3a_648_10_label_fv1c.npy'), ('sequence_1k_339_10_fv1c.npy', 'sequence_1k_339_10_label_fv1c.npy'), ('sequence_1l_398_9_fv1c.npy', 'sequence_1l_398_9_label_fv1c.npy'), ('sequence_1d_129_10_fv1c.npy', 'sequence_1d_129_10_label_fv1c.npy'), ('sequence_1l_408_9_fv1c.npy', 'sequence_1l_408_9_label_fv1c.npy'), ('sequence_1k_348_9_fv1c.npy', 'sequence_1k_348_9_label_fv1c.npy'), ('sequence_3a_568_9_fv1c.npy', 'sequence_3a_568_9_label_fv1c.npy'), ('sequence_1l_438_9_fv1c.npy', 'sequence_1l_438_9_label_fv1c.npy'), ('sequence_1b_38_9_fv1c.npy', 'sequence_1b_38_9_label_fv1c.npy'), ('sequence_2c_509_10_fv1c.npy', 'sequence_2c_509_10_label_fv1c.npy'), ('sequence_1k_329_10_fv1c.npy', 'sequence_1k_329_10_label_fv1c.npy'), ('sequence_1k_349_10_fv1c.npy', 'sequence_1k_349_10_label_fv1c.npy'), ('sequence_1k_359_10_fv1c.npy', 'sequence_1k_359_10_label_fv1c.npy'), ('sequence_1c_119_10_fv1c.npy', 'sequence_1c_119_10_label_fv1c.npy'), ('sequence_1k_299_10_fv1c.npy', 'sequence_1k_299_10_label_fv1c.npy'), ('sequence_1l_378_9_fv1c.npy', 'sequence_1l_378_9_label_fv1c.npy'), ('sequence_3a_588_10_fv1c.npy', 'sequence_3a_588_10_label_fv1c.npy'), ('sequence_2b_469_10_fv1c.npy', 'sequence_2b_469_10_label_fv1c.npy'), ('sequence_1b_39_10_fv1c.npy', 'sequence_1b_39_10_label_fv1c.npy'), ('sequence_1b_58_9_fv1c.npy', 'sequence_1b_58_9_label_fv1c.npy'), ('sequence_2b_458_9_fv1c.npy', 'sequence_2b_458_9_label_fv1c.npy'), ('sequence_1c_108_9_fv1c.npy', 'sequence_1c_108_9_label_fv1c.npy'), ('sequence_3a_708_10_fv1c.npy', 'sequence_3a_708_10_label_fv1c.npy'), ('sequence_1l_429_10_fv1c.npy', 'sequence_1l_429_10_label_fv1c.npy'), ('sequence_2c_528_9_fv1c.npy', 'sequence_2c_528_9_label_fv1c.npy'), ('sequence_1c_118_9_fv1c.npy', 'sequence_1c_118_9_label_fv1c.npy'), ('sequence_2b_448_9_fv1c.npy', 'sequence_2b_448_9_label_fv1c.npy'), ('sequence_2b_479_10_fv1c.npy', 'sequence_2b_479_10_label_fv1c.npy'), ('sequence_1l_418_9_fv1c.npy', 'sequence_1l_418_9_label_fv1c.npy'), ('sequence_1d_168_9_fv1c.npy', 'sequence_1d_168_9_label_fv1c.npy'), ('sequence_3a_607_9_fv1c.npy', 'sequence_3a_607_9_label_fv1c.npy'), ('sequence_3a_688_10_fv1c.npy', 'sequence_3a_688_10_label_fv1c.npy'), ('sequence_1b_48_9_fv1c.npy', 'sequence_1b_48_9_label_fv1c.npy'), ('sequence_2c_519_10_fv1c.npy', 'sequence_2c_519_10_label_fv1c.npy'), ('sequence_1c_89_10_fv1c.npy', 'sequence_1c_89_10_label_fv1c.npy'), ('sequence_3a_627_9_fv1c.npy', 'sequence_3a_627_9_label_fv1c.npy'), ('sequence_2b_478_9_fv1c.npy', 'sequence_2b_478_9_label_fv1c.npy'), ('sequence_2b_459_10_fv1c.npy', 'sequence_2b_459_10_label_fv1c.npy'), ('sequence_1b_8_9_fv1c.npy', 'sequence_1b_8_9_label_fv1c.npy'), ('sequence_3a_569_10_fv1c.npy', 'sequence_3a_569_10_label_fv1c.npy'), ('sequence_2c_559_10_fv1c.npy', 'sequence_2c_559_10_label_fv1c.npy'), ('sequence_1d_258_9_fv1c.npy', 'sequence_1d_258_9_label_fv1c.npy'), ('sequence_2b_488_9_fv1c.npy', 'sequence_2b_488_9_label_fv1c.npy'), ('sequence_1l_409_10_fv1c.npy', 'sequence_1l_409_10_label_fv1c.npy'), ('sequence_1d_139_10_fv1c.npy', 'sequence_1d_139_10_label_fv1c.npy'), ('sequence_2b_489_10_fv1c.npy', 'sequence_2b_489_10_label_fv1c.npy'), ('sequence_1l_439_10_fv1c.npy', 'sequence_1l_439_10_label_fv1c.npy'), ('sequence_2b_498_9_fv1c.npy', 'sequence_2b_498_9_label_fv1c.npy'), ('sequence_2c_518_9_fv1c.npy', 'sequence_2c_518_9_label_fv1c.npy'), ('sequence_1c_88_9_fv1c.npy', 'sequence_1c_88_9_label_fv1c.npy'), ('sequence_2c_529_10_fv1c.npy', 'sequence_2c_529_10_label_fv1c.npy'), ('sequence_1l_389_10_fv1c.npy', 'sequence_1l_389_10_label_fv1c.npy'), ('sequence_1d_128_9_fv1c.npy', 'sequence_1d_128_9_label_fv1c.npy'), ('sequence_1c_68_9_fv1c.npy', 'sequence_1c_68_9_label_fv1c.npy'), ('sequence_1d_228_9_fv1c.npy', 'sequence_1d_228_9_label_fv1c.npy'), ('sequence_3a_608_10_fv1c.npy', 'sequence_3a_608_10_label_fv1c.npy'), ('sequence_1d_138_9_fv1c.npy', 'sequence_1d_138_9_label_fv1c.npy'), ('sequence_1k_298_9_fv1c.npy', 'sequence_1k_298_9_label_fv1c.npy'), ('sequence_1d_169_10_fv1c.npy', 'sequence_1d_169_10_label_fv1c.npy'), ('sequence_1k_309_10_fv1c.npy', 'sequence_1k_309_10_label_fv1c.npy'), ('sequence_1d_198_9_fv1c.npy', 'sequence_1d_198_9_label_fv1c.npy'), ('sequence_2c_548_9_fv1c.npy', 'sequence_2c_548_9_label_fv1c.npy'), ('sequence_3a_668_10_fv1c.npy', 'sequence_3a_668_10_label_fv1c.npy'), ('sequence_1c_99_10_fv1c.npy', 'sequence_1c_99_10_label_fv1c.npy'), ('sequence_3a_667_9_fv1c.npy', 'sequence_3a_667_9_label_fv1c.npy'), ('sequence_3a_687_9_fv1c.npy', 'sequence_3a_687_9_label_fv1c.npy'), ('sequence_2b_499_10_fv1c.npy', 'sequence_2b_499_10_label_fv1c.npy'), ('sequence_1l_369_10_fv1c.npy', 'sequence_1l_369_10_label_fv1c.npy'), ('sequence_1k_289_10_fv1c.npy', 'sequence_1k_289_10_label_fv1c.npy'), ('sequence_1k_338_9_fv1c.npy', 'sequence_1k_338_9_label_fv1c.npy'), ('sequence_1l_399_10_fv1c.npy', 'sequence_1l_399_10_label_fv1c.npy'), ('sequence_1b_29_10_fv1c.npy', 'sequence_1b_29_10_label_fv1c.npy'), ('sequence_2b_468_9_fv1c.npy', 'sequence_2b_468_9_label_fv1c.npy'), ('sequence_1k_318_9_fv1c.npy', 'sequence_1k_318_9_label_fv1c.npy'), ('sequence_1c_69_10_fv1c.npy', 'sequence_1c_69_10_label_fv1c.npy'), ('sequence_1l_388_9_fv1c.npy', 'sequence_1l_388_9_label_fv1c.npy'), ('sequence_1d_229_10_fv1c.npy', 'sequence_1d_229_10_label_fv1c.npy'), ('sequence_1k_319_10_fv1c.npy', 'sequence_1k_319_10_label_fv1c.npy'), ('sequence_1c_109_10_fv1c.npy', 'sequence_1c_109_10_label_fv1c.npy'), ('sequence_1c_78_9_fv1c.npy', 'sequence_1c_78_9_label_fv1c.npy'), ('sequence_2c_539_10_fv1c.npy', 'sequence_2c_539_10_label_fv1c.npy'), ('sequence_1c_98_9_fv1c.npy', 'sequence_1c_98_9_label_fv1c.npy'), ('sequence_2c_508_9_fv1c.npy', 'sequence_2c_508_9_label_fv1c.npy'), ('sequence_1b_18_9_fv1c.npy', 'sequence_1b_18_9_label_fv1c.npy')]
Test set read.
[('sequence_2b_484_5_fv1c.npy', 'sequence_2b_484_5_label_fv1c.npy'), ('sequence_1l_413_4_fv1c.npy', 'sequence_1l_413_4_label_fv1c.npy'), ('sequence_1d_161_2_fv1c.npy', 'sequence_1d_161_2_label_fv1c.npy'), ('sequence_2c_551_2_fv1c.npy', 'sequence_2c_551_2_label_fv1c.npy'), ('sequence_1l_411_2_fv1c.npy', 'sequence_1l_411_2_label_fv1c.npy'), ('sequence_3a_561_2_fv1c.npy', 'sequence_3a_561_2_label_fv1c.npy'), ('sequence_3a_690_12_fv1c.npy', 'sequence_3a_690_12_label_fv1c.npy'), ('sequence_1c_105_6_fv1c.npy', 'sequence_1c_105_6_label_fv1c.npy'), ('sequence_1d_197_8_fv1c.npy', 'sequence_1d_197_8_label_fv1c.npy'), ('sequence_3a_715_17_fv1c.npy', 'sequence_3a_715_17_label_fv1c.npy'), ('sequence_1b_15_6_fv1c.npy', 'sequence_1b_15_6_label_fv1c.npy'), ('sequence_2b_485_6_fv1c.npy', 'sequence_2b_485_6_label_fv1c.npy'), ('sequence_3a_600_2_fv1c.npy', 'sequence_3a_600_2_label_fv1c.npy'), ('sequence_3a_664_6_fv1c.npy', 'sequence_3a_664_6_label_fv1c.npy'), ('sequence_3a_646_8_fv1c.npy', 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In [2]:

    

i = 0 
unique_load_sequences = [] #fill these with the 4-pairs as a numpy array
unique_load_sequences_aslists = []
sequences_4pairs_list = []
sequences_not4pairs_list = []
filename_loadseq_dict_seqidkey = {} #key is the sequence ID, value is its position in the list. 
four_pairs_condition = False
print("train_set size: ",len(train_set_filenames))
for files in train_set_filenames:
    #print("filename", files)
    i += 1
    data_load_path = train_path + '/data/' + files[0]
    label_load_path = train_path + '/label/' + files[1]
    # print("data/label load path: {} \n {}".format(data_load_path,label_load_path))
    train_array = np.load(data_load_path)
    train_label_array = np.load(label_load_path)[:, 1:]
    
    #check if the repeating unit is actually 4 tuples. 
    #four_pair_condition = (train_array[0:5,-2] == train_array[4:8,-2]) and (train_array[0:5,-1] == train_array[4:8,-1]) error. 
    #numpy version 
    four_pair_condition = np.array_equal(train_array[0:4,-2:],train_array[4:8,-2:]) 

    
    if four_pair_condition == True:
        #print(str(files[0])," is in.")
        load_sequence = train_array[0:4,-2:]
        #if len(unique_load_sequences) != 0 :
        array_equality = False
        if len(unique_load_sequences) == 0:
            unique_load_sequences.append(load_sequence)
            #unique_load_sequences_aslists.append(np.ndarray.tolist(load_sequence)) #need to be flattened into a list to make it hashable for the list-operators to work
            unique_load_sequences_aslists.append(str(np.ndarray.flatten(load_sequence)))
        if len(unique_load_sequences) != 0:
            for load_seq in unique_load_sequences:
                if np.array_equal(load_seq,load_sequence) == True:
                    #print("equal array found in list?", np.array_equal(load_seq,load_sequence))
                    array_equality = True
            if array_equality == False:
                unique_load_sequences.append(load_sequence) #the load types
                unique_load_sequences_aslists.append(str(np.ndarray.flatten(load_sequence)))
            filename_loadseq_dict_seqidkey[str(files[0])[:-4]] = unique_load_sequences_aslists.index(str(np.ndarray.flatten(load_sequence)))
        sequences_4pairs_list.append(str(files[0]))
    if four_pair_condition == False:
        sequences_not4pairs_list.append(str(files[0]))
        
    
    #print("train_array's last two columns",train_array[0:20,-2:])
#unique_load_sequences = set(unique_load_sequences)
#print("there are", len(unique_load_sequences), " load sequences")
#print("the unique load sequences are:", unique_load_sequences)
print("sequences with 4-pair repeating units: ",len(sequences_4pairs_list))
print("sequences WITHOUT 4-pair repeating units: ",sequences_not4pairs_list)


    

    




('train_set size: ', 611)
('sequences with 4-pair repeating units: ', 357)
('sequences WITHOUT 4-pair repeating units: ', ['sequence_1l_413_4_fv1c.npy', 'sequence_1l_411_2_fv1c.npy', 'sequence_3a_561_2_fv1c.npy', 'sequence_3a_690_12_fv1c.npy', 'sequence_3a_715_17_fv1c.npy', 'sequence_3a_600_2_fv1c.npy', 'sequence_3a_664_6_fv1c.npy', 'sequence_3a_646_8_fv1c.npy', 'sequence_1k_337_8_fv1c.npy', 'sequence_3a_618_20_fv1c.npy', 'sequence_3a_570_11_fv1c.npy', 'sequence_1l_394_5_fv1c.npy', 'sequence_1l_404_5_fv1c.npy', 'sequence_3a_703_5_fv1c.npy', 'sequence_1k_321_2_fv1c.npy', 'sequence_3a_584_6_fv1c.npy', 'sequence_1k_292_3_fv1c.npy', 'sequence_1l_371_2_fv1c.npy', 'sequence_1l_402_3_fv1c.npy', 'sequence_3a_650_12_fv1c.npy', 'sequence_1k_344_5_fv1c.npy', 'sequence_1l_401_2_fv1c.npy', 'sequence_3a_718_20_fv1c.npy', 'sequence_3a_661_3_fv1c.npy', 'sequence_1l_364_5_fv1c.npy', 'sequence_1k_357_8_fv1c.npy', 'sequence_1k_341_2_fv1c.npy', 'sequence_1l_397_8_fv1c.npy', 'sequence_3a_582_4_fv1c.npy', 'sequence_3a_603_5_fv1c.npy', 'sequence_1l_386_7_fv1c.npy', 'sequence_3a_659_1_fv1c.npy', 'sequence_3a_698_20_fv1c.npy', 'sequence_3a_613_15_fv1c.npy', 'sequence_1l_396_7_fv1c.npy', 'sequence_1k_322_3_fv1c.npy', 'sequence_3a_649_11_fv1c.npy', 'sequence_3a_593_15_fv1c.npy', 'sequence_1k_293_4_fv1c.npy', 'sequence_1l_395_6_fv1c.npy', 'sequence_1k_285_6_fv1c.npy', 'sequence_1k_307_8_fv1c.npy', 'sequence_3a_655_17_fv1c.npy', 'sequence_1l_387_8_fv1c.npy', 'sequence_3a_685_7_fv1c.npy', 'sequence_3a_673_15_fv1c.npy', 'sequence_1k_331_2_fv1c.npy', 'sequence_1k_334_5_fv1c.npy', 'sequence_3a_574_15_fv1c.npy', 'sequence_3a_581_3_fv1c.npy', 'sequence_1k_284_5_fv1c.npy', 'sequence_1l_382_3_fv1c.npy', 'sequence_1k_280_1_fv1c.npy', 'sequence_1l_433_4_fv1c.npy', 'sequence_3a_645_7_fv1c.npy', 'sequence_3a_578_19_fv1c.npy', 'sequence_3a_670_12_fv1c.npy', 'sequence_3a_677_19_fv1c.npy', 'sequence_3a_567_8_fv1c.npy', 'sequence_1k_346_7_fv1c.npy', 'sequence_3a_609_11_fv1c.npy', 'sequence_1k_351_2_fv1c.npy', 'sequence_3a_699_1_fv1c.npy', 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'sequence_1l_380_1_fv1c.npy', 'sequence_1k_297_8_fv1c.npy', 'sequence_3a_572_13_fv1c.npy', 'sequence_3a_656_18_fv1c.npy', 'sequence_1l_437_8_fv1c.npy', 'sequence_1l_426_7_fv1c.npy', 'sequence_1l_393_4_fv1c.npy', 'sequence_1k_354_5_fv1c.npy', 'sequence_1l_405_6_fv1c.npy', 'sequence_3a_606_8_fv1c.npy', 'sequence_3a_641_3_fv1c.npy', 'sequence_3a_665_7_fv1c.npy', 'sequence_1k_283_4_fv1c.npy', 'sequence_3a_652_14_fv1c.npy', 'sequence_3a_595_17_fv1c.npy', 'sequence_3a_605_7_fv1c.npy', 'sequence_3a_573_14_fv1c.npy', 'sequence_1k_355_6_fv1c.npy', 'sequence_3a_577_18_fv1c.npy', 'sequence_3a_602_4_fv1c.npy', 'sequence_3a_693_15_fv1c.npy', 'sequence_3a_666_8_fv1c.npy', 'sequence_1l_375_6_fv1c.npy', 'sequence_3a_635_17_fv1c.npy', 'sequence_3a_702_4_fv1c.npy', 'sequence_1l_415_6_fv1c.npy', 'sequence_3a_630_12_fv1c.npy'])

In [3]:

    

twelve_pair_condition = np.array_equal(train_array[0:12,-2:],train_array[12:24,-2:]) 
sequences_12pairs_list = []
sequences_not4_not12_pairs_list = []

#gotta run the 4-pair filter first to make sure the 12-pair subset list is ok. Hence the separate loop. 
for unusual_seqs in sequences_not4pairs_list: #12 repeating units. 
    data_load_path = train_path + '/data/' + unusual_seqs
    train_array = np.load(data_load_path)
    
    if twelve_pair_condition == True:
        #print(str(unusual_seqs)," is in.")
        load_sequence = train_array[0:12,-2:]
        #if len(unique_load_sequences) != 0 :
        array_equality = False
        if len(unique_load_sequences) == 0:
            unique_load_sequences.append(load_sequence)
            #unique_load_sequences_aslists.append(np.ndarray.tolist(load_sequence))
            unique_load_sequences_aslists.append(str(np.ndarray.flatten(load_sequence)))
        if len(unique_load_sequences) != 0:
            for load_seq in unique_load_sequences:
                if np.array_equal(load_seq,load_sequence) == True:
                    #print("equal array found in list?", np.array_equal(load_seq,load_sequence))
                    array_equality = True
            if array_equality == False:
                unique_load_sequences.append(load_sequence) #the load types
                #unique_load_sequences_aslists.append(np.ndarray.tolist(load_sequence))
                unique_load_sequences_aslists.append(str(np.ndarray.flatten(load_sequence)))
                                        
        sequences_12pairs_list.append(str(unusual_seqs))
        filename_loadseq_dict_seqidkey[str(unusual_seqs)[:-4]] = unique_load_sequences_aslists.index(str(np.ndarray.flatten(load_sequence)))
    if twelve_pair_condition == False:
        sequences_not4_not12_pairs_list.append(str(unusual_seqs))


    

In [4]:

    

print("There are ",len(sequences_12pairs_list), " sequences with 12-pairs")        
print("There are ", len(sequences_not4_not12_pairs_list), "sequences that have neither 4 nor 12 repeating units")
print("seqs that aren't 4 or 12: ",sequences_not4_not12_pairs_list)
print("there are ",len(unique_load_sequences), " different load sequences in the ", len(train_set_filenames), "sequences in the training set")
#print("unique load seqs", unique_load_sequences)


    

    




('There are ', 254, ' sequences with 12-pairs')
('There are ', 0, 'sequences that have neither 4 nor 12 repeating units')
("seqs that aren't 4 or 12: ", [])
('there are ', 27, ' different load sequences in the ', 611, 'sequences in the training set')

In [5]:

    

#just query this. e.g. print dict[sequence id] 
print("the dict with seq id as key: ", filename_loadseq_dict_seqidkey)


    

    




