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\"}))),_style||(_style=/*#__PURE__*/React.createElement(\"style\",null)),_use||(_use=/*#__PURE__*/React.createElement(\"use\",{id:\"Background\",href:\"#img1\",x:95,y:66})));}var ForwardRef=/*#__PURE__*/React.forwardRef(SvgPago);export default __webpack_public_path__+\"static/media/pago.f2297feb4548c70cc46c64eb5a90d9d1.svg\";export{ForwardRef as ReactComponent};","import React from \"react\";\nimport { useRef, useState } from \"react\";\nimport Container from '@mui/material/Container';\nimport { useTheme } from '@mui/material/styles';\nimport Box from '@mui/material/Box';\nimport Stack from '@mui/material/Stack';\nimport Paper from '@mui/material/Paper';\nimport Typography from '@mui/material/Typography';\nimport MobileStepper from '@mui/material/MobileStepper';\nimport Button from '@mui/material/Button';\nimport KeyboardArrowLeft from '@mui/icons-material/KeyboardArrowLeft';\nimport KeyboardArrowRight from '@mui/icons-material/KeyboardArrowRight';\n\nimport anuncio from '../../assets/slideanuncios/anuncio.png';\nimport anuncio2 from '../../assets/slideanuncios/anuncio2.png';\nimport anuncio3 from '../../assets/slideanuncios/anuncio3.png';\nimport anuncioEstrenaton from '../../assets/slideanuncios/estrenaton2022.png';\nimport anuncioEstrenatonMovil from '../../assets/slideanuncios/estrenaton2022Movil.jpg';\n\nimport casaCompracasa from '../../assets/casa_compracasa.svg';\nimport ubicacionCompracasa from '../../assets/foto2.svg';\nimport ArrowForwardIcon from '@mui/icons-material/ArrowForward';\n\nimport { Grid } from \"@mui/material\";\nimport Carousel from \"react-material-ui-carousel\";\nimport { useNavigate } from \"react-router-dom\";\n\nconst SlideCompraEnUnSoloDiaItem = ({ datos }) => {\n let navigate = useNavigate();\n\n return (\n \n \n \n \n Compra una casa Pecsa en un solo día
\n \n \n \n \n { datos.textoMD }\n } sx={{ color:'#f5323f' }} onClick={()=>{ navigate('/tienda') }}>\n VER MÁS\n \n \n \n { datos.textoXS } \n \n \n \n \n \n \n \n );\n}\n\nexport { SlideCompraEnUnSoloDiaItem };","import React from \"react\";\nimport { useRef, useState } from \"react\";\nimport Container from '@mui/material/Container';\nimport { useTheme } from '@mui/material/styles';\nimport Box from '@mui/material/Box';\nimport Stack from '@mui/material/Stack';\nimport Paper from '@mui/material/Paper';\nimport Typography from '@mui/material/Typography';\nimport MobileStepper from '@mui/material/MobileStepper';\nimport Button from '@mui/material/Button';\nimport KeyboardArrowLeft from '@mui/icons-material/KeyboardArrowLeft';\nimport KeyboardArrowRight from '@mui/icons-material/KeyboardArrowRight';\n\nimport anuncio from '../../assets/slideanuncios/anuncio.png';\nimport anuncio2 from '../../assets/slideanuncios/anuncio2.png';\nimport anuncio3 from '../../assets/slideanuncios/anuncio3.png';\nimport anuncioEstrenaton from '../../assets/slideanuncios/estrenaton2022.png';\nimport anuncioEstrenatonMovil from '../../assets/slideanuncios/estrenaton2022Movil.jpg';\n\nimport casaCompracasa from '../../assets/casa_compracasa.svg';\nimport ubicacionCompracasa from '../../assets/foto2.svg';\nimport pagoCompracasa from '../../assets/pago.svg';\nimport ArrowForwardIcon from '@mui/icons-material/ArrowForward';\n\nimport { Grid } from \"@mui/material\";\nimport Carousel from \"react-material-ui-carousel\";\nimport { SlideCompraEnUnSoloDiaItem } from \"./SlideComprarEnUnSoloDiaItem\";\n\nconst items = [\n {\n textoMD: \n Elige el modelo que mas te guste\n ,\n textoXS: \n Elige el modelo que mas te guste\n ,\n imagen: casaCompracasa,\n },\n {\n textoMD: \n Escoge la mejor ubicacion para ti\n ,\n textoXS: \n Escoge la mejor ubicacion para ti\n ,\n imagen: ubicacionCompracasa,\n },\n {\n textoMD: \n Realiza tu pago y listo, habrás separado tu nueva casa.\n ,\n textoXS: \n Realiza tu pago y listo, habrás separado tu nueva casa.\n ,\n imagen: pagoCompracasa,\n },\n];\n\nconst SlideCompraEnUnSoloDia = () => {\n return (\n \n \n \n \n {\n items.map((item)=>(\n \n ))\n }\n \n \n \n\n );\n}\n\nexport { SlideCompraEnUnSoloDia };","import { Button, Grid, Typography } from \"@mui/material\";\nimport { Box, Container } from \"@mui/system\";\nimport { useNavigate } from \"react-router-dom\";\n\nconst BannerOpciones = () => {\n let navigate = useNavigate();\n const reedirigirCalendly = () =>{\n window.open('https://calendly.com/casas-pecsa')\n }\n const reedirigirCompracasa = () =>{\n navigate('/tienda');\n }\n return(\n \n \n \n \n \n \n Visita el Fraccionamiento\n \n \n \n Visita nuestros fraccionamientos y conoce de primera mano nuestros modelos o bien agenda una cita virtual con nuestros asesores y obten asesoria online.\n \n \n \n \n \n \n \n \n Amor a primera vista\n \n \n \n ¿Ya te enamoraste de nuestros modelos? ¡No esperes más! Separa tu casa en este instante completamente online. Rapido, comodo y seguro.\n \n \n \n \n \n \n \n \n );\n}\n\nexport { BannerOpciones }","import { Helmet } from \"react-helmet\";\n\nconst MetaPixel = () => {\n return(\n \n {/* */}\n \n \n {/* */}\n \n );\n}\n\nexport { MetaPixel }","import { Backdrop, Box, Button, CircularProgress, Link, TextField, Typography, styled } from \"@mui/material\";\nimport { useState } from \"react\";\nimport ClearRoundedIcon from '@mui/icons-material/ClearRounded';\nimport moment from \"moment/moment\";\nimport axios from \"axios\";\nimport useMediaQuery from '@mui/material/useMediaQuery';\nimport { useTheme } from '@mui/material/styles';\n\nconst regalos = [\n { id: 1, regalo: 'TV', video: 'https://storage.casaspecsa.com/Promociones/La+magia+de+comprar+tu+casa+Pecsa/premios/TV+C.mp4', videoMovil: 'https://storage.casaspecsa.com/Promociones/La+magia+de+comprar+tu+casa+Pecsa/premios/TV+C+(1).mp4'},\n { id: 2, regalo: 'Minisplit', video: 'https://storage.casaspecsa.com/Promociones/La+magia+de+comprar+tu+casa+Pecsa/premios/Mini+C.mp4', videoMovil: 'https://storage.casaspecsa.com/Promociones/La+magia+de+comprar+tu+casa+Pecsa/premios/Mini+C+(1).mp4'},\n { id: 3, regalo: 'Descuento', video: 'https://storage.casaspecsa.com/Promociones/La+magia+de+comprar+tu+casa+Pecsa/premios/Descuento+C.mp4', videoMovil: 'https://storage.casaspecsa.com/Promociones/La+magia+de+comprar+tu+casa+Pecsa/premios/Descuento+C+(1).mp4'}\n]\nconst BotonParaPromo = styled(Button)({\n backgroundColor:'#5adcc9',\n color:'#000',\n m:2,\n borderRadius:'12px',\n textTransform:'none',\n '&:hover':{\n backgroundColor:'#5adcc9',\n\n }\n});\nconst stylePecsaMoffin = {\n \"& label.Mui-focused\": {\n color: \"#5adcc9\",\n // backgroundColor:'rgba(5, 5, 5, 0.2)'\n },\n \"& .MuiInput-underline:after\": {\n borderBottomColor: \"#5adcc9\",\n \n },\n \"& .MuiOutlinedInput-root\": {\n // '& fieldset': {\n // borderColor: 'black',\n // },\n backgroundColor:'#fff',\n \"&:hover fieldset\": {\n borderColor: \"#5adcc9\"\n },\n \"&.Mui-focused fieldset\": {\n borderColor: \"#5adcc9\"\n }\n }\n}\n\nconst PopUpPromocion = () => {\n const [open, setOpen] = useState(true);\n const getOpen = () => {\n setOpen(!open);\n reestaurarValores();\n }\n const [jugar, setJugar] = useState(false);\n const [mostrarCanjear, setMostrarCanjear] = useState(false);\n const [gano, setGano] = useState(false);\n const [buttonGanoText, setButtonGanoText] = useState('Enviar');\n const [buttonGanoDisabled, setButtonGanoDisabled] = useState(false);\n\n const reestaurarValores = () => {\n setJugar(false);\n setMostrarCanjear(false);\n setGano(false);\n setButtonGanoText('Enviar');\n }\n\n const generarAleatorio = (inicio, fin) => {\n // Generar un número decimal aleatorio entre 0 y 1\n const numeroAleatorio = Math.random();\n\n // Escalar y desplazar el número para que esté dentro del rango\n const numeroEnRango = numeroAleatorio * (fin - inicio + 1) + inicio;\n\n // Redondear el número al entero más cercano (opcional)\n // Si no deseas redondear, simplemente devuelve `numeroEnRango`.\n return Math.floor(numeroEnRango);\n }\n const [numeroAleatorio, setNumeroAleatorio] = useState(generarAleatorio(0, 2));\n\n const [isLoading, setIsLoading] = useState(true);\n const handleVideoLoaded = () => {\n // console.log(isLoading);\n setIsLoading(false);\n // console.log(isLoading);\n };\n\n const theme = useTheme();\n const fullScreen = useMediaQuery(theme.breakpoints.down('md'));\n\n const sendEmail = (event) => {\n event.preventDefault();\n setButtonGanoText('Enviando...')\n setButtonGanoDisabled(true);\n const data = new FormData(event.currentTarget);\n\n axios.post(`${process.env.REACT_APP_API_URL}/api/email/sendPromo`, {\n nombre: data.get('nombre'),\n email: data.get('email'),\n fecha: moment().format('DD MMMM YYYY, h:mm:ss a'),\n regalo: numeroAleatorio,\n })\n .then((response) => {\n console.log(response.data)\n getOpen();\n reestaurarValores();\n setButtonGanoDisabled(false);\n })\n .catch((error) => {\n console.log(error)\n setButtonGanoText('Enviar');\n setButtonGanoDisabled(false);\n })\n }\n return(\n \n \n {\n jugar ? (\n gano ? (\n \n \n \n \n \n \n \n \n \n \n \n Escribe tu correo electronico para que recibas los pasos para reclamar tu premio.\n \n \n \n \n Al hacer click en el botón enviar aceptas {\n window.open('https://storage.casaspecsa.com/Promociones/La+magia+de+comprar+tu+casa+Pecsa/terminosYCondiciones/Terminos+y+condiciones+agosto-compressed_2.pdf','_blank');\n }}\n sx={{ color:'#5adcc9' }}\n >\n términos y condiciones de la promoción\n \n \n setOpen(!open)}\n type=\"submit\"\n disabled={buttonGanoDisabled}\n >\n { buttonGanoText }\n \n \n \n \n \n \n ) : (\n \n { \n isLoading && (\n \n \n \n )\n } \n setMostrarCanjear(true)}\n />\n {/* */}\n \n {\n mostrarCanjear && (\n setGano(true)}\n >\n Canjear\n \n )\n } \n \n \n )\n ) : (\n \n \n \n \n \n \n \n \n \n \n \n \n setJugar(true)}\n >\n Jugar\n \n \n \n \n )\n } \n \n \n \n );\n}\n\nexport { PopUpPromocion }","import defineProperty from \"./defineProperty.js\";\nfunction ownKeys(e, r) {\n var t = Object.keys(e);\n if (Object.getOwnPropertySymbols) {\n var o = Object.getOwnPropertySymbols(e);\n r && (o = o.filter(function (r) {\n return Object.getOwnPropertyDescriptor(e, r).enumerable;\n })), t.push.apply(t, o);\n }\n return t;\n}\nexport default function _objectSpread2(e) {\n for (var r = 1; r < arguments.length; r++) {\n var t = null != arguments[r] ? arguments[r] : {};\n r % 2 ? ownKeys(Object(t), !0).forEach(function (r) {\n defineProperty(e, r, t[r]);\n }) : Object.getOwnPropertyDescriptors ? Object.defineProperties(e, Object.getOwnPropertyDescriptors(t)) : ownKeys(Object(t)).forEach(function (r) {\n Object.defineProperty(e, r, Object.getOwnPropertyDescriptor(t, r));\n });\n }\n return e;\n}","import * as React from \"react\";\nconst WhitePecsaLogo = (props) => (\n \n);\nexport default WhitePecsaLogo;\n","import WhitePecsaLogo from \"../Icons/WhitePecsaLogo\";\nimport { Countdown } from \"../Countdown\";\n\nconst textItems = [\n \"Últimos días de precios 2024\",\n ,\n // ,\n].map((text, index) => ({\n id: index,\n text,\n}));\n\nexport const extendedTextItems = [\n ...textItems,\n ...textItems,\n ...textItems,\n ...textItems,\n ...textItems,\n ...textItems,\n ...textItems,\n ...textItems,\n ...textItems,\n];\n","import React from \"react\";\n\nimport { Helmet } from \"react-helmet\";\n\nimport { SlideAnuncios } from \"../../components/SlideAnuncios\";\nimport { ModelosCarrusel } from \"../../components/ModelosCarrusel\";\nimport { SolicitaCredito } from \"../../components/Banners/SolicitaCredito\";\nimport { FraccionamientosCarrusel } from \"../../components/FraccionamientosCarrusel\";\nimport { OchoRazones } from \"../../components/OchoRazones\";\n\nimport Box from \"@mui/material/Box\";\nimport { SlideCompraCasa } from \"../../components/SlideCompraCasa\";\nimport { OpinionesFamiliaPecsa } from \"../../components/OpinionesFamiliaPecsa\";\nimport { VideoBlogBanner } from \"../../components/VideoBlogBanner\";\nimport { SlideCompraEnUnSoloDia } from \"../../components/SlideCompraEnUnSoloDia\";\nimport { BannerOpciones } from \"../../components/BannerOpciones\";\nimport PortadaVideo from \"../../components/SlideAnuncios/PortadaVideo\";\nimport { VideoHome } from \"../../components/VideoHome\";\nimport { PopupYoutube } from \"../../components/Popups/PopupYoutube\";\nimport { PopupFoto } from \"../../components/Popups/PopupFoto\";\nimport { PopUpPromocion } from \"../../components/PopUpPromocion\";\nimport { MetaPixel } from \"../../components/MetaPixel\";\nimport { useNavigate } from \"react-router-dom\";\nimport { Typography } from \"@mui/material\";\nimport { InfiniteScrollHorizontal } from \"../../components/InfiniteHorizontalBanner\";\n\nconst Home = (props) => {\n let navigate = useNavigate();\n // console.log('estoy en Home', props);\n return (\n \n
\n Casas Pecsa | Home\n \n \n
\n\n {/* Contenedor de scroll infinito. Comentar/Descomentar todo el Box */}\n {/*
\n \n */}\n\n
\n \n \n {/*
\n \n */}\n
\n \n \n
\n \n \n
\n \n \n
\n \n \n {/*
\n \n */}\n
\n \n \n
\n \n \n
\n \n \n
\n \n \n {/*
*/}\n {/* navigate(\"/tienda/192T-102Bosques_De_Las_Lomas_Residencial\")} /> */}\n {/* */}\n \n );\n};\n\nexport { Home };\n","import * as React from 'react';\nimport { useEffect, useState } from \"react\";\nimport axios from 'axios';\nimport { useNavigate } from \"react-router-dom\";\nimport { Card, CardActionArea, CardContent, Grid, Typography } from \"@mui/material\";\nimport { Box, Container } from \"@mui/system\";\n\nimport HomeIcon from '@mui/icons-material/Home';\nimport CorporateFareIcon from '@mui/icons-material/CorporateFare';\nimport GroupIcon from '@mui/icons-material/Group';\nimport SellIcon from '@mui/icons-material/Sell';\n\nconst getPermisosDeAcceso = (perfil) => {\n const permisos = [\n { name: 'Administrador', accesos: [1,2,3,4,5,6,7] },\n { name: 'Asesor', accesos: [] },\n { name: 'Diseño', accesos: [] },\n { name: 'Tramitador', accesos: [3] },\n ]\n // console.log('llego');\n return permisos.find((element)=>element.name === perfil);\n}\n\nconst CargasMenu = () =>{\n let navigate = useNavigate();\n \n const [usuario, setUsuario] = useState(null);\n\n const [permisos, setPermisos] = useState(null);\n\n const getAcceso = (index) =>{\n if(permisos){\n return permisos.accesos.includes(index)\n }\n }\n\n useEffect(() => {\n const token = !localStorage.getItem('token') ? '': localStorage.getItem('token');\n \n axios.get(`${process.env.REACT_APP_API_URL}/api/usuarios/by-folio/${localStorage.getItem('id')}`, \n {\n headers: {\n Authorization: `Bearer ${token}`\n }\n })\n .then((response) => {\n // handle success\n console.log(response.data);\n setUsuario(response.data);\n // setPermisos(getPermisosDeAcceso(usuario.perfil.nombre))\n })\n .catch((error) => {\n // handle success\n console.log(error);\n navigate('/usuarios/login');\n })\n .then(() => {\n // handle success\n console.log('sae');\n });\n }, []);\n useEffect(() => {\n if(usuario){\n setPermisos(getPermisosDeAcceso(usuario.perfil.nombre))\n console.log('hey',usuario);\n }\n // console.log('hey',usuario);\n }, [usuario]);\n\n return(\n \n \n \n \n Carga de informacion
\n \n \n \n {\n getAcceso(1) && (\n \n \n { navigate('/cargas/inventario')}}>\n \n \n \n Inventario\n \n \n \n \n \n )\n }\n {\n getAcceso(2) && (\n \n \n { navigate('/cargas/modelos')}}>\n \n \n \n Modelos\n \n \n \n \n \n )\n }\n {\n getAcceso(3) && (\n \n \n { navigate('/cargas/clientes')}}>\n \n \n \n Clientes\n \n \n \n \n \n )\n }\n {\n getAcceso(4) && (\n \n \n { navigate('/cargas/promociones')}}>\n \n \n \n Promociones\n \n \n \n \n \n )\n }\n {\n getAcceso(5) && (\n \n \n { navigate('/cargas/empresas')}}>\n \n \n \n Empresas\n \n \n \n \n \n )\n }\n {\n getAcceso(6) && (\n \n \n { navigate('/cargas/usuarios')}}>\n \n \n \n Usuarios\n \n \n \n \n \n )\n }\n {\n getAcceso(7) && (\n \n \n { navigate('/cargas/asesores')}}>\n \n \n \n Asesores\n \n \n \n \n \n )\n }\n \n \n \n \n
\n );\n}\n\nexport { CargasMenu }","import { Outlet, useNavigate } from \"react-router-dom\";\n\nconst Cargas = (props) => {\n let navigate = useNavigate();\n //const [personajes, setPersonajes] = useState(props.datos.personajes || []);\n\n return(\n \n \n
\n );\n}\n\nexport { Cargas }","import { Route, Routes, useNavigate } from \"react-router-dom\";\nimport { Button, Card, CardActionArea, CardContent, CardMedia, Container, Grid, Typography } from \"@mui/material\";\nimport { Box } from \"@mui/system\";\nimport AddIcon from '@mui/icons-material/Add';\nimport AddCircleOutlineIcon from '@mui/icons-material/AddCircleOutline';\n\n\n\n\nconst MenuFraccionamientos = (props) => {\n let navigate = useNavigate();\n //console.log('menu', props);\n\n \n return(\n \n \n \n Fraccionamientos
\n \n \n \n \n \n { navigate('add')}}\n sx={{\n height:250\n }}\n >\n \n \n \n Agregar Fraccionamiento\n \n \n \n \n \n {\n props.inventario.map((producto) => ( \n \n \n {/* { navigate(`${producto.folio}`, {state:{fraccionamiento:producto}}) }}> */}\n { navigate(`${producto.folio}`) }}\n sx={{\n height:250\n }}\n >\n \n \n \n { producto.nombre}\n \n \n \n \n \n ))\n }\n \n \n \n {/* {\n props.productos.length < 1 ? nada
:\n props.productos.map((producto) => ( \n {producto.name}
\n ))\n } */}\n \n );\n}\nexport { MenuFraccionamientos }","import React, {useState,useEffect} from 'react';\nimport axios from 'axios';\nimport { BrowserRouter as Router, Routes, Route, useNavigate, useLocation, useSearchParams, Link, NavLink, Navigate } from \"react-router-dom\";\nimport { Container } from \"@mui/system\";\nimport { MenuFraccionamientos } from \"../../components/MenuFraccionamientos\";\nimport { Skeleton } from '@mui/material';\n\nconst Inventario = () => {\n let navigate = useNavigate();\n const location = useLocation();\n //console.log(location.state.inventario);\n\n //const [inventario, setInventario] = useState(location.state.inventario);\n const [inventario, setInventario] = useState([]);\n\n useEffect(() => {\n const token = !localStorage.getItem('token') ? '': localStorage.getItem('token');\n \n axios.get(`${process.env.REACT_APP_API_URL}/api/producto/fraccionamientos`, {\n headers: {\n Authorization: `Bearer ${token}`\n }\n })\n .then((response) => {\n // handle success\n console.log(response);\n setInventario(response.data) \n console.log('se valido Inventario');\n })\n .catch((error) => {\n // handle success\n console.log(error);\n navigate('/usuarios/login');\n })\n .then(() => {\n // handle success\n console.log('sae');\n });\n }, [setInventario]);\n\n return(\n \n \n {\n !inventario ? \n () : \n ()\n } \n \n
\n );\n}\n\nexport { Inventario }","import Visibility from '@mui/icons-material/Visibility';\nimport VisibilityOff from '@mui/icons-material/VisibilityOff';\nimport { FormControl, IconButton, InputAdornment, InputLabel, OutlinedInput } from '@mui/material';\nimport { useState } from \"react\";\n\nconst stylePecsaInputs = {\n \"& label.Mui-focused\": {\n color: \"#f5323f\"\n },\n \"& .MuiInput-underline:after\": {\n borderBottomColor: \"#d9d9d9\"\n },\n \"& .MuiOutlinedInput-root\": {\n // '& fieldset': {\n // borderColor: 'black',\n // },\n \"&:hover fieldset\": {\n borderColor: \"#d9d9d9\"\n },\n \"&.Mui-focused fieldset\": {\n borderColor: \"#d9d9d9\"\n }\n }\n}\n\nconst CustomInputPassword = ({ id = \"outlined-adornment-password\", label = \"Contraseña\", name = \"password\", handleChange, required = false, endAdornment, autoFocus = false }) => {\n const [showPassword, setShowPassword] = useState(false);\n\n const handleClickShowPassword = () => setShowPassword((show) => !show);\n\n const handleMouseDownPassword = (event) => {\n event.preventDefault();\n };\n\n return(\n \n { label }\n \n { endAdornment }\n \n { showPassword ? : }\n \n \n }\n autoComplete=\"current-password\"\n label={label}\n onChange={(e)=>handleChange(e.target.value)}\n autoFocus={autoFocus}\n />\n \n );\n}\n\nexport { CustomInputPassword }","import axios from \"axios\";\nimport { BrowserRouter as Router, Routes, Route, useNavigate, useLocation, Navigate, Link } from \"react-router-dom\";\nimport { Alert, Avatar, Button, Checkbox, Container, Divider, FormControlLabel, Grid, Snackbar, TextField, Typography } from \"@mui/material\"\nimport { Box } from \"@mui/system\";\n\nimport LockOutlinedIcon from '@mui/icons-material/LockOutlined';\nimport { useState } from \"react\";\n\nimport logo from '../../assets/logo.svg'\nimport { CustomInputPassword } from \"../../components/CustomInputPassword\";\n\nconst LoginUsuarios = () => {\n let navigate = useNavigate();\n let location = useLocation();\n \n const [perfil, setPerfil] = useState(null);\n // console.log(location);\n const [open, setOpen] = useState(false);\n\n const MostarAlert = () => {\n setOpen(true);\n };\n \n const CerrarAlert = (event, reason) => {\n if (reason === 'clickaway') {\n return;\n }\n \n setOpen(false);\n };\n\n const login = (event) => {\n event.preventDefault();\n const data = new FormData(event.currentTarget);\n // console.log({\n // email: data.get('email'),\n // password: data.get('password'),\n // });\n axios.post(`${process.env.REACT_APP_API_URL}/api/usuarios/login`, {\n email: data.get('email'),\n password: data.get('password'),\n })\n .then((response) => {\n // handle success\n console.log('logiiiiiiinnnn',response);\n localStorage.setItem('token', response.data.token);\n localStorage.setItem('id', response.data.user.folio);\n localStorage.setItem('type', 'usuario');\n setPerfil(response.data.user.perfil.nombre);\n if(response.data.user.perfil.nombre === \"Administrador\"){\n navigate('/cargas');\n }\n if(response.data.user.perfil.nombre === \"Asesor\"){\n navigate(`/espacio-asesor/${response.data.user.folio}/perfil`);\n }\n // navigate('/usuarios');\n })\n .catch((error) => {\n // handle success\n console.log(error);\n navigate('/usuarios/login');\n })\n .then(() => {\n // handle success\n console.log('sabe'); \n });\n };\n return(\n \n \n \n \n \n {/* \n \n */}\n \n Iniciar Sesion Usuarios Pecsa\n \n \n \n {/* */}\n \n {/* }\n label=\"Remember me\"\n /> */}\n \n {/* \n \n \n {\"¿Olvido su contraseña? O ¿Separo su casa y no sabe cuál es su contraseña? Clic Aquí\"}\n \n \n \n ó */}\n \n {/* */}\n \n \n \n \n \n \n Usuario no encontrado\n \n \n \n );\n}\n\nexport { LoginUsuarios }","import { useState } from \"react\";\nimport { useNavigate, useLocation } from \"react-router-dom\";\nimport { Button, Card, CardActionArea, CardContent, CardMedia, Container, Grid, Typography } from \"@mui/material\";\nimport { Box } from \"@mui/system\";\nimport AddIcon from '@mui/icons-material/Add';\nimport AddCircleOutlineIcon from '@mui/icons-material/AddCircleOutline';\nimport ApartmentSharpIcon from '@mui/icons-material/ApartmentSharp';\n\n\nconst MenuAmenidades = ({ fraccionamiento }) => {\n let navigate = useNavigate();\n const location = useLocation();\n // console.log(props.modelo);\n\n // const [fachadas, setFachadas] = useState(props.modelo.fachadas);\n\n return(\n \n \n \n Amenidades
\n \n \n \n \n \n { navigate('addAmenidad', {state:{fraccionamiento:fraccionamiento}} )}}>\n \n \n \n Agregar Amenidad\n \n \n \n \n \n {\n fraccionamiento.amenidades.map((amenidad)=>(\n \n \n { }}>\n \n \n \n {amenidad.nombre}\n \n \n \n \n \n ))\n }\n {/* {\n fachadas.map((fachada) => ( \n \n \n {\n fachada.fotos.length > 0 ? (\n // {fachada.nombre + 'hey'}
\n \n ) : (\n // {fachada.fotos[0].url}
\n \n )\n }\n \n { navigate(`${fachada.id}`) }}>\n \n \n Fachada: { fachada.nombre}\n \n \n \n \n \n ))\n } */}\n \n \n \n \n );\n}\n\nexport { MenuAmenidades }","import React, {useState,useEffect} from 'react';\nimport axios from \"axios\";\nimport { BrowserRouter as Router, Routes, Route, useNavigate, useLocation, Link, NavLink, Navigate } from \"react-router-dom\";\nimport { Autocomplete, Button, ButtonBase, Checkbox, Container, Divider, FormControl, Grid, IconButton, InputLabel, MenuItem, Select, Stack, TextareaAutosize, TextField, Typography } from '@mui/material';\nimport { Box } from '@mui/system';\n\nimport AddAPhotoIcon from '@mui/icons-material/AddAPhoto';\nimport sinlogo from '../../assets/sinlogo.png';\n\nimport StarOutlineRoundedIcon from '@mui/icons-material/StarOutlineRounded';\nimport StarRoundedIcon from '@mui/icons-material/StarRounded';\nimport ClearIcon from '@mui/icons-material/Clear';\n\nimport AddIcon from '@mui/icons-material/Add';\n\nimport { MenuAmenidades } from '../../components/MenuAmenidades';\n\nconst label = { inputProps: { 'aria-label': 'Checkbox demo' } };\n\nconst descripciones = [\n {title: 'Norte', desc: 'Al norte de la ciudad'},\n {title: 'Sur', desc: 'Al sur de la ciudad'},\n {title: 'Oriente', desc: 'Al oriente de la ciudad'},\n {title: 'Poniente', desc: 'Al poniente de la ciudad'},\n];\n\nconst AddFraccionamiento = () => {\n let navigate = useNavigate();\n const location = useLocation();\n //const [personaje, setPersonaje] = useState(location.state.personaje);\n\n const [ciudades, setCiudades] = useState([]);\n const [selectedCiudad, setSelectedCiudad] = useState(null);\n\n const [promociones, setPromociones] = useState([]);\n const [selectedPromocion, setSelectedPromocion] = useState(null);\n\n const [selectedDescripcion, setSelectedDescripcion] = useState(null);\n\n const [linkUbicacion, setLinkUbicacion] = useState(null);\n\n // const [selectedImage, setSelectedImage] = useState(null);\n // const [imageUrl, setImageUrl] = useState(null);\n const [selectedImage, setSelectedImage] = useState([]);\n const [imageUrl, setImageUrl] = useState(null);\n\n const [storageImages, setStorageImages] = useState([]);\n const [selectedImages, setSelectedImages] = useState([]);\n const [imagesUrl, setImagesUrl] = useState([]);\n\n const [storageImagesAmenidades, setStorageImagesAmenidades] = useState([]);\n const [selectedImageAmenidades, setSelectedImageAmenidades] = useState([]);\n const [imagesAmenidadesUrl, setImagesAmenidadesUrl] = useState([]);\n\n const [selectedMapa, setSelectedMapa] = useState([]);\n const [mapaUrl, setMapaUrl] = useState(null);\n const [mapaUrlAWS, setMapaUrlAWS] = useState(null);\n\n const [principal, setPrincipal] = useState(0);\n\n const [amenidad, setAmenidad] = useState(null);\n const [amenidades, setAmenidades] = useState([]);\n\n const add = (event) => {\n event.preventDefault();\n const data = new FormData(event.currentTarget);\n // // // const data = new FormData(); \n const token = !localStorage.getItem('token') ? '': localStorage.getItem('token');\n const folio = !localStorage.getItem('id') ? '': localStorage.getItem('id');\n //var formData = new FormData();\n // // // data.append('idMpio', selectedCiudad.id);\n data.set('idMpio', selectedCiudad.id);\n data.set('principal', principal);\n // data.set('amenidades', JSON.stringify(amenidades));\n // amenidades.map((amenidadesIt) => {\n // data.append('amenidades', [JSON.stringify(amenidadesIt)]);\n // })\n // console.log(data.get('amenidades')); return;\n\n data.delete('prefotosImg');\n storageImages.map((foto) => {\n data.append('fotosImg', foto);\n })\n data.delete('preAmenidadImg');\n // storageImagesAmenidades.map((foto) => {\n // data.append('fotosAmenidadesImg', foto);\n // })\n data.set('LinkUbicacion', linkUbicacion);\n data.set('folioCreateBy', folio);\n // let reader = new FileReader();\n // reader.readAsDataURL(selectedImage);\n data.set('idPromocion', selectedPromocion);\n const prueba = {\n nombre: data.get('nombre'),\n idMpio: selectedCiudad.id,\n logoImg: selectedImage,\n fotosImg: storageImages,\n folioCreateBy: folio,\n }\n //console.log('prueba::::', prueba); return;\n // console.log('data::::', data.get('fotosImg')); return;\n\n // for(const pair of data.entries()) {\n // console.log(`${pair[0]}, ${pair[1]}`);\n // }\n // return;\n axios.post(`${process.env.REACT_APP_API_URL}/api/fraccionamiento/`, \n // // {\n // // nombre: data.get('nombre'),\n // // idMpio: selectedCiudad.id,\n // // logoImg: selectedImage,\n // // fotosImg: storageImages,\n // // folioCreateBy: folio,\n // // },\n data,\n {\n headers: { \n Authorization: `Bearer ${token}`, \n 'Content-Type': 'multipart/form-data', \n //ContentType: 'multipart/form-data'\n } \n })\n .then((response) => {\n // handle success \n console.log(response);\n console.log('success axio');\n // navigate(-1, { replace: true });\n })\n .catch((error) => {\n // handle success\n console.log(error);\n // navigate('/usuarios/login');\n })\n .then(() => {\n // handle success\n console.log('sabe');\n });\n }\n\n const getPromocion = (event) => {\n setSelectedPromocion(event.target.value);\n };\n const removeImagen = (index) => {\n storageImages.splice(index, 1);\n imagesUrl.splice(index, 1);\n setStorageImages(storageImages);\n setImagesUrl(imagesUrl);\n }\n const AddAmenidad = () => { \n console.log(amenidad);\n setAmenidades([...amenidades, amenidad]);\n for (let i = 0; i < selectedImageAmenidades.length; i++) {\n setStorageImagesAmenidades([...storageImagesAmenidades, selectedImageAmenidades[i]])\n }\n console.log(amenidades);\n console.log(storageImagesAmenidades);\n }\n useEffect(()=>{\n console.log('selectedImageAmenidades',selectedImageAmenidades);\n for (let i = 0; i < selectedImageAmenidades.length; i++) {\n setAmenidad({...amenidad, foto:selectedImageAmenidades[i]})\n \n }\n },[selectedImageAmenidades]);\n useEffect(() => {\n const token = !localStorage.getItem('token') ? '': localStorage.getItem('token');\n \n if (selectedImage.length > 0) {\n setImageUrl(URL.createObjectURL(selectedImage[0]));\n }\n if (selectedImages.length > 0) {\n // console.log('imagenes seleccionsadoas: ', selectedImages);\n for (let i = 0; i < selectedImages.length; i++) {\n if(!storageImages.find( element => element === selectedImages[i])){\n storageImages.push(selectedImages[i]);\n imagesUrl.push(URL.createObjectURL(selectedImages[i]))\n } \n }\n // console.log('imagenes en storage: ', storageImages);\n // console.log('imagenes seleccionadoas: ', imagesUrl);\n }\n\n axios.get(`${process.env.REACT_APP_API_URL}/api/producto/createview`, {\n headers: {\n Authorization: `Bearer ${token}`\n }\n })\n .then((response) => {\n // handle success\n console.log(response);\n setCiudades(response.data.ciudades);\n setPromociones(response.data.promociones);\n })\n .catch((error) => {\n // handle success\n console.log(error);\n // navigate('/usuarios/login');\n })\n .then(() => {\n // handle success\n });\n }, [setCiudades, selectedImage, selectedImages, imagesUrl]);\n return(\n \n \n Nuevo Fraccionamiento
