class WordScoreChunk2:
    Dwords = {"dejected": -2.2, "dejecting": -2.3, "dejects": -2.0, "delay": -1.3, "delayed": -0.9, "delectable": 2.9, "delectables": 1.4, "delectably": 2.8, "delicate": 0.2, "delicately": 1.0, "delicates": 0.6, "delicatessen": 0.4, "delicatessens": 0.4, "delicious": 2.7, "deliciously": 1.9, "deliciousness": 1.8, "delight": 2.9, "delighted": 2.3, "delightedly": 2.4, "delightedness": 2.1, "delighter": 2.0, "delighters": 2.6, "delightful": 2.8, "delightfully": 2.7, "delightfulness": 2.1, "delighting": 1.6, "delights": 2.0, "delightsome": 2.3, "demand": -0.5, "demanded": -0.9, "demanding": -0.9, "demonstration": 0.4, "demoralized": -1.6, "denied": -1.9, "denier": -1.5, "deniers": -1.1, "denies": -1.8, "denounce": -1.4, "denounces": -1.9, "deny": -1.4, "denying": -1.4, "depress": -2.2, "depressant": -1.6, "depressants": -1.6, "depressed": -2.3, "depresses": -2.2, "depressible": -1.7, "depressing": -1.6, "depressingly": -2.3, "depression": -2.7, "depressions": -2.2, "depressive": -1.6, "depressively": -2.1, "depressives": -1.5, "depressor": -1.8, "depressors": -1.7, "depressurization": -0.3, "depressurizations": -0.4, "depressurize": -0.5, "depressurized": -0.3, "depressurizes": -0.3, "depressurizing": -0.7, "deprival": -2.1, "deprivals": -1.2, "deprivation": -1.8, "deprivations": -1.8, "deprive": -2.1, "deprived": -2.1, "depriver": -1.6, "deprivers": -1.4, "deprives": -1.7, "depriving": -2.0, "derail": -1.2, "derailed": -1.4, "derails": -1.3, "deride": -1.1, "derided": -0.8, "derides": -1.0, "deriding": -1.5, "derision": -1.2, "desirable": 1.3, "desire": 1.7, "desired": 1.1, "desirous": 1.3, "despair": -1.3, "despaired": -2.7, "despairer": -1.3, "despairers": -1.3, "despairing": -2.3, "despairingly": -2.2, "despairs": -2.7, "desperate": -1.3, "desperately": -1.6, "desperateness": -1.5, "desperation": -2.0, "desperations": -2.2, "despise": -1.4, "despised": -1.7, "despisement": -2.4, "despisements": -2.5, "despiser": -1.8, "despisers": -1.6, "despises": -2.0, "despising": -2.7, "despondent": -2.1, "destroy": -2.5, "destroyed": -2.2, "destroyer": -2.0, "destroyers": -2.3, "destroying": -2.6, "destroys": -2.6, "destruct": -2.4, "destructed": -1.9, "destructibility": -1.8, "destructible": -1.5, "destructing": -2.5, "destruction": -2.7, "destructionist": -2.6, "destructionists": -2.1, "destructions": -2.3, "destructive": -3.0, "destructively": -2.4, "destructiveness": -2.4, "destructivity": -2.2, "destructs": -2.4, "detached": -0.5, "detain": -1.8, "detained": -1.7, "detention": -1.5, "determinable": 0.9, "determinableness": 0.2, "determinably": 0.9, "determinacy": 1.0, "determinant": 0.2, "determinantal": -0.3, "determinate": 0.8, "determinately": 1.2, "determinateness": 1.1, "determination": 1.7, "determinations": 0.8, "determinative": 1.1, "determinatives": 0.9, "determinator": 1.1, "determined": 1.4, "devastate": -3.1, "devastated": -3.0, "devastates": -2.8, "devastating": -3.3, "devastatingly": -2.4, "devastation": -1.8, "devastations": -1.9, "devastative": -3.2, "devastator": -2.8, "devastators": -2.9, "devil": -3.4, "deviled": -1.6, "devilfish": -0.8, "devilfishes": -0.6, "deviling": -2.2, "devilish": -2.1, "devilishly": -1.6, "devilishness": -2.3, "devilkin": -2.4, "devilled": -2.3, "devilling": -1.8, "devilment": -1.9, "devilments": -1.1, "devilries": -1.6, "devilry": -2.8, "devils": -2.7, "deviltries": -1.5, "deviltry": -2.8, "devilwood": -0.8, "devilwoods": -1.0, "devote": 1.4, "devoted": 1.7, "devotedly": 1.6, "devotedness": 2.0, "devotee": 1.6, "devotees": 0.5, "devotement": 1.5, "devotements": 1.1, "devotes": 1.6, "devoting": 2.1, "devotion": 2.0, "devotional": 1.2, "devotionally": 2.2, "devotionals": 1.2, "devotions": 1.8, "diamond": 1.4, "dick": -2.3, "dickhead": -3.1, "die": -2.9, "died": -2.6, "difficult": -1.5, "difficulties": -1.2, "difficultly": -1.7, "difficulty": -1.4, "diffident": -1.0, "dignified": 2.2, "dignifies": 2.0, "dignify": 1.8, "dignifying": 2.1, "dignitaries": 0.6, "dignitary": 1.9, "dignities": 1.4, "dignity": 1.7, "dilemma": -0.7, "dipshit": -2.1, "dire": -2.0, "direful": -3.1, "dirt": -1.4, "dirtier": -1.4, "dirtiest": -2.4, "dirty": -1.9, "disabling": -2.1, "disadvantage": -1.8, "disadvantaged": -1.7, "disadvantageous": -1.8, "disadvantageously": -2.1, "disadvantageousness": -1.6, "disadvantages": -1.7, "disagree": -1.6, "disagreeable": -1.7, "disagreeableness": -1.7, "disagreeablenesses": -1.9, "disagreeably": -1.5, "disagreed": -1.3, "disagreeing": -1.4, "disagreement": -1.5, "disagreements": -1.8, "disagrees": -1.3, "disappear": -0.9, "disappeared": -0.9, "disappears": -1.4, "disappoint": -1.7, "disappointed": -2.1, "disappointedly": -1.7, "disappointing": -2.2, "disappointingly": -1.9, "disappointment": -2.3, "disappointments": -2.0, "disappoints": -1.6, "disaster": -3.1, "disasters": -2.6, "disastrous": -2.9, "disbelieve": -1.2, "discard": -1.0, "discarded": -1.4, "discarding": -0.7, "discards": -1.0, "discomfort": -1.8, "discomfortable": -1.6, "discomforted": -1.6, "discomforting": -1.6, "discomforts": -1.3, "disconsolate": -2.3, "disconsolation": -1.7, "discontented": -1.8, "discord": -1.7, "discounted": 0.2, "discourage": -1.8, "discourageable": -1.2, "discouraged": -1.7, "discouragement": -2.0, "discouragements": -1.8, "discourager": -1.7, "discouragers": -1.9, "discourages": -1.9, "discouraging": -1.9, "discouragingly": -1.8, "discredited": -1.9, "disdain": -2.1, "disgrace": -2.2, "disgraced": -2.0, "disguise": -1.0, "disguised": -1.1, "disguises": -1.0, "disguising": -1.3, "disgust": -2.9, "disgusted": -2.4, "disgustedly": -3.0, "disgustful": -2.6, "disgusting": -2.4, "disgustingly": -2.9, "disgusts": -2.1, "dishearten": -2.0, "disheartened": -2.2, "disheartening": -1.8, "dishearteningly": -2.0, "disheartenment": -2.3, "disheartenments": -2.2, "disheartens": -2.2, "dishonest": -2.7, "disillusion": -1.0, "disillusioned": -1.9, "disillusioning": -1.3, "disillusionment": -1.7, "disillusionments": -1.5, "disillusions": -1.6, "disinclined": -1.1, "disjointed": -1.3, "dislike": -1.6, "disliked": -1.7, "dislikes": -1.7, "disliking": -1.3, "dismal": -3.0, "dismay": -1.8, "dismayed": -1.9, "dismaying": -2.2, "dismayingly": -1.9, "dismays": -1.8, "disorder": -1.7, "disorganized": -1.2, "disoriented": -1.5, "disparage": -2.0, "disparaged": -1.4, "disparages": -1.6, "disparaging": -2.2, "displeased": -1.9, "dispute": -1.7, "disputed": -1.4, "disputes": -1.1, "disputing": -1.7, "disqualified": -1.8, "disquiet": -1.3, "disregard": -1.1, "disregarded": -1.6, "disregarding": -0.9, "disregards": -1.4, "disrespect": -1.8, "disrespected": -2.0, "disruption": -1.5, "disruptions": -1.4, "disruptive": -1.3, "dissatisfaction": -2.2, "dissatisfactions": -1.9, "dissatisfactory": -2.0, "dissatisfied": -1.6, "dissatisfies": -1.8, "dissatisfy": -2.2, "dissatisfying": -2.4, "distort": -1.3, "distorted": -1.7, "distorting": -1.1, "distorts": -1.4, "distract": -1.2, "distractable": -1.3, "distracted": -1.4, "distractedly": -0.9, "distractibility": -1.3, "distractible": -1.5, "distracting": -1.2, "distractingly": -1.4, "distraction": -1.6, "distractions": -1.0, "distractive": -1.6, "distracts": -1.3, "distraught": -2.6, "distress": -2.4, "distressed": -1.8, "distresses": -1.6, "distressful": -2.2, "distressfully": -1.7, "distressfulness": -2.4, "distressing": -1.7, "distressingly": -2.2, "distrust": -1.8, "distrusted": -2.4, "distrustful": -2.1, "distrustfully": -1.8, "distrustfulness": -1.6, "distrusting": -2.1, "distrusts": -1.3, "disturb": -1.7, "disturbance": -1.6, "disturbances": -1.4, "disturbed": -1.6, "disturber": -1.4, "disturbers": -2.1, "disturbing": -2.3, "disturbingly": -2.3, "disturbs": -1.9, "dithering": -0.5, "divination": 1.7, "divinations": 1.1, "divinatory": 1.6, "divine": 2.6, "divined": 0.8, "divinely": 2.9, "diviner": 0.3, "diviners": 1.2, "divines": 0.8, "divinest": 2.7, "diving": 0.3, "divining": 0.9, "divinise": 0.5, "divinities": 1.8, "divinity": 2.7, "divinize": 2.3, "dizzy": -0.9, "dodging": -0.4, "dodgy": -0.9, "dolorous": -2.2, "dominance": 0.8, "dominances": -0.1, "dominantly": 0.2, "dominants": 0.2, "dominate": -0.5, "dominates": 0.2, "dominating": -1.2, "domination": -0.2, "dominations": -0.3, "dominative": -0.7, "dominators": -0.4, "dominatrices": -0.2, "dominatrix": -0.5, "dominatrixes": 0.6, "doom": -1.7, "doomed": -3.2, "doomful": -2.1, "dooming": -2.8, "dooms": -1.1, "doomsayer": -0.7, "doomsayers": -1.7, "doomsaying": -1.5, "doomsayings": -1.5, "doomsday": -2.8, "doomsdayer": -2.2, "doomsdays": -2.4, "doomster": -2.2, "doomsters": -1.6, "doomy": -1.1, "dork": -1.4, "dorkier": -1.1, "dorkiest": -1.2, "dorks": -0.5, "dorky": -1.1, "doubt": -1.5, "doubtable": -1.5, "doubted": -1.1, "doubter": -1.6, "doubters": -1.3, "doubtful": -1.4, "doubtfully": -1.2, "doubtfulness": -1.2, "doubting": -1.4, "doubtingly": -1.4, "doubtless": 0.9, "doubtlessly": 1.2, "doubtlessness": 0.8, "doubts": -1.2, "douche": -1.5, "douchebag": -3.0, "downcast": -1.8, "downhearted": -2.3, "downside": -1.0, "drag": -0.9, "dragged": -0.2, "drags": -0.7, "drained": -1.5, "dread": -2.0, "dreaded": -2.7, "dreadful": -1.9, "dreadfully": -2.7, "dreadfulness": -3.2, "dreadfuls": -2.4, "dreading": -2.4, "dreadlock": -0.4, "dreadlocks": -0.2, "dreadnought": -0.6, "dreadnoughts": -0.4, "dreads": -1.4, "dream": 1.0, "dreams": 1.7, "dreary": -1.4, "droopy": -0.8, "drop": -1.1, "drown": -2.7, "drowned": -2.9, "drowns": -2.2, "drunk": -1.4, "dubious": -1.5, "dud": -1.0, "dull": -1.7, "dullard": -1.6, "dullards": -1.8, "dulled": -1.5, "duller": -1.7, "dullest": -1.7, "dulling": -1.1, "dullish": -1.1, "dullness": -1.4, "dullnesses": -1.9, "dulls": -1.0, "dullsville": -2.4, "dully": -1.1, "dumb": -2.3, "dumbass": -2.6, "dumbbell": -0.8, "dumbbells": -0.2, "dumbcane": -0.3, "dumbcanes": -0.6, "dumbed": -1.4, "dumber": -1.5, "dumbest": -2.3, "dumbfound": -0.1, "dumbfounded": -1.6, "dumbfounder": -1.0, "dumbfounders": -1.0, "dumbfounding": -0.8, "dumbfounds": -0.3, "dumbhead": -2.6, "dumbheads": -1.9, "dumbing": -0.5, "dumbly": -1.3, "dumbness": -1.9, "dumbs": -1.5, "dumbstruck": -1.0, "dumbwaiter": 0.2, "dumbwaiters": -0.1, "dump": -1.6, "dumpcart": -0.6, "dumped": -1.7, "dumper": -1.2, "dumpers": -0.8, "dumpier": -1.4, "dumpiest": -1.6, "dumpiness": -1.2, "dumping": -1.3, "dumpings": -1.1, "dumpish": -1.8, "dumpling": 0.4, "dumplings": -0.3, "dumps": -1.7, "dumpster": -0.6, "dumpsters": -1.0, "dumpy": -1.7, "dupe": -1.5, "duped": -1.8, "dwell": 0.5, "dwelled": 0.4, "dweller": 0.3, "dwellers": -0.3, "dwelling": 0.1, "dwells": -0.1, "dynamic": 1.6, "dynamical": 1.2, "dynamically": 1.5, "dynamics": 1.1, "dynamism": 1.6, "dynamisms": 1.2, "dynamist": 1.4, "dynamistic": 1.5, "dynamists": 0.9, "dynamite": 0.7, "dynamited": -0.9, "dynamiter": -1.2, "dynamiters": 0.4, "dynamites": -0.3, "dynamitic": 0.9, "dynamiting": 0.2, "dynamometer": 0.3, "dynamometers": 0.3, "dynamometric": 0.3, "dynamometry": 0.6, "dynamos": 0.3, "dynamotor": 0.6, "dysfunction": -1.8, "eager": 1.5, "eagerly": 1.6, "eagerness": 1.7, "eagers": 1.6, "earnest": 2.3, "ease": 1.5, "eased": 1.2, "easeful": 1.5, "easefully": 1.4, "easel": 0.3, "easement": 1.6, "easements": 0.4, "eases": 1.3, "easier": 1.8, "easiest": 1.8, "easily": 1.4, "easiness": 1.6, "easing": 1.0, "easy": 1.9, "easygoing": 1.3, "easygoingness": 1.5, "ecstacy": 3.3, "ecstasies": 2.3, "ecstasy": 2.9, "ecstatic": 2.3, "ecstatically": 2.8, "ecstatics": 2.9, "eerie": -1.5, "eery": -0.9, "effective": 2.1, "effectively": 1.9, "efficiencies": 1.6, "efficiency": 1.5, "efficient": 1.8, "efficiently": 1.7, "effin": -2.3, "egotism": -1.4, "egotisms": -1.0, "egotist": -2.3, "egotistic": -1.4, "egotistical": -0.9, "egotistically": -1.8, "egotists": -1.7, "elated": 3.2, "elation": 1.5, "elegance": 2.1, "elegances": 1.8, "elegancies": 1.6, "elegancy": 2.1, "elegant": 2.1, "elegantly": 1.9, "embarrass": -1.2, "embarrassable": -1.6, "embarrassed": -1.5, "embarrassedly": -1.1, "embarrasses": -1.7, "embarrassing": -1.6, "embarrassingly": -1.7, "embarrassment": -1.9, "embarrassments": -1.7, "embittered": -0.4, "embrace": 1.3, "emergency": -1.6, "emotional": 0.6, "empathetic": 1.7, "emptied": -0.7, "emptier": -0.7, "emptiers": -0.7, "empties": -0.7, "emptiest": -1.8, "emptily": -1.0, "emptiness": -1.9, "emptinesses": -1.5, "emptins": -0.3, "empty": -0.8, "emptying": -0.6, "enchanted": 1.6, "encourage": 2.3, "encouraged": 1.5, "encouragement": 1.8, "encouragements": 2.1, "encourager": 1.5, "encouragers": 1.5, "encourages": 1.9, "encouraging": 2.4, "encouragingly": 2.0, "endorse": 1.3, "endorsed": 1.0, "endorsement": 1.3, "endorses": 1.4, "enemies": -2.2, "enemy": -2.5, "energetic": 1.9, "energetically": 1.8, "energetics": 0.3, "energies": 0.9, "energise": 2.2, "energised": 2.1, "energises": 2.2, "energising": 1.9, "energization": 1.6, "energizations": 1.5, "energize": 2.1, "energized": 2.3, "energizer": 2.1, "energizers": 1.7, "energizes": 2.1, "energizing": 2.0, "energy": 1.1, "engage": 1.4, "engaged": 1.7, "engagement": 2.0, "engagements": 0.6, "engager": 1.1, "engagers": 1.0, "engages": 1.0, "engaging": 1.4, "engagingly": 1.5, "engrossed": 0.6, "enjoy": 2.2, "enjoyable": 1.9, "enjoyableness": 1.9, "enjoyably": 1.8, "enjoyed": 2.3, "enjoyer": 2.2, "enjoyers": 2.2, "enjoying": 2.4, "enjoyment": 2.6, "enjoyments": 2.0, "enjoys": 2.3, "enlighten": 2.3, "enlightened": 2.2, "enlightening": 2.3, "enlightens": 1.7, "ennui": -1.2, "enrage": -2.6, "enraged": -1.7, "enrages": -1.8, "enraging": -2.8, "enrapture": 3.0, "enslave": -3.1, "enslaved": -1.7, "enslaves": -1.6, "ensure": 1.6, "ensuring": 1.1, "enterprising": 2.3, "entertain": 1.3, "entertained": 1.7, "entertainer": 1.6, "entertainers": 1.0, "entertaining": 1.9, "entertainingly": 1.9, "entertainment": 1.8, "entertainments": 2.3, "entertains": 2.4, "enthral": 0.4, "enthuse": 1.6, "enthused": 2.0, "enthuses": 1.7, "enthusiasm": 1.9, "enthusiasms": 2.0, "enthusiast": 1.5, "enthusiastic": 2.2, "enthusiastically": 2.6, 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"grime": -1.5, "grimed": -1.2, "grimes": -1.0, "grimier": -1.6, "grimiest": -0.7, "grimily": -0.7, "griminess": -1.6, "griming": -0.7, "grimly": -1.3, "grimmer": -1.5, "grimmest": -0.8, "grimness": -0.8, "grimy": -1.8, "grin": 2.1, "grinned": 1.1, "grinner": 1.1, "grinners": 1.6, "grinning": 1.5, "grins": 0.9, "gross": -2.1, "grossed": -0.4, "grosser": -0.3, "grosses": -0.8, "grossest": -2.1, "grossing": -0.3, "grossly": -0.9, "grossness": -1.8, "grossular": -0.3, "grossularite": -0.1, "grossularites": -0.7, "grossulars": -0.3, "grouch": -2.2, "grouched": -0.8, "grouches": -0.9, "grouchier": -2.0, "grouchiest": -2.3, "grouchily": -1.4, "grouchiness": -2.0, "grouching": -1.7, "grouchy": -1.9, "growing": 0.7, "growth": 1.6, "guarantee": 1.0, "guilt": -1.1, "guiltier": -2.0, "guiltiest": -1.7, "guiltily": -1.1, "guiltiness": -1.8, "guiltless": 0.8, "guiltlessly": 0.7, "guiltlessness": 0.6, "guilts": -1.4, "guilty": -1.8, "gullibility": -1.6, "gullible": -1.5, "gun": -1.4, "h8": -2.7, "ha": 1.4, "hacked": -1.7, "haha": 2.0, "hahaha": 2.6, "hahas": 1.8, "hail": 0.3, "hailed": 0.9, "hallelujah": 3.0, "handsome": 2.2, "handsomely": 1.9, "handsomeness": 2.4, "handsomer": 2.0, "handsomest": 2.6, "hapless": -1.4, "haplessness": -1.4, "happier": 2.4, "happiest": 3.2, "happily": 2.6, "happiness": 2.6, "happing": 1.1, "happy": 2.7, "harass": -2.2, "harassed": -2.5, "harasser": -2.4, "harassers": -2.8, "harasses": -2.5, "harassing": -2.5, "harassment": -2.5, "harassments": -2.6, "hard": -0.4, "hardier": -0.6, "hardship": -1.3, "hardy": 1.7, "harm": -2.5, "harmed": -2.1, "harmfully": -2.6, "harmfulness": -2.6, "harming": -2.6, "harmless": 1.0, "harmlessly": 1.4, "harmlessness": 0.8, "harmonic": 1.8, "harmonica": 0.6, "harmonically": 2.1, "harmonicas": 0.1, "harmonicist": 0.5, "harmonicists": 0.9, "harmonics": 1.5, "harmonies": 1.3, "harmonious": 2.0, "harmoniously": 1.9, "harmoniousness": 1.8, "harmonise": 1.8, "harmonised": 1.3, "harmonising": 1.4, "harmonium": 0.9, "harmoniums": 0.8, "harmonization": 1.9, "harmonizations": 0.9, "harmonize": 1.7, "harmonized": 1.6, "harmonizer": 1.6, "harmonizers": 1.6, "harmonizes": 1.5, "harmonizing": 1.4, "harmony": 1.7, "harms": -2.2, "harried": -1.4, "harsh": -1.9, "harsher": -2.2, "harshest": -2.9, "hate": -2.7, "hated": -3.2, "hateful": -2.2, "hatefully": -2.3, "hatefulness": -3.6, "hater": -1.8, "haters": -2.2, "hates": -1.9, "hating": -2.3, "hatred": -3.2, "haunt": -1.7, "haunted": -2.1, "haunting": -1.1, "haunts": -1.0, "havoc": -2.9, "healthy": 1.7, "heartbreak": -2.7, "heartbreaker": -2.2, "heartbreakers": -2.1, "heartbreaking": -2.0, "heartbreakingly": -1.8, "heartbreaks": -1.8, "heartbroken": -3.3, "heartfelt": 2.5, "heartless": -2.2, "heartlessly": -2.8, "heartlessness": -2.8, "heartwarming": 2.1, "heaven": 2.3, "heavenlier": 3.0, "heavenliest": 2.7, "heavenliness": 2.7, "heavenlinesses": 2.3, "heavenly": 3.0, "heavens": 1.7, "heavenward": 1.4, "heavenwards": 1.2, "heavyhearted": -2.1, "heh": -0.6, "hell": -3.6, "hellish": -3.2, "help": 1.7, "helper": 1.4, "helpers": 1.1, "helpful": 1.8, "helpfully": 2.3, "helpfulness": 1.9, "helping": 1.2, "helpless": -2.0, "helplessly": -1.4, "helplessness": -2.1, "helplessnesses": -1.7, "helps": 1.6, "hero": 2.6, "heroes": 2.3, "heroic": 2.6, "heroical": 2.9, "heroically": 2.4, "heroicomic": 1.0, "heroicomical": 1.1, "heroics": 2.4, "heroin": -2.2, "heroine": 2.7, "heroines": 1.8, "heroinism": -2.0, "heroism": 2.8, "heroisms": 2.2, "heroize": 2.1, "heroized": 2.0, "heroizes": 2.2, "heroizing": 1.9, "heron": 0.1, "heronries": 0.7, "heronry": 0.1, "herons": 0.5, "heros": 1.3, "hesitance": -0.9, "hesitancies": -1.0, "hesitancy": -0.9, "hesitant": -1.0, "hesitantly": -1.2, "hesitate": -1.1, "hesitated": -1.3, "hesitater": -1.4, "hesitaters": -1.4, "hesitates": -1.4, "hesitating": -1.4, "hesitatingly": -1.5, "hesitation": -1.1, "hesitations": -1.1, "hid": -0.4, "hide": -0.7, "hides": -0.7, "hiding": -1.2, "highlight": 1.4, "hilarious": 1.7, "hindrance": -1.7, "hoax": -1.1, "holiday": 1.7, "holidays": 1.6, "homesick": -0.7, "homesickness": -1.8, "homesicknesses": -1.8, "honest": 2.3, "honester": 1.9, "honestest": 3.0, "honesties": 1.8, "honestly": 2.0, "honesty": 2.2, "honor": 2.2, "honorability": 2.2, "honorable": 2.5, "honorableness": 2.2, "honorably": 2.4, "honoraria": 0.6, "honoraries": 1.5, "honorarily": 1.9, "honorarium": 0.7, "honorariums": 1.0, "honorary": 1.4, "honored": 2.8, "honoree": 2.1, "honorees": 2.3, "honorer": 1.7, "honorers": 1.3, "honorific": 1.4, "honorifically": 2.2, "honorifics": 1.7, "honoring": 2.3, "honors": 2.3, "honour": 2.7, "honourable": 2.1, "honoured": 2.2, "honourer": 1.8, "honourers": 1.6, "honouring": 2.1, "honours": 2.2, "hooligan": -1.5, "hooliganism": -2.1, "hooligans": -1.1, "hooray": 2.3, "hope": 1.9, "hoped": 1.6, "hopeful": 2.3, "hopefully": 1.7, "hopefulness": 1.6, "hopeless": -2.0, "hopelessly": -2.2, "hopelessness": -3.1, "hopes": 1.8, "hoping": 1.8, "horrendous": -2.8, "horrendously": -1.9, "horrent": -0.9, "horrible": -2.5, "horribleness": -2.4, "horribles": -2.1, "horribly": -2.4, "horrid": -2.5, "horridly": -1.4, "horridness": -2.3, "horridnesses": -3.0, "horrific": -3.4, "horrifically": -2.9, "horrified": -2.5, "horrifies": -2.9, "horrify": -2.5, "horrifying": -2.7, "horrifyingly": -3.3, "horror": -2.7, "horrors": -2.7, "hostile": -1.6, "hostilely": -2.2, "hostiles": -1.3}