('the dict with seq id as key: ', {'sequence_1k_294_5_fv1c': 23, 'sequence_1d_161_2_fv1c': 1, 'sequence_1c_61_2_fv1c': 7, 'sequence_1d_269_20_fv1c': 5, 'sequence_3a_676_18_fv1c': 12, 'sequence_1k_315_6_fv1c': 3, 'sequence_1d_172_13_fv1c': 1, 'sequence_1c_113_4_fv1c': 5, 'sequence_3a_604_6_fv1c': 16, 'sequence_1d_207_18_fv1c': 3, 'sequence_3a_586_8_fv1c': 23, 'sequence_1c_104_5_fv1c': 0, 'sequence_1d_136_7_fv1c': 4, 'sequence_2b_466_7_fv1c': 1, 'sequence_1d_126_7_fv1c': 7, 'sequence_2b_450_1_fv1c': 4, 'sequence_1d_246_27_fv1c': 0, 'sequence_3a_564_5_fv1c': 13, 'sequence_1d_263_14_fv1c': 5, 'sequence_2c_557_8_fv1c': 2, 'sequence_1l_373_4_fv1c': 24, 'sequence_2b_483_4_fv1c': 0, 'sequence_1k_333_4_fv1c': 17, 'sequence_3a_623_5_fv1c': 3, 'sequence_1d_141_12_fv1c': 4, 'sequence_3a_649_11_fv1c': 21, 'sequence_3a_620_2_fv1c': 3, 'sequence_1k_336_7_fv1c': 17, 'sequence_3a_645_7_fv1c': 18, 'sequence_1k_330_1_fv1c': 17, 'sequence_3a_626_8_fv1c': 3, 'sequence_2c_524_5_fv1c': 10, 'sequence_1l_422_3_fv1c': 14, 'sequence_2c_503_4_fv1c': 9, 'sequence_2c_512_3_fv1c': 6, 'sequence_1c_85_6_fv1c': 1, 'sequence_1d_251_2_fv1c': 5, 'sequence_1l_376_7_fv1c': 24, 'sequence_1k_317_8_fv1c': 3, 'sequence_1k_320_1_fv1c': 18, 'sequence_1c_96_7_fv1c': 3, 'sequence_1d_224_5_fv1c': 0, 'sequence_1d_243_24_fv1c': 0, 'sequence_1l_392_3_fv1c': 20, 'sequence_1d_124_5_fv1c': 7, 'sequence_1l_415_6_fv1c': 12, 'sequence_3a_709_11_fv1c': 15, 'sequence_3a_682_4_fv1c': 25, 'sequence_3a_578_19_fv1c': 26, 'sequence_1d_277_28_fv1c': 5, 'sequence_1k_344_5_fv1c': 25, 'sequence_1l_400_1_fv1c': 21, 'sequence_1d_230_11_fv1c': 0, 'sequence_1l_395_6_fv1c': 20, 'sequence_1c_117_8_fv1c': 5, 'sequence_1d_242_23_fv1c': 0, 'sequence_1c_66_7_fv1c': 7, 'sequence_1l_414_5_fv1c': 12, 'sequence_1c_86_7_fv1c': 1, 'sequence_3a_610_12_fv1c': 19, 'sequence_2b_457_8_fv1c': 4, 'sequence_1d_151_22_fv1c': 4, 'sequence_1d_181_22_fv1c': 1, 'sequence_1c_76_7_fv1c': 4, 'sequence_2c_556_7_fv1c': 2, 'sequence_3a_580_2_fv1c': 23, 'sequence_3a_675_17_fv1c': 12, 'sequence_1d_188_29_fv1c': 1, 'sequence_1c_82_3_fv1c': 1, 'sequence_1l_367_8_fv1c': 26, 'sequence_1b_11_2_fv1c': 4, 'sequence_1c_80_1_fv1c': 1, 'sequence_1l_366_7_fv1c': 26, 'sequence_1b_12_3_fv1c': 4, 'sequence_3a_612_14_fv1c': 19, 'sequence_2b_472_3_fv1c': 3, 'sequence_1l_387_8_fv1c': 19, 'sequence_1d_240_21_fv1c': 0, 'sequence_3a_596_18_fv1c': 24, 'sequence_3a_593_15_fv1c': 24, 'sequence_2b_446_7_fv1c': 7, 'sequence_2b_445_6_fv1c': 7, 'sequence_3a_664_6_fv1c': 17, 'sequence_1d_163_4_fv1c': 1, 'sequence_1l_402_3_fv1c': 21, 'sequence_3a_643_5_fv1c': 18, 'sequence_1k_296_7_fv1c': 23, 'sequence_3a_706_8_fv1c': 22, 'sequence_1c_116_7_fv1c': 5, 'sequence_3a_714_16_fv1c': 15, 'sequence_1k_337_8_fv1c': 17, 'sequence_1c_112_3_fv1c': 5, 'sequence_3a_575_16_fv1c': 26, 'sequence_2c_541_2_fv1c': 11, 'sequence_1k_334_5_fv1c': 17, 'sequence_3a_684_6_fv1c': 25, 'sequence_2b_467_8_fv1c': 1, 'sequence_1d_226_7_fv1c': 0, 'sequence_2b_455_6_fv1c': 4, 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'sequence_3a_624_6_fv1c': 3, 'sequence_2c_507_8_fv1c': 9, 'sequence_1l_374_5_fv1c': 24, 'sequence_1d_204_15_fv1c': 3, 'sequence_2c_501_2_fv1c': 9, 'sequence_1k_326_7_fv1c': 18, 'sequence_1d_233_14_fv1c': 0, 'sequence_3a_670_12_fv1c': 12, 'sequence_1d_268_19_fv1c': 5, 'sequence_1l_416_7_fv1c': 12, 'sequence_3a_609_11_fv1c': 16, 'sequence_1k_302_3_fv1c': 16, 'sequence_1b_42_3_fv1c': 0, 'sequence_3a_567_8_fv1c': 13, 'sequence_3a_630_12_fv1c': 20, 'sequence_1d_142_13_fv1c': 4, 'sequence_3a_705_7_fv1c': 22, 'sequence_1b_40_1_fv1c': 0, 'sequence_1k_323_4_fv1c': 18, 'sequence_2b_486_7_fv1c': 0, 'sequence_3a_713_15_fv1c': 15, 'sequence_1k_354_5_fv1c': 22, 'sequence_3a_577_18_fv1c': 26, 'sequence_1d_192_3_fv1c': 3, 'sequence_1d_164_5_fv1c': 1, 'sequence_1l_431_2_fv1c': 15, 'sequence_1l_396_7_fv1c': 20, 'sequence_1d_237_18_fv1c': 0, 'sequence_1k_286_7_fv1c': 13, 'sequence_1l_436_7_fv1c': 15, 'sequence_3a_694_16_fv1c': 14, 'sequence_1d_185_26_fv1c': 1, 'sequence_1d_241_22_fv1c': 0, 'sequence_1l_421_2_fv1c': 14, 'sequence_3a_561_2_fv1c': 13, 'sequence_2c_510_1_fv1c': 6, 'sequence_1d_221_2_fv1c': 0, 'sequence_1l_406_7_fv1c': 21, 'sequence_1c_93_4_fv1c': 3, 'sequence_1d_173_14_fv1c': 1, 'sequence_3a_617_19_fv1c': 19, 'sequence_1l_407_8_fv1c': 21, 'sequence_1k_350_1_fv1c': 22, 'sequence_1d_123_4_fv1c': 7, 'sequence_1d_176_17_fv1c': 1, 'sequence_3a_701_3_fv1c': 22, 'sequence_1l_405_6_fv1c': 21, 'sequence_1b_23_4_fv1c': 1, 'sequence_1l_381_2_fv1c': 19, 'sequence_3a_663_5_fv1c': 17, 'sequence_1c_100_1_fv1c': 0, 'sequence_1k_282_3_fv1c': 13, 'sequence_1c_64_5_fv1c': 7, 'sequence_1d_182_23_fv1c': 1, 'sequence_3a_636_18_fv1c': 20, 'sequence_3a_658_20_fv1c': 21, 'sequence_3a_689_11_fv1c': 14, 'sequence_2b_454_5_fv1c': 4, 'sequence_2c_553_4_fv1c': 2, 'sequence_2b_451_2_fv1c': 4, 'sequence_3a_634_16_fv1c': 20, 'sequence_3a_660_2_fv1c': 17, 'sequence_1d_137_8_fv1c': 4, 'sequence_3a_717_19_fv1c': 15, 'sequence_3a_673_15_fv1c': 12, 'sequence_3a_641_3_fv1c': 18, 'sequence_1c_115_6_fv1c': 5, 'sequence_3a_613_15_fv1c': 19, 'sequence_1c_62_3_fv1c': 7, 'sequence_3a_685_7_fv1c': 25, 'sequence_1d_212_23_fv1c': 3, 'sequence_1l_364_5_fv1c': 26, 'sequence_1l_380_1_fv1c': 19, 'sequence_1k_346_7_fv1c': 25, 'sequence_1k_327_8_fv1c': 18, 'sequence_1d_257_8_fv1c': 5, 'sequence_1d_219_30_fv1c': 3, 'sequence_2c_516_7_fv1c': 6, 'sequence_3a_700_2_fv1c': 22, 'sequence_2c_552_3_fv1c': 2, 'sequence_3a_695_17_fv1c': 14, 'sequence_1c_74_5_fv1c': 4, 'sequence_1d_264_15_fv1c': 5, 'sequence_1b_20_1_fv1c': 1, 'sequence_1l_377_8_fv1c': 24, 'sequence_1d_265_16_fv1c': 5, 'sequence_1d_120_1_fv1c': 7, 'sequence_1d_157_28_fv1c': 4, 'sequence_1b_16_7_fv1c': 4, 'sequence_1b_26_7_fv1c': 1, 'sequence_1c_83_4_fv1c': 1, 'sequence_3a_702_4_fv1c': 22, 'sequence_1b_27_8_fv1c': 1, 'sequence_1d_152_23_fv1c': 4, 'sequence_1c_92_3_fv1c': 3, 'sequence_1k_351_2_fv1c': 22, 'sequence_3a_651_13_fv1c': 21, 'sequence_1d_125_6_fv1c': 7, 'sequence_1c_107_8_fv1c': 0, 'sequence_1k_325_6_fv1c': 18, 'sequence_2c_537_8_fv1c': 8, 'sequence_2c_515_6_fv1c': 6, 'sequence_3a_622_4_fv1c': 3, 'sequence_1d_130_1_fv1c': 4, 'sequence_2c_555_6_fv1c': 2, 'sequence_1k_332_3_fv1c': 17, 'sequence_1d_206_17_fv1c': 3, 'sequence_2b_470_1_fv1c': 3, 'sequence_1c_111_2_fv1c': 5, 'sequence_2c_511_2_fv1c': 6, 'sequence_2b_473_4_fv1c': 3, 'sequence_1d_244_25_fv1c': 0, 'sequence_1d_166_7_fv1c': 1, 'sequence_1b_57_8_fv1c': 5, 'sequence_3a_677_19_fv1c': 12, 'sequence_3a_704_6_fv1c': 22, 'sequence_3a_640_2_fv1c': 18, 'sequence_2c_540_1_fv1c': 11, 'sequence_1d_267_18_fv1c': 5, 'sequence_1k_322_3_fv1c': 18, 'sequence_1l_363_4_fv1c': 26, 'sequence_3a_614_16_fv1c': 19, 'sequence_1k_303_4_fv1c': 16, 'sequence_3a_584_6_fv1c': 23, 'sequence_1b_1_2_fv1c': 7, 'sequence_1k_335_6_fv1c': 17, 'sequence_2c_532_3_fv1c': 8, 'sequence_3a_699_1_fv1c': 22, 'sequence_1d_178_19_fv1c': 1, 'sequence_3a_665_7_fv1c': 17, 'sequence_1d_220_1_fv1c': 0, 'sequence_1b_0_1_fv1c': 7, 'sequence_2b_476_7_fv1c': 3, 'sequence_1c_110_1_fv1c': 5, 'sequence_1d_209_20_fv1c': 3, 'sequence_3a_656_18_fv1c': 21, 'sequence_3a_597_19_fv1c': 24, 'sequence_3a_659_1_fv1c': 17, 'sequence_1d_231_12_fv1c': 0, 'sequence_1k_311_2_fv1c': 3, 'sequence_1c_106_7_fv1c': 0, 'sequence_1d_279_30_fv1c': 5, 'sequence_1d_189_30_fv1c': 1, 'sequence_1k_331_2_fv1c': 17, 'sequence_2b_496_7_fv1c': 5, 'sequence_1l_411_2_fv1c': 12, 'sequence_3a_565_6_fv1c': 13, 'sequence_1d_200_11_fv1c': 3, 'sequence_1c_97_8_fv1c': 3, 'sequence_1l_365_6_fv1c': 26, 'sequence_3a_693_15_fv1c': 14, 'sequence_1b_47_8_fv1c': 0, 'sequence_2c_542_3_fv1c': 11, 'sequence_1d_273_24_fv1c': 5, 'sequence_1c_87_8_fv1c': 1, 'sequence_1b_41_2_fv1c': 0, 'sequence_1l_394_5_fv1c': 20, 'sequence_1d_274_25_fv1c': 5, 'sequence_1l_384_5_fv1c': 19, 'sequence_2b_442_3_fv1c': 7, 'sequence_1k_313_4_fv1c': 3, 'sequence_1d_271_22_fv1c': 5, 'sequence_3a_661_3_fv1c': 17, 'sequence_1l_403_4_fv1c': 21, 'sequence_1k_321_2_fv1c': 18, 'sequence_2c_527_8_fv1c': 10, 'sequence_1d_217_28_fv1c': 3, 'sequence_1k_357_8_fv1c': 22, 'sequence_2b_441_2_fv1c': 7, 'sequence_2b_461_2_fv1c': 1, 'sequence_1d_249_30_fv1c': 0, 'sequence_1c_65_6_fv1c': 7, 'sequence_1d_247_28_fv1c': 0, 'sequence_1d_191_2_fv1c': 3, 'sequence_1d_121_2_fv1c': 7, 'sequence_1k_281_2_fv1c': 13, 'sequence_3a_599_1_fv1c': 16, 'sequence_2b_482_3_fv1c': 0, 'sequence_3a_631_13_fv1c': 20, 'sequence_2b_460_1_fv1c': 1, 'sequence_1c_60_1_fv1c': 7, 'sequence_1k_345_6_fv1c': 25, 'sequence_1b_14_5_fv1c': 4, 'sequence_1d_144_15_fv1c': 4, 'sequence_2b_495_6_fv1c': 5, 'sequence_3a_691_13_fv1c': 14, 'sequence_3a_712_14_fv1c': 15, 'sequence_1c_84_5_fv1c': 1, 'sequence_3a_681_3_fv1c': 25, 'sequence_1l_426_7_fv1c': 14, 'sequence_1d_145_16_fv1c': 4, 'sequence_3a_595_17_fv1c': 24, 'sequence_3a_576_17_fv1c': 26, 'sequence_1d_158_29_fv1c': 4, 'sequence_3a_592_14_fv1c': 24, 'sequence_1d_135_6_fv1c': 4, 'sequence_1d_213_24_fv1c': 3, 'sequence_1d_148_19_fv1c': 4, 'sequence_1b_33_4_fv1c': 3, 'sequence_1k_355_6_fv1c': 22, 'sequence_3a_589_11_fv1c': 24, 'sequence_1l_433_4_fv1c': 15, 'sequence_1d_153_24_fv1c': 4, 'sequence_1c_102_3_fv1c': 0, 'sequence_1c_75_6_fv1c': 4, 'sequence_1b_43_4_fv1c': 0, 'sequence_1c_70_1_fv1c': 4, 'sequence_2c_517_8_fv1c': 6, 'sequence_1l_432_3_fv1c': 15, 'sequence_3a_619_1_fv1c': 3, 'sequence_1c_101_2_fv1c': 0, 'sequence_1l_386_7_fv1c': 19, 'sequence_1b_17_8_fv1c': 4, 'sequence_1d_140_11_fv1c': 4, 'sequence_1l_370_1_fv1c': 24, 'sequence_1d_150_21_fv1c': 4, 'sequence_1k_290_1_fv1c': 23, 'sequence_2b_471_2_fv1c': 3, 'sequence_2c_502_3_fv1c': 9, 'sequence_1k_307_8_fv1c': 16, 'sequence_1d_232_13_fv1c': 0, 'sequence_1b_36_7_fv1c': 3, 'sequence_1d_245_26_fv1c': 0, 'sequence_2b_440_1_fv1c': 7, 'sequence_1l_393_4_fv1c': 20, 'sequence_1d_214_25_fv1c': 3, 'sequence_1d_197_8_fv1c': 3, 'sequence_3a_637_19_fv1c': 20, 'sequence_1k_314_5_fv1c': 3, 'sequence_3a_615_17_fv1c': 19, 'sequence_1l_424_5_fv1c': 14, 'sequence_1d_202_13_fv1c': 3, 'sequence_1d_256_7_fv1c': 5, 'sequence_2b_497_8_fv1c': 5, 'sequence_3a_710_12_fv1c': 15, 'sequence_3a_703_5_fv1c': 22, 'sequence_1c_63_4_fv1c': 7, 'sequence_3a_642_4_fv1c': 18, 'sequence_3a_680_2_fv1c': 25, 'sequence_1b_15_6_fv1c': 4, 'sequence_3a_611_13_fv1c': 19, 'sequence_3a_598_20_fv1c': 24, 'sequence_1d_193_4_fv1c': 3, 'sequence_1l_427_8_fv1c': 14, 'sequence_3a_625_7_fv1c': 3, 'sequence_1k_356_7_fv1c': 22, 'sequence_1b_7_8_fv1c': 7, 'sequence_1d_170_11_fv1c': 1, 'sequence_1l_412_3_fv1c': 12, 'sequence_2b_464_5_fv1c': 1, 'sequence_1d_195_6_fv1c': 3, 'sequence_1d_155_26_fv1c': 4, 'sequence_3a_563_4_fv1c': 26, 'sequence_3a_679_1_fv1c': 25, 'sequence_1l_410_1_fv1c': 12, 'sequence_1d_134_5_fv1c': 4, 'sequence_3a_639_1_fv1c': 18, 'sequence_1c_103_4_fv1c': 0, 'sequence_3a_605_7_fv1c': 16, 'sequence_1b_10_1_fv1c': 4, 'sequence_1d_174_15_fv1c': 1, 'sequence_1d_180_21_fv1c': 1, 'sequence_1k_293_4_fv1c': 23, 'sequence_2c_545_6_fv1c': 11, 'sequence_1d_270_21_fv1c': 5, 'sequence_1d_234_15_fv1c': 0, 'sequence_3a_678_20_fv1c': 12, 'sequence_1k_283_4_fv1c': 13, 'sequence_1d_250_1_fv1c': 5, 'sequence_1d_122_3_fv1c': 7, 'sequence_1k_301_2_fv1c': 16, 'sequence_2b_444_5_fv1c': 7, 'sequence_1b_46_7_fv1c': 0, 'sequence_2c_550_1_fv1c': 2, 'sequence_1d_132_3_fv1c': 4, 'sequence_3a_696_18_fv1c': 14, 'sequence_1l_383_4_fv1c': 19, 'sequence_3a_711_13_fv1c': 15, 'sequence_1d_235_16_fv1c': 0, 'sequence_2c_504_5_fv1c': 9, 'sequence_1l_437_8_fv1c': 15, 'sequence_2c_505_6_fv1c': 9, 'sequence_2c_536_7_fv1c': 8, 'sequence_1k_342_3_fv1c': 25, 'sequence_3a_579_1_fv1c': 23, 'sequence_3a_582_4_fv1c': 23, 'sequence_3a_606_8_fv1c': 16, 'sequence_3a_657_19_fv1c': 21, 'sequence_3a_662_4_fv1c': 17, 'sequence_2c_535_6_fv1c': 8, 'sequence_1l_382_3_fv1c': 19, 'sequence_1k_352_3_fv1c': 22, 'sequence_1b_56_7_fv1c': 5, 'sequence_1k_341_2_fv1c': 25, 'sequence_1k_312_3_fv1c': 3, 'sequence_1d_218_29_fv1c': 3, 'sequence_1l_360_1_fv1c': 26, 'sequence_1d_184_25_fv1c': 1, 'sequence_1d_187_28_fv1c': 1, 'sequence_1l_361_2_fv1c': 26, 'sequence_2b_456_7_fv1c': 4, 'sequence_1c_72_3_fv1c': 4, 'sequence_1d_149_20_fv1c': 4, 'sequence_1c_105_6_fv1c': 0, 'sequence_1d_196_7_fv1c': 3, 'sequence_1b_53_4_fv1c': 5, 'sequence_1d_179_20_fv1c': 1, 'sequence_2b_484_5_fv1c': 0, 'sequence_1d_208_19_fv1c': 3, 'sequence_2b_447_8_fv1c': 7, 'sequence_2b_485_6_fv1c': 0, 'sequence_1d_261_12_fv1c': 5, 'sequence_1k_280_1_fv1c': 13, 'sequence_1k_287_8_fv1c': 13, 'sequence_1d_183_24_fv1c': 1, 'sequence_2c_547_8_fv1c': 11, 'sequence_3a_616_18_fv1c': 19, 'sequence_3a_716_18_fv1c': 15, 'sequence_1b_30_1_fv1c': 3, 'sequence_1l_385_6_fv1c': 19, 'sequence_3a_715_17_fv1c': 15, 'sequence_3a_621_3_fv1c': 3, 'sequence_1d_248_29_fv1c': 0, 'sequence_1d_133_4_fv1c': 4, 'sequence_3a_629_11_fv1c': 3, 'sequence_1l_425_6_fv1c': 14, 'sequence_1l_372_3_fv1c': 24, 'sequence_1l_371_2_fv1c': 24, 'sequence_3a_683_5_fv1c': 25, 'sequence_1d_131_2_fv1c': 4, 'sequence_3a_674_16_fv1c': 12, 'sequence_1d_210_21_fv1c': 3, 'sequence_1l_397_8_fv1c': 20, 'sequence_3a_650_12_fv1c': 21, 'sequence_1l_362_3_fv1c': 26, 'sequence_1d_146_17_fv1c': 4, 'sequence_1d_272_23_fv1c': 5, 'sequence_1k_306_7_fv1c': 16, 'sequence_3a_585_7_fv1c': 23, 'sequence_3a_573_14_fv1c': 13, 'sequence_2b_443_4_fv1c': 7, 'sequence_1d_275_26_fv1c': 5, 'sequence_2b_494_5_fv1c': 5, 'sequence_3a_635_17_fv1c': 20, 'sequence_3a_601_3_fv1c': 16, 'sequence_1d_227_8_fv1c': 0, 'sequence_1b_24_5_fv1c': 1, 'sequence_3a_560_1_fv1c': 26, 'sequence_1d_266_17_fv1c': 5, 'sequence_2b_463_4_fv1c': 1, 'sequence_1d_190_1_fv1c': 3, 'sequence_3a_618_20_fv1c': 19, 'sequence_2c_514_5_fv1c': 6, 'sequence_1d_254_5_fv1c': 5, 'sequence_2b_493_4_fv1c': 5, 'sequence_2c_543_4_fv1c': 11, 'sequence_1d_225_6_fv1c': 0, 'sequence_3a_566_7_fv1c': 26, 'sequence_3a_655_17_fv1c': 21, 'sequence_1d_162_3_fv1c': 1, 'sequence_1d_223_4_fv1c': 0, 'sequence_1d_222_3_fv1c': 0, 'sequence_1k_324_5_fv1c': 18, 'sequence_1d_143_14_fv1c': 4, 'sequence_1d_154_25_fv1c': 4, 'sequence_3a_570_11_fv1c': 13, 'sequence_1d_165_6_fv1c': 1, 'sequence_1d_160_1_fv1c': 1, 'sequence_1d_171_12_fv1c': 1, 'sequence_3a_690_12_fv1c': 14, 'sequence_3a_672_14_fv1c': 12, 'sequence_1l_434_5_fv1c': 15, 'sequence_3a_686_8_fv1c': 25, 'sequence_3a_603_5_fv1c': 16, 'sequence_2c_546_7_fv1c': 11, 'sequence_1d_252_3_fv1c': 5, 'sequence_1l_391_2_fv1c': 20, 'sequence_1l_435_6_fv1c': 15, 'sequence_1d_215_26_fv1c': 3, 'sequence_2c_526_7_fv1c': 10, 'sequence_1b_37_8_fv1c': 3, 'sequence_1d_276_27_fv1c': 5, 'sequence_2c_525_6_fv1c': 10, 'sequence_1d_205_16_fv1c': 3, 'sequence_1d_262_13_fv1c': 5, 'sequence_2b_480_1_fv1c': 0, 'sequence_1k_295_6_fv1c': 23, 'sequence_2c_523_4_fv1c': 10, 'sequence_3a_669_11_fv1c': 12, 'sequence_2c_551_2_fv1c': 2, 'sequence_3a_574_15_fv1c': 26, 'sequence_1k_343_4_fv1c': 25, 'sequence_1k_340_1_fv1c': 25, 'sequence_3a_646_8_fv1c': 18, 'sequence_3a_583_5_fv1c': 23, 'sequence_2c_533_4_fv1c': 8, 'sequence_1l_401_2_fv1c': 21, 'sequence_1k_285_6_fv1c': 13, 'sequence_1b_31_2_fv1c': 3, 'sequence_2c_544_5_fv1c': 11, 'sequence_1l_413_4_fv1c': 12, 'sequence_1l_420_1_fv1c': 14, 'sequence_2c_506_7_fv1c': 9, 'sequence_1k_347_8_fv1c': 25, 'sequence_1b_54_5_fv1c': 5, 'sequence_1b_35_6_fv1c': 3, 'sequence_1d_147_18_fv1c': 4, 'sequence_1b_44_5_fv1c': 0, 'sequence_1l_423_4_fv1c': 14, 'sequence_1b_55_6_fv1c': 5, 'sequence_2b_465_6_fv1c': 1, 'sequence_1d_203_14_fv1c': 3, 'sequence_1d_253_4_fv1c': 5, 'sequence_3a_638_20_fv1c': 20, 'sequence_1l_417_8_fv1c': 12, 'sequence_2b_475_6_fv1c': 3, 'sequence_3a_671_13_fv1c': 12, 'sequence_1l_390_1_fv1c': 20, 'sequence_1c_114_5_fv1c': 5, 'sequence_2c_534_5_fv1c': 8, 'sequence_1d_127_8_fv1c': 7, 'sequence_1k_310_1_fv1c': 3, 'sequence_2b_462_3_fv1c': 1, 'sequence_2b_491_2_fv1c': 5, 'sequence_3a_572_13_fv1c': 13, 'sequence_3a_594_16_fv1c': 24, 'sequence_2b_487_8_fv1c': 0, 'sequence_3a_602_4_fv1c': 16, 'sequence_1b_6_7_fv1c': 7, 'sequence_1b_34_5_fv1c': 3, 'sequence_3a_562_3_fv1c': 13, 'sequence_1c_77_8_fv1c': 4, 'sequence_3a_600_2_fv1c': 16, 'sequence_1k_304_5_fv1c': 16, 'sequence_1b_22_3_fv1c': 1, 'sequence_2b_492_3_fv1c': 5, 'sequence_1c_81_2_fv1c': 1, 'sequence_1b_5_6_fv1c': 7, 'sequence_3a_571_12_fv1c': 26, 'sequence_1d_239_20_fv1c': 0, 'sequence_1c_90_1_fv1c': 3, 'sequence_3a_666_8_fv1c': 17, 'sequence_1c_67_8_fv1c': 7, 'sequence_3a_590_12_fv1c': 23})

In [6]:

    