\n \n \n \n \n ciudad.estado.nombre}\n getOptionLabel={(ciudad) => ciudad.nombre}\n value={selectedCiudad}\n onChange={(event, newCiudad)=>{setSelectedCiudad(newCiudad);}}\n sx={{ width: 400 }}\n renderInput={(params) => }\n />\n { setLinkUbicacion(e.target.value) }} name=\"linkUbicacion\" required/>\n \n Ubicacion dentro de la ciudad\n \n \n \n \n \n \n \n {\n imageUrl ? selectedImage ? (\n \n ) : (\n \n ) : (\n \n )\n }\n \n \n \n \n \n \n \n \n \n \n \n \n {\n imagesUrl && (\n imagesUrl.map((imagen, index) => (\n \n { setImageUrl(imagen) } }\n >\n {/*
*/}\n } \n checkedIcon={}\n sx={{\n position: 'absolute',\n bottom:0, \n left:0,\n }}\n checked={index === principal ? (true) : (false)} \n onChange={ () => { setPrincipal(index) } }\n />\n { removeImagen(index) } }\n >\n \n \n {/* { } }\n >\n \n */}\n \n \n ))\n )\n }\n\n \n \n \n \n\n \n \n \n \n \n \n { setAmenidad({...amenidad, nombre: e.target.value }) }} variant=\"outlined\" name=\"nombreAmenidad\"/>\n \n \n { setAmenidad({...amenidad, descripcion: e.target.value }) }}\n />\n \n \n \n \n \n } onClick={AddAmenidad}>\n Agregar\n \n \n {\n amenidades.length > 0 && (\n amenidades.map((amenidadItem) => (\n \n Nombre: {amenidadItem.nombre}\n Descripcion: {amenidadItem.descripcion}\n \n )) \n )\n }\n \n \n \n \n {\n mapaUrlAWS && !mapaUrl ? (\n \n ) :\n mapaUrl ? selectedImage ? (\n \n ) : (\n \n ) : (\n \n )\n }\n \n \n \n \n \n \n \n \n Promociones\n \n \n \n \n \n \n \n \n );\n}\n\nexport { AddFraccionamiento }","import { Outlet, useNavigate } from \"react-router-dom\";\n\nconst CargasFraccionamiento = (props) => {\n let navigate = useNavigate();\n //const [personajes, setPersonajes] = useState(props.datos.personajes || []);\n\n return(\n \n \n
\n );\n}\n\nexport { CargasFraccionamiento }","import * as React from 'react';\nimport { useEffect, useState } from \"react\";\nimport { Avatar, Button, Container, Divider, Grid, Stack } from \"@mui/material\";\nimport Box from '@mui/material/Box';\nimport Typography from '@mui/material/Typography';\nimport m146 from '../../assets/bosques_logo.png';\nimport DeleteIcon from '@mui/icons-material/Delete';\nimport EditIcon from '@mui/icons-material/Edit';\nimport LocationOnIcon from '@mui/icons-material/LocationOn';\nimport { useNavigate } from 'react-router-dom';\n\nconst DatosFraccionamiento = (props) => {\n let navigate = useNavigate();\n const[fraccionamiento, setFraccionamiento] = useState(props.datos);\n //const fraccionamiento = props.datos;\n //console.log(fraccionamiento);\n useEffect(() => {\n setFraccionamiento(props.datos);\n }, [setFraccionamiento]);\n return(\n \n \n \n }\n spacing={2}\n \n >\n \n \n \n {fraccionamiento.nombre}\n \n \n Folio: {fraccionamiento.folio}\n \n \n Saltillo, Coahuila\n \n \n }\n sx={{ \n width: '100%',\n marginTop: 2\n }}\n onClick={ () => { navigate('edit') } }\n >\n Editar\n \n }\n sx={{ \n width: '100%',\n marginTop: 1\n }}\n >\n Borrar\n \n \n \n \n \n \n \n );\n}\n\nexport { DatosFraccionamiento }","import { useNavigate, useLocation } from \"react-router-dom\";\nimport { Button, Card, CardActionArea, CardContent, CardMedia, Container, Grid, Typography } from \"@mui/material\";\nimport { Box } from \"@mui/system\";\nimport AddIcon from '@mui/icons-material/Add';\nimport AddCircleOutlineIcon from '@mui/icons-material/AddCircleOutline';\nimport ApartmentSharpIcon from '@mui/icons-material/ApartmentSharp';\n\nconst MenuEtapas = (props) => {\n let navigate = useNavigate();\n const location = useLocation();\n console.log(location.state || 'nada');\n\n return(\n \n \n \n Etapas
\n \n \n \n \n \n { navigate('add', {state:{fraccionamiento:props.fraccionamiento}})}}>\n \n \n \n Agregar Etapa\n \n \n \n \n \n {\n props.fraccionamiento.etapas.map((etapa) => ( \n \n \n { navigate(`${etapa.folio}`, {state:{etapa:etapa}}) }}>\n \n\n \n \n { etapa.nombre}\n \n \n \n \n \n ))\n }\n \n \n \n \n );\n}\n\nexport { MenuEtapas }","import { Box, Skeleton } from '@mui/material';\nimport React, {useState,useEffect} from 'react';\nimport axios from 'axios';\nimport { BrowserRouter as Router, Routes, Route, useNavigate, useLocation, useParams, Link, NavLink, Navigate } from \"react-router-dom\";\nimport { DatosFraccionamiento } from '../../components/DatosFraccionamiento';\nimport { MenuEtapas } from '../../components/MenuEtapas';\nimport { MenuAmenidades } from '../../components/MenuAmenidades';\n\nconst InventarioFraccionamiento = () => {\n let navigate = useNavigate();\n const location = useLocation();\n let parametros = useParams();\n //const [fraccionamiento, setFraccionamiento] = useState(location.state.fraccionamiento);\n const [fraccionamiento, setFraccionamiento] = useState(null);\n //console.log(location.state.fraccionamiento);\n //console.log(parametros);\n\n useEffect(() => {\n const token = !localStorage.getItem('token') ? '': localStorage.getItem('token');\n \n\n axios.get(`${process.env.REACT_APP_API_URL}/api/fraccionamiento/${parametros.fraccionamiento}`, {\n headers: {\n Authorization: `Bearer ${token}`\n }\n })\n .then((response) => {\n // handle success\n console.log(response);\n setFraccionamiento(response.data) \n })\n .catch((error) => {\n // handle success\n console.log(error);\n navigate('/usuarios/login');\n })\n .then(() => {\n // handle success\n console.log('sae');\n });\n }, [setFraccionamiento]);\n\n return(\n \n \n \n {\n !fraccionamiento ?\n :\n \n }\n \n \n {\n !fraccionamiento ?\n :\n \n } \n \n \n {\n !fraccionamiento ?\n :\n \n } \n \n \n
\n );\n}\n\nexport { InventarioFraccionamiento }","import { useEffect, useState } from \"react\";\nimport axios from \"axios\";\nimport { BrowserRouter as Router, Routes, Route, useNavigate, useLocation, Link, NavLink, Navigate } from \"react-router-dom\";\n\nimport { Autocomplete, Avatar, Button, Container, Divider, Grid, Input, Stack, TextField } from \"@mui/material\"\nimport { Box } from \"@mui/system\";\nimport UploadFileIcon from '@mui/icons-material/UploadFile';\n\nconst AddEtapa = () => {\n let navigate = useNavigate();\n const location = useLocation();\n const [fraccionamiento, setFraccionamiento] = useState(location.state.fraccionamiento);\n //console.log(location.state);\n\n const [ciudades, setCiudades] = useState([]);\n const [selectedCiudadNotaria, setSelectedCiudadNotaria] = useState(null);\n const [selectedCiudadRegistro, setSelectedCiudadRegistro] = useState(null);\n\n // const [fraccionamientos, setFraccionamientos] = useState([]);\n // const [selectedFraccionamiento, setSelectedFraccionamiento] = useState(null);\n\n const [empresas, setEmpresas] = useState([]);\n const [selectedEmpresa, setSelectedEmpresa] = useState(null);\n\n const add = (event) => {\n event.preventDefault();\n const data = new FormData(event.currentTarget);\n const token = !localStorage.getItem('token') ? '': localStorage.getItem('token');\n const folio = !localStorage.getItem('id') ? '': localStorage.getItem('id');\n // console.log('archivoLicenciaContruccion:::', data.get('archivoLicenciaContruccion'));\n // console.log('archivoFactibilidadAgua:::', data.get('archivoFactibilidadAgua'));\n // console.log('archivoFactibilidadLuz:::', data.get('archivoFactibilidadLuz'));\n // console.log('archivoEscrituras:::', data.get('archivoEscrituras'));\n // return;\n data.set('idFraccionamiento', fraccionamiento.id);\n data.set('idEmpresa', selectedEmpresa.id);\n data.set('ciudadNotaria', selectedCiudadNotaria.id);\n data.set('ciudadRegistro', selectedCiudadRegistro.id);\n data.set('folioCreateBy', folio);\n\n axios.post(`${process.env.REACT_APP_API_URL}/api/etapa/`, \n // {\n // nombre: data.get('nombre'),\n // codigoPostal: data.get('codigoPostal'),\n // idFraccionamiento: fraccionamiento.id,\n // idEmpresa: selectedEmpresa.id,\n // numeroLicencia: data.get('numeroLicencia'),\n // licenciaOtorgadaPor: data.get('licenciaOtorgadaPor'),\n // fechaOtorgamientoLicencia: data.get('fechaOtorgamientoLicencia'),\n // // archivoLicenciaContruccion: \n // // archivoFactibilidadAgua: \n // // archivoFactibilidadLuz: \n // numeroEscritura: data.get('numeroEscritura'),\n // notaria: data.get('notaria'),\n // titularNotaria: data.get('titularNotaria'),\n // ciudadNotaria: selectedCiudadNotaria.id,\n // ciudadRegistro: selectedCiudadRegistro.id,\n // numeroPredio: data.get('numeroPredio'),\n // fechaEscrituracion: data.get('fechaEscrituracion'),\n // // archivoEscrituras: \n // folioCreateBy: folio,\n // },\n data,\n {\n headers: {\n Authorization: `Bearer ${token}`,\n 'Content-Type': 'multipart/form-data', \n } \n })\n .then((response) => {\n // handle success \n console.log(response);\n console.log('success axio'); \n fraccionamiento.etapas.push(response.data);\n //navigate(`/cargas/inventario/${fraccionamiento.folio.toString()}`, {state:{fraccionamiento:fraccionamiento}});\n //navigate(-1, { replace: true });\n })\n .catch((error) => {\n // handle success\n console.log(error);\n // navigate('/usuarios/login');\n })\n .then(() => {\n // handle success\n console.log('sabe');\n });\n }\n\n useEffect(() => {\n const token = !localStorage.getItem('token') ? '': localStorage.getItem('token');\n \n axios.get(`${process.env.REACT_APP_API_URL}/api/etapa/createview`, {\n headers: {\n Authorization: `Bearer ${token}`\n }\n })\n .then((response) => {\n // handle success\n console.log(response);\n setCiudades(response.data.ciudades);\n setEmpresas(response.data.empresas);\n })\n .catch((error) => {\n // handle success\n console.log(error);\n // navigate('/usuarios/login');\n })\n .then(() => {\n // handle success\n });\n }, [setCiudades]);\n\n return(\n \n \n Nueva Etapa
\n \n \n \n \n \n {/* fraccionamiento.nombre}\n value={selectedFraccionamiento}\n onChange={(event, newFraccionamiento)=>{setSelectedFraccionamiento(newFraccionamiento);}}\n sx={{ width: 300 }}\n renderInput={(params) => }\n /> */}\n empresa.nombre}\n value={selectedEmpresa}\n onChange={(event, newEmpresa)=>{setSelectedEmpresa(newEmpresa);}}\n sx={{ width: 300 }}\n renderInput={(params) => }\n />\n \n \n \n \n \n \n \n \n \n ciudad.estado.nombre}\n getOptionLabel={(ciudad) => ciudad.nombre}\n value={selectedCiudadNotaria}\n onChange={(event, newCiudad)=>{setSelectedCiudadNotaria(newCiudad);}}\n sx={{ width: 300 }}\n renderInput={(params) => }\n />\n ciudad.estado.nombre}\n getOptionLabel={(ciudad) => ciudad.nombre}\n value={selectedCiudadRegistro}\n onChange={(event, newCiudad)=>{setSelectedCiudadRegistro(newCiudad);}}\n sx={{ width: 300 }}\n renderInput={(params) => }\n />\n \n \n \n \n \n \n \n \n \n );\n}\n\nexport { AddEtapa }","import { Outlet, useNavigate } from \"react-router-dom\";\n\nconst CargasEtapa = (props) => {\n let navigate = useNavigate();\n //const [personajes, setPersonajes] = useState(props.datos.personajes || []);\n\n return(\n \n \n
\n );\n}\n\nexport { CargasEtapa }","import * as React from 'react';\nimport { useEffect, useState } from \"react\";\nimport { Button, Container, Divider, Grid, Stack } from \"@mui/material\";\nimport Box from '@mui/material/Box';\nimport Typography from '@mui/material/Typography';\nimport DeleteIcon from '@mui/icons-material/Delete';\nimport EditIcon from '@mui/icons-material/Edit';\nimport ApartmentSharpIcon from '@mui/icons-material/ApartmentSharp';\nimport MapsHomeWorkSharpIcon from '@mui/icons-material/MapsHomeWorkSharp';\nimport LocationOnIcon from '@mui/icons-material/LocationOn';\nimport { useNavigate } from 'react-router-dom';\n\nconst DatosEtapa = (props) => {\n let navigate = useNavigate();\n const[etapa, setEtapa] = useState(props.datos);\n return(\n \n \n \n }\n spacing={4}\n width='100%'\n >\n \n \n \n \n \n {etapa.nombre}\n \n \n Folio: {etapa.folio}\n \n \n Nombre Fraccionamiento\n \n \n Empresa\n \n \n Saltillo, Coahuila\n \n \n }\n sx={{ \n width: '100%',\n marginTop: 2\n }}\n onClick={()=>{navigate('edit')}}\n >\n Editar\n \n }\n sx={{ \n width: '100%',\n marginTop: 1\n }}\n >\n Borrar\n \n \n \n \n \n \n \n );\n}\n\nexport { DatosEtapa }","import { useNavigate, useLocation } from \"react-router-dom\";\nimport { Button, Card, CardActionArea, CardContent, CardMedia, Container, Grid, Typography } from \"@mui/material\";\nimport { Box } from \"@mui/system\";\nimport AddIcon from '@mui/icons-material/Add';\nimport AddCircleOutlineIcon from '@mui/icons-material/AddCircleOutline';\nimport SignpostSharpIcon from '@mui/icons-material/SignpostSharp';\n\nconst MenuManzanas = (props) => {\n let navigate = useNavigate();\n const location = useLocation();\n console.log(location.state || 'nada');\n\n return(\n \n \n \n Manzanas
\n \n \n \n \n \n { navigate('add', {state:{etapa:props.etapa}}) }}>\n \n \n \n Agregar Manzana\n \n \n \n \n \n {\n props.etapa.manzanas.map((manzana) => ( \n \n \n {/* { navigate(`/${manzana.folio}`) }}> */}\n { navigate(`${manzana.folio}`, {state:{manzana:manzana}}) }}>\n \n {/* */}\n \n \n { manzana.nombre}\n \n \n \n \n \n ))\n }\n \n \n \n \n );\n}\n\nexport { MenuManzanas }","import React, {useState,useEffect} from 'react';\nimport axios from 'axios';\nimport { BrowserRouter as Router, Routes, Route, useNavigate, useLocation, useParams, Link, NavLink, Navigate } from \"react-router-dom\";\nimport { Box, Skeleton } from '@mui/material';\nimport { DatosEtapa } from '../../components/DatosEtapa';\nimport { MenuManzanas } from '../../components/MenuManzanas';\n\nconst InventarioEtapa = () => {\n let navigate = useNavigate();\n const location = useLocation();\n let parametros = useParams();\n // const [etapa, setEtapa] = useState(location.state.etapa);\n const [etapa, setEtapa] = useState(null);\n //console.log(location.state.etapa);\n console.log(parametros);\n\n useEffect(() => {\n const token = !localStorage.getItem('token') ? '': localStorage.getItem('token');\n \n\n axios.get(`${process.env.REACT_APP_API_URL}/api/etapa/${parametros.etapa}`, {\n headers: {\n Authorization: `Bearer ${token}`\n }\n })\n .then((response) => {\n // handle success\n console.log(response);\n setEtapa(response.data) \n })\n .catch((error) => {\n // handle success\n console.log(error);\n navigate('/usuarios/login');\n })\n .then(() => {\n // handle success\n console.log('sae');\n });\n }, [setEtapa]);\n\n return(\n \n \n {\n !etapa ?\n :\n \n } \n \n \n {\n !etapa ?\n :\n \n } \n \n
\n );\n}\n\nexport { InventarioEtapa }","import { useEffect, useState } from \"react\";\nimport axios from \"axios\";\nimport { useNavigate, useLocation, useParams } from \"react-router-dom\";\n\nimport { Autocomplete, Avatar, Button, Container, Divider, Grid, Stack, TextField } from \"@mui/material\"\nimport { Box } from \"@mui/system\";\nimport AddAPhotoIcon from '@mui/icons-material/AddAPhoto';\n\nconst AddManzana = () => {\n let navigate = useNavigate();\n const location = useLocation();\n const parametros = useParams();\n const [etapa, setEtapa] = useState(location.state.etapa);\n // const [etapas, setEtapas] = useState([]);\n // const [selectedEtapa, setSelectedEtapa] = useState(null);\n\n const add = (event) => {\n event.preventDefault();\n const data = new FormData(event.currentTarget);\n const token = !localStorage.getItem('token') ? '': localStorage.getItem('token');\n const folio = !localStorage.getItem('id') ? '': localStorage.getItem('id');\n\n data.set('idEtapa', etapa.id);\n data.set('folioCreateBy', folio);\n\n axios.post(`${process.env.REACT_APP_API_URL}/api/manzana/`, \n {\n nombre: data.get('nombre'), \n idEtapa: etapa.id,\n\n folioCreateBy: folio,\n },\n {\n headers: {\n Authorization: `Bearer ${token}`\n } \n })\n .then((response) => {\n // handle success \n console.log(response);\n console.log('success axio');\n //navigate(-1, { replace: true });\n //etapa.manzanas.push(response.data);\n // navigate(`/cargas/inventario/${etapa.folio.toString()}`, {state:{etapa:etapa}});\n navigate(-1, { replace: true });\n })\n .catch((error) => {\n // handle success\n console.log(error);\n // navigate('/usuarios/login');\n })\n .then(() => {\n // handle success\n console.log('sabe');\n });\n }\n\n useEffect(() => {\n const token = !localStorage.getItem('token') ? '': localStorage.getItem('token');\n \n axios.get(`${process.env.REACT_APP_API_URL}/api/manzana/createview`, {\n headers: {\n Authorization: `Bearer ${token}`\n }\n })\n .then((response) => {\n // handle success\n console.log(response);\n //setEtapas(response.data.etapas);\n })\n .catch((error) => {\n // handle success\n console.log(error);\n // navigate('/usuarios/login');\n })\n .then(() => {\n // handle success\n });\n }, []);\n return(\n \n \n Nueva Manzana
\n \n }>\n \n \n {/* etapa.nombre}\n value={selectedEtapa}\n onChange={(event, newEtapa)=>{setSelectedEtapa(newEtapa);}}\n sx={{ width: 300 }}\n renderInput={(params) => }\n /> */}\n \n \n {/* */}\n {/* \n \n \n \n */}\n \n \n \n \n );\n}\n\nexport { AddManzana }","import { Outlet, useNavigate } from \"react-router-dom\";\n\nconst CargasManzana = (props) => {\n let navigate = useNavigate();\n //const [personajes, setPersonajes] = useState(props.datos.personajes || []);\n\n return(\n \n \n
\n );\n}\n\nexport { CargasManzana }","import * as React from 'react';\nimport { useEffect, useState } from \"react\";\nimport { Button, Container, Divider, Grid, Stack } from \"@mui/material\";\nimport Box from '@mui/material/Box';\nimport Typography from '@mui/material/Typography';\nimport DeleteIcon from '@mui/icons-material/Delete';\nimport EditIcon from '@mui/icons-material/Edit';\nimport LocationOnIcon from '@mui/icons-material/LocationOn';\nimport SignpostSharpIcon from '@mui/icons-material/SignpostSharp';\nimport { useNavigate } from 'react-router-dom';\n\nconst DatosManzana = (props) => {\n const navigate = useNavigate();\n const[manzana, setManzana] = useState(props.datos);\n return(\n \n \n \n }\n spacing={2}\n \n >\n {/* */}\n \n \n \n \n \n {manzana.nombre}\n \n \n Folio: {manzana.folio}\n \n \n Fraccionamiento\n \n \n Etapa\n \n \n Saltillo, Coahuila\n \n \n }\n sx={{ \n width: '100%',\n marginTop: 2\n }}\n onClick={()=>navigate('edit')}\n >\n Editar\n \n }\n sx={{ \n width: '100%',\n marginTop: 1\n }}\n >\n Borrar\n \n \n \n \n \n {/* \n }\n spacing={2}\n sx={{ backgroundColor: 'greenyellow' }}\n >\n \n \n \n {manzana.nombre}\n \n \n Folio: {manzana.folio}\n \n \n \n \n \n }>\n Editar\n \n }>\n Borrar\n \n */}\n \n \n );\n}\n\nexport { DatosManzana }","import { useNavigate } from \"react-router-dom\";\nimport { Button, Card, CardActionArea, CardActions, CardContent, CardMedia, Container, FormControl, FormControlLabel, Grid, InputAdornment, InputLabel, MenuItem, Select, Switch, TextField, Typography } from \"@mui/material\";\nimport { Box } from \"@mui/system\";\nimport AddIcon from '@mui/icons-material/Add';\nimport AddCircleOutlineIcon from '@mui/icons-material/AddCircleOutline';\nimport { useEffect, useState } from \"react\";\nimport axios from \"axios\";\n\nconst label = { inputProps: { 'aria-label': 'Switch demo' } };\n\nconst MenuLotes = ({ manzana }) => {\n let navigate = useNavigate();\n console.log({ manzana });\n const [listaEstatus, setListaEstatus] = useState([]);\n const [listaPromociones, setListaPromociones] = useState([]);\n\n const disabledLote = (e) => {\n const token = !localStorage.getItem('token') ? '': localStorage.getItem('token');\n const folio = !localStorage.getItem('id') ? '': localStorage.getItem('id'); \n\n axios.post(`${process.env.REACT_APP_API_URL}/api/lote/update`, \n {\n id: e.target.value,\n disponible: e.target.checked,\n },\n {\n headers: { \n Authorization: `Bearer ${token}`,\n } \n }\n )\n .then((response) => {\n // handle success \n console.log(response);\n console.log('success axio');\n //navigate(-1, { replace: true });\n })\n .catch((error) => {\n // handle success\n console.log(error);\n // navigate('/usuarios/login');\n })\n .then(() => {\n // handle success\n console.log('sabe');\n });\n }\n\n const changeEstatusLote = (e, idLote) => {\n const token = !localStorage.getItem('token') ? '': localStorage.getItem('token');\n const folio = !localStorage.getItem('id') ? '': localStorage.getItem('id'); \n\n axios.post(`${process.env.REACT_APP_API_URL}/api/lote/update`, \n {\n id: idLote,\n idEstatus: e.target.value,\n },\n {\n headers: { \n Authorization: `Bearer ${token}`,\n } \n }\n )\n .then((response) => {\n // handle success \n console.log(response);\n console.log('success axio');\n //navigate(-1, { replace: true });\n })\n .catch((error) => {\n // handle success\n console.log(error);\n // navigate('/usuarios/login');\n })\n .then(() => {\n // handle success\n console.log('sabe');\n });\n }\n\n const changePromocionLote = (e, idLote) => {\n const token = !localStorage.getItem('token') ? '': localStorage.getItem('token');\n const folio = !localStorage.getItem('id') ? '': localStorage.getItem('id'); \n\n axios.post(`${process.env.REACT_APP_API_URL}/api/lote/update`, \n {\n id: idLote,\n idPromocion: e.target.value,\n },\n {\n headers: { \n Authorization: `Bearer ${token}`,\n } \n }\n )\n .then((response) => {\n // handle success \n console.log(response);\n console.log('success axio');\n //navigate(-1, { replace: true });\n })\n .catch((error) => {\n // handle success\n console.log(error);\n // navigate('/usuarios/login');\n })\n .then(() => {\n // handle success\n console.log('sabe');\n });\n }\n\n const changePrecioLote = (value, idLote) => {\n // console.log(value, idLote);\n const token = !localStorage.getItem('token') ? '': localStorage.getItem('token');\n const folio = !localStorage.getItem('id') ? '': localStorage.getItem('id'); \n\n axios.post(`${process.env.REACT_APP_API_URL}/api/lote/update`, \n {\n id: idLote,\n precio: value,\n },\n {\n headers: { \n Authorization: `Bearer ${token}`,\n } \n }\n )\n .then((response) => {\n // handle success \n console.log(response);\n console.log('success axio');\n //navigate(-1, { replace: true });\n })\n .catch((error) => {\n // handle success\n console.log(error);\n // navigate('/usuarios/login');\n })\n .then(() => {\n // handle success\n console.log('sabe');\n });\n }\n\n const [isChecked, setIsChecked] = useState(() =>\n manzana.lotes.map((lote) => lote.disponible)\n );\n const [estatusLote, setEstatusLote] = useState(() =>\n manzana.lotes.map((lote) => lote.idEstatus)\n );\n const [promocionLote, setPromocionLote] = useState(() =>\n manzana.lotes.map((lote) => lote.idPromocion)\n );\n const [precioLote, setPrecioLote] = useState(() =>\n manzana.lotes.map((lote) => lote.precio)\n );\n const handleChange = (lote) => (event) => {\n console.log(lote.id, event.target.value);\n }\n const changeEstatusLoteFront = (index, e, lote) => {\n //console.log(index, e.target, lote); return;\n setEstatusLote((estatus) => {\n return estatus.map((c, i) => {\n if (i === index) return e.target.value;\n return c;\n });\n });\n changeEstatusLote(e, lote.id);\n // if(e.target.checked){\n // console.log('se mostrara disponible', e.target.value);\n // }\n // else{\n // console.log('se mostrara No disponible', e.target.value);\n // }\n };\n const isCheckboxChecked = (index, e) => {\n setIsChecked((isChecked) => {\n return isChecked.map((c, i) => {\n if (i === index) return e.target.checked;\n return c;\n });\n });\n disabledLote(e)\n if(e.target.checked){\n console.log('se mostrara disponible', e.target.value);\n }\n else{\n console.log('se mostrara No disponible', e.target.value);\n }\n };\n const changePromocionLoteFront = (index, e, lote) => {\n //console.log(index, e.target, lote); return;\n setPromocionLote((estatus) => {\n return estatus.map((c, i) => {\n if (i === index) return e.target.value;\n return c;\n });\n });\n changePromocionLote(e, lote.id);\n // if(e.target.checked){\n // console.log('se mostrara disponible', e.target.value);\n // }\n // else{\n // console.log('se mostrara No disponible', e.target.value);\n // }\n };\n const changePrecioLoteFront = (index, e) => {\n // console.log('PRECIO A CAMBIAR',index, e.target, lote); return;\n setPrecioLote((estatus) => {\n return estatus.map((c, i) => {\n if (i === index) return e.target.value;\n return c;\n });\n });\n // changePromocionLote(e, lote.id);\n // if(e.target.checked){\n // console.log('se mostrara disponible', e.target.value);\n // }\n // else{\n // console.log('se mostrara No disponible', e.target.value);\n // }\n };\n useEffect(() => {\n const token = !localStorage.getItem('token') ? '': localStorage.getItem('token');\n \n\n axios.get(`${process.env.REACT_APP_API_URL}/api/estatus`, {\n headers: {\n Authorization: `Bearer ${token}`\n }\n })\n .then((response) => {\n // handle success\n console.log(response);\n setListaEstatus(response.data);\n })\n .catch((error) => {\n // handle success\n console.log(error);\n navigate('/usuarios/login');\n })\n .then(() => {\n // handle success\n console.log('sae');\n });\n axios.get(`${process.env.REACT_APP_API_URL}/api/promocion/only-active`, {\n headers: {\n Authorization: `Bearer ${token}`\n }\n })\n .then((response) => {\n // handle success\n console.log(response);\n setListaPromociones(response.data);\n })\n .catch((error) => {\n // handle success\n console.log(error);\n navigate('/usuarios/login');\n })\n .then(() => {\n // handle success\n console.log('sae');\n });\n }, []);\n return(\n \n \n \n Lotes
\n \n \n \n \n \n { navigate('add', {state:{manzana:manzana}}) }}>\n \n \n \n Agregar Lote\n \n \n \n \n \n {\n manzana.lotes.map((lote, index) => ( \n \n \n {/* { navigate(`/${lote.folio}`) }}> */}\n { navigate(`${lote.folio}`) }}>\n \n {\n lote.fotos.length > 0 ? (\n \n \n ) : (\n \n )\n } \n \n Lote:\n \n \n { lote.numeroDeLote}\n \n \n \n \n \n \n \n \n Estatus\n \n \n \n \n { isCheckboxChecked(index, e); }}\n />\n }\n label=\"Disponible\"\n labelPlacement=\"start\"\n />\n {/* { isCheckboxChecked(index, e); }}\n /> */}\n \n \n \n Promocion\n \n \n \n \n \n $\n // endAdornment: tipoDescuento === 2 ? (%) : null,\n }}\n defaultValue={precioLote[index]}\n onChange={(e) => changePrecioLoteFront(index, e)}\n name=\"descuento\"\n />\n \n \n \n \n \n \n \n \n \n \n ))\n }\n \n \n \n {/* {\n productos.length < 1 ? nada
:\n productos.map((producto) => ( \n {producto.name}
\n ))\n } */}\n \n );\n}\n\nexport { MenuLotes }","import React, {useState,useEffect} from 'react';\nimport axios from 'axios';\nimport { BrowserRouter as Router, Routes, Route, useNavigate, useLocation, useParams, Link, NavLink, Navigate } from \"react-router-dom\";\nimport { Box, Skeleton } from '@mui/material';\nimport { DatosManzana } from '../../components/DatosManzana';\nimport { MenuLotes } from '../../components/MenuLotes';\n\nconst InventarioManzana = () => {\n let navigate = useNavigate();\n const location = useLocation();\n let parametros = useParams();\n // const [etapa, setEtapa] = useState(location.state.etapa);\n const [manzana, setManzana] = useState(null);\n //console.log(location.state.etapa);\n console.log(parametros);\n \n useEffect(() => {\n const token = !localStorage.getItem('token') ? '': localStorage.getItem('token');\n \n\n axios.get(`${process.env.REACT_APP_API_URL}/api/manzana/${parametros.manzana}`, {\n headers: {\n Authorization: `Bearer ${token}`\n }\n })\n .then((response) => {\n // handle success\n console.log(response);\n setManzana(response.data) \n })\n .catch((error) => {\n // handle success\n console.log(error);\n navigate('/usuarios/login');\n })\n .then(() => {\n // handle success\n console.log('sae');\n });\n }, [setManzana]);\n\n return(\n \n \n {\n !manzana ?\n :\n \n } \n \n \n {\n !manzana ?\n :\n \n } \n \n