class WordScoreChunk3:
    Dwords ={"hostilities": -2.1, "hostility": -2.5, "huckster": -0.9, "hug": 2.1, "huge": 1.3, "huggable": 1.6, "hugged": 1.7, "hugger": 1.6, "huggers": 1.8, "hugging": 1.8, "hugs": 2.2, "humerous": 1.4, "humiliate": -2.5, "humiliated": -1.4, "humiliates": -1.0, "humiliating": -1.2, "humiliatingly": -2.6, "humiliation": -2.7, "humiliations": -2.4, "humor": 1.1, "humoral": 0.6, "humored": 1.2, "humoresque": 1.2, "humoresques": 0.9, "humoring": 2.1, "humorist": 1.2, "humoristic": 1.5, "humorists": 1.3, "humorless": -1.3, "humorlessness": -1.4, "humorous": 1.6, "humorously": 2.3, "humorousness": 2.4, "humors": 1.6, "humour": 2.1, "humoured": 1.1, "humouring": 1.7, "humourous": 2.0, "hunger": -1.0, "hurrah": 2.6, "hurrahed": 1.9, "hurrahing": 2.4, "hurrahs": 2.1, "hurray": 2.7, "hurrayed": 1.8, "hurraying": 1.2, "hurrays": 2.4, "hurt": -2.4, "hurter": -2.3, "hurters": -1.9, "hurtful": -2.4, "hurtfully": -2.6, "hurtfulness": -1.9, "hurting": -1.7, "hurtle": -0.3, "hurtled": -0.6, "hurtles": -1.0, "hurtless": 0.3, "hurtling": -1.4, "hurts": -2.1, "hypocritical": -2.0, "hysteria": -1.9, "hysterical": -0.1, "hysterics": -1.8, "ideal": 2.4, "idealess": -1.9, "idealise": 1.4, "idealised": 2.1, "idealises": 2.0, "idealising": 0.6, "idealism": 1.7, "idealisms": 0.8, "idealist": 1.6, "idealistic": 1.8, "idealistically": 1.7, "idealists": 0.7, "idealities": 1.5, "ideality": 1.9, "idealization": 1.8, "idealizations": 1.4, "idealize": 1.2, "idealized": 1.8, "idealizer": 1.3, "idealizers": 1.9, "idealizes": 2.0, "idealizing": 1.4, "idealless": -1.7, "ideally": 1.8, "idealogues": 0.5, "idealogy": 0.8, "ideals": 0.8, "idiot": -2.3, "idiotic": -2.6, "ignorable": -1.0, "ignorami": -1.9, "ignoramus": -1.9, "ignoramuses": -2.3, "ignorance": -1.5, "ignorances": -1.2, "ignorant": -1.1, "ignorantly": -1.6, "ignorantness": -1.1, "ignore": -1.5, "ignored": -1.3, "ignorer": -1.3, "ignorers": -0.7, "ignores": -1.1, "ignoring": -1.7, "ill": -1.8, "illegal": -2.6, "illiteracy": -1.9, "illness": -1.7, "illnesses": -2.2, "imbecile": -2.2, "immobilized": -1.2, "immoral": -2.0, "immoralism": -1.6, "immoralist": -2.1, "immoralists": -1.7, "immoralities": -1.1, "immorality": -0.6, "immorally": -2.1, "immortal": 1.0, "immune": 1.2, "impatience": -1.8, "impatiens": -0.2, "impatient": -1.2, "impatiently": -1.7, "imperfect": -1.3, "impersonal": -1.3, "impolite": -1.6, "impolitely": -1.8, "impoliteness": -1.8, "impolitenesses": -2.3, "importance": 1.5, "importancies": 0.4, "importancy": 1.4, "important": 0.8, "importantly": 1.3, "impose": -1.2, "imposed": -0.3, "imposes": -0.4, "imposing": -0.4, "impotent": -1.1, "impress": 1.9, "impressed": 2.1, "impresses": 2.1, "impressibility": 1.2, "impressible": 0.8, "impressing": 2.5, "impression": 0.9, "impressionable": 0.2, "impressionism": 0.8, "impressionisms": 0.5, "impressionist": 1.0, "impressionistic": 1.5, "impressionistically": 1.6, "impressionists": 0.5, "impressions": 0.9, "impressive": 2.3, "impressively": 2.0, "impressiveness": 1.7, "impressment": -0.4, "impressments": 0.5, "impressure": 0.6, "imprisoned": -2.0, "improve": 1.9, "improved": 2.1, "improvement": 2.0, "improvements": 1.3, "improver": 1.8, "improvers": 1.3, "improves": 1.8, "improving": 1.8, "inability": -1.7, "inaction": -1.0, "inadequacies": -1.7, "inadequacy": -1.7, "inadequate": -1.7, "inadequately": -1.0, "inadequateness": -1.7, "inadequatenesses": -1.6, "incapable": -1.6, "incapacitated": -1.9, "incensed": -2.0, "incentive": 1.5, "incentives": 1.3, "incompetence": -2.3, "incompetent": -2.1, "inconsiderate": -1.9, "inconvenience": -1.5, "inconvenient": -1.4, "increase": 1.3, "increased": 1.1, "indecision": -0.8, "indecisions": -1.1, "indecisive": -1.0, "indecisively": -0.7, "indecisiveness": -1.3, "indecisivenesses": -0.9, "indestructible": 0.6, "indifference": -0.2, "indifferent": -0.8, "indignant": -1.8, "indignation": -2.4, "indoctrinate": -1.4, "indoctrinated": -0.4, "indoctrinates": -0.6, "indoctrinating": -0.7, "ineffective": -0.5, "ineffectively": -1.3, "ineffectiveness": -1.3, "ineffectual": -1.2, "ineffectuality": -1.6, "ineffectually": -1.1, "ineffectualness": -1.3, "infatuated": 0.2, "infatuation": 0.6, "infected": -2.2, "inferior": -1.7, "inferiorities": -1.9, "inferiority": -1.1, "inferiorly": -2.0, "inferiors": -0.5, "inflamed": -1.4, "influential": 1.9, "infringement": -2.1, "infuriate": -2.2, "infuriated": -3.0, "infuriates": -2.6, "infuriating": -2.4, "inhibin": -0.2, "inhibit": -1.6, "inhibited": -0.4, "inhibiting": -0.4, "inhibition": -1.5, "inhibitions": -0.8, "inhibitive": -1.4, "inhibitor": -0.3, "inhibitors": -1.0, "inhibitory": -1.0, "inhibits": -0.9, "injured": -1.7, "injury": -1.8, "injustice": -2.7, "innocence": 1.6, "innocency": 1.9, "innocent": 1.4, "innocenter": 0.9, "innocently": 1.4, "innocents": 1.1, "innovate": 2.2, "innovates": 2.0, "innovation": 1.6, "innovative": 1.9, "inquisition": -1.2, "inquisitive": 0.7, "insane": -1.7, "insanity": -2.7, "insecure": -1.8, "insecurely": -1.4, "insecureness": -1.8, "insecurities": -1.8, "insecurity": -1.8, "insensitive": -0.9, "insensitivity": -1.8, "insignificant": -1.4, "insincere": -1.8, "insincerely": -1.9, "insincerity": -1.4, "insipid": -2.0, "inspiration": 2.4, "inspirational": 2.3, "inspirationally": 2.3, "inspirations": 2.1, "inspirator": 1.9, "inspirators": 1.2, "inspiratory": 1.5, "inspire": 2.7, "inspired": 2.2, "inspirer": 2.2, "inspirers": 2.0, "inspires": 1.9, "inspiring": 1.8, "inspiringly": 2.6, "inspirit": 1.9, "inspirited": 1.3, "inspiriting": 1.8, "inspiritingly": 2.1, "inspirits": 0.8, "insult": -2.3, "insulted": -2.3, "insulter": -2.0, "insulters": -2.0, "insulting": -2.2, "insultingly": -2.3, "insults": -1.8, "intact": 0.8, "integrity": 1.6, "intellect": 2.0, "intellection": 0.6, "intellections": 0.8, "intellective": 1.7, "intellectively": 0.8, "intellects": 1.8, "intellectual": 2.3, "intellectualism": 2.2, "intellectualist": 2.0, "intellectualistic": 1.3, "intellectualists": 0.8, "intellectualities": 1.7, "intellectuality": 1.7, "intellectualization": 1.5, "intellectualize": 1.5, "intellectualized": 1.2, "intellectualizes": 1.8, "intellectualizing": 0.8, "intellectually": 1.4, "intellectualness": 1.5, "intellectuals": 1.6, "intelligence": 2.1, "intelligencer": 1.5, "intelligencers": 1.6, "intelligences": 1.6, "intelligent": 2.0, "intelligential": 1.9, "intelligently": 2.0, "intelligentsia": 1.5, "intelligibility": 1.5, "intelligible": 1.4, "intelligibleness": 1.5, "intelligibly": 1.2, "intense": 0.3, "interest": 2.0, "interested": 1.7, "interestedly": 1.5, "interesting": 1.7, "interestingly": 1.7, "interestingness": 1.8, "interests": 1.0, "interrogated": -1.6, "interrupt": -1.4, "interrupted": -1.2, "interrupter": -1.1, "interrupters": -1.3, "interruptible": -1.3, "interrupting": -1.2, "interruption": -1.5, "interruptions": -1.7, "interruptive": -1.4, "interruptor": -1.3, "interrupts": -1.3, "intimidate": -0.8, "intimidated": -1.9, "intimidates": -1.3, 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"partyer": 1.2, "partyers": 1.1, "partying": 1.6, "passion": 2.0, "passional": 1.6, "passionate": 2.4, "passionately": 2.4, "passionateness": 2.3, "passionflower": 0.3, "passionflowers": 0.4, "passionless": -1.9, "passions": 2.2, "passive": 0.8, "passively": -0.7, "pathetic": -2.7, "pathetical": -1.2, "pathetically": -1.8, "pay": -0.4, "peace": 2.5, "peaceable": 1.7, "peaceableness": 1.8, "peaceably": 2.0, "peaceful": 2.2, "peacefuller": 1.9, "peacefullest": 3.1, "peacefully": 2.4, "peacefulness": 2.1, "peacekeeper": 1.6, "peacekeepers": 1.6, "peacekeeping": 2.0, "peacekeepings": 1.6, "peacemaker": 2.0, "peacemakers": 2.4, "peacemaking": 1.7, "peacenik": 0.8, "peaceniks": 0.7, "peaces": 2.1, "peacetime": 2.2, "peacetimes": 2.1, "peculiar": 0.6, "peculiarities": 0.1, "peculiarity": 0.6, "peculiarly": -0.4, "penalty": -2.0, "pensive": 0.3, "perfect": 2.7, "perfecta": 1.4, "perfectas": 0.6, "perfected": 2.7, "perfecter": 1.8, "perfecters": 1.4, "perfectest": 3.1, "perfectibilities": 2.1, "perfectibility": 1.8, "perfectible": 1.5, "perfecting": 2.3, "perfection": 2.7, "perfectionism": 1.3, "perfectionist": 1.5, "perfectionistic": 0.7, "perfectionists": 0.1, "perfections": 2.5, "perfective": 1.2, "perfectively": 2.1, "perfectiveness": 0.9, "perfectives": 0.9, "perfectivity": 2.2, "perfectly": 3.2, "perfectness": 3.0, "perfecto": 1.3, "perfects": 1.6, "peril": -1.7, "perjury": -1.9, "perpetrator": -2.2, "perpetrators": -1.0, "perplexed": -1.3, "persecute": -2.1, "persecuted": -1.3, "persecutes": -1.2, "persecuting": -1.5, "perturbed": -1.4, "perverse": -1.8, "perversely": -2.2, "perverseness": -2.1, "perversenesses": -0.5, "perversion": -1.3, "perversions": -1.2, "perversities": -1.1, "perversity": -2.6, "perversive": -2.1, "pervert": -2.3, "perverted": -2.5, "pervertedly": -1.2, "pervertedness": -1.2, "perverter": -1.7, "perverters": -0.6, "perverting": -1.0, "perverts": -2.8, "pesky": -1.2, "pessimism": -1.5, "pessimisms": -2.0, "pessimist": -1.5, "pessimistic": -1.5, "pessimistically": -2.0, "pessimists": -1.0, "petrifaction": -1.9, "petrifactions": -0.3, "petrification": -0.1, "petrifications": -0.4, "petrified": -2.5, "petrifies": -2.3, "petrify": -1.7, "petrifying": -2.6, "pettier": -0.3, "pettiest": -1.3, "petty": -0.8, "phobia": -1.6, "phobias": -2.0, "phobic": -1.2, "phobics": -1.3, "picturesque": 1.6, "pileup": -1.1, "pique": -1.1, "piqued": 0.1, "piss": -1.7, "pissant": -1.5, "pissants": -2.5, "pissed": -3.2, "pisser": -2.0, "pissers": -1.4, "pisses": -1.4, "pissing": -1.7, "pissoir": -0.8, "piteous": -1.2, "pitiable": -1.1, "pitiableness": -1.1, "pitiably": -1.1, "pitied": -1.3, "pitier": -1.2, "pitiers": -1.3, "pities": -1.2, "pitiful": -2.2, "pitifuller": -1.8, "pitifullest": -1.1, "pitifully": -1.2, "pitifulness": -1.2, "pitiless": -1.8, "pitilessly": -2.1, "pitilessness": -0.5, "pity": -1.2, "pitying": -1.4, "pityingly": -1.0, "pityriasis": -0.8, "play": 1.4, "played": 1.4, "playful": 1.9, "playfully": 1.6, "playfulness": 1.2, "playing": 0.8, "plays": 1.0, "pleasant": 2.3, "pleasanter": 1.5, "pleasantest": 2.6, "pleasantly": 2.1, "pleasantness": 2.3, "pleasantnesses": 2.3, "pleasantries": 1.3, "pleasantry": 2.0, "please": 1.3, "pleased": 1.9, "pleaser": 1.7, "pleasers": 1.0, "pleases": 1.7, "pleasing": 2.4, "pleasurability": 1.9, "pleasurable": 2.4, "pleasurableness": 2.4, "pleasurably": 2.6, "pleasure": 2.7, "pleasured": 2.3, "pleasureless": -1.6, "pleasures": 1.9, "pleasuring": 2.8, "poised": 1.0, "poison": -2.5, "poisoned": -2.2, "poisoner": -2.7, "poisoners": -3.1, "poisoning": -2.8, "poisonings": -2.4, "poisonous": -2.7, "poisonously": -2.9, "poisons": -2.7, "poisonwood": -1.0, "pollute": -2.3, "polluted": -2.0, "polluter": -1.8, "polluters": -2.0, "pollutes": -2.2, "poor": -2.1, "poorer": -1.5, "poorest": -2.5, "popular": 1.8, "popularise": 1.6, "popularised": 1.1, "popularises": 0.5, "popularising": 1.2, "popularities": 1.6, "popularity": 2.1, "popularization": 1.3, "popularizations": 0.9, "popularize": 1.3, "popularized": 1.9, "popularizer": 1.8, "popularizers": 1.0, "popularizes": 1.4, "popularizing": 1.5, "popularly": 1.8, "positive": 2.6, "positively": 2.4, "positiveness": 2.3, "positivenesses": 2.2, "positiver": 2.3, "positives": 2.4, "positivest": 2.9, "positivism": 1.6, "positivisms": 1.8, "positivist": 2.0, "positivistic": 1.9, "positivists": 1.7, "positivities": 2.6, "positivity": 2.3, "possessive": -0.9, "postpone": -0.9, "postponed": -0.8, "postpones": -1.1, "postponing": -0.5, "poverty": -2.3, "powerful": 1.8, "powerless": -2.2, "praise": 2.6, "praised": 2.2, "praiser": 2.0, "praisers": 2.0, "praises": 2.4, "praiseworthily": 1.9, "praiseworthiness": 2.4, "praiseworthy": 2.6, "praising": 2.5, "pray": 1.3, "praying": 1.5, "prays": 1.4, "prblm": -1.6, "prblms": -2.3, "precious": 2.7, "preciously": 2.2, "preciousness": 1.9, "prejudice": -2.3, "prejudiced": -1.9, "prejudices": -1.8, "prejudicial": -2.6, "prejudicially": -1.5, "prejudicialness": -2.4, "prejudicing": -1.8, "prepared": 0.9, "pressure": -1.2, "pressured": -0.9, "pressureless": 1.0, "pressures": -1.3, "pressuring": -1.4, "pressurise": -0.6, "pressurised": -0.4, "pressurises": -0.8, "pressurising": -0.6, "pressurizations": -0.3, "pressurize": -0.7, "pressurized": 0.1, "pressurizer": 0.1, "pressurizers": -0.7, "pressurizes": -0.2, "pressurizing": -0.2, "pretend": -0.4, "pretending": 0.4, "pretends": -0.4, "prettied": 1.6, "prettier": 2.1, "pretties": 1.7, "prettiest": 2.7, "pretty": 2.2, "prevent": 0.1, "prevented": 0.1, "preventing": -0.1, "prevents": 0.3, "prick": -1.4, "pricked": -0.6, "pricker": -0.3, "prickers": -0.2, "pricket": -0.5, "prickets": 0.3, "pricking": -0.9, "prickle": -1.0, "prickled": -0.2, "prickles": -0.8, "pricklier": -1.6, "prickliest": -1.4, "prickliness": -0.6, "prickling": -0.8, "prickly": -0.9, "pricks": -0.9, "pricky": -0.6, "pride": 1.4, "prison": -2.3, "prisoner": -2.5, "prisoners": -2.3, "privilege": 1.5, "privileged": 1.9, "privileges": 1.6, "privileging": 0.7, "prize": 2.3, "prized": 2.4, "prizefight": -0.1, "prizefighter": 1.0, "prizefighters": -0.1, "prizefighting": 0.4, "prizefights": 0.3, "prizer": 1.0, "prizers": 0.8, "prizes": 2.0, "prizewinner": 2.3, "prizewinners": 2.4, "prizewinning": 3.0, "proactive": 1.8, "problem": -1.7, "problematic": -1.9, "problematical": -1.8, "problematically": -2.0, "problematics": -1.3, "problems": -1.7, "profit": 1.9, "profitabilities": 1.1, "profitability": 1.1, "profitable": 1.9, "profitableness": 2.4, "profitably": 1.6, "profited": 1.3, "profiteer": 0.8, "profiteered": -0.5, "profiteering": -0.6, "profiteers": 0.5, "profiter": 0.7, "profiterole": 0.4, "profiteroles": 0.5, "profiting": 1.6, "profitless": -1.5, "profits": 1.9, "profitwise": 0.9, "progress": 1.8, "prominent": 1.3, "promiscuities": -0.8, "promiscuity": -1.8, "promiscuous": -0.3, "promiscuously": -1.5, "promiscuousness": -0.9, "promise": 1.3, "promised": 1.5, "promisee": 0.8, "promisees": 1.1, "promiser": 1.3, "promisers": 1.6, "promises": 1.6, "promising": 1.7, "promisingly": 1.2, "promisor": 1.0, "promisors": 0.4, "promissory": 0.9, "promote": 1.6, "promoted": 1.8, "promotes": 1.4, "promoting": 1.5, "propaganda": -1.0, "prosecute": -1.7, "prosecuted": -1.6, "prosecutes": -1.8, "prosecution": -2.2, "prospect": 1.2, "prospects": 1.2, "prosperous": 2.1, "protect": 1.6, "protected": 1.9, "protects": 1.3, "protest": -1.0, "protested": -0.5, "protesters": -0.9, "protesting": -1.8, "protests": -0.9, "proud": 2.1, "prouder": 2.2, "proudest": 2.6, "proudful": 1.9, "proudhearted": 1.4, "proudly": 2.6, "provoke": -1.7, "provoked": -1.1, "provokes": -1.3, "provoking": -0.8, "pseudoscience": -1.2, "puke": -2.4, "puked": -1.8, "pukes": -1.9, "puking": -1.8, "pukka": 2.8, "punish": -2.4, "punishabilities": -1.7, "punishability": -1.6, "punishable": -1.9, "punished": -2.0, "punisher": -1.9, "punishers": -2.6, "punishes": -2.1, "punishing": -2.6, "punishment": -2.2, "punishments": -1.8, "punitive": -2.3, "pushy": -1.1, "puzzled": -0.7, "quaking": -1.5, "questionable": -1.2, "questioned": -0.4, "questioning": -0.4, "racism": -3.1, "racist": -3.0, "racists": -2.5, "radian": 0.4, "radiance": 1.4, "radiances": 1.1, "radiancies": 0.8, "radiancy": 1.4, "radians": 0.2, "radiant": 2.1, "radiantly": 1.3, "radiants": 1.2, "rage": -2.6, "raged": -2.0, "ragee": -0.4, "rageful": -2.8, "rages": -2.1, "raging": -2.4, "rainy": -0.3, "rancid": -2.5, "rancidity": -2.6, "rancidly": -2.5, "rancidness": -2.6, "rancidnesses": -1.6, "rant": -1.4, "ranter": -1.2, "ranters": -1.2, "rants": -1.3, "rape": -3.7, "raped": -3.6, "raper": -3.4, "rapers": -3.6, "rapes": -3.5, "rapeseeds": -0.5, "raping": -3.8, "rapist": -3.9, "rapists": -3.3, "rapture": 0.6, "raptured": 0.9, "raptures": 0.7, "rapturous": 1.7, "rash": -1.7, "ratified": 0.6, "reach": 0.1, "reached": 0.4, "reaches": 0.2, "reaching": 0.8, "readiness": 1.0, "ready": 1.5, "reassurance": 1.5, "reassurances": 1.4, "reassure": 1.4, "reassured": 1.7, "reassures": 1.5, "reassuring": 1.7, "reassuringly": 1.8, "rebel": -0.6, "rebeldom": -1.5, "rebelled": -1.0, "rebelling": -1.1, "rebellion": -0.5, "rebellions": -1.1, "rebellious": -1.2, "rebelliously": -1.8, "rebelliousness": -1.2, "rebels": -0.8, "recession": -1.8, "reckless": -1.7, "recommend": 1.5, "recommended": 0.8, "recommends": 0.9, "redeemed": 1.3, "reek": -2.4, "reeked": -2.0, "reeker": -1.7, "reekers": -1.5, "reeking": -2.0, "refuse": -1.2, "refused": -1.2, "refusing": -1.7, "regret": -1.8, "regretful": -1.9, "regretfully": -1.9, "regretfulness": -1.6, "regrets": -1.5, "regrettable": -2.3, "regrettably": -2.0, "regretted": -1.6, "regretter": -1.6, "regretters": -2.0, "regretting": -1.7, "reinvigorate": 2.3, "reinvigorated": 1.9, "reinvigorates": 1.8, "reinvigorating": 1.7, "reinvigoration": 2.2, "reject": -1.7, "rejected": -2.3, "rejectee": -2.3, "rejectees": -1.8, "rejecter": -1.6, "rejecters": -1.8, "rejecting": -2.0, "rejectingly": -1.7, "rejection": -2.5, "rejections": -2.1, "rejective": -1.8, "rejector": -1.8, "rejects": -2.2, "rejoice": 1.9, "rejoiced": 2.0, "rejoices": 2.1, "rejoicing": 2.8, "relax": 1.9, "relaxant": 1.0, "relaxants": 0.7, "relaxation": 2.4, "relaxations": 1.0, "relaxed": 2.2, "relaxedly": 1.5, "relaxedness": 2.0, "relaxer": 1.6, "relaxers": 1.4, "relaxes": 1.5, "relaxin": 1.7, "relaxing": 2.2, "relaxins": 1.2, "relentless": 0.2, "reliant": 0.5, "relief": 2.1, "reliefs": 1.3, "relievable": 1.1, "relieve": 1.5, "relieved": 1.6, "relievedly": 1.4, "reliever": 1.5, "relievers": 1.0, "relieves": 1.5, "relieving": 1.5, "relievo": 1.3, "relishing": 1.6, "reluctance": -1.4, "reluctancy": -1.6, "reluctant": -1.0, "reluctantly": -0.4, "remarkable": 2.6, "remorse": -1.1, "remorseful": -0.9, "remorsefully": -0.7, "remorsefulness": -0.7, "remorseless": -2.3, "remorselessly": -2.0, "remorselessness": -2.8, "repetitive": -1.0, "repress": -1.4, "repressed": -1.3, "represses": -1.3, "repressible": -1.5, "repressing": -1.8, "repression": -1.6, "repressions": -1.7, "repressive": -1.4, "repressively": -1.7, "repressiveness": -1.0, "repressor": -1.4, "repressors": -2.2, "repressurize": -0.3, "repressurized": 0.1, "repressurizes": 0.1, "repressurizing": -0.1, "repulse": -2.8, "repulsed": -2.2, "rescue": 2.3, "rescued": 1.8, "rescues": 1.3, "resent": -0.7, "resented": -1.6, "resentence": -1.0, "resentenced": -0.8, "resentences": -0.6, "resentencing": 0.2, "resentful": -2.1, "resentfully": -1.4, "resentfulness": -2.0, "resenting": -1.2, "resentment": -1.9, "resentments": -1.9, "resents": -1.2, "resign": -1.4, "resignation": -1.2, "resignations": -1.2, "resigned": -1.0, "resignedly": -0.7, "resignedness": -0.8, "resigner": -1.2, "resigners": -1.0, "resigning": -0.9, "resigns": -1.3}