#make the reverse. load-sequence as key, sequence ID (as lists) as values. 
#dict[key].append(value as list)
filename_loadseq_dict_loadlistposkey = {}

for key,value in filename_loadseq_dict_seqidkey.iteritems():
    print("key",key,"value",value)
    load_sequence_aslist = str(unique_load_sequences_aslists[value])
    #this arrangement below is necessary because dict has to have a list as its value. 
    #casting the list-cast ndarray as a string means.. parsing it back to numpy requires some creativity. 
    if filename_loadseq_dict_loadlistposkey.get(str(load_sequence_aslist)) != None:
        filename_loadseq_dict_loadlistposkey[str(load_sequence_aslist)].append(key)
    if filename_loadseq_dict_loadlistposkey.get(str(load_sequence_aslist)) == None: 
        content_list = []
        content_list.append(key)
        filename_loadseq_dict_loadlistposkey[str(load_sequence_aslist)] = content_list

        
#print(filename_loadseq_dict_loadlistposkey)
# fl_llpk_df = pd.DataFrame.from_dict(filename_loadseq_dict_loadlistposkey,orient='index')
# fl_sik_df = pd.DataFrame.from_dict(filename_loadseq_dict_seqidkey,orient='index')
# fl_llpk_df.to_csv(analysis_path + "file_loadseq_loadlistkey_TRAIN.csv")
# fl_sik_df.to_csv(analysis_path + "file_loadseq_seqid_key_TRAIN.csv")


    

    




('key', 'sequence_1k_294_5_fv1c', 'value', 23)
('key', 'sequence_1d_161_2_fv1c', 'value', 1)
('key', 'sequence_1c_61_2_fv1c', 'value', 7)
('key', 'sequence_1d_269_20_fv1c', 'value', 5)
('key', 'sequence_3a_676_18_fv1c', 'value', 12)
('key', 'sequence_1k_315_6_fv1c', 'value', 3)
('key', 'sequence_1d_172_13_fv1c', 'value', 1)
('key', 'sequence_1c_113_4_fv1c', 'value', 5)
('key', 'sequence_3a_604_6_fv1c', 'value', 16)
('key', 'sequence_1d_207_18_fv1c', 'value', 3)
('key', 'sequence_3a_586_8_fv1c', 'value', 23)
('key', 'sequence_1c_104_5_fv1c', 'value', 0)
('key', 'sequence_1d_136_7_fv1c', 'value', 4)
('key', 'sequence_2b_466_7_fv1c', 'value', 1)
('key', 'sequence_1d_126_7_fv1c', 'value', 7)
('key', 'sequence_2b_450_1_fv1c', 'value', 4)
('key', 'sequence_1d_246_27_fv1c', 'value', 0)
('key', 'sequence_3a_564_5_fv1c', 'value', 13)
('key', 'sequence_1d_263_14_fv1c', 'value', 5)
('key', 'sequence_2c_557_8_fv1c', 'value', 2)
('key', 'sequence_1l_373_4_fv1c', 'value', 24)
('key', 'sequence_2b_483_4_fv1c', 'value', 0)
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('key', 'sequence_3a_623_5_fv1c', 'value', 3)
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('key', 'sequence_1d_135_6_fv1c', 'value', 4)
('key', 'sequence_1d_213_24_fv1c', 'value', 3)
('key', 'sequence_1d_148_19_fv1c', 'value', 4)
('key', 'sequence_1b_33_4_fv1c', 'value', 3)
('key', 'sequence_1k_355_6_fv1c', 'value', 22)
('key', 'sequence_3a_589_11_fv1c', 'value', 24)
('key', 'sequence_1l_433_4_fv1c', 'value', 15)
('key', 'sequence_1d_153_24_fv1c', 'value', 4)
('key', 'sequence_1c_102_3_fv1c', 'value', 0)
('key', 'sequence_1c_75_6_fv1c', 'value', 4)
('key', 'sequence_1b_43_4_fv1c', 'value', 0)
('key', 'sequence_1c_70_1_fv1c', 'value', 4)
('key', 'sequence_2c_517_8_fv1c', 'value', 6)
('key', 'sequence_1l_432_3_fv1c', 'value', 15)
('key', 'sequence_3a_619_1_fv1c', 'value', 3)
('key', 'sequence_1c_101_2_fv1c', 'value', 0)
('key', 'sequence_1l_386_7_fv1c', 'value', 19)
('key', 'sequence_1b_17_8_fv1c', 'value', 4)
('key', 'sequence_1d_140_11_fv1c', 'value', 4)
('key', 'sequence_1l_370_1_fv1c', 'value', 24)
('key', 'sequence_1d_150_21_fv1c', 'value', 4)
('key', 'sequence_1k_290_1_fv1c', 'value', 23)
('key', 'sequence_2b_471_2_fv1c', 'value', 3)
('key', 'sequence_2c_502_3_fv1c', 'value', 9)
('key', 'sequence_1k_307_8_fv1c', 'value', 16)
('key', 'sequence_1d_232_13_fv1c', 'value', 0)
('key', 'sequence_1b_36_7_fv1c', 'value', 3)
('key', 'sequence_1d_245_26_fv1c', 'value', 0)
('key', 'sequence_2b_440_1_fv1c', 'value', 7)
('key', 'sequence_1l_393_4_fv1c', 'value', 20)
('key', 'sequence_1d_214_25_fv1c', 'value', 3)
('key', 'sequence_1d_197_8_fv1c', 'value', 3)
('key', 'sequence_3a_637_19_fv1c', 'value', 20)
('key', 'sequence_1k_314_5_fv1c', 'value', 3)
('key', 'sequence_3a_615_17_fv1c', 'value', 19)
('key', 'sequence_1l_424_5_fv1c', 'value', 14)
('key', 'sequence_1d_202_13_fv1c', 'value', 3)
('key', 'sequence_1d_256_7_fv1c', 'value', 5)
('key', 'sequence_2b_497_8_fv1c', 'value', 5)
('key', 'sequence_3a_710_12_fv1c', 'value', 15)
('key', 'sequence_3a_703_5_fv1c', 'value', 22)
('key', 'sequence_1c_63_4_fv1c', 'value', 7)
('key', 'sequence_3a_642_4_fv1c', 'value', 18)
('key', 'sequence_3a_680_2_fv1c', 'value', 25)
('key', 'sequence_1b_15_6_fv1c', 'value', 4)
('key', 'sequence_3a_611_13_fv1c', 'value', 19)
('key', 'sequence_3a_598_20_fv1c', 'value', 24)
('key', 'sequence_1d_193_4_fv1c', 'value', 3)
('key', 'sequence_1l_427_8_fv1c', 'value', 14)
('key', 'sequence_3a_625_7_fv1c', 'value', 3)
('key', 'sequence_1k_356_7_fv1c', 'value', 22)
('key', 'sequence_1b_7_8_fv1c', 'value', 7)
('key', 'sequence_1d_170_11_fv1c', 'value', 1)
('key', 'sequence_1l_412_3_fv1c', 'value', 12)
('key', 'sequence_2b_464_5_fv1c', 'value', 1)
('key', 'sequence_1d_195_6_fv1c', 'value', 3)
('key', 'sequence_1d_155_26_fv1c', 'value', 4)
('key', 'sequence_3a_563_4_fv1c', 'value', 26)
('key', 'sequence_3a_679_1_fv1c', 'value', 25)
('key', 'sequence_1l_410_1_fv1c', 'value', 12)
('key', 'sequence_1d_134_5_fv1c', 'value', 4)
('key', 'sequence_3a_639_1_fv1c', 'value', 18)
('key', 'sequence_1c_103_4_fv1c', 'value', 0)
('key', 'sequence_3a_605_7_fv1c', 'value', 16)
('key', 'sequence_1b_10_1_fv1c', 'value', 4)
('key', 'sequence_1d_174_15_fv1c', 'value', 1)
('key', 'sequence_1d_180_21_fv1c', 'value', 1)
('key', 'sequence_1k_293_4_fv1c', 'value', 23)
('key', 'sequence_2c_545_6_fv1c', 'value', 11)
('key', 'sequence_1d_270_21_fv1c', 'value', 5)
('key', 'sequence_1d_234_15_fv1c', 'value', 0)
('key', 'sequence_3a_678_20_fv1c', 'value', 12)
('key', 'sequence_1k_283_4_fv1c', 'value', 13)
('key', 'sequence_1d_250_1_fv1c', 'value', 5)
('key', 'sequence_1d_122_3_fv1c', 'value', 7)
('key', 'sequence_1k_301_2_fv1c', 'value', 16)
('key', 'sequence_2b_444_5_fv1c', 'value', 7)
('key', 'sequence_1b_46_7_fv1c', 'value', 0)
('key', 'sequence_2c_550_1_fv1c', 'value', 2)
('key', 'sequence_1d_132_3_fv1c', 'value', 4)
('key', 'sequence_3a_696_18_fv1c', 'value', 14)
('key', 'sequence_1l_383_4_fv1c', 'value', 19)
('key', 'sequence_3a_711_13_fv1c', 'value', 15)
('key', 'sequence_1d_235_16_fv1c', 'value', 0)
('key', 'sequence_2c_504_5_fv1c', 'value', 9)
('key', 'sequence_1l_437_8_fv1c', 'value', 15)
('key', 'sequence_2c_505_6_fv1c', 'value', 9)
('key', 'sequence_2c_536_7_fv1c', 'value', 8)
('key', 'sequence_1k_342_3_fv1c', 'value', 25)
('key', 'sequence_3a_579_1_fv1c', 'value', 23)
('key', 'sequence_3a_582_4_fv1c', 'value', 23)
('key', 'sequence_3a_606_8_fv1c', 'value', 16)
('key', 'sequence_3a_657_19_fv1c', 'value', 21)
('key', 'sequence_3a_662_4_fv1c', 'value', 17)
('key', 'sequence_2c_535_6_fv1c', 'value', 8)
('key', 'sequence_1l_382_3_fv1c', 'value', 19)
('key', 'sequence_1k_352_3_fv1c', 'value', 22)
('key', 'sequence_1b_56_7_fv1c', 'value', 5)
('key', 'sequence_1k_341_2_fv1c', 'value', 25)
('key', 'sequence_1k_312_3_fv1c', 'value', 3)
('key', 'sequence_1d_218_29_fv1c', 'value', 3)
('key', 'sequence_1l_360_1_fv1c', 'value', 26)
('key', 'sequence_1d_184_25_fv1c', 'value', 1)
('key', 'sequence_1d_187_28_fv1c', 'value', 1)
('key', 'sequence_1l_361_2_fv1c', 'value', 26)
('key', 'sequence_2b_456_7_fv1c', 'value', 4)
('key', 'sequence_1c_72_3_fv1c', 'value', 4)
('key', 'sequence_1d_149_20_fv1c', 'value', 4)
('key', 'sequence_1c_105_6_fv1c', 'value', 0)
('key', 'sequence_1d_196_7_fv1c', 'value', 3)
('key', 'sequence_1b_53_4_fv1c', 'value', 5)
('key', 'sequence_1d_179_20_fv1c', 'value', 1)
('key', 'sequence_2b_484_5_fv1c', 'value', 0)
('key', 'sequence_1d_208_19_fv1c', 'value', 3)
('key', 'sequence_2b_447_8_fv1c', 'value', 7)
('key', 'sequence_2b_485_6_fv1c', 'value', 0)
('key', 'sequence_1d_261_12_fv1c', 'value', 5)
('key', 'sequence_1k_280_1_fv1c', 'value', 13)
('key', 'sequence_1k_287_8_fv1c', 'value', 13)
('key', 'sequence_1d_183_24_fv1c', 'value', 1)
('key', 'sequence_2c_547_8_fv1c', 'value', 11)
('key', 'sequence_3a_616_18_fv1c', 'value', 19)
('key', 'sequence_3a_716_18_fv1c', 'value', 15)
('key', 'sequence_1b_30_1_fv1c', 'value', 3)
('key', 'sequence_1l_385_6_fv1c', 'value', 19)
('key', 'sequence_3a_715_17_fv1c', 'value', 15)
('key', 'sequence_3a_621_3_fv1c', 'value', 3)
('key', 'sequence_1d_248_29_fv1c', 'value', 0)
('key', 'sequence_1d_133_4_fv1c', 'value', 4)
('key', 'sequence_3a_629_11_fv1c', 'value', 3)
('key', 'sequence_1l_425_6_fv1c', 'value', 14)
('key', 'sequence_1l_372_3_fv1c', 'value', 24)
('key', 'sequence_1l_371_2_fv1c', 'value', 24)
('key', 'sequence_3a_683_5_fv1c', 'value', 25)
('key', 'sequence_1d_131_2_fv1c', 'value', 4)
('key', 'sequence_3a_674_16_fv1c', 'value', 12)
('key', 'sequence_1d_210_21_fv1c', 'value', 3)
('key', 'sequence_1l_397_8_fv1c', 'value', 20)
('key', 'sequence_3a_650_12_fv1c', 'value', 21)
('key', 'sequence_1l_362_3_fv1c', 'value', 26)
('key', 'sequence_1d_146_17_fv1c', 'value', 4)
('key', 'sequence_1d_272_23_fv1c', 'value', 5)
('key', 'sequence_1k_306_7_fv1c', 'value', 16)
('key', 'sequence_3a_585_7_fv1c', 'value', 23)
('key', 'sequence_3a_573_14_fv1c', 'value', 13)
('key', 'sequence_2b_443_4_fv1c', 'value', 7)
('key', 'sequence_1d_275_26_fv1c', 'value', 5)
('key', 'sequence_2b_494_5_fv1c', 'value', 5)
('key', 'sequence_3a_635_17_fv1c', 'value', 20)
('key', 'sequence_3a_601_3_fv1c', 'value', 16)
('key', 'sequence_1d_227_8_fv1c', 'value', 0)
('key', 'sequence_1b_24_5_fv1c', 'value', 1)
('key', 'sequence_3a_560_1_fv1c', 'value', 26)
('key', 'sequence_1d_266_17_fv1c', 'value', 5)
('key', 'sequence_2b_463_4_fv1c', 'value', 1)
('key', 'sequence_1d_190_1_fv1c', 'value', 3)
('key', 'sequence_3a_618_20_fv1c', 'value', 19)
('key', 'sequence_2c_514_5_fv1c', 'value', 6)
('key', 'sequence_1d_254_5_fv1c', 'value', 5)
('key', 'sequence_2b_493_4_fv1c', 'value', 5)
('key', 'sequence_2c_543_4_fv1c', 'value', 11)
('key', 'sequence_1d_225_6_fv1c', 'value', 0)
('key', 'sequence_3a_566_7_fv1c', 'value', 26)
('key', 'sequence_3a_655_17_fv1c', 'value', 21)
('key', 'sequence_1d_162_3_fv1c', 'value', 1)
('key', 'sequence_1d_223_4_fv1c', 'value', 0)
('key', 'sequence_1d_222_3_fv1c', 'value', 0)
('key', 'sequence_1k_324_5_fv1c', 'value', 18)
('key', 'sequence_1d_143_14_fv1c', 'value', 4)
('key', 'sequence_1d_154_25_fv1c', 'value', 4)
('key', 'sequence_3a_570_11_fv1c', 'value', 13)
('key', 'sequence_1d_165_6_fv1c', 'value', 1)
('key', 'sequence_1d_160_1_fv1c', 'value', 1)
('key', 'sequence_1d_171_12_fv1c', 'value', 1)
('key', 'sequence_3a_690_12_fv1c', 'value', 14)
('key', 'sequence_3a_672_14_fv1c', 'value', 12)
('key', 'sequence_1l_434_5_fv1c', 'value', 15)
('key', 'sequence_3a_686_8_fv1c', 'value', 25)
('key', 'sequence_3a_603_5_fv1c', 'value', 16)
('key', 'sequence_2c_546_7_fv1c', 'value', 11)
('key', 'sequence_1d_252_3_fv1c', 'value', 5)
('key', 'sequence_1l_391_2_fv1c', 'value', 20)
('key', 'sequence_1l_435_6_fv1c', 'value', 15)
('key', 'sequence_1d_215_26_fv1c', 'value', 3)
('key', 'sequence_2c_526_7_fv1c', 'value', 10)
('key', 'sequence_1b_37_8_fv1c', 'value', 3)
('key', 'sequence_1d_276_27_fv1c', 'value', 5)
('key', 'sequence_2c_525_6_fv1c', 'value', 10)
('key', 'sequence_1d_205_16_fv1c', 'value', 3)
('key', 'sequence_1d_262_13_fv1c', 'value', 5)
('key', 'sequence_2b_480_1_fv1c', 'value', 0)
('key', 'sequence_1k_295_6_fv1c', 'value', 23)
('key', 'sequence_2c_523_4_fv1c', 'value', 10)
('key', 'sequence_3a_669_11_fv1c', 'value', 12)
('key', 'sequence_2c_551_2_fv1c', 'value', 2)
('key', 'sequence_3a_574_15_fv1c', 'value', 26)
('key', 'sequence_1k_343_4_fv1c', 'value', 25)
('key', 'sequence_1k_340_1_fv1c', 'value', 25)
('key', 'sequence_3a_646_8_fv1c', 'value', 18)
('key', 'sequence_3a_583_5_fv1c', 'value', 23)
('key', 'sequence_2c_533_4_fv1c', 'value', 8)
('key', 'sequence_1l_401_2_fv1c', 'value', 21)
('key', 'sequence_1k_285_6_fv1c', 'value', 13)
('key', 'sequence_1b_31_2_fv1c', 'value', 3)
('key', 'sequence_2c_544_5_fv1c', 'value', 11)
('key', 'sequence_1l_413_4_fv1c', 'value', 12)
('key', 'sequence_1l_420_1_fv1c', 'value', 14)
('key', 'sequence_2c_506_7_fv1c', 'value', 9)
('key', 'sequence_1k_347_8_fv1c', 'value', 25)
('key', 'sequence_1b_54_5_fv1c', 'value', 5)
('key', 'sequence_1b_35_6_fv1c', 'value', 3)
('key', 'sequence_1d_147_18_fv1c', 'value', 4)
('key', 'sequence_1b_44_5_fv1c', 'value', 0)
('key', 'sequence_1l_423_4_fv1c', 'value', 14)
('key', 'sequence_1b_55_6_fv1c', 'value', 5)
('key', 'sequence_2b_465_6_fv1c', 'value', 1)
('key', 'sequence_1d_203_14_fv1c', 'value', 3)
('key', 'sequence_1d_253_4_fv1c', 'value', 5)
('key', 'sequence_3a_638_20_fv1c', 'value', 20)
('key', 'sequence_1l_417_8_fv1c', 'value', 12)
('key', 'sequence_2b_475_6_fv1c', 'value', 3)
('key', 'sequence_3a_671_13_fv1c', 'value', 12)
('key', 'sequence_1l_390_1_fv1c', 'value', 20)
('key', 'sequence_1c_114_5_fv1c', 'value', 5)
('key', 'sequence_2c_534_5_fv1c', 'value', 8)
('key', 'sequence_1d_127_8_fv1c', 'value', 7)
('key', 'sequence_1k_310_1_fv1c', 'value', 3)
('key', 'sequence_2b_462_3_fv1c', 'value', 1)
('key', 'sequence_2b_491_2_fv1c', 'value', 5)
('key', 'sequence_3a_572_13_fv1c', 'value', 13)
('key', 'sequence_3a_594_16_fv1c', 'value', 24)
('key', 'sequence_2b_487_8_fv1c', 'value', 0)
('key', 'sequence_3a_602_4_fv1c', 'value', 16)
('key', 'sequence_1b_6_7_fv1c', 'value', 7)
('key', 'sequence_1b_34_5_fv1c', 'value', 3)
('key', 'sequence_3a_562_3_fv1c', 'value', 13)
('key', 'sequence_1c_77_8_fv1c', 'value', 4)
('key', 'sequence_3a_600_2_fv1c', 'value', 16)
('key', 'sequence_1k_304_5_fv1c', 'value', 16)
('key', 'sequence_1b_22_3_fv1c', 'value', 1)
('key', 'sequence_2b_492_3_fv1c', 'value', 5)
('key', 'sequence_1c_81_2_fv1c', 'value', 1)
('key', 'sequence_1b_5_6_fv1c', 'value', 7)
('key', 'sequence_3a_571_12_fv1c', 'value', 26)
('key', 'sequence_1d_239_20_fv1c', 'value', 0)
('key', 'sequence_1c_90_1_fv1c', 'value', 3)
('key', 'sequence_3a_666_8_fv1c', 'value', 17)
('key', 'sequence_1c_67_8_fv1c', 'value', 7)
('key', 'sequence_3a_590_12_fv1c', 'value', 23)

In [7]:

    

#now let's play with the test set. 

i = 0 
unique_load_sequences = [] #fill these with the 4-pairs as a numpy array
unique_load_sequences_aslists = []
sequences_4pairs_list = []
sequences_not4pairs_list = []
filename_loadseq_dict_seqidkey = {} #key is the sequence ID, value is its position in the list. 
four_pairs_condition = False
print("test_set size: ",len(test_set_filenames))
for files in test_set_filenames:
    #print("filename", files)
    i += 1
    data_load_path = test_path + '/data/' + files[0]
    label_load_path = test_path + '/label/' + files[1]
    # print("data/label load path: {} \n {}".format(data_load_path,label_load_path))
    test_array = np.load(data_load_path)
    test_label_array = np.load(label_load_path)[:, 1:]
    
    #check if the repeating unit is actually 4 tuples. 
    #four_pair_condition = (test_array[0:5,-2] == test_array[4:8,-2]) and (test_array[0:5,-1] == test_array[4:8,-1]) error. 
    #numpy version 
    four_pair_condition = np.array_equal(test_array[0:4,-2:],test_array[4:8,-2:]) 

    
    if four_pair_condition == True:
        #print(str(files[0])," is in.")
        load_sequence = test_array[0:4,-2:]
        #if len(unique_load_sequences) != 0 :
        array_equality = False
        if len(unique_load_sequences) == 0:
            unique_load_sequences.append(load_sequence)
            #unique_load_sequences_aslists.append(np.ndarray.tolist(load_sequence)) #need to be flattened into a list to make it hashable for the list-operators to work
            unique_load_sequences_aslists.append(str(np.ndarray.flatten(load_sequence)))
        if len(unique_load_sequences) != 0:
            for load_seq in unique_load_sequences:
                if np.array_equal(load_seq,load_sequence) == True:
                    #print("equal array found in list?", np.array_equal(load_seq,load_sequence))
                    array_equality = True
            if array_equality == False:
                unique_load_sequences.append(load_sequence) #the load types
                #unique_load_sequences_aslists.append(np.ndarray.tolist(load_sequence))
                unique_load_sequences_aslists.append(str(np.ndarray.flatten(load_sequence)))
            filename_loadseq_dict_seqidkey[str(files[0])[:-4]] = unique_load_sequences_aslists.index(str(np.ndarray.flatten(load_sequence)))
        sequences_4pairs_list.append(str(files[0]))
    if four_pair_condition == False:
        sequences_not4pairs_list.append(str(files[0]))
        
    
    #print("test_array's last two columns",test_array[0:20,-2:])
#unique_load_sequences = set(unique_load_sequences)
#print("there are", len(unique_load_sequences), " load sequences")
#print("the unique load sequences are:", unique_load_sequences)
print("sequences with 4-pair repeating units: ",len(sequences_4pairs_list))
print("sequences WITHOUT 4-pair repeating units: ",sequences_not4pairs_list)

twelve_pair_condition = np.array_equal(test_array[0:12,-2:],test_array[12:24,-2:]) 
sequences_12pairs_list = []
sequences_not4_not12_pairs_list = []