\n );\n}\n\nexport { InventarioManzana }","var _defs, _rect;\n\nvar _excluded = [\"title\", \"titleId\"];\n\nfunction _extends() { _extends = Object.assign ? Object.assign.bind() : function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); }\n\nfunction _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; }\n\nfunction _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; }\n\nimport * as React from \"react\";\n\nfunction SvgBanioIcon(_ref, svgRef) {\n var title = _ref.title,\n titleId = _ref.titleId,\n props = _objectWithoutProperties(_ref, _excluded);\n\n return /*#__PURE__*/React.createElement(\"svg\", _extends({\n xmlns: \"http://www.w3.org/2000/svg\",\n xmlnsXlink: \"http://www.w3.org/1999/xlink\",\n width: 24,\n height: 24,\n viewBox: \"0 0 24 24\",\n ref: svgRef,\n \"aria-labelledby\": titleId\n }, props), title ? /*#__PURE__*/React.createElement(\"title\", {\n id: titleId\n }, title) : null, _defs || (_defs = /*#__PURE__*/React.createElement(\"defs\", null, /*#__PURE__*/React.createElement(\"pattern\", {\n id: \"pattern\",\n preserveAspectRatio: \"xMidYMid slice\",\n width: \"100%\",\n height: \"100%\",\n viewBox: \"0 0 512 512\"\n }, /*#__PURE__*/React.createElement(\"image\", {\n width: 512,\n height: 512,\n xlinkHref: 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\"\n })))), _rect || (_rect = /*#__PURE__*/React.createElement(\"rect\", {\n id: \"Image_1\",\n \"data-name\": \"Image 1\",\n width: 24,\n height: 24,\n fill: \"url(#pattern)\"\n })));\n}\n\nvar ForwardRef = /*#__PURE__*/React.forwardRef(SvgBanioIcon);\nexport default __webpack_public_path__ + \"static/media/banio_icon.10035c4838b0da34491d5c08bf633234.svg\";\nexport { ForwardRef as ReactComponent };","var _defs, _rect;\n\nvar _excluded = [\"title\", \"titleId\"];\n\nfunction _extends() { _extends = Object.assign ? Object.assign.bind() : function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); }\n\nfunction _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; }\n\nfunction _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; }\n\nimport * as React from \"react\";\n\nfunction SvgTerrenoIcon(_ref, svgRef) {\n var title = _ref.title,\n titleId = _ref.titleId,\n props = _objectWithoutProperties(_ref, _excluded);\n\n return /*#__PURE__*/React.createElement(\"svg\", _extends({\n xmlns: \"http://www.w3.org/2000/svg\",\n xmlnsXlink: \"http://www.w3.org/1999/xlink\",\n width: 24,\n height: 24,\n viewBox: \"0 0 24 24\",\n ref: svgRef,\n \"aria-labelledby\": titleId\n }, props), title ? /*#__PURE__*/React.createElement(\"title\", {\n id: titleId\n }, title) : null, _defs || (_defs = /*#__PURE__*/React.createElement(\"defs\", null, /*#__PURE__*/React.createElement(\"pattern\", {\n id: \"pattern\",\n preserveAspectRatio: \"xMidYMid slice\",\n width: \"100%\",\n height: \"100%\",\n viewBox: \"0 0 512 512\"\n }, /*#__PURE__*/React.createElement(\"image\", {\n width: 512,\n height: 512,\n xlinkHref: 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})))), _rect || (_rect = /*#__PURE__*/React.createElement(\"rect\", {\n id: \"Image_1\",\n \"data-name\": \"Image 1\",\n width: 24,\n height: 24,\n fill: \"url(#pattern)\"\n })));\n}\n\nvar ForwardRef = /*#__PURE__*/React.forwardRef(SvgTerrenoIcon);\nexport default __webpack_public_path__ + \"static/media/terreno_icon.795c57353a0a64ea8ab726455b4de067.svg\";\nexport { ForwardRef as ReactComponent };","var _defs, _rect;\n\nvar _excluded = [\"title\", \"titleId\"];\n\nfunction _extends() { _extends = Object.assign ? Object.assign.bind() : function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); }\n\nfunction _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; }\n\nfunction _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; }\n\nimport * as React from \"react\";\n\nfunction SvgConstruccionIcon(_ref, svgRef) {\n var title = _ref.title,\n titleId = _ref.titleId,\n props = _objectWithoutProperties(_ref, _excluded);\n\n return /*#__PURE__*/React.createElement(\"svg\", _extends({\n xmlns: \"http://www.w3.org/2000/svg\",\n xmlnsXlink: \"http://www.w3.org/1999/xlink\",\n width: 24,\n height: 24,\n viewBox: \"0 0 24 24\",\n ref: svgRef,\n \"aria-labelledby\": titleId\n }, props), title ? /*#__PURE__*/React.createElement(\"title\", {\n id: titleId\n }, title) : null, _defs || (_defs = /*#__PURE__*/React.createElement(\"defs\", null, /*#__PURE__*/React.createElement(\"pattern\", {\n id: \"pattern\",\n preserveAspectRatio: \"xMidYMid slice\",\n width: \"100%\",\n height: \"100%\",\n viewBox: \"0 0 512 512\"\n }, /*#__PURE__*/React.createElement(\"image\", {\n width: 512,\n height: 512,\n xlinkHref: 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\"\n })))), _rect || (_rect = /*#__PURE__*/React.createElement(\"rect\", {\n id: \"Image_1\",\n \"data-name\": \"Image 1\",\n width: 24,\n height: 24,\n fill: \"url(#pattern)\"\n })));\n}\n\nvar ForwardRef = /*#__PURE__*/React.forwardRef(SvgConstruccionIcon);\nexport default __webpack_public_path__ + \"static/media/construccion_icon.238727a4f2104c3ea685a4d21375ff62.svg\";\nexport { ForwardRef as ReactComponent };","var _defs, _rect;\n\nvar _excluded = [\"title\", \"titleId\"];\n\nfunction _extends() { _extends = Object.assign ? Object.assign.bind() : function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); }\n\nfunction _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; }\n\nfunction _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; }\n\nimport * as React from \"react\";\n\nfunction SvgHabitacionesIcon(_ref, svgRef) {\n var title = _ref.title,\n titleId = _ref.titleId,\n props = _objectWithoutProperties(_ref, _excluded);\n\n return /*#__PURE__*/React.createElement(\"svg\", _extends({\n xmlns: \"http://www.w3.org/2000/svg\",\n xmlnsXlink: \"http://www.w3.org/1999/xlink\",\n width: 24,\n height: 24,\n viewBox: \"0 0 24 24\",\n ref: svgRef,\n \"aria-labelledby\": titleId\n }, props), title ? /*#__PURE__*/React.createElement(\"title\", {\n id: titleId\n }, title) : null, _defs || (_defs = /*#__PURE__*/React.createElement(\"defs\", null, /*#__PURE__*/React.createElement(\"pattern\", {\n id: \"pattern\",\n preserveAspectRatio: \"xMidYMid slice\",\n width: \"100%\",\n height: \"100%\",\n viewBox: \"0 0 512 512\"\n }, /*#__PURE__*/React.createElement(\"image\", {\n width: 512,\n height: 512,\n xlinkHref: 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Object.assign.bind() : function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; }; return _extends.apply(this, arguments); }\n\nfunction _objectWithoutProperties(source, excluded) { if (source == null) return {}; var target = _objectWithoutPropertiesLoose(source, excluded); var key, i; if (Object.getOwnPropertySymbols) { var sourceSymbolKeys = Object.getOwnPropertySymbols(source); for (i = 0; i < sourceSymbolKeys.length; i++) { key = sourceSymbolKeys[i]; if (excluded.indexOf(key) >= 0) continue; if (!Object.prototype.propertyIsEnumerable.call(source, key)) continue; target[key] = source[key]; } } return target; }\n\nfunction _objectWithoutPropertiesLoose(source, excluded) { if (source == null) return {}; var target = {}; var sourceKeys = Object.keys(source); var key, i; for (i = 0; i < sourceKeys.length; i++) { key = sourceKeys[i]; if (excluded.indexOf(key) >= 0) continue; target[key] = source[key]; } return target; }\n\nimport * as React from \"react\";\n\nfunction SvgCasita(_ref, svgRef) {\n var title = _ref.title,\n titleId = _ref.titleId,\n props = _objectWithoutProperties(_ref, _excluded);\n\n return /*#__PURE__*/React.createElement(\"svg\", _extends({\n xmlns: \"http://www.w3.org/2000/svg\",\n xmlnsXlink: \"http://www.w3.org/1999/xlink\",\n width: 24,\n height: 24,\n viewBox: \"0 0 24 24\",\n ref: svgRef,\n \"aria-labelledby\": titleId\n }, props), title ? /*#__PURE__*/React.createElement(\"title\", {\n id: titleId\n }, title) : null, _defs || (_defs = /*#__PURE__*/React.createElement(\"defs\", null, /*#__PURE__*/React.createElement(\"pattern\", {\n id: \"pattern\",\n preserveAspectRatio: \"xMidYMid slice\",\n width: \"100%\",\n height: \"100%\",\n viewBox: \"0 0 512 512\"\n }, /*#__PURE__*/React.createElement(\"image\", {\n width: 512,\n height: 512,\n xlinkHref: 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\"\n })))), _rect || (_rect = /*#__PURE__*/React.createElement(\"rect\", {\n id: \"Image_1\",\n \"data-name\": \"Image 1\",\n width: 24,\n height: 24,\n fill: \"url(#pattern)\"\n })));\n}\n\nvar ForwardRef = /*#__PURE__*/React.forwardRef(SvgCasita);\nexport default __webpack_public_path__ + \"static/media/casita.7a703725d04e33e8d3960f7a66a40700.svg\";\nexport { ForwardRef as ReactComponent };","// Polyfills\n\nif ( Number.EPSILON === undefined ) {\n\n\tNumber.EPSILON = Math.pow( 2, - 52 );\n\n}\n\nif ( Number.isInteger === undefined ) {\n\n\t// Missing in IE\n\t// https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Number/isInteger\n\n\tNumber.isInteger = function ( value ) {\n\n\t\treturn typeof value === 'number' && isFinite( value ) && Math.floor( value ) === value;\n\n\t};\n\n}\n\n//\n\nif ( Math.sign === undefined ) {\n\n\t// https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Math/sign\n\n\tMath.sign = function ( x ) {\n\n\t\treturn ( x < 0 ) ? - 1 : ( x > 0 ) ? 1 : + x;\n\n\t};\n\n}\n\nif ( 'name' in Function.prototype === false ) {\n\n\t// Missing in IE\n\t// https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Function/name\n\n\tObject.defineProperty( Function.prototype, 'name', {\n\n\t\tget: function () {\n\n\t\t\treturn this.toString().match( /^\\s*function\\s*([^\\(\\s]*)/ )[ 1 ];\n\n\t\t}\n\n\t} );\n\n}\n\nif ( Object.assign === undefined ) {\n\n\t// Missing in IE\n\t// https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Object/assign\n\n\t( function () {\n\n\t\tObject.assign = function ( target ) {\n\n\t\t\tif ( target === undefined || target === null ) {\n\n\t\t\t\tthrow new TypeError( 'Cannot convert undefined or null to object' );\n\n\t\t\t}\n\n\t\t\tvar output = Object( target );\n\n\t\t\tfor ( var index = 1; index < arguments.length; index ++ ) {\n\n\t\t\t\tvar source = arguments[ index ];\n\n\t\t\t\tif ( source !== undefined && source !== null ) {\n\n\t\t\t\t\tfor ( var nextKey in source ) {\n\n\t\t\t\t\t\tif ( Object.prototype.hasOwnProperty.call( source, nextKey ) ) {\n\n\t\t\t\t\t\t\toutput[ nextKey ] = source[ nextKey ];\n\n\t\t\t\t\t\t}\n\n\t\t\t\t\t}\n\n\t\t\t\t}\n\n\t\t\t}\n\n\t\t\treturn output;\n\n\t\t};\n\n\t} )();\n\n}\n\n/**\n * https://github.com/mrdoob/eventdispatcher.js/\n */\n\nfunction EventDispatcher() {}\n\nObject.assign( EventDispatcher.prototype, {\n\n\taddEventListener: function ( type, listener ) {\n\n\t\tif ( this._listeners === undefined ) this._listeners = {};\n\n\t\tvar listeners = this._listeners;\n\n\t\tif ( listeners[ type ] === undefined ) {\n\n\t\t\tlisteners[ type ] = [];\n\n\t\t}\n\n\t\tif ( listeners[ type ].indexOf( listener ) === - 1 ) {\n\n\t\t\tlisteners[ type ].push( listener );\n\n\t\t}\n\n\t},\n\n\thasEventListener: function ( type, listener ) {\n\n\t\tif ( this._listeners === undefined ) return false;\n\n\t\tvar listeners = this._listeners;\n\n\t\treturn listeners[ type ] !== undefined && listeners[ type ].indexOf( listener ) !== - 1;\n\n\t},\n\n\tremoveEventListener: function ( type, listener ) {\n\n\t\tif ( this._listeners === undefined ) return;\n\n\t\tvar listeners = this._listeners;\n\t\tvar listenerArray = listeners[ type ];\n\n\t\tif ( listenerArray !== undefined ) {\n\n\t\t\tvar index = listenerArray.indexOf( listener );\n\n\t\t\tif ( index !== - 1 ) {\n\n\t\t\t\tlistenerArray.splice( index, 1 );\n\n\t\t\t}\n\n\t\t}\n\n\t},\n\n\tdispatchEvent: function ( event ) {\n\n\t\tif ( this._listeners === undefined ) return;\n\n\t\tvar listeners = this._listeners;\n\t\tvar listenerArray = listeners[ event.type ];\n\n\t\tif ( listenerArray !== undefined ) {\n\n\t\t\tevent.target = this;\n\n\t\t\tvar array = listenerArray.slice( 0 );\n\n\t\t\tfor ( var i = 0, l = array.length; i < l; i ++ ) {\n\n\t\t\t\tarray[ i ].call( this, event );\n\n\t\t\t}\n\n\t\t}\n\n\t}\n\n} );\n\nvar REVISION = '105';\nvar MOUSE = { LEFT: 0, MIDDLE: 1, RIGHT: 2 };\nvar CullFaceNone = 0;\nvar CullFaceBack = 1;\nvar CullFaceFront = 2;\nvar CullFaceFrontBack = 3;\nvar FrontFaceDirectionCW = 0;\nvar FrontFaceDirectionCCW = 1;\nvar BasicShadowMap = 0;\nvar PCFShadowMap = 1;\nvar PCFSoftShadowMap = 2;\nvar FrontSide = 0;\nvar BackSide = 1;\nvar DoubleSide = 2;\nvar FlatShading = 1;\nvar SmoothShading = 2;\nvar NoColors = 0;\nvar FaceColors = 1;\nvar VertexColors = 2;\nvar NoBlending = 0;\nvar NormalBlending = 1;\nvar AdditiveBlending = 2;\nvar SubtractiveBlending = 3;\nvar MultiplyBlending = 4;\nvar CustomBlending = 5;\nvar AddEquation = 100;\nvar SubtractEquation = 101;\nvar ReverseSubtractEquation = 102;\nvar MinEquation = 103;\nvar MaxEquation = 104;\nvar ZeroFactor = 200;\nvar OneFactor = 201;\nvar SrcColorFactor = 202;\nvar OneMinusSrcColorFactor = 203;\nvar SrcAlphaFactor = 204;\nvar OneMinusSrcAlphaFactor = 205;\nvar DstAlphaFactor = 206;\nvar OneMinusDstAlphaFactor = 207;\nvar DstColorFactor = 208;\nvar OneMinusDstColorFactor = 209;\nvar SrcAlphaSaturateFactor = 210;\nvar NeverDepth = 0;\nvar AlwaysDepth = 1;\nvar LessDepth = 2;\nvar LessEqualDepth = 3;\nvar EqualDepth = 4;\nvar GreaterEqualDepth = 5;\nvar GreaterDepth = 6;\nvar NotEqualDepth = 7;\nvar MultiplyOperation = 0;\nvar MixOperation = 1;\nvar AddOperation = 2;\nvar NoToneMapping = 0;\nvar LinearToneMapping = 1;\nvar ReinhardToneMapping = 2;\nvar Uncharted2ToneMapping = 3;\nvar CineonToneMapping = 4;\nvar ACESFilmicToneMapping = 5;\n\nvar UVMapping = 300;\nvar CubeReflectionMapping = 301;\nvar CubeRefractionMapping = 302;\nvar EquirectangularReflectionMapping = 303;\nvar EquirectangularRefractionMapping = 304;\nvar SphericalReflectionMapping = 305;\nvar CubeUVReflectionMapping = 306;\nvar CubeUVRefractionMapping = 307;\nvar RepeatWrapping = 1000;\nvar ClampToEdgeWrapping = 1001;\nvar MirroredRepeatWrapping = 1002;\nvar NearestFilter = 1003;\nvar NearestMipMapNearestFilter = 1004;\nvar NearestMipMapLinearFilter = 1005;\nvar LinearFilter = 1006;\nvar LinearMipMapNearestFilter = 1007;\nvar LinearMipMapLinearFilter = 1008;\nvar UnsignedByteType = 1009;\nvar ByteType = 1010;\nvar ShortType = 1011;\nvar UnsignedShortType = 1012;\nvar IntType = 1013;\nvar UnsignedIntType = 1014;\nvar FloatType = 1015;\nvar HalfFloatType = 1016;\nvar UnsignedShort4444Type = 1017;\nvar UnsignedShort5551Type = 1018;\nvar UnsignedShort565Type = 1019;\nvar UnsignedInt248Type = 1020;\nvar AlphaFormat = 1021;\nvar RGBFormat = 1022;\nvar RGBAFormat = 1023;\nvar LuminanceFormat = 1024;\nvar LuminanceAlphaFormat = 1025;\nvar RGBEFormat = RGBAFormat;\nvar DepthFormat = 1026;\nvar DepthStencilFormat = 1027;\nvar RedFormat = 1028;\nvar RGB_S3TC_DXT1_Format = 33776;\nvar RGBA_S3TC_DXT1_Format = 33777;\nvar RGBA_S3TC_DXT3_Format = 33778;\nvar RGBA_S3TC_DXT5_Format = 33779;\nvar RGB_PVRTC_4BPPV1_Format = 35840;\nvar RGB_PVRTC_2BPPV1_Format = 35841;\nvar RGBA_PVRTC_4BPPV1_Format = 35842;\nvar RGBA_PVRTC_2BPPV1_Format = 35843;\nvar RGB_ETC1_Format = 36196;\nvar RGBA_ASTC_4x4_Format = 37808;\nvar RGBA_ASTC_5x4_Format = 37809;\nvar RGBA_ASTC_5x5_Format = 37810;\nvar RGBA_ASTC_6x5_Format = 37811;\nvar RGBA_ASTC_6x6_Format = 37812;\nvar RGBA_ASTC_8x5_Format = 37813;\nvar RGBA_ASTC_8x6_Format = 37814;\nvar RGBA_ASTC_8x8_Format = 37815;\nvar RGBA_ASTC_10x5_Format = 37816;\nvar RGBA_ASTC_10x6_Format = 37817;\nvar RGBA_ASTC_10x8_Format = 37818;\nvar RGBA_ASTC_10x10_Format = 37819;\nvar RGBA_ASTC_12x10_Format = 37820;\nvar RGBA_ASTC_12x12_Format = 37821;\nvar LoopOnce = 2200;\nvar LoopRepeat = 2201;\nvar LoopPingPong = 2202;\nvar InterpolateDiscrete = 2300;\nvar InterpolateLinear = 2301;\nvar InterpolateSmooth = 2302;\nvar ZeroCurvatureEnding = 2400;\nvar ZeroSlopeEnding = 2401;\nvar WrapAroundEnding = 2402;\nvar TrianglesDrawMode = 0;\nvar TriangleStripDrawMode = 1;\nvar TriangleFanDrawMode = 2;\nvar LinearEncoding = 3000;\nvar sRGBEncoding = 3001;\nvar GammaEncoding = 3007;\nvar RGBEEncoding = 3002;\nvar LogLuvEncoding = 3003;\nvar RGBM7Encoding = 3004;\nvar RGBM16Encoding = 3005;\nvar RGBDEncoding = 3006;\nvar BasicDepthPacking = 3200;\nvar RGBADepthPacking = 3201;\nvar TangentSpaceNormalMap = 0;\nvar ObjectSpaceNormalMap = 1;\n\n/**\n * @author alteredq / http://alteredqualia.com/\n * @author mrdoob / http://mrdoob.com/\n */\n\nvar _Math = {\n\n\tDEG2RAD: Math.PI / 180,\n\tRAD2DEG: 180 / Math.PI,\n\n\tgenerateUUID: ( function () {\n\n\t\t// http://stackoverflow.com/questions/105034/how-to-create-a-guid-uuid-in-javascript/21963136#21963136\n\n\t\tvar lut = [];\n\n\t\tfor ( var i = 0; i < 256; i ++ ) {\n\n\t\t\tlut[ i ] = ( i < 16 ? '0' : '' ) + ( i ).toString( 16 );\n\n\t\t}\n\n\t\treturn function generateUUID() {\n\n\t\t\tvar d0 = Math.random() * 0xffffffff | 0;\n\t\t\tvar d1 = Math.random() * 0xffffffff | 0;\n\t\t\tvar d2 = Math.random() * 0xffffffff | 0;\n\t\t\tvar d3 = Math.random() * 0xffffffff | 0;\n\t\t\tvar uuid = lut[ d0 & 0xff ] + lut[ d0 >> 8 & 0xff ] + lut[ d0 >> 16 & 0xff ] + lut[ d0 >> 24 & 0xff ] + '-' +\n\t\t\t\tlut[ d1 & 0xff ] + lut[ d1 >> 8 & 0xff ] + '-' + lut[ d1 >> 16 & 0x0f | 0x40 ] + lut[ d1 >> 24 & 0xff ] + '-' +\n\t\t\t\tlut[ d2 & 0x3f | 0x80 ] + lut[ d2 >> 8 & 0xff ] + '-' + lut[ d2 >> 16 & 0xff ] + lut[ d2 >> 24 & 0xff ] +\n\t\t\t\tlut[ d3 & 0xff ] + lut[ d3 >> 8 & 0xff ] + lut[ d3 >> 16 & 0xff ] + lut[ d3 >> 24 & 0xff ];\n\n\t\t\t// .toUpperCase() here flattens concatenated strings to save heap memory space.\n\t\t\treturn uuid.toUpperCase();\n\n\t\t};\n\n\t} )(),\n\n\tclamp: function ( value, min, max ) {\n\n\t\treturn Math.max( min, Math.min( max, value ) );\n\n\t},\n\n\t// compute euclidian modulo of m % n\n\t// https://en.wikipedia.org/wiki/Modulo_operation\n\n\teuclideanModulo: function ( n, m ) {\n\n\t\treturn ( ( n % m ) + m ) % m;\n\n\t},\n\n\t// Linear mapping from range to range \n\n\tmapLinear: function ( x, a1, a2, b1, b2 ) {\n\n\t\treturn b1 + ( x - a1 ) * ( b2 - b1 ) / ( a2 - a1 );\n\n\t},\n\n\t// https://en.wikipedia.org/wiki/Linear_interpolation\n\n\tlerp: function ( x, y, t ) {\n\n\t\treturn ( 1 - t ) * x + t * y;\n\n\t},\n\n\t// http://en.wikipedia.org/wiki/Smoothstep\n\n\tsmoothstep: function ( x, min, max ) {\n\n\t\tif ( x <= min ) return 0;\n\t\tif ( x >= max ) return 1;\n\n\t\tx = ( x - min ) / ( max - min );\n\n\t\treturn x * x * ( 3 - 2 * x );\n\n\t},\n\n\tsmootherstep: function ( x, min, max ) {\n\n\t\tif ( x <= min ) return 0;\n\t\tif ( x >= max ) return 1;\n\n\t\tx = ( x - min ) / ( max - min );\n\n\t\treturn x * x * x * ( x * ( x * 6 - 15 ) + 10 );\n\n\t},\n\n\t// Random integer from interval\n\n\trandInt: function ( low, high ) {\n\n\t\treturn low + Math.floor( Math.random() * ( high - low + 1 ) );\n\n\t},\n\n\t// Random float from interval\n\n\trandFloat: function ( low, high ) {\n\n\t\treturn low + Math.random() * ( high - low );\n\n\t},\n\n\t// Random float from <-range/2, range/2> interval\n\n\trandFloatSpread: function ( range ) {\n\n\t\treturn range * ( 0.5 - Math.random() );\n\n\t},\n\n\tdegToRad: function ( degrees ) {\n\n\t\treturn degrees * _Math.DEG2RAD;\n\n\t},\n\n\tradToDeg: function ( radians ) {\n\n\t\treturn radians * _Math.RAD2DEG;\n\n\t},\n\n\tisPowerOfTwo: function ( value ) {\n\n\t\treturn ( value & ( value - 1 ) ) === 0 && value !== 0;\n\n\t},\n\n\tceilPowerOfTwo: function ( value ) {\n\n\t\treturn Math.pow( 2, Math.ceil( Math.log( value ) / Math.LN2 ) );\n\n\t},\n\n\tfloorPowerOfTwo: function ( value ) {\n\n\t\treturn Math.pow( 2, Math.floor( Math.log( value ) / Math.LN2 ) );\n\n\t}\n\n};\n\n/**\n * @author mrdoob / http://mrdoob.com/\n * @author philogb / http://blog.thejit.org/\n * @author egraether / http://egraether.com/\n * @author zz85 / http://www.lab4games.net/zz85/blog\n */\n\nfunction Vector2( x, y ) {\n\n\tthis.x = x || 0;\n\tthis.y = y || 0;\n\n}\n\nObject.defineProperties( Vector2.prototype, {\n\n\t\"width\": {\n\n\t\tget: function () {\n\n\t\t\treturn this.x;\n\n\t\t},\n\n\t\tset: function ( value ) {\n\n\t\t\tthis.x = value;\n\n\t\t}\n\n\t},\n\n\t\"height\": {\n\n\t\tget: function () {\n\n\t\t\treturn this.y;\n\n\t\t},\n\n\t\tset: function ( value ) {\n\n\t\t\tthis.y = value;\n\n\t\t}\n\n\t}\n\n} );\n\nObject.assign( Vector2.prototype, {\n\n\tisVector2: true,\n\n\tset: function ( x, y ) {\n\n\t\tthis.x = x;\n\t\tthis.y = y;\n\n\t\treturn this;\n\n\t},\n\n\tsetScalar: function ( scalar ) {\n\n\t\tthis.x = scalar;\n\t\tthis.y = scalar;\n\n\t\treturn this;\n\n\t},\n\n\tsetX: function ( x ) {\n\n\t\tthis.x = x;\n\n\t\treturn this;\n\n\t},\n\n\tsetY: function ( y ) {\n\n\t\tthis.y = y;\n\n\t\treturn this;\n\n\t},\n\n\tsetComponent: function ( index, value ) {\n\n\t\tswitch ( index ) {\n\n\t\t\tcase 0: this.x = value; break;\n\t\t\tcase 1: this.y = value; break;\n\t\t\tdefault: throw new Error( 'index is out of range: ' + index );\n\n\t\t}\n\n\t\treturn this;\n\n\t},\n\n\tgetComponent: function ( index ) {\n\n\t\tswitch ( index ) {\n\n\t\t\tcase 0: return this.x;\n\t\t\tcase 1: return this.y;\n\t\t\tdefault: throw new Error( 'index is out of range: ' + index );\n\n\t\t}\n\n\t},\n\n\tclone: function () {\n\n\t\treturn new this.constructor( this.x, this.y );\n\n\t},\n\n\tcopy: function ( v ) {\n\n\t\tthis.x = v.x;\n\t\tthis.y = v.y;\n\n\t\treturn this;\n\n\t},\n\n\tadd: function ( v, w ) {\n\n\t\tif ( w !== undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Vector2: .add() now only accepts one argument. Use .addVectors( a, b ) instead.' );\n\t\t\treturn this.addVectors( v, w );\n\n\t\t}\n\n\t\tthis.x += v.x;\n\t\tthis.y += v.y;\n\n\t\treturn this;\n\n\t},\n\n\taddScalar: function ( s ) {\n\n\t\tthis.x += s;\n\t\tthis.y += s;\n\n\t\treturn this;\n\n\t},\n\n\taddVectors: function ( a, b ) {\n\n\t\tthis.x = a.x + b.x;\n\t\tthis.y = a.y + b.y;\n\n\t\treturn this;\n\n\t},\n\n\taddScaledVector: function ( v, s ) {\n\n\t\tthis.x += v.x * s;\n\t\tthis.y += v.y * s;\n\n\t\treturn this;\n\n\t},\n\n\tsub: function ( v, w ) {\n\n\t\tif ( w !== undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Vector2: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.' );\n\t\t\treturn this.subVectors( v, w );\n\n\t\t}\n\n\t\tthis.x -= v.x;\n\t\tthis.y -= v.y;\n\n\t\treturn this;\n\n\t},\n\n\tsubScalar: function ( s ) {\n\n\t\tthis.x -= s;\n\t\tthis.y -= s;\n\n\t\treturn this;\n\n\t},\n\n\tsubVectors: function ( a, b ) {\n\n\t\tthis.x = a.x - b.x;\n\t\tthis.y = a.y - b.y;\n\n\t\treturn this;\n\n\t},\n\n\tmultiply: function ( v ) {\n\n\t\tthis.x *= v.x;\n\t\tthis.y *= v.y;\n\n\t\treturn this;\n\n\t},\n\n\tmultiplyScalar: function ( scalar ) {\n\n\t\tthis.x *= scalar;\n\t\tthis.y *= scalar;\n\n\t\treturn this;\n\n\t},\n\n\tdivide: function ( v ) {\n\n\t\tthis.x /= v.x;\n\t\tthis.y /= v.y;\n\n\t\treturn this;\n\n\t},\n\n\tdivideScalar: function ( scalar ) {\n\n\t\treturn this.multiplyScalar( 1 / scalar );\n\n\t},\n\n\tapplyMatrix3: function ( m ) {\n\n\t\tvar x = this.x, y = this.y;\n\t\tvar e = m.elements;\n\n\t\tthis.x = e[ 0 ] * x + e[ 3 ] * y + e[ 6 ];\n\t\tthis.y = e[ 1 ] * x + e[ 4 ] * y + e[ 7 ];\n\n\t\treturn this;\n\n\t},\n\n\tmin: function ( v ) {\n\n\t\tthis.x = Math.min( this.x, v.x );\n\t\tthis.y = Math.min( this.y, v.y );\n\n\t\treturn this;\n\n\t},\n\n\tmax: function ( v ) {\n\n\t\tthis.x = Math.max( this.x, v.x );\n\t\tthis.y = Math.max( this.y, v.y );\n\n\t\treturn this;\n\n\t},\n\n\tclamp: function ( min, max ) {\n\n\t\t// assumes min < max, componentwise\n\n\t\tthis.x = Math.max( min.x, Math.min( max.x, this.x ) );\n\t\tthis.y = Math.max( min.y, Math.min( max.y, this.y ) );\n\n\t\treturn this;\n\n\t},\n\n\tclampScalar: function ( minVal, maxVal ) {\n\n\t\tthis.x = Math.max( minVal, Math.min( maxVal, this.x ) );\n\t\tthis.y = Math.max( minVal, Math.min( maxVal, this.y ) );\n\n\t\treturn this;\n\n\t},\n\n\tclampLength: function ( min, max ) {\n\n\t\tvar length = this.length();\n\n\t\treturn this.divideScalar( length || 1 ).multiplyScalar( Math.max( min, Math.min( max, length ) ) );\n\n\t},\n\n\tfloor: function () {\n\n\t\tthis.x = Math.floor( this.x );\n\t\tthis.y = Math.floor( this.y );\n\n\t\treturn this;\n\n\t},\n\n\tceil: function () {\n\n\t\tthis.x = Math.ceil( this.x );\n\t\tthis.y = Math.ceil( this.y );\n\n\t\treturn this;\n\n\t},\n\n\tround: function () {\n\n\t\tthis.x = Math.round( this.x );\n\t\tthis.y = Math.round( this.y );\n\n\t\treturn this;\n\n\t},\n\n\troundToZero: function () {\n\n\t\tthis.x = ( this.x < 0 ) ? Math.ceil( this.x ) : Math.floor( this.x );\n\t\tthis.y = ( this.y < 0 ) ? Math.ceil( this.y ) : Math.floor( this.y );\n\n\t\treturn this;\n\n\t},\n\n\tnegate: function () {\n\n\t\tthis.x = - this.x;\n\t\tthis.y = - this.y;\n\n\t\treturn this;\n\n\t},\n\n\tdot: function ( v ) {\n\n\t\treturn this.x * v.x + this.y * v.y;\n\n\t},\n\n\tcross: function ( v ) {\n\n\t\treturn this.x * v.y - this.y * v.x;\n\n\t},\n\n\tlengthSq: function () {\n\n\t\treturn this.x * this.x + this.y * this.y;\n\n\t},\n\n\tlength: function () {\n\n\t\treturn Math.sqrt( this.x * this.x + this.y * this.y );\n\n\t},\n\n\tmanhattanLength: function () {\n\n\t\treturn Math.abs( this.x ) + Math.abs( this.y );\n\n\t},\n\n\tnormalize: function () {\n\n\t\treturn this.divideScalar( this.length() || 1 );\n\n\t},\n\n\tangle: function () {\n\n\t\t// computes the angle in radians with respect to the positive x-axis\n\n\t\tvar angle = Math.atan2( this.y, this.x );\n\n\t\tif ( angle < 0 ) angle += 2 * Math.PI;\n\n\t\treturn angle;\n\n\t},\n\n\tdistanceTo: function ( v ) {\n\n\t\treturn Math.sqrt( this.distanceToSquared( v ) );\n\n\t},\n\n\tdistanceToSquared: function ( v ) {\n\n\t\tvar dx = this.x - v.x, dy = this.y - v.y;\n\t\treturn dx * dx + dy * dy;\n\n\t},\n\n\tmanhattanDistanceTo: function ( v ) {\n\n\t\treturn Math.abs( this.x - v.x ) + Math.abs( this.y - v.y );\n\n\t},\n\n\tsetLength: function ( length ) {\n\n\t\treturn this.normalize().multiplyScalar( length );\n\n\t},\n\n\tlerp: function ( v, alpha ) {\n\n\t\tthis.x += ( v.x - this.x ) * alpha;\n\t\tthis.y += ( v.y - this.y ) * alpha;\n\n\t\treturn this;\n\n\t},\n\n\tlerpVectors: function ( v1, v2, alpha ) {\n\n\t\treturn this.subVectors( v2, v1 ).multiplyScalar( alpha ).add( v1 );\n\n\t},\n\n\tequals: function ( v ) {\n\n\t\treturn ( ( v.x === this.x ) && ( v.y === this.y ) );\n\n\t},\n\n\tfromArray: function ( array, offset ) {\n\n\t\tif ( offset === undefined ) offset = 0;\n\n\t\tthis.x = array[ offset ];\n\t\tthis.y = array[ offset + 1 ];\n\n\t\treturn this;\n\n\t},\n\n\ttoArray: function ( array, offset ) {\n\n\t\tif ( array === undefined ) array = [];\n\t\tif ( offset === undefined ) offset = 0;\n\n\t\tarray[ offset ] = this.x;\n\t\tarray[ offset + 1 ] = this.y;\n\n\t\treturn array;\n\n\t},\n\n\tfromBufferAttribute: function ( attribute, index, offset ) {\n\n\t\tif ( offset !== undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Vector2: offset has been removed from .fromBufferAttribute().' );\n\n\t\t}\n\n\t\tthis.x = attribute.getX( index );\n\t\tthis.y = attribute.getY( index );\n\n\t\treturn this;\n\n\t},\n\n\trotateAround: function ( center, angle ) {\n\n\t\tvar c = Math.cos( angle ), s = Math.sin( angle );\n\n\t\tvar x = this.x - center.x;\n\t\tvar y = this.y - center.y;\n\n\t\tthis.x = x * c - y * s + center.x;\n\t\tthis.y = x * s + y * c + center.y;\n\n\t\treturn this;\n\n\t}\n\n} );\n\n/**\n * @author mikael emtinger / http://gomo.se/\n * @author alteredq / http://alteredqualia.com/\n * @author WestLangley / http://github.com/WestLangley\n * @author bhouston / http://clara.io\n */\n\nfunction Quaternion( x, y, z, w ) {\n\n\tthis._x = x || 0;\n\tthis._y = y || 0;\n\tthis._z = z || 0;\n\tthis._w = ( w !== undefined ) ? w : 1;\n\n}\n\nObject.assign( Quaternion, {\n\n\tslerp: function ( qa, qb, qm, t ) {\n\n\t\treturn qm.copy( qa ).slerp( qb, t );\n\n\t},\n\n\tslerpFlat: function ( dst, dstOffset, src0, srcOffset0, src1, srcOffset1, t ) {\n\n\t\t// fuzz-free, array-based Quaternion SLERP operation\n\n\t\tvar x0 = src0[ srcOffset0 + 0 ],\n\t\t\ty0 = src0[ srcOffset0 + 1 ],\n\t\t\tz0 = src0[ srcOffset0 + 2 ],\n\t\t\tw0 = src0[ srcOffset0 + 3 ],\n\n\t\t\tx1 = src1[ srcOffset1 + 0 ],\n\t\t\ty1 = src1[ srcOffset1 + 1 ],\n\t\t\tz1 = src1[ srcOffset1 + 2 ],\n\t\t\tw1 = src1[ srcOffset1 + 3 ];\n\n\t\tif ( w0 !== w1 || x0 !== x1 || y0 !== y1 || z0 !== z1 ) {\n\n\t\t\tvar s = 1 - t,\n\n\t\t\t\tcos = x0 * x1 + y0 * y1 + z0 * z1 + w0 * w1,\n\n\t\t\t\tdir = ( cos >= 0 ? 