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common_vars.py
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import numpy
from io import StringIO
W = 1
# Generated using numpy.random.normal(0,1, 400)
alpha = StringIO(u" -1.48947048 0.71336163 -0.76224473 -2.39050327 -1.80731613 0.32582241 -0.24885309 -1.18344062 0.54010852 0.20128018 0.72139241 -0.95830204 -0.57774412 -0.7193498 1.7580711 -0.75232954 -0.45617427 0.12841815 1.76885731 -0.79749443 0.22251757 0.483441 -0.10781235 -2.18438962 0.60285895 -0.5560307 -0.59596188 0.62110995 -0.29098158 -0.33257556 0.5951485 -0.73742485 -0.3495096 -0.34494956 1.29137345 0.6603728 -0.65467095 0.16485491 2.86029158 0.58606523 2.01455471 0.11913697 0.31301853 -0.17777457 0.65640364 0.45864482 1.17890675 -0.70817934 -1.69078402 -0.62301257 -2.46766769 -0.11465831 1.42168523 -1.86524556 -0.26841738 -0.11371167 -0.7614555 -0.49092101 0.48136063 -0.24926131 -0.05767393 -0.66958166 0.49095712 0.73221227 -1.47464472 -0.34472819 -0.38109477 -0.37064617 -1.19656747 -1.41078713 -1.52229952 0.63569108 1.31919116 0.54964379 -0.05554477 -0.64243767 -1.54941984 0.71556438 -2.61061658 -0.36341904 0.93973923 -0.2698301 1.06143638 0.66151419 -0.90811544 1.287573 0.89575729 0.21746579 -1.44691143 0.11903301 0.55338445 1.3972953 0.38078257 -0.48248742 -0.79288282 0.26848279 1.10477348 -0.45628211 -1.75286078 0.21941945 -0.20566036 -2.22288432 -0.2940249 0.58089027 1.07050979 -0.06988481 -1.67636643 -1.41538267 -0.26093582 -0.76910946 0.27314048 -0.12833866 -0.94192741 -0.59553864 -2.00182621 -0.21311869 0.20500594 1.88778921 -0.27053353 -1.17909073 0.76109831 0.7494378 -0.22932452 -0.48226666 1.13554479 -1.5007225 0.11844697 0.50058878 0.70376079 0.30985101 -0.42124774 1.63562261 0.42465435 -0.27510877 -1.73762302 0.78429024 -0.65747819 1.01126233 -0.12837026 1.3086113 -0.05590944 0.84287435 0.78192226 0.28186378 0.88422356 1.75421496 -0.44463358 -0.78562306 0.14049968 -0.01752382 0.43202304 -0.59263216 2.03398015 0.36760829 0.55792258 0.99164731 -0.17698347 2.92094117 1.30909411 0.13875348 -1.53072166 1.20915718 -1.13490909 0.76392868 -0.43061076 0.43412278 -0.10372002 -0.68599355 1.19257177 0.45073775 -0.34670496 -1.54109339 -0.20852486 0.5071136 -0.72379186 -0.39099063 1.03715425 0.80592764 -0.76445892 -0.60639062 1.66182649 -1.50443233 -0.20514094 0.2784393 0.3970306 0.80264169 -0.47727888 2.63057696 0.28886513 2.07620257 -1.38307363 -1.93740169 -0.92187285 -1.60997653 0.54581197 -0.8514182 -1.13153758 0.98445791 -1.63739951 0.50942528 0.54342917 -2.50823079 0.88492967 1.98005235 -0.31687008 -0.35589369 0.19191931 -1.94345343 0.59335074 -0.1427504 0.70098113 -0.73739547 1.79415337 0.2672015 -1.33087588 -0.69759636 -1.71886625 -1.39504097 -0.39713164 0.23338851 0.66332149 0.74644337 0.32393006 -0.04281859 0.16061127 0.63457117 -0.60207231 0.11385587 -1.46809303 -0.01100432 -0.58452214 1.63011349 -0.71587517 0.16496326 -0.53223682 0.9829394 -0.98602773 -0.33284861 -0.61341919 0.64961038 -1.55694148 -0.53857052 -0.47747642 0.0414776 -0.77507978 -0.79777607 -1.17101902 0.29900351 0.26309184 0.59777603 -0.78819115 1.00077374 0.19953391 -1.70564318 0.37341733 1.14304716 -0.19460056 0.70637215 0.00982724 1.03723818 1.16327922 1.68045458 -0.57268879 -1.47224002 -1.60395742 0.59752317 0.27805218 -1.55159419 -0.69616243 0.0237781 -0.39764911 0.20589307 0.34596474 0.51627106 -0.15866831 -0.9532324 -0.01840771 0.39901268 -0.51623772 0.3781907 2.34102804 0.53564462 1.25582657 0.67877751 1.60939854 0.2036258 0.09935712 1.72939993 -0.07026736 0.82562045 -0.73808561 -0.3217688 -1.1870847 -0.02072422 0.41040494 1.90773962 -1.19789287 -1.57596823 1.43511759 1.0573697 1.06126907 0.2556618 0.33036263 2.2149623 -1.28761687 -0.41699618 -1.45197419 0.25240701 0.47584712 0.22501629 0.753759 0.21775838 -1.00646219 -0.97442323 -0.59048717 -0.55705854 0.69323908 -0.6426252 1.18866269 -1.49951168 -0.14860592 -0.24555863 0.60690823 0.37895364 0.16059609 0.67115935 -0.24451805 1.03758259 -1.69115754 0.70536332 -0.59810489 -0.26579461 1.19508252 0.94613678 -0.68622042 -1.08587122 1.11724451 2.16045451 1.45107886 1.79307674 0.13551954 0.23389761 0.94379802 -0.27613088 -0.37340296 -0.69737345 -0.6900231 0.33164467 0.07174116 0.51524603 0.69590643 0.72442552 -0.44010218 1.21243991 -1.02460307 -0.36597343 -2.26630598 -0.04389867 0.69221299 0.68565081 -0.36363271 1.15555017 -0.10950136 -0.50837917 -0.84114494 0.64121087 -0.70697273 -1.18774571 -1.33002211 0.39014702 0.33478443 0.56157097 0.97424006 -0.66721883 -0.10808331 0.13335318 -0.43836212 0.93602979 -1.32364132 2.10419921 -0.49526129 -0.02884042 -2.72523309 -0.42644753 -0.7548172 -0.37952602 -0.42487295 1.03167847 -1.22782112 -0.81804881 0.05957715 -0.21603708 1.35146501 -1.34496634 -0.09549702 0.97281038 -0.0628809 -0.72222825 1.21042016 -0.8436308")
# Generated using numpy.random.uniform(0, W)
beta = 0.09699146111524193