#gotta run the 4-pair filter first to make sure the 12-pair subset list is ok. Hence the separate loop. 
for unusual_seqs in sequences_not4pairs_list: #12 repeating units. 
    data_load_path = test_path + '/data/' + unusual_seqs
    test_array = np.load(data_load_path)
    
    if twelve_pair_condition == True:
        #print(str(unusual_seqs)," is in.")
        load_sequence = test_array[0:12,-2:]
        #if len(unique_load_sequences) != 0 :
        array_equality = False
        if len(unique_load_sequences) == 0:
            unique_load_sequences.append(load_sequence)
            #unique_load_sequences_aslists.append(np.ndarray.tolist(load_sequence))
            unique_load_sequences_aslists.append(str(np.ndarray.flatten(load_sequence)))
        if len(unique_load_sequences) != 0:
            for load_seq in unique_load_sequences:
                if np.array_equal(load_seq,load_sequence) == True:
                    #print("equal array found in list?", np.array_equal(load_seq,load_sequence))
                    array_equality = True
            if array_equality == False:
                unique_load_sequences.append(load_sequence) #the load types
                #unique_load_sequences_aslists.append(np.ndarray.tolist(load_sequence))
                unique_load_sequences_aslists.append(str(np.ndarray.flatten(load_sequence)))
        sequences_12pairs_list.append(str(unusual_seqs))
        filename_loadseq_dict_seqidkey[str(unusual_seqs)[:-4]] = unique_load_sequences_aslists.index(str(np.ndarray.flatten(load_sequence)))
    if twelve_pair_condition == False:
        sequences_not4_not12_pairs_list.append(str(unusual_seqs))

print("There are ",len(sequences_12pairs_list), " sequences with 12-pairs")        
print("There are ", len(sequences_not4_not12_pairs_list), "sequences that have neither 4 nor 12 repeating units")
print("seqs that aren't 4 or 12: ",sequences_not4_not12_pairs_list)
print("there are ",len(unique_load_sequences), " different load sequences in the ", len(test_set_filenames), "sequences in the training set")
#print("unique load seqs", unique_load_sequences)

for key,value in filename_loadseq_dict_seqidkey.iteritems():
    print("key",key,"value",value)
    load_sequence_aslist = str(unique_load_sequences_aslists[value])
    #this arrangement below is necessary because dict has to have a list as its value. 
    #casting the list-cast ndarray as a string means.. parsing it back to numpy requires some creativity. 
    if filename_loadseq_dict_loadlistposkey.get(str(load_sequence_aslist)) != None:
        filename_loadseq_dict_loadlistposkey[str(load_sequence_aslist)].append(key)
    if filename_loadseq_dict_loadlistposkey.get(str(load_sequence_aslist)) == None: 
        content_list = []
        content_list.append(key)
        filename_loadseq_dict_loadlistposkey[str(load_sequence_aslist)] = content_list

print(filename_loadseq_dict_loadlistposkey)
fl_llpk_test_df = pd.DataFrame.from_dict(filename_loadseq_dict_loadlistposkey,orient='index')
fl_sik_test_df = pd.DataFrame.from_dict(filename_loadseq_dict_seqidkey,orient='index')

fl_llpk_test_df.to_csv(analysis_path + "file_loadseq_loadlistkey_TEST.csv")
fl_sik_test_df.to_csv(analysis_path + "file_loadseq_seqid_key_TEST.csv")

#append the string of the loadseq #file_loadseq_seqid_key_TEST.csv
loadseq_loadlist_df = pd.DataFrame(unique_load_sequences_aslists,columns=['loadseq'])
loadseq_loadlist_df.to_csv(analysis_path + "load_list_code.csv")
# for item in loadseq_loadlist_df:
#     loadseq_loadlist_df.loc[]


    

    




('test_set size: ', 108)
('sequences with 4-pair repeating units: ', 63)
('sequences WITHOUT 4-pair repeating units: ', ['sequence_1l_419_10_fv1c.npy', 'sequence_3a_628_10_fv1c.npy', 'sequence_1l_428_9_fv1c.npy', 'sequence_1k_308_9_fv1c.npy', 'sequence_1k_358_9_fv1c.npy', 'sequence_3a_587_9_fv1c.npy', 'sequence_3a_647_9_fv1c.npy', 'sequence_1k_328_9_fv1c.npy', 'sequence_1k_288_9_fv1c.npy', 'sequence_3a_707_9_fv1c.npy', 'sequence_1l_379_10_fv1c.npy', 'sequence_1l_368_9_fv1c.npy', 'sequence_3a_648_10_fv1c.npy', 'sequence_1k_339_10_fv1c.npy', 'sequence_1l_398_9_fv1c.npy', 'sequence_1l_408_9_fv1c.npy', 'sequence_1k_348_9_fv1c.npy', 'sequence_3a_568_9_fv1c.npy', 'sequence_1l_438_9_fv1c.npy', 'sequence_1k_329_10_fv1c.npy', 'sequence_1k_349_10_fv1c.npy', 'sequence_1k_359_10_fv1c.npy', 'sequence_1k_299_10_fv1c.npy', 'sequence_1l_378_9_fv1c.npy', 'sequence_3a_588_10_fv1c.npy', 'sequence_3a_708_10_fv1c.npy', 'sequence_1l_429_10_fv1c.npy', 'sequence_1l_418_9_fv1c.npy', 'sequence_3a_607_9_fv1c.npy', 'sequence_3a_688_10_fv1c.npy', 'sequence_3a_569_10_fv1c.npy', 'sequence_1l_409_10_fv1c.npy', 'sequence_1l_439_10_fv1c.npy', 'sequence_1l_389_10_fv1c.npy', 'sequence_3a_608_10_fv1c.npy', 'sequence_1k_298_9_fv1c.npy', 'sequence_1k_309_10_fv1c.npy', 'sequence_3a_668_10_fv1c.npy', 'sequence_3a_667_9_fv1c.npy', 'sequence_3a_687_9_fv1c.npy', 'sequence_1l_369_10_fv1c.npy', 'sequence_1k_289_10_fv1c.npy', 'sequence_1k_338_9_fv1c.npy', 'sequence_1l_399_10_fv1c.npy', 'sequence_1l_388_9_fv1c.npy'])
('There are ', 45, ' sequences with 12-pairs')
('There are ', 0, 'sequences that have neither 4 nor 12 repeating units')
("seqs that aren't 4 or 12: ", [])
('there are ', 27, ' different load sequences in the ', 108, 'sequences in the training set')
('key', 'sequence_1d_168_9_fv1c', 'value', 0)
('key', 'sequence_1b_29_10_fv1c', 'value', 0)
('key', 'sequence_1c_119_10_fv1c', 'value', 3)
('key', 'sequence_1b_8_9_fv1c', 'value', 6)
('key', 'sequence_2c_538_9_fv1c', 'value', 7)
('key', 'sequence_3a_628_10_fv1c', 'value', 13)
('key', 'sequence_2b_469_10_fv1c', 'value', 0)
('key', 'sequence_1l_398_9_fv1c', 'value', 13)
('key', 'sequence_1b_49_10_fv1c', 'value', 1)
('key', 'sequence_3a_687_9_fv1c', 'value', 23)
('key', 'sequence_2c_529_10_fv1c', 'value', 10)
('key', 'sequence_1l_368_9_fv1c', 'value', 20)
('key', 'sequence_2b_468_9_fv1c', 'value', 0)
('key', 'sequence_3a_607_9_fv1c', 'value', 15)
('key', 'sequence_1k_308_9_fv1c', 'value', 15)
('key', 'sequence_1c_88_9_fv1c', 'value', 0)
('key', 'sequence_1k_288_9_fv1c', 'value', 19)
('key', 'sequence_1b_58_9_fv1c', 'value', 3)
('key', 'sequence_1b_48_9_fv1c', 'value', 1)
('key', 'sequence_1l_369_10_fv1c', 'value', 20)
('key', 'sequence_2b_499_10_fv1c', 'value', 3)
('key', 'sequence_2c_528_9_fv1c', 'value', 10)
('key', 'sequence_3a_668_10_fv1c', 'value', 21)
('key', 'sequence_1k_318_9_fv1c', 'value', 8)
('key', 'sequence_3a_568_9_fv1c', 'value', 19)
('key', 'sequence_3a_688_10_fv1c', 'value', 23)
('key', 'sequence_1c_98_9_fv1c', 'value', 8)
('key', 'sequence_1c_89_10_fv1c', 'value', 0)
('key', 'sequence_3a_667_9_fv1c', 'value', 21)
('key', 'sequence_1d_199_10_fv1c', 'value', 8)
('key', 'sequence_1d_129_10_fv1c', 'value', 6)
('key', 'sequence_2c_548_9_fv1c', 'value', 2)
('key', 'sequence_1b_59_10_fv1c', 'value', 3)
('key', 'sequence_1l_418_9_fv1c', 'value', 12)
('key', 'sequence_1b_28_9_fv1c', 'value', 0)
('key', 'sequence_1l_408_9_fv1c', 'value', 22)
('key', 'sequence_2c_509_10_fv1c', 'value', 9)
('key', 'sequence_1l_438_9_fv1c', 'value', 24)
('key', 'sequence_1k_299_10_fv1c', 'value', 25)
('key', 'sequence_2c_558_9_fv1c', 'value', 5)
('key', 'sequence_2b_488_9_fv1c', 'value', 1)
('key', 'sequence_1k_348_9_fv1c', 'value', 23)
('key', 'sequence_1k_349_10_fv1c', 'value', 23)
('key', 'sequence_1l_378_9_fv1c', 'value', 17)
('key', 'sequence_2c_549_10_fv1c', 'value', 2)
('key', 'sequence_1l_389_10_fv1c', 'value', 26)
('key', 'sequence_2c_508_9_fv1c', 'value', 9)
('key', 'sequence_1d_139_10_fv1c', 'value', 4)
('key', 'sequence_1k_298_9_fv1c', 'value', 25)
('key', 'sequence_2c_518_9_fv1c', 'value', 11)
('key', 'sequence_1k_338_9_fv1c', 'value', 21)
('key', 'sequence_1k_329_10_fv1c', 'value', 18)
('key', 'sequence_3a_627_9_fv1c', 'value', 8)
('key', 'sequence_2b_479_10_fv1c', 'value', 8)
('key', 'sequence_1d_228_9_fv1c', 'value', 1)
('key', 'sequence_1l_419_10_fv1c', 'value', 12)
('key', 'sequence_1l_379_10_fv1c', 'value', 17)
('key', 'sequence_3a_588_10_fv1c', 'value', 25)
('key', 'sequence_1c_118_9_fv1c', 'value', 3)
('key', 'sequence_1l_439_10_fv1c', 'value', 24)
('key', 'sequence_1b_18_9_fv1c', 'value', 4)
('key', 'sequence_1k_358_9_fv1c', 'value', 16)
('key', 'sequence_3a_647_9_fv1c', 'value', 18)
('key', 'sequence_1c_78_9_fv1c', 'value', 4)
('key', 'sequence_2c_559_10_fv1c', 'value', 5)
('key', 'sequence_1l_429_10_fv1c', 'value', 14)
('key', 'sequence_3a_587_9_fv1c', 'value', 17)
('key', 'sequence_2b_458_9_fv1c', 'value', 4)
('key', 'sequence_1c_99_10_fv1c', 'value', 8)
('key', 'sequence_3a_648_10_fv1c', 'value', 18)
('key', 'sequence_1c_69_10_fv1c', 'value', 6)
('key', 'sequence_1k_289_10_fv1c', 'value', 19)
('key', 'sequence_3a_708_10_fv1c', 'value', 16)
('key', 'sequence_1l_399_10_fv1c', 'value', 13)
('key', 'sequence_2b_478_9_fv1c', 'value', 8)
('key', 'sequence_2b_489_10_fv1c', 'value', 1)
('key', 'sequence_1k_309_10_fv1c', 'value', 15)
('key', 'sequence_1d_138_9_fv1c', 'value', 4)
('key', 'sequence_1d_128_9_fv1c', 'value', 6)
('key', 'sequence_1l_409_10_fv1c', 'value', 22)
('key', 'sequence_1d_169_10_fv1c', 'value', 0)
('key', 'sequence_2b_459_10_fv1c', 'value', 4)
('key', 'sequence_3a_707_9_fv1c', 'value', 16)
('key', 'sequence_2c_519_10_fv1c', 'value', 11)
('key', 'sequence_1c_109_10_fv1c', 'value', 1)
('key', 'sequence_1d_258_9_fv1c', 'value', 3)
('key', 'sequence_1k_339_10_fv1c', 'value', 21)
('key', 'sequence_1b_19_10_fv1c', 'value', 4)
('key', 'sequence_1c_79_10_fv1c', 'value', 4)
('key', 'sequence_3a_608_10_fv1c', 'value', 26)
('key', 'sequence_1b_39_10_fv1c', 'value', 8)
('key', 'sequence_1c_108_9_fv1c', 'value', 1)
('key', 'sequence_2b_498_9_fv1c', 'value', 3)
('key', 'sequence_1b_9_10_fv1c', 'value', 6)
('key', 'sequence_2c_539_10_fv1c', 'value', 7)
('key', 'sequence_2b_449_10_fv1c', 'value', 6)
('key', 'sequence_1k_328_9_fv1c', 'value', 18)
('key', 'sequence_3a_569_10_fv1c', 'value', 20)
('key', 'sequence_1b_38_9_fv1c', 'value', 8)
('key', 'sequence_1c_68_9_fv1c', 'value', 6)
('key', 'sequence_1d_198_9_fv1c', 'value', 8)
('key', 'sequence_1k_359_10_fv1c', 'value', 16)
('key', 'sequence_1k_319_10_fv1c', 'value', 8)
('key', 'sequence_2b_448_9_fv1c', 'value', 6)
('key', 'sequence_1d_229_10_fv1c', 'value', 1)
('key', 'sequence_1l_428_9_fv1c', 'value', 14)
('key', 'sequence_1d_259_10_fv1c', 'value', 3)
('key', 'sequence_1l_388_9_fv1c', 'value', 26)
{'[  1.428  14.28   14.28    0.75    0.75   16.28   16.28    1.428   1.428\n  14.28   14.28    1.75    1.75   14.28   14.28    1.428   1.428  14.28\n  14.28    0.75    0.75   14.28   14.28    1.428]': ['sequence_3a_649_11_fv1c', 'sequence_1l_400_1_fv1c', 'sequence_1l_402_3_fv1c', 'sequence_3a_654_16_fv1c', 'sequence_3a_652_14_fv1c', 'sequence_1l_404_5_fv1c', 'sequence_3a_653_15_fv1c', 'sequence_1l_406_7_fv1c', 'sequence_1l_407_8_fv1c', 'sequence_1l_405_6_fv1c', 'sequence_3a_658_20_fv1c', 'sequence_3a_651_13_fv1c', 'sequence_3a_656_18_fv1c', 'sequence_1l_403_4_fv1c', 'sequence_3a_657_19_fv1c', 'sequence_3a_650_12_fv1c', 'sequence_3a_655_17_fv1c', 'sequence_1l_401_2_fv1c', 'sequence_1l_408_9_fv1c', 'sequence_1l_409_10_fv1c'], '[  1.028  13.28   13.28    1.25    1.25   13.28   13.28    1.028]': ['sequence_2c_512_3_fv1c', 'sequence_2c_513_4_fv1c', 'sequence_2c_510_1_fv1c', 'sequence_2c_516_7_fv1c', 'sequence_2c_515_6_fv1c', 'sequence_2c_511_2_fv1c', 'sequence_2c_517_8_fv1c', 'sequence_2c_514_5_fv1c', 'sequence_2c_518_9_fv1c', 'sequence_2c_519_10_fv1c'], '[  1.428  14.28   14.28    0.75    0.75   16.28   16.28    1.428]': ['sequence_1c_104_5_fv1c', 'sequence_1d_246_27_fv1c', 'sequence_2b_483_4_fv1c', 'sequence_1d_224_5_fv1c', 'sequence_1d_243_24_fv1c', 'sequence_1d_230_11_fv1c', 'sequence_1d_242_23_fv1c', 'sequence_1d_240_21_fv1c', 'sequence_1d_226_7_fv1c', 'sequence_1d_238_19_fv1c', 'sequence_1d_236_17_fv1c', 'sequence_1b_45_6_fv1c', 'sequence_2b_481_2_fv1c', 'sequence_1d_233_14_fv1c', 'sequence_1b_42_3_fv1c', 'sequence_1b_40_1_fv1c', 'sequence_2b_486_7_fv1c', 'sequence_1d_237_18_fv1c', 'sequence_1d_241_22_fv1c', 'sequence_1d_221_2_fv1c', 'sequence_1c_100_1_fv1c', 'sequence_1c_107_8_fv1c', 'sequence_1d_244_25_fv1c', 'sequence_1d_220_1_fv1c', 'sequence_1d_231_12_fv1c', 'sequence_1c_106_7_fv1c', 'sequence_1b_47_8_fv1c', 'sequence_1b_41_2_fv1c', 'sequence_1d_249_30_fv1c', 'sequence_1d_247_28_fv1c', 'sequence_2b_482_3_fv1c', 'sequence_1c_102_3_fv1c', 'sequence_1b_43_4_fv1c', 'sequence_1c_101_2_fv1c', 'sequence_1d_232_13_fv1c', 'sequence_1d_245_26_fv1c', 'sequence_1c_103_4_fv1c', 'sequence_1d_234_15_fv1c', 'sequence_1b_46_7_fv1c', 'sequence_1d_235_16_fv1c', 'sequence_1c_105_6_fv1c', 'sequence_2b_484_5_fv1c', 'sequence_2b_485_6_fv1c', 'sequence_1d_248_29_fv1c', 'sequence_1d_227_8_fv1c', 'sequence_1d_225_6_fv1c', 'sequence_1d_223_4_fv1c', 'sequence_1d_222_3_fv1c', 'sequence_2b_480_1_fv1c', 'sequence_1b_44_5_fv1c', 'sequence_2b_487_8_fv1c', 'sequence_1d_239_20_fv1c', 'sequence_1b_49_10_fv1c', 'sequence_1b_48_9_fv1c', 'sequence_2b_488_9_fv1c', 'sequence_1d_228_9_fv1c', 'sequence_2b_489_10_fv1c', 'sequence_1c_109_10_fv1c', 'sequence_1c_108_9_fv1c', 'sequence_1d_229_10_fv1c'], '[  1.428  14.28   14.28    1.25    1.25   14.28   14.28    1.428   1.428\n  14.28   14.28    1.      1.     14.28   14.28    1.428   1.428  13.5\n  13.5     1.      1.     11.5    11.5     1.428]': ['sequence_1l_373_4_fv1c', 'sequence_1l_376_7_fv1c', 'sequence_3a_596_18_fv1c', 'sequence_3a_593_15_fv1c', 'sequence_3a_591_13_fv1c', 'sequence_1l_375_6_fv1c', 'sequence_1l_374_5_fv1c', 'sequence_1l_377_8_fv1c', 'sequence_3a_597_19_fv1c', 'sequence_3a_595_17_fv1c', 'sequence_3a_592_14_fv1c', 'sequence_3a_589_11_fv1c', 'sequence_1l_370_1_fv1c', 'sequence_3a_598_20_fv1c', 'sequence_1l_372_3_fv1c', 'sequence_1l_371_2_fv1c', 'sequence_3a_594_16_fv1c', 'sequence_1l_378_9_fv1c', 'sequence_1l_379_10_fv1c', 'sequence_3a_587_9_fv1c'], '[  1.028  12.28   12.28    1.028   1.028  17.28   17.28    1.028]': ['sequence_2c_557_8_fv1c', 'sequence_2c_556_7_fv1c', 'sequence_2c_554_5_fv1c', 'sequence_2c_553_4_fv1c', 'sequence_2c_552_3_fv1c', 'sequence_2c_555_6_fv1c', 'sequence_2c_550_1_fv1c', 'sequence_2c_551_2_fv1c', 'sequence_2c_558_9_fv1c', 'sequence_2c_559_10_fv1c'], '[  1.028  15.28   15.28    1.75    1.75   10.28   10.28    1.028]': ['sequence_2c_524_5_fv1c', 'sequence_2c_520_1_fv1c', 'sequence_2c_521_2_fv1c', 'sequence_2c_522_3_fv1c', 'sequence_2c_527_8_fv1c', 'sequence_2c_526_7_fv1c', 'sequence_2c_525_6_fv1c', 'sequence_2c_523_4_fv1c', 'sequence_2c_529_10_fv1c', 'sequence_2c_528_9_fv1c'], '[  1.428  14.28   14.28    0.5     0.5    16.28   16.28    1.428   1.428\n  14.28   14.28    1.      1.     14.28   14.28    1.428   1.428  14.28\n  14.28    0.5     0.5    16.28   16.28    1.428]': ['sequence_3a_706_8_fv1c', 'sequence_1k_353_4_fv1c', 'sequence_3a_705_7_fv1c', 'sequence_1k_354_5_fv1c', 'sequence_1k_350_1_fv1c', 'sequence_3a_701_3_fv1c', 'sequence_3a_700_2_fv1c', 'sequence_3a_702_4_fv1c', 'sequence_1k_351_2_fv1c', 'sequence_3a_704_6_fv1c', 'sequence_3a_699_1_fv1c', 'sequence_1k_357_8_fv1c', 'sequence_1k_355_6_fv1c', 'sequence_3a_703_5_fv1c', 'sequence_1k_356_7_fv1c', 'sequence_1k_352_3_fv1c', 'sequence_1k_358_9_fv1c', 'sequence_3a_708_10_fv1c', 'sequence_3a_707_9_fv1c', 'sequence_1k_359_10_fv1c'], '[  1.428  12.28   12.28    1.428   1.428  16.28   16.28    1.428]': ['sequence_1d_269_20_fv1c', 'sequence_1c_113_4_fv1c', 'sequence_1d_263_14_fv1c', 'sequence_1d_251_2_fv1c', 'sequence_1d_277_28_fv1c', 'sequence_1c_117_8_fv1c', 'sequence_1c_116_7_fv1c', 'sequence_1c_112_3_fv1c', 'sequence_1b_50_1_fv1c', 'sequence_1b_51_2_fv1c', 'sequence_1d_255_6_fv1c', 'sequence_1d_278_29_fv1c', 'sequence_1b_52_3_fv1c', 'sequence_1d_260_11_fv1c', 'sequence_2b_490_1_fv1c', 'sequence_1d_268_19_fv1c', 'sequence_1c_115_6_fv1c', 'sequence_1d_257_8_fv1c', 'sequence_1d_264_15_fv1c', 'sequence_1d_265_16_fv1c', 'sequence_1c_111_2_fv1c', 'sequence_1b_57_8_fv1c', 'sequence_1d_267_18_fv1c', 'sequence_1c_110_1_fv1c', 'sequence_1d_279_30_fv1c', 'sequence_2b_496_7_fv1c', 'sequence_1d_273_24_fv1c', 'sequence_1d_274_25_fv1c', 'sequence_1d_271_22_fv1c', 'sequence_2b_495_6_fv1c', 'sequence_1d_256_7_fv1c', 'sequence_2b_497_8_fv1c', 'sequence_1d_270_21_fv1c', 'sequence_1d_250_1_fv1c', 'sequence_1b_56_7_fv1c', 'sequence_1b_53_4_fv1c', 'sequence_1d_261_12_fv1c', 'sequence_1d_272_23_fv1c', 'sequence_1d_275_26_fv1c', 'sequence_2b_494_5_fv1c', 'sequence_1d_266_17_fv1c', 'sequence_1d_254_5_fv1c', 'sequence_2b_493_4_fv1c', 'sequence_1d_252_3_fv1c', 'sequence_1d_276_27_fv1c', 'sequence_1d_262_13_fv1c', 'sequence_1b_54_5_fv1c', 'sequence_1b_55_6_fv1c', 'sequence_1d_253_4_fv1c', 'sequence_1c_114_5_fv1c', 'sequence_2b_491_2_fv1c', 'sequence_2b_492_3_fv1c', 'sequence_1c_119_10_fv1c', 'sequence_1b_58_9_fv1c', 'sequence_2b_499_10_fv1c', 'sequence_1b_59_10_fv1c', 'sequence_1c_118_9_fv1c', 'sequence_1d_258_9_fv1c', 'sequence_2b_498_9_fv1c', 'sequence_1d_259_10_fv1c'], '[  1.428  14.28   14.28    1.428   1.428  14.28   14.28    1.428   1.428\n  14.28   14.28    0.75    0.75   16.28   16.28    1.428   1.428  12.28\n  12.28    1.428   1.428  16.28   16.28    1.428]': ['sequence_1l_422_3_fv1c', 'sequence_3a_698_20_fv1c', 'sequence_3a_697_19_fv1c', 'sequence_3a_692_14_fv1c', 'sequence_3a_694_16_fv1c', 'sequence_1l_421_2_fv1c', 'sequence_3a_689_11_fv1c', 'sequence_3a_695_17_fv1c', 'sequence_3a_693_15_fv1c', 'sequence_3a_691_13_fv1c', 'sequence_1l_426_7_fv1c', 'sequence_1l_424_5_fv1c', 'sequence_1l_427_8_fv1c', 'sequence_3a_696_18_fv1c', 'sequence_1l_425_6_fv1c', 'sequence_3a_690_12_fv1c', 'sequence_1l_420_1_fv1c', 'sequence_1l_423_4_fv1c', 'sequence_1l_429_10_fv1c', 'sequence_1l_428_9_fv1c'], '[  1.428  14.28   14.28    1.25    1.25   14.28   14.28    1.428   1.428\n  14.28   14.28    1.75    1.75   14.28   14.28    1.428   1.428  14.28\n  14.28    1.25    1.25   14.28   14.28    1.428]': ['sequence_1k_294_5_fv1c', 'sequence_3a_586_8_fv1c', 'sequence_3a_580_2_fv1c', 'sequence_1k_296_7_fv1c', 'sequence_1k_292_3_fv1c', 'sequence_3a_581_3_fv1c', 'sequence_1k_297_8_fv1c', 'sequence_1k_291_2_fv1c', 'sequence_3a_584_6_fv1c', 'sequence_1k_290_1_fv1c', 'sequence_1k_293_4_fv1c', 'sequence_3a_579_1_fv1c', 'sequence_3a_582_4_fv1c', 'sequence_3a_585_7_fv1c', 'sequence_1k_295_6_fv1c', 'sequence_3a_583_5_fv1c', 'sequence_3a_590_12_fv1c', 'sequence_1k_299_10_fv1c', 'sequence_1k_298_9_fv1c', 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0.75   16.28   16.28    1.428   1.428\n  12.28   12.28    1.428   1.428  16.28   16.28    1.428   1.428  14.28\n  14.28    0.75    0.75   16.28   16.28    1.428]': ['sequence_3a_645_7_fv1c', 'sequence_1k_320_1_fv1c', 'sequence_3a_643_5_fv1c', 'sequence_3a_644_6_fv1c', 'sequence_1k_326_7_fv1c', 'sequence_1k_323_4_fv1c', 'sequence_3a_641_3_fv1c', 'sequence_1k_327_8_fv1c', 'sequence_1k_325_6_fv1c', 'sequence_3a_640_2_fv1c', 'sequence_1k_322_3_fv1c', 'sequence_1k_321_2_fv1c', 'sequence_3a_642_4_fv1c', 'sequence_3a_639_1_fv1c', 'sequence_1k_324_5_fv1c', 'sequence_3a_646_8_fv1c', 'sequence_1k_329_10_fv1c', 'sequence_3a_647_9_fv1c', 'sequence_3a_648_10_fv1c', 'sequence_1k_328_9_fv1c'], '[  1.428  14.28   14.28    0.75    0.75   14.28   14.28    1.428]': ['sequence_1k_315_6_fv1c', 'sequence_1d_207_18_fv1c', 'sequence_3a_623_5_fv1c', 'sequence_3a_620_2_fv1c', 'sequence_3a_626_8_fv1c', 'sequence_1k_317_8_fv1c', 'sequence_1c_96_7_fv1c', 'sequence_2b_472_3_fv1c', 'sequence_1d_216_27_fv1c', 'sequence_1c_94_5_fv1c', 'sequence_1b_32_3_fv1c', 'sequence_1k_316_7_fv1c', 'sequence_1d_211_22_fv1c', 'sequence_1c_91_2_fv1c', 'sequence_2b_477_8_fv1c', 'sequence_2b_474_5_fv1c', 'sequence_1d_194_5_fv1c', 'sequence_1c_95_6_fv1c', 'sequence_1d_201_12_fv1c', 'sequence_3a_624_6_fv1c', 'sequence_1d_204_15_fv1c', 'sequence_1d_192_3_fv1c', 'sequence_1c_93_4_fv1c', 'sequence_1d_212_23_fv1c', 'sequence_1d_219_30_fv1c', 'sequence_1c_92_3_fv1c', 'sequence_3a_622_4_fv1c', 'sequence_1d_206_17_fv1c', 'sequence_2b_470_1_fv1c', 'sequence_2b_473_4_fv1c', 'sequence_2b_476_7_fv1c', 'sequence_1d_209_20_fv1c', 'sequence_1k_311_2_fv1c', 'sequence_1d_200_11_fv1c', 'sequence_1c_97_8_fv1c', 'sequence_1k_313_4_fv1c', 'sequence_1d_217_28_fv1c', 'sequence_1d_191_2_fv1c', 'sequence_1d_213_24_fv1c', 'sequence_1b_33_4_fv1c', 'sequence_3a_619_1_fv1c', 'sequence_2b_471_2_fv1c', 'sequence_1b_36_7_fv1c', 'sequence_1d_214_25_fv1c', 'sequence_1d_197_8_fv1c', 'sequence_1k_314_5_fv1c', 'sequence_1d_202_13_fv1c', 'sequence_1d_193_4_fv1c', 'sequence_3a_625_7_fv1c', 'sequence_1d_195_6_fv1c', 'sequence_1k_312_3_fv1c', 'sequence_1d_218_29_fv1c', 'sequence_1d_196_7_fv1c', 'sequence_1d_208_19_fv1c', 'sequence_1b_30_1_fv1c', 'sequence_3a_621_3_fv1c', 'sequence_3a_629_11_fv1c', 'sequence_1d_210_21_fv1c', 'sequence_1d_190_1_fv1c', 'sequence_1d_215_26_fv1c', 'sequence_1b_37_8_fv1c', 'sequence_1d_205_16_fv1c', 'sequence_1b_31_2_fv1c', 'sequence_1b_35_6_fv1c', 'sequence_1d_203_14_fv1c', 'sequence_2b_475_6_fv1c', 'sequence_1k_310_1_fv1c', 'sequence_1b_34_5_fv1c', 'sequence_1c_90_1_fv1c', 'sequence_1k_318_9_fv1c', 'sequence_1c_98_9_fv1c', 'sequence_1d_199_10_fv1c', 'sequence_3a_627_9_fv1c', 'sequence_2b_479_10_fv1c', 'sequence_1c_99_10_fv1c', 'sequence_2b_478_9_fv1c', 'sequence_1b_39_10_fv1c', 'sequence_1b_38_9_fv1c', 'sequence_1d_198_9_fv1c', 'sequence_1k_319_10_fv1c'], '[  1.428  14.28   14.28    0.5     0.5    16.28   16.28    1.428   1.428\n  12.28   12.28    1.428   1.428  16.28   16.28    1.428   1.428  14.28\n  14.28    1.428   1.428  14.28   14.28    1.428]': ['sequence_3a_709_11_fv1c', 'sequence_3a_714_16_fv1c', 'sequence_1l_430_1_fv1c', 'sequence_3a_718_20_fv1c', 'sequence_3a_713_15_fv1c', 'sequence_1l_431_2_fv1c', 'sequence_1l_436_7_fv1c', 'sequence_3a_717_19_fv1c', 'sequence_3a_712_14_fv1c', 'sequence_1l_433_4_fv1c', 'sequence_1l_432_3_fv1c', 'sequence_3a_710_12_fv1c', 'sequence_3a_711_13_fv1c', 'sequence_1l_437_8_fv1c', 'sequence_3a_716_18_fv1c', 'sequence_3a_715_17_fv1c', 'sequence_1l_434_5_fv1c', 'sequence_1l_435_6_fv1c', 'sequence_1l_438_9_fv1c', 'sequence_1l_439_10_fv1c'], '[  1.028  11.28   11.28    1.      1.     10.28   10.28    1.028]': ['sequence_2c_503_4_fv1c', 'sequence_2c_500_1_fv1c', 'sequence_2c_507_8_fv1c', 'sequence_2c_501_2_fv1c', 'sequence_2c_502_3_fv1c', 'sequence_2c_504_5_fv1c', 'sequence_2c_505_6_fv1c', 'sequence_2c_506_7_fv1c', 'sequence_2c_509_10_fv1c', 'sequence_2c_508_9_fv1c']}