1 : - 1 ),\n\t\t\t\tsqrSin = 1 - cos * cos;\n\n\t\t\t// Skip the Slerp for tiny steps to avoid numeric problems:\n\t\t\tif ( sqrSin > Number.EPSILON ) {\n\n\t\t\t\tvar sin = Math.sqrt( sqrSin ),\n\t\t\t\t\tlen = Math.atan2( sin, cos * dir );\n\n\t\t\t\ts = Math.sin( s * len ) / sin;\n\t\t\t\tt = Math.sin( t * len ) / sin;\n\n\t\t\t}\n\n\t\t\tvar tDir = t * dir;\n\n\t\t\tx0 = x0 * s + x1 * tDir;\n\t\t\ty0 = y0 * s + y1 * tDir;\n\t\t\tz0 = z0 * s + z1 * tDir;\n\t\t\tw0 = w0 * s + w1 * tDir;\n\n\t\t\t// Normalize in case we just did a lerp:\n\t\t\tif ( s === 1 - t ) {\n\n\t\t\t\tvar f = 1 / Math.sqrt( x0 * x0 + y0 * y0 + z0 * z0 + w0 * w0 );\n\n\t\t\t\tx0 *= f;\n\t\t\t\ty0 *= f;\n\t\t\t\tz0 *= f;\n\t\t\t\tw0 *= f;\n\n\t\t\t}\n\n\t\t}\n\n\t\tdst[ dstOffset ] = x0;\n\t\tdst[ dstOffset + 1 ] = y0;\n\t\tdst[ dstOffset + 2 ] = z0;\n\t\tdst[ dstOffset + 3 ] = w0;\n\n\t}\n\n} );\n\nObject.defineProperties( Quaternion.prototype, {\n\n\tx: {\n\n\t\tget: function () {\n\n\t\t\treturn this._x;\n\n\t\t},\n\n\t\tset: function ( value ) {\n\n\t\t\tthis._x = value;\n\t\t\tthis.onChangeCallback();\n\n\t\t}\n\n\t},\n\n\ty: {\n\n\t\tget: function () {\n\n\t\t\treturn this._y;\n\n\t\t},\n\n\t\tset: function ( value ) {\n\n\t\t\tthis._y = value;\n\t\t\tthis.onChangeCallback();\n\n\t\t}\n\n\t},\n\n\tz: {\n\n\t\tget: function () {\n\n\t\t\treturn this._z;\n\n\t\t},\n\n\t\tset: function ( value ) {\n\n\t\t\tthis._z = value;\n\t\t\tthis.onChangeCallback();\n\n\t\t}\n\n\t},\n\n\tw: {\n\n\t\tget: function () {\n\n\t\t\treturn this._w;\n\n\t\t},\n\n\t\tset: function ( value ) {\n\n\t\t\tthis._w = value;\n\t\t\tthis.onChangeCallback();\n\n\t\t}\n\n\t}\n\n} );\n\nObject.assign( Quaternion.prototype, {\n\n\tisQuaternion: true,\n\n\tset: function ( x, y, z, w ) {\n\n\t\tthis._x = x;\n\t\tthis._y = y;\n\t\tthis._z = z;\n\t\tthis._w = w;\n\n\t\tthis.onChangeCallback();\n\n\t\treturn this;\n\n\t},\n\n\tclone: function () {\n\n\t\treturn new this.constructor( this._x, this._y, this._z, this._w );\n\n\t},\n\n\tcopy: function ( quaternion ) {\n\n\t\tthis._x = quaternion.x;\n\t\tthis._y = quaternion.y;\n\t\tthis._z = quaternion.z;\n\t\tthis._w = quaternion.w;\n\n\t\tthis.onChangeCallback();\n\n\t\treturn this;\n\n\t},\n\n\tsetFromEuler: function ( euler, update ) {\n\n\t\tif ( ! ( euler && euler.isEuler ) ) {\n\n\t\t\tthrow new Error( 'THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.' );\n\n\t\t}\n\n\t\tvar x = euler._x, y = euler._y, z = euler._z, order = euler.order;\n\n\t\t// http://www.mathworks.com/matlabcentral/fileexchange/\n\t\t// \t20696-function-to-convert-between-dcm-euler-angles-quaternions-and-euler-vectors/\n\t\t//\tcontent/SpinCalc.m\n\n\t\tvar cos = Math.cos;\n\t\tvar sin = Math.sin;\n\n\t\tvar c1 = cos( x / 2 );\n\t\tvar c2 = cos( y / 2 );\n\t\tvar c3 = cos( z / 2 );\n\n\t\tvar s1 = sin( x / 2 );\n\t\tvar s2 = sin( y / 2 );\n\t\tvar s3 = sin( z / 2 );\n\n\t\tif ( order === 'XYZ' ) {\n\n\t\t\tthis._x = s1 * c2 * c3 + c1 * s2 * s3;\n\t\t\tthis._y = c1 * s2 * c3 - s1 * c2 * s3;\n\t\t\tthis._z = c1 * c2 * s3 + s1 * s2 * c3;\n\t\t\tthis._w = c1 * c2 * c3 - s1 * s2 * s3;\n\n\t\t} else if ( order === 'YXZ' ) {\n\n\t\t\tthis._x = s1 * c2 * c3 + c1 * s2 * s3;\n\t\t\tthis._y = c1 * s2 * c3 - s1 * c2 * s3;\n\t\t\tthis._z = c1 * c2 * s3 - s1 * s2 * c3;\n\t\t\tthis._w = c1 * c2 * c3 + s1 * s2 * s3;\n\n\t\t} else if ( order === 'ZXY' ) {\n\n\t\t\tthis._x = s1 * c2 * c3 - c1 * s2 * s3;\n\t\t\tthis._y = c1 * s2 * c3 + s1 * c2 * s3;\n\t\t\tthis._z = c1 * c2 * s3 + s1 * s2 * c3;\n\t\t\tthis._w = c1 * c2 * c3 - s1 * s2 * s3;\n\n\t\t} else if ( order === 'ZYX' ) {\n\n\t\t\tthis._x = s1 * c2 * c3 - c1 * s2 * s3;\n\t\t\tthis._y = c1 * s2 * c3 + s1 * c2 * s3;\n\t\t\tthis._z = c1 * c2 * s3 - s1 * s2 * c3;\n\t\t\tthis._w = c1 * c2 * c3 + s1 * s2 * s3;\n\n\t\t} else if ( order === 'YZX' ) {\n\n\t\t\tthis._x = s1 * c2 * c3 + c1 * s2 * s3;\n\t\t\tthis._y = c1 * s2 * c3 + s1 * c2 * s3;\n\t\t\tthis._z = c1 * c2 * s3 - s1 * s2 * c3;\n\t\t\tthis._w = c1 * c2 * c3 - s1 * s2 * s3;\n\n\t\t} else if ( order === 'XZY' ) {\n\n\t\t\tthis._x = s1 * c2 * c3 - c1 * s2 * s3;\n\t\t\tthis._y = c1 * s2 * c3 - s1 * c2 * s3;\n\t\t\tthis._z = c1 * c2 * s3 + s1 * s2 * c3;\n\t\t\tthis._w = c1 * c2 * c3 + s1 * s2 * s3;\n\n\t\t}\n\n\t\tif ( update !== false ) this.onChangeCallback();\n\n\t\treturn this;\n\n\t},\n\n\tsetFromAxisAngle: function ( axis, angle ) {\n\n\t\t// http://www.euclideanspace.com/maths/geometry/rotations/conversions/angleToQuaternion/index.htm\n\n\t\t// assumes axis is normalized\n\n\t\tvar halfAngle = angle / 2, s = Math.sin( halfAngle );\n\n\t\tthis._x = axis.x * s;\n\t\tthis._y = axis.y * s;\n\t\tthis._z = axis.z * s;\n\t\tthis._w = Math.cos( halfAngle );\n\n\t\tthis.onChangeCallback();\n\n\t\treturn this;\n\n\t},\n\n\tsetFromRotationMatrix: function ( m ) {\n\n\t\t// http://www.euclideanspace.com/maths/geometry/rotations/conversions/matrixToQuaternion/index.htm\n\n\t\t// assumes the upper 3x3 of m is a pure rotation matrix (i.e, unscaled)\n\n\t\tvar te = m.elements,\n\n\t\t\tm11 = te[ 0 ], m12 = te[ 4 ], m13 = te[ 8 ],\n\t\t\tm21 = te[ 1 ], m22 = te[ 5 ], m23 = te[ 9 ],\n\t\t\tm31 = te[ 2 ], m32 = te[ 6 ], m33 = te[ 10 ],\n\n\t\t\ttrace = m11 + m22 + m33,\n\t\t\ts;\n\n\t\tif ( trace > 0 ) {\n\n\t\t\ts = 0.5 / Math.sqrt( trace + 1.0 );\n\n\t\t\tthis._w = 0.25 / s;\n\t\t\tthis._x = ( m32 - m23 ) * s;\n\t\t\tthis._y = ( m13 - m31 ) * s;\n\t\t\tthis._z = ( m21 - m12 ) * s;\n\n\t\t} else if ( m11 > m22 && m11 > m33 ) {\n\n\t\t\ts = 2.0 * Math.sqrt( 1.0 + m11 - m22 - m33 );\n\n\t\t\tthis._w = ( m32 - m23 ) / s;\n\t\t\tthis._x = 0.25 * s;\n\t\t\tthis._y = ( m12 + m21 ) / s;\n\t\t\tthis._z = ( m13 + m31 ) / s;\n\n\t\t} else if ( m22 > m33 ) {\n\n\t\t\ts = 2.0 * Math.sqrt( 1.0 + m22 - m11 - m33 );\n\n\t\t\tthis._w = ( m13 - m31 ) / s;\n\t\t\tthis._x = ( m12 + m21 ) / s;\n\t\t\tthis._y = 0.25 * s;\n\t\t\tthis._z = ( m23 + m32 ) / s;\n\n\t\t} else {\n\n\t\t\ts = 2.0 * Math.sqrt( 1.0 + m33 - m11 - m22 );\n\n\t\t\tthis._w = ( m21 - m12 ) / s;\n\t\t\tthis._x = ( m13 + m31 ) / s;\n\t\t\tthis._y = ( m23 + m32 ) / s;\n\t\t\tthis._z = 0.25 * s;\n\n\t\t}\n\n\t\tthis.onChangeCallback();\n\n\t\treturn this;\n\n\t},\n\n\tsetFromUnitVectors: function ( vFrom, vTo ) {\n\n\t\t// assumes direction vectors vFrom and vTo are normalized\n\n\t\tvar EPS = 0.000001;\n\n\t\tvar r = vFrom.dot( vTo ) + 1;\n\n\t\tif ( r < EPS ) {\n\n\t\t\tr = 0;\n\n\t\t\tif ( Math.abs( vFrom.x ) > Math.abs( vFrom.z ) ) {\n\n\t\t\t\tthis._x = - vFrom.y;\n\t\t\t\tthis._y = vFrom.x;\n\t\t\t\tthis._z = 0;\n\t\t\t\tthis._w = r;\n\n\t\t\t} else {\n\n\t\t\t\tthis._x = 0;\n\t\t\t\tthis._y = - vFrom.z;\n\t\t\t\tthis._z = vFrom.y;\n\t\t\t\tthis._w = r;\n\n\t\t\t}\n\n\t\t} else {\n\n\t\t\t// crossVectors( vFrom, vTo ); // inlined to avoid cyclic dependency on Vector3\n\n\t\t\tthis._x = vFrom.y * vTo.z - vFrom.z * vTo.y;\n\t\t\tthis._y = vFrom.z * vTo.x - vFrom.x * vTo.z;\n\t\t\tthis._z = vFrom.x * vTo.y - vFrom.y * vTo.x;\n\t\t\tthis._w = r;\n\n\t\t}\n\n\t\treturn this.normalize();\n\n\t},\n\n\tangleTo: function ( q ) {\n\n\t\treturn 2 * Math.acos( Math.abs( _Math.clamp( this.dot( q ), - 1, 1 ) ) );\n\n\t},\n\n\trotateTowards: function ( q, step ) {\n\n\t\tvar angle = this.angleTo( q );\n\n\t\tif ( angle === 0 ) return this;\n\n\t\tvar t = Math.min( 1, step / angle );\n\n\t\tthis.slerp( q, t );\n\n\t\treturn this;\n\n\t},\n\n\tinverse: function () {\n\n\t\t// quaternion is assumed to have unit length\n\n\t\treturn this.conjugate();\n\n\t},\n\n\tconjugate: function () {\n\n\t\tthis._x *= - 1;\n\t\tthis._y *= - 1;\n\t\tthis._z *= - 1;\n\n\t\tthis.onChangeCallback();\n\n\t\treturn this;\n\n\t},\n\n\tdot: function ( v ) {\n\n\t\treturn this._x * v._x + this._y * v._y + this._z * v._z + this._w * v._w;\n\n\t},\n\n\tlengthSq: function () {\n\n\t\treturn this._x * this._x + this._y * this._y + this._z * this._z + this._w * this._w;\n\n\t},\n\n\tlength: function () {\n\n\t\treturn Math.sqrt( this._x * this._x + this._y * this._y + this._z * this._z + this._w * this._w );\n\n\t},\n\n\tnormalize: function () {\n\n\t\tvar l = this.length();\n\n\t\tif ( l === 0 ) {\n\n\t\t\tthis._x = 0;\n\t\t\tthis._y = 0;\n\t\t\tthis._z = 0;\n\t\t\tthis._w = 1;\n\n\t\t} else {\n\n\t\t\tl = 1 / l;\n\n\t\t\tthis._x = this._x * l;\n\t\t\tthis._y = this._y * l;\n\t\t\tthis._z = this._z * l;\n\t\t\tthis._w = this._w * l;\n\n\t\t}\n\n\t\tthis.onChangeCallback();\n\n\t\treturn this;\n\n\t},\n\n\tmultiply: function ( q, p ) {\n\n\t\tif ( p !== undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead.' );\n\t\t\treturn this.multiplyQuaternions( q, p );\n\n\t\t}\n\n\t\treturn this.multiplyQuaternions( this, q );\n\n\t},\n\n\tpremultiply: function ( q ) {\n\n\t\treturn this.multiplyQuaternions( q, this );\n\n\t},\n\n\tmultiplyQuaternions: function ( a, b ) {\n\n\t\t// from http://www.euclideanspace.com/maths/algebra/realNormedAlgebra/quaternions/code/index.htm\n\n\t\tvar qax = a._x, qay = a._y, qaz = a._z, qaw = a._w;\n\t\tvar qbx = b._x, qby = b._y, qbz = b._z, qbw = b._w;\n\n\t\tthis._x = qax * qbw + qaw * qbx + qay * qbz - qaz * qby;\n\t\tthis._y = qay * qbw + qaw * qby + qaz * qbx - qax * qbz;\n\t\tthis._z = qaz * qbw + qaw * qbz + qax * qby - qay * qbx;\n\t\tthis._w = qaw * qbw - qax * qbx - qay * qby - qaz * qbz;\n\n\t\tthis.onChangeCallback();\n\n\t\treturn this;\n\n\t},\n\n\tslerp: function ( qb, t ) {\n\n\t\tif ( t === 0 ) return this;\n\t\tif ( t === 1 ) return this.copy( qb );\n\n\t\tvar x = this._x, y = this._y, z = this._z, w = this._w;\n\n\t\t// http://www.euclideanspace.com/maths/algebra/realNormedAlgebra/quaternions/slerp/\n\n\t\tvar cosHalfTheta = w * qb._w + x * qb._x + y * qb._y + z * qb._z;\n\n\t\tif ( cosHalfTheta < 0 ) {\n\n\t\t\tthis._w = - qb._w;\n\t\t\tthis._x = - qb._x;\n\t\t\tthis._y = - qb._y;\n\t\t\tthis._z = - qb._z;\n\n\t\t\tcosHalfTheta = - cosHalfTheta;\n\n\t\t} else {\n\n\t\t\tthis.copy( qb );\n\n\t\t}\n\n\t\tif ( cosHalfTheta >= 1.0 ) {\n\n\t\t\tthis._w = w;\n\t\t\tthis._x = x;\n\t\t\tthis._y = y;\n\t\t\tthis._z = z;\n\n\t\t\treturn this;\n\n\t\t}\n\n\t\tvar sqrSinHalfTheta = 1.0 - cosHalfTheta * cosHalfTheta;\n\n\t\tif ( sqrSinHalfTheta <= Number.EPSILON ) {\n\n\t\t\tvar s = 1 - t;\n\t\t\tthis._w = s * w + t * this._w;\n\t\t\tthis._x = s * x + t * this._x;\n\t\t\tthis._y = s * y + t * this._y;\n\t\t\tthis._z = s * z + t * this._z;\n\n\t\t\treturn this.normalize();\n\n\t\t}\n\n\t\tvar sinHalfTheta = Math.sqrt( sqrSinHalfTheta );\n\t\tvar halfTheta = Math.atan2( sinHalfTheta, cosHalfTheta );\n\t\tvar ratioA = Math.sin( ( 1 - t ) * halfTheta ) / sinHalfTheta,\n\t\t\tratioB = Math.sin( t * halfTheta ) / sinHalfTheta;\n\n\t\tthis._w = ( w * ratioA + this._w * ratioB );\n\t\tthis._x = ( x * ratioA + this._x * ratioB );\n\t\tthis._y = ( y * ratioA + this._y * ratioB );\n\t\tthis._z = ( z * ratioA + this._z * ratioB );\n\n\t\tthis.onChangeCallback();\n\n\t\treturn this;\n\n\t},\n\n\tequals: function ( quaternion ) {\n\n\t\treturn ( quaternion._x === this._x ) && ( quaternion._y === this._y ) && ( quaternion._z === this._z ) && ( quaternion._w === this._w );\n\n\t},\n\n\tfromArray: function ( array, offset ) {\n\n\t\tif ( offset === undefined ) offset = 0;\n\n\t\tthis._x = array[ offset ];\n\t\tthis._y = array[ offset + 1 ];\n\t\tthis._z = array[ offset + 2 ];\n\t\tthis._w = array[ offset + 3 ];\n\n\t\tthis.onChangeCallback();\n\n\t\treturn this;\n\n\t},\n\n\ttoArray: function ( array, offset ) {\n\n\t\tif ( array === undefined ) array = [];\n\t\tif ( offset === undefined ) offset = 0;\n\n\t\tarray[ offset ] = this._x;\n\t\tarray[ offset + 1 ] = this._y;\n\t\tarray[ offset + 2 ] = this._z;\n\t\tarray[ offset + 3 ] = this._w;\n\n\t\treturn array;\n\n\t},\n\n\tonChange: function ( callback ) {\n\n\t\tthis.onChangeCallback = callback;\n\n\t\treturn this;\n\n\t},\n\n\tonChangeCallback: function () {}\n\n} );\n\n/**\n * @author mrdoob / http://mrdoob.com/\n * @author kile / http://kile.stravaganza.org/\n * @author philogb / http://blog.thejit.org/\n * @author mikael emtinger / http://gomo.se/\n * @author egraether / http://egraether.com/\n * @author WestLangley / http://github.com/WestLangley\n */\n\nfunction Vector3( x, y, z ) {\n\n\tthis.x = x || 0;\n\tthis.y = y || 0;\n\tthis.z = z || 0;\n\n}\n\nObject.assign( Vector3.prototype, {\n\n\tisVector3: true,\n\n\tset: function ( x, y, z ) {\n\n\t\tthis.x = x;\n\t\tthis.y = y;\n\t\tthis.z = z;\n\n\t\treturn this;\n\n\t},\n\n\tsetScalar: function ( scalar ) {\n\n\t\tthis.x = scalar;\n\t\tthis.y = scalar;\n\t\tthis.z = scalar;\n\n\t\treturn this;\n\n\t},\n\n\tsetX: function ( x ) {\n\n\t\tthis.x = x;\n\n\t\treturn this;\n\n\t},\n\n\tsetY: function ( y ) {\n\n\t\tthis.y = y;\n\n\t\treturn this;\n\n\t},\n\n\tsetZ: function ( z ) {\n\n\t\tthis.z = z;\n\n\t\treturn this;\n\n\t},\n\n\tsetComponent: function ( index, value ) {\n\n\t\tswitch ( index ) {\n\n\t\t\tcase 0: this.x = value; break;\n\t\t\tcase 1: this.y = value; break;\n\t\t\tcase 2: this.z = value; break;\n\t\t\tdefault: throw new Error( 'index is out of range: ' + index );\n\n\t\t}\n\n\t\treturn this;\n\n\t},\n\n\tgetComponent: function ( index ) {\n\n\t\tswitch ( index ) {\n\n\t\t\tcase 0: return this.x;\n\t\t\tcase 1: return this.y;\n\t\t\tcase 2: return this.z;\n\t\t\tdefault: throw new Error( 'index is out of range: ' + index );\n\n\t\t}\n\n\t},\n\n\tclone: function () {\n\n\t\treturn new this.constructor( this.x, this.y, this.z );\n\n\t},\n\n\tcopy: function ( v ) {\n\n\t\tthis.x = v.x;\n\t\tthis.y = v.y;\n\t\tthis.z = v.z;\n\n\t\treturn this;\n\n\t},\n\n\tadd: function ( v, w ) {\n\n\t\tif ( w !== undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Vector3: .add() now only accepts one argument. Use .addVectors( a, b ) instead.' );\n\t\t\treturn this.addVectors( v, w );\n\n\t\t}\n\n\t\tthis.x += v.x;\n\t\tthis.y += v.y;\n\t\tthis.z += v.z;\n\n\t\treturn this;\n\n\t},\n\n\taddScalar: function ( s ) {\n\n\t\tthis.x += s;\n\t\tthis.y += s;\n\t\tthis.z += s;\n\n\t\treturn this;\n\n\t},\n\n\taddVectors: function ( a, b ) {\n\n\t\tthis.x = a.x + b.x;\n\t\tthis.y = a.y + b.y;\n\t\tthis.z = a.z + b.z;\n\n\t\treturn this;\n\n\t},\n\n\taddScaledVector: function ( v, s ) {\n\n\t\tthis.x += v.x * s;\n\t\tthis.y += v.y * s;\n\t\tthis.z += v.z * s;\n\n\t\treturn this;\n\n\t},\n\n\tsub: function ( v, w ) {\n\n\t\tif ( w !== undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Vector3: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.' );\n\t\t\treturn this.subVectors( v, w );\n\n\t\t}\n\n\t\tthis.x -= v.x;\n\t\tthis.y -= v.y;\n\t\tthis.z -= v.z;\n\n\t\treturn this;\n\n\t},\n\n\tsubScalar: function ( s ) {\n\n\t\tthis.x -= s;\n\t\tthis.y -= s;\n\t\tthis.z -= s;\n\n\t\treturn this;\n\n\t},\n\n\tsubVectors: function ( a, b ) {\n\n\t\tthis.x = a.x - b.x;\n\t\tthis.y = a.y - b.y;\n\t\tthis.z = a.z - b.z;\n\n\t\treturn this;\n\n\t},\n\n\tmultiply: function ( v, w ) {\n\n\t\tif ( w !== undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Vector3: .multiply() now only accepts one argument. Use .multiplyVectors( a, b ) instead.' );\n\t\t\treturn this.multiplyVectors( v, w );\n\n\t\t}\n\n\t\tthis.x *= v.x;\n\t\tthis.y *= v.y;\n\t\tthis.z *= v.z;\n\n\t\treturn this;\n\n\t},\n\n\tmultiplyScalar: function ( scalar ) {\n\n\t\tthis.x *= scalar;\n\t\tthis.y *= scalar;\n\t\tthis.z *= scalar;\n\n\t\treturn this;\n\n\t},\n\n\tmultiplyVectors: function ( a, b ) {\n\n\t\tthis.x = a.x * b.x;\n\t\tthis.y = a.y * b.y;\n\t\tthis.z = a.z * b.z;\n\n\t\treturn this;\n\n\t},\n\n\tapplyEuler: function () {\n\n\t\tvar quaternion = new Quaternion();\n\n\t\treturn function applyEuler( euler ) {\n\n\t\t\tif ( ! ( euler && euler.isEuler ) ) {\n\n\t\t\t\tconsole.error( 'THREE.Vector3: .applyEuler() now expects an Euler rotation rather than a Vector3 and order.' );\n\n\t\t\t}\n\n\t\t\treturn this.applyQuaternion( quaternion.setFromEuler( euler ) );\n\n\t\t};\n\n\t}(),\n\n\tapplyAxisAngle: function () {\n\n\t\tvar quaternion = new Quaternion();\n\n\t\treturn function applyAxisAngle( axis, angle ) {\n\n\t\t\treturn this.applyQuaternion( quaternion.setFromAxisAngle( axis, angle ) );\n\n\t\t};\n\n\t}(),\n\n\tapplyMatrix3: function ( m ) {\n\n\t\tvar x = this.x, y = this.y, z = this.z;\n\t\tvar e = m.elements;\n\n\t\tthis.x = e[ 0 ] * x + e[ 3 ] * y + e[ 6 ] * z;\n\t\tthis.y = e[ 1 ] * x + e[ 4 ] * y + e[ 7 ] * z;\n\t\tthis.z = e[ 2 ] * x + e[ 5 ] * y + e[ 8 ] * z;\n\n\t\treturn this;\n\n\t},\n\n\tapplyMatrix4: function ( m ) {\n\n\t\tvar x = this.x, y = this.y, z = this.z;\n\t\tvar e = m.elements;\n\n\t\tvar w = 1 / ( e[ 3 ] * x + e[ 7 ] * y + e[ 11 ] * z + e[ 15 ] );\n\n\t\tthis.x = ( e[ 0 ] * x + e[ 4 ] * y + e[ 8 ] * z + e[ 12 ] ) * w;\n\t\tthis.y = ( e[ 1 ] * x + e[ 5 ] * y + e[ 9 ] * z + e[ 13 ] ) * w;\n\t\tthis.z = ( e[ 2 ] * x + e[ 6 ] * y + e[ 10 ] * z + e[ 14 ] ) * w;\n\n\t\treturn this;\n\n\t},\n\n\tapplyQuaternion: function ( q ) {\n\n\t\tvar x = this.x, y = this.y, z = this.z;\n\t\tvar qx = q.x, qy = q.y, qz = q.z, qw = q.w;\n\n\t\t// calculate quat * vector\n\n\t\tvar ix = qw * x + qy * z - qz * y;\n\t\tvar iy = qw * y + qz * x - qx * z;\n\t\tvar iz = qw * z + qx * y - qy * x;\n\t\tvar iw = - qx * x - qy * y - qz * z;\n\n\t\t// calculate result * inverse quat\n\n\t\tthis.x = ix * qw + iw * - qx + iy * - qz - iz * - qy;\n\t\tthis.y = iy * qw + iw * - qy + iz * - qx - ix * - qz;\n\t\tthis.z = iz * qw + iw * - qz + ix * - qy - iy * - qx;\n\n\t\treturn this;\n\n\t},\n\n\tproject: function ( camera ) {\n\n\t\treturn this.applyMatrix4( camera.matrixWorldInverse ).applyMatrix4( camera.projectionMatrix );\n\n\t},\n\n\tunproject: function ( camera ) {\n\n\t\treturn this.applyMatrix4( camera.projectionMatrixInverse ).applyMatrix4( camera.matrixWorld );\n\n\t},\n\n\ttransformDirection: function ( m ) {\n\n\t\t// input: THREE.Matrix4 affine matrix\n\t\t// vector interpreted as a direction\n\n\t\tvar x = this.x, y = this.y, z = this.z;\n\t\tvar e = m.elements;\n\n\t\tthis.x = e[ 0 ] * x + e[ 4 ] * y + e[ 8 ] * z;\n\t\tthis.y = e[ 1 ] * x + e[ 5 ] * y + e[ 9 ] * z;\n\t\tthis.z = e[ 2 ] * x + e[ 6 ] * y + e[ 10 ] * z;\n\n\t\treturn this.normalize();\n\n\t},\n\n\tdivide: function ( v ) {\n\n\t\tthis.x /= v.x;\n\t\tthis.y /= v.y;\n\t\tthis.z /= v.z;\n\n\t\treturn this;\n\n\t},\n\n\tdivideScalar: function ( scalar ) {\n\n\t\treturn this.multiplyScalar( 1 / scalar );\n\n\t},\n\n\tmin: function ( v ) {\n\n\t\tthis.x = Math.min( this.x, v.x );\n\t\tthis.y = Math.min( this.y, v.y );\n\t\tthis.z = Math.min( this.z, v.z );\n\n\t\treturn this;\n\n\t},\n\n\tmax: function ( v ) {\n\n\t\tthis.x = Math.max( this.x, v.x );\n\t\tthis.y = Math.max( this.y, v.y );\n\t\tthis.z = Math.max( this.z, v.z );\n\n\t\treturn this;\n\n\t},\n\n\tclamp: function ( min, max ) {\n\n\t\t// assumes min < max, componentwise\n\n\t\tthis.x = Math.max( min.x, Math.min( max.x, this.x ) );\n\t\tthis.y = Math.max( min.y, Math.min( max.y, this.y ) );\n\t\tthis.z = Math.max( min.z, Math.min( max.z, this.z ) );\n\n\t\treturn this;\n\n\t},\n\n\tclampScalar: function ( minVal, maxVal ) {\n\n\t\tthis.x = Math.max( minVal, Math.min( maxVal, this.x ) );\n\t\tthis.y = Math.max( minVal, Math.min( maxVal, this.y ) );\n\t\tthis.z = Math.max( minVal, Math.min( maxVal, this.z ) );\n\n\t\treturn this;\n\n\t},\n\n\tclampLength: function ( min, max ) {\n\n\t\tvar length = this.length();\n\n\t\treturn this.divideScalar( length || 1 ).multiplyScalar( Math.max( min, Math.min( max, length ) ) );\n\n\t},\n\n\tfloor: function () {\n\n\t\tthis.x = Math.floor( this.x );\n\t\tthis.y = Math.floor( this.y );\n\t\tthis.z = Math.floor( this.z );\n\n\t\treturn this;\n\n\t},\n\n\tceil: function () {\n\n\t\tthis.x = Math.ceil( this.x );\n\t\tthis.y = Math.ceil( this.y );\n\t\tthis.z = Math.ceil( this.z );\n\n\t\treturn this;\n\n\t},\n\n\tround: function () {\n\n\t\tthis.x = Math.round( this.x );\n\t\tthis.y = Math.round( this.y );\n\t\tthis.z = Math.round( this.z );\n\n\t\treturn this;\n\n\t},\n\n\troundToZero: function () {\n\n\t\tthis.x = ( this.x < 0 ) ? Math.ceil( this.x ) : Math.floor( this.x );\n\t\tthis.y = ( this.y < 0 ) ? Math.ceil( this.y ) : Math.floor( this.y );\n\t\tthis.z = ( this.z < 0 ) ? Math.ceil( this.z ) : Math.floor( this.z );\n\n\t\treturn this;\n\n\t},\n\n\tnegate: function () {\n\n\t\tthis.x = - this.x;\n\t\tthis.y = - this.y;\n\t\tthis.z = - this.z;\n\n\t\treturn this;\n\n\t},\n\n\tdot: function ( v ) {\n\n\t\treturn this.x * v.x + this.y * v.y + this.z * v.z;\n\n\t},\n\n\t// TODO lengthSquared?\n\n\tlengthSq: function () {\n\n\t\treturn this.x * this.x + this.y * this.y + this.z * this.z;\n\n\t},\n\n\tlength: function () {\n\n\t\treturn Math.sqrt( this.x * this.x + this.y * this.y + this.z * this.z );\n\n\t},\n\n\tmanhattanLength: function () {\n\n\t\treturn Math.abs( this.x ) + Math.abs( this.y ) + Math.abs( this.z );\n\n\t},\n\n\tnormalize: function () {\n\n\t\treturn this.divideScalar( this.length() || 1 );\n\n\t},\n\n\tsetLength: function ( length ) {\n\n\t\treturn this.normalize().multiplyScalar( length );\n\n\t},\n\n\tlerp: function ( v, alpha ) {\n\n\t\tthis.x += ( v.x - this.x ) * alpha;\n\t\tthis.y += ( v.y - this.y ) * alpha;\n\t\tthis.z += ( v.z - this.z ) * alpha;\n\n\t\treturn this;\n\n\t},\n\n\tlerpVectors: function ( v1, v2, alpha ) {\n\n\t\treturn this.subVectors( v2, v1 ).multiplyScalar( alpha ).add( v1 );\n\n\t},\n\n\tcross: function ( v, w ) {\n\n\t\tif ( w !== undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Vector3: .cross() now only accepts one argument. Use .crossVectors( a, b ) instead.' );\n\t\t\treturn this.crossVectors( v, w );\n\n\t\t}\n\n\t\treturn this.crossVectors( this, v );\n\n\t},\n\n\tcrossVectors: function ( a, b ) {\n\n\t\tvar ax = a.x, ay = a.y, az = a.z;\n\t\tvar bx = b.x, by = b.y, bz = b.z;\n\n\t\tthis.x = ay * bz - az * by;\n\t\tthis.y = az * bx - ax * bz;\n\t\tthis.z = ax * by - ay * bx;\n\n\t\treturn this;\n\n\t},\n\n\tprojectOnVector: function ( vector ) {\n\n\t\tvar scalar = vector.dot( this ) / vector.lengthSq();\n\n\t\treturn this.copy( vector ).multiplyScalar( scalar );\n\n\t},\n\n\tprojectOnPlane: function () {\n\n\t\tvar v1 = new Vector3();\n\n\t\treturn function projectOnPlane( planeNormal ) {\n\n\t\t\tv1.copy( this ).projectOnVector( planeNormal );\n\n\t\t\treturn this.sub( v1 );\n\n\t\t};\n\n\t}(),\n\n\treflect: function () {\n\n\t\t// reflect incident vector off plane orthogonal to normal\n\t\t// normal is assumed to have unit length\n\n\t\tvar v1 = new Vector3();\n\n\t\treturn function reflect( normal ) {\n\n\t\t\treturn this.sub( v1.copy( normal ).multiplyScalar( 2 * this.dot( normal ) ) );\n\n\t\t};\n\n\t}(),\n\n\tangleTo: function ( v ) {\n\n\t\tvar theta = this.dot( v ) / ( Math.sqrt( this.lengthSq() * v.lengthSq() ) );\n\n\t\t// clamp, to handle numerical problems\n\n\t\treturn Math.acos( _Math.clamp( theta, - 1, 1 ) );\n\n\t},\n\n\tdistanceTo: function ( v ) {\n\n\t\treturn Math.sqrt( this.distanceToSquared( v ) );\n\n\t},\n\n\tdistanceToSquared: function ( v ) {\n\n\t\tvar dx = this.x - v.x, dy = this.y - v.y, dz = this.z - v.z;\n\n\t\treturn dx * dx + dy * dy + dz * dz;\n\n\t},\n\n\tmanhattanDistanceTo: function ( v ) {\n\n\t\treturn Math.abs( this.x - v.x ) + Math.abs( this.y - v.y ) + Math.abs( this.z - v.z );\n\n\t},\n\n\tsetFromSpherical: function ( s ) {\n\n\t\treturn this.setFromSphericalCoords( s.radius, s.phi, s.theta );\n\n\t},\n\n\tsetFromSphericalCoords: function ( radius, phi, theta ) {\n\n\t\tvar sinPhiRadius = Math.sin( phi ) * radius;\n\n\t\tthis.x = sinPhiRadius * Math.sin( theta );\n\t\tthis.y = Math.cos( phi ) * radius;\n\t\tthis.z = sinPhiRadius * Math.cos( theta );\n\n\t\treturn this;\n\n\t},\n\n\tsetFromCylindrical: function ( c ) {\n\n\t\treturn this.setFromCylindricalCoords( c.radius, c.theta, c.y );\n\n\t},\n\n\tsetFromCylindricalCoords: function ( radius, theta, y ) {\n\n\t\tthis.x = radius * Math.sin( theta );\n\t\tthis.y = y;\n\t\tthis.z = radius * Math.cos( theta );\n\n\t\treturn this;\n\n\t},\n\n\tsetFromMatrixPosition: function ( m ) {\n\n\t\tvar e = m.elements;\n\n\t\tthis.x = e[ 12 ];\n\t\tthis.y = e[ 13 ];\n\t\tthis.z = e[ 14 ];\n\n\t\treturn this;\n\n\t},\n\n\tsetFromMatrixScale: function ( m ) {\n\n\t\tvar sx = this.setFromMatrixColumn( m, 0 ).length();\n\t\tvar sy = this.setFromMatrixColumn( m, 1 ).length();\n\t\tvar sz = this.setFromMatrixColumn( m, 2 ).length();\n\n\t\tthis.x = sx;\n\t\tthis.y = sy;\n\t\tthis.z = sz;\n\n\t\treturn this;\n\n\t},\n\n\tsetFromMatrixColumn: function ( m, index ) {\n\n\t\treturn this.fromArray( m.elements, index * 4 );\n\n\t},\n\n\tequals: function ( v ) {\n\n\t\treturn ( ( v.x === this.x ) && ( v.y === this.y ) && ( v.z === this.z ) );\n\n\t},\n\n\tfromArray: function ( array, offset ) {\n\n\t\tif ( offset === undefined ) offset = 0;\n\n\t\tthis.x = array[ offset ];\n\t\tthis.y = array[ offset + 1 ];\n\t\tthis.z = array[ offset + 2 ];\n\n\t\treturn this;\n\n\t},\n\n\ttoArray: function ( array, offset ) {\n\n\t\tif ( array === undefined ) array = [];\n\t\tif ( offset === undefined ) offset = 0;\n\n\t\tarray[ offset ] = this.x;\n\t\tarray[ offset + 1 ] = this.y;\n\t\tarray[ offset + 2 ] = this.z;\n\n\t\treturn array;\n\n\t},\n\n\tfromBufferAttribute: function ( attribute, index, offset ) {\n\n\t\tif ( offset !== undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Vector3: offset has been removed from .fromBufferAttribute().' );\n\n\t\t}\n\n\t\tthis.x = attribute.getX( index );\n\t\tthis.y = attribute.getY( index );\n\t\tthis.z = attribute.getZ( index );\n\n\t\treturn this;\n\n\t}\n\n} );\n\n/**\n * @author alteredq / http://alteredqualia.com/\n * @author WestLangley / http://github.com/WestLangley\n * @author bhouston / http://clara.io\n * @author tschw\n */\n\nfunction Matrix3() {\n\n\tthis.elements = [\n\n\t\t1, 0, 0,\n\t\t0, 1, 0,\n\t\t0, 0, 1\n\n\t];\n\n\tif ( arguments.length > 0 ) {\n\n\t\tconsole.error( 'THREE.Matrix3: the constructor no longer reads arguments. use .set() instead.' );\n\n\t}\n\n}\n\nObject.assign( Matrix3.prototype, {\n\n\tisMatrix3: true,\n\n\tset: function ( n11, n12, n13, n21, n22, n23, n31, n32, n33 ) {\n\n\t\tvar te = this.elements;\n\n\t\tte[ 0 ] = n11; te[ 1 ] = n21; te[ 2 ] = n31;\n\t\tte[ 3 ] = n12; te[ 4 ] = n22; te[ 5 ] = n32;\n\t\tte[ 6 ] = n13; te[ 7 ] = n23; te[ 8 ] = n33;\n\n\t\treturn this;\n\n\t},\n\n\tidentity: function () {\n\n\t\tthis.set(\n\n\t\t\t1, 0, 0,\n\t\t\t0, 1, 0,\n\t\t\t0, 0, 1\n\n\t\t);\n\n\t\treturn this;\n\n\t},\n\n\tclone: function () {\n\n\t\treturn new this.constructor().fromArray( this.elements );\n\n\t},\n\n\tcopy: function ( m ) {\n\n\t\tvar te = this.elements;\n\t\tvar me = m.elements;\n\n\t\tte[ 0 ] = me[ 0 ]; te[ 1 ] = me[ 1 ]; te[ 2 ] = me[ 2 ];\n\t\tte[ 3 ] = me[ 3 ]; te[ 4 ] = me[ 4 ]; te[ 5 ] = me[ 5 ];\n\t\tte[ 6 ] = me[ 6 ]; te[ 7 ] = me[ 7 ]; te[ 8 ] = me[ 8 ];\n\n\t\treturn this;\n\n\t},\n\n\tsetFromMatrix4: function ( m ) {\n\n\t\tvar me = m.elements;\n\n\t\tthis.set(\n\n\t\t\tme[ 0 ], me[ 4 ], me[ 8 ],\n\t\t\tme[ 1 ], me[ 5 ], me[ 9 ],\n\t\t\tme[ 2 ], me[ 6 ], me[ 10 ]\n\n\t\t);\n\n\t\treturn this;\n\n\t},\n\n\tapplyToBufferAttribute: function () {\n\n\t\tvar v1 = new Vector3();\n\n\t\treturn function applyToBufferAttribute( attribute ) {\n\n\t\t\tfor ( var i = 0, l = attribute.count; i < l; i ++ ) {\n\n\t\t\t\tv1.x = attribute.getX( i );\n\t\t\t\tv1.y = attribute.getY( i );\n\t\t\t\tv1.z = attribute.getZ( i );\n\n\t\t\t\tv1.applyMatrix3( this );\n\n\t\t\t\tattribute.setXYZ( i, v1.x, v1.y, v1.z );\n\n\t\t\t}\n\n\t\t\treturn attribute;\n\n\t\t};\n\n\t}(),\n\n\tmultiply: function ( m ) {\n\n\t\treturn this.multiplyMatrices( this, m );\n\n\t},\n\n\tpremultiply: function ( m ) {\n\n\t\treturn this.multiplyMatrices( m, this );\n\n\t},\n\n\tmultiplyMatrices: function ( a, b ) {\n\n\t\tvar ae = a.elements;\n\t\tvar be = b.elements;\n\t\tvar te = this.elements;\n\n\t\tvar a11 = ae[ 0 ], a12 = ae[ 3 ], a13 = ae[ 6 ];\n\t\tvar a21 = ae[ 1 ], a22 = ae[ 4 ], a23 = ae[ 7 ];\n\t\tvar a31 = ae[ 2 ], a32 = ae[ 5 ], a33 = ae[ 8 ];\n\n\t\tvar b11 = be[ 0 ], b12 = be[ 3 ], b13 = be[ 6 ];\n\t\tvar b21 = be[ 1 ], b22 = be[ 4 ], b23 = be[ 7 ];\n\t\tvar b31 = be[ 2 ], b32 = be[ 5 ], b33 = be[ 8 ];\n\n\t\tte[ 0 ] = a11 * b11 + a12 * b21 + a13 * b31;\n\t\tte[ 3 ] = a11 * b12 + a12 * b22 + a13 * b32;\n\t\tte[ 6 ] = a11 * b13 + a12 * b23 + a13 * b33;\n\n\t\tte[ 1 ] = a21 * b11 + a22 * b21 + a23 * b31;\n\t\tte[ 4 ] = a21 * b12 + a22 * b22 + a23 * b32;\n\t\tte[ 7 ] = a21 * b13 + a22 * b23 + a23 * b33;\n\n\t\tte[ 2 ] = a31 * b11 + a32 * b21 + a33 * b31;\n\t\tte[ 5 ] = a31 * b12 + a32 * b22 + a33 * b32;\n\t\tte[ 8 ] = a31 * b13 + a32 * b23 + a33 * b33;\n\n\t\treturn this;\n\n\t},\n\n\tmultiplyScalar: function ( s ) {\n\n\t\tvar te = this.elements;\n\n\t\tte[ 0 ] *= s; te[ 3 ] *= s; te[ 6 ] *= s;\n\t\tte[ 1 ] *= s; te[ 4 ] *= s; te[ 7 ] *= s;\n\t\tte[ 2 ] *= s; te[ 5 ] *= s; te[ 8 ] *= s;\n\n\t\treturn this;\n\n\t},\n\n\tdeterminant: function () {\n\n\t\tvar te = this.elements;\n\n\t\tvar a = te[ 0 ], b = te[ 1 ], c = te[ 2 ],\n\t\t\td = te[ 3 ], e = te[ 4 ], f = te[ 5 ],\n\t\t\tg = te[ 6 ], h = te[ 7 ], i = te[ 8 ];\n\n\t\treturn a * e * i - a * f * h - b * d * i + b * f * g + c * d * h - c * e * g;\n\n\t},\n\n\tgetInverse: function ( matrix, throwOnDegenerate ) {\n\n\t\tif ( matrix && matrix.isMatrix4 ) {\n\n\t\t\tconsole.error( \"THREE.Matrix3: .getInverse() no longer takes a Matrix4 argument.\" );\n\n\t\t}\n\n\t\tvar me = matrix.elements,\n\t\t\tte = this.elements,\n\n\t\t\tn11 = me[ 0 ], n21 = me[ 1 ], n31 = me[ 2 ],\n\t\t\tn12 = me[ 3 ], n22 = me[ 4 ], n32 = me[ 5 ],\n\t\t\tn13 = me[ 6 ], n23 = me[ 7 ], n33 = me[ 8 ],\n\n\t\t\tt11 = n33 * n22 - n32 * n23,\n\t\t\tt12 = n32 * n13 - n33 * n12,\n\t\t\tt13 = n23 * n12 - n22 * n13,\n\n\t\t\tdet = n11 * t11 + n21 * t12 + n31 * t13;\n\n\t\tif ( det === 0 ) {\n\n\t\t\tvar msg = \"THREE.Matrix3: .getInverse() can't invert matrix, determinant is 0\";\n\n\t\t\tif ( throwOnDegenerate === true ) {\n\n\t\t\t\tthrow new Error( msg );\n\n\t\t\t} else {\n\n\t\t\t\tconsole.warn( msg );\n\n\t\t\t}\n\n\t\t\treturn this.identity();\n\n\t\t}\n\n\t\tvar detInv = 1 / det;\n\n\t\tte[ 0 ] = t11 * detInv;\n\t\tte[ 1 ] = ( n31 * n23 - n33 * n21 ) * detInv;\n\t\tte[ 2 ] = ( n32 * n21 - n31 * n22 ) * detInv;\n\n\t\tte[ 3 ] = t12 * detInv;\n\t\tte[ 4 ] = ( n33 * n11 - n31 * n13 ) * detInv;\n\t\tte[ 5 ] = ( n31 * n12 - n32 * n11 ) * detInv;\n\n\t\tte[ 6 ] = t13 * detInv;\n\t\tte[ 7 ] = ( n21 * n13 - n23 * n11 ) * detInv;\n\t\tte[ 8 ] = ( n22 * n11 - n21 * n12 ) * detInv;\n\n\t\treturn this;\n\n\t},\n\n\ttranspose: function () {\n\n\t\tvar tmp, m = this.elements;\n\n\t\ttmp = m[ 1 ]; m[ 1 ] = m[ 3 ]; m[ 3 ] = tmp;\n\t\ttmp = m[ 2 ]; m[ 2 ] = m[ 6 ]; m[ 6 ] = tmp;\n\t\ttmp = m[ 5 ]; m[ 5 ] = m[ 7 ]; m[ 7 ] = tmp;\n\n\t\treturn this;\n\n\t},\n\n\tgetNormalMatrix: function ( matrix4 ) {\n\n\t\treturn this.setFromMatrix4( matrix4 ).getInverse( this ).transpose();\n\n\t},\n\n\ttransposeIntoArray: function ( r ) {\n\n\t\tvar m = this.elements;\n\n\t\tr[ 0 ] = m[ 0 ];\n\t\tr[ 1 ] = m[ 3 ];\n\t\tr[ 2 ] = m[ 6 ];\n\t\tr[ 3 ] = m[ 1 ];\n\t\tr[ 4 ] = m[ 4 ];\n\t\tr[ 5 ] = m[ 7 ];\n\t\tr[ 6 ] = m[ 2 ];\n\t\tr[ 7 ] = m[ 5 ];\n\t\tr[ 8 ] = m[ 8 ];\n\n\t\treturn this;\n\n\t},\n\n\tsetUvTransform: function ( tx, ty, sx, sy, rotation, cx, cy ) {\n\n\t\tvar c = Math.cos( rotation );\n\t\tvar s = Math.sin( rotation );\n\n\t\tthis.set(\n\t\t\tsx * c, sx * s, - sx * ( c * cx + s * cy ) + cx + tx,\n\t\t\t- sy * s, sy * c, - sy * ( - s * cx + c * cy ) + cy + ty,\n\t\t\t0, 0, 1\n\t\t);\n\n\t},\n\n\tscale: function ( sx, sy ) {\n\n\t\tvar te = this.elements;\n\n\t\tte[ 0 ] *= sx; te[ 3 ] *= sx; te[ 6 ] *= sx;\n\t\tte[ 1 ] *= sy; te[ 4 ] *= sy; te[ 7 ] *= sy;\n\n\t\treturn this;\n\n\t},\n\n\trotate: function ( theta ) {\n\n\t\tvar c = Math.cos( theta );\n\t\tvar s = Math.sin( theta );\n\n\t\tvar te = this.elements;\n\n\t\tvar a11 = te[ 0 ], a12 = te[ 3 ], a13 = te[ 6 ];\n\t\tvar a21 = te[ 1 ], a22 = te[ 4 ], a23 = te[ 7 ];\n\n\t\tte[ 0 ] = c * a11 + s * a21;\n\t\tte[ 3 ] = c * a12 + s * a22;\n\t\tte[ 6 ] = c * a13 + s * a23;\n\n\t\tte[ 1 ] = - s * a11 + c * a21;\n\t\tte[ 4 ] = - s * a12 + c * a22;\n\t\tte[ 7 ] = - s * a13 + c * a23;\n\n\t\treturn this;\n\n\t},\n\n\ttranslate: function ( tx, ty ) {\n\n\t\tvar te = this.elements;\n\n\t\tte[ 0 ] += tx * te[ 2 ]; te[ 3 ] += tx * te[ 5 ]; te[ 6 ] += tx * te[ 8 ];\n\t\tte[ 1 ] += ty * te[ 2 ]; te[ 4 ] += ty * te[ 5 ]; te[ 7 ] += ty * te[ 8 ];\n\n\t\treturn this;\n\n\t},\n\n\tequals: function ( matrix ) {\n\n\t\tvar te = this.elements;\n\t\tvar me = matrix.elements;\n\n\t\tfor ( var i = 0; i < 9; i ++ ) {\n\n\t\t\tif ( te[ i ] !