class WordScoreChunk4:
    Dwords ={"resolute": 1.1, "resolvable": 1.0, "resolve": 1.6, "resolved": 0.7, "resolvent": 0.7, "resolvents": 0.4, "resolver": 0.7, "resolvers": 1.4, "resolves": 0.7, "resolving": 1.6, "respect": 2.1, "respectabilities": 1.8, "respectability": 2.4, "respectable": 1.9, "respectableness": 1.2, "respectably": 1.7, "respected": 2.1, "respecter": 2.1, "respecters": 1.6, "respectful": 2.0, "respectfully": 1.7, "respectfulness": 1.9, "respectfulnesses": 1.3, "respecting": 2.2, "respective": 1.8, "respectively": 1.4, "respectiveness": 1.1, "respects": 1.3, "responsible": 1.3, "responsive": 1.5, "restful": 1.5, "restless": -1.1, "restlessly": -1.4, "restlessness": -1.2, "restore": 1.2, "restored": 1.4, "restores": 1.2, "restoring": 1.2, "restrict": -1.6, "restricted": -1.6, "restricting": -1.6, "restriction": -1.1, "restricts": -1.3, "retained": 0.1, "retard": -2.4, "retarded": -2.7, "retreat": 0.8, "revenge": -2.4, "revenged": -0.9, "revengeful": -2.4, "revengefully": -1.4, 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-0.8, "wellborn": 1.8, "welldoer": 2.5, "welldoers": 1.6, "welled": 0.4, "wellhead": 0.1, "wellheads": 0.5, "wellhole": -0.1, "wellies": 0.4, "welling": 1.6, "wellness": 1.9, "wells": 1.0, "wellsite": 0.5, "wellspring": 1.5, "wellsprings": 1.4, "welly": 0.2, "wept": -2.0, "whimsical": 0.3, "whine": -1.5, "whined": -0.9, "whiner": -1.2, "whiners": -0.6, "whines": -1.8, "whiney": -1.3, "whining": -0.9, "whitewash": 0.1, "whore": -3.3, "whored": -2.8, "whoredom": -2.1, "whoredoms": -2.4, "whorehouse": -1.1, "whorehouses": -1.9, "whoremaster": -1.9, "whoremasters": -1.5, "whoremonger": -2.6, "whoremongers": -2.0, "whores": -3.0, "whoreson": -2.2, "whoresons": -2.5, "wicked": -2.4, "wickeder": -2.2, "wickedest": -2.9, "wickedly": -2.1, "wickedness": -2.1, "wickednesses": -2.2, "widowed": -2.1, "willingness": 1.1, "wimp": -1.4, "wimpier": -1.0, "wimpiest": -0.9, "wimpiness": -1.2, "wimpish": -1.6, "wimpishness": -0.2, "wimple": -0.2, "wimples": -0.3, "wimps": -1.0, "wimpy": -0.9, "win": 2.8, "winnable": 1.8, "winned": 1.8, "winner": 2.8, "winners": 2.1, "winning": 2.4, "winningly": 2.3, "winnings": 2.5, "winnow": -0.3, "winnower": -0.1, "winnowers": -0.2, "winnowing": -0.1, "winnows": -0.2, "wins": 2.7, "wisdom": 2.4, "wise": 2.1, "wiseacre": -1.2, "wiseacres": -0.1, "wiseass": -1.8, "wiseasses": -1.5, "wisecrack": -0.1, "wisecracked": -0.5, "wisecracker": -0.1, "wisecrackers": 0.1, "wisecracking": -0.6, "wisecracks": -0.3, "wised": 1.5, "wiseguys": 0.3, "wiselier": 0.9, "wiseliest": 1.6, "wisely": 1.8, "wiseness": 1.9, "wisenheimer": -1.0, "wisenheimers": -1.4, "wisents": 0.4, "wiser": 1.2, "wises": 1.3, "wisest": 2.1, "wisewomen": 1.3, "wish": 1.7, "wishes": 0.6, "wishing": 0.9, "witch": -1.5, "withdrawal": 0.1, "woe": -1.8, "woebegone": -2.6, "woebegoneness": -1.1, "woeful": -1.9, "woefully": -1.7, "woefulness": -2.1, "woes": -1.9, "woesome": -1.2, "won": 2.7, "wonderful": 2.7, "wonderfully": 2.9, "wonderfulness": 2.9, "woo": 2.1, "woohoo": 2.3, "woot": 1.8, "worn": -1.2, "worried": -1.2, "worriedly": -2.0, "worrier": -1.8, "worriers": -1.7, "worries": -1.8, "worriment": -1.5, "worriments": -1.9, "worrisome": -1.7, "worrisomely": -2.0, "worrisomeness": -1.9, "worrit": -2.1, "worrits": -1.2, "worry": -1.9, "worrying": -1.4, "worrywart": -1.8, "worrywarts": -1.5, "worse": -2.1, "worsen": -2.3, "worsened": -1.9, "worsening": -2.0, "worsens": -2.1, "worser": -2.0, "worship": 1.2, "worshiped": 2.4, "worshiper": 1.0, "worshipers": 0.9, "worshipful": 0.7, "worshipfully": 1.1, "worshipfulness": 1.6, "worshiping": 1.0, "worshipless": -0.6, "worshipped": 2.7, "worshipper": 0.6, "worshippers": 0.8, "worshipping": 1.6, "worships": 1.4, "worst": -3.1, "worth": 0.9, "worthless": -1.9, "worthwhile": 1.4, "worthy": 1.9, "wow": 2.8, "wowed": 2.6, "wowing": 2.5, "wows": 2.0, "wowser": -1.1, "wowsers": 1.0, "wrathful": -2.7, "wreck": -1.9, "wrong": -2.1, "wronged": -1.9, "yay": 2.4, "yeah": 1.2, "yearning": 0.5, "yeees": 1.7, "yep": 1.2, "yes": 1.7, "youthful": 1.3, "yucky": -1.8, "yummy": 2.4, "zealot": -1.9, "zealots": -0.8, "zealous": 0.5, "{:": 1.8, "|-0": -1.2, "|-:": -0.8, "|-:>": -1.6, "|-o": -1.2, "|:": -0.5, "|;-)": 2.2, "|=": -0.4, "|^:": -1.1, "|o:": -0.9, "||-:": -2.3, "}:": -2.1, "}:(": -2.0, "}:)": 0.4, "}:-(": -2.1, "}:-)": 0.3}

import numpy as np
import pandas as pd
from collections import deque
import random
from gym import spaces
import math
from scipy.stats import linregress
import random

class TradingEnv(object):
    
    def __init__(self, obs_len=10, df=None):
        
        self.window = obs_len
        self.data = df
        
        if df is None:
            self.data = self.dummy_data()
            
        x = list(self.data.columns.get_level_values(0))
        x = list(dict.fromkeys(x))
        
        self.SymbolList = x
        self.CountIter = -1
        self.MaxCount = len(x)
        self.action_space = spaces.Box(-1, +1, (1,), dtype=np.float32)
        
    def dummy_data(self):
        x1 = np.zeros(100)
        close = {'symbol_1':x1,'symbol_2': x1,'symbol_3':x1}
        vol = {'symbol_1':x1,'symbol_2': x1,'symbol_3':x1}
        score = {'symbol_1':x1,'symbol_2': x1,'symbol_3':x1}
        y = {'close':close, 'volume':vol, 'score':score}
        dict_of_df = {k: pd.DataFrame(v) for k,v in y.items()}
        df = pd.concat(dict_of_df, axis=1)
        v = pd.Categorical(df.columns.get_level_values(0), 
                           categories=['close', 'volume', 'score'], 
                           ordered=True)
        v2 = pd.Categorical(df.columns.get_level_values(1), 
                            categories=['symbol_1', 'symbol_2', 'symbol_3'],
                            ordered=True)
        df.columns = pd.MultiIndex.from_arrays([v2,v])
        return df.sort_index(axis=1, level=[0, 1])
        
    def reset(self, randomIndex=False):
        """reset and set data to zero or null"""
        
        # randomly pick stock if true
        if randomIndex:
            self.CountIter = random.randint(0, int(self.MaxCount))
        
        # iterate through list to train
        if self.CountIter + 1 >= self.MaxCount:
            self.CountIter = -1
        
        self.CountIter += 1
        
        # used to identify what symbol data is used
        self.sym = self.SymbolList[self.CountIter]
        
        df = self.data[self.sym]
        
        self.close = df['close'].values
        self.volume = df['volume'].values
        self.news = df['score'].values
        self.returns = df['close'].pct_change().values
        
        # start index so rolling window is full of data
        self.ts_index = self.window + 1

        # get obs
        c_window, v_window, n_window = self.on_data()
        observations = self.next_observation(close_window=c_window, volume_window=v_window, news_window=n_window)
        
        # get obs space
        self.observation_space = spaces.Box(-np.inf, np.inf, shape=(len(observations),), dtype=np.float32)
        
        # used to calc strategy perf
        self.strat_returns = []
        
        return observations
        
    def std(self,x):
        '''generate standard deviation'''
        y = (x - x.mean())/x.std()
        return y[-1]
        
    def exponential_regression(self, data):
        log_c = np.log(data)
        x = np.arange(len(log_c))
        slope, _, rvalue, _, _ = linregress(x, log_c)
        return (1 + slope) * (rvalue ** 2)
        
    def regression(self, data):
        x = np.arange(len(data))
        slope, _, rvalue, _, _ = linregress(x, data)
        return (1 + slope) * (rvalue ** 2) 
    
    def next_observation(self, close_window, volume_window, news_window):
        '''return observations'''
        
        # calculate log of closing prices (feature 1)
        sed = news_window.mean() / 100
        
        # log diff of price
        #diff_c = np.diff(np.log(close_window)).sum()*100
        
        # log diff of volume
        #diff_v = np.diff(np.log(volume_window)).sum()*5
        
        # volitility of price
        #std = np.log(close_window).std()
        
        # how well the exp slope is correlated with itself 
        #exp_reg = self.exponential_regression(close_window)
        
        # how well the slope is correlated with itself 
        lin_reg = self.regression(close_window)
        
        # last std value of close
        col = self.std(close_window)
        
        # last std value of volume
        vol = self.std(volume_window)
        
        # last std value of sediment
        seddir = self.std(news_window)
        
        # combine mutliple features
        obs = np.concatenate(([sed],[col],[lin_reg],[vol],[seddir]), axis=0)
        
        # if nan replace nans with zero
        where_are_NaNs = np.isnan(obs)
        obs[where_are_NaNs] = 0
        
        return obs

    def on_data(self):
        '''update data'''
        # where are we in index?
        step = self.ts_index
        
        close_window = self.close[step-self.window:step]
        volume_window = self.volume[step-self.window:step]
        news_window = self.volume[step-self.window:step]
        
        return close_window, volume_window, news_window
        
        
    def get_reward(self, trade=0):
        '''rewards for trading performance'''
        
        # where are we in index?
        step = self.ts_index
        
        # calculate reward 
        reward = self.returns[step] * trade
        
        self.strat_returns.append(reward)
        
        return reward if np.isfinite(reward) else 0
        

    def normalize(self,x):
        ''' 
        greater than 0.15 == buy scaled 0-1
        in between +0.15 & -0.15 == do nothing
        less than -0.15 == sell scaled 0-1
        '''
        return np.round((1/0.95*x)-0.05264,3)
        
    def step(self, action):
        '''step through envoirment'''
        
        done = False
        
        # get action from neural network
        action = float(action[0])
        
        # buy sell do nothing logic
        if action >= 0.05:
            # bet size buy
            size = np.clip(self.normalize(abs(action)),0,1)
            
        elif action <= -0.05:
            # bet size sell
            size = -(np.clip(self.normalize(abs(action)),0,1))
        
        else:
            # do nothing
            size = 0

        # if done, break and return final values
        if self.ts_index + 2 >= len(self.close):

            done = True
            
            # get reward
            reward = self.get_reward(trade=size)
            
            # step through next iteration of data
            c_window, v_window, n_window = self.on_data()
            
            # gets obs
            observations = self.next_observation(close_window=c_window, volume_window=v_window, news_window=n_window)
            
            return observations, reward, done, self.ts_index
            
        # get reward
        reward = self.get_reward(trade=size)
        
        # add one to timestep index (different from current index)
        self.ts_index += 1

        # step through next iteration of data
        c_window, v_window, n_window = self.on_data()
        
        # get features
        observations = self.next_observation(close_window=c_window, volume_window=v_window, news_window=n_window)
        
        return observations, reward, done, self.ts_index

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from network import Actor, Critic
import base64
from io import BytesIO
import json

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")


class TD3(object):
    """Agent class that handles the training of the networks and provides outputs as actions
    
        Args:
            state_dim (int): state size
            action_dim (int): action size
            max_action (float): highest action to take
            device (device): cuda or cpu to process tensors
            env (env): gym environment to use
    
    """
    def __init__(self, algo, state_dim, action_dim, max_action, seed):
        self.algo = algo
        self.actor = Actor(state_dim, action_dim, max_action, seed).to(device)
        self.actor_target = Actor(state_dim, action_dim, max_action, seed).to(device)
        self.actor_target.load_state_dict(self.actor.state_dict())
        self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=1e-3)

        self.critic = Critic(state_dim, action_dim, seed).to(device)
        self.critic_target = Critic(state_dim, action_dim, seed).to(device)
        self.critic_target.load_state_dict(self.critic.state_dict())
        self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=1e-3)

        self.max_action = max_action

        #np.random.seed(seed=seed)
        

    def select_action(self, state, noise=0.1):
        """Select an appropriate action from the agent policy
        
            Args:
                state (array): current state of environment
                noise (float): how much noise to add to acitons
                
            Returns:
                action (float): action clipped within action range
        
        """
        
        state = torch.FloatTensor(state.reshape(1, -1)).to(device)
        
        action = self.actor(state).cpu().data.numpy().flatten()
        if noise != 0: 
            action = (action + np.random.normal(0, noise, size=1))
            
        return action.clip(-self.max_action, self.max_action)

    
    def train(self, replay_buffer, iterations, batch_size=100, discount=0.99, tau=0.005, policy_noise=0.2, noise_clip=0.5, policy_freq=2):
        """Train and update actor and critic networks
        
            Args:
                replay_buffer (ReplayBuffer): buffer for experience replay
                iterations (int): how many times to run training
                batch_size(int): batch size to sample from replay buffer
                discount (float): discount factor
                tau (float): soft update for main networks to target networks
                
            Return:
                actor_loss (float): loss from actor network
                critic_loss (float): loss from critic network
        
        """
        
        for it in range(iterations):

            # Sample replay buffer 
            x, y, u, r, d = replay_buffer.sample(batch_size)
            state = torch.FloatTensor(x).to(device)
            action = torch.FloatTensor(u).to(device)
            next_state = torch.FloatTensor(y).to(device)
            done = torch.FloatTensor(1 - d).to(device)
            reward = torch.FloatTensor(r).to(device)

            # Select action according to policy and add clipped noise 
            noise = torch.FloatTensor(u).data.normal_(0, policy_noise).to(device)
            noise = noise.clamp(-noise_clip, noise_clip)
            next_action = (self.actor_target(next_state) + noise).clamp(-self.max_action, self.max_action)

            # Compute the target Q value
            target_Q1, target_Q2 = self.critic_target(next_state, next_action)
            target_Q = torch.min(target_Q1, target_Q2)
            target_Q = reward + (done * discount * target_Q).detach()

            # Get current Q estimates
            current_Q1, current_Q2 = self.critic(state, action)

            # Compute critic loss
            critic_loss = F.mse_loss(current_Q1, target_Q) + F.mse_loss(current_Q2, target_Q) 

            # Optimize the critic
            self.critic_optimizer.zero_grad()
            critic_loss.backward()
            self.critic_optimizer.step()

            # Delayed policy updates
            if it % policy_freq == 0:

                # Compute actor loss
                actor_loss = -self.critic.Q1(state, self.actor(state)).mean()

                # Optimize the actor 
                self.actor_optimizer.zero_grad()
                actor_loss.backward()
                self.actor_optimizer.step()

                # Update the frozen target models
                for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
                    target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)

                for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
                    target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)

    def save(self, filename):
        torch.save(self.actor.state_dict(), '%s_actor.pth' % (filename))
        torch.save(self.critic.state_dict(), '%s_critic.pth' % (filename))

    def load(self, filename):
        self.actor.load_state_dict(torch.load('%s_actor.pth' % (filename)))
        self.critic.load_state_dict(torch.load('%s_critic.pth' % (filename)))

import numpy as np
import pickle

# Expects tuples of (state, next_state, action, reward, done)
class ReplayBuffer(object):
    """Buffer to store tuples of experience replay"""
    
    def __init__(self, algo, max_size=1000000):
        """
        Args:
            max_size (int): total amount of tuples to store
        """
        self.algo = algo
        
        self.storage = []
        self.max_size = max_size
        self.ptr = 0

    def add(self, data):
        """Add experience tuples to buffer
        
        Args:
            data (tuple): experience replay tuple
        """
        
        if len(self.storage) == self.max_size:
            self.storage[int(self.ptr)] = data
            self.ptr = (self.ptr + 1) % self.max_size
        else:
            self.storage.append(data)
            
    def save(self, name='ReplayBuff'):
        '''Save replay buffer for live trading'''
        self.algo.ObjectStore.Save(name, str(self.storage))
        self.algo.Log("{} - Saving Replay Buffer!: {}".format(self.algo.Time, len(self.storage)))
    
    def load(self, name='ReplayBuff'):
        '''Save replay buffer for live trading'''
        self.storage = eval(self.algo.ObjectStore.ReadBytes("key"))
        self.algo.Log("{} - Loading Replay Buffer!: {}".format(self.algo.Time, len(self.storage)))

    def sample(self, batch_size):
        """Samples a random amount of experiences from buffer of batch size
        
        Args:
            batch_size (int): size of sample
        """
        
        ind = np.random.randint(0, len(self.storage), size=batch_size)
        states, actions, next_states, rewards, dones = [], [], [], [], []

        for i in ind: 
            s, a, s_, r, d = self.storage[i]
            states.append(np.array(s, copy=False))
            actions.append(np.array(a, copy=False))
            next_states.append(np.array(s_, copy=False))
            rewards.append(np.array(r, copy=False))
            dones.append(np.array(d, copy=False))

        return np.array(states), np.array(actions), np.array(next_states), np.array(rewards).reshape(-1, 1), np.array(dones).reshape(-1, 1)

import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F

def hidden_init(layer):
    fan_in = layer.weight.data.size()[0]
    lim = 1. / np.sqrt(fan_in)
    return (-lim, lim)

class Actor(nn.Module):
    """Initialize parameters and build model.
        Args:
            state_size (int): Dimension of each state
            action_size (int): Dimension of each action
            max_action (float): highest action to take
            seed (int): Random seed
            h1_units (int): Number of nodes in first hidden layer
            h2_units (int): Number of nodes in second hidden layer
            
        Return:
            action output of network with tanh activation
    """
    
    def __init__(self, state_dim, action_dim, max_action, seed):
        super(Actor, self).__init__()
        self.seed = torch.manual_seed(seed)

        self.l1 = nn.Linear(state_dim, 400)
        self.l2 = nn.Linear(400, 300)
        self.l3 = nn.Linear(300, action_dim)

        self.max_action = max_action


    def forward(self, x):
        x = F.relu(self.l1(x))
        x = F.relu(self.l2(x))
        x = self.max_action * torch.tanh(self.l3(x)) 
        return x



class Critic(nn.Module):
    """Initialize parameters and build model.
        Args:
            state_size (int): Dimension of each state
            action_size (int): Dimension of each action
            max_action (float): highest action to take
            seed (int): Random seed
            h1_units (int): Number of nodes in first hidden layer
            h2_units (int): Number of nodes in second hidden layer
            
        Return:
            value output of network 
    """
    
    def __init__(self, state_dim, action_dim, seed):
        super(Critic, self).__init__()
        self.seed = torch.manual_seed(seed)

        # Q1 architecture
        self.l1 = nn.Linear(state_dim + action_dim, 400)
        self.l2 = nn.Linear(400, 300)
        self.l3 = nn.Linear(300, 1)

        # Q2 architecture
        self.l4 = nn.Linear(state_dim + action_dim, 400)
        self.l5 = nn.Linear(400, 300)
        self.l6 = nn.Linear(300, 1)



    def forward(self, x, u):
        xu = torch.cat([x, u], 1)

        x1 = F.relu(self.l1(xu))

        x1 = F.relu(self.l2(x1))

        x1 = self.l3(x1)

        x2 = F.relu(self.l4(xu))

        x2 = F.relu(self.l5(x2))

        x2 = self.l6(x2)
        return x1, x2


    def Q1(self, x, u):
        xu = torch.cat([x, u], 1)

        x1 = F.relu(self.l1(xu))

        x1 = F.relu(self.l2(x1))

        x1 = self.l3(x1)
        return x1

import nltk
from nltk.tokenize import word_tokenize
from itertools import chain
import re

# we cant have one big file full of keys and values so I split it up
from SedimentDict_1 import WordScoreChunk1 as wsc1
from SedimentDict_2 import WordScoreChunk2 as wsc2
from SedimentDict_3 import WordScoreChunk3 as wsc3
from SedimentDict_4 import WordScoreChunk4 as wsc4

class WordScore:
    def __init__(self):
        ws1 = wsc1.DWords
        ws2 = wsc2.Dwords
        ws3 = wsc3.Dwords
        ws4 = wsc4.Dwords
        
        # combine all python dictionaries 
        self.wordScores = dict(chain.from_iterable(d.items() for d in (ws1, ws2, ws3, ws4)))
        
    def score(self, article):
        '''Get sediment score'''
        try:
            words = word_tokenize(article)
            words = re.sub("[^a-zA-Z]", " ", str(words))
        except:
            return 0
        return sum([self.wordScores[word] for word in words if word in self.wordScores])

class WordScoreChunk1:
    DWords = {"$:": -1.5, "%)": -0.4, "%-)": -1.5, "&-:": -0.4, "&:": -0.7, "( \'}{\' )": 1.6, "(%": -0.9, "(\'-:": 2.2, "(\':": 2.3, "((-:": 2.1, "(*": 1.1, "(-%": -0.7, "(-*": 1.3, "(-:": 1.6, "(-:0": 2.8, "(-:<": -0.4, "(-:o": 1.5, 
    "(-:O": 1.5, "(-:{": -0.1, "(-:|>*": 1.9, "(-;": 1.3, "(-;|": 2.1, "(8": 2.6, "(:": 2.2, "(:0": 2.4, "(:<": -0.2, "(:o": 2.5, "(:O": 2.5, "(;": 1.1, "(;<": 0.3, "(=": 2.2, "(?:": 2.1, "(^:": 1.5, "(^;": 1.5, "(^;0": 2.0, "(^;o": 1.9, "(o:": 1.6,
    ")\':": -2.0, ")-\':": -2.1, ")-:": -2.1, ")-:<": -2.2, ")-:{": -2.1, "):": -1.8, "):<": -1.9, "):{": -2.3, ");<": -2.6, "*)": 0.6, "*-)": 0.3, "*-:": 2.1, "*-;": 2.4, "*:": 1.9, "*<|:-)": 1.6, "*\\\\0/*": 2.3, "*^:": 1.6, ",-:": 1.2, "---\'-;-{@": 2.3,
    "--<--<@": 2.2, ".-:": -1.2, "..###-:": -1.7, "..###:": -1.9, "/-:": -1.3, "/:": -1.3, "/:<": -1.4, "/=": -0.9, "/^:": -1.0, "/o:": -1.4, "0-8": 0.1, "0-|": -1.2, "0:)": 1.9, "0:-)": 1.4, "0:-3": 1.5, "0:03": 1.9, "0;^)": 1.6, "0_o": -0.3, "10q": 2.1, "1337": 2.1,
    "143": 3.2, "1432": 2.6, "14aa41": 2.4, "182": -2.9, "187": -3.1, "2g2b4g": 2.8, "2g2bt": -0.1, "2qt": 2.1, "3:(": -2.2, "3:)": 0.5, "3:-(": -2.3, "3:-)": -1.4, "4col": -2.2, "4q": -3.1, "5fs": 1.5, "8)": 1.9, "8-d": 1.7, "8-o": -0.3, "86": -1.6, "8d": 2.9,
    ":###..": -2.4, ":$": -0.2, ":&": -0.6, ":\'(": -2.2, ":\')": 2.3, ":\'-(": -2.4, ":\'-)": 2.7, ":(": -1.9, ":)": 2.0, ":*": 2.5, ":-###..": -2.5, ":-&": -0.5, ":-(": -1.5, ":-)": 1.3, ":-))": 2.8, ":-*": 1.7, ":-,": 1.1, ":-.": -0.9, ":-/": -1.2, ":-<": -1.5,
    ":-d": 2.3, ":-D": 2.3, ":-o": 0.1, ":-p": 1.5, ":-[": -1.6, ":-\\\\": -0.9, ":-c": -1.3, ":-|": -0.7, ":-||": -2.5, ":-\\u00de": 0.9, ":/": -1.4, ":3": 2.3, ":<": -2.1, ":>": 2.1, ":?)": 1.3, ":?c": -1.6, ":@": -2.5, ":d": 2.3, ":D": 2.3, ":l": -1.7, ":o": -0.4,
    ":p": 1.4, ":s": -1.2, ":[": -2.0, ":\\\\": -1.3, ":]": 2.2, ":^)": 2.1, ":^*": 2.6, ":^/": -1.2, ":^\\\\": -1.0, ":^|": -1.0, ":c": -2.1, ":c)": 2.0, ":o)": 2.1, ":o/": -1.4, ":o\\\\": -1.1, ":o|": -0.6, ":{": -1.9, ":|": -0.4, ":}": 2.1, ":\\u00de": 1.1, ";)": 0.9,
    ";-)": 1.0, ";-*": 2.2, ";-]": 0.7, ";d": 0.8, ";D": 0.8, ";]": 0.6, ";^)": 1.4, "</3": -3.0, "<3": 1.9, "<:": 2.1, "<:-|": -1.4, "=)": 2.2, "-3": 2.0, "#NAME?": 1.3, "=/": -1.4, "3": 2.1, "=]": 1.6, "=|": -0.8, ">-:": -2.0, ">.<": -1.3, ">:": -2.1, ">:(": -2.7,
    ">:)": 0.4, ">:-(": -2.7, ">:-)": -0.4, ">:/": -1.6, ">:o": -1.2, ">:p": 1.0, ">:[": -2.1, ">:\\\\": -1.7, ">;(": -2.9, ">;)": 0.1, ">_>^": 2.1, "@:": -2.1, "@>-->--": 2.1, "@}-;-\'---": 2.2, "aas": 2.5, "aayf": 2.7, "afu": -2.9, "alol": 2.8, "ambw": 2.9,
    "aml": 3.4, "atab": -1.9, "awol": -1.3, "ayc": 0.2, "ayor": -1.2, "Aug-00": 0.3, "bfd": -2.7, "bfe": -2.6, "bff": 2.9, "bffn": 1.0, "bl": 2.3, "bsod": -2.2, "btd": -2.1, "btdt": -0.1, "bz": 0.4, "b^d": 2.6, "cwot": -2.3, "d-\':": -2.5, "d8": -3.2, "d:": 1.2,
    "d:<": -3.2, "d;": -2.9, "d=": 1.5, "doa": -2.3, "dx": -3.0, "ez": 1.5, "fav": 2.0, "fcol": -1.8, "ff": 1.8, "ffs": -2.8, "fkm": -2.4, "foaf": 1.8, "ftw": 2.0, "fu": -3.7, "fubar": -3.0, "fwb": 2.5, "fyi": 0.8, "fysa": 0.4, "g1": 1.4, "gg": 1.2, "gga": 1.7, 
    "gigo": -0.6, "gj": 2.0, "gl": 1.3, "gla": 2.5, "gn": 1.2, "gr8": 2.7, "grrr": -0.4, "gt": 1.1, "h&k": 2.3, "hagd": 2.2, "hagn": 2.2, "hago": 1.2, "hak": 1.9, "hand": 2.2, "hho1/2k": 1.4, "hhoj": 2.0, "hhok": 0.9, "hugz": 2.0, "hi5": 1.9, "idk": -0.4, "ijs": 0.7, 
    "ilu": 3.4, "iluaaf": 2.7, "ily": 3.4, "ily2": 2.6, "iou": 0.7, "iyq": 2.3, "j/j": 2.0, "j/k": 1.6, "j/p": 1.4, "j/t": -0.2, "j/w": 1.0, "j4f": 1.4, "j4g": 1.7, "jho": 0.8, "jhomf": 1.0, "jj": 1.0, "jk": 0.9, "jp": 0.8, "jt": 0.9, "jw": 1.6, "jealz": -1.2, 
    "k4y": 2.3, "kfy": 2.3, "kia": -3.2, "kk": 1.5, "kmuf": 2.2, "l": 2.0, "l&r": 2.2, "laoj": 1.3, "lmao": 2.9, "lmbao": 1.8, "lmfao": 2.5, "lmso": 2.7, "lol": 1.8, "lolz": 2.7, "lts": 1.6, "ly": 2.6, "ly4e": 2.7, "lya": 3.3, "lyb": 3.0, "lyl": 3.1, "lylab": 2.7, 
    "lylas": 2.6, "lylb": 1.6, "m8": 1.4, "mia": -1.2, "mml": 2.0, "mofo": -2.4, "muah": 2.3, "mubar": -1.0, "musm": 0.9, "mwah": 2.5, "n1": 1.9, "nbd": 1.3, "nbif": -0.5, "nfc": -2.7, "nfw": -2.4, "nh": 2.2, "nimby": -0.8, "nimjd": -0.7, "nimq": -0.2, "nimy": -1.4,
    "nitl": -1.5, "nme": -2.1, "noyb": -0.7, "np": 1.4, "ntmu": 1.4, "o-8": -0.5, "o-:": -0.3, "o-|": -1.1, "o.o": -0.8, "O.o": -0.6, "o.O": -0.6, "o:": -0.2, "o:)": 1.5, "o:-)": 2.0, "o:-3": 2.2, "o:3": 2.3, "o:<": -0.3, "o;^)": 1.6, "ok": 1.2, "o_o": -0.5, "O_o": -0.5, 
    "o_O": -0.5, "pita": -2.4, "pls": 0.3, "plz": 0.3, "pmbi": 0.8, "pmfji": 0.3, "pmji": 0.7, "po": -2.6, "ptl": 2.6, "pu": -1.1, "qq": -2.2, "qt": 1.8, "r&r": 2.4, "rofl": 2.7, "roflmao": 2.5, "rotfl": 2.6, "rotflmao": 2.8, "rotflmfao": 2.5, "rotflol": 3.0, "rotgl": 2.9,
    "rotglmao": 1.8, "s:": -1.1, "sapfu": -1.1, "sete": 2.8, "sfete": 2.7, "sgtm": 2.4, "slap": 0.6, "slaw": 2.1, "smh": -1.3, "snafu": -2.5, "sob": -1.0, "swak": 2.3, "tgif": 2.3, "thks": 1.4, "thx": 1.5, "tia": 2.3, "tmi": -0.3, "tnx": 1.1, "TRUE": 1.8, "tx": 1.5, "txs": 1.1,
    "ty": 1.6, "tyvm": 2.5, "urw": 1.9, "vbg": 2.1, "vbs": 3.1, "vip": 2.3, "vwd": 2.6, "vwp": 2.1, "wag": -0.2, "wd": 2.7, "wilco": 0.9, "wp": 1.0, "wtf": -2.8, "wtg": 2.1, "wth": -2.4, "x-d": 2.6, "x-p": 1.7, "xd": 2.8, "xlnt": 3.0, "xoxo": 3.0, "xoxozzz": 2.3,
    "xp": 1.6, "xqzt": 1.6, "xtc": 0.8, "yolo": 1.1, "yoyo": 0.4, "yvw": 1.6, "yw": 1.8, "ywia": 2.5, "zzz": -1.2, "[-;": 0.5, "[:": 1.3, "[;": 1.0, "[=": 1.7, "\\\\-:": -1.0, "\\\\:": -1.0, "\\\\:<": -1.7, "\\\\=": -1.1, "\\\\^:": -1.3, "\\\\o/": 2.2, "\\\\o:": -1.2, 
    "]-:": -2.1, "]:": -1.6, "]:<": -2.5, "^<_<": 1.4, "^urs": -2.8, "abandon": -1.9, "abandoned": -2.0, "abandoner": -1.9, "abandoners": -1.9, "abandoning": -1.6, "abandonment": -2.4, "abandonments": -1.7, "abandons": -1.3, "abducted": -2.3, "abduction": -2.8,
    "abductions": -2.0, "abhor": -2.0, "abhorred": -2.4, "abhorrent": -3.1, "abhors": -2.9, "abilities": 1.0, "ability": 1.3, "aboard": 0.1, "absentee": -1.1, "absentees": -0.8, "absolve": 1.2, "absolved": 1.5, "absolves": 1.3, "absolving": 1.6, "abuse": -3.2,
    "abused": -2.3, "abuser": -2.6, "abusers": -2.6, "abuses": -2.6, "abusing": -2.0, "abusive": -3.2, "abusively": -2.8, "abusiveness": -2.5, "abusivenesses": -3.0, "accept": 1.6, "acceptabilities": 1.6, "acceptability": 1.1, "acceptable": 1.3, "acceptableness": 1.3,
    "acceptably": 1.5, "acceptance": 2.0, "acceptances": 1.7, "acceptant": 1.6, "acceptation": 1.3, "acceptations": 0.9, "accepted": 1.1, "accepting": 1.6, "accepts": 1.3, "accident": -2.1, "accidental": -0.3, "accidentally": -1.4, "accidents": -1.3, "accomplish": 1.8, 
    "accomplished": 1.9, "accomplishes": 1.7, "accusation": -1.0, "accusations": -1.3, "accuse": -0.8, "accused": -1.2, "accuses": -1.4, "accusing": -0.7, "ache": -1.6, "ached": -1.6, "aches": -1.0, "achievable": 1.3, "aching": -2.2, "acquit": 0.8, "acquits": 0.1, 
    "acquitted": 1.0, "acquitting": 1.3, "acrimonious": -1.7, "active": 1.7, "actively": 1.3, "activeness": 0.6, "activenesses": 0.8, "actives": 1.1, "adequate": 0.9, "admirability": 2.4, "admirable": 2.6, "admirableness": 2.2, "admirably": 2.5, "admiral": 1.3, 
    "admirals": 1.5, "admiralties": 1.6, "admiralty": 1.2, "admiration": 2.5, "admirations": 1.6, "admire": 2.1, "admired": 2.3, "admirer": 1.8, "admirers": 1.7, "admires": 1.5, "admiring": 1.6, "admiringly": 2.3, "admit": 0.8, "admits": 1.2, "admitted": 0.4, 
    "admonished": -1.9, "adopt": 0.7, "adopts": 0.7, "adorability": 2.2, "adorable": 2.2, "adorableness": 2.5, "adorably": 2.1, "adoration": 2.9, "adorations": 2.2, "adore": 2.6, "adored": 1.8, "adorer": 1.7, "adorers": 2.1, "adores": 1.6, "adoring": 2.6, "adoringly": 2.4,
    "adorn": 0.9, "adorned": 0.8, "adorner": 1.3, "adorners": 0.9, "adorning": 1.0, "adornment": 1.3, "adornments": 0.8, "adorns": 0.5, "advanced": 1.0, "advantage": 1.0, "advantaged": 1.4, "advantageous": 1.5, "advantageously": 1.9, "advantageousness": 1.6, "advantages": 1.5,
    "advantaging": 1.6, "adventure": 1.3, "adventured": 1.3, "adventurer": 1.2, "adventurers": 0.9, "adventures": 1.4, "adventuresome": 1.7, "adventuresomeness": 1.3, "adventuress": 0.8, "adventuresses": 1.4, "adventuring": 2.3, "adventurism": 1.5, "adventurist": 1.4, 
    "adventuristic": 1.7, "adventurists": 1.2, "adventurous": 1.4, "adventurously": 1.3, "adventurousness": 1.8, "adversarial": -1.5, "adversaries": -1.0, "adversary": -0.8, "adversative": -1.2, "adversatively": -0.1, "adversatives": -1.0, "adverse": -1.5, "adversely": -0.8,
    "adverseness": -0.6, "adversities": -1.5, "adversity": -1.8, "affected": -0.6, "affection": 2.4, "affectional": 1.9, "affectionally": 1.5, "affectionate": 1.9, "affectionately": 2.2, "affectioned": 1.8, "affectionless": -2.0, "affections": 1.5, "afflicted": -1.5,
    "affronted": 0.2, "aggravate": -2.5, "aggravated": -1.9, "aggravates": -1.9, "aggravating": -1.2, "aggress": -1.3, "aggressed": -1.4, "aggresses": -0.5, "aggressing": -0.6, "aggression": -1.2, "aggressions": -1.3, "aggressive": -0.6, "aggressively": -1.3, 
    "aggressiveness": -1.8, "aggressivities": -1.4, "aggressivity": -0.6, "aggressor": -0.8, "aggressors": -0.9, "aghast": -1.9, "agitate": -1.7, "agitated": -2.0, "agitatedly": -1.6, "agitates": -1.4, "agitating": -1.8, "agitation": -1.0, "agitational": -1.2, 
    "agitations": -1.3, "agitative": -1.3, "agitato": -0.1, "agitator": -1.4, "agitators": -2.1, "agog": 1.9, "agonise": -2.1, "agonised": -2.3, "agonises": -2.4, "agonising": -1.5, "agonize": -2.3, "agonized": -2.2, "agonizes": -2.3, "agonizing": -2.7, "agonizingly": -2.3,
    "agony": -1.8, "agree": 1.5, "agreeability": 1.9, "agreeable": 1.8, "agreeableness": 1.8, "agreeablenesses": 1.3, "agreeably": 1.6, "agreed": 1.1, "agreeing": 1.4, "agreement": 2.2, "agreements": 1.1, "agrees": 0.8, "alarm": -1.4, "alarmed": -1.4, "alarming": -0.5,
    "alarmingly": -2.6, "alarmism": -0.3, "alarmists": -1.1, "alarms": -1.1, "alas": -1.1, "alert": 1.2, "alienation": -1.1, "alive": 1.6, "allergic": -1.2, "allow": 0.9, "alone": -1.0, "alright": 1.0, "amaze": 2.5, "amazed": 2.2, "amazedly": 2.1, "amazement": 2.5,
    "amazements": 2.2, "amazes": 2.2, "amazing": 2.8, "amazon": 0.7, "amazonite": 0.2, "amazons": -0.1, "amazonstone": 1.0, "amazonstones": 0.2, "ambitious": 2.1, "ambivalent": 0.5, "amor": 3.0, "amoral": -1.6, "amoralism": -0.7, "amoralisms": -0.7, "amoralities": -1.2,
    "amorality": -1.5, "amorally": -1.0, "amoretti": 0.2, "amoretto": 0.6, "amorettos": 0.3, "amorino": 1.2, "amorist": 1.6, "amoristic": 1.0, "amorists": 0.1, "amoroso": 2.3, "amorous": 1.8, "amorously": 2.3, "amorousness": 2.0, "amorphous": -0.2, "amorphously": 0.1,
    "amorphousness": 0.3, "amort": -2.1, "amortise": 0.5, "amortised": -0.2, "amortises": 0.1, "amortizable": 0.5, "amortization": 0.6, "amortizations": 0.2, "amortize": -0.1, "amortized": 0.8, "amortizes": 0.6, "amortizing": 0.8, "amusable": 0.7, "amuse": 1.7, "amused": 1.8, 
    "amusedly": 2.2, "amusement": 1.5, "amusements": 1.5, "amuser": 1.1, "amusers": 1.3, "amuses": 1.7, "amusia": 0.3, "amusias": -0.4, "amusing": 1.6, "amusingly": 0.8, "amusingness": 1.8, "amusive": 1.7, "anger": -2.7, "angered": -2.3, "angering": -2.2, "angerly": -1.9, 
    "angers": -2.3, "angrier": -2.3, "angriest": -3.1, "angrily": -1.8, "angriness": -1.7, "angry": -2.3, "anguish": -2.9, "anguished": -1.8, "anguishes": -2.1, "anguishing": -2.7, "animosity": -1.9, "annoy": -1.9, "annoyance": -1.3, "annoyances": -1.8, "annoyed": -1.6,
    "annoyer": -2.2, "annoyers": -1.5, "annoying": -1.7, "annoys": -1.8, "antagonism": -1.9, "antagonisms": -1.2, "antagonist": -1.9, "antagonistic": -1.7, "antagonistically": -2.2, "antagonists": -1.7, "antagonize": -2.0, "antagonized": -1.4, "antagonizes": -0.5,
    "antagonizing": -2.7, "anti": -1.3, "anticipation": 0.4, "anxieties": -0.6, "anxiety": -0.7, "anxious": -1.0, "anxiously": -0.9, "anxiousness": -1.0, "aok": 2.0, "apathetic": -1.2, "apathetically": -0.4, "apathies": -0.6, "apathy": -1.2, "apeshit": -0.9, 
    "apocalyptic": -3.4, "apologise": 1.6, "apologised": 0.4, "apologises": 0.8, "apologising": 0.2, "apologize": 0.4, "apologized": 1.3, "apologizes": 1.5, "apologizing": -0.3, "apology": 0.2, "appall": -2.4, "appalled": -2.0, "appalling": -1.5, "appallingly": -2.0, 
    "appalls": -1.9, "appease": 1.1, "appeased": 0.9, "appeases": 0.9, "appeasing": 1.0, "applaud": 2.0, "applauded": 1.5, "applauding": 2.1, "applauds": 1.4, "applause": 1.8, "appreciate": 1.7, "appreciated": 2.3, "appreciates": 2.3, "appreciating": 1.9,
    "appreciation": 2.3, "appreciations": 1.7, "appreciative": 2.6, "appreciatively": 1.8, "appreciativeness": 1.6, "appreciator": 2.6, "appreciators": 1.5, "appreciatory": 1.7, "apprehensible": 1.1, "apprehensibly": -0.2, "apprehension": -2.1, "apprehensions": -0.9,
    "apprehensively": -0.3, "apprehensiveness": -0.7, "approval": 2.1, "approved": 1.8, "approves": 1.7, "ardent": 2.1, "arguable": -1.0, "arguably": -1.0, "argue": -1.4, "argued": -1.5, "arguer": -1.6, "arguers": -1.4, "argues": -1.6, "arguing": -2.0, "argument": -1.5,
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    "crush": -0.6, "crushed": -1.8, "crushes": -1.9, "crushing": -1.5, "cry": -2.1, "crying": -2.1, "cunt": -2.2, "cunts": -2.9, "curious": 1.3, "curse": -2.5, "cut": -1.1, "cute": 2.0, "cutely": 1.3, "cuteness": 2.3, "cutenesses": 1.9, "cuter": 2.3, 
    "cutes": 1.8, "cutesie": 1.0, "cutesier": 1.5, "cutesiest": 2.2, "cutest": 2.8, "cutesy": 2.1, "cutey": 2.1, "cuteys": 1.5, "cutie": 1.5, "cutiepie": 2.0, "cuties": 2.2, "cuts": -1.2, "cutting": -0.5, "cynic": -1.4, "cynical": -1.6, "cynically": -1.3, 
    "cynicism": -1.7, "cynicisms": -1.7, "cynics": -0.3, "d-:": 1.6, "damage": -2.2, "damaged": -1.9, "damager": -1.9, "damagers": -2.0, "damages": -1.9, "damaging": -2.3, "damagingly": -2.0, "damn": -1.7, "damnable": -1.7, "damnableness": -1.8, "damnably": -1.7,
    "damnation": -2.6, "damnations": -1.4, "damnatory": -2.6, "damned": -1.6, "damnedest": -0.5, "damnified": -2.8, "damnifies": -1.8, "damnify": -2.2, "damnifying": -2.4, "damning": -1.4, "damningly": -2.0, "damnit": -2.4, "damns": -2.2, "danger": -2.4, 
    "dangered": -2.4, "dangering": -2.5, "dangerous": -2.1, "dangerously": -2.0, "dangerousness": -2.0, "dangers": -2.2, "daredevil": 0.5, "daring": 1.5, "daringly": 2.1, "daringness": 1.4, "darings": 0.4, "darkest": -2.2, "darkness": -1.0, "darling": 2.8, 
    "darlingly": 1.6, "darlingness": 2.3, "darlings": 2.2, "dauntless": 2.3, "daze": -0.7, "dazed": -0.7, "dazedly": -0.4, "dazedness": -0.5, "dazes": -0.3, "dead": -3.3, "deadlock": -1.4, "deafening": -1.2, "dear": 1.6, "dearer": 1.9, "dearest": 2.6, 
    "dearie": 2.2, "dearies": 1.0, "dearly": 1.8, "dearness": 2.0, "dears": 1.9, "dearth": -2.3, "dearths": -0.9, "deary": 1.9, "death": -2.9, "debonair": 0.8, "debt": -1.5, "decay": -1.7, "decayed": -1.6, "decayer": -1.6, "decayers": -1.6, "decaying": -1.7,
    "decays": -1.7, "deceit": -2.0, "deceitful": -1.9, "deceive": -1.7, "deceived": -1.9, "deceives": -1.6, "deceiving": -1.4, "deception": -1.9, "decisive": 0.9, "dedicated": 2.0, "defeat": -2.0, "defeated": -2.1, "defeater": -1.4, "defeaters": -0.9,
    "defeating": -1.6, "defeatism": -1.3, "defeatist": -1.7, "defeatists": -2.1, "defeats": -1.3, "defeature": -1.9, "defeatures": -1.5, "defect": -1.4, "defected": -1.7, "defecting": -1.8, "defection": -1.4, "defections": -1.5, "defective": -1.9, "defectively": -2.1,
    "defectiveness": -1.8, "defectives": -1.8, "defector": -1.9, "defectors": -1.3, "defects": -1.7, "defence": 0.4, "defenceman": 0.4, "defencemen": 0.6, "defences": -0.2, "defender": 0.4, "defenders": 0.3, "defense": 0.5, "defenseless": -1.4, "defenselessly": -1.1,
    "defenselessness": -1.3, "defenseman": 0.1, "defensemen": -0.4, "defenses": 0.7, "defensibility": 0.4, "defensible": 0.8, "defensibly": 0.1, "defensive": 0.1, "defensively": -0.6, "defensiveness": -0.4, "defensives": -0.3, "defer": -1.2, "deferring": -0.7,
    "defiant": -0.9, "deficit": -1.7, "definite": 1.1, "definitely": 1.7, "degradable": -1.0, "degradation": -2.4, "degradations": -1.5, "degradative": -2.0, "degrade": -1.9, "degraded": -1.8, "degrader": -2.0, "degraders": -2.0, "degrades": -2.1,
    "degrading": -2.8, "degradingly": -2.7, "dehumanize": -1.8, "dehumanized": -1.9, "dehumanizes": -1.5, "dehumanizing": -2.4, "deject": -2.2}

from QuantConnect.Data.Custom.Tiingo import *


from sediment import WordScore
from env import TradingEnv
from explore import Runner
from agent import TD3

import pandas as pd
import numpy as np

import math

class TwinDelayedDDPG(QCAlgorithm):
    '''Continuous Twin Delayed DDPG model '''

    def Initialize(self):
        
        # settings
        self.FeatureWindow = 10
        self.LookBack = 100 * 2
        self.Test = 20 * 2
        self.LastDataNum = -1
        
        live = False
        
        self.SetStartDate(2019, 1, 1)
        #self.SetEndDate(2019, 10, 31)
        #self.SetBrokerageModel(BrokerageName.Alpaca, AccountType.Cash)
        
        self.symbolDataBySymbol = {}
        self.SymbolList = ['SPY','AAPL','NVDA','AMZN','MSFT','GOOGL','TSLA']#,'MKTX','ABMD','ALGN','AVGO','ULTA','TTWO','FTNT','MA','TGT','TSN']

        self.SecurityList = []
        self.NewsList = []
        
        for symbol in self.SymbolList:
            security = self.AddEquity(symbol, Resolution.Daily)
            self.SecurityList.append(security.Symbol)
            self.symbolDataBySymbol[security.Symbol] = SymbolData(self, security.Symbol, self.FeatureWindow,  Resolution.Daily)
            
        for symbol in self.SecurityList:
            news = self.AddData(TiingoNews, symbol)
            self.NewsList.append(news.Symbol)
            
        self.SetBenchmark("SPY")
        
        self.WS = WordScore()
        
        env = TradingEnv(self.FeatureWindow)
        env.reset()
        
        self.environment = env
        self.observationRun = False
        self.modelIsTraining = False
        
        state_size = env.observation_space.shape[0]
        action_dim = env.action_space.shape[0]
        max_action = float(env.action_space.high[0])
        seed = 0
        
        sizes = (state_size, action_dim, max_action, seed)
        
        self.runnerObj = Runner(self, n_episodes=170, batch_size=5, gamma=0.99, tau=0.005, noise=0.2,\
        noise_clip=0.5, explore_noise=0.1, policy_frequency=2, sizes=sizes)
        
        self.AI_TradeAgent = TD3(self, state_dim=state_size, action_dim=action_dim, max_action=max_action, seed=seed)

        if live:
            self.runnerObj.replay_buffer.load(name='ReplayBuff')
        
        # Set TrainingMethod to be executed immediately
        self.Train(self.TrainingMethod)
        
        # Set TrainingMethod to be executed at 8:00 am every Sunday once a month
        self.Train(self.DateRules.Every(DayOfWeek.Sunday), self.TimeRules.At(6, 0), self.TrainingMethod)
        
    def TrainTimeCheck(self):
        '''Check if new month then Train data'''
        
        today = self.Time
        # can change to month, week, or day for trigger
        weekNum = today.strftime("%V")
        dayNum = today.strftime("%e")
        monthNum = today.strftime("%m")
        
        # trigger logic
        if self.LastDataNum == -1: #self.LastDataNum != monthNum and int(monthNum) % 2 == 0 or 
            self.LastDataNum = monthNum
            # New month time to train TD3!
            return True
        return False
        
    
    def HistoricalData(self, lookBack=100):
        
        historyData = self.History(self.SecurityList, lookBack, Resolution.Daily)
        historyNews = self.History(TiingoNews, self.NewsList, lookBack, Resolution.Daily)
        historyData.dropna(inplace=True)
        historyNews.dropna(inplace=True)
        
        pricesX = {}
        volumeX = {}
        newsX = {}
        
        for symbol in self.SecurityList:
            if not historyData.empty:
                pricesX[symbol.Value] = list(historyData.loc[str(symbol.Value)]['close'])[:-1]
                volumeX[symbol.Value] = list(historyData.loc[str(symbol.Value)]['volume'])[:-1]
            
            # what is the len of data?
            maxValue = len(pricesX[symbol.Value])
                
        for symbol in self.NewsList:
            if not historyNews.empty:
                df = historyNews.loc[symbol].apply(lambda row : self.WS.score(row['description']), axis = 1)
                df.index = pd.to_datetime(df.index, utc=True)
                df = df.resample('1D').sum().fillna(0)
                time = self.Time
                days = pd.date_range(time - timedelta(lookBack), time, freq='D')
                x = np.zeros(lookBack+1)
                x[:] = np.nan
                data = pd.DataFrame({'score':x},index=days)
                data['score'] = df
                data.fillna(0,inplace=True)
                newsX[symbol.Value] = list(data['score'])[-maxValue:]
                
        dictOfDict = {'close':pricesX, 'volume':volumeX, 'score':newsX}
        
        dictOfDf = {k: pd.DataFrame(v) for k,v in dictOfDict.items()}
        
        df = pd.concat(dictOfDf, axis=1)
        
        v1 = pd.Categorical(df.columns.get_level_values(0), 
                           categories=['close', 'volume', 'score'], 
                           ordered=True)
                           
        v2 = pd.Categorical(df.columns.get_level_values(1), 
                            categories=self.SymbolList,
                            ordered=True)
                            
        df.columns = pd.MultiIndex.from_arrays([v2,v1])
        
        return df.sort_index(axis=1, level=[0, 1])
        
    def TrainingMethod(self):
        
        # check to see if we should train the model
        train = self.TrainTimeCheck()
        
        if not train:
            return
        
        # get historical data
        x = self.LookBack
        df = self.HistoricalData(x)
        
        # create envoirments training and testing
        #Env = TradingEnv(obs_len=self.FeatureWindow, df=df)
        trainEnv = TradingEnv(obs_len=self.FeatureWindow, df=df.iloc[:-self.Test])
        testEnv = TradingEnv(obs_len=self.FeatureWindow, df=df.iloc[-self.Test:])
        
        # run observations only once to fill replay buffer with samples
        if not self.observationRun:
            self.runnerObj.observe(trainEnv, 1000)
            self.observationRun = True
        
        # set model to train data
        self.modelIsTraining = True
        self.runnerObj.train(testEnv,testEnv)
        self.modelIsTraining = False
        
    def OnOrderEvent(self, orderEvent):
        self.Debug("{} {}".format(self.Time, orderEvent.ToString()))

    def OnEndOfAlgorithm(self):
        # Save Replay Buffer!
        self.runnerObj.replay_buffer.save(name='ReplayBuff')
        self.Log("{} - TotalPortfolioValue: {}".format(self.Time, self.Portfolio.TotalPortfolioValue))
        self.Log("{} - CashBook: {}".format(self.Time, self.Portfolio.CashBook))

class SymbolData:
    
    def __init__(self, algo, symbol, window, resolution):
        
        self.algo = algo
        self.symbol = symbol
        self.window = window
        self.resolution = resolution
        
        # add each symbol to consolidator
        self.timeConsolidator = TradeBarConsolidator(timedelta(days = 1))
        self.timeConsolidator.DataConsolidated += self.TimeConsolidator
        self.algo.SubscriptionManager.AddConsolidator(symbol, self.timeConsolidator)
        
        # add each symbol to tiingo news
        self.newsAsset = algo.AddData(TiingoNews, symbol)
        
        # temp info used to check changes before trades
        self.weight_temp = 0

        # we will store the historical window here, and keep it a fixed length in update
        self.history_close = []
        self.history_volume = []
        self.history_news = []
        
        # how much of one asset can we buy/short
        self.max_pos = 1 / len(algo.SymbolList)
        self.max_short_pos = 0.0
        
    def update(self, close, volume, symbol):
        '''Update symbols and news with historical data or live data'''
        
        # update history, retain length
        if len(self.history_close)==0:
            hist_df = self.algo.History([self.symbol], timedelta(days=20), self.resolution)
            
            # Case where no data to return for this asset. New asset?
            if 'close' not in hist_df.columns:
                return
            
            hist_df.dropna(inplace=True)
            hist_df.reset_index(level=[0,1],inplace=True)
            hist_df.set_index('time', inplace=True)
            hist_df.dropna(inplace=True)
            
            # store the target time series
            self.history_close = hist_df.close.values[-self.window:]
            self.history_volume = hist_df.volume.values[-self.window:]

        if len(self.history_close) < self.window:
            self.history_close = np.append(self.history_close, close)
            self.history_volume = np.append(self.history_volume, volume)

        else:
            self.history_close = np.append(self.history_close, close)[1:]
            self.history_volume = np.append(self.history_volume, volume)[1:]
        
        if len(self.history_news)==0:
            hist_df = self.algo.History(TiingoNews, self.newsAsset.Symbol, timedelta(days=20), Resolution.Daily)
            hist_df.dropna(inplace=True)
            if not hist_df.empty:
                df = hist_df.loc[self.newsAsset.Symbol].apply(lambda row : self.algo.WS.score(row['description']), axis = 1)
                df.index = pd.to_datetime(df.index, utc=True)
                df = df.resample('1D').sum().fillna(0)
                self.history_news = df.values[-self.window:]
            else:
                self.history_news = np.zeros(self.window)
            
        else:
            hist_df = self.algo.History(TiingoNews, self.newsAsset.Symbol, timedelta(days=1), Resolution.Daily)
            hist_df.dropna(inplace=True)
            if not hist_df.empty:
                df = hist_df.loc[self.newsAsset.Symbol].apply(lambda row : self.algo.WS.score(row['description'] if \
                'description' in row else np.nan), axis = 1)
                
                df.index = pd.to_datetime(df.index, utc=True)
                a = df.resample('1D').sum().fillna(0)
                self.history_news = np.append(self.history_news, np.array(a))[1:]
            else:
                self.history_news = np.append(self.history_news, 0)[1:]
                
    def TimeConsolidator(self, sender, bar):
        '''Live data will be streammed here'''
        
        # return if modle is training
        if self.algo.modelIsTraining:
            self.algo.Debug("Retun, model still training")
            return
        
        # load best AI model
        self.algo.AI_TradeAgent.load("best_avg")
        
        symbol = bar.Symbol
        price = bar.Close
        vol = bar.Volume
        
        # update data arrays with new data
        self.update(price, vol, symbol)
        
        # Check current portfolio for changes
        if self.algo.Securities[symbol].Invested:
            currentweight = (self.algo.Portfolio[symbol].Quantity * price) /self.algo.Portfolio.TotalPortfolioValue
        else:
            currentweight = 0.0
        
        # set ratio currently invested for this symbol
        weight = currentweight
        
        # re-use code in env and get observations with new data
        new_obs = self.algo.environment.next_observation(close_window = self.history_close, \
            volume_window = self.history_volume, news_window=self.history_news)

        # get action from agent
        action = self.algo.AI_TradeAgent.select_action(np.array(new_obs), noise=0)
        
        # View Action
        #self.algo.Debug("{} {} -> {}".format(symbol, new_obs, action))
        
        if action > 0.05:
            
            weight += np.clip(self.algo.environment.normalize(abs(float(action))),0,1) * 0.3
            weight = np.clip(round(weight,4),self.max_short_pos,self.max_pos)
            
            if weight > self.weight_temp:
                self.algo.SetHoldings(symbol, weight, False)
                self.weight_temp = weight
            
        elif action < -0.05:
            
            weight += -(np.clip(self.algo.environment.normalize(abs(float(action))),0,1)) * 0.3
            weight = np.clip(round(weight,4),self.max_short_pos,self.max_pos)
            
            if weight < self.weight_temp:
                self.algo.SetHoldings(symbol, weight, False)
                self.weight_temp = weight
            else:
                pass
        
        else:
            pass

from collections import deque
import pandas as pd
import numpy as np
import gym
import sys

from buffer import ReplayBuffer
from agent import TD3

class Runner:
    """Carries out the environment steps and adds experiences to memory"""
    
    def __init__(self, algo, n_episodes=100, batch_size=32, gamma=0.99, tau=0.005, noise=0.2, noise_clip=0.5, explore_noise=0.1,\
            policy_frequency=2, sizes=None):
                
        self.algo = algo
        self.n_episodes = n_episodes
        self.batch_size = batch_size
        self.gamma = gamma
        self.tau = tau
        self.noise = noise
        self.noise_clip = noise_clip
        self.explore_noise = explore_noise
        self.policy_frequency = policy_frequency
        self.replay_buffer = ReplayBuffer(algo)
        
        self.agent = TD3(self, state_dim=sizes[0], action_dim=sizes[1], max_action=sizes[2], seed=sizes[3])

    def evaluate_policy(self, TestEnv, eval_episodes=1):
        """
        run several episodes using the best agent policy
            
            Args:
                policy (agent): agent to evaluate
                env (env): gym environment
                eval_episodes (int): how many test episodes to run
                render (bool): show training
            
            Returns:
                avg_reward (float): average reward over the number of evaluations    
        """
        
        avg_reward = 0.
        for i in range(eval_episodes):
            obs = TestEnv.reset()
            done = False
            while not done:
                action = self.agent.select_action(np.array(obs), noise=0)
                obs, reward, done, _ = TestEnv.step(action)
                avg_reward += reward
                if action <= -0.05:
                    self.algo.Log("Action {}.".format(action))
                
        #self.algo.Log("Eval {}.".format(round(avg_reward,2)))

        avg_reward /= eval_episodes
        
        return avg_reward
    
    def observe(self, TrainEnv, observation_steps):
        """
        run episodes while taking random actions and filling replay_buffer
        
            Args:
                env (env): gym environment
                replay_buffer(ReplayBuffer): buffer to store experience replay
                observation_steps (int): how many steps to observe for    
        """
        
        time_steps = 0
        obs = TrainEnv.reset()
        done = False
        
        while time_steps < observation_steps:
            action = TrainEnv.action_space.sample()
            new_obs, reward, done, _ = TrainEnv.step(action)
    
            self.replay_buffer.add((obs, new_obs, action, reward, done))
    
            obs = new_obs
            time_steps += 1
    
            if done:
                obs = TrainEnv.reset()
                done = False
    
            #self.algo.Log("Populating Buffer {}/{}.".format(time_steps, observation_steps))
            #sys.stdout.flush()
        
    def train(self, TrainEnv, TestEnv):
        """
        Train the agent for exploration steps    
            Args:
                agent (Agent): agent to use
                env (environment): gym environment
                exploration (int): how many training steps to run    
        """
        
        scores = []
        scores_avg = []
        scores_window = deque(maxlen=25)
        
        eval_reward_best = -1000
        
        self.algo.Debug("{} | Training..".format(self.algo.Time))
        
        eval_reward = self.evaluate_policy(TestEnv, int(TestEnv.MaxCount))

        if eval_reward > eval_reward_best:
            eval_reward_best = eval_reward
            self.algo.Debug("Last Model Tested |"+str(eval_reward_best))
            self.agent.save("best_avg")
        
        for i_episode in range(1, self.n_episodes+1):
            
            obs = TrainEnv.reset()
            score = 0
            done = False
            episode_timesteps = 0
            
            while not done:
                
                action = self.agent.select_action(np.array(obs), noise=self.explore_noise)
                new_obs, reward, done, _ = TrainEnv.step(action)
                self.replay_buffer.add((obs, new_obs, action, reward, done))
                obs = new_obs
                score += reward
                
                episode_timesteps += 1
                
                scores_window.append(score)
                scores.append(score)
                scores_avg.append(np.mean(scores_window))
                
                self.agent.train(self.replay_buffer, episode_timesteps, self.batch_size, self.gamma, self.tau, self.noise, self.noise_clip, self.policy_frequency)
                    
            # how often do we want to eval model?
            if i_episode % 1 == 0:
                # get eval over all symbols and save best avg
                eval_reward = self.evaluate_policy(TestEnv, int(TestEnv.MaxCount))
                
                if eval_reward > eval_reward_best:
                    eval_reward_best = eval_reward
                    self.algo.Debug(str(i_episode)+"| Best Model! |"+str(round(eval_reward_best,3)))
                    self.agent.save("best_avg")
                    #self.algo.Log("{} {} {} {} {}".format(episode_timesteps, i_episode, score, eval_reward))