In [8]:

    

#run the train set again to make the combined (test+train) dict; most of the lists aren't reset. 

i = 0 
# unique_load_sequences = [] #fill these with the 4-pairs as a numpy array
# unique_load_sequences_aslists = []
sequences_4pairs_list = []
sequences_not4pairs_list = []
# filename_loadseq_dict_seqidkey = {} #key is the sequence ID, value is its position in the list. 
four_pairs_condition = False
print("train_set size: ",len(train_set_filenames))
for files in train_set_filenames:
    #print("filename", files)
    i += 1
    data_load_path = train_path + '/data/' + files[0]
    label_load_path = train_path + '/label/' + files[1]
    # print("data/label load path: {} \n {}".format(data_load_path,label_load_path))
    train_array = np.load(data_load_path)
    train_label_array = np.load(label_load_path)[:, 1:]
    
    #check if the repeating unit is actually 4 tuples. 
    #four_pair_condition = (train_array[0:5,-2] == train_array[4:8,-2]) and (train_array[0:5,-1] == train_array[4:8,-1]) error. 
    #numpy version 
    four_pair_condition = np.array_equal(train_array[0:4,-2:],train_array[4:8,-2:]) 

    
    if four_pair_condition == True:
        #print(str(files[0])," is in.")
        load_sequence = train_array[0:4,-2:]
        #if len(unique_load_sequences) != 0 :
        array_equality = False
        if len(unique_load_sequences) == 0:
            unique_load_sequences.append(load_sequence)
            #unique_load_sequences_aslists.append(np.ndarray.tolist(load_sequence)) #need to be flattened into a list to make it hashable for the list-operators to work
            unique_load_sequences_aslists.append(str(np.ndarray.flatten(load_sequence)))
        if len(unique_load_sequences) != 0:
            for load_seq in unique_load_sequences:
                if np.array_equal(load_seq,load_sequence) == True:
                    #print("equal array found in list?", np.array_equal(load_seq,load_sequence))
                    array_equality = True
            if array_equality == False:
                unique_load_sequences.append(load_sequence) #the load types
                #unique_load_sequences_aslists.append(np.ndarray.tolist(load_sequence))
                unique_load_sequences_aslists.append(str(np.ndarray.flatten(load_sequence)))
            filename_loadseq_dict_seqidkey[str(files[0])] = unique_load_sequences_aslists.index(str(np.ndarray.flatten(load_sequence)))
        sequences_4pairs_list.append(str(files[0]))
    if four_pair_condition == False:
        sequences_not4pairs_list.append(str(files[0]))
        
    
    #print("train_array's last two columns",train_array[0:20,-2:])
#unique_load_sequences = set(unique_load_sequences)
#print("there are", len(unique_load_sequences), " load sequences")
#print("the unique load sequences are:", unique_load_sequences)
print("sequences with 4-pair repeating units: ",len(sequences_4pairs_list))
print("sequences WITHOUT 4-pair repeating units: ",sequences_not4pairs_list)

twelve_pair_condition = np.array_equal(train_array[0:12,-2:],train_array[12:24,-2:]) 
sequences_12pairs_list = []
sequences_not4_not12_pairs_list = []

#gotta run the 4-pair filter first to make sure the 12-pair subset list is ok. Hence the separate loop. 
for unusual_seqs in sequences_not4pairs_list: #12 repeating units. 
    data_load_path = train_path + '/data/' + unusual_seqs
    train_array = np.load(data_load_path)
    
    if twelve_pair_condition == True:
        #print(str(unusual_seqs)," is in.")
        load_sequence = train_array[0:12,-2:]
        #if len(unique_load_sequences) != 0 :
        array_equality = False
        if len(unique_load_sequences) == 0:
            unique_load_sequences.append(load_sequence)
            #unique_load_sequences_aslists.append(np.ndarray.tolist(load_sequence))
            unique_load_sequences_aslists.append(str(np.ndarray.flatten(load_sequence)))
        if len(unique_load_sequences) != 0:
            for load_seq in unique_load_sequences:
                if np.array_equal(load_seq,load_sequence) == True:
                    #print("equal array found in list?", np.array_equal(load_seq,load_sequence))
                    array_equality = True
            if array_equality == False:
                unique_load_sequences.append(load_sequence) #the load types
                #unique_load_sequences_aslists.append(np.ndarray.tolist(load_sequence))
                unique_load_sequences_aslists.append(str(np.ndarray.flatten(load_sequence)))
        sequences_12pairs_list.append(str(unusual_seqs))
        filename_loadseq_dict_seqidkey[str(unusual_seqs)] = unique_load_sequences_aslists.index(str(np.ndarray.flatten(load_sequence)))
    if twelve_pair_condition == False:
        sequences_not4_not12_pairs_list.append(str(unusual_seqs))

print("There are ",len(sequences_12pairs_list), " sequences with 12-pairs")        
print("There are ", len(sequences_not4_not12_pairs_list), "sequences that have neither 4 nor 12 repeating units")
print("seqs that aren't 4 or 12: ",sequences_not4_not12_pairs_list)
print("there are ",len(unique_load_sequences), " different load sequences in the ", len(train_set_filenames), "sequences in the training set")
#print("unique load seqs", unique_load_sequences)

for key,value in filename_loadseq_dict_seqidkey.iteritems():
    print("key",key,"value",value)
    load_sequence_aslist = str(unique_load_sequences_aslists[value])
    #this arrangement below is necessary because dict has to have a list as its value. 
    #casting the list-cast ndarray as a string means.. parsing it back to numpy requires some creativity. 
    if filename_loadseq_dict_loadlistposkey.get(str(load_sequence_aslist)) != None:
        filename_loadseq_dict_loadlistposkey[str(load_sequence_aslist)].append(key)
    if filename_loadseq_dict_loadlistposkey.get(str(load_sequence_aslist)) == None: 
        content_list = []
        content_list.append(key)
        filename_loadseq_dict_loadlistposkey[str(load_sequence_aslist)] = content_list

print(filename_loadseq_dict_loadlistposkey)
fl_llpk_combined_df = pd.DataFrame.from_dict(filename_loadseq_dict_loadlistposkey,orient='index')
fl_sik_combined_df = pd.DataFrame.from_dict(filename_loadseq_dict_seqidkey,orient='index')
fl_llpk_combined_df.to_csv(analysis_path + "file_loadseq_loadlistkey_COMBINED.csv")
fl_sik_combined_df.to_csv(analysis_path + "file_loadseq_seqid_key_COMBINED.csv")


    

    