== me[ i ] ) return false;\n\n\t\t}\n\n\t\treturn true;\n\n\t},\n\n\tfromArray: function ( array, offset ) {\n\n\t\tif ( offset === undefined ) offset = 0;\n\n\t\tfor ( var i = 0; i < 9; i ++ ) {\n\n\t\t\tthis.elements[ i ] = array[ i + offset ];\n\n\t\t}\n\n\t\treturn this;\n\n\t},\n\n\ttoArray: function ( array, offset ) {\n\n\t\tif ( array === undefined ) array = [];\n\t\tif ( offset === undefined ) offset = 0;\n\n\t\tvar te = this.elements;\n\n\t\tarray[ offset ] = te[ 0 ];\n\t\tarray[ offset + 1 ] = te[ 1 ];\n\t\tarray[ offset + 2 ] = te[ 2 ];\n\n\t\tarray[ offset + 3 ] = te[ 3 ];\n\t\tarray[ offset + 4 ] = te[ 4 ];\n\t\tarray[ offset + 5 ] = te[ 5 ];\n\n\t\tarray[ offset + 6 ] = te[ 6 ];\n\t\tarray[ offset + 7 ] = te[ 7 ];\n\t\tarray[ offset + 8 ] = te[ 8 ];\n\n\t\treturn array;\n\n\t}\n\n} );\n\n/**\n * @author mrdoob / http://mrdoob.com/\n * @author alteredq / http://alteredqualia.com/\n * @author szimek / https://github.com/szimek/\n */\n\nvar _canvas;\n\nvar ImageUtils = {\n\n\tgetDataURL: function ( image ) {\n\n\t\tvar canvas;\n\n\t\tif ( typeof HTMLCanvasElement == 'undefined' ) {\n\n\t\t\treturn image.src;\n\n\t\t} else if ( image instanceof HTMLCanvasElement ) {\n\n\t\t\tcanvas = image;\n\n\t\t} else {\n\n\t\t\tif ( _canvas === undefined ) _canvas = document.createElementNS( 'http://www.w3.org/1999/xhtml', 'canvas' );\n\n\t\t\t_canvas.width = image.width;\n\t\t\t_canvas.height = image.height;\n\n\t\t\tvar context = _canvas.getContext( '2d' );\n\n\t\t\tif ( image instanceof ImageData ) {\n\n\t\t\t\tcontext.putImageData( image, 0, 0 );\n\n\t\t\t} else {\n\n\t\t\t\tcontext.drawImage( image, 0, 0, image.width, image.height );\n\n\t\t\t}\n\n\t\t\tcanvas = _canvas;\n\n\t\t}\n\n\t\tif ( canvas.width > 2048 || canvas.height > 2048 ) {\n\n\t\t\treturn canvas.toDataURL( 'image/jpeg', 0.6 );\n\n\t\t} else {\n\n\t\t\treturn canvas.toDataURL( 'image/png' );\n\n\t\t}\n\n\t}\n\n};\n\n/**\n * @author mrdoob / http://mrdoob.com/\n * @author alteredq / http://alteredqualia.com/\n * @author szimek / https://github.com/szimek/\n */\n\nvar textureId = 0;\n\nfunction Texture( image, mapping, wrapS, wrapT, magFilter, minFilter, format, type, anisotropy, encoding ) {\n\n\tObject.defineProperty( this, 'id', { value: textureId ++ } );\n\n\tthis.uuid = _Math.generateUUID();\n\n\tthis.name = '';\n\n\tthis.image = image !== undefined ? image : Texture.DEFAULT_IMAGE;\n\tthis.mipmaps = [];\n\n\tthis.mapping = mapping !== undefined ? mapping : Texture.DEFAULT_MAPPING;\n\n\tthis.wrapS = wrapS !== undefined ? wrapS : ClampToEdgeWrapping;\n\tthis.wrapT = wrapT !== undefined ? wrapT : ClampToEdgeWrapping;\n\n\tthis.magFilter = magFilter !== undefined ? magFilter : LinearFilter;\n\tthis.minFilter = minFilter !== undefined ? minFilter : LinearMipMapLinearFilter;\n\n\tthis.anisotropy = anisotropy !== undefined ? anisotropy : 1;\n\n\tthis.format = format !== undefined ? format : RGBAFormat;\n\tthis.type = type !== undefined ? type : UnsignedByteType;\n\n\tthis.offset = new Vector2( 0, 0 );\n\tthis.repeat = new Vector2( 1, 1 );\n\tthis.center = new Vector2( 0, 0 );\n\tthis.rotation = 0;\n\n\tthis.matrixAutoUpdate = true;\n\tthis.matrix = new Matrix3();\n\n\tthis.generateMipmaps = true;\n\tthis.premultiplyAlpha = false;\n\tthis.flipY = true;\n\tthis.unpackAlignment = 4;\t// valid values: 1, 2, 4, 8 (see http://www.khronos.org/opengles/sdk/docs/man/xhtml/glPixelStorei.xml)\n\n\t// Values of encoding !== THREE.LinearEncoding only supported on map, envMap and emissiveMap.\n\t//\n\t// Also changing the encoding after already used by a Material will not automatically make the Material\n\t// update. You need to explicitly call Material.needsUpdate to trigger it to recompile.\n\tthis.encoding = encoding !== undefined ? encoding : LinearEncoding;\n\n\tthis.version = 0;\n\tthis.onUpdate = null;\n\n}\n\nTexture.DEFAULT_IMAGE = undefined;\nTexture.DEFAULT_MAPPING = UVMapping;\n\nTexture.prototype = Object.assign( Object.create( EventDispatcher.prototype ), {\n\n\tconstructor: Texture,\n\n\tisTexture: true,\n\n\tupdateMatrix: function () {\n\n\t\tthis.matrix.setUvTransform( this.offset.x, this.offset.y, this.repeat.x, this.repeat.y, this.rotation, this.center.x, this.center.y );\n\n\t},\n\n\tclone: function () {\n\n\t\treturn new this.constructor().copy( this );\n\n\t},\n\n\tcopy: function ( source ) {\n\n\t\tthis.name = source.name;\n\n\t\tthis.image = source.image;\n\t\tthis.mipmaps = source.mipmaps.slice( 0 );\n\n\t\tthis.mapping = source.mapping;\n\n\t\tthis.wrapS = source.wrapS;\n\t\tthis.wrapT = source.wrapT;\n\n\t\tthis.magFilter = source.magFilter;\n\t\tthis.minFilter = source.minFilter;\n\n\t\tthis.anisotropy = source.anisotropy;\n\n\t\tthis.format = source.format;\n\t\tthis.type = source.type;\n\n\t\tthis.offset.copy( source.offset );\n\t\tthis.repeat.copy( source.repeat );\n\t\tthis.center.copy( source.center );\n\t\tthis.rotation = source.rotation;\n\n\t\tthis.matrixAutoUpdate = source.matrixAutoUpdate;\n\t\tthis.matrix.copy( source.matrix );\n\n\t\tthis.generateMipmaps = source.generateMipmaps;\n\t\tthis.premultiplyAlpha = source.premultiplyAlpha;\n\t\tthis.flipY = source.flipY;\n\t\tthis.unpackAlignment = source.unpackAlignment;\n\t\tthis.encoding = source.encoding;\n\n\t\treturn this;\n\n\t},\n\n\ttoJSON: function ( meta ) {\n\n\t\tvar isRootObject = ( meta === undefined || typeof meta === 'string' );\n\n\t\tif ( ! isRootObject && meta.textures[ this.uuid ] !== undefined ) {\n\n\t\t\treturn meta.textures[ this.uuid ];\n\n\t\t}\n\n\t\tvar output = {\n\n\t\t\tmetadata: {\n\t\t\t\tversion: 4.5,\n\t\t\t\ttype: 'Texture',\n\t\t\t\tgenerator: 'Texture.toJSON'\n\t\t\t},\n\n\t\t\tuuid: this.uuid,\n\t\t\tname: this.name,\n\n\t\t\tmapping: this.mapping,\n\n\t\t\trepeat: [ this.repeat.x, this.repeat.y ],\n\t\t\toffset: [ this.offset.x, this.offset.y ],\n\t\t\tcenter: [ this.center.x, this.center.y ],\n\t\t\trotation: this.rotation,\n\n\t\t\twrap: [ this.wrapS, this.wrapT ],\n\n\t\t\tformat: this.format,\n\t\t\ttype: this.type,\n\t\t\tencoding: this.encoding,\n\n\t\t\tminFilter: this.minFilter,\n\t\t\tmagFilter: this.magFilter,\n\t\t\tanisotropy: this.anisotropy,\n\n\t\t\tflipY: this.flipY,\n\n\t\t\tpremultiplyAlpha: this.premultiplyAlpha,\n\t\t\tunpackAlignment: this.unpackAlignment\n\n\t\t};\n\n\t\tif ( this.image !== undefined ) {\n\n\t\t\t// TODO: Move to THREE.Image\n\n\t\t\tvar image = this.image;\n\n\t\t\tif ( image.uuid === undefined ) {\n\n\t\t\t\timage.uuid = _Math.generateUUID(); // UGH\n\n\t\t\t}\n\n\t\t\tif ( ! isRootObject && meta.images[ image.uuid ] === undefined ) {\n\n\t\t\t\tvar url;\n\n\t\t\t\tif ( Array.isArray( image ) ) {\n\n\t\t\t\t\t// process array of images e.g. CubeTexture\n\n\t\t\t\t\turl = [];\n\n\t\t\t\t\tfor ( var i = 0, l = image.length; i < l; i ++ ) {\n\n\t\t\t\t\t\turl.push( ImageUtils.getDataURL( image[ i ] ) );\n\n\t\t\t\t\t}\n\n\t\t\t\t} else {\n\n\t\t\t\t\t// process single image\n\n\t\t\t\t\turl = ImageUtils.getDataURL( image );\n\n\t\t\t\t}\n\n\t\t\t\tmeta.images[ image.uuid ] = {\n\t\t\t\t\tuuid: image.uuid,\n\t\t\t\t\turl: url\n\t\t\t\t};\n\n\t\t\t}\n\n\t\t\toutput.image = image.uuid;\n\n\t\t}\n\n\t\tif ( ! isRootObject ) {\n\n\t\t\tmeta.textures[ this.uuid ] = output;\n\n\t\t}\n\n\t\treturn output;\n\n\t},\n\n\tdispose: function () {\n\n\t\tthis.dispatchEvent( { type: 'dispose' } );\n\n\t},\n\n\ttransformUv: function ( uv ) {\n\n\t\tif ( this.mapping !== UVMapping ) return uv;\n\n\t\tuv.applyMatrix3( this.matrix );\n\n\t\tif ( uv.x < 0 || uv.x > 1 ) {\n\n\t\t\tswitch ( this.wrapS ) {\n\n\t\t\t\tcase RepeatWrapping:\n\n\t\t\t\t\tuv.x = uv.x - Math.floor( uv.x );\n\t\t\t\t\tbreak;\n\n\t\t\t\tcase ClampToEdgeWrapping:\n\n\t\t\t\t\tuv.x = uv.x < 0 ? 0 : 1;\n\t\t\t\t\tbreak;\n\n\t\t\t\tcase MirroredRepeatWrapping:\n\n\t\t\t\t\tif ( Math.abs( Math.floor( uv.x ) % 2 ) === 1 ) {\n\n\t\t\t\t\t\tuv.x = Math.ceil( uv.x ) - uv.x;\n\n\t\t\t\t\t} else {\n\n\t\t\t\t\t\tuv.x = uv.x - Math.floor( uv.x );\n\n\t\t\t\t\t}\n\t\t\t\t\tbreak;\n\n\t\t\t}\n\n\t\t}\n\n\t\tif ( uv.y < 0 || uv.y > 1 ) {\n\n\t\t\tswitch ( this.wrapT ) {\n\n\t\t\t\tcase RepeatWrapping:\n\n\t\t\t\t\tuv.y = uv.y - Math.floor( uv.y );\n\t\t\t\t\tbreak;\n\n\t\t\t\tcase ClampToEdgeWrapping:\n\n\t\t\t\t\tuv.y = uv.y < 0 ? 0 : 1;\n\t\t\t\t\tbreak;\n\n\t\t\t\tcase MirroredRepeatWrapping:\n\n\t\t\t\t\tif ( Math.abs( Math.floor( uv.y ) % 2 ) === 1 ) {\n\n\t\t\t\t\t\tuv.y = Math.ceil( uv.y ) - uv.y;\n\n\t\t\t\t\t} else {\n\n\t\t\t\t\t\tuv.y = uv.y - Math.floor( uv.y );\n\n\t\t\t\t\t}\n\t\t\t\t\tbreak;\n\n\t\t\t}\n\n\t\t}\n\n\t\tif ( this.flipY ) {\n\n\t\t\tuv.y = 1 - uv.y;\n\n\t\t}\n\n\t\treturn uv;\n\n\t}\n\n} );\n\nObject.defineProperty( Texture.prototype, \"needsUpdate\", {\n\n\tset: function ( value ) {\n\n\t\tif ( value === true ) this.version ++;\n\n\t}\n\n} );\n\n/**\n * @author supereggbert / http://www.paulbrunt.co.uk/\n * @author philogb / http://blog.thejit.org/\n * @author mikael emtinger / http://gomo.se/\n * @author egraether / http://egraether.com/\n * @author WestLangley / http://github.com/WestLangley\n */\n\nfunction Vector4( x, y, z, w ) {\n\n\tthis.x = x || 0;\n\tthis.y = y || 0;\n\tthis.z = z || 0;\n\tthis.w = ( w !== undefined ) ? w : 1;\n\n}\n\nObject.assign( Vector4.prototype, {\n\n\tisVector4: true,\n\n\tset: function ( x, y, z, w ) {\n\n\t\tthis.x = x;\n\t\tthis.y = y;\n\t\tthis.z = z;\n\t\tthis.w = w;\n\n\t\treturn this;\n\n\t},\n\n\tsetScalar: function ( scalar ) {\n\n\t\tthis.x = scalar;\n\t\tthis.y = scalar;\n\t\tthis.z = scalar;\n\t\tthis.w = scalar;\n\n\t\treturn this;\n\n\t},\n\n\tsetX: function ( x ) {\n\n\t\tthis.x = x;\n\n\t\treturn this;\n\n\t},\n\n\tsetY: function ( y ) {\n\n\t\tthis.y = y;\n\n\t\treturn this;\n\n\t},\n\n\tsetZ: function ( z ) {\n\n\t\tthis.z = z;\n\n\t\treturn this;\n\n\t},\n\n\tsetW: function ( w ) {\n\n\t\tthis.w = w;\n\n\t\treturn this;\n\n\t},\n\n\tsetComponent: function ( index, value ) {\n\n\t\tswitch ( index ) {\n\n\t\t\tcase 0: this.x = value; break;\n\t\t\tcase 1: this.y = value; break;\n\t\t\tcase 2: this.z = value; break;\n\t\t\tcase 3: this.w = value; break;\n\t\t\tdefault: throw new Error( 'index is out of range: ' + index );\n\n\t\t}\n\n\t\treturn this;\n\n\t},\n\n\tgetComponent: function ( index ) {\n\n\t\tswitch ( index ) {\n\n\t\t\tcase 0: return this.x;\n\t\t\tcase 1: return this.y;\n\t\t\tcase 2: return this.z;\n\t\t\tcase 3: return this.w;\n\t\t\tdefault: throw new Error( 'index is out of range: ' + index );\n\n\t\t}\n\n\t},\n\n\tclone: function () {\n\n\t\treturn new this.constructor( this.x, this.y, this.z, this.w );\n\n\t},\n\n\tcopy: function ( v ) {\n\n\t\tthis.x = v.x;\n\t\tthis.y = v.y;\n\t\tthis.z = v.z;\n\t\tthis.w = ( v.w !== undefined ) ? v.w : 1;\n\n\t\treturn this;\n\n\t},\n\n\tadd: function ( v, w ) {\n\n\t\tif ( w !== undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead.' );\n\t\t\treturn this.addVectors( v, w );\n\n\t\t}\n\n\t\tthis.x += v.x;\n\t\tthis.y += v.y;\n\t\tthis.z += v.z;\n\t\tthis.w += v.w;\n\n\t\treturn this;\n\n\t},\n\n\taddScalar: function ( s ) {\n\n\t\tthis.x += s;\n\t\tthis.y += s;\n\t\tthis.z += s;\n\t\tthis.w += s;\n\n\t\treturn this;\n\n\t},\n\n\taddVectors: function ( a, b ) {\n\n\t\tthis.x = a.x + b.x;\n\t\tthis.y = a.y + b.y;\n\t\tthis.z = a.z + b.z;\n\t\tthis.w = a.w + b.w;\n\n\t\treturn this;\n\n\t},\n\n\taddScaledVector: function ( v, s ) {\n\n\t\tthis.x += v.x * s;\n\t\tthis.y += v.y * s;\n\t\tthis.z += v.z * s;\n\t\tthis.w += v.w * s;\n\n\t\treturn this;\n\n\t},\n\n\tsub: function ( v, w ) {\n\n\t\tif ( w !== undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead.' );\n\t\t\treturn this.subVectors( v, w );\n\n\t\t}\n\n\t\tthis.x -= v.x;\n\t\tthis.y -= v.y;\n\t\tthis.z -= v.z;\n\t\tthis.w -= v.w;\n\n\t\treturn this;\n\n\t},\n\n\tsubScalar: function ( s ) {\n\n\t\tthis.x -= s;\n\t\tthis.y -= s;\n\t\tthis.z -= s;\n\t\tthis.w -= s;\n\n\t\treturn this;\n\n\t},\n\n\tsubVectors: function ( a, b ) {\n\n\t\tthis.x = a.x - b.x;\n\t\tthis.y = a.y - b.y;\n\t\tthis.z = a.z - b.z;\n\t\tthis.w = a.w - b.w;\n\n\t\treturn this;\n\n\t},\n\n\tmultiplyScalar: function ( scalar ) {\n\n\t\tthis.x *= scalar;\n\t\tthis.y *= scalar;\n\t\tthis.z *= scalar;\n\t\tthis.w *= scalar;\n\n\t\treturn this;\n\n\t},\n\n\tapplyMatrix4: function ( m ) {\n\n\t\tvar x = this.x, y = this.y, z = this.z, w = this.w;\n\t\tvar e = m.elements;\n\n\t\tthis.x = e[ 0 ] * x + e[ 4 ] * y + e[ 8 ] * z + e[ 12 ] * w;\n\t\tthis.y = e[ 1 ] * x + e[ 5 ] * y + e[ 9 ] * z + e[ 13 ] * w;\n\t\tthis.z = e[ 2 ] * x + e[ 6 ] * y + e[ 10 ] * z + e[ 14 ] * w;\n\t\tthis.w = e[ 3 ] * x + e[ 7 ] * y + e[ 11 ] * z + e[ 15 ] * w;\n\n\t\treturn this;\n\n\t},\n\n\tdivideScalar: function ( scalar ) {\n\n\t\treturn this.multiplyScalar( 1 / scalar );\n\n\t},\n\n\tsetAxisAngleFromQuaternion: function ( q ) {\n\n\t\t// http://www.euclideanspace.com/maths/geometry/rotations/conversions/quaternionToAngle/index.htm\n\n\t\t// q is assumed to be normalized\n\n\t\tthis.w = 2 * Math.acos( q.w );\n\n\t\tvar s = Math.sqrt( 1 - q.w * q.w );\n\n\t\tif ( s < 0.0001 ) {\n\n\t\t\tthis.x = 1;\n\t\t\tthis.y = 0;\n\t\t\tthis.z = 0;\n\n\t\t} else {\n\n\t\t\tthis.x = q.x / s;\n\t\t\tthis.y = q.y / s;\n\t\t\tthis.z = q.z / s;\n\n\t\t}\n\n\t\treturn this;\n\n\t},\n\n\tsetAxisAngleFromRotationMatrix: function ( m ) {\n\n\t\t// http://www.euclideanspace.com/maths/geometry/rotations/conversions/matrixToAngle/index.htm\n\n\t\t// assumes the upper 3x3 of m is a pure rotation matrix (i.e, unscaled)\n\n\t\tvar angle, x, y, z,\t\t// variables for result\n\t\t\tepsilon = 0.01,\t\t// margin to allow for rounding errors\n\t\t\tepsilon2 = 0.1,\t\t// margin to distinguish between 0 and 180 degrees\n\n\t\t\tte = m.elements,\n\n\t\t\tm11 = te[ 0 ], m12 = te[ 4 ], m13 = te[ 8 ],\n\t\t\tm21 = te[ 1 ], m22 = te[ 5 ], m23 = te[ 9 ],\n\t\t\tm31 = te[ 2 ], m32 = te[ 6 ], m33 = te[ 10 ];\n\n\t\tif ( ( Math.abs( m12 - m21 ) < epsilon ) &&\n\t\t ( Math.abs( m13 - m31 ) < epsilon ) &&\n\t\t ( Math.abs( m23 - m32 ) < epsilon ) ) {\n\n\t\t\t// singularity found\n\t\t\t// first check for identity matrix which must have +1 for all terms\n\t\t\t// in leading diagonal and zero in other terms\n\n\t\t\tif ( ( Math.abs( m12 + m21 ) < epsilon2 ) &&\n\t\t\t ( Math.abs( m13 + m31 ) < epsilon2 ) &&\n\t\t\t ( Math.abs( m23 + m32 ) < epsilon2 ) &&\n\t\t\t ( Math.abs( m11 + m22 + m33 - 3 ) < epsilon2 ) ) {\n\n\t\t\t\t// this singularity is identity matrix so angle = 0\n\n\t\t\t\tthis.set( 1, 0, 0, 0 );\n\n\t\t\t\treturn this; // zero angle, arbitrary axis\n\n\t\t\t}\n\n\t\t\t// otherwise this singularity is angle = 180\n\n\t\t\tangle = Math.PI;\n\n\t\t\tvar xx = ( m11 + 1 ) / 2;\n\t\t\tvar yy = ( m22 + 1 ) / 2;\n\t\t\tvar zz = ( m33 + 1 ) / 2;\n\t\t\tvar xy = ( m12 + m21 ) / 4;\n\t\t\tvar xz = ( m13 + m31 ) / 4;\n\t\t\tvar yz = ( m23 + m32 ) / 4;\n\n\t\t\tif ( ( xx > yy ) && ( xx > zz ) ) {\n\n\t\t\t\t// m11 is the largest diagonal term\n\n\t\t\t\tif ( xx < epsilon ) {\n\n\t\t\t\t\tx = 0;\n\t\t\t\t\ty = 0.707106781;\n\t\t\t\t\tz = 0.707106781;\n\n\t\t\t\t} else {\n\n\t\t\t\t\tx = Math.sqrt( xx );\n\t\t\t\t\ty = xy / x;\n\t\t\t\t\tz = xz / x;\n\n\t\t\t\t}\n\n\t\t\t} else if ( yy > zz ) {\n\n\t\t\t\t// m22 is the largest diagonal term\n\n\t\t\t\tif ( yy < epsilon ) {\n\n\t\t\t\t\tx = 0.707106781;\n\t\t\t\t\ty = 0;\n\t\t\t\t\tz = 0.707106781;\n\n\t\t\t\t} else {\n\n\t\t\t\t\ty = Math.sqrt( yy );\n\t\t\t\t\tx = xy / y;\n\t\t\t\t\tz = yz / y;\n\n\t\t\t\t}\n\n\t\t\t} else {\n\n\t\t\t\t// m33 is the largest diagonal term so base result on this\n\n\t\t\t\tif ( zz < epsilon ) {\n\n\t\t\t\t\tx = 0.707106781;\n\t\t\t\t\ty = 0.707106781;\n\t\t\t\t\tz = 0;\n\n\t\t\t\t} else {\n\n\t\t\t\t\tz = Math.sqrt( zz );\n\t\t\t\t\tx = xz / z;\n\t\t\t\t\ty = yz / z;\n\n\t\t\t\t}\n\n\t\t\t}\n\n\t\t\tthis.set( x, y, z, angle );\n\n\t\t\treturn this; // return 180 deg rotation\n\n\t\t}\n\n\t\t// as we have reached here there are no singularities so we can handle normally\n\n\t\tvar s = Math.sqrt( ( m32 - m23 ) * ( m32 - m23 ) +\n\t\t ( m13 - m31 ) * ( m13 - m31 ) +\n\t\t ( m21 - m12 ) * ( m21 - m12 ) ); // used to normalize\n\n\t\tif ( Math.abs( s ) < 0.001 ) s = 1;\n\n\t\t// prevent divide by zero, should not happen if matrix is orthogonal and should be\n\t\t// caught by singularity test above, but I've left it in just in case\n\n\t\tthis.x = ( m32 - m23 ) / s;\n\t\tthis.y = ( m13 - m31 ) / s;\n\t\tthis.z = ( m21 - m12 ) / s;\n\t\tthis.w = Math.acos( ( m11 + m22 + m33 - 1 ) / 2 );\n\n\t\treturn this;\n\n\t},\n\n\tmin: function ( v ) {\n\n\t\tthis.x = Math.min( this.x, v.x );\n\t\tthis.y = Math.min( this.y, v.y );\n\t\tthis.z = Math.min( this.z, v.z );\n\t\tthis.w = Math.min( this.w, v.w );\n\n\t\treturn this;\n\n\t},\n\n\tmax: function ( v ) {\n\n\t\tthis.x = Math.max( this.x, v.x );\n\t\tthis.y = Math.max( this.y, v.y );\n\t\tthis.z = Math.max( this.z, v.z );\n\t\tthis.w = Math.max( this.w, v.w );\n\n\t\treturn this;\n\n\t},\n\n\tclamp: function ( min, max ) {\n\n\t\t// assumes min < max, componentwise\n\n\t\tthis.x = Math.max( min.x, Math.min( max.x, this.x ) );\n\t\tthis.y = Math.max( min.y, Math.min( max.y, this.y ) );\n\t\tthis.z = Math.max( min.z, Math.min( max.z, this.z ) );\n\t\tthis.w = Math.max( min.w, Math.min( max.w, this.w ) );\n\n\t\treturn this;\n\n\t},\n\n\tclampScalar: function () {\n\n\t\tvar min, max;\n\n\t\treturn function clampScalar( minVal, maxVal ) {\n\n\t\t\tif ( min === undefined ) {\n\n\t\t\t\tmin = new Vector4();\n\t\t\t\tmax = new Vector4();\n\n\t\t\t}\n\n\t\t\tmin.set( minVal, minVal, minVal, minVal );\n\t\t\tmax.set( maxVal, maxVal, maxVal, maxVal );\n\n\t\t\treturn this.clamp( min, max );\n\n\t\t};\n\n\t}(),\n\n\tclampLength: function ( min, max ) {\n\n\t\tvar length = this.length();\n\n\t\treturn this.divideScalar( length || 1 ).multiplyScalar( Math.max( min, Math.min( max, length ) ) );\n\n\t},\n\n\tfloor: function () {\n\n\t\tthis.x = Math.floor( this.x );\n\t\tthis.y = Math.floor( this.y );\n\t\tthis.z = Math.floor( this.z );\n\t\tthis.w = Math.floor( this.w );\n\n\t\treturn this;\n\n\t},\n\n\tceil: function () {\n\n\t\tthis.x = Math.ceil( this.x );\n\t\tthis.y = Math.ceil( this.y );\n\t\tthis.z = Math.ceil( this.z );\n\t\tthis.w = Math.ceil( this.w );\n\n\t\treturn this;\n\n\t},\n\n\tround: function () {\n\n\t\tthis.x = Math.round( this.x );\n\t\tthis.y = Math.round( this.y );\n\t\tthis.z = Math.round( this.z );\n\t\tthis.w = Math.round( this.w );\n\n\t\treturn this;\n\n\t},\n\n\troundToZero: function () {\n\n\t\tthis.x = ( this.x < 0 ) ? Math.ceil( this.x ) : Math.floor( this.x );\n\t\tthis.y = ( this.y < 0 ) ? Math.ceil( this.y ) : Math.floor( this.y );\n\t\tthis.z = ( this.z < 0 ) ? Math.ceil( this.z ) : Math.floor( this.z );\n\t\tthis.w = ( this.w < 0 ) ? Math.ceil( this.w ) : Math.floor( this.w );\n\n\t\treturn this;\n\n\t},\n\n\tnegate: function () {\n\n\t\tthis.x = - this.x;\n\t\tthis.y = - this.y;\n\t\tthis.z = - this.z;\n\t\tthis.w = - this.w;\n\n\t\treturn this;\n\n\t},\n\n\tdot: function ( v ) {\n\n\t\treturn this.x * v.x + this.y * v.y + this.z * v.z + this.w * v.w;\n\n\t},\n\n\tlengthSq: function () {\n\n\t\treturn this.x * this.x + this.y * this.y + this.z * this.z + this.w * this.w;\n\n\t},\n\n\tlength: function () {\n\n\t\treturn Math.sqrt( this.x * this.x + this.y * this.y + this.z * this.z + this.w * this.w );\n\n\t},\n\n\tmanhattanLength: function () {\n\n\t\treturn Math.abs( this.x ) + Math.abs( this.y ) + Math.abs( this.z ) + Math.abs( this.w );\n\n\t},\n\n\tnormalize: function () {\n\n\t\treturn this.divideScalar( this.length() || 1 );\n\n\t},\n\n\tsetLength: function ( length ) {\n\n\t\treturn this.normalize().multiplyScalar( length );\n\n\t},\n\n\tlerp: function ( v, alpha ) {\n\n\t\tthis.x += ( v.x - this.x ) * alpha;\n\t\tthis.y += ( v.y - this.y ) * alpha;\n\t\tthis.z += ( v.z - this.z ) * alpha;\n\t\tthis.w += ( v.w - this.w ) * alpha;\n\n\t\treturn this;\n\n\t},\n\n\tlerpVectors: function ( v1, v2, alpha ) {\n\n\t\treturn this.subVectors( v2, v1 ).multiplyScalar( alpha ).add( v1 );\n\n\t},\n\n\tequals: function ( v ) {\n\n\t\treturn ( ( v.x === this.x ) && ( v.y === this.y ) && ( v.z === this.z ) && ( v.w === this.w ) );\n\n\t},\n\n\tfromArray: function ( array, offset ) {\n\n\t\tif ( offset === undefined ) offset = 0;\n\n\t\tthis.x = array[ offset ];\n\t\tthis.y = array[ offset + 1 ];\n\t\tthis.z = array[ offset + 2 ];\n\t\tthis.w = array[ offset + 3 ];\n\n\t\treturn this;\n\n\t},\n\n\ttoArray: function ( array, offset ) {\n\n\t\tif ( array === undefined ) array = [];\n\t\tif ( offset === undefined ) offset = 0;\n\n\t\tarray[ offset ] = this.x;\n\t\tarray[ offset + 1 ] = this.y;\n\t\tarray[ offset + 2 ] = this.z;\n\t\tarray[ offset + 3 ] = this.w;\n\n\t\treturn array;\n\n\t},\n\n\tfromBufferAttribute: function ( attribute, index, offset ) {\n\n\t\tif ( offset !== undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Vector4: offset has been removed from .fromBufferAttribute().' );\n\n\t\t}\n\n\t\tthis.x = attribute.getX( index );\n\t\tthis.y = attribute.getY( index );\n\t\tthis.z = attribute.getZ( index );\n\t\tthis.w = attribute.getW( index );\n\n\t\treturn this;\n\n\t}\n\n} );\n\n/**\n * @author szimek / https://github.com/szimek/\n * @author alteredq / http://alteredqualia.com/\n * @author Marius Kintel / https://github.com/kintel\n */\n\n/*\n In options, we can specify:\n * Texture parameters for an auto-generated target texture\n * depthBuffer/stencilBuffer: Booleans to indicate if we should generate these buffers\n*/\nfunction WebGLRenderTarget( width, height, options ) {\n\n\tthis.width = width;\n\tthis.height = height;\n\n\tthis.scissor = new Vector4( 0, 0, width, height );\n\tthis.scissorTest = false;\n\n\tthis.viewport = new Vector4( 0, 0, width, height );\n\n\toptions = options || {};\n\n\tthis.texture = new Texture( undefined, undefined, options.wrapS, options.wrapT, options.magFilter, options.minFilter, options.format, options.type, options.anisotropy, options.encoding );\n\n\tthis.texture.generateMipmaps = options.generateMipmaps !== undefined ? options.generateMipmaps : false;\n\tthis.texture.minFilter = options.minFilter !== undefined ? options.minFilter : LinearFilter;\n\n\tthis.depthBuffer = options.depthBuffer !== undefined ? options.depthBuffer : true;\n\tthis.stencilBuffer = options.stencilBuffer !== undefined ? options.stencilBuffer : true;\n\tthis.depthTexture = options.depthTexture !== undefined ? options.depthTexture : null;\n\n}\n\nWebGLRenderTarget.prototype = Object.assign( Object.create( EventDispatcher.prototype ), {\n\n\tconstructor: WebGLRenderTarget,\n\n\tisWebGLRenderTarget: true,\n\n\tsetSize: function ( width, height ) {\n\n\t\tif ( this.width !== width || this.height !== height ) {\n\n\t\t\tthis.width = width;\n\t\t\tthis.height = height;\n\n\t\t\tthis.dispose();\n\n\t\t}\n\n\t\tthis.viewport.set( 0, 0, width, height );\n\t\tthis.scissor.set( 0, 0, width, height );\n\n\t},\n\n\tclone: function () {\n\n\t\treturn new this.constructor().copy( this );\n\n\t},\n\n\tcopy: function ( source ) {\n\n\t\tthis.width = source.width;\n\t\tthis.height = source.height;\n\n\t\tthis.viewport.copy( source.viewport );\n\n\t\tthis.texture = source.texture.clone();\n\n\t\tthis.depthBuffer = source.depthBuffer;\n\t\tthis.stencilBuffer = source.stencilBuffer;\n\t\tthis.depthTexture = source.depthTexture;\n\n\t\treturn this;\n\n\t},\n\n\tdispose: function () {\n\n\t\tthis.dispatchEvent( { type: 'dispose' } );\n\n\t}\n\n} );\n\n/**\n * @author Mugen87 / https://github.com/Mugen87\n * @author Matt DesLauriers / @mattdesl\n */\n\nfunction WebGLMultisampleRenderTarget( width, height, options ) {\n\n\tWebGLRenderTarget.call( this, width, height, options );\n\n\tthis.samples = 4;\n\n}\n\nWebGLMultisampleRenderTarget.prototype = Object.assign( Object.create( WebGLRenderTarget.prototype ), {\n\n\tconstructor: WebGLMultisampleRenderTarget,\n\n\tisWebGLMultisampleRenderTarget: true,\n\n\tcopy: function ( source ) {\n\n\t\tWebGLRenderTarget.prototype.copy.call( this, source );\n\n\t\tthis.samples = source.samples;\n\n\t\treturn this;\n\n\t}\n\n} );\n\n/**\n * @author alteredq / http://alteredqualia.com\n */\n\nfunction WebGLRenderTargetCube( width, height, options ) {\n\n\tWebGLRenderTarget.call( this, width, height, options );\n\n}\n\nWebGLRenderTargetCube.prototype = Object.create( WebGLRenderTarget.prototype );\nWebGLRenderTargetCube.prototype.constructor = WebGLRenderTargetCube;\n\nWebGLRenderTargetCube.prototype.isWebGLRenderTargetCube = true;\n\n/**\n * @author alteredq / http://alteredqualia.com/\n */\n\nfunction DataTexture( data, width, height, format, type, mapping, wrapS, wrapT, magFilter, minFilter, anisotropy, encoding ) {\n\n\tTexture.call( this, null, mapping, wrapS, wrapT, magFilter, minFilter, format, type, anisotropy, encoding );\n\n\tthis.image = { data: data, width: width, height: height };\n\n\tthis.magFilter = magFilter !== undefined ? magFilter : NearestFilter;\n\tthis.minFilter = minFilter !== undefined ? minFilter : NearestFilter;\n\n\tthis.generateMipmaps = false;\n\tthis.flipY = false;\n\tthis.unpackAlignment = 1;\n\n}\n\nDataTexture.prototype = Object.create( Texture.prototype );\nDataTexture.prototype.constructor = DataTexture;\n\nDataTexture.prototype.isDataTexture = true;\n\n/**\n * @author bhouston / http://clara.io\n * @author WestLangley / http://github.com/WestLangley\n */\n\nfunction Box3( min, max ) {\n\n\tthis.min = ( min !== undefined ) ? min : new Vector3( + Infinity, + Infinity, + Infinity );\n\tthis.max = ( max !== undefined ) ? max : new Vector3( - Infinity, - Infinity, - Infinity );\n\n}\n\nObject.assign( Box3.prototype, {\n\n\tisBox3: true,\n\n\tset: function ( min, max ) {\n\n\t\tthis.min.copy( min );\n\t\tthis.max.copy( max );\n\n\t\treturn this;\n\n\t},\n\n\tsetFromArray: function ( array ) {\n\n\t\tvar minX = + Infinity;\n\t\tvar minY = + Infinity;\n\t\tvar minZ = + Infinity;\n\n\t\tvar maxX = - Infinity;\n\t\tvar maxY = - Infinity;\n\t\tvar maxZ = - Infinity;\n\n\t\tfor ( var i = 0, l = array.length; i < l; i += 3 ) {\n\n\t\t\tvar x = array[ i ];\n\t\t\tvar y = array[ i + 1 ];\n\t\t\tvar z = array[ i + 2 ];\n\n\t\t\tif ( x < minX ) minX = x;\n\t\t\tif ( y < minY ) minY = y;\n\t\t\tif ( z < minZ ) minZ = z;\n\n\t\t\tif ( x > maxX ) maxX = x;\n\t\t\tif ( y > maxY ) maxY = y;\n\t\t\tif ( z > maxZ ) maxZ = z;\n\n\t\t}\n\n\t\tthis.min.set( minX, minY, minZ );\n\t\tthis.max.set( maxX, maxY, maxZ );\n\n\t\treturn this;\n\n\t},\n\n\tsetFromBufferAttribute: function ( attribute ) {\n\n\t\tvar minX = + Infinity;\n\t\tvar minY = + Infinity;\n\t\tvar minZ = + Infinity;\n\n\t\tvar maxX = - Infinity;\n\t\tvar maxY = - Infinity;\n\t\tvar maxZ = - Infinity;\n\n\t\tfor ( var i = 0, l = attribute.count; i < l; i ++ ) {\n\n\t\t\tvar x = attribute.getX( i );\n\t\t\tvar y = attribute.getY( i );\n\t\t\tvar z = attribute.getZ( i );\n\n\t\t\tif ( x < minX ) minX = x;\n\t\t\tif ( y < minY ) minY = y;\n\t\t\tif ( z < minZ ) minZ = z;\n\n\t\t\tif ( x > maxX ) maxX = x;\n\t\t\tif ( y > maxY ) maxY = y;\n\t\t\tif ( z > maxZ ) maxZ = z;\n\n\t\t}\n\n\t\tthis.min.set( minX, minY, minZ );\n\t\tthis.max.set( maxX, maxY, maxZ );\n\n\t\treturn this;\n\n\t},\n\n\tsetFromPoints: function ( points ) {\n\n\t\tthis.makeEmpty();\n\n\t\tfor ( var i = 0, il = points.length; i < il; i ++ ) {\n\n\t\t\tthis.expandByPoint( points[ i ] );\n\n\t\t}\n\n\t\treturn this;\n\n\t},\n\n\tsetFromCenterAndSize: function () {\n\n\t\tvar v1 = new Vector3();\n\n\t\treturn function setFromCenterAndSize( center, size ) {\n\n\t\t\tvar halfSize = v1.copy( size ).multiplyScalar( 0.5 );\n\n\t\t\tthis.min.copy( center ).sub( halfSize );\n\t\t\tthis.max.copy( center ).add( halfSize );\n\n\t\t\treturn this;\n\n\t\t};\n\n\t}(),\n\n\tsetFromObject: function ( object ) {\n\n\t\tthis.makeEmpty();\n\n\t\treturn this.expandByObject( object );\n\n\t},\n\n\tclone: function () {\n\n\t\treturn new this.constructor().copy( this );\n\n\t},\n\n\tcopy: function ( box ) {\n\n\t\tthis.min.copy( box.min );\n\t\tthis.max.copy( box.max );\n\n\t\treturn this;\n\n\t},\n\n\tmakeEmpty: function () {\n\n\t\tthis.min.x = this.min.y = this.min.z = + Infinity;\n\t\tthis.max.x = this.max.y = this.max.z = - Infinity;\n\n\t\treturn this;\n\n\t},\n\n\tisEmpty: function () {\n\n\t\t// this is a more robust check for empty than ( volume <= 0 ) because volume can get positive with two negative axes\n\n\t\treturn ( this.max.x < this.min.x ) || ( this.max.y < this.min.y ) || ( this.max.z < this.min.z );\n\n\t},\n\n\tgetCenter: function ( target ) {\n\n\t\tif ( target === undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Box3: .getCenter() target is now required' );\n\t\t\ttarget = new Vector3();\n\n\t\t}\n\n\t\treturn this.isEmpty() ? target.set( 0, 0, 0 ) : target.addVectors( this.min, this.max ).multiplyScalar( 0.5 );\n\n\t},\n\n\tgetSize: function ( target ) {\n\n\t\tif ( target === undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Box3: .getSize() target is now required' );\n\t\t\ttarget = new Vector3();\n\n\t\t}\n\n\t\treturn this.isEmpty() ? target.set( 0, 0, 0 ) : target.subVectors( this.max, this.min );\n\n\t},\n\n\texpandByPoint: function ( point ) {\n\n\t\tthis.min.min( point );\n\t\tthis.max.max( point );\n\n\t\treturn this;\n\n\t},\n\n\texpandByVector: function ( vector ) {\n\n\t\tthis.min.sub( vector );\n\t\tthis.max.add( vector );\n\n\t\treturn this;\n\n\t},\n\n\texpandByScalar: function ( scalar ) {\n\n\t\tthis.min.addScalar( - scalar );\n\t\tthis.max.addScalar( scalar );\n\n\t\treturn this;\n\n\t},\n\n\texpandByObject: function () {\n\n\t\t// Computes the world-axis-aligned bounding box of an object (including its children),\n\t\t// accounting for both the object's, and children's, world transforms\n\n\t\tvar scope, i, l;\n\n\t\tvar v1 = new Vector3();\n\n\t\tfunction traverse( node ) {\n\n\t\t\tvar geometry = node.geometry;\n\n\t\t\tif ( geometry !== undefined ) {\n\n\t\t\t\tif ( geometry.isGeometry ) {\n\n\t\t\t\t\tvar vertices = geometry.vertices;\n\n\t\t\t\t\tfor ( i = 0, l = vertices.length; i < l; i ++ ) {\n\n\t\t\t\t\t\tv1.copy( vertices[ i ] );\n\t\t\t\t\t\tv1.applyMatrix4( node.matrixWorld );\n\n\t\t\t\t\t\tscope.expandByPoint( v1 );\n\n\t\t\t\t\t}\n\n\t\t\t\t} else if ( geometry.isBufferGeometry ) {\n\n\t\t\t\t\tvar attribute = geometry.attributes.position;\n\n\t\t\t\t\tif ( attribute !== undefined ) {\n\n\t\t\t\t\t\tfor ( i = 0, l = attribute.count; i < l; i ++ ) {\n\n\t\t\t\t\t\t\tv1.fromBufferAttribute( attribute, i ).applyMatrix4( node.matrixWorld );\n\n\t\t\t\t\t\t\tscope.expandByPoint( v1 );\n\n\t\t\t\t\t\t}\n\n\t\t\t\t\t}\n\n\t\t\t\t}\n\n\t\t\t}\n\n\t\t}\n\n\t\treturn function expandByObject( object ) {\n\n\t\t\tscope = this;\n\n\t\t\tobject.updateMatrixWorld( true );\n\n\t\t\tobject.traverse( traverse );\n\n\t\t\treturn this;\n\n\t\t};\n\n\t}(),\n\n\tcontainsPoint: function ( point ) {\n\n\t\treturn point.x < this.min.x || point.x > this.max.x ||\n\t\t\tpoint.y < this.min.y || point.y > this.max.y ||\n\t\t\tpoint.z < this.min.z || point.z > this.max.z ? false : true;\n\n\t},\n\n\tcontainsBox: function ( box ) {\n\n\t\treturn this.min.x <= box.min.x && box.max.x <= this.max.x &&\n\t\t\tthis.min.y <= box.min.y && box.max.y <= this.max.y &&\n\t\t\tthis.min.z <= box.min.z && box.max.z <= this.max.z;\n\n\t},\n\n\tgetParameter: function ( point, target ) {\n\n\t\t// This can potentially have a divide by zero if the box\n\t\t// has a size dimension of 0.\n\n\t\tif ( target === undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Box3: .getParameter() target is now required' );\n\t\t\ttarget = new Vector3();\n\n\t\t}\n\n\t\treturn target.set(\n\t\t\t( point.x - this.min.x ) / ( this.max.x - this.min.x ),\n\t\t\t( point.y - this.min.y ) / ( this.max.y - this.min.y ),\n\t\t\t( point.z - this.min.z ) / ( this.max.z - this.min.z )\n\t\t);\n\n\t},\n\n\tintersectsBox: function ( box ) {\n\n\t\t// using 6 splitting planes to rule out intersections.\n\t\treturn box.max.x < this.min.x || box.min.x > this.max.x ||\n\t\t\tbox.max.y < this.min.y || box.min.y > this.max.y ||\n\t\t\tbox.max.z < this.min.z || box.min.z > this.max.z ? false : true;\n\n\t},\n\n\tintersectsSphere: ( function () {\n\n\t\tvar closestPoint = new Vector3();\n\n\t\treturn function intersectsSphere( sphere ) {\n\n\t\t\t// Find the point on the AABB closest to the sphere center.\n\t\t\tthis.clampPoint( sphere.center, closestPoint );\n\n\t\t\t// If that point is inside the sphere, the AABB and sphere intersect.\n\t\t\treturn closestPoint.distanceToSquared( sphere.center ) <= ( sphere.radius * sphere.radius );\n\n\t\t};\n\n\t} )(),\n\n\tintersectsPlane: function ( plane ) {\n\n\t\t// We compute the minimum and maximum dot product values. If those values\n\t\t// are on the same side (back or front) of the plane, then there is no intersection.\n\n\t\tvar min, max;\n\n\t\tif ( plane.normal.x > 0 ) {\n\n\t\t\tmin = plane.normal.x * this.min.x;\n\t\t\tmax = plane.normal.x * this.max.x;\n\n\t\t} else {\n\n\t\t\tmin = plane.normal.x * this.max.x;\n\t\t\tmax = plane.normal.x * this.min.x;\n\n\t\t}\n\n\t\tif ( plane.normal.y > 0 ) {\n\n\t\t\tmin += plane.normal.y * this.min.y;\n\t\t\tmax += plane.normal.y * this.max.y;\n\n\t\t} else {\n\n\t\t\tmin += plane.normal.y * this.max.y;\n\t\t\tmax += plane.normal.y * this.min.y;\n\n\t\t}\n\n\t\tif ( plane.normal.z > 0 ) {\n\n\t\t\tmin += plane.normal.z * this.min.z;\n\t\t\tmax += plane.normal.z * this.max.z;\n\n\t\t} else {\n\n\t\t\tmin += plane.normal.z * this.max.z;\n\t\t\tmax += plane.normal.z * this.min.z;\n\n\t\t}\n\n\t\treturn ( min <= - plane.constant && max >= - plane.constant );\n\n\t},\n\n\tintersectsTriangle: ( function () {\n\n\t\t// triangle centered vertices\n\t\tvar v0 = new Vector3();\n\t\tvar v1 = new Vector3();\n\t\tvar v2 = new Vector3();\n\n\t\t// triangle edge vectors\n\t\tvar f0 = new Vector3();\n\t\tvar f1 = new Vector3();\n\t\tvar f2 = new Vector3();\n\n\t\tvar testAxis = new Vector3();\n\n\t\tvar center = new Vector3();\n\t\tvar extents = new Vector3();\n\n\t\tvar triangleNormal = new Vector3();\n\n\t\tfunction satForAxes( axes ) {\n\n\t\t\tvar i, j;\n\n\t\t\tfor ( i = 0, j = axes.length - 3; i <= j; i += 3 ) {\n\n\t\t\t\ttestAxis.fromArray( axes, i );\n\t\t\t\t// project the aabb onto the seperating axis\n\t\t\t\tvar r = extents.x * Math.abs( testAxis.x ) + extents.y * Math.abs( testAxis.y ) + extents.z * Math.abs( testAxis.z );\n\t\t\t\t// project all 3 vertices of the triangle onto the seperating axis\n\t\t\t\tvar p0 = v0.dot( testAxis );\n\t\t\t\tvar p1 = v1.dot( testAxis );\n\t\t\t\tvar p2 = v2.dot( testAxis );\n\t\t\t\t// actual test, basically see if either of the most extreme of the triangle points intersects r\n\t\t\t\tif ( Math.max( - Math.max( p0, p1, p2 ), Math.min( p0, p1, p2 ) ) > r ) {\n\n\t\t\t\t\t// points of the projected triangle are outside the projected half-length of the aabb\n\t\t\t\t\t// the axis is seperating and we can exit\n\t\t\t\t\treturn false;\n\n\t\t\t\t}\n\n\t\t\t}\n\n\t\t\treturn true;\n\n\t\t}\n\n\t\treturn function intersectsTriangle( triangle ) {\n\n\t\t\tif ( this.isEmpty() ) {\n\n\t\t\t\treturn false;\n\n\t\t\t}\n\n\t\t\t// compute box center and extents\n\t\t\tthis.getCenter( center );\n\t\t\textents.subVectors( this.max, center );\n\n\t\t\t// translate triangle to aabb origin\n\t\t\tv0.subVectors( triangle.a, center );\n\t\t\tv1.subVectors( triangle.b, center );\n\t\t\tv2.subVectors( triangle.c, center );\n\n\t\t\t// compute edge vectors for triangle\n\t\t\tf0.subVectors( v1, v0 );\n\t\t\tf1.subVectors( v2, v1 );\n\t\t\tf2.subVectors( v0, v2 );\n\n\t\t\t// test against axes that are given by cross product combinations of the edges of the triangle and the edges of the aabb\n\t\t\t// make an axis testing of each of the 3 sides of the aabb against each of the 3 sides of the triangle = 9 axis of separation\n\t\t\t// axis_ij = u_i x f_j (u0, u1, u2 = face normals of aabb = x,y,z axes vectors since aabb is axis aligned)\n\t\t\tvar axes = [\n\t\t\t\t0, - f0.z, f0.y, 0, - f1.z, f1.y, 0, - f2.z, f2.y,\n\t\t\t\tf0.z, 0, - f0.x, f1.z, 0, - f1.x, f2.z, 0, - f2.x,\n\t\t\t\t- f0.y, f0.x, 0, - f1.y, f1.x, 0, - f2.y, f2.x, 0\n\t\t\t];\n\t\t\tif ( ! satForAxes( axes ) ) {\n\n\t\t\t\treturn false;\n\n\t\t\t}\n\n\t\t\t// test 3 face normals from the aabb\n\t\t\taxes = [ 1, 0, 0, 0, 1, 0, 0, 0, 1 ];\n\t\t\tif ( ! satForAxes( axes ) ) {\n\n\t\t\t\treturn false;\n\n\t\t\t}\n\n\t\t\t// finally testing the face normal of the triangle\n\t\t\t// use already existing triangle edge vectors here\n\t\t\ttriangleNormal.crossVectors( f0, f1 );\n\t\t\taxes = [ triangleNormal.x, triangleNormal.y, triangleNormal.z ];\n\t\t\treturn satForAxes( axes );\n\n\t\t};\n\n\t} )(),\n\n\tclampPoint: function ( point, target ) {\n\n\t\tif ( target === undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Box3: .clampPoint() target is now required' );\n\t\t\ttarget = new Vector3();\n\n\t\t}\n\n\t\treturn target.copy( point ).clamp( this.min, this.max );\n\n\t},\n\n\tdistanceToPoint: function () {\n\n\t\tvar v1 = new Vector3();\n\n\t\treturn function distanceToPoint( point ) {\n\n\t\t\tvar clampedPoint = v1.copy( point ).clamp( this.min, this.max );\n\t\t\treturn clampedPoint.sub( point ).length();\n\n\t\t};\n\n\t}(),\n\n\tgetBoundingSphere: function () {\n\n\t\tvar v1 = new Vector3();\n\n\t\treturn function getBoundingSphere( target ) {\n\n\t\t\tif ( target === undefined ) {\n\n\t\t\t\tconsole.error( 'THREE.Box3: .getBoundingSphere() target is now required' );\n\t\t\t\t//target = new Sphere(); // removed to avoid cyclic dependency\n\n\t\t\t}\n\n\t\t\tthis.getCenter( target.center );\n\n\t\t\ttarget.radius = this.getSize( v1 ).length() * 0.5;\n\n\t\t\treturn target;\n\n\t\t};\n\n\t}(),\n\n\tintersect: function ( box ) {\n\n\t\tthis.min.max( box.min );\n\t\tthis.max.min( box.max );\n\n\t\t// ensure that if there is no overlap, the result is fully empty, not slightly empty with non-inf/+inf values that will cause subsequence intersects to erroneously return valid values.\n\t\tif ( this.isEmpty() ) this.makeEmpty();\n\n\t\treturn this;\n\n\t},\n\n\tunion: function ( box ) {\n\n\t\tthis.min.min( box.min );\n\t\tthis.max.max( box.max );\n\n\t\treturn this;\n\n\t},\n\n\tapplyMatrix4: function () {\n\n\t\tvar points = [\n\t\t\tnew Vector3(),\n\t\t\tnew Vector3(),\n\t\t\tnew Vector3(),\n\t\t\tnew Vector3(),\n\t\t\tnew Vector3(),\n\t\t\tnew Vector3(),\n\t\t\tnew Vector3(),\n\t\t\tnew Vector3()\n\t\t];\n\n\t\treturn function applyMatrix4( matrix ) {\n\n\t\t\t// transform of empty box is an empty box.\n\t\t\tif ( this.isEmpty() ) return this;\n\n\t\t\t// NOTE: I am using a binary pattern to specify all 2^3 combinations below\n\t\t\tpoints[ 0 ].set( this.min.x, this.min.y, this.min.z ).applyMatrix4( matrix ); // 000\n\t\t\tpoints[ 1 ].set( this.min.x, this.min.y, this.max.z ).applyMatrix4( matrix ); // 001\n\t\t\tpoints[ 2 ].set( this.min.x, this.max.y, this.min.z ).applyMatrix4( matrix ); // 010\n\t\t\tpoints[ 3 ].set( this.min.x, this.max.y, this.max.z ).applyMatrix4( matrix ); // 011\n\t\t\tpoints[ 4 ].set( this.max.x, this.min.y, this.min.z ).applyMatrix4( matrix ); // 100\n\t\t\tpoints[ 5 ].set( this.max.x, this.min.y, this.max.z ).applyMatrix4( matrix ); // 101\n\t\t\tpoints[ 6 ].set( this.max.x, this.max.y, this.min.z ).applyMatrix4( matrix ); // 110\n\t\t\tpoints[ 7 ].set( this.max.x, this.max.y, this.max.z ).applyMatrix4( matrix ); // 111\n\n\t\t\tthis.setFromPoints( points );\n\n\t\t\treturn this;\n\n\t\t};\n\n\t}(),\n\n\ttranslate: function ( offset ) {\n\n\t\tthis.min.add( offset );\n\t\tthis.max.add( offset );\n\n\t\treturn this;\n\n\t},\n\n\tequals: function ( box ) {\n\n\t\treturn box.min.equals( this.min ) && box.max.equals( this.max );\n\n\t}\n\n} );\n\n/**\n * @author bhouston / http://clara.io\n * @author mrdoob / http://mrdoob.com/\n */\n\nfunction Sphere( center, radius ) {\n\n\tthis.center = ( center !== undefined ) ? center : new Vector3();\n\tthis.radius = ( radius !== undefined ) ? radius : 0;\n\n}\n\nObject.assign( Sphere.prototype, {\n\n\tset: function ( center, radius ) {\n\n\t\tthis.center.copy( center );\n\t\tthis.radius = radius;\n\n\t\treturn this;\n\n\t},\n\n\tsetFromPoints: function () {\n\n\t\tvar box = new Box3();\n\n\t\treturn function setFromPoints( points, optionalCenter ) {\n\n\t\t\tvar center = this.center;\n\n\t\t\tif ( optionalCenter !== undefined ) {\n\n\t\t\t\tcenter.copy( optionalCenter );\n\n\t\t\t} else {\n\n\t\t\t\tbox.setFromPoints( points ).getCenter( center );\n\n\t\t\t}\n\n\t\t\tvar maxRadiusSq = 0;\n\n\t\t\tfor ( var i = 0, il = points.length; i < il; i ++ ) {\n\n\t\t\t\tmaxRadiusSq = Math.max( maxRadiusSq, center.distanceToSquared( points[ i ] ) );\n\n\t\t\t}\n\n\t\t\tthis.radius = Math.sqrt( maxRadiusSq );\n\n\t\t\treturn this;\n\n\t\t};\n\n\t}(),\n\n\tclone: function () {\n\n\t\treturn new this.constructor().copy( this );\n\n\t},\n\n\tcopy: function ( sphere ) {\n\n\t\tthis.center.copy( sphere.center );\n\t\tthis.radius = sphere.radius;\n\n\t\treturn this;\n\n\t},\n\n\tempty: function () {\n\n\t\treturn ( this.radius <= 0 );\n\n\t},\n\n\tcontainsPoint: function ( point ) {\n\n\t\treturn ( point.distanceToSquared( this.center ) <= ( this.radius * this.radius ) );\n\n\t},\n\n\tdistanceToPoint: function ( point ) {\n\n\t\treturn ( point.distanceTo( this.center ) - this.radius );\n\n\t},\n\n\tintersectsSphere: function ( sphere ) {\n\n\t\tvar radiusSum = this.radius + sphere.radius;\n\n\t\treturn sphere.center.distanceToSquared( this.center ) <= ( radiusSum * radiusSum );\n\n\t},\n\n\tintersectsBox: function ( box ) {\n\n\t\treturn box.intersectsSphere( this );\n\n\t},\n\n\tintersectsPlane: function ( plane ) {\n\n\t\treturn Math.abs( plane.distanceToPoint( this.center ) ) <= this.radius;\n\n\t},\n\n\tclampPoint: function ( point, target ) {\n\n\t\tvar deltaLengthSq = this.center.distanceToSquared( point );\n\n\t\tif ( target === undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Sphere: .clampPoint() target is now required' );\n\t\t\ttarget = new Vector3();\n\n\t\t}\n\n\t\ttarget.copy( point );\n\n\t\tif ( deltaLengthSq > ( this.radius * this.radius ) ) {\n\n\t\t\ttarget.sub( this.center ).normalize();\n\t\t\ttarget.multiplyScalar( this.radius ).add( this.center );\n\n\t\t}\n\n\t\treturn target;\n\n\t},\n\n\tgetBoundingBox: function ( target ) {\n\n\t\tif ( target === undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Sphere: .getBoundingBox() target is now required' );\n\t\t\ttarget = new Box3();\n\n\t\t}\n\n\t\ttarget.set( this.center, this.center );\n\t\ttarget.expandByScalar( this.radius );\n\n\t\treturn target;\n\n\t},\n\n\tapplyMatrix4: function ( matrix ) {\n\n\t\tthis.center.applyMatrix4( matrix );\n\t\tthis.radius = this.radius * matrix.getMaxScaleOnAxis();\n\n\t\treturn this;\n\n\t},\n\n\ttranslate: function ( offset ) {\n\n\t\tthis.center.add( offset );\n\n\t\treturn this;\n\n\t},\n\n\tequals: function ( sphere ) {\n\n\t\treturn sphere.center.equals( this.center ) && ( sphere.radius === this.radius );\n\n\t}\n\n} );\n\n/**\n * @author bhouston / http://clara.io\n */\n\nfunction Plane( normal, constant ) {\n\n\t// normal is assumed to be normalized\n\n\tthis.normal = ( normal !== undefined ) ? normal : new Vector3( 1, 0, 0 );\n\tthis.constant = ( constant !== undefined ) ? constant : 0;\n\n}\n\nObject.assign( Plane.prototype, {\n\n\tset: function ( normal, constant ) {\n\n\t\tthis.normal.copy( normal );\n\t\tthis.constant = constant;\n\n\t\treturn this;\n\n\t},\n\n\tsetComponents: function ( x, y, z, w ) {\n\n\t\tthis.normal.set( x, y, z );\n\t\tthis.constant = w;\n\n\t\treturn this;\n\n\t},\n\n\tsetFromNormalAndCoplanarPoint: function ( normal, point ) {\n\n\t\tthis.normal.copy( normal );\n\t\tthis.constant = - point.dot( this.normal );\n\n\t\treturn this;\n\n\t},\n\n\tsetFromCoplanarPoints: function () {\n\n\t\tvar v1 = new Vector3();\n\t\tvar v2 = new Vector3();\n\n\t\treturn function setFromCoplanarPoints( a, b, c ) {\n\n\t\t\tvar normal = v1.subVectors( c, b ).cross( v2.subVectors( a, b ) ).normalize();\n\n\t\t\t// Q: should an error be thrown if normal is zero (e.g. degenerate plane)?\n\n\t\t\tthis.setFromNormalAndCoplanarPoint( normal, a );\n\n\t\t\treturn this;\n\n\t\t};\n\n\t}(),\n\n\tclone: function () {\n\n\t\treturn new this.constructor().copy( this );\n\n\t},\n\n\tcopy: function ( plane ) {\n\n\t\tthis.normal.copy( plane.normal );\n\t\tthis.constant = plane.constant;\n\n\t\treturn this;\n\n\t},\n\n\tnormalize: function () {\n\n\t\t// Note: will lead to a divide by zero if the plane is invalid.\n\n\t\tvar inverseNormalLength = 1.0 / this.normal.length();\n\t\tthis.normal.multiplyScalar( inverseNormalLength );\n\t\tthis.constant *= inverseNormalLength;\n\n\t\treturn this;\n\n\t},\n\n\tnegate: function () {\n\n\t\tthis.constant *= - 1;\n\t\tthis.normal.negate();\n\n\t\treturn this;\n\n\t},\n\n\tdistanceToPoint: function ( point ) {\n\n\t\treturn this.normal.dot( point ) + this.constant;\n\n\t},\n\n\tdistanceToSphere: function ( sphere ) {\n\n\t\treturn this.distanceToPoint( sphere.center ) - sphere.radius;\n\n\t},\n\n\tprojectPoint: function ( point, target ) {\n\n\t\tif ( target === undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Plane: .projectPoint() target is now required' );\n\t\t\ttarget = new Vector3();\n\n\t\t}\n\n\t\treturn target.copy( this.normal ).multiplyScalar( - this.distanceToPoint( point ) ).add( point );\n\n\t},\n\n\tintersectLine: function () {\n\n\t\tvar v1 = new Vector3();\n\n\t\treturn function intersectLine( line, target ) {\n\n\t\t\tif ( target === undefined ) {\n\n\t\t\t\tconsole.warn( 'THREE.Plane: .intersectLine() target is now required' );\n\t\t\t\ttarget = new Vector3();\n\n\t\t\t}\n\n\t\t\tvar direction = line.delta( v1 );\n\n\t\t\tvar denominator = this.normal.dot( direction );\n\n\t\t\tif ( denominator === 0 ) {\n\n\t\t\t\t// line is coplanar, return origin\n\t\t\t\tif ( this.distanceToPoint( line.start ) === 0 ) {\n\n\t\t\t\t\treturn target.copy( line.start );\n\n\t\t\t\t}\n\n\t\t\t\t// Unsure if this is the correct method to handle this case.\n\t\t\t\treturn undefined;\n\n\t\t\t}\n\n\t\t\tvar t = - ( line.start.dot( this.normal ) + this.constant ) / denominator;\n\n\t\t\tif ( t < 0 || t > 1 ) {\n\n\t\t\t\treturn undefined;\n\n\t\t\t}\n\n\t\t\treturn target.copy( direction ).multiplyScalar( t ).add( line.start );\n\n\t\t};\n\n\t}(),\n\n\tintersectsLine: function ( line ) {\n\n\t\t// Note: this tests if a line intersects the plane, not whether it (or its end-points) are coplanar with it.\n\n\t\tvar startSign = this.distanceToPoint( line.start );\n\t\tvar endSign = this.distanceToPoint( line.end );\n\n\t\treturn ( startSign < 0 && endSign > 0 ) || ( endSign < 0 && startSign > 0 );\n\n\t},\n\n\tintersectsBox: function ( box ) {\n\n\t\treturn box.intersectsPlane( this );\n\n\t},\n\n\tintersectsSphere: function ( sphere ) {\n\n\t\treturn sphere.intersectsPlane( this );\n\n\t},\n\n\tcoplanarPoint: function ( target ) {\n\n\t\tif ( target === undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Plane: .coplanarPoint() target is now required' );\n\t\t\ttarget = new Vector3();\n\n\t\t}\n\n\t\treturn target.copy( this.normal ).multiplyScalar( - this.constant );\n\n\t},\n\n\tapplyMatrix4: function () {\n\n\t\tvar v1 = new Vector3();\n\t\tvar m1 = new Matrix3();\n\n\t\treturn function applyMatrix4( matrix, optionalNormalMatrix ) {\n\n\t\t\tvar normalMatrix = optionalNormalMatrix || m1.getNormalMatrix( matrix );\n\n\t\t\tvar referencePoint = this.coplanarPoint( v1 ).applyMatrix4( matrix );\n\n\t\t\tvar normal = this.normal.applyMatrix3( normalMatrix ).normalize();\n\n\t\t\tthis.constant = - referencePoint.dot( normal );\n\n\t\t\treturn this;\n\n\t\t};\n\n\t}(),\n\n\ttranslate: function ( offset ) {\n\n\t\tthis.constant -= offset.dot( this.normal );\n\n\t\treturn this;\n\n\t},\n\n\tequals: function ( plane ) {\n\n\t\treturn plane.normal.equals( this.normal ) && ( plane.constant === this.constant );\n\n\t}\n\n} );\n\n/**\n * @author mrdoob / http://mrdoob.com/\n * @author alteredq / http://alteredqualia.com/\n * @author bhouston / http://clara.io\n */\n\nfunction Frustum( p0, p1, p2, p3, p4, p5 ) {\n\n\tthis.planes = [\n\n\t\t( p0 !== undefined ) ? p0 : new Plane(),\n\t\t( p1 !== undefined ) ? p1 : new Plane(),\n\t\t( p2 !== undefined ) ? p2 : new Plane(),\n\t\t( p3 !== undefined ) ? p3 : new Plane(),\n\t\t( p4 !== undefined ) ? p4 : new Plane(),\n\t\t( p5 !== undefined ) ? p5 : new Plane()\n\n\t];\n\n}\n\nObject.assign( Frustum.prototype, {\n\n\tset: function ( p0, p1, p2, p3, p4, p5 ) {\n\n\t\tvar planes = this.planes;\n\n\t\tplanes[ 0 ].copy( p0 );\n\t\tplanes[ 1 ].copy( p1 );\n\t\tplanes[ 2 ].copy( p2 );\n\t\tplanes[ 3 ].copy( p3 );\n\t\tplanes[ 4 ].copy( p4 );\n\t\tplanes[ 5 ].copy( p5 );\n\n\t\treturn this;\n\n\t},\n\n\tclone: function () {\n\n\t\treturn new this.constructor().copy( this );\n\n\t},\n\n\tcopy: function ( frustum ) {\n\n\t\tvar planes = this.planes;\n\n\t\tfor ( var i = 0; i < 6; i ++ ) {\n\n\t\t\tplanes[ i ].copy( frustum.planes[ i ] );\n\n\t\t}\n\n\t\treturn this;\n\n\t},\n\n\tsetFromMatrix: function ( m ) {\n\n\t\tvar planes = this.planes;\n\t\tvar me = m.elements;\n\t\tvar me0 = me[ 0 ], me1 = me[ 1 ], me2 = me[ 2 ], me3 = me[ 3 ];\n\t\tvar me4 = me[ 4 ], me5 = me[ 5 ], me6 = me[ 6 ], me7 = me[ 7 ];\n\t\tvar me8 = me[ 8 ], me9 = me[ 9 ], me10 = me[ 10 ], me11 = me[ 11 ];\n\t\tvar me12 = me[ 12 ], me13 = me[ 13 ], me14 = me[ 14 ], me15 = me[ 15 ];\n\n\t\tplanes[ 0 ].setComponents( me3 - me0, me7 - me4, me11 - me8, me15 - me12 ).normalize();\n\t\tplanes[ 1 ].setComponents( me3 + me0, me7 + me4, me11 + me8, me15 + me12 ).normalize();\n\t\tplanes[ 2 ].setComponents( me3 + me1, me7 + me5, me11 + me9, me15 + me13 ).normalize();\n\t\tplanes[ 3 ].setComponents( me3 - me1, me7 - me5, me11 - me9, me15 - me13 ).normalize();\n\t\tplanes[ 4 ].setComponents( me3 - me2, me7 - me6, me11 - me10, me15 - me14 ).normalize();\n\t\tplanes[ 5 ].setComponents( me3 + me2, me7 + me6, me11 + me10, me15 + me14 ).normalize();\n\n\t\treturn this;\n\n\t},\n\n\tintersectsObject: function () {\n\n\t\tvar sphere = new Sphere();\n\n\t\treturn function intersectsObject( object ) {\n\n\t\t\tvar geometry = object.geometry;\n\n\t\t\tif ( geometry.boundingSphere === null )\n\t\t\t\tgeometry.computeBoundingSphere();\n\n\t\t\tsphere.copy( geometry.boundingSphere )\n\t\t\t\t.applyMatrix4( object.matrixWorld );\n\n\t\t\treturn this.intersectsSphere( sphere );\n\n\t\t};\n\n\t}(),\n\n\tintersectsSprite: function () {\n\n\t\tvar sphere = new Sphere();\n\n\t\treturn function intersectsSprite( sprite ) {\n\n\t\t\tsphere.center.set( 0, 0, 0 );\n\t\t\tsphere.radius = 0.7071067811865476;\n\t\t\tsphere.applyMatrix4( sprite.matrixWorld );\n\n\t\t\treturn this.intersectsSphere( sphere );\n\n\t\t};\n\n\t}(),\n\n\tintersectsSphere: function ( sphere ) {\n\n\t\tvar planes = this.planes;\n\t\tvar center = sphere.center;\n\t\tvar negRadius = - sphere.radius;\n\n\t\tfor ( var i = 0; i < 6; i ++ ) {\n\n\t\t\tvar distance = planes[ i ].distanceToPoint( center );\n\n\t\t\tif ( distance < negRadius ) {\n\n\t\t\t\treturn false;\n\n\t\t\t}\n\n\t\t}\n\n\t\treturn true;\n\n\t},\n\n\tintersectsBox: function () {\n\n\t\tvar p = new Vector3();\n\n\t\treturn function intersectsBox( box ) {\n\n\t\t\tvar planes = this.planes;\n\n\t\t\tfor ( var i = 0; i < 6; i ++ ) {\n\n\t\t\t\tvar plane = planes[ i ];\n\n\t\t\t\t// corner at max distance\n\n\t\t\t\tp.x = plane.normal.x > 0 ? box.max.x : box.min.x;\n\t\t\t\tp.y = plane.normal.y > 0 ? box.max.y : box.min.y;\n\t\t\t\tp.z = plane.normal.z > 0 ? box.max.z : box.min.z;\n\n\t\t\t\tif ( plane.distanceToPoint( p ) < 0 ) {\n\n\t\t\t\t\treturn false;\n\n\t\t\t\t}\n\n\t\t\t}\n\n\t\t\treturn true;\n\n\t\t};\n\n\t}(),\n\n\tcontainsPoint: function ( point ) {\n\n\t\tvar planes = this.planes;\n\n\t\tfor ( var i = 0; i < 6; i ++ ) {\n\n\t\t\tif ( planes[ i ].distanceToPoint( point ) < 0 ) {\n\n\t\t\t\treturn false;\n\n\t\t\t}\n\n\t\t}\n\n\t\treturn true;\n\n\t}\n\n} );\n\n/**\n * @author mrdoob / http://mrdoob.com/\n * @author supereggbert / http://www.paulbrunt.co.uk/\n * @author philogb / http://blog.thejit.org/\n * @author jordi_ros / http://plattsoft.com\n * @author D1plo1d / http://github.com/D1plo1d\n * @author alteredq / http://alteredqualia.com/\n * @author mikael emtinger / http://gomo.se/\n * @author timknip / http://www.floorplanner.com/\n * @author bhouston / http://clara.io\n * @author WestLangley / http://github.com/WestLangley\n */\n\nfunction Matrix4() {\n\n\tthis.elements = [\n\n\t\t1, 0, 0, 0,\n\t\t0, 1, 0, 0,\n\t\t0, 0, 1, 0,\n\t\t0, 0, 0, 1\n\n\t];\n\n\tif ( arguments.length > 0 ) {\n\n\t\tconsole.error( 'THREE.Matrix4: the constructor no longer reads arguments. use .set() instead.' );\n\n\t}\n\n}\n\nObject.assign( Matrix4.prototype, {\n\n\tisMatrix4: true,\n\n\tset: function ( n11, n12, n13, n14, n21, n22, n23, n24, n31, n32, n33, n34, n41, n42, n43, n44 ) {\n\n\t\tvar te = this.elements;\n\n\t\tte[ 0 ] = n11; te[ 4 ] = n12; te[ 8 ] = n13; te[ 12 ] = n14;\n\t\tte[ 1 ] = n21; te[ 5 ] = n22; te[ 9 ] = n23; te[ 13 ] = n24;\n\t\tte[ 2 ] = n31; te[ 6 ] = n32; te[ 10 ] = n33; te[ 14 ] = n34;\n\t\tte[ 3 ] = n41; te[ 7 ] = n42; te[ 11 ] = n43; te[ 15 ] = n44;\n\n\t\treturn this;\n\n\t},\n\n\tidentity: function () {\n\n\t\tthis.set(\n\n\t\t\t1, 0, 0, 0,\n\t\t\t0, 1, 0, 0,\n\t\t\t0, 0, 1, 0,\n\t\t\t0, 0, 0, 1\n\n\t\t);\n\n\t\treturn this;\n\n\t},\n\n\tclone: function () {\n\n\t\treturn new Matrix4().fromArray( this.elements );\n\n\t},\n\n\tcopy: function ( m ) {\n\n\t\tvar te = this.elements;\n\t\tvar me = m.elements;\n\n\t\tte[ 0 ] = me[ 0 ]; te[ 1 ] = me[ 1 ]; te[ 2 ] = me[ 2 ]; te[ 3 ] = me[ 3 ];\n\t\tte[ 4 ] = me[ 4 ]; te[ 5 ] = me[ 5 ]; te[ 6 ] = me[ 6 ]; te[ 7 ] = me[ 7 ];\n\t\tte[ 8 ] = me[ 8 ]; te[ 9 ] = me[ 9 ]; te[ 10 ] = me[ 10 ]; te[ 11 ] = me[ 11 ];\n\t\tte[ 12 ] = me[ 12 ]; te[ 13 ] = me[ 13 ]; te[ 14 ] = me[ 14 ]; te[ 15 ] = me[ 15 ];\n\n\t\treturn this;\n\n\t},\n\n\tcopyPosition: function ( m ) {\n\n\t\tvar te = this.elements, me = m.elements;\n\n\t\tte[ 12 ] = me[ 12 ];\n\t\tte[ 13 ] = me[ 13 ];\n\t\tte[ 14 ] = me[ 14 ];\n\n\t\treturn this;\n\n\t},\n\n\textractBasis: function ( xAxis, yAxis, zAxis ) {\n\n\t\txAxis.setFromMatrixColumn( this, 0 );\n\t\tyAxis.setFromMatrixColumn( this, 1 );\n\t\tzAxis.setFromMatrixColumn( this, 2 );\n\n\t\treturn this;\n\n\t},\n\n\tmakeBasis: function ( xAxis, yAxis, zAxis ) {\n\n\t\tthis.set(\n\t\t\txAxis.x, yAxis.x, zAxis.x, 0,\n\t\t\txAxis.y, yAxis.y, zAxis.y, 0,\n\t\t\txAxis.z, yAxis.z, zAxis.z, 0,\n\t\t\t0, 0, 0, 1\n\t\t);\n\n\t\treturn this;\n\n\t},\n\n\textractRotation: function () {\n\n\t\tvar v1 = new Vector3();\n\n\t\treturn function extractRotation( m ) {\n\n\t\t\t// this method does not support reflection matrices\n\n\t\t\tvar te = this.elements;\n\t\t\tvar me = m.elements;\n\n\t\t\tvar scaleX = 1 / v1.setFromMatrixColumn( m, 0 ).length();\n\t\t\tvar scaleY = 1 / v1.setFromMatrixColumn( m, 1 ).length();\n\t\t\tvar scaleZ = 1 / v1.setFromMatrixColumn( m, 2 ).length();\n\n\t\t\tte[ 0 ] = me[ 0 ] * scaleX;\n\t\t\tte[ 1 ] = me[ 1 ] * scaleX;\n\t\t\tte[ 2 ] = me[ 2 ] * scaleX;\n\t\t\tte[ 3 ] = 0;\n\n\t\t\tte[ 4 ] = me[ 4 ] * scaleY;\n\t\t\tte[ 5 ] = me[ 5 ] * scaleY;\n\t\t\tte[ 6 ] = me[ 6 ] * scaleY;\n\t\t\tte[ 7 ] = 0;\n\n\t\t\tte[ 8 ] = me[ 8 ] * scaleZ;\n\t\t\tte[ 9 ] = me[ 9 ] * scaleZ;\n\t\t\tte[ 10 ] = me[ 10 ] * scaleZ;\n\t\t\tte[ 11 ] = 0;\n\n\t\t\tte[ 12 ] = 0;\n\t\t\tte[ 13 ] = 0;\n\t\t\tte[ 14 ] = 0;\n\t\t\tte[ 15 ] = 1;\n\n\t\t\treturn this;\n\n\t\t};\n\n\t}(),\n\n\tmakeRotationFromEuler: function ( euler ) {\n\n\t\tif ( ! ( euler && euler.isEuler ) ) {\n\n\t\t\tconsole.error( 'THREE.Matrix4: .makeRotationFromEuler() now expects a Euler rotation rather than a Vector3 and order.' );\n\n\t\t}\n\n\t\tvar te = this.elements;\n\n\t\tvar x = euler.x, y = euler.y, z = euler.z;\n\t\tvar a = Math.cos( x ), b = Math.sin( x );\n\t\tvar c = Math.cos( y ), d = Math.sin( y );\n\t\tvar e = Math.cos( z ), f = Math.sin( z );\n\n\t\tif ( euler.order === 'XYZ' ) {\n\n\t\t\tvar ae = a * e, af = a * f, be = b * e, bf = b * f;\n\n\t\t\tte[ 0 ] = c * e;\n\t\t\tte[ 4 ] = - c * f;\n\t\t\tte[ 8 ] = d;\n\n\t\t\tte[ 1 ] = af + be * d;\n\t\t\tte[ 5 ] = ae - bf * d;\n\t\t\tte[ 9 ] = - b * c;\n\n\t\t\tte[ 2 ] = bf - ae * d;\n\t\t\tte[ 6 ] = be + af * d;\n\t\t\tte[ 10 ] = a * c;\n\n\t\t} else if ( euler.order === 'YXZ' ) {\n\n\t\t\tvar ce = c * e, cf = c * f, de = d * e, df = d * f;\n\n\t\t\tte[ 0 ] = ce + df * b;\n\t\t\tte[ 4 ] = de * b - cf;\n\t\t\tte[ 8 ] = a * d;\n\n\t\t\tte[ 1 ] = a * f;\n\t\t\tte[ 5 ] = a * e;\n\t\t\tte[ 9 ] = - b;\n\n\t\t\tte[ 2 ] = cf * b - de;\n\t\t\tte[ 6 ] = df + ce * b;\n\t\t\tte[ 10 ] = a * c;\n\n\t\t} else if ( euler.order === 'ZXY' ) {\n\n\t\t\tvar ce = c * e, cf = c * f, de = d * e, df = d * f;\n\n\t\t\tte[ 0 ] = ce - df * b;\n\t\t\tte[ 4 ] = - a * f;\n\t\t\tte[ 8 ] = de + cf * b;\n\n\t\t\tte[ 1 ] = cf + de * b;\n\t\t\tte[ 5 ] = a * e;\n\t\t\tte[ 9 ] = df - ce * b;\n\n\t\t\tte[ 2 ] = - a * d;\n\t\t\tte[ 6 ] = b;\n\t\t\tte[ 10 ] = a * c;\n\n\t\t} else if ( euler.order === 'ZYX' ) {\n\n\t\t\tvar ae = a * e, af = a * f, be = b * e, bf = b * f;\n\n\t\t\tte[ 0 ] = c * e;\n\t\t\tte[ 4 ] = be * d - af;\n\t\t\tte[ 8 ] = ae * d + bf;\n\n\t\t\tte[ 1 ] = c * f;\n\t\t\tte[ 5 ] = bf * d + ae;\n\t\t\tte[ 9 ] = af * d - be;\n\n\t\t\tte[ 2 ] = - d;\n\t\t\tte[ 6 ] = b * c;\n\t\t\tte[ 10 ] = a * c;\n\n\t\t} else if ( euler.order === 'YZX' ) {\n\n\t\t\tvar ac = a * c, ad = a * d, bc = b * c, bd = b * d;\n\n\t\t\tte[ 0 ] = c * e;\n\t\t\tte[ 4 ] = bd - ac * f;\n\t\t\tte[ 8 ] = bc * f + ad;\n\n\t\t\tte[ 1 ] = f;\n\t\t\tte[ 5 ] = a * e;\n\t\t\tte[ 9 ] = - b * e;\n\n\t\t\tte[ 2 ] = - d * e;\n\t\t\tte[ 6 ] = ad * f + bc;\n\t\t\tte[ 10 ] = ac - bd * f;\n\n\t\t} else if ( euler.order === 'XZY' ) {\n\n\t\t\tvar ac = a * c, ad = a * d, bc = b * c, bd = b * d;\n\n\t\t\tte[ 0 ] = c * e;\n\t\t\tte[ 4 ] = - f;\n\t\t\tte[ 8 ] = d * e;\n\n\t\t\tte[ 1 ] = ac * f + bd;\n\t\t\tte[ 5 ] = a * e;\n\t\t\tte[ 9 ] = ad * f - bc;\n\n\t\t\tte[ 2 ] = bc * f - ad;\n\t\t\tte[ 6 ] = b * e;\n\t\t\tte[ 10 ] = bd * f + ac;\n\n\t\t}\n\n\t\t// bottom row\n\t\tte[ 3 ] = 0;\n\t\tte[ 7 ] = 0;\n\t\tte[ 11 ] = 0;\n\n\t\t// last column\n\t\tte[ 12 ] = 0;\n\t\tte[ 13 ] = 0;\n\t\tte[ 14 ] = 0;\n\t\tte[ 15 ] = 1;\n\n\t\treturn this;\n\n\t},\n\n\tmakeRotationFromQuaternion: function () {\n\n\t\tvar zero = new Vector3( 0, 0, 0 );\n\t\tvar one = new Vector3( 1, 1, 1 );\n\n\t\treturn function makeRotationFromQuaternion( q ) {\n\n\t\t\treturn this.compose( zero, q, one );\n\n\t\t};\n\n\t}(),\n\n\tlookAt: function () {\n\n\t\tvar x = new Vector3();\n\t\tvar y = new Vector3();\n\t\tvar z = new Vector3();\n\n\t\treturn function lookAt( eye, target, up ) {\n\n\t\t\tvar te = this.elements;\n\n\t\t\tz.subVectors( eye, target );\n\n\t\t\tif ( z.lengthSq() === 0 ) {\n\n\t\t\t\t// eye and target are in the same position\n\n\t\t\t\tz.z = 1;\n\n\t\t\t}\n\n\t\t\tz.normalize();\n\t\t\tx.crossVectors( up, z );\n\n\t\t\tif ( x.lengthSq() === 0 ) {\n\n\t\t\t\t// up and z are parallel\n\n\t\t\t\tif ( Math.abs( up.z ) === 1 ) {\n\n\t\t\t\t\tz.x += 0.0001;\n\n\t\t\t\t} else {\n\n\t\t\t\t\tz.z += 0.0001;\n\n\t\t\t\t}\n\n\t\t\t\tz.normalize();\n\t\t\t\tx.crossVectors( up, z );\n\n\t\t\t}\n\n\t\t\tx.normalize();\n\t\t\ty.crossVectors( z, x );\n\n\t\t\tte[ 0 ] = x.x; te[ 4 ] = y.x; te[ 8 ] = z.x;\n\t\t\tte[ 1 ] = x.y; te[ 5 ] = y.y; te[ 9 ] = z.y;\n\t\t\tte[ 2 ] = x.z; te[ 6 ] = y.z; te[ 10 ] = z.z;\n\n\t\t\treturn this;\n\n\t\t};\n\n\t}(),\n\n\tmultiply: function ( m, n ) {\n\n\t\tif ( n !== undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead.' );\n\t\t\treturn this.multiplyMatrices( m, n );\n\n\t\t}\n\n\t\treturn this.multiplyMatrices( this, m );\n\n\t},\n\n\tpremultiply: function ( m ) {\n\n\t\treturn this.multiplyMatrices( m, this );\n\n\t},\n\n\tmultiplyMatrices: function ( a, b ) {\n\n\t\tvar ae = a.elements;\n\t\tvar be = b.elements;\n\t\tvar te = this.elements;\n\n\t\tvar a11 = ae[ 0 ], a12 = ae[ 4 ], a13 = ae[ 8 ], a14 = ae[ 12 ];\n\t\tvar a21 = ae[ 1 ], a22 = ae[ 5 ], a23 = ae[ 9 ], a24 = ae[ 13 ];\n\t\tvar a31 = ae[ 2 ], a32 = ae[ 6 ], a33 = ae[ 10 ], a34 = ae[ 14 ];\n\t\tvar a41 = ae[ 3 ], a42 = ae[ 7 ], a43 = ae[ 11 ], a44 = ae[ 15 ];\n\n\t\tvar b11 = be[ 0 ], b12 = be[ 4 ], b13 = be[ 8 ], b14 = be[ 12 ];\n\t\tvar b21 = be[ 1 ], b22 = be[ 5 ], b23 = be[ 9 ], b24 = be[ 13 ];\n\t\tvar b31 = be[ 2 ], b32 = be[ 6 ], b33 = be[ 10 ], b34 = be[ 14 ];\n\t\tvar b41 = be[ 3 ], b42 = be[ 7 ], b43 = be[ 11 ], b44 = be[ 15 ];\n\n\t\tte[ 0 ] = a11 * b11 + a12 * b21 + a13 * b31 + a14 * b41;\n\t\tte[ 4 ] = a11 * b12 + a12 * b22 + a13 * b32 + a14 * b42;\n\t\tte[ 8 ] = a11 * b13 + a12 * b23 + a13 * b33 + a14 * b43;\n\t\tte[ 12 ] = a11 * b14 + a12 * b24 + a13 * b34 + a14 * b44;\n\n\t\tte[ 1 ] = a21 * b11 + a22 * b21 + a23 * b31 + a24 * b41;\n\t\tte[ 5 ] = a21 * b12 + a22 * b22 + a23 * b32 + a24 * b42;\n\t\tte[ 9 ] = a21 * b13 + a22 * b23 + a23 * b33 + a24 * b43;\n\t\tte[ 13 ] = a21 * b14 + a22 * b24 + a23 * b34 + a24 * b44;\n\n\t\tte[ 2 ] = a31 * b11 + a32 * b21 + a33 * b31 + a34 * b41;\n\t\tte[ 6 ] = a31 * b12 + a32 * b22 + a33 * b32 + a34 * b42;\n\t\tte[ 10 ] = a31 * b13 + a32 * b23 + a33 * b33 + a34 * b43;\n\t\tte[ 14 ] = a31 * b14 + a32 * b24 + a33 * b34 + a34 * b44;\n\n\t\tte[ 3 ] = a41 * b11 + a42 * b21 + a43 * b31 + a44 * b41;\n\t\tte[ 7 ] = a41 * b12 + a42 * b22 + a43 * b32 + a44 * b42;\n\t\tte[ 11 ] = a41 * b13 + a42 * b23 + a43 * b33 + a44 * b43;\n\t\tte[ 15 ] = a41 * b14 + a42 * b24 + a43 * b34 + a44 * b44;\n\n\t\treturn this;\n\n\t},\n\n\tmultiplyScalar: function ( s ) {\n\n\t\tvar te = this.elements;\n\n\t\tte[ 0 ] *= s; te[ 4 ] *= s; te[ 8 ] *= s; te[ 12 ] *= s;\n\t\tte[ 1 ] *= s; te[ 5 ] *= s; te[ 9 ] *= s; te[ 13 ] *= s;\n\t\tte[ 2 ] *= s; te[ 6 ] *= s; te[ 10 ] *= s; te[ 14 ] *= s;\n\t\tte[ 3 ] *= s; te[ 7 ] *= s; te[ 11 ] *= s; te[ 15 ] *= s;\n\n\t\treturn this;\n\n\t},\n\n\tapplyToBufferAttribute: function () {\n\n\t\tvar v1 = new Vector3();\n\n\t\treturn function applyToBufferAttribute( attribute ) {\n\n\t\t\tfor ( var i = 0, l = attribute.count; i < l; i ++ ) {\n\n\t\t\t\tv1.x = attribute.getX( i );\n\t\t\t\tv1.y = attribute.getY( i );\n\t\t\t\tv1.z = attribute.getZ( i );\n\n\t\t\t\tv1.applyMatrix4( this );\n\n\t\t\t\tattribute.setXYZ( i, v1.x, v1.y, v1.z );\n\n\t\t\t}\n\n\t\t\treturn attribute;\n\n\t\t};\n\n\t}(),\n\n\tdeterminant: function () {\n\n\t\tvar te = this.elements;\n\n\t\tvar n11 = te[ 0 ], n12 = te[ 4 ], n13 = te[ 8 ], n14 = te[ 12 ];\n\t\tvar n21 = te[ 1 ], n22 = te[ 5 ], n23 = te[ 9 ], n24 = te[ 13 ];\n\t\tvar n31 = te[ 2 ], n32 = te[ 6 ], n33 = te[ 10 ], n34 = te[ 14 ];\n\t\tvar n41 = te[ 3 ], n42 = te[ 7 ], n43 = te[ 11 ], n44 = te[ 15 ];\n\n\t\t//TODO: make this more efficient\n\t\t//( based on http://www.euclideanspace.com/maths/algebra/matrix/functions/inverse/fourD/index.htm )\n\n\t\treturn (\n\t\t\tn41 * (\n\t\t\t\t+ n14 * n23 * n32\n\t\t\t\t - n13 * n24 * n32\n\t\t\t\t - n14 * n22 * n33\n\t\t\t\t + n12 * n24 * n33\n\t\t\t\t + n13 * n22 * n34\n\t\t\t\t - n12 * n23 * n34\n\t\t\t) +\n\t\t\tn42 * (\n\t\t\t\t+ n11 * n23 * n34\n\t\t\t\t - n11 * n24 * n33\n\t\t\t\t + n14 * n21 * n33\n\t\t\t\t - n13 * n21 * n34\n\t\t\t\t + n13 * n24 * n31\n\t\t\t\t - n14 * n23 * n31\n\t\t\t) +\n\t\t\tn43 * (\n\t\t\t\t+ n11 * n24 * n32\n\t\t\t\t - n11 * n22 * n34\n\t\t\t\t - n14 * n21 * n32\n\t\t\t\t + n12 * n21 * n34\n\t\t\t\t + n14 * n22 * n31\n\t\t\t\t - n12 * n24 * n31\n\t\t\t) +\n\t\t\tn44 * (\n\t\t\t\t- n13 * n22 * n31\n\t\t\t\t - n11 * n23 * n32\n\t\t\t\t + n11 * n22 * n33\n\t\t\t\t + n13 * n21 * n32\n\t\t\t\t - n12 * n21 * n33\n\t\t\t\t + n12 * n23 * n31\n\t\t\t)\n\n\t\t);\n\n\t},\n\n\ttranspose: function () {\n\n\t\tvar te = this.elements;\n\t\tvar tmp;\n\n\t\ttmp = te[ 1 ]; te[ 1 ] = te[ 4 ]; te[ 4 ] = tmp;\n\t\ttmp = te[ 2 ]; te[ 2 ] = te[ 8 ]; te[ 8 ] = tmp;\n\t\ttmp = te[ 6 ]; te[ 6 ] = te[ 9 ]; te[ 9 ] = tmp;\n\n\t\ttmp = te[ 3 ]; te[ 3 ] = te[ 12 ]; te[ 12 ] = tmp;\n\t\ttmp = te[ 7 ]; te[ 7 ] = te[ 13 ]; te[ 13 ] = tmp;\n\t\ttmp = te[ 11 ]; te[ 11 ] = te[ 14 ]; te[ 14 ] = tmp;\n\n\t\treturn this;\n\n\t},\n\n\tsetPosition: function ( x, y, z ) {\n\n\t\tvar te = this.elements;\n\n\t\tif ( x.isVector3 ) {\n\n\t\t\tte[ 12 ] = x.x;\n\t\t\tte[ 13 ] = x.y;\n\t\t\tte[ 14 ] = x.z;\n\n\t\t} else {\n\n\t\t\tte[ 12 ] = x;\n\t\t\tte[ 13 ] = y;\n\t\t\tte[ 14 ] = z;\n\n\t\t}\n\n\t\treturn this;\n\n\t},\n\n\tgetInverse: function ( m, throwOnDegenerate ) {\n\n\t\t// based on http://www.euclideanspace.com/maths/algebra/matrix/functions/inverse/fourD/index.htm\n\t\tvar te = this.elements,\n\t\t\tme = m.elements,\n\n\t\t\tn11 = me[ 0 ], n21 = me[ 1 ], n31 = me[ 2 ], n41 = me[ 3 ],\n\t\t\tn12 = me[ 4 ], n22 = me[ 5 ], n32 = me[ 6 ], n42 = me[ 7 ],\n\t\t\tn13 = me[ 8 ], n23 = me[ 9 ], n33 = me[ 10 ], n43 = me[ 11 ],\n\t\t\tn14 = me[ 12 ], n24 = me[ 13 ], n34 = me[ 14 ], n44 = me[ 15 ],\n\n\t\t\tt11 = n23 * n34 * n42 - n24 * n33 * n42 + n24 * n32 * n43 - n22 * n34 * n43 - n23 * n32 * n44 + n22 * n33 * n44,\n\t\t\tt12 = n14 * n33 * n42 - n13 * n34 * n42 - n14 * n32 * n43 + n12 * n34 * n43 + n13 * n32 * n44 - n12 * n33 * n44,\n\t\t\tt13 = n13 * n24 * n42 - n14 * n23 * n42 + n14 * n22 * n43 - n12 * n24 * n43 - n13 * n22 * n44 + n12 * n23 * n44,\n\t\t\tt14 = n14 * n23 * n32 - n13 * n24 * n32 - n14 * n22 * n33 + n12 * n24 * n33 + n13 * n22 * n34 - n12 * n23 * n34;\n\n\t\tvar det = n11 * t11 + n21 * t12 + n31 * t13 + n41 * t14;\n\n\t\tif ( det === 0 ) {\n\n\t\t\tvar msg = \"THREE.Matrix4: .getInverse() can't invert matrix, determinant is 0\";\n\n\t\t\tif ( throwOnDegenerate === true ) {\n\n\t\t\t\tthrow new Error( msg );\n\n\t\t\t} else {\n\n\t\t\t\tconsole.warn( msg );\n\n\t\t\t}\n\n\t\t\treturn this.identity();\n\n\t\t}\n\n\t\tvar detInv = 1 / det;\n\n\t\tte[ 0 ] = t11 * detInv;\n\t\tte[ 1 ] = ( n24 * n33 * n41 - n23 * n34 * n41 - n24 * n31 * n43 + n21 * n34 * n43 + n23 * n31 * n44 - n21 * n33 * n44 ) * detInv;\n\t\tte[ 2 ] = ( n22 * n34 * n41 - n24 * n32 * n41 + n24 * n31 * n42 - n21 * n34 * n42 - n22 * n31 * n44 + n21 * n32 * n44 ) * detInv;\n\t\tte[ 3 ] = ( n23 * n32 * n41 - n22 * n33 * n41 - n23 * n31 * n42 + n21 * n33 * n42 + n22 * n31 * n43 - n21 * n32 * n43 ) * detInv;\n\n\t\tte[ 4 ] = t12 * detInv;\n\t\tte[ 5 ] = ( n13 * n34 * n41 - n14 * n33 * n41 + n14 * n31 * n43 - n11 * n34 * n43 - n13 * n31 * n44 + n11 * n33 * n44 ) * detInv;\n\t\tte[ 6 ] = ( n14 * n32 * n41 - n12 * n34 * n41 - n14 * n31 * n42 + n11 * n34 * n42 + n12 * n31 * n44 - n11 * n32 * n44 ) * detInv;\n\t\tte[ 7 ] = ( n12 * n33 * n41 - n13 * n32 * n41 + n13 * n31 * n42 - n11 * n33 * n42 - n12 * n31 * n43 + n11 * n32 * n43 ) * detInv;\n\n\t\tte[ 8 ] = t13 * detInv;\n\t\tte[ 9 ] = ( n14 * n23 * n41 - n13 * n24 * n41 - n14 * n21 * n43 + n11 * n24 * n43 + n13 * n21 * n44 - n11 * n23 * n44 ) * detInv;\n\t\tte[ 10 ] = ( n12 * n24 * n41 - n14 * n22 * n41 + n14 * n21 * n42 - n11 * n24 * n42 - n12 * n21 * n44 + n11 * n22 * n44 ) * detInv;\n\t\tte[ 11 ] = ( n13 * n22 * n41 - n12 * n23 * n41 - n13 * n21 * n42 + n11 * n23 * n42 + n12 * n21 * n43 - n11 * n22 * n43 ) * detInv;\n\n\t\tte[ 12 ] = t14 * detInv;\n\t\tte[ 13 ] = ( n13 * n24 * n31 - n14 * n23 * n31 + n14 * n21 * n33 - n11 * n24 * n33 - n13 * n21 * n34 + n11 * n23 * n34 ) * detInv;\n\t\tte[ 14 ] = ( n14 * n22 * n31 - n12 * n24 * n31 - n14 * n21 * n32 + n11 * n24 * n32 + n12 * n21 * n34 - n11 * n22 * n34 ) * detInv;\n\t\tte[ 15 ] = ( n12 * n23 * n31 - n13 * n22 * n31 + n13 * n21 * n32 - n11 * n23 * n32 - n12 * n21 * n33 + n11 * n22 * n33 ) * detInv;\n\n\t\treturn this;\n\n\t},\n\n\tscale: function ( v ) {\n\n\t\tvar te = this.elements;\n\t\tvar x = v.x, y = v.y, z = v.z;\n\n\t\tte[ 0 ] *= x; te[ 4 ] *= y; te[ 8 ] *= z;\n\t\tte[ 1 ] *= x; te[ 5 ] *= y; te[ 9 ] *= z;\n\t\tte[ 2 ] *= x; te[ 6 ] *= y; te[ 10 ] *= z;\n\t\tte[ 3 ] *= x; te[ 7 ] *= y; te[ 11 ] *= z;\n\n\t\treturn this;\n\n\t},\n\n\tgetMaxScaleOnAxis: function () {\n\n\t\tvar te = this.elements;\n\n\t\tvar scaleXSq = te[ 0 ] * te[ 0 ] + te[ 1 ] * te[ 1 ] + te[ 2 ] * te[ 2 ];\n\t\tvar scaleYSq = te[ 4 ] * te[ 4 ] + te[ 5 ] * te[ 5 ] + te[ 6 ] * te[ 6 ];\n\t\tvar scaleZSq = te[ 8 ] * te[ 8 ] + te[ 9 ] * te[ 9 ] + te[ 10 ] * te[ 10 ];\n\n\t\treturn Math.sqrt( Math.max( scaleXSq, scaleYSq, scaleZSq ) );\n\n\t},\n\n\tmakeTranslation: function ( x, y, z ) {\n\n\t\tthis.set(\n\n\t\t\t1, 0, 0, x,\n\t\t\t0, 1, 0, y,\n\t\t\t0, 0, 1, z,\n\t\t\t0, 0, 0, 1\n\n\t\t);\n\n\t\treturn this;\n\n\t},\n\n\tmakeRotationX: function ( theta ) {\n\n\t\tvar c = Math.cos( theta ), s = Math.sin( theta );\n\n\t\tthis.set(\n\n\t\t\t1, 0, 0, 0,\n\t\t\t0, c, - s, 0,\n\t\t\t0, s, c, 0,\n\t\t\t0, 0, 0, 1\n\n\t\t);\n\n\t\treturn this;\n\n\t},\n\n\tmakeRotationY: function ( theta ) {\n\n\t\tvar c = Math.cos( theta ), s = Math.sin( theta );\n\n\t\tthis.set(\n\n\t\t\t c, 0, s, 0,\n\t\t\t 0, 1, 0, 0,\n\t\t\t- s, 0, c, 0,\n\t\t\t 0, 0, 0, 1\n\n\t\t);\n\n\t\treturn this;\n\n\t},\n\n\tmakeRotationZ: function ( theta ) {\n\n\t\tvar c = Math.cos( theta ), s = Math.sin( theta );\n\n\t\tthis.set(\n\n\t\t\tc, - s, 0, 0,\n\t\t\ts, c, 0, 0,\n\t\t\t0, 0, 1, 0,\n\t\t\t0, 0, 0, 1\n\n\t\t);\n\n\t\treturn this;\n\n\t},\n\n\tmakeRotationAxis: function ( axis, angle ) {\n\n\t\t// Based on http://www.gamedev.net/reference/articles/article1199.asp\n\n\t\tvar c = Math.cos( angle );\n\t\tvar s = Math.sin( angle );\n\t\tvar t = 1 - c;\n\t\tvar x = axis.x, y = axis.y, z = axis.z;\n\t\tvar tx = t * x, ty = t * y;\n\n\t\tthis.set(\n\n\t\t\ttx * x + c, tx * y - s * z, tx * z + s * y, 0,\n\t\t\ttx * y + s * z, ty * y + c, ty * z - s * x, 0,\n\t\t\ttx * z - s * y, ty * z + s * x, t * z * z + c, 0,\n\t\t\t0, 0, 0, 1\n\n\t\t);\n\n\t\t return this;\n\n\t},\n\n\tmakeScale: function ( x, y, z ) {\n\n\t\tthis.set(\n\n\t\t\tx, 0, 0, 0,\n\t\t\t0, y, 0, 0,\n\t\t\t0, 0, z, 0,\n\t\t\t0, 0, 0, 1\n\n\t\t);\n\n\t\treturn this;\n\n\t},\n\n\tmakeShear: function ( x, y, z ) {\n\n\t\tthis.set(\n\n\t\t\t1, y, z, 0,\n\t\t\tx, 1, z, 0,\n\t\t\tx, y, 1, 0,\n\t\t\t0, 0, 0, 1\n\n\t\t);\n\n\t\treturn this;\n\n\t},\n\n\tcompose: function ( position, quaternion, scale ) {\n\n\t\tvar te = this.elements;\n\n\t\tvar x = quaternion._x, y = quaternion._y, z = quaternion._z, w = quaternion._w;\n\t\tvar x2 = x + x,\ty2 = y + y, z2 = z + z;\n\t\tvar xx = x * x2, xy = x * y2, xz = x * z2;\n\t\tvar yy = y * y2, yz = y * z2, zz = z * z2;\n\t\tvar wx = w * x2, wy = w * y2, wz = w * z2;\n\n\t\tvar sx = scale.x, sy = scale.y, sz = scale.z;\n\n\t\tte[ 0 ] = ( 1 - ( yy + zz ) ) * sx;\n\t\tte[ 1 ] = ( xy + wz ) * sx;\n\t\tte[ 2 ] = ( xz - wy ) * sx;\n\t\tte[ 3 ] = 0;\n\n\t\tte[ 4 ] = ( xy - wz ) * sy;\n\t\tte[ 5 ] = ( 1 - ( xx + zz ) ) * sy;\n\t\tte[ 6 ] = ( yz + wx ) * sy;\n\t\tte[ 7 ] = 0;\n\n\t\tte[ 8 ] = ( xz + wy ) * sz;\n\t\tte[ 9 ] = ( yz - wx ) * sz;\n\t\tte[ 10 ] = ( 1 - ( xx + yy ) ) * sz;\n\t\tte[ 11 ] = 0;\n\n\t\tte[ 12 ] = position.x;\n\t\tte[ 13 ] = position.y;\n\t\tte[ 14 ] = position.z;\n\t\tte[ 15 ] = 1;\n\n\t\treturn this;\n\n\t},\n\n\tdecompose: function () {\n\n\t\tvar vector = new Vector3();\n\t\tvar matrix = new Matrix4();\n\n\t\treturn function decompose( position, quaternion, scale ) {\n\n\t\t\tvar te = this.elements;\n\n\t\t\tvar sx = vector.set( te[ 0 ], te[ 1 ], te[ 2 ] ).length();\n\t\t\tvar sy = vector.set( te[ 4 ], te[ 5 ], te[ 6 ] ).length();\n\t\t\tvar sz = vector.set( te[ 8 ], te[ 9 ], te[ 10 ] ).length();\n\n\t\t\t// if determine is negative, we need to invert one scale\n\t\t\tvar det = this.determinant();\n\t\t\tif ( det < 0 ) sx = - sx;\n\n\t\t\tposition.x = te[ 12 ];\n\t\t\tposition.y = te[ 13 ];\n\t\t\tposition.z = te[ 14 ];\n\n\t\t\t// scale the rotation part\n\t\t\tmatrix.copy( this );\n\n\t\t\tvar invSX = 1 / sx;\n\t\t\tvar invSY = 1 / sy;\n\t\t\tvar invSZ = 1 / sz;\n\n\t\t\tmatrix.elements[ 0 ] *= invSX;\n\t\t\tmatrix.elements[ 1 ] *= invSX;\n\t\t\tmatrix.elements[ 2 ] *= invSX;\n\n\t\t\tmatrix.elements[ 4 ] *= invSY;\n\t\t\tmatrix.elements[ 5 ] *= invSY;\n\t\t\tmatrix.elements[ 6 ] *= invSY;\n\n\t\t\tmatrix.elements[ 8 ] *= invSZ;\n\t\t\tmatrix.elements[ 9 ] *= invSZ;\n\t\t\tmatrix.elements[ 10 ] *= invSZ;\n\n\t\t\tquaternion.setFromRotationMatrix( matrix );\n\n\t\t\tscale.x = sx;\n\t\t\tscale.y = sy;\n\t\t\tscale.z = sz;\n\n\t\t\treturn this;\n\n\t\t};\n\n\t}(),\n\n\tmakePerspective: function ( left, right, top, bottom, near, far ) {\n\n\t\tif ( far === undefined ) {\n\n\t\t\tconsole.warn( 'THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.' );\n\n\t\t}\n\n\t\tvar te = this.elements;\n\t\tvar x = 2 * near / ( right - left );\n\t\tvar y = 2 * near / ( top - bottom );\n\n\t\tvar a = ( right + left ) / ( right - left );\n\t\tvar b = ( top + bottom ) / ( top - bottom );\n\t\tvar c = - ( far + near ) / ( far - near );\n\t\tvar d = - 2 * far * near / ( far - near );\n\n\t\tte[ 0 ] = x;\tte[ 4 ] = 0;\tte[ 8 ] = a;\tte[ 12 ] = 0;\n\t\tte[ 1 ] = 0;\tte[ 5 ] = y;\tte[ 9 ] = b;\tte[ 13 ] = 0;\n\t\tte[ 2 ] = 0;\tte[ 6 ] = 0;\tte[ 10 ] = c;\tte[ 14 ] = d;\n\t\tte[ 3 ] = 0;\tte[ 7 ] = 0;\tte[ 11 ] = - 1;\tte[ 15 ] = 0;\n\n\t\treturn this;\n\n\t},\n\n\tmakeOrthographic: function ( left, right, top, bottom, near, far ) {\n\n\t\tvar te = this.elements;\n\t\tvar w = 1.0 / ( right - left );\n\t\tvar h = 1.0 / ( top - bottom );\n\t\tvar p = 1.0 / ( far - near );\n\n\t\tvar x = ( right + left ) * w;\n\t\tvar y = ( top + bottom ) * h;\n\t\tvar z = ( far + near ) * p;\n\n\t\tte[ 0 ] = 2 * w;\tte[ 4 ] = 0;\tte[ 8 ] = 0;\tte[ 12 ] = - x;\n\t\tte[ 1 ] = 0;\tte[ 5 ] = 2 * h;\tte[ 9 ] = 0;\tte[ 13 ] = - y;\n\t\tte[ 2 ] = 0;\tte[ 6 ] = 0;\tte[ 10 ] = - 2 * p;\tte[ 14 ] = - z;\n\t\tte[ 3 ] = 0;\tte[ 7 ] = 0;\tte[ 11 ] = 0;\tte[ 15 ] = 1;\n\n\t\treturn this;\n\n\t},\n\n\tequals: function ( matrix ) {\n\n\t\tvar te = this.elements;\n\t\tvar me = matrix.elements;\n\n\t\tfor ( var i = 0; i < 16; i ++ ) {\n\n\t\t\tif ( te[ i ] !== me[ i ] ) return false;\n\n\t\t}\n\n\t\treturn true;\n\n\t},\n\n\tfromArray: function ( array, offset ) {\n\n\t\tif ( offset === undefined ) offset = 0;\n\n\t\tfor ( var i = 0; i < 16; i ++ ) {\n\n\t\t\tthis.elements[ i ] = array[ i + offset ];\n\n\t\t}\n\n\t\treturn this;\n\n\t},\n\n\ttoArray: function ( array, offset ) {\n\n\t\tif ( array === undefined ) array = [];\n\t\tif ( offset === undefined ) offset = 0;\n\n\t\tvar te = this.elements;\n\n\t\tarray[ offset ] = te[ 0 ];\n\t\tarray[ offset + 1 ] = te[ 1 ];\n\t\tarray[ offset + 2 ] = te[ 2 ];\n\t\tarray[ offset + 3 ] = te[ 3 ];\n\n\t\tarray[ offset + 4 ] = te[ 4 ];\n\t\tarray[ offset + 5 ] = te[ 5 ];\n\t\tarray[ offset + 6 ] = te[ 6 ];\n\t\tarray[ offset + 7 ] = te[ 7 ];\n\n\t\tarray[ offset + 8 ] = te[ 8 ];\n\t\tarray[ offset + 9 ] = te[ 9 ];\n\t\tarray[ offset + 10 ] = te[ 10 ];\n\t\tarray[ offset + 11 ] = te[ 11 ];\n\n\t\tarray[ offset + 12 ] = te[ 12 ];\n\t\tarray[ offset + 13 ] = te[ 13 ];\n\t\tarray[ offset + 14 ] = te[ 14 ];\n\t\tarray[ offset + 15 ] = te[ 15 ];\n\n\t\treturn array;\n\n\t}\n\n} );\n\nvar alphamap_fragment = \"#ifdef USE_ALPHAMAP\\n\\tdiffuseColor.a *= texture2D( alphaMap, vUv ).g;\\n#endif\";\n\nvar alphamap_pars_fragment = \"#ifdef USE_ALPHAMAP\\n\\tuniform sampler2D alphaMap;\\n#endif\";\n\nvar alphatest_fragment = \"#ifdef ALPHATEST\\n\\tif ( diffuseColor.a < ALPHATEST ) discard;\\n#endif\";\n\nvar aomap_fragment = \"#ifdef USE_AOMAP\\n\\tfloat ambientOcclusion = ( texture2D( aoMap, vUv2 ).r - 1.0 ) * aoMapIntensity + 1.0;\\n\\treflectedLight.indirectDiffuse *= ambientOcclusion;\\n\\t#if defined( USE_ENVMAP ) && defined( PHYSICAL )\\n\\t\\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\\n\\t\\treflectedLight.indirectSpecular *= computeSpecularOcclusion( dotNV, ambientOcclusion, material.specularRoughness );\\n\\t#endif\\n#endif\";\n\nvar aomap_pars_fragment = \"#ifdef USE_AOMAP\\n\\tuniform sampler2D aoMap;\\n\\tuniform float aoMapIntensity;\\n#endif\";\n\nvar begin_vertex = \"vec3 transformed = vec3( position );\";\n\nvar beginnormal_vertex = \"vec3 objectNormal = vec3( normal );\\n#ifdef USE_TANGENT\\n\\tvec3 objectTangent = vec3( tangent.xyz );\\n#endif\";\n\nvar bsdfs = \"vec2 integrateSpecularBRDF( const in float dotNV, const in float roughness ) {\\n\\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\\n\\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\\n\\tvec4 r = roughness * c0 + c1;\\n\\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\\n\\treturn vec2( -1.04, 1.04 ) * a004 + r.zw;\\n}\\nfloat punctualLightIntensityToIrradianceFactor( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\\n#if defined ( PHYSICALLY_CORRECT_LIGHTS )\\n\\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\\n\\tif( cutoffDistance > 0.0 ) {\\n\\t\\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\\n\\t}\\n\\treturn distanceFalloff;\\n#else\\n\\tif( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\\n\\t\\treturn pow( saturate( -lightDistance / cutoffDistance + 1.0 ), decayExponent );\\n\\t}\\n\\treturn 1.0;\\n#endif\\n}\\nvec3 BRDF_Diffuse_Lambert( const in vec3 diffuseColor ) {\\n\\treturn RECIPROCAL_PI * diffuseColor;\\n}\\nvec3 F_Schlick( const in vec3 specularColor, const in float dotLH ) {\\n\\tfloat fresnel = exp2( ( -5.55473 * dotLH - 6.98316 ) * dotLH );\\n\\treturn ( 1.0 - specularColor ) * fresnel + specularColor;\\n}\\nvec3 F_Schlick_RoughnessDependent( const in vec3 F0, const in float dotNV, const in float roughness ) {\\n\\tfloat fresnel = exp2( ( -5.55473 * dotNV - 6.98316 ) * dotNV );\\n\\tvec3 Fr = max( vec3( 1.0 - roughness ), F0 ) - F0;\\n\\treturn Fr * fresnel + F0;\\n}\\nfloat G_GGX_Smith( const in float alpha, const in float dotNL, const in float dotNV ) {\\n\\tfloat a2 = pow2( alpha );\\n\\tfloat gl = dotNL + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\\n\\tfloat gv = dotNV + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\\n\\treturn 1.0 / ( gl * gv );\\n}\\nfloat G_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\\n\\tfloat a2 = pow2( alpha );\\n\\tfloat gv = dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\\n\\tfloat gl = dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\\n\\treturn 0.5 / max( gv + gl, EPSILON );\\n}\\nfloat D_GGX( const in float alpha, const in float dotNH ) {\\n\\tfloat a2 = pow2( alpha );\\n\\tfloat denom = pow2( dotNH ) * ( a2 - 1.0 ) + 1.0;\\n\\treturn RECIPROCAL_PI * a2 / pow2( denom );\\n}\\nvec3 BRDF_Specular_GGX( const in IncidentLight incidentLight, const in GeometricContext geometry, const in vec3 specularColor, const in float roughness ) {\\n\\tfloat alpha = pow2( roughness );\\n\\tvec3 halfDir = normalize( incidentLight.direction + geometry.viewDir );\\n\\tfloat dotNL = saturate( dot( geometry.normal, incidentLight.direction ) );\\n\\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\\n\\tfloat dotNH = saturate( dot( geometry.normal, halfDir ) );\\n\\tfloat dotLH = saturate( dot( incidentLight.direction, halfDir ) );\\n\\tvec3 F = F_Schlick( specularColor, dotLH );\\n\\tfloat G = G_GGX_SmithCorrelated( alpha, dotNL, dotNV );\\n\\tfloat D = D_GGX( alpha, dotNH );\\n\\treturn F * ( G * D );\\n}\\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\\n\\tconst float LUT_SIZE = 64.0;\\n\\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\\n\\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\\n\\tfloat dotNV = saturate( dot( N, V ) );\\n\\tvec2 uv = vec2( roughness, sqrt( 1.0 - dotNV ) );\\n\\tuv = uv * LUT_SCALE + LUT_BIAS;\\n\\treturn uv;\\n}\\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\\n\\tfloat l = length( f );\\n\\treturn max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\\n}\\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\\n\\tfloat x = dot( v1, v2 );\\n\\tfloat y = abs( x );\\n\\tfloat a = 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;\\n\\tfloat b = 3.4175940 + ( 4.1616724 + y ) * y;\\n\\tfloat v = a / b;\\n\\tfloat theta_sintheta = ( x > 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\\n\\treturn cross( v1, v2 ) * theta_sintheta;\\n}\\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\\n\\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\\n\\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\\n\\tvec3 lightNormal = cross( v1, v2 );\\n\\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\\n\\tvec3 T1, T2;\\n\\tT1 = normalize( V - N * dot( V, N ) );\\n\\tT2 = - cross( N, T1 );\\n\\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\\n\\tvec3 coords[ 4 ];\\n\\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\\n\\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\\n\\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\\n\\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\\n\\tcoords[ 0 ] = normalize( coords[ 0 ] );\\n\\tcoords[ 1 ] = normalize( coords[ 1 ] );\\n\\tcoords[ 2 ] = normalize( coords[ 2 ] );\\n\\tcoords[ 3 ] = normalize( coords[ 3 ] );\\n\\tvec3 vectorFormFactor = vec3( 0.0 );\\n\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\\n\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\\n\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\\n\\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\\n\\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\\n\\treturn vec3( result );\\n}\\nvec3 BRDF_Specular_GGX_Environment( const in GeometricContext geometry, const in vec3 specularColor, const in float roughness ) {\\n\\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\\n\\tvec2 brdf = integrateSpecularBRDF( dotNV, roughness );\\n\\treturn specularColor * brdf.x + brdf.y;\\n}\\nvoid BRDF_Specular_Multiscattering_Environment( const in GeometricContext geometry, const in vec3 specularColor, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\\n\\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\\n\\tvec3 F = F_Schlick_RoughnessDependent( specularColor, dotNV, roughness );\\n\\tvec2 brdf = integrateSpecularBRDF( dotNV, roughness );\\n\\tvec3 FssEss = F * brdf.x + brdf.y;\\n\\tfloat Ess = brdf.x + brdf.y;\\n\\tfloat Ems = 1.0 - Ess;\\n\\tvec3 Favg = specularColor + ( 1.0 - specularColor ) * 0.047619;\\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\\n\\tsingleScatter += FssEss;\\n\\tmultiScatter += Fms * Ems;\\n}\\nfloat G_BlinnPhong_Implicit( ) {\\n\\treturn 0.25;\\n}\\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\\n\\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\\n}\\nvec3 BRDF_Specular_BlinnPhong( const in IncidentLight incidentLight, const in GeometricContext geometry, const in vec3 specularColor, const in float shininess ) {\\n\\tvec3 halfDir = normalize( incidentLight.direction + geometry.viewDir );\\n\\tfloat dotNH = saturate( dot( geometry.normal, halfDir ) );\\n\\tfloat dotLH = saturate( dot( incidentLight.direction, halfDir ) );\\n\\tvec3 F = F_Schlick( specularColor, dotLH );\\n\\tfloat G = G_BlinnPhong_Implicit( );\\n\\tfloat D = D_BlinnPhong( shininess, dotNH );\\n\\treturn F * ( G * D );\\n}\\nfloat GGXRoughnessToBlinnExponent( const in float ggxRoughness ) {\\n\\treturn ( 2.0 / pow2( ggxRoughness + 0.0001 ) - 2.0 );\\n}\\nfloat BlinnExponentToGGXRoughness( const in float blinnExponent ) {\\n\\treturn sqrt( 2.0 / ( blinnExponent + 2.0 ) );\\n}\";\n\nvar bumpmap_pars_fragment = \"#ifdef USE_BUMPMAP\\n\\tuniform sampler2D bumpMap;\\n\\tuniform float bumpScale;\\n\\tvec2 dHdxy_fwd() {\\n\\t\\tvec2 dSTdx = dFdx( vUv );\\n\\t\\tvec2 dSTdy = dFdy( vUv );\\n\\t\\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\\n\\t\\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\\n\\t\\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\\n\\t\\treturn vec2( dBx, dBy );\\n\\t}\\n\\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy ) {\\n\\t\\tvec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\\n\\t\\tvec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\\n\\t\\tvec3 vN = surf_norm;\\n\\t\\tvec3 R1 = cross( vSigmaY, vN );\\n\\t\\tvec3 R2 = cross( vN, vSigmaX );\\n\\t\\tfloat fDet = dot( vSigmaX, R1 );\\n\\t\\tfDet *= ( float( gl_FrontFacing ) * 2.0 - 1.0 );\\n\\t\\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\\n\\t\\treturn normalize( abs( fDet ) * surf_norm - vGrad );\\n\\t}\\n#endif\";\n\nvar clipping_planes_fragment = \"#if NUM_CLIPPING_PLANES > 0\\n\\tvec4 plane;\\n\\t#pragma unroll_loop\\n\\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\\n\\t\\tplane = clippingPlanes[ i ];\\n\\t\\tif ( dot( vViewPosition, plane.xyz ) > plane.w ) discard;\\n\\t}\\n\\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\\n\\t\\tbool clipped = true;\\n\\t\\t#pragma unroll_loop\\n\\t\\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\\n\\t\\t\\tplane = clippingPlanes[ i ];\\n\\t\\t\\tclipped = ( dot( vViewPosition, plane.xyz ) > plane.w ) && clipped;\\n\\t\\t}\\n\\t\\tif ( clipped ) discard;\\n\\t#endif\\n#endif\";\n\nvar clipping_planes_pars_fragment = \"#if NUM_CLIPPING_PLANES > 0\\n\\t#if ! defined( PHYSICAL ) && ! defined( PHONG ) && ! defined( MATCAP )\\n\\t\\tvarying vec3 vViewPosition;\\n\\t#endif\\n\\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\\n#endif\";\n\nvar clipping_planes_pars_vertex = \"#if NUM_CLIPPING_PLANES > 0 && ! defined( PHYSICAL ) && ! defined( PHONG ) && ! defined( MATCAP )\\n\\tvarying vec3 vViewPosition;\\n#endif\";\n\nvar clipping_planes_vertex = \"#if NUM_CLIPPING_PLANES > 0 && ! defined( PHYSICAL ) && ! defined( PHONG ) && ! defined( MATCAP )\\n\\tvViewPosition = - mvPosition.xyz;\\n#endif\";\n\nvar color_fragment = \"#ifdef USE_COLOR\\n\\tdiffuseColor.rgb *= vColor;\\n#endif\";\n\nvar color_pars_fragment = \"#ifdef USE_COLOR\\n\\tvarying vec3 vColor;\\n#endif\";\n\nvar color_pars_vertex = \"#ifdef USE_COLOR\\n\\tvarying vec3 vColor;\\n#endif\";\n\nvar color_vertex = \"#ifdef USE_COLOR\\n\\tvColor.xyz = color.xyz;\\n#endif\";\n\nvar common = \"#define PI 3.14159265359\\n#define PI2 6.28318530718\\n#define PI_HALF 1.5707963267949\\n#define RECIPROCAL_PI 0.31830988618\\n#define RECIPROCAL_PI2 0.15915494\\n#define LOG2 1.442695\\n#define EPSILON 1e-6\\n#define saturate(a) clamp( a, 0.0, 1.0 )\\n#define whiteCompliment(a) ( 1.0 - saturate( a ) )\\nfloat pow2( const in float x ) { return x*x; }\\nfloat pow3( const in float x ) { return x*x*x; }\\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\\nhighp float rand( const in vec2 uv ) {\\n\\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\\n\\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\\n\\treturn fract(sin(sn) * c);\\n}\\nstruct IncidentLight {\\n\\tvec3 color;\\n\\tvec3 direction;\\n\\tbool visible;\\n};\\nstruct ReflectedLight {\\n\\tvec3 directDiffuse;\\n\\tvec3 directSpecular;\\n\\tvec3 indirectDiffuse;\\n\\tvec3 indirectSpecular;\\n};\\nstruct GeometricContext {\\n\\tvec3 position;\\n\\tvec3 normal;\\n\\tvec3 viewDir;\\n};\\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\\n\\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\\n}\\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\\n\\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\\n}\\nvec3 projectOnPlane(in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\\n\\tfloat distance = dot( planeNormal, point - pointOnPlane );\\n\\treturn - distance * planeNormal + point;\\n}\\nfloat sideOfPlane( in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\\n\\treturn sign( dot( point - pointOnPlane, planeNormal ) );\\n}\\nvec3 linePlaneIntersect( in vec3 pointOnLine, in vec3 lineDirection, in vec3 pointOnPlane, in vec3 planeNormal ) {\\n\\treturn lineDirection * ( dot( planeNormal, pointOnPlane - pointOnLine ) / dot( planeNormal, lineDirection ) ) + pointOnLine;\\n}\\nmat3 transposeMat3( const in mat3 m ) {\\n\\tmat3 tmp;\\n\\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\\n\\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\\n\\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\\n\\treturn tmp;\\n}\\nfloat linearToRelativeLuminance( const in vec3 color ) {\\n\\tvec3 weights = vec3( 0.2126, 0.7152, 0.0722 );\\n\\treturn dot( weights, color.rgb );\\n}\";\n\nvar cube_uv_reflection_fragment = \"#ifdef ENVMAP_TYPE_CUBE_UV\\n#define cubeUV_textureSize (1024.0)\\nint getFaceFromDirection(vec3 direction) {\\n\\tvec3 absDirection = abs(direction);\\n\\tint face = -1;\\n\\tif( absDirection.x > absDirection.z ) {\\n\\t\\tif(absDirection.x > absDirection.y )\\n\\t\\t\\tface = direction.x > 0.0 ? 0 : 3;\\n\\t\\telse\\n\\t\\t\\tface = direction.y > 0.0 ? 1 : 4;\\n\\t}\\n\\telse {\\n\\t\\tif(absDirection.z > absDirection.y )\\n\\t\\t\\tface = direction.z > 0.0 ? 2 : 5;\\n\\t\\telse\\n\\t\\t\\tface = direction.y > 0.0 ? 1 : 4;\\n\\t}\\n\\treturn face;\\n}\\n#define cubeUV_maxLods1 (log2(cubeUV_textureSize*0.25) - 1.0)\\n#define cubeUV_rangeClamp (exp2((6.0 - 1.0) * 2.0))\\nvec2 MipLevelInfo( vec3 vec, float roughnessLevel, float roughness ) {\\n\\tfloat scale = exp2(cubeUV_maxLods1 - roughnessLevel);\\n\\tfloat dxRoughness = dFdx(roughness);\\n\\tfloat dyRoughness = dFdy(roughness);\\n\\tvec3 dx = dFdx( vec * scale * dxRoughness );\\n\\tvec3 dy = dFdy( vec * scale * dyRoughness );\\n\\tfloat d = max( dot( dx, dx ), dot( dy, dy ) );\\n\\td = clamp(d, 1.0, cubeUV_rangeClamp);\\n\\tfloat mipLevel = 0.5 * log2(d);\\n\\treturn vec2(floor(mipLevel), fract(mipLevel));\\n}\\n#define cubeUV_maxLods2 (log2(cubeUV_textureSize*0.25) - 2.0)\\n#define cubeUV_rcpTextureSize (1.0 / cubeUV_textureSize)\\nvec2 getCubeUV(vec3 direction, float roughnessLevel, float mipLevel) {\\n\\tmipLevel = roughnessLevel > cubeUV_maxLods2 - 3.0 ? 0.0 : mipLevel;\\n\\tfloat a = 16.0 * cubeUV_rcpTextureSize;\\n\\tvec2 exp2_packed = exp2( vec2( roughnessLevel, mipLevel ) );\\n\\tvec2 rcp_exp2_packed = vec2( 1.0 ) / exp2_packed;\\n\\tfloat powScale = exp2_packed.x * exp2_packed.y;\\n\\tfloat scale = rcp_exp2_packed.x * rcp_exp2_packed.y * 0.25;\\n\\tfloat mipOffset = 0.75*(1.0 - rcp_exp2_packed.y) * rcp_exp2_packed.x;\\n\\tbool bRes = mipLevel == 0.0;\\n\\tscale = bRes && (scale < a) ? a : scale;\\n\\tvec3 r;\\n\\tvec2 offset;\\n\\tint face = getFaceFromDirection(direction);\\n\\tfloat rcpPowScale = 1.0 / powScale;\\n\\tif( face == 0) {\\n\\t\\tr = vec3(direction.x, -direction.z, direction.y);\\n\\t\\toffset = vec2(0.0+mipOffset,0.75 * rcpPowScale);\\n\\t\\toffset.y = bRes && (offset.y < 2.0*a) ? a : offset.y;\\n\\t}\\n\\telse if( face == 1) {\\n\\t\\tr = vec3(direction.y, direction.x, direction.z);\\n\\t\\toffset = vec2(scale+mipOffset, 0.75 * rcpPowScale);\\n\\t\\toffset.y = bRes && (offset.y < 2.0*a) ? a : offset.y;\\n\\t}\\n\\telse if( face == 2) {\\n\\t\\tr = vec3(direction.z, direction.x, direction.y);\\n\\t\\toffset = vec2(2.0*scale+mipOffset, 0.75 * rcpPowScale);\\n\\t\\toffset.y = bRes && (offset.y < 2.0*a) ? a : offset.y;\\n\\t}\\n\\telse if( face == 3) {\\n\\t\\tr = vec3(direction.x, direction.z, direction.y);\\n\\t\\toffset = vec2(0.0+mipOffset,0.5 * rcpPowScale);\\n\\t\\toffset.y = bRes && (offset.y < 2.0*a) ? 0.0 : offset.y;\\n\\t}\\n\\telse if( face == 4) {\\n\\t\\tr = vec3(direction.y, direction.x, -direction.z);\\n\\t\\toffset = vec2(scale+mipOffset, 0.5 * rcpPowScale);\\n\\t\\toffset.y = bRes && (offset.y < 2.0*a) ? 0.0 : offset.y;\\n\\t}\\n\\telse {\\n\\t\\tr = vec3(direction.z, -direction.x, direction.y);\\n\\t\\toffset = vec2(2.0*scale+mipOffset, 0.5 * rcpPowScale);\\n\\t\\toffset.y = bRes && (offset.y < 2.0*a) ? 0.0 : offset.y;\\n\\t}\\n\\tr = normalize(r);\\n\\tfloat texelOffset = 0.5 * cubeUV_rcpTextureSize;\\n\\tvec2 s = ( r.yz / abs( r.x ) + vec2( 1.0 ) ) * 0.5;\\n\\tvec2 base = offset + vec2( texelOffset );\\n\\treturn base + s * ( scale - 2.0 * texelOffset );\\n}\\n#define cubeUV_maxLods3 (log2(cubeUV_textureSize*0.25) - 3.0)\\nvec4 textureCubeUV( sampler2D envMap, vec3 reflectedDirection, float roughness ) {\\n\\tfloat roughnessVal = roughness* cubeUV_maxLods3;\\n\\tfloat r1 = floor(roughnessVal);\\n\\tfloat r2 = r1 + 1.0;\\n\\tfloat t = fract(roughnessVal);\\n\\tvec2 mipInfo = MipLevelInfo(reflectedDirection, r1, roughness);\\n\\tfloat s = mipInfo.y;\\n\\tfloat level0 = mipInfo.x;\\n\\tfloat level1 = level0 + 1.0;\\n\\tlevel1 = level1 > 5.0 ? 5.0 : level1;\\n\\tlevel0 += min( floor( s + 0.5 ), 5.0 );\\n\\tvec2 uv_10 = getCubeUV(reflectedDirection, r1, level0);\\n\\tvec4 color10 = envMapTexelToLinear(texture2D(envMap, uv_10));\\n\\tvec2 uv_20 = getCubeUV(reflectedDirection, r2, level0);\\n\\tvec4 color20 = envMapTexelToLinear(texture2D(envMap, uv_20));\\n\\tvec4 result = mix(color10, color20, t);\\n\\treturn vec4(result.rgb, 1.0);\\n}\\n#endif\";\n\nvar defaultnormal_vertex = \"vec3 transformedNormal = normalMatrix * objectNormal;\\n#ifdef FLIP_SIDED\\n\\ttransformedNormal = - transformedNormal;\\n#endif\\n#ifdef USE_TANGENT\\n\\tvec3 transformedTangent = normalMatrix * objectTangent;\\n\\t#ifdef FLIP_SIDED\\n\\t\\ttransformedTangent = - transformedTangent;\\n\\t#endif\\n#endif\";\n\nvar displacementmap_pars_vertex = \"#ifdef USE_DISPLACEMENTMAP\\n\\tuniform sampler2D displacementMap;\\n\\tuniform float displacementScale;\\n\\tuniform float displacementBias;\\n#endif\";\n\nvar displacementmap_vertex = \"#ifdef USE_DISPLACEMENTMAP\\n\\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, uv ).x * displacementScale + displacementBias );\\n#endif\";\n\nvar emissivemap_fragment = \"#ifdef USE_EMISSIVEMAP\\n\\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\\n\\temissiveColor.rgb = emissiveMapTexelToLinear( emissiveColor ).rgb;\\n\\ttotalEmissiveRadiance *= emissiveColor.rgb;\\n#endif\";\n\nvar emissivemap_pars_fragment = \"#ifdef USE_EMISSIVEMAP\\n\\tuniform sampler2D emissiveMap;\\n#endif\";\n\nvar encodings_fragment = \"gl_FragColor = linearToOutputTexel( gl_FragColor );\";\n\nvar encodings_pars_fragment = \"\\nvec4 LinearToLinear( in vec4 value ) {\\n\\treturn value;\\n}\\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\\n\\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\\n}\\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\\n\\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\\n}\\nvec4 sRGBToLinear( in vec4 value ) {\\n\\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\\n}\\nvec4 LinearTosRGB( in vec4 value ) {\\n\\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\\n}\\nvec4 RGBEToLinear( in vec4 value ) {\\n\\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\\n}\\nvec4 LinearToRGBE( in vec4 value ) {\\n\\tfloat maxComponent = max( max( value.r, value.g ), value.b );\\n\\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\\n\\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\\n}\\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\\n\\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\\n}\\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\\n\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\n\\tfloat M = clamp( maxRGB / maxRange, 0.0, 1.0 );\\n\\tM = ceil( M * 255.0 ) / 255.0;\\n\\treturn vec4( value.rgb / ( M * maxRange ), M );\\n}\\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\\n\\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\\n}\\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\\n\\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\\n\\tfloat D = max( maxRange / maxRGB, 1.0 );\\n\\tD = min( floor( D ) / 255.0, 1.0 );\\n\\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\\n}\\nconst mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\\nvec4 LinearToLogLuv( in vec4 value ) {\\n\\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\\n\\tXp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\\n\\tvec4 vResult;\\n\\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\\n\\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\\n\\tvResult.w = fract( Le );\\n\\tvResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\\n\\treturn vResult;\\n}\\nconst mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\\nvec4 LogLuvToLinear( in vec4 value ) {\\n\\tfloat Le = value.z * 255.0 + value.w;\\n\\tvec3 Xp_Y_XYZp;\\n\\tXp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );\\n\\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\\n\\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\\n\\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\\n\\treturn vec4( max( vRGB, 0.0 ), 1.0 );\\n}\";\n\nvar envmap_fragment = \"#ifdef USE_ENVMAP\\n\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\\n\\t\\tvec3 cameraToVertex = normalize( vWorldPosition - cameraPosition );\\n\\t\\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\\n\\t\\t#ifdef ENVMAP_MODE_REFLECTION\\n\\t\\t\\tvec3 reflectVec = reflect( cameraToVertex, worldNormal );\\n\\t\\t#else\\n\\t\\t\\tvec3 reflectVec = refract( cameraToVertex, worldNormal, refractionRatio );\\n\\t\\t#endif\\n\\t#else\\n\\t\\tvec3 reflectVec = vReflect;\\n\\t#endif\\n\\t#ifdef ENVMAP_TYPE_CUBE\\n\\t\\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\\n\\t#elif defined( ENVMAP_TYPE_EQUIREC )\\n\\t\\tvec2 sampleUV;\\n\\t\\treflectVec = normalize( reflectVec );\\n\\t\\tsampleUV.y = asin( clamp( reflectVec.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\\n\\t\\tsampleUV.x = atan( reflectVec.z, reflectVec.x ) * RECIPROCAL_PI2 + 0.5;\\n\\t\\tvec4 envColor = texture2D( envMap, sampleUV );\\n\\t#elif defined( ENVMAP_TYPE_SPHERE )\\n\\t\\treflectVec = normalize( reflectVec );\\n\\t\\tvec3 reflectView = normalize( ( viewMatrix * vec4( reflectVec, 0.0 ) ).xyz + vec3( 0.0, 0.0, 1.0 ) );\\n\\t\\tvec4 envColor = texture2D( envMap, reflectView.xy * 0.5 + 0.5 );\\n\\t#else\\n\\t\\tvec4 envColor = vec4( 0.0 );\\n\\t#endif\\n\\tenvColor = envMapTexelToLinear( envColor );\\n\\t#ifdef ENVMAP_BLENDING_MULTIPLY\\n\\t\\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\\n\\t#elif defined( ENVMAP_BLENDING_MIX )\\n\\t\\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\\n\\t#elif defined( ENVMAP_BLENDING_ADD )\\n\\t\\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\\n\\t#endif\\n#endif\";\n\nvar envmap_pars_fragment = \"#if defined( USE_ENVMAP ) || defined( PHYSICAL )\\n\\tuniform float reflectivity;\\n\\tuniform float envMapIntensity;\\n#endif\\n#ifdef USE_ENVMAP\\n\\t#if ! defined( PHYSICAL ) && ( defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG ) )\\n\\t\\tvarying vec3 vWorldPosition;\\n\\t#endif\\n\\t#ifdef ENVMAP_TYPE_CUBE\\n\\t\\tuniform samplerCube envMap;\\n\\t#else\\n\\t\\tuniform sampler2D envMap;\\n\\t#endif\\n\\tuniform float flipEnvMap;\\n\\tuniform int maxMipLevel;\\n\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG ) || defined( PHYSICAL )\\n\\t\\tuniform float refractionRatio;\\n\\t#else\\n\\t\\tvarying vec3 vReflect;\\n\\t#endif\\n#endif\";\n\nvar envmap_pars_vertex = \"#ifdef USE_ENVMAP\\n\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\\n\\t\\tvarying vec3 vWorldPosition;\\n\\t#else\\n\\t\\tvarying vec3 vReflect;\\n\\t\\tuniform float refractionRatio;\\n\\t#endif\\n#endif\";\n\nvar envmap_vertex = \"#ifdef USE_ENVMAP\\n\\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\\n\\t\\tvWorldPosition = worldPosition.xyz;\\n\\t#else\\n\\t\\tvec3 cameraToVertex = normalize( worldPosition.xyz - cameraPosition );\\n\\t\\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\\n\\t\\t#ifdef ENVMAP_MODE_REFLECTION\\n\\t\\t\\tvReflect = reflect( cameraToVertex, worldNormal );\\n\\t\\t#else\\n\\t\\t\\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\\n\\t\\t#endif\\n\\t#endif\\n#endif\";\n\nvar fog_vertex = \"#ifdef USE_FOG\\n\\tfogDepth = -mvPosition.z;\\n#endif\";\n\nvar fog_pars_vertex = \"#ifdef USE_FOG\\n\\tvarying float fogDepth;\\n#endif\";\n\nvar fog_fragment = \"#ifdef USE_FOG\\n\\t#ifdef FOG_EXP2\\n\\t\\tfloat fogFactor = whiteCompliment( exp2( - fogDensity * fogDensity * fogDepth * fogDepth * LOG2 ) );\\n\\t#else\\n\\t\\tfloat fogFactor = smoothstep( fogNear, fogFar, fogDepth );\\n\\t#endif\\n\\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\\n#endif\";\n\nvar fog_pars_fragment = \"#ifdef USE_FOG\\n\\tuniform vec3 fogColor;\\n\\tvarying float fogDepth;\\n\\t#ifdef FOG_EXP2\\n\\t\\tuniform float fogDensity;\\n\\t#else\\n\\t\\tuniform float fogNear;\\n\\t\\tuniform float fogFar;\\n\\t#endif\\n#endif\";\n\nvar gradientmap_pars_fragment = \"#ifdef TOON\\n\\tuniform sampler2D gradientMap;\\n\\tvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\\n\\t\\tfloat dotNL = dot( normal, lightDirection );\\n\\t\\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\\n\\t\\t#ifdef USE_GRADIENTMAP\\n\\t\\t\\treturn texture2D( gradientMap, coord ).rgb;\\n\\t\\t#else\\n\\t\\t\\treturn ( coord.x < 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\\n\\t\\t#endif\\n\\t}\\n#endif\";\n\nvar lightmap_fragment = \"#ifdef USE_LIGHTMAP\\n\\treflectedLight.indirectDiffuse += PI * texture2D( lightMap, vUv2 ).xyz * lightMapIntensity;\\n#endif\";\n\nvar lightmap_pars_fragment = \"#ifdef USE_LIGHTMAP\\n\\tuniform sampler2D lightMap;\\n\\tuniform float lightMapIntensity;\\n#endif\";\n\nvar lights_lambert_vertex = \"vec3 diffuse = vec3( 1.0 );\\nGeometricContext geometry;\\ngeometry.position = mvPosition.xyz;\\ngeometry.normal = normalize( transformedNormal );\\ngeometry.viewDir = normalize( -mvPosition.xyz );\\nGeometricContext backGeometry;\\nbackGeometry.position = geometry.position;\\nbackGeometry.normal = -geometry.normal;\\nbackGeometry.viewDir = geometry.viewDir;\\nvLightFront = vec3( 0.0 );\\nvIndirectFront = vec3( 0.0 );\\n#ifdef DOUBLE_SIDED\\n\\tvLightBack = vec3( 0.0 );\\n\\tvIndirectBack = vec3( 0.0 );\\n#endif\\nIncidentLight directLight;\\nfloat dotNL;\\nvec3 directLightColor_Diffuse;\\n#if NUM_POINT_LIGHTS > 0\\n\\t#pragma unroll_loop\\n\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\n\\t\\tgetPointDirectLightIrradiance( pointLights[ i ], geometry, directLight );\\n\\t\\tdotNL = dot( geometry.normal, directLight.direction );\\n\\t\\tdirectLightColor_Diffuse = PI * directLight.color;\\n\\t\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\n\\t\\t#ifdef DOUBLE_SIDED\\n\\t\\t\\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\\n\\t\\t#endif\\n\\t}\\n#endif\\n#if NUM_SPOT_LIGHTS > 0\\n\\t#pragma unroll_loop\\n\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\n\\t\\tgetSpotDirectLightIrradiance( spotLights[ i ], geometry, directLight );\\n\\t\\tdotNL = dot( geometry.normal, directLight.direction );\\n\\t\\tdirectLightColor_Diffuse = PI * directLight.color;\\n\\t\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\n\\t\\t#ifdef DOUBLE_SIDED\\n\\t\\t\\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\\n\\t\\t#endif\\n\\t}\\n#endif\\n#if NUM_DIR_LIGHTS > 0\\n\\t#pragma unroll_loop\\n\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\n\\t\\tgetDirectionalDirectLightIrradiance( directionalLights[ i ], geometry, directLight );\\n\\t\\tdotNL = dot( geometry.normal, directLight.direction );\\n\\t\\tdirectLightColor_Diffuse = PI * directLight.color;\\n\\t\\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\\n\\t\\t#ifdef DOUBLE_SIDED\\n\\t\\t\\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\\n\\t\\t#endif\\n\\t}\\n#endif\\n#if NUM_HEMI_LIGHTS > 0\\n\\t#pragma unroll_loop\\n\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\n\\t\\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\\n\\t\\t#ifdef DOUBLE_SIDED\\n\\t\\t\\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry );\\n\\t\\t#endif\\n\\t}\\n#endif\";\n\nvar lights_pars_begin = \"uniform vec3 ambientLightColor;\\nuniform vec3 lightProbe[ 9 ];\\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\\n\\tfloat x = normal.x, y = normal.y, z = normal.z;\\n\\tvec3 result = shCoefficients[ 0 ] * 0.886227;\\n\\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\\n\\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\\n\\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\\n\\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\\n\\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\\n\\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\\n\\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\\n\\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\\n\\treturn result;\\n}\\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in GeometricContext geometry ) {\\n\\tvec3 worldNormal = inverseTransformDirection( geometry.normal, viewMatrix );\\n\\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\\n\\treturn irradiance;\\n}\\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\\n\\tvec3 irradiance = ambientLightColor;\\n\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\n\\t\\tirradiance *= PI;\\n\\t#endif\\n\\treturn irradiance;\\n}\\n#if NUM_DIR_LIGHTS > 0\\n\\tstruct DirectionalLight {\\n\\t\\tvec3 direction;\\n\\t\\tvec3 color;\\n\\t\\tint shadow;\\n\\t\\tfloat shadowBias;\\n\\t\\tfloat shadowRadius;\\n\\t\\tvec2 shadowMapSize;\\n\\t};\\n\\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\\n\\tvoid getDirectionalDirectLightIrradiance( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight directLight ) {\\n\\t\\tdirectLight.color = directionalLight.color;\\n\\t\\tdirectLight.direction = directionalLight.direction;\\n\\t\\tdirectLight.visible = true;\\n\\t}\\n#endif\\n#if NUM_POINT_LIGHTS > 0\\n\\tstruct PointLight {\\n\\t\\tvec3 position;\\n\\t\\tvec3 color;\\n\\t\\tfloat distance;\\n\\t\\tfloat decay;\\n\\t\\tint shadow;\\n\\t\\tfloat shadowBias;\\n\\t\\tfloat shadowRadius;\\n\\t\\tvec2 shadowMapSize;\\n\\t\\tfloat shadowCameraNear;\\n\\t\\tfloat shadowCameraFar;\\n\\t};\\n\\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\\n\\tvoid getPointDirectLightIrradiance( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight directLight ) {\\n\\t\\tvec3 lVector = pointLight.position - geometry.position;\\n\\t\\tdirectLight.direction = normalize( lVector );\\n\\t\\tfloat lightDistance = length( lVector );\\n\\t\\tdirectLight.color = pointLight.color;\\n\\t\\tdirectLight.color *= punctualLightIntensityToIrradianceFactor( lightDistance, pointLight.distance, pointLight.decay );\\n\\t\\tdirectLight.visible = ( directLight.color != vec3( 0.0 ) );\\n\\t}\\n#endif\\n#if NUM_SPOT_LIGHTS > 0\\n\\tstruct SpotLight {\\n\\t\\tvec3 position;\\n\\t\\tvec3 direction;\\n\\t\\tvec3 color;\\n\\t\\tfloat distance;\\n\\t\\tfloat decay;\\n\\t\\tfloat coneCos;\\n\\t\\tfloat penumbraCos;\\n\\t\\tint shadow;\\n\\t\\tfloat shadowBias;\\n\\t\\tfloat shadowRadius;\\n\\t\\tvec2 shadowMapSize;\\n\\t};\\n\\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\\n\\tvoid getSpotDirectLightIrradiance( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight directLight ) {\\n\\t\\tvec3 lVector = spotLight.position - geometry.position;\\n\\t\\tdirectLight.direction = normalize( lVector );\\n\\t\\tfloat lightDistance = length( lVector );\\n\\t\\tfloat angleCos = dot( directLight.direction, spotLight.direction );\\n\\t\\tif ( angleCos > spotLight.coneCos ) {\\n\\t\\t\\tfloat spotEffect = smoothstep( spotLight.coneCos, spotLight.penumbraCos, angleCos );\\n\\t\\t\\tdirectLight.color = spotLight.color;\\n\\t\\t\\tdirectLight.color *= spotEffect * punctualLightIntensityToIrradianceFactor( lightDistance, spotLight.distance, spotLight.decay );\\n\\t\\t\\tdirectLight.visible = true;\\n\\t\\t} else {\\n\\t\\t\\tdirectLight.color = vec3( 0.0 );\\n\\t\\t\\tdirectLight.visible = false;\\n\\t\\t}\\n\\t}\\n#endif\\n#if NUM_RECT_AREA_LIGHTS > 0\\n\\tstruct RectAreaLight {\\n\\t\\tvec3 color;\\n\\t\\tvec3 position;\\n\\t\\tvec3 halfWidth;\\n\\t\\tvec3 halfHeight;\\n\\t};\\n\\tuniform sampler2D ltc_1;\\tuniform sampler2D ltc_2;\\n\\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\\n#endif\\n#if NUM_HEMI_LIGHTS > 0\\n\\tstruct HemisphereLight {\\n\\t\\tvec3 direction;\\n\\t\\tvec3 skyColor;\\n\\t\\tvec3 groundColor;\\n\\t};\\n\\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\\n\\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in GeometricContext geometry ) {\\n\\t\\tfloat dotNL = dot( geometry.normal, hemiLight.direction );\\n\\t\\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\\n\\t\\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\\n\\t\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\n\\t\\t\\tirradiance *= PI;\\n\\t\\t#endif\\n\\t\\treturn irradiance;\\n\\t}\\n#endif\";\n\nvar envmap_physical_pars_fragment = \"#if defined( USE_ENVMAP ) && defined( PHYSICAL )\\n\\tvec3 getLightProbeIndirectIrradiance( const in GeometricContext geometry, const in int maxMIPLevel ) {\\n\\t\\tvec3 worldNormal = inverseTransformDirection( geometry.normal, viewMatrix );\\n\\t\\t#ifdef ENVMAP_TYPE_CUBE\\n\\t\\t\\tvec3 queryVec = vec3( flipEnvMap * worldNormal.x, worldNormal.yz );\\n\\t\\t\\t#ifdef TEXTURE_LOD_EXT\\n\\t\\t\\t\\tvec4 envMapColor = textureCubeLodEXT( envMap, queryVec, float( maxMIPLevel ) );\\n\\t\\t\\t#else\\n\\t\\t\\t\\tvec4 envMapColor = textureCube( envMap, queryVec, float( maxMIPLevel ) );\\n\\t\\t\\t#endif\\n\\t\\t\\tenvMapColor.rgb = envMapTexelToLinear( envMapColor ).rgb;\\n\\t\\t#elif defined( ENVMAP_TYPE_CUBE_UV )\\n\\t\\t\\tvec3 queryVec = vec3( flipEnvMap * worldNormal.x, worldNormal.yz );\\n\\t\\t\\tvec4 envMapColor = textureCubeUV( envMap, queryVec, 1.0 );\\n\\t\\t#else\\n\\t\\t\\tvec4 envMapColor = vec4( 0.0 );\\n\\t\\t#endif\\n\\t\\treturn PI * envMapColor.rgb * envMapIntensity;\\n\\t}\\n\\tfloat getSpecularMIPLevel( const in float blinnShininessExponent, const in int maxMIPLevel ) {\\n\\t\\tfloat maxMIPLevelScalar = float( maxMIPLevel );\\n\\t\\tfloat desiredMIPLevel = maxMIPLevelScalar + 0.79248 - 0.5 * log2( pow2( blinnShininessExponent ) + 1.0 );\\n\\t\\treturn clamp( desiredMIPLevel, 0.0, maxMIPLevelScalar );\\n\\t}\\n\\tvec3 getLightProbeIndirectRadiance( const in GeometricContext geometry, const in float blinnShininessExponent, const in int maxMIPLevel ) {\\n\\t\\t#ifdef ENVMAP_MODE_REFLECTION\\n\\t\\t\\tvec3 reflectVec = reflect( -geometry.viewDir, geometry.normal );\\n\\t\\t#else\\n\\t\\t\\tvec3 reflectVec = refract( -geometry.viewDir, geometry.normal, refractionRatio );\\n\\t\\t#endif\\n\\t\\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\\n\\t\\tfloat specularMIPLevel = getSpecularMIPLevel( blinnShininessExponent, maxMIPLevel );\\n\\t\\t#ifdef ENVMAP_TYPE_CUBE\\n\\t\\t\\tvec3 queryReflectVec = vec3( flipEnvMap * reflectVec.x, reflectVec.yz );\\n\\t\\t\\t#ifdef TEXTURE_LOD_EXT\\n\\t\\t\\t\\tvec4 envMapColor = textureCubeLodEXT( envMap, queryReflectVec, specularMIPLevel );\\n\\t\\t\\t#else\\n\\t\\t\\t\\tvec4 envMapColor = textureCube( envMap, queryReflectVec, specularMIPLevel );\\n\\t\\t\\t#endif\\n\\t\\t\\tenvMapColor.rgb = envMapTexelToLinear( envMapColor ).rgb;\\n\\t\\t#elif defined( ENVMAP_TYPE_CUBE_UV )\\n\\t\\t\\tvec3 queryReflectVec = vec3( flipEnvMap * reflectVec.x, reflectVec.yz );\\n\\t\\t\\tvec4 envMapColor = textureCubeUV( envMap, queryReflectVec, BlinnExponentToGGXRoughness(blinnShininessExponent ));\\n\\t\\t#elif defined( ENVMAP_TYPE_EQUIREC )\\n\\t\\t\\tvec2 sampleUV;\\n\\t\\t\\tsampleUV.y = asin( clamp( reflectVec.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\\n\\t\\t\\tsampleUV.x = atan( reflectVec.z, reflectVec.x ) * RECIPROCAL_PI2 + 0.5;\\n\\t\\t\\t#ifdef TEXTURE_LOD_EXT\\n\\t\\t\\t\\tvec4 envMapColor = texture2DLodEXT( envMap, sampleUV, specularMIPLevel );\\n\\t\\t\\t#else\\n\\t\\t\\t\\tvec4 envMapColor = texture2D( envMap, sampleUV, specularMIPLevel );\\n\\t\\t\\t#endif\\n\\t\\t\\tenvMapColor.rgb = envMapTexelToLinear( envMapColor ).rgb;\\n\\t\\t#elif defined( ENVMAP_TYPE_SPHERE )\\n\\t\\t\\tvec3 reflectView = normalize( ( viewMatrix * vec4( reflectVec, 0.0 ) ).xyz + vec3( 0.0,0.0,1.0 ) );\\n\\t\\t\\t#ifdef TEXTURE_LOD_EXT\\n\\t\\t\\t\\tvec4 envMapColor = texture2DLodEXT( envMap, reflectView.xy * 0.5 + 0.5, specularMIPLevel );\\n\\t\\t\\t#else\\n\\t\\t\\t\\tvec4 envMapColor = texture2D( envMap, reflectView.xy * 0.5 + 0.5, specularMIPLevel );\\n\\t\\t\\t#endif\\n\\t\\t\\tenvMapColor.rgb = envMapTexelToLinear( envMapColor ).rgb;\\n\\t\\t#endif\\n\\t\\treturn envMapColor.rgb * envMapIntensity;\\n\\t}\\n#endif\";\n\nvar lights_phong_fragment = \"BlinnPhongMaterial material;\\nmaterial.diffuseColor = diffuseColor.rgb;\\nmaterial.specularColor = specular;\\nmaterial.specularShininess = shininess;\\nmaterial.specularStrength = specularStrength;\";\n\nvar lights_phong_pars_fragment = \"varying vec3 vViewPosition;\\n#ifndef FLAT_SHADED\\n\\tvarying vec3 vNormal;\\n#endif\\nstruct BlinnPhongMaterial {\\n\\tvec3\\tdiffuseColor;\\n\\tvec3\\tspecularColor;\\n\\tfloat\\tspecularShininess;\\n\\tfloat\\tspecularStrength;\\n};\\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\n\\t#ifdef TOON\\n\\t\\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\\n\\t#else\\n\\t\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\n\\t\\tvec3 irradiance = dotNL * directLight.color;\\n\\t#endif\\n\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\n\\t\\tirradiance *= PI;\\n\\t#endif\\n\\treflectedLight.directDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\\n\\treflectedLight.directSpecular += irradiance * BRDF_Specular_BlinnPhong( directLight, geometry, material.specularColor, material.specularShininess ) * material.specularStrength;\\n}\\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\\n\\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\\n}\\n#define RE_Direct\\t\\t\\t\\tRE_Direct_BlinnPhong\\n#define RE_IndirectDiffuse\\t\\tRE_IndirectDiffuse_BlinnPhong\\n#define Material_LightProbeLOD( material )\\t(0)\";\n\nvar lights_physical_fragment = \"PhysicalMaterial material;\\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\\nmaterial.specularRoughness = clamp( roughnessFactor, 0.04, 1.0 );\\n#ifdef STANDARD\\n\\tmaterial.specularColor = mix( vec3( DEFAULT_SPECULAR_COEFFICIENT ), diffuseColor.rgb, metalnessFactor );\\n#else\\n\\tmaterial.specularColor = mix( vec3( MAXIMUM_SPECULAR_COEFFICIENT * pow2( reflectivity ) ), diffuseColor.rgb, metalnessFactor );\\n\\tmaterial.clearCoat = saturate( clearCoat );\\tmaterial.clearCoatRoughness = clamp( clearCoatRoughness, 0.04, 1.0 );\\n#endif\";\n\nvar lights_physical_pars_fragment = \"struct PhysicalMaterial {\\n\\tvec3\\tdiffuseColor;\\n\\tfloat\\tspecularRoughness;\\n\\tvec3\\tspecularColor;\\n\\t#ifndef STANDARD\\n\\t\\tfloat clearCoat;\\n\\t\\tfloat clearCoatRoughness;\\n\\t#endif\\n};\\n#define MAXIMUM_SPECULAR_COEFFICIENT 0.16\\n#define DEFAULT_SPECULAR_COEFFICIENT 0.04\\nfloat clearCoatDHRApprox( const in float roughness, const in float dotNL ) {\\n\\treturn DEFAULT_SPECULAR_COEFFICIENT + ( 1.0 - DEFAULT_SPECULAR_COEFFICIENT ) * ( pow( 1.0 - dotNL, 5.0 ) * pow( 1.0 - roughness, 2.0 ) );\\n}\\n#if NUM_RECT_AREA_LIGHTS > 0\\n\\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\n\\t\\tvec3 normal = geometry.normal;\\n\\t\\tvec3 viewDir = geometry.viewDir;\\n\\t\\tvec3 position = geometry.position;\\n\\t\\tvec3 lightPos = rectAreaLight.position;\\n\\t\\tvec3 halfWidth = rectAreaLight.halfWidth;\\n\\t\\tvec3 halfHeight = rectAreaLight.halfHeight;\\n\\t\\tvec3 lightColor = rectAreaLight.color;\\n\\t\\tfloat roughness = material.specularRoughness;\\n\\t\\tvec3 rectCoords[ 4 ];\\n\\t\\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight;\\t\\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\\n\\t\\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\\n\\t\\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\\n\\t\\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\\n\\t\\tvec4 t1 = texture2D( ltc_1, uv );\\n\\t\\tvec4 t2 = texture2D( ltc_2, uv );\\n\\t\\tmat3 mInv = mat3(\\n\\t\\t\\tvec3( t1.x, 0, t1.y ),\\n\\t\\t\\tvec3( 0, 1, 0 ),\\n\\t\\t\\tvec3( t1.z, 0, t1.w )\\n\\t\\t);\\n\\t\\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\\n\\t\\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\\n\\t\\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\\n\\t}\\n#endif\\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\n\\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\\n\\tvec3 irradiance = dotNL * directLight.color;\\n\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\n\\t\\tirradiance *= PI;\\n\\t#endif\\n\\t#ifndef STANDARD\\n\\t\\tfloat clearCoatDHR = material.clearCoat * clearCