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unit = dfhack.gui.getSelectedUnit()

PlusPlus = {700, 1200, 1400, 1500, 1600, 1800, 2500}  
Plus = {450, 950, 1150, 1250, 1350, 1550, 2250}	
Normal = {200, 700, 900, 1000, 1100, 1300, 2000}
Minus = {150, 600, 800, 900, 1000, 1100, 1500}

PhysicalPlus = {"STRENGTH", "TOUGHNESS", "ANALYTICAL_ABILITY", "FOCUS", "CREATIVITY", "PATIENCE", "MEMORY"}

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unit = dfhack.gui.getSelectedUnit()

PlusPlus = {700, 1200, 1400, 1500, 1600, 1800, 2500}	
Plus = {450, 950, 1150, 1250, 1350, 1550, 2250}	
Normal = {200, 700, 900, 1000, 1100, 1300, 2000}
Minus = {150, 600, 800, 900, 1000, 1100, 1500}

Mental_Attribute_Names = {
Mental_PlusPlus = {"FOCUS", "SPATIAL_SENSE"},
Mental_Plus = {"ANALYTICAL_ABILITY", "FOCUS", "CREATIVITY", "PATIENCE", "MEMORY"},

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-- Bugs

--[[
...rk\MasterworkDF v3e\Dwarf Fortress\hack\scripts/test.lua:199: Cannot read fie
ld unit_attribute[].6: index out of bounds.
stack traceback:
        [C]: in function '__index'
        ...rk\MasterworkDF v3e\Dwarf Fortress\hack\scripts/test.lua:199: in func
tion 'boostValue'
        ...rk\MasterworkDF v3e\Dwarf Fortress\hack\scripts/test.lua:61: in funct

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-- Bugs

-- --------------------------------------------------------------------------
-- Functions
function boostAttributes(selectedUnitCaste, unit)

--[[ these were not easy to get, see code...
df.physical_attribute_type
0       STRENGTH
1       AGILITY

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-- Bugs

-- --------------------------------------------------------------------------
-- Classes
TraitArrayBins = defclass(TraitArrayBins)
TraitArrayBins.ATTR = {
  	name = "",
		traitMin = 0,
		traitMed = 0,
		traitMax = 0

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TraitArrayBins = {}
 
TraitArrayBins.__index = TraitArrayBins
 
function TraitArrayBins.create(name, traitMin, traitMed, traitMax)
	local trait = {}
	setmetatable(trait,TraitArrayBins)

	trait.name = name
	trait.traitMin = traitMin

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import os
os.environ['R_HOME'] = '/mnt/distvol/R/4.0.5/lib64/R/'
import rpy2.robjects as ro
from rpy2.robjects.packages import importr
from rpy2.robjects import pandas2ri
pandas2ri.activate()

k = 4

grdevices = importr('grDevices')

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import sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split
import numpy as np
import shap
shap.initjs()

# select a set of background examples to take an expectation over
background = np.array(data)
#[np.random.choice(X_train.shape[0],100,replace=False)]

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trf = zca.ZCA().fit(transformed.iloc[:,1:])

X = pd.DataFrame(trf.transform(transformed.iloc[:,1:]))
X.columns=pd.DataFrame(transformed.iloc[:,1:]).columns
X.index = all_data.index
#from sklearn.preprocessing import StandardScaler

y = pd.DataFrame((transformed.iloc[:,0]-np.mean(transformed.iloc[:,0]))/np.std(transformed.iloc[:,0]))
y.columns=pd.DataFrame(transformed.iloc[:,0]).columns
y.index = all_data.index

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converted = pd.DataFrame()

	def ecdf2(values):
			co = len(values)
			externalArray = pd.DataFrame()
			for d in range(0,len(values.columns)):
				internalArray = []
				for i in range(0,len(values.iloc[:,d])):
					a = ( \
						sum( ( values.iloc[:,d] <= values.iloc[:,d][i] ) ) + \