('train_set size: ', 611)
('sequences with 4-pair repeating units: ', 357)
('sequences WITHOUT 4-pair repeating units: ', ['sequence_1l_413_4_fv1c.npy', 'sequence_1l_411_2_fv1c.npy', 'sequence_3a_561_2_fv1c.npy', 'sequence_3a_690_12_fv1c.npy', 'sequence_3a_715_17_fv1c.npy', 'sequence_3a_600_2_fv1c.npy', 'sequence_3a_664_6_fv1c.npy', 'sequence_3a_646_8_fv1c.npy', 'sequence_1k_337_8_fv1c.npy', 'sequence_3a_618_20_fv1c.npy', 'sequence_3a_570_11_fv1c.npy', 'sequence_1l_394_5_fv1c.npy', 'sequence_1l_404_5_fv1c.npy', 'sequence_3a_703_5_fv1c.npy', 'sequence_1k_321_2_fv1c.npy', 'sequence_3a_584_6_fv1c.npy', 'sequence_1k_292_3_fv1c.npy', 'sequence_1l_371_2_fv1c.npy', 'sequence_1l_402_3_fv1c.npy', 'sequence_3a_650_12_fv1c.npy', 'sequence_1k_344_5_fv1c.npy', 'sequence_1l_401_2_fv1c.npy', 'sequence_3a_718_20_fv1c.npy', 'sequence_3a_661_3_fv1c.npy', 'sequence_1l_364_5_fv1c.npy', 'sequence_1k_357_8_fv1c.npy', 'sequence_1k_341_2_fv1c.npy', 'sequence_1l_397_8_fv1c.npy', 'sequence_3a_582_4_fv1c.npy', 'sequence_3a_603_5_fv1c.npy', 'sequence_1l_386_7_fv1c.npy', 'sequence_3a_659_1_fv1c.npy', 'sequence_3a_698_20_fv1c.npy', 'sequence_3a_613_15_fv1c.npy', 'sequence_1l_396_7_fv1c.npy', 'sequence_1k_322_3_fv1c.npy', 'sequence_3a_649_11_fv1c.npy', 'sequence_3a_593_15_fv1c.npy', 'sequence_1k_293_4_fv1c.npy', 'sequence_1l_395_6_fv1c.npy', 'sequence_1k_285_6_fv1c.npy', 'sequence_1k_307_8_fv1c.npy', 'sequence_3a_655_17_fv1c.npy', 'sequence_1l_387_8_fv1c.npy', 'sequence_3a_685_7_fv1c.npy', 'sequence_3a_673_15_fv1c.npy', 'sequence_1k_331_2_fv1c.npy', 'sequence_1k_334_5_fv1c.npy', 'sequence_3a_574_15_fv1c.npy', 'sequence_3a_581_3_fv1c.npy', 'sequence_1k_284_5_fv1c.npy', 'sequence_1l_382_3_fv1c.npy', 'sequence_1k_280_1_fv1c.npy', 'sequence_1l_433_4_fv1c.npy', 'sequence_3a_645_7_fv1c.npy', 'sequence_3a_578_19_fv1c.npy', 'sequence_3a_670_12_fv1c.npy', 'sequence_3a_677_19_fv1c.npy', 'sequence_3a_567_8_fv1c.npy', 'sequence_1k_346_7_fv1c.npy', 'sequence_3a_609_11_fv1c.npy', 'sequence_1k_351_2_fv1c.npy', 'sequence_3a_699_1_fv1c.npy', 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'sequence_1l_372_3_fv1c.npy', 'sequence_3a_714_16_fv1c.npy', 'sequence_1l_423_4_fv1c.npy', 'sequence_3a_689_11_fv1c.npy', 'sequence_1l_416_7_fv1c.npy', 'sequence_1k_330_1_fv1c.npy', 'sequence_3a_712_14_fv1c.npy', 'sequence_3a_696_18_fv1c.npy', 'sequence_3a_682_4_fv1c.npy', 'sequence_3a_691_13_fv1c.npy', 'sequence_3a_660_2_fv1c.npy', 'sequence_1k_304_5_fv1c.npy', 'sequence_3a_657_19_fv1c.npy', 'sequence_3a_562_3_fv1c.npy', 'sequence_3a_639_1_fv1c.npy', 'sequence_3a_596_18_fv1c.npy', 'sequence_1l_406_7_fv1c.npy', 'sequence_3a_594_16_fv1c.npy', 'sequence_1l_417_8_fv1c.npy', 'sequence_1l_430_1_fv1c.npy', 'sequence_1l_370_1_fv1c.npy', 'sequence_1k_340_1_fv1c.npy', 'sequence_3a_611_13_fv1c.npy', 'sequence_1k_327_8_fv1c.npy', 'sequence_3a_643_5_fv1c.npy', 'sequence_1k_350_1_fv1c.npy', 'sequence_3a_640_2_fv1c.npy', 'sequence_3a_586_8_fv1c.npy', 'sequence_3a_662_4_fv1c.npy', 'sequence_1k_320_1_fv1c.npy', 'sequence_3a_653_15_fv1c.npy', 'sequence_1k_287_8_fv1c.npy', 'sequence_3a_612_14_fv1c.npy', 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'sequence_1k_282_3_fv1c.npy', 'sequence_1k_306_7_fv1c.npy', 'sequence_1l_410_1_fv1c.npy', 'sequence_3a_676_18_fv1c.npy', 'sequence_3a_716_18_fv1c.npy', 'sequence_1k_294_5_fv1c.npy', 'sequence_1l_403_4_fv1c.npy', 'sequence_1l_436_7_fv1c.npy', 'sequence_3a_678_20_fv1c.npy', 'sequence_3a_610_12_fv1c.npy', 'sequence_3a_616_18_fv1c.npy', 'sequence_1l_383_4_fv1c.npy', 'sequence_3a_566_7_fv1c.npy', 'sequence_1l_427_8_fv1c.npy', 'sequence_3a_644_6_fv1c.npy', 'sequence_1k_281_2_fv1c.npy', 'sequence_1l_434_5_fv1c.npy', 'sequence_3a_615_17_fv1c.npy', 'sequence_3a_642_4_fv1c.npy', 'sequence_1k_290_1_fv1c.npy', 'sequence_3a_680_2_fv1c.npy', 'sequence_3a_694_16_fv1c.npy', 'sequence_3a_585_7_fv1c.npy', 'sequence_3a_634_16_fv1c.npy', 'sequence_1l_420_1_fv1c.npy', 'sequence_1k_352_3_fv1c.npy', 'sequence_1k_343_4_fv1c.npy', 'sequence_3a_583_5_fv1c.npy', 'sequence_3a_592_14_fv1c.npy', 'sequence_3a_598_20_fv1c.npy', 'sequence_3a_589_11_fv1c.npy', 'sequence_3a_674_16_fv1c.npy', 'sequence_1l_435_6_fv1c.npy', 'sequence_1l_373_4_fv1c.npy', 'sequence_1k_324_5_fv1c.npy', 'sequence_1k_286_7_fv1c.npy', 'sequence_1k_336_7_fv1c.npy', 'sequence_1l_374_5_fv1c.npy', 'sequence_3a_564_5_fv1c.npy', 'sequence_3a_681_3_fv1c.npy', 'sequence_3a_632_14_fv1c.npy', 'sequence_3a_590_12_fv1c.npy', 'sequence_1l_367_8_fv1c.npy', 'sequence_1k_342_3_fv1c.npy', 'sequence_3a_701_3_fv1c.npy', 'sequence_1l_432_3_fv1c.npy', 'sequence_1l_362_3_fv1c.npy', 'sequence_3a_669_11_fv1c.npy', 'sequence_3a_580_2_fv1c.npy', 'sequence_3a_671_13_fv1c.npy', 'sequence_3a_697_19_fv1c.npy', 'sequence_3a_686_8_fv1c.npy', 'sequence_3a_711_13_fv1c.npy', 'sequence_3a_704_6_fv1c.npy', 'sequence_1k_303_4_fv1c.npy', 'sequence_3a_633_15_fv1c.npy', 'sequence_3a_684_6_fv1c.npy', 'sequence_3a_658_20_fv1c.npy', 'sequence_3a_591_13_fv1c.npy', 'sequence_1l_377_8_fv1c.npy', 'sequence_1l_360_1_fv1c.npy', 'sequence_1l_425_6_fv1c.npy', 'sequence_3a_638_20_fv1c.npy', 'sequence_1l_363_4_fv1c.npy', 'sequence_3a_614_16_fv1c.npy', 'sequence_1l_380_1_fv1c.npy', 'sequence_1k_297_8_fv1c.npy', 'sequence_3a_572_13_fv1c.npy', 'sequence_3a_656_18_fv1c.npy', 'sequence_1l_437_8_fv1c.npy', 'sequence_1l_426_7_fv1c.npy', 'sequence_1l_393_4_fv1c.npy', 'sequence_1k_354_5_fv1c.npy', 'sequence_1l_405_6_fv1c.npy', 'sequence_3a_606_8_fv1c.npy', 'sequence_3a_641_3_fv1c.npy', 'sequence_3a_665_7_fv1c.npy', 'sequence_1k_283_4_fv1c.npy', 'sequence_3a_652_14_fv1c.npy', 'sequence_3a_595_17_fv1c.npy', 'sequence_3a_605_7_fv1c.npy', 'sequence_3a_573_14_fv1c.npy', 'sequence_1k_355_6_fv1c.npy', 'sequence_3a_577_18_fv1c.npy', 'sequence_3a_602_4_fv1c.npy', 'sequence_3a_693_15_fv1c.npy', 'sequence_3a_666_8_fv1c.npy', 'sequence_1l_375_6_fv1c.npy', 'sequence_3a_635_17_fv1c.npy', 'sequence_3a_702_4_fv1c.npy', 'sequence_1l_415_6_fv1c.npy', 'sequence_3a_630_12_fv1c.npy'])
('There are ', 254, ' sequences with 12-pairs')
('There are ', 0, 'sequences that have neither 4 nor 12 repeating units')
("seqs that aren't 4 or 12: ", [])
('there are ', 27, ' different load sequences in the ', 611, 'sequences in the training set')
('key', 'sequence_1b_29_10_fv1c', 'value', 0)
('key', 'sequence_3a_651_13_fv1c.npy', 'value', 22)
('key', 'sequence_1l_416_7_fv1c.npy', 'value', 12)
('key', 'sequence_3a_628_10_fv1c', 'value', 13)
('key', 'sequence_3a_664_6_fv1c.npy', 'value', 21)
('key', 'sequence_1d_175_16_fv1c.npy', 'value', 0)
('key', 'sequence_3a_614_16_fv1c.npy', 'value', 26)
('key', 'sequence_3a_637_19_fv1c.npy', 'value', 13)
('key', 'sequence_1c_92_3_fv1c.npy', 'value', 8)
('key', 'sequence_1l_433_4_fv1c.npy', 'value', 24)
('key', 'sequence_1d_200_11_fv1c.npy', 'value', 8)
('key', 'sequence_3a_573_14_fv1c.npy', 'value', 19)
('key', 'sequence_3a_607_9_fv1c', 'value', 15)
('key', 'sequence_3a_663_5_fv1c.npy', 'value', 21)
('key', 'sequence_1k_308_9_fv1c', 'value', 15)
('key', 'sequence_1d_127_8_fv1c.npy', 'value', 6)
('key', 'sequence_1l_390_1_fv1c.npy', 'value', 13)
('key', 'sequence_3a_671_13_fv1c.npy', 'value', 12)
('key', 'sequence_3a_679_1_fv1c.npy', 'value', 23)
('key', 'sequence_1k_300_1_fv1c.npy', 'value', 15)
('key', 'sequence_1d_251_2_fv1c.npy', 'value', 3)
('key', 'sequence_1l_363_4_fv1c.npy', 'value', 20)
('key', 'sequence_3a_560_1_fv1c.npy', 'value', 20)
('key', 'sequence_1d_155_26_fv1c.npy', 'value', 4)
('key', 'sequence_1d_212_23_fv1c.npy', 'value', 8)
('key', 'sequence_3a_593_15_fv1c.npy', 'value', 17)
('key', 'sequence_2c_528_9_fv1c', 'value', 10)
('key', 'sequence_2b_493_4_fv1c.npy', 'value', 3)
('key', 'sequence_1d_203_14_fv1c.npy', 'value', 8)
('key', 'sequence_2b_453_4_fv1c.npy', 'value', 4)
('key', 'sequence_3a_632_14_fv1c.npy', 'value', 13)
('key', 'sequence_1c_107_8_fv1c.npy', 'value', 1)
('key', 'sequence_1k_354_5_fv1c.npy', 'value', 16)
('key', 'sequence_1d_192_3_fv1c.npy', 'value', 8)
('key', 'sequence_1k_280_1_fv1c.npy', 'value', 19)
('key', 'sequence_3a_564_5_fv1c.npy', 'value', 19)
('key', 'sequence_2b_463_4_fv1c.npy', 'value', 0)
('key', 'sequence_2b_480_1_fv1c.npy', 'value', 1)
('key', 'sequence_2b_466_7_fv1c.npy', 'value', 0)
('key', 'sequence_2c_548_9_fv1c', 'value', 2)
('key', 'sequence_1l_436_7_fv1c.npy', 'value', 24)
('key', 'sequence_3a_682_4_fv1c.npy', 'value', 23)
('key', 'sequence_2b_447_8_fv1c.npy', 'value', 6)
('key', 'sequence_2b_477_8_fv1c.npy', 'value', 8)
('key', 'sequence_2c_541_2_fv1c.npy', 'value', 2)
('key', 'sequence_3a_701_3_fv1c.npy', 'value', 16)
('key', 'sequence_3a_592_14_fv1c.npy', 'value', 17)
('key', 'sequence_1b_12_3_fv1c.npy', 'value', 4)
('key', 'sequence_2c_536_7_fv1c.npy', 'value', 7)
('key', 'sequence_1k_316_7_fv1c.npy', 'value', 8)
('key', 'sequence_3a_649_11_fv1c.npy', 'value', 22)
('key', 'sequence_1d_125_6_fv1c.npy', 'value', 6)
('key', 'sequence_1d_204_15_fv1c.npy', 'value', 8)
('key', 'sequence_3a_602_4_fv1c.npy', 'value', 15)
('key', 'sequence_1c_74_5_fv1c.npy', 'value', 4)
('key', 'sequence_1b_56_7_fv1c.npy', 'value', 3)
('key', 'sequence_1l_377_8_fv1c.npy', 'value', 17)
('key', 'sequence_1d_252_3_fv1c.npy', 'value', 3)
('key', 'sequence_2c_550_1_fv1c.npy', 'value', 5)
('key', 'sequence_1k_307_8_fv1c.npy', 'value', 15)
('key', 'sequence_1d_183_24_fv1c.npy', 'value', 0)
('key', 'sequence_1k_287_8_fv1c.npy', 'value', 19)
('key', 'sequence_1d_171_12_fv1c.npy', 'value', 0)
('key', 'sequence_1k_348_9_fv1c', 'value', 23)
('key', 'sequence_2c_514_5_fv1c.npy', 'value', 11)
('key', 'sequence_1c_85_6_fv1c.npy', 'value', 0)
('key', 'sequence_1k_349_10_fv1c', 'value', 23)
('key', 'sequence_1l_378_9_fv1c', 'value', 17)
('key', 'sequence_3a_693_15_fv1c.npy', 'value', 14)
('key', 'sequence_1d_193_4_fv1c.npy', 'value', 8)
('key', 'sequence_1d_120_1_fv1c.npy', 'value', 6)
('key', 'sequence_1d_146_17_fv1c.npy', 'value', 4)
('key', 'sequence_1d_178_19_fv1c.npy', 'value', 0)
('key', 'sequence_2c_554_5_fv1c.npy', 'value', 5)
('key', 'sequence_2c_518_9_fv1c', 'value', 11)
('key', 'sequence_1l_410_1_fv1c.npy', 'value', 12)
('key', 'sequence_1c_87_8_fv1c.npy', 'value', 0)
('key', 'sequence_3a_621_3_fv1c.npy', 'value', 8)
('key', 'sequence_1d_274_25_fv1c.npy', 'value', 3)
('key', 'sequence_1b_21_2_fv1c.npy', 'value', 0)
('key', 'sequence_1d_156_27_fv1c.npy', 'value', 4)
('key', 'sequence_1d_209_20_fv1c.npy', 'value', 8)
('key', 'sequence_1k_303_4_fv1c.npy', 'value', 15)
('key', 'sequence_1d_228_9_fv1c', 'value', 1)
('key', 'sequence_3a_703_5_fv1c.npy', 'value', 16)
('key', 'sequence_1d_196_7_fv1c.npy', 'value', 8)
('key', 'sequence_1l_402_3_fv1c.npy', 'value', 22)
('key', 'sequence_3a_717_19_fv1c.npy', 'value', 24)
('key', 'sequence_1d_126_7_fv1c.npy', 'value', 6)
('key', 'sequence_1l_376_7_fv1c.npy', 'value', 17)
('key', 'sequence_1l_413_4_fv1c.npy', 'value', 12)
('key', 'sequence_1c_118_9_fv1c', 'value', 3)
('key', 'sequence_1l_394_5_fv1c.npy', 'value', 13)
('key', 'sequence_1b_1_2_fv1c.npy', 'value', 6)
('key', 'sequence_1k_305_6_fv1c.npy', 'value', 15)
('key', 'sequence_1d_176_17_fv1c.npy', 'value', 0)
('key', 'sequence_1d_123_4_fv1c.npy', 'value', 6)
('key', 'sequence_1k_358_9_fv1c', 'value', 16)
('key', 'sequence_3a_647_9_fv1c', 'value', 18)
('key', 'sequence_1d_226_7_fv1c.npy', 'value', 1)
('key', 'sequence_1b_0_1_fv1c.npy', 'value', 6)
('key', 'sequence_2b_476_7_fv1c.npy', 'value', 8)
('key', 'sequence_1c_110_1_fv1c.npy', 'value', 3)
('key', 'sequence_2b_475_6_fv1c.npy', 'value', 8)
('key', 'sequence_1k_334_5_fv1c.npy', 'value', 21)
('key', 'sequence_1d_232_13_fv1c.npy', 'value', 1)
('key', 'sequence_1d_246_27_fv1c.npy', 'value', 1)
('key', 'sequence_3a_578_19_fv1c.npy', 'value', 20)
('key', 'sequence_3a_638_20_fv1c.npy', 'value', 13)
('key', 'sequence_1d_140_11_fv1c.npy', 'value', 4)
('key', 'sequence_1d_262_13_fv1c.npy', 'value', 3)
('key', 'sequence_2b_464_5_fv1c.npy', 'value', 0)
('key', 'sequence_1b_54_5_fv1c.npy', 'value', 3)
('key', 'sequence_2c_534_5_fv1c.npy', 'value', 7)
('key', 'sequence_1d_190_1_fv1c.npy', 'value', 8)
('key', 'sequence_1k_337_8_fv1c.npy', 'value', 21)
('key', 'sequence_3a_586_8_fv1c.npy', 'value', 25)
('key', 'sequence_3a_648_10_fv1c', 'value', 18)
('key', 'sequence_1d_144_15_fv1c.npy', 'value', 4)
('key', 'sequence_1k_326_7_fv1c.npy', 'value', 18)
('key', 'sequence_1l_388_9_fv1c', 'value', 26)
('key', 'sequence_3a_700_2_fv1c.npy', 'value', 16)
('key', 'sequence_1k_331_2_fv1c.npy', 'value', 21)
('key', 'sequence_2b_478_9_fv1c', 'value', 8)
('key', 'sequence_1l_361_2_fv1c.npy', 'value', 20)
('key', 'sequence_1c_103_4_fv1c.npy', 'value', 1)
('key', 'sequence_2b_481_2_fv1c.npy', 'value', 1)
('key', 'sequence_1b_31_2_fv1c.npy', 'value', 8)
('key', 'sequence_1c_83_4_fv1c.npy', 'value', 0)
('key', 'sequence_1l_409_10_fv1c', 'value', 22)
('key', 'sequence_1c_113_4_fv1c.npy', 'value', 3)
('key', 'sequence_2b_459_10_fv1c', 'value', 4)
('key', 'sequence_2c_520_1_fv1c.npy', 'value', 10)
('key', 'sequence_2c_519_10_fv1c', 'value', 11)
('key', 'sequence_1d_181_22_fv1c.npy', 'value', 0)
('key', 'sequence_1d_276_27_fv1c.npy', 'value', 3)
('key', 'sequence_1c_109_10_fv1c', 'value', 1)
('key', 'sequence_1k_339_10_fv1c', 'value', 21)
('key', 'sequence_3a_629_11_fv1c.npy', 'value', 8)
('key', 'sequence_1b_5_6_fv1c.npy', 'value', 6)
('key', 'sequence_3a_655_17_fv1c.npy', 'value', 22)
('key', 'sequence_1d_166_7_fv1c.npy', 'value', 0)
('key', 'sequence_1b_57_8_fv1c.npy', 'value', 3)
('key', 'sequence_1b_36_7_fv1c.npy', 'value', 8)
('key', 'sequence_1d_254_5_fv1c.npy', 'value', 3)
('key', 'sequence_3a_658_20_fv1c.npy', 'value', 22)
('key', 'sequence_1c_75_6_fv1c.npy', 'value', 4)
('key', 'sequence_1c_79_10_fv1c', 'value', 4)
('key', 'sequence_1b_39_10_fv1c', 'value', 8)
('key', 'sequence_1l_403_4_fv1c.npy', 'value', 22)
('key', 'sequence_2b_470_1_fv1c.npy', 'value', 8)
('key', 'sequence_1d_165_6_fv1c.npy', 'value', 0)
('key', 'sequence_3a_661_3_fv1c.npy', 'value', 21)
('key', 'sequence_2b_491_2_fv1c.npy', 'value', 3)
('key', 'sequence_1b_24_5_fv1c.npy', 'value', 0)
('key', 'sequence_2b_452_3_fv1c.npy', 'value', 4)
('key', 'sequence_1c_71_2_fv1c.npy', 'value', 4)
('key', 'sequence_2c_502_3_fv1c.npy', 'value', 9)
('key', 'sequence_1d_264_15_fv1c.npy', 'value', 3)
('key', 'sequence_3a_630_12_fv1c.npy', 'value', 13)
('key', 'sequence_1d_238_19_fv1c.npy', 'value', 1)
('key', 'sequence_2b_442_3_fv1c.npy', 'value', 6)
('key', 'sequence_1k_328_9_fv1c', 'value', 18)
('key', 'sequence_3a_569_10_fv1c', 'value', 20)
('key', 'sequence_1d_147_18_fv1c.npy', 'value', 4)
('key', 'sequence_1l_407_8_fv1c.npy', 'value', 22)
('key', 'sequence_3a_617_19_fv1c.npy', 'value', 26)
('key', 'sequence_3a_673_15_fv1c.npy', 'value', 12)
('key', 'sequence_1c_96_7_fv1c.npy', 'value', 8)
('key', 'sequence_1d_256_7_fv1c.npy', 'value', 3)
('key', 'sequence_3a_713_15_fv1c.npy', 'value', 24)
('key', 'sequence_1d_222_3_fv1c.npy', 'value', 1)
('key', 'sequence_1c_101_2_fv1c.npy', 'value', 1)
('key', 'sequence_2b_492_3_fv1c.npy', 'value', 3)
('key', 'sequence_3a_603_5_fv1c.npy', 'value', 15)
('key', 'sequence_3a_620_2_fv1c.npy', 'value', 8)
('key', 'sequence_1d_124_5_fv1c.npy', 'value', 6)
('key', 'sequence_1d_260_11_fv1c.npy', 'value', 3)
('key', 'sequence_3a_584_6_fv1c.npy', 'value', 25)
('key', 'sequence_1d_218_29_fv1c.npy', 'value', 8)
('key', 'sequence_1d_184_25_fv1c.npy', 'value', 0)
('key', 'sequence_2b_444_5_fv1c.npy', 'value', 6)
('key', 'sequence_1b_40_1_fv1c.npy', 'value', 1)
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('key', 'sequence_1c_119_10_fv1c', 'value', 3)
('key', 'sequence_2c_538_9_fv1c', 'value', 7)
('key', 'sequence_1k_282_3_fv1c.npy', 'value', 19)
('key', 'sequence_3a_582_4_fv1c.npy', 'value', 25)
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('key', 'sequence_3a_646_8_fv1c.npy', 'value', 18)
('key', 'sequence_2b_469_10_fv1c', 'value', 0)
('key', 'sequence_1c_108_9_fv1c', 'value', 1)
('key', 'sequence_2c_529_10_fv1c', 'value', 10)
('key', 'sequence_2c_506_7_fv1c.npy', 'value', 9)
('key', 'sequence_2c_507_8_fv1c.npy', 'value', 9)
('key', 'sequence_2c_517_8_fv1c.npy', 'value', 11)
('key', 'sequence_2b_468_9_fv1c', 'value', 0)
('key', 'sequence_3a_576_17_fv1c.npy', 'value', 20)
('key', 'sequence_3a_567_8_fv1c.npy', 'value', 19)
('key', 'sequence_1d_239_20_fv1c.npy', 'value', 1)
('key', 'sequence_1k_317_8_fv1c.npy', 'value', 8)
('key', 'sequence_1d_131_2_fv1c.npy', 'value', 4)
('key', 'sequence_1d_267_18_fv1c.npy', 'value', 3)
('key', 'sequence_1b_58_9_fv1c', 'value', 3)
('key', 'sequence_2b_472_3_fv1c.npy', 'value', 8)
('key', 'sequence_1c_64_5_fv1c.npy', 'value', 6)
('key', 'sequence_1c_70_1_fv1c.npy', 'value', 4)
('key', 'sequence_3a_669_11_fv1c.npy', 'value', 12)
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('key', 'sequence_1d_201_12_fv1c.npy', 'value', 8)
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('key', 'sequence_1d_186_27_fv1c.npy', 'value', 0)
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('key', 'sequence_2c_533_4_fv1c.npy', 'value', 7)
('key', 'sequence_1d_269_20_fv1c.npy', 'value', 3)
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('key', 'sequence_2b_465_6_fv1c.npy', 'value', 0)
('key', 'sequence_1d_137_8_fv1c.npy', 'value', 4)
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('key', 'sequence_1b_6_7_fv1c.npy', 'value', 6)
('key', 'sequence_1c_72_3_fv1c.npy', 'value', 4)
('key', 'sequence_1k_299_10_fv1c', 'value', 25)
('key', 'sequence_2c_558_9_fv1c', 'value', 5)
('key', 'sequence_3a_598_20_fv1c.npy', 'value', 17)
('key', 'sequence_1c_73_4_fv1c.npy', 'value', 4)
('key', 'sequence_1k_323_4_fv1c.npy', 'value', 18)
('key', 'sequence_3a_715_17_fv1c.npy', 'value', 24)
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'sequence_1k_286_7_fv1c', 'sequence_3a_561_2_fv1c', 'sequence_1k_282_3_fv1c', 'sequence_3a_565_6_fv1c', 'sequence_1k_281_2_fv1c', 'sequence_1k_283_4_fv1c', 'sequence_1k_280_1_fv1c', 'sequence_1k_287_8_fv1c', 'sequence_3a_573_14_fv1c', 'sequence_3a_570_11_fv1c', 'sequence_1k_285_6_fv1c', 'sequence_3a_572_13_fv1c', 'sequence_3a_562_3_fv1c', 'sequence_1k_288_9_fv1c', 'sequence_3a_568_9_fv1c', 'sequence_1k_289_10_fv1c', 'sequence_3a_573_14_fv1c.npy', 'sequence_1k_280_1_fv1c.npy', 'sequence_3a_564_5_fv1c.npy', 'sequence_1k_287_8_fv1c.npy', 'sequence_1k_282_3_fv1c.npy', 'sequence_3a_567_8_fv1c.npy', 'sequence_3a_562_3_fv1c.npy', 'sequence_3a_568_9_fv1c', 'sequence_1k_283_4_fv1c.npy', 'sequence_3a_561_2_fv1c.npy', 'sequence_1k_285_6_fv1c.npy', 'sequence_1k_286_7_fv1c.npy', 'sequence_1k_281_2_fv1c.npy', 'sequence_1k_289_10_fv1c', 'sequence_3a_572_13_fv1c.npy', 'sequence_1k_284_5_fv1c.npy', 'sequence_1k_288_9_fv1c', 'sequence_3a_570_11_fv1c.npy', 'sequence_3a_565_6_fv1c.npy'], '[  1.428  14.28   14.28    1.      1.     14.28   14.28    1.428]': ['sequence_1c_61_2_fv1c', 'sequence_1d_126_7_fv1c', 'sequence_1d_124_5_fv1c', 'sequence_1c_66_7_fv1c', 'sequence_2b_446_7_fv1c', 'sequence_2b_445_6_fv1c', 'sequence_1b_4_5_fv1c', 'sequence_1b_3_4_fv1c', 'sequence_1b_2_3_fv1c', 'sequence_1d_123_4_fv1c', 'sequence_1c_64_5_fv1c', 'sequence_1c_62_3_fv1c', 'sequence_1d_120_1_fv1c', 'sequence_1d_125_6_fv1c', 'sequence_1b_1_2_fv1c', 'sequence_1b_0_1_fv1c', 'sequence_2b_442_3_fv1c', 'sequence_2b_441_2_fv1c', 'sequence_1c_65_6_fv1c', 'sequence_1d_121_2_fv1c', 'sequence_1c_60_1_fv1c', 'sequence_2b_440_1_fv1c', 'sequence_1c_63_4_fv1c', 'sequence_1b_7_8_fv1c', 'sequence_1d_122_3_fv1c', 'sequence_2b_444_5_fv1c', 'sequence_2b_447_8_fv1c', 'sequence_2b_443_4_fv1c', 'sequence_1d_127_8_fv1c', 'sequence_1b_6_7_fv1c', 'sequence_1b_5_6_fv1c', 'sequence_1c_67_8_fv1c', 'sequence_1b_8_9_fv1c', 'sequence_1d_129_10_fv1c', 'sequence_1c_69_10_fv1c', 'sequence_1d_128_9_fv1c', 'sequence_1b_9_10_fv1c', 'sequence_2b_449_10_fv1c', 'sequence_1c_68_9_fv1c', 'sequence_2b_448_9_fv1c', 'sequence_1d_127_8_fv1c.npy', 'sequence_2b_447_8_fv1c.npy', 'sequence_1d_125_6_fv1c.npy', 'sequence_1d_120_1_fv1c.npy', 'sequence_1d_126_7_fv1c.npy', 'sequence_1b_1_2_fv1c.npy', 'sequence_1d_123_4_fv1c.npy', 'sequence_1b_0_1_fv1c.npy', 'sequence_1b_5_6_fv1c.npy', 'sequence_2b_442_3_fv1c.npy', 'sequence_1d_124_5_fv1c.npy', 'sequence_2b_444_5_fv1c.npy', 'sequence_1c_64_5_fv1c.npy', 'sequence_1c_67_8_fv1c.npy', 'sequence_1b_6_7_fv1c.npy', 'sequence_1b_3_4_fv1c.npy', 'sequence_2b_445_6_fv1c.npy', 'sequence_1c_66_7_fv1c.npy', 'sequence_1b_4_5_fv1c.npy', 'sequence_2b_440_1_fv1c.npy', 'sequence_2b_443_4_fv1c.npy', 'sequence_1b_9_10_fv1c', 'sequence_1b_2_3_fv1c.npy', 'sequence_1b_8_9_fv1c', 'sequence_1d_122_3_fv1c.npy', 'sequence_1d_129_10_fv1c', 'sequence_1d_121_2_fv1c.npy', 'sequence_1c_63_4_fv1c.npy', 'sequence_1c_62_3_fv1c.npy', 'sequence_1c_65_6_fv1c.npy', 'sequence_1d_128_9_fv1c', 'sequence_1b_7_8_fv1c.npy', 'sequence_2b_449_10_fv1c', 'sequence_1c_60_1_fv1c.npy', 'sequence_2b_441_2_fv1c.npy', 'sequence_1c_61_2_fv1c.npy', 'sequence_1c_68_9_fv1c', 'sequence_1c_69_10_fv1c', 'sequence_2b_446_7_fv1c.npy', 'sequence_2b_448_9_fv1c'], '[  1.428  14.28   14.28    0.75    0.75   14.28   14.28    1.428   1.428\n  14.28   14.28    1.25    1.25   14.28   14.28    1.428   1.428  14.28\n  14.28    1.75    1.75   14.28   14.28    1.428]': ['sequence_1l_392_3_fv1c', 'sequence_1l_395_6_fv1c', 'sequence_3a_632_14_fv1c', 'sequence_3a_633_15_fv1c', 'sequence_3a_630_12_fv1c', 'sequence_1l_396_7_fv1c', 'sequence_3a_636_18_fv1c', 'sequence_3a_634_16_fv1c', 'sequence_1l_394_5_fv1c', 'sequence_3a_631_13_fv1c', 'sequence_1l_393_4_fv1c', 'sequence_3a_637_19_fv1c', 'sequence_1l_397_8_fv1c', 'sequence_3a_635_17_fv1c', 'sequence_1l_391_2_fv1c', 'sequence_3a_638_20_fv1c', 'sequence_1l_390_1_fv1c', 'sequence_3a_628_10_fv1c', 'sequence_1l_398_9_fv1c', 'sequence_1l_399_10_fv1c', 'sequence_3a_628_10_fv1c', 'sequence_3a_637_19_fv1c.npy', 'sequence_1l_390_1_fv1c.npy', 'sequence_3a_632_14_fv1c.npy', 'sequence_1l_394_5_fv1c.npy', 'sequence_3a_638_20_fv1c.npy', 'sequence_3a_630_12_fv1c.npy', 'sequence_1l_391_2_fv1c.npy', 'sequence_3a_636_18_fv1c.npy', 'sequence_1l_399_10_fv1c', 'sequence_1l_396_7_fv1c.npy', 'sequence_3a_635_17_fv1c.npy', 'sequence_1l_397_8_fv1c.npy', 'sequence_3a_631_13_fv1c.npy', 'sequence_1l_395_6_fv1c.npy', 'sequence_1l_392_3_fv1c.npy', 'sequence_1l_393_4_fv1c.npy', 'sequence_1l_398_9_fv1c', 'sequence_3a_634_16_fv1c.npy', 'sequence_3a_633_15_fv1c.npy'], '[  1.428  14.28   14.28    0.75    0.75   16.28   16.28    1.428   1.428\n  12.28   12.28    1.428   1.428  16.28   16.28    1.428   1.428  14.28\n  14.28    0.75    0.75   16.28   16.28    1.428]': ['sequence_3a_645_7_fv1c', 'sequence_1k_320_1_fv1c', 'sequence_3a_643_5_fv1c', 'sequence_3a_644_6_fv1c', 'sequence_1k_326_7_fv1c', 'sequence_1k_323_4_fv1c', 'sequence_3a_641_3_fv1c', 'sequence_1k_327_8_fv1c', 'sequence_1k_325_6_fv1c', 'sequence_3a_640_2_fv1c', 'sequence_1k_322_3_fv1c', 'sequence_1k_321_2_fv1c', 'sequence_3a_642_4_fv1c', 'sequence_3a_639_1_fv1c', 'sequence_1k_324_5_fv1c', 'sequence_3a_646_8_fv1c', 'sequence_1k_329_10_fv1c', 'sequence_3a_647_9_fv1c', 'sequence_3a_648_10_fv1c', 'sequence_1k_328_9_fv1c', 'sequence_3a_647_9_fv1c', 'sequence_3a_648_10_fv1c', 'sequence_1k_326_7_fv1c.npy', 'sequence_1k_328_9_fv1c', 'sequence_3a_646_8_fv1c.npy', 'sequence_3a_642_4_fv1c.npy', 'sequence_1k_323_4_fv1c.npy', 'sequence_3a_643_5_fv1c.npy', 'sequence_1k_321_2_fv1c.npy', 'sequence_3a_645_7_fv1c.npy', 'sequence_1k_327_8_fv1c.npy', 'sequence_1k_320_1_fv1c.npy', 'sequence_3a_641_3_fv1c.npy', 'sequence_3a_640_2_fv1c.npy', 'sequence_1k_325_6_fv1c.npy', 'sequence_3a_644_6_fv1c.npy', 'sequence_1k_322_3_fv1c.npy', 'sequence_1k_329_10_fv1c', 'sequence_3a_639_1_fv1c.npy', 'sequence_1k_324_5_fv1c.npy'], '[  1.428  14.28   14.28    0.75    0.75   14.28   14.28    1.428]': ['sequence_1k_315_6_fv1c', 'sequence_1d_207_18_fv1c', 'sequence_3a_623_5_fv1c', 'sequence_3a_620_2_fv1c', 'sequence_3a_626_8_fv1c', 'sequence_1k_317_8_fv1c', 'sequence_1c_96_7_fv1c', 'sequence_2b_472_3_fv1c', 'sequence_1d_216_27_fv1c', 'sequence_1c_94_5_fv1c', 'sequence_1b_32_3_fv1c', 'sequence_1k_316_7_fv1c', 'sequence_1d_211_22_fv1c', 'sequence_1c_91_2_fv1c', 'sequence_2b_477_8_fv1c', 'sequence_2b_474_5_fv1c', 'sequence_1d_194_5_fv1c', 'sequence_1c_95_6_fv1c', 'sequence_1d_201_12_fv1c', 'sequence_3a_624_6_fv1c', 'sequence_1d_204_15_fv1c', 'sequence_1d_192_3_fv1c', 'sequence_1c_93_4_fv1c', 'sequence_1d_212_23_fv1c', 'sequence_1d_219_30_fv1c', 'sequence_1c_92_3_fv1c', 'sequence_3a_622_4_fv1c', 'sequence_1d_206_17_fv1c', 'sequence_2b_470_1_fv1c', 'sequence_2b_473_4_fv1c', 'sequence_2b_476_7_fv1c', 'sequence_1d_209_20_fv1c', 'sequence_1k_311_2_fv1c', 'sequence_1d_200_11_fv1c', 'sequence_1c_97_8_fv1c', 'sequence_1k_313_4_fv1c', 'sequence_1d_217_28_fv1c', 'sequence_1d_191_2_fv1c', 'sequence_1d_213_24_fv1c', 'sequence_1b_33_4_fv1c', 'sequence_3a_619_1_fv1c', 'sequence_2b_471_2_fv1c', 'sequence_1b_36_7_fv1c', 'sequence_1d_214_25_fv1c', 'sequence_1d_197_8_fv1c', 'sequence_1k_314_5_fv1c', 'sequence_1d_202_13_fv1c', 'sequence_1d_193_4_fv1c', 'sequence_3a_625_7_fv1c', 'sequence_1d_195_6_fv1c', 'sequence_1k_312_3_fv1c', 'sequence_1d_218_29_fv1c', 'sequence_1d_196_7_fv1c', 'sequence_1d_208_19_fv1c', 'sequence_1b_30_1_fv1c', 'sequence_3a_621_3_fv1c', 'sequence_3a_629_11_fv1c', 'sequence_1d_210_21_fv1c', 'sequence_1d_190_1_fv1c', 'sequence_1d_215_26_fv1c', 'sequence_1b_37_8_fv1c', 'sequence_1d_205_16_fv1c', 'sequence_1b_31_2_fv1c', 'sequence_1b_35_6_fv1c', 'sequence_1d_203_14_fv1c', 'sequence_2b_475_6_fv1c', 'sequence_1k_310_1_fv1c', 'sequence_1b_34_5_fv1c', 'sequence_1c_90_1_fv1c', 'sequence_1k_318_9_fv1c', 'sequence_1c_98_9_fv1c', 'sequence_1d_199_10_fv1c', 'sequence_3a_627_9_fv1c', 'sequence_2b_479_10_fv1c', 'sequence_1c_99_10_fv1c', 'sequence_2b_478_9_fv1c', 'sequence_1b_39_10_fv1c', 'sequence_1b_38_9_fv1c', 'sequence_1d_198_9_fv1c', 'sequence_1k_319_10_fv1c', 'sequence_1c_92_3_fv1c.npy', 'sequence_1d_200_11_fv1c.npy', 'sequence_1d_212_23_fv1c.npy', 'sequence_1d_203_14_fv1c.npy', 'sequence_1d_192_3_fv1c.npy', 'sequence_2b_477_8_fv1c.npy', 'sequence_1k_316_7_fv1c.npy', 'sequence_1d_204_15_fv1c.npy', 'sequence_1d_193_4_fv1c.npy', 'sequence_3a_621_3_fv1c.npy', 'sequence_1d_209_20_fv1c.npy', 'sequence_1d_196_7_fv1c.npy', 'sequence_2b_476_7_fv1c.npy', 'sequence_2b_475_6_fv1c.npy', 'sequence_1d_190_1_fv1c.npy', 'sequence_2b_478_9_fv1c', 'sequence_1b_31_2_fv1c.npy', 'sequence_3a_629_11_fv1c.npy', 'sequence_1b_36_7_fv1c.npy', 'sequence_1b_39_10_fv1c', 'sequence_2b_470_1_fv1c.npy', 'sequence_1c_96_7_fv1c.npy', 'sequence_3a_620_2_fv1c.npy', 'sequence_1d_218_29_fv1c.npy', 'sequence_1k_317_8_fv1c.npy', 'sequence_2b_472_3_fv1c.npy', 'sequence_1d_201_12_fv1c.npy', 'sequence_1d_191_2_fv1c.npy', 'sequence_1d_217_28_fv1c.npy', 'sequence_1c_91_2_fv1c.npy', 'sequence_1c_99_10_fv1c', 'sequence_2b_473_4_fv1c.npy', 'sequence_1k_310_1_fv1c.npy', 'sequence_1k_313_4_fv1c.npy', 'sequence_1d_210_21_fv1c.npy', 'sequence_1k_312_3_fv1c.npy', 'sequence_1b_37_8_fv1c.npy', 'sequence_1b_38_9_fv1c', 'sequence_3a_624_6_fv1c.npy', 'sequence_1c_93_4_fv1c.npy', 'sequence_3a_622_4_fv1c.npy', 'sequence_1d_207_18_fv1c.npy', 'sequence_1d_197_8_fv1c.npy', 'sequence_1d_202_13_fv1c.npy', 'sequence_1k_314_5_fv1c.npy', 'sequence_3a_625_7_fv1c.npy', 'sequence_1c_95_6_fv1c.npy', 'sequence_1d_199_10_fv1c', 'sequence_1d_194_5_fv1c.npy', 'sequence_1d_214_25_fv1c.npy', 'sequence_1d_208_19_fv1c.npy', 'sequence_3a_627_9_fv1c', 'sequence_1b_34_5_fv1c.npy', 'sequence_2b_479_10_fv1c', 'sequence_1d_205_16_fv1c.npy', 'sequence_1k_311_2_fv1c.npy', 'sequence_1d_219_30_fv1c.npy', 'sequence_1k_315_6_fv1c.npy', 'sequence_3a_619_1_fv1c.npy', 'sequence_1c_97_8_fv1c.npy', 'sequence_1d_213_24_fv1c.npy', 'sequence_3a_626_8_fv1c.npy', 'sequence_1b_32_3_fv1c.npy', 'sequence_1d_215_26_fv1c.npy', 'sequence_2b_474_5_fv1c.npy', 'sequence_1k_318_9_fv1c', 'sequence_1c_98_9_fv1c', 'sequence_3a_623_5_fv1c.npy', 'sequence_1b_35_6_fv1c.npy', 'sequence_2b_471_2_fv1c.npy', 'sequence_1d_195_6_fv1c.npy', 'sequence_1b_33_4_fv1c.npy', 'sequence_1b_30_1_fv1c.npy', 'sequence_1c_90_1_fv1c.npy', 'sequence_1d_216_27_fv1c.npy', 'sequence_1d_198_9_fv1c', 'sequence_1d_206_17_fv1c.npy', 'sequence_1d_211_22_fv1c.npy', 'sequence_1c_94_5_fv1c.npy', 'sequence_1k_319_10_fv1c'], '[  1.428  14.28   14.28    0.5     0.5    16.28   16.28    1.428   1.428\n  12.28   12.28    1.428   1.428  16.28   16.28    1.428   1.428  14.28\n  14.28    1.428   1.428  14.28   14.28    1.428]': ['sequence_3a_709_11_fv1c', 'sequence_3a_714_16_fv1c', 'sequence_1l_430_1_fv1c', 'sequence_3a_718_20_fv1c', 'sequence_3a_713_15_fv1c', 'sequence_1l_431_2_fv1c', 'sequence_1l_436_7_fv1c', 'sequence_3a_717_19_fv1c', 'sequence_3a_712_14_fv1c', 'sequence_1l_433_4_fv1c', 'sequence_1l_432_3_fv1c', 'sequence_3a_710_12_fv1c', 'sequence_3a_711_13_fv1c', 'sequence_1l_437_8_fv1c', 'sequence_3a_716_18_fv1c', 'sequence_3a_715_17_fv1c', 'sequence_1l_434_5_fv1c', 'sequence_1l_435_6_fv1c', 'sequence_1l_438_9_fv1c', 'sequence_1l_439_10_fv1c', 'sequence_1l_433_4_fv1c.npy', 'sequence_1l_436_7_fv1c.npy', 'sequence_3a_717_19_fv1c.npy', 'sequence_3a_713_15_fv1c.npy', 'sequence_3a_715_17_fv1c.npy', 'sequence_3a_709_11_fv1c.npy', 'sequence_3a_710_12_fv1c.npy', 'sequence_3a_718_20_fv1c.npy', 'sequence_1l_438_9_fv1c', 'sequence_1l_430_1_fv1c.npy', 'sequence_3a_716_18_fv1c.npy', 'sequence_3a_714_16_fv1c.npy', 'sequence_3a_711_13_fv1c.npy', 'sequence_1l_435_6_fv1c.npy', 'sequence_1l_439_10_fv1c', 'sequence_1l_434_5_fv1c.npy', 'sequence_1l_437_8_fv1c.npy', 'sequence_3a_712_14_fv1c.npy', 'sequence_1l_432_3_fv1c.npy', 'sequence_1l_431_2_fv1c.npy'], '[  1.028  11.28   11.28    1.      1.     10.28   10.28    1.028]': ['sequence_2c_503_4_fv1c', 'sequence_2c_500_1_fv1c', 'sequence_2c_507_8_fv1c', 'sequence_2c_501_2_fv1c', 'sequence_2c_502_3_fv1c', 'sequence_2c_504_5_fv1c', 'sequence_2c_505_6_fv1c', 'sequence_2c_506_7_fv1c', 'sequence_2c_509_10_fv1c', 'sequence_2c_508_9_fv1c', 'sequence_2c_502_3_fv1c.npy', 'sequence_2c_506_7_fv1c.npy', 'sequence_2c_507_8_fv1c.npy', 'sequence_2c_508_9_fv1c', 'sequence_2c_503_4_fv1c.npy', 'sequence_2c_501_2_fv1c.npy', 'sequence_2c_505_6_fv1c.npy', 'sequence_2c_509_10_fv1c', 'sequence_2c_504_5_fv1c.npy', 'sequence_2c_500_1_fv1c.npy']}

In [9]:

    

print(len(unique_load_sequences))
print(unique_load_sequences[0].shape)
for item in unique_load_sequences:
    print(item.shape)


    

    




27
(4, 2)
(4, 2)
(4, 2)
(4, 2)
(4, 2)
(4, 2)
(4, 2)
(4, 2)
(4, 2)
(4, 2)
(4, 2)
(4, 2)
(4, 2)
(12, 2)
(12, 2)
(12, 2)
(12, 2)
(12, 2)
(12, 2)
(12, 2)
(12, 2)
(12, 2)
(12, 2)
(12, 2)
(12, 2)
(12, 2)
(12, 2)
(12, 2)

In [10]:

    

flattened_loadseq_list = []
for item in unique_load_sequences:
    flattened_loadseq_list.append(np.ndarray.flatten(item))
print(flattened_loadseq_list[0].shape,flattened_loadseq_list[-1].shape)


    

    




((8,), (24,))

In [11]:

    

sigma1 = flattened_loadseq_list[0]
sigma2 = flattened_loadseq_list[-1]
print(np.mean(sigma1), np.median(sigma1), np.std(sigma1))
print(flattened_loadseq_list[0])


    

    




(7.9344999999999999, 8.0150000000000006, 6.3465211533563801)
[  1.428  14.28   14.28    1.75    1.75   14.28   14.28    1.428]

In [12]:

    

#flattened_loadseq_list.sort() #doesn't work
#try to pad
flattened_eq_len_loadseq_list = []
long_shape = flattened_loadseq_list[-1].shape
for item in flattened_loadseq_list:
    if item.shape == (8,):
        temp_padded_array = np.zeros(shape=long_shape)
        temp_padded_array[0:8]=item
        temp_padded_array[8:16]=item
        temp_padded_array[16:24]=item
        flattened_eq_len_loadseq_list.append(temp_padded_array)
    if item.shape == (24,):
        flattened_eq_len_loadseq_list.append(item)

# for item in flattened_eq_len_loadseq_list:
#     print(item.shape)
any(item.shape != (24,) for item in flattened_eq_len_loadseq_list)


    

    
Out[12]:





False

In [13]:

    

print(str(flattened_eq_len_loadseq_list[0])) # you can cast this as a string. yay.


    

    




[  1.428  14.28   14.28    1.75    1.75   14.28   14.28    1.428   1.428
  14.28   14.28    1.75    1.75   14.28   14.28    1.428   1.428  14.28
  14.28    1.75    1.75   14.28   14.28    1.428]

In [14]:

    

posn=0
for item in flattened_eq_len_loadseq_list:
    print(posn,np.mean(item), np.median(item), np.std(item))
    posn+=1


    

    




(0, 7.9344999999999999, 8.0150000000000006, 6.3465211533563801)
(1, 8.1844999999999999, 7.8540000000000001, 7.1346745370759557)
(2, 8.0845000000000002, 7.6539999999999999, 7.230828496790668)
(3, 7.8539999999999992, 6.8540000000000001, 6.5797778077986795)
(4, 7.8094999999999999, 7.8540000000000001, 6.4708060355723847)
(5, 7.9040000000000008, 6.6539999999999999, 7.0996039326148335)
(6, 7.7469999999999999, 7.8540000000000001, 6.5347522523811099)
(7, 5.5844999999999994, 5.6539999999999999, 4.6965285850295855)
(8, 7.6844999999999999, 7.8540000000000001, 6.5998546006711383)
(9, 5.8969999999999994, 5.6539999999999999, 4.8957927856476937)
(10, 7.0844999999999994, 6.0149999999999997, 5.9689932777646844)
(11, 7.2094999999999994, 7.2649999999999997, 6.0710073916937377)
(12, 7.9076666666666666, 6.8540000000000001, 6.7794900906254663)
(13, 7.8094999999999999, 8.0150000000000006, 6.4740248236059967)
(14, 7.9641666666666664, 6.8540000000000001, 6.722185976715874)
(15, 7.8511666666666668, 8.0150000000000006, 6.4331539417993788)
(16, 7.9969999999999999, 7.8540000000000001, 6.9877502578917818)
(17, 7.4711666666666661, 6.4640000000000004, 6.2585519868594375)
(18, 8.0743333333333336, 6.8540000000000001, 6.9563749579472463)
(19, 7.7678333333333329, 7.8540000000000001, 6.5135732363700001)
(20, 7.8019999999999996, 8.0, 5.1613007404464755)
(21, 7.8539999999999992, 6.8540000000000001, 6.5289209930380787)
(22, 7.9344999999999999, 8.0150000000000006, 6.7048487989414545)
(23, 7.9433333333333325, 7.8540000000000001, 6.6952581893622458)
(24, 7.9433333333333325, 6.8540000000000001, 6.7448609243548452)
(25, 7.8511666666666668, 8.0150000000000006, 6.4299146940081728)
(26, 7.8303333333333329, 8.0150000000000006, 6.4516374579550639)

In [15]:

    

print(flattened_eq_len_loadseq_list[21])


    

    




[  1.428  12.28   12.28    1.428   1.428  16.28   16.28    1.428   1.428
  14.28   14.28    1.428   1.428  14.28   14.28    1.428   1.428  12.28
  12.28    1.428   1.428  16.28   16.28    1.428]

In [16]:

    

#create a dataframe that correlates each sequence (in both sets) to the equal-lengthened array
#fl_llpk_combined_df = pd.DataFrame.from_dict(filename_loadseq_dict_loadlistposkey,orient='index')
#fl_sik_combined_df = pd.DataFrame.from_dict(filename_loadseq_dict_seqidkey,orient='index')
filename_loadseq_dict_seqidkey_equallength = {}
print(filename_loadseq_dict_seqidkey.keys())
print(len(filename_loadseq_dict_seqidkey.keys()))


    

    




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'sequence_1d_192_3_fv1c.npy', 'sequence_1k_280_1_fv1c.npy', 'sequence_3a_564_5_fv1c.npy', 'sequence_2b_463_4_fv1c.npy', 'sequence_2b_480_1_fv1c.npy', 'sequence_2b_466_7_fv1c.npy', 'sequence_2c_548_9_fv1c', 'sequence_1l_436_7_fv1c.npy', 'sequence_3a_682_4_fv1c.npy', 'sequence_2b_447_8_fv1c.npy', 'sequence_2b_477_8_fv1c.npy', 'sequence_2c_541_2_fv1c.npy', 'sequence_3a_701_3_fv1c.npy', 'sequence_3a_592_14_fv1c.npy', 'sequence_1b_12_3_fv1c.npy', 'sequence_2c_536_7_fv1c.npy', 'sequence_1k_316_7_fv1c.npy', 'sequence_3a_649_11_fv1c.npy', 'sequence_1d_125_6_fv1c.npy', 'sequence_1d_204_15_fv1c.npy', 'sequence_3a_602_4_fv1c.npy', 'sequence_1c_74_5_fv1c.npy', 'sequence_1b_56_7_fv1c.npy', 'sequence_1l_377_8_fv1c.npy', 'sequence_1d_252_3_fv1c.npy', 'sequence_2c_550_1_fv1c.npy', 'sequence_1k_307_8_fv1c.npy', 'sequence_1d_183_24_fv1c.npy', 'sequence_1k_287_8_fv1c.npy', 'sequence_1d_171_12_fv1c.npy', 'sequence_1k_348_9_fv1c', 'sequence_2c_514_5_fv1c.npy', 'sequence_1c_85_6_fv1c.npy', 'sequence_1k_349_10_fv1c', 'sequence_1l_378_9_fv1c', 'sequence_3a_693_15_fv1c.npy', 'sequence_1d_193_4_fv1c.npy', 'sequence_1d_120_1_fv1c.npy', 'sequence_1d_146_17_fv1c.npy', 'sequence_1d_178_19_fv1c.npy', 'sequence_2c_554_5_fv1c.npy', 'sequence_2c_518_9_fv1c', 'sequence_1l_410_1_fv1c.npy', 'sequence_1c_87_8_fv1c.npy', 'sequence_3a_621_3_fv1c.npy', 'sequence_1d_274_25_fv1c.npy', 'sequence_1b_21_2_fv1c.npy', 'sequence_1d_156_27_fv1c.npy', 'sequence_1d_209_20_fv1c.npy', 'sequence_1k_303_4_fv1c.npy', 'sequence_1d_228_9_fv1c', 'sequence_3a_703_5_fv1c.npy', 'sequence_1d_196_7_fv1c.npy', 'sequence_1l_402_3_fv1c.npy', 'sequence_3a_717_19_fv1c.npy', 'sequence_1d_126_7_fv1c.npy', 'sequence_1l_376_7_fv1c.npy', 'sequence_1l_413_4_fv1c.npy', 'sequence_1c_118_9_fv1c', 'sequence_1l_394_5_fv1c.npy', 'sequence_1b_1_2_fv1c.npy', 'sequence_1k_305_6_fv1c.npy', 'sequence_1d_176_17_fv1c.npy', 'sequence_1d_123_4_fv1c.npy', 'sequence_1k_358_9_fv1c', 'sequence_3a_647_9_fv1c', 'sequence_1d_226_7_fv1c.npy', 'sequence_1b_0_1_fv1c.npy', 'sequence_2b_476_7_fv1c.npy', 'sequence_1c_110_1_fv1c.npy', 'sequence_2b_475_6_fv1c.npy', 'sequence_1k_334_5_fv1c.npy', 'sequence_1d_232_13_fv1c.npy', 'sequence_1d_246_27_fv1c.npy', 'sequence_3a_578_19_fv1c.npy', 'sequence_3a_638_20_fv1c.npy', 'sequence_1d_140_11_fv1c.npy', 'sequence_1d_262_13_fv1c.npy', 'sequence_2b_464_5_fv1c.npy', 'sequence_1b_54_5_fv1c.npy', 'sequence_2c_534_5_fv1c.npy', 'sequence_1d_190_1_fv1c.npy', 'sequence_1k_337_8_fv1c.npy', 'sequence_3a_586_8_fv1c.npy', 'sequence_3a_648_10_fv1c', 'sequence_1d_144_15_fv1c.npy', 'sequence_1k_326_7_fv1c.npy', 'sequence_1l_388_9_fv1c', 'sequence_3a_700_2_fv1c.npy', 'sequence_1k_331_2_fv1c.npy', 'sequence_2b_478_9_fv1c', 'sequence_1l_361_2_fv1c.npy', 'sequence_1c_103_4_fv1c.npy', 'sequence_2b_481_2_fv1c.npy', 'sequence_1b_31_2_fv1c.npy', 'sequence_1c_83_4_fv1c.npy', 'sequence_1l_409_10_fv1c', 'sequence_1c_113_4_fv1c.npy', 'sequence_2b_459_10_fv1c', 'sequence_2c_520_1_fv1c.npy', 'sequence_2c_519_10_fv1c', 'sequence_1d_181_22_fv1c.npy', 'sequence_1d_276_27_fv1c.npy', 'sequence_1c_109_10_fv1c', 'sequence_1k_339_10_fv1c', 'sequence_3a_629_11_fv1c.npy', 'sequence_1b_5_6_fv1c.npy', 'sequence_3a_655_17_fv1c.npy', 'sequence_1d_166_7_fv1c.npy', 'sequence_1b_57_8_fv1c.npy', 'sequence_1b_36_7_fv1c.npy', 'sequence_1d_254_5_fv1c.npy', 'sequence_3a_658_20_fv1c.npy', 'sequence_1c_75_6_fv1c.npy', 'sequence_1c_79_10_fv1c', 'sequence_1b_39_10_fv1c', 'sequence_1l_403_4_fv1c.npy', 'sequence_2b_470_1_fv1c.npy', 'sequence_1d_165_6_fv1c.npy', 'sequence_3a_661_3_fv1c.npy', 'sequence_2b_491_2_fv1c.npy', 'sequence_1b_24_5_fv1c.npy', 'sequence_2b_452_3_fv1c.npy', 'sequence_1c_71_2_fv1c.npy', 'sequence_2c_502_3_fv1c.npy', 'sequence_1d_264_15_fv1c.npy', 'sequence_3a_630_12_fv1c.npy', 'sequence_1d_238_19_fv1c.npy', 'sequence_2b_442_3_fv1c.npy', 'sequence_1k_328_9_fv1c', 'sequence_3a_569_10_fv1c', 'sequence_1d_147_18_fv1c.npy', 'sequence_1l_407_8_fv1c.npy', 'sequence_3a_617_19_fv1c.npy', 'sequence_3a_673_15_fv1c.npy', 'sequence_1c_96_7_fv1c.npy', 'sequence_1d_256_7_fv1c.npy', 'sequence_3a_713_15_fv1c.npy', 'sequence_1d_222_3_fv1c.npy', 'sequence_1c_101_2_fv1c.npy', 'sequence_2b_492_3_fv1c.npy', 'sequence_3a_603_5_fv1c.npy', 'sequence_3a_620_2_fv1c.npy', 'sequence_1d_124_5_fv1c.npy', 'sequence_1d_260_11_fv1c.npy', 'sequence_3a_584_6_fv1c.npy', 'sequence_1d_218_29_fv1c.npy', 'sequence_1d_184_25_fv1c.npy', 'sequence_2b_444_5_fv1c.npy', 'sequence_1b_40_1_fv1c.npy', 'sequence_1c_114_5_fv1c.npy', 'sequence_1c_119_10_fv1c', 'sequence_2c_538_9_fv1c', 'sequence_1k_282_3_fv1c.npy', 'sequence_3a_582_4_fv1c.npy', 'sequence_1d_247_28_fv1c.npy', 'sequence_3a_646_8_fv1c.npy', 'sequence_2b_469_10_fv1c', 'sequence_1c_108_9_fv1c', 'sequence_2c_529_10_fv1c', 'sequence_2c_506_7_fv1c.npy', 'sequence_2c_507_8_fv1c.npy', 'sequence_2c_517_8_fv1c.npy', 'sequence_2b_468_9_fv1c', 'sequence_3a_576_17_fv1c.npy', 'sequence_3a_567_8_fv1c.npy', 'sequence_1d_239_20_fv1c.npy', 'sequence_1k_317_8_fv1c.npy', 'sequence_1d_131_2_fv1c.npy', 'sequence_1d_267_18_fv1c.npy', 'sequence_1b_58_9_fv1c', 'sequence_2b_472_3_fv1c.npy', 'sequence_1c_64_5_fv1c.npy', 'sequence_1c_70_1_fv1c.npy', 'sequence_3a_669_11_fv1c.npy', 'sequence_1b_47_8_fv1c.npy', 'sequence_1c_67_8_fv1c.npy', 'sequence_3a_590_12_fv1c.npy', 'sequence_3a_562_3_fv1c.npy', 'sequence_1c_106_7_fv1c.npy', 'sequence_3a_650_12_fv1c.npy', 'sequence_1l_405_6_fv1c.npy', 'sequence_3a_668_10_fv1c', 'sequence_1b_48_9_fv1c', 'sequence_3a_568_9_fv1c', 'sequence_1d_157_28_fv1c.npy', 'sequence_1d_249_30_fv1c.npy', 'sequence_2b_461_2_fv1c.npy', 'sequence_3a_672_14_fv1c.npy', 'sequence_1d_201_12_fv1c.npy', 'sequence_1l_421_2_fv1c.npy', 'sequence_1l_414_5_fv1c.npy', 'sequence_1d_186_27_fv1c.npy', 'sequence_3a_642_4_fv1c.npy', 'sequence_1k_357_8_fv1c.npy', 'sequence_2c_533_4_fv1c.npy', 'sequence_1d_269_20_fv1c.npy', 'sequence_1l_418_9_fv1c', 'sequence_1k_335_6_fv1c.npy', 'sequence_1d_163_4_fv1c.npy', 'sequence_2b_465_6_fv1c.npy', 'sequence_1d_137_8_fv1c.npy', 'sequence_1d_273_24_fv1c.npy', 'sequence_1b_6_7_fv1c.npy', 'sequence_1c_72_3_fv1c.npy', 'sequence_1k_299_10_fv1c', 'sequence_2c_558_9_fv1c', 'sequence_3a_598_20_fv1c.npy', 'sequence_1c_73_4_fv1c.npy', 'sequence_1k_323_4_fv1c.npy', 'sequence_3a_715_17_fv1c.npy', 'sequence_1l_391_2_fv1c.npy', 'sequence_1k_283_4_fv1c.npy', 'sequence_3a_685_7_fv1c.npy', 'sequence_1d_191_2_fv1c.npy', 'sequence_3a_688_10_fv1c', 'sequence_3a_676_18_fv1c.npy', 'sequence_2b_456_7_fv1c.npy', 'sequence_1d_268_19_fv1c.npy', 'sequence_1d_235_16_fv1c.npy', 'sequence_3a_561_2_fv1c.npy', 'sequence_2c_508_9_fv1c', 'sequence_2c_511_2_fv1c.npy', 'sequence_3a_656_18_fv1c.npy', 'sequence_1k_338_9_fv1c', 'sequence_2c_503_4_fv1c.npy', 'sequence_1d_220_1_fv1c.npy', 'sequence_3a_681_3_fv1c.npy', 'sequence_3a_636_18_fv1c.npy', 'sequence_1b_3_4_fv1c.npy', 'sequence_1b_55_6_fv1c.npy', 'sequence_3a_695_17_fv1c.npy', 'sequence_1l_425_6_fv1c.npy', 'sequence_1b_44_5_fv1c.npy', 'sequence_2b_445_6_fv1c.npy', 'sequence_1l_400_1_fv1c.npy', 'sequence_3a_652_14_fv1c.npy', 'sequence_1d_160_1_fv1c.npy', 'sequence_1d_243_24_fv1c.npy', 'sequence_3a_643_5_fv1c.npy', 'sequence_1d_277_28_fv1c.npy', 'sequence_1d_134_5_fv1c.npy', 'sequence_1d_217_28_fv1c.npy', 'sequence_1b_18_9_fv1c', 'sequence_3a_680_2_fv1c.npy', 'sequence_3a_709_11_fv1c.npy', 'sequence_2c_516_7_fv1c.npy', 'sequence_1c_78_9_fv1c', 'sequence_1c_117_8_fv1c.npy', 'sequence_2b_490_1_fv1c.npy', 'sequence_1d_236_17_fv1c.npy', 'sequence_1d_188_29_fv1c.npy', 'sequence_1c_66_7_fv1c.npy', 'sequence_1d_265_16_fv1c.npy', 'sequence_1c_91_2_fv1c.npy', 'sequence_1l_429_10_fv1c', 'sequence_2b_454_5_fv1c.npy', 'sequence_2c_527_8_fv1c.npy', 'sequence_1b_4_5_fv1c.npy', 'sequence_2b_458_9_fv1c', 'sequence_1c_112_3_fv1c.npy', 'sequence_3a_589_11_fv1c.npy', 'sequence_1c_99_10_fv1c', 'sequence_3a_662_4_fv1c.npy', 'sequence_2c_513_4_fv1c.npy', 'sequence_1k_352_3_fv1c.npy', 'sequence_3a_660_2_fv1c.npy', 'sequence_3a_683_5_fv1c.npy', 'sequence_2b_440_1_fv1c.npy', 'sequence_1c_116_7_fv1c.npy', 'sequence_1l_399_10_fv1c', 'sequence_1d_275_26_fv1c.npy', 'sequence_2b_443_4_fv1c.npy', 'sequence_1d_182_23_fv1c.npy', 'sequence_2b_489_10_fv1c', 'sequence_3a_659_1_fv1c.npy', 'sequence_2c_553_4_fv1c.npy', 'sequence_2c_537_8_fv1c.npy', 'sequence_1c_102_3_fv1c.npy', 'sequence_1d_169_10_fv1c', 'sequence_1k_292_3_fv1c.npy', 'sequence_2b_473_4_fv1c.npy', 'sequence_2c_525_6_fv1c.npy', 'sequence_1k_310_1_fv1c.npy', 'sequence_2c_501_2_fv1c.npy', 'sequence_1d_185_26_fv1c.npy', 'sequence_1d_271_22_fv1c.npy', 'sequence_1k_313_4_fv1c.npy', 'sequence_1l_404_5_fv1c.npy', 'sequence_2b_460_1_fv1c.npy', 'sequence_1l_396_7_fv1c.npy', 'sequence_1k_285_6_fv1c.npy', 'sequence_3a_608_10_fv1c', 'sequence_1d_210_21_fv1c.npy', 'sequence_1c_105_6_fv1c.npy', 'sequence_1d_230_11_fv1c.npy', 'sequence_3a_591_13_fv1c.npy', 'sequence_1b_9_10_fv1c', 'sequence_1d_189_30_fv1c.npy', 'sequence_3a_704_6_fv1c.npy', 'sequence_1l_371_2_fv1c.npy', 'sequence_1b_26_7_fv1c.npy', 'sequence_2c_532_3_fv1c.npy', 'sequence_1l_426_7_fv1c.npy', 'sequence_1d_154_25_fv1c.npy', 'sequence_2b_450_1_fv1c.npy', 'sequence_3a_635_17_fv1c.npy', 'sequence_3a_691_13_fv1c.npy', 'sequence_1k_312_3_fv1c.npy', 'sequence_1b_37_8_fv1c.npy', 'sequence_1b_43_4_fv1c.npy', 'sequence_1b_52_3_fv1c.npy', 'sequence_1b_38_9_fv1c', 'sequence_3a_710_12_fv1c.npy', 'sequence_3a_624_6_fv1c.npy', 'sequence_3a_718_20_fv1c.npy', 'sequence_1d_233_14_fv1c.npy', 'sequence_1d_164_5_fv1c.npy', 'sequence_3a_684_6_fv1c.npy', 'sequence_1b_2_3_fv1c.npy', 'sequence_1b_46_7_fv1c.npy', 'sequence_1c_93_4_fv1c.npy', 'sequence_1l_397_8_fv1c.npy', 'sequence_2c_505_6_fv1c.npy', 'sequence_1d_168_9_fv1c', 'sequence_3a_622_4_fv1c.npy', 'sequence_1d_207_18_fv1c.npy', 'sequence_1b_8_9_fv1c', 'sequence_2c_551_2_fv1c.npy', 'sequence_3a_690_12_fv1c.npy', 'sequence_1d_197_8_fv1c.npy', 'sequence_3a_581_3_fv1c.npy', 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'sequence_2b_455_6_fv1c.npy', 'sequence_1k_315_6_fv1c.npy', 'sequence_1c_115_6_fv1c.npy', 'sequence_1c_62_3_fv1c.npy', 'sequence_1l_423_4_fv1c.npy', 'sequence_1d_231_12_fv1c.npy', 'sequence_3a_577_18_fv1c.npy', 'sequence_1b_53_4_fv1c.npy', 'sequence_1d_143_14_fv1c.npy', 'sequence_1d_257_8_fv1c.npy', 'sequence_2b_451_2_fv1c.npy', 'sequence_1d_177_18_fv1c.npy', 'sequence_1b_22_3_fv1c.npy', 'sequence_1c_65_6_fv1c.npy', 'sequence_2b_484_5_fv1c.npy', 'sequence_3a_666_8_fv1c.npy', 'sequence_1d_141_12_fv1c.npy', 'sequence_3a_585_7_fv1c.npy', 'sequence_1k_289_10_fv1c', 'sequence_2c_546_7_fv1c.npy', 'sequence_3a_708_10_fv1c', 'sequence_1l_430_1_fv1c.npy', 'sequence_3a_605_7_fv1c.npy', 'sequence_1c_81_2_fv1c.npy', 'sequence_1d_179_20_fv1c.npy', 'sequence_1k_309_10_fv1c', 'sequence_1d_128_9_fv1c', 'sequence_3a_716_18_fv1c.npy', 'sequence_1k_351_2_fv1c.npy', 'sequence_3a_572_13_fv1c.npy', 'sequence_1b_7_8_fv1c.npy', 'sequence_3a_618_20_fv1c.npy', 'sequence_1b_51_2_fv1c.npy', 'sequence_1d_244_25_fv1c.npy', 'sequence_1b_16_7_fv1c.npy', 'sequence_2b_457_8_fv1c.npy', 'sequence_1d_149_20_fv1c.npy', 'sequence_2c_512_3_fv1c.npy', 'sequence_3a_678_20_fv1c.npy', 'sequence_1k_284_5_fv1c.npy', 'sequence_1b_11_2_fv1c.npy', 'sequence_3a_641_3_fv1c.npy', 'sequence_3a_653_15_fv1c.npy', 'sequence_3a_619_1_fv1c.npy', 'sequence_1l_393_4_fv1c.npy', 'sequence_1k_290_1_fv1c.npy', 'sequence_3a_687_9_fv1c', 'sequence_1l_380_1_fv1c.npy', 'sequence_3a_606_8_fv1c.npy', 'sequence_2c_539_10_fv1c', 'sequence_1k_295_6_fv1c.npy', 'sequence_1b_27_8_fv1c.npy', 'sequence_1d_152_23_fv1c.npy', 'sequence_1l_427_8_fv1c.npy', 'sequence_2b_449_10_fv1c', 'sequence_3a_616_18_fv1c.npy', 'sequence_1l_375_6_fv1c.npy', 'sequence_1l_415_6_fv1c.npy', 'sequence_3a_611_13_fv1c.npy', 'sequence_1d_237_18_fv1c.npy', 'sequence_3a_640_2_fv1c.npy', 'sequence_2c_526_7_fv1c.npy', 'sequence_1c_86_7_fv1c.npy', 'sequence_1d_253_4_fv1c.npy', 'sequence_3a_566_7_fv1c.npy', 'sequence_1b_50_1_fv1c.npy', 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'sequence_1d_174_15_fv1c.npy', 'sequence_1k_288_9_fv1c', 'sequence_2b_498_9_fv1c', 'sequence_1d_187_28_fv1c.npy', 'sequence_3a_612_14_fv1c.npy', 'sequence_2b_474_5_fv1c.npy', 'sequence_1l_439_10_fv1c', 'sequence_1l_417_8_fv1c.npy', 'sequence_1d_278_29_fv1c.npy', 'sequence_1k_318_9_fv1c', 'sequence_1k_304_5_fv1c.npy', 'sequence_1c_88_9_fv1c', 'sequence_3a_594_16_fv1c.npy', 'sequence_1d_224_5_fv1c.npy', 'sequence_1d_130_1_fv1c.npy', 'sequence_1c_60_1_fv1c.npy', 'sequence_1k_301_2_fv1c.npy', 'sequence_1c_98_9_fv1c', 'sequence_1c_89_10_fv1c', 'sequence_1d_170_11_fv1c.npy', 'sequence_3a_667_9_fv1c', 'sequence_2c_547_8_fv1c.npy', 'sequence_1b_59_10_fv1c', 'sequence_2c_504_5_fv1c.npy', 'sequence_3a_601_3_fv1c.npy', 'sequence_2b_441_2_fv1c.npy', 'sequence_3a_623_5_fv1c.npy', 'sequence_1l_434_5_fv1c.npy', 'sequence_1b_35_6_fv1c.npy', 'sequence_1d_242_23_fv1c.npy', 'sequence_1k_355_6_fv1c.npy', 'sequence_3a_644_6_fv1c.npy', 'sequence_1l_437_8_fv1c.npy', 'sequence_3a_657_19_fv1c.npy', 'sequence_3a_575_16_fv1c.npy', 'sequence_1k_322_3_fv1c.npy', 'sequence_3a_596_18_fv1c.npy', 'sequence_3a_712_14_fv1c.npy', 'sequence_1d_223_4_fv1c.npy', 'sequence_1l_383_4_fv1c.npy', 'sequence_3a_574_15_fv1c.npy', 'sequence_1k_297_8_fv1c.npy', 'sequence_2c_549_10_fv1c', 'sequence_1c_61_2_fv1c.npy', 'sequence_3a_707_9_fv1c', 'sequence_1d_139_10_fv1c', 'sequence_1l_420_1_fv1c.npy', 'sequence_1k_298_9_fv1c', 'sequence_1d_135_6_fv1c.npy', 'sequence_1k_329_10_fv1c', 'sequence_1b_13_4_fv1c.npy', 'sequence_2c_540_1_fv1c.npy', 'sequence_2c_557_8_fv1c.npy', 'sequence_2c_552_3_fv1c.npy', 'sequence_3a_609_11_fv1c.npy', 'sequence_2b_496_7_fv1c.npy', 'sequence_3a_583_5_fv1c.npy', 'sequence_1b_45_6_fv1c.npy', 'sequence_2b_471_2_fv1c.npy', 'sequence_2b_486_7_fv1c.npy', 'sequence_1b_15_6_fv1c.npy', 'sequence_1d_195_6_fv1c.npy', 'sequence_1l_419_10_fv1c', 'sequence_3a_692_14_fv1c.npy', 'sequence_1d_279_30_fv1c.npy', 'sequence_1c_68_9_fv1c', 'sequence_3a_570_11_fv1c.npy', 'sequence_3a_588_10_fv1c', 'sequence_3a_580_2_fv1c.npy', 'sequence_1b_33_4_fv1c.npy', 'sequence_3a_613_15_fv1c.npy', 'sequence_1k_291_2_fv1c.npy', 'sequence_3a_702_4_fv1c.npy', 'sequence_3a_705_7_fv1c.npy', 'sequence_3a_571_12_fv1c.npy', 'sequence_2c_559_10_fv1c', 'sequence_2c_530_1_fv1c.npy', 'sequence_1d_234_15_fv1c.npy', 'sequence_1c_84_5_fv1c.npy', 'sequence_3a_587_9_fv1c', 'sequence_1d_240_21_fv1c.npy', 'sequence_1d_270_21_fv1c.npy', 'sequence_2c_545_6_fv1c.npy', 'sequence_1k_342_3_fv1c.npy', 'sequence_3a_675_17_fv1c.npy', 'sequence_2c_500_1_fv1c.npy', 'sequence_3a_639_1_fv1c.npy', 'sequence_1b_30_1_fv1c.npy', 'sequence_1d_153_24_fv1c.npy', 'sequence_1c_90_1_fv1c.npy', 'sequence_1d_221_2_fv1c.npy', 'sequence_1d_161_2_fv1c.npy', 'sequence_1c_69_10_fv1c', 'sequence_1d_132_3_fv1c.npy', 'sequence_2b_467_8_fv1c.npy', 'sequence_3a_706_8_fv1c.npy', 'sequence_1k_324_5_fv1c.npy', 'sequence_1d_136_7_fv1c.npy', 'sequence_3a_689_11_fv1c.npy', 'sequence_1k_344_5_fv1c.npy', 'sequence_1l_384_5_fv1c.npy', 'sequence_1l_370_1_fv1c.npy', 'sequence_1l_381_2_fv1c.npy', 'sequence_1l_422_3_fv1c.npy', 'sequence_1d_216_27_fv1c.npy', 'sequence_1d_255_6_fv1c.npy', 'sequence_3a_610_12_fv1c.npy', 'sequence_1d_162_3_fv1c.npy', 'sequence_1k_294_5_fv1c.npy', 'sequence_1l_424_5_fv1c.npy', 'sequence_3a_615_17_fv1c.npy', 'sequence_1k_353_4_fv1c.npy', 'sequence_2c_531_2_fv1c.npy', 'sequence_1d_198_9_fv1c', 'sequence_3a_699_1_fv1c.npy', 'sequence_1d_258_9_fv1c', 'sequence_2c_535_6_fv1c.npy', 'sequence_3a_565_6_fv1c.npy', 'sequence_2b_495_6_fv1c.npy', 'sequence_1b_20_1_fv1c.npy', 'sequence_3a_604_6_fv1c.npy', 'sequence_3a_634_16_fv1c.npy', 'sequence_1d_206_17_fv1c.npy', 'sequence_1b_19_10_fv1c', 'sequence_1k_356_7_fv1c.npy', 'sequence_1k_302_3_fv1c.npy', 'sequence_2b_446_7_fv1c.npy', 'sequence_1d_225_6_fv1c.npy', 'sequence_1l_432_3_fv1c.npy', 'sequence_2c_515_6_fv1c.npy', 'sequence_1l_411_2_fv1c.npy', 'sequence_1c_77_8_fv1c.npy', 'sequence_3a_600_2_fv1c.npy', 'sequence_1l_360_1_fv1c.npy', 'sequence_1d_229_10_fv1c', 'sequence_2c_510_1_fv1c.npy', 'sequence_1d_266_17_fv1c.npy', 'sequence_3a_633_15_fv1c.npy', 'sequence_1d_250_1_fv1c.npy', 'sequence_3a_599_1_fv1c.npy', 'sequence_1l_431_2_fv1c.npy', 'sequence_1l_374_5_fv1c.npy', 'sequence_1d_167_8_fv1c.npy', 'sequence_1d_159_30_fv1c.npy', 'sequence_1d_211_22_fv1c.npy', 'sequence_1c_94_5_fv1c.npy', 'sequence_1b_41_2_fv1c.npy', 'sequence_1d_263_14_fv1c.npy', 'sequence_1d_272_23_fv1c.npy', 'sequence_1d_158_29_fv1c.npy', 'sequence_1k_319_10_fv1c', 'sequence_2b_448_9_fv1c', 'sequence_1k_293_4_fv1c.npy', 'sequence_3a_670_12_fv1c.npy', 'sequence_3a_694_16_fv1c.npy']
719

In [17]:

    

# for key, value in filename_loadseq_dict_seqidkey:
#     filename_loadseq_dict_seqidkey_equallength[key] = 
loadseq_df = pd.DataFrame(flattened_loadseq_list)
loadseq_df.to_csv(analysis_path+"loadseq_flattened.csv")


    

In [18]:

    

#fl_sik_test_df file-load sequenceid-key-TEST
#loadseq_loadlist_df
fl_sik_test_df.index.rename(name='seq_name',inplace=True)
fl_sik_test_df.columns = ['loadseq_id']

loadseq_loadlist_df.index.rename(name='loadseq_id',inplace=True)
loadseq_loadlist_df.columns = ['loadseq']


print(fl_sik_test_df.columns,fl_sik_test_df.index)
print(loadseq_loadlist_df.columns,loadseq_loadlist_df.index)
complete_seqname_loadseqlist_df = pd.merge(fl_sik_test_df,loadseq_loadlist_df,how='left',right_index=True,left_on='loadseq_id')
print(complete_seqname_loadseqlist_df.head(2))


    

    




(Index([u'loadseq_id'], dtype='object'), Index([u'sequence_1d_168_9_fv1c', u'sequence_1b_29_10_fv1c',
       u'sequence_1c_119_10_fv1c', u'sequence_1b_8_9_fv1c',
       u'sequence_2c_538_9_fv1c', u'sequence_3a_628_10_fv1c',
       u'sequence_2b_469_10_fv1c', u'sequence_1l_398_9_fv1c',
       u'sequence_1b_49_10_fv1c', u'sequence_3a_687_9_fv1c',
       ...
       u'sequence_1b_38_9_fv1c', u'sequence_1c_68_9_fv1c',
       u'sequence_1d_198_9_fv1c', u'sequence_1k_359_10_fv1c',
       u'sequence_1k_319_10_fv1c', u'sequence_2b_448_9_fv1c',
       u'sequence_1d_229_10_fv1c', u'sequence_1l_428_9_fv1c',
       u'sequence_1d_259_10_fv1c', u'sequence_1l_388_9_fv1c'],
      dtype='object', name=u'seq_name', length=108))
(Index([u'loadseq'], dtype='object'), RangeIndex(start=0, stop=27, step=1, name=u'loadseq_id'))
                        loadseq_id  \
seq_name                             
sequence_1d_168_9_fv1c           0   
sequence_1b_29_10_fv1c           0   

                                                                  loadseq  
seq_name                                                                   
sequence_1d_168_9_fv1c  [  1.428  14.28   14.28    1.75    1.75   14.2...  
sequence_1b_29_10_fv1c  [  1.428  14.28   14.28    1.75    1.75   14.2...  

In [19]:

    

complete_seqname_loadseqlist_df.to_csv(analysis_path+"complete_seqname_loadseqlist.csv")


    

In [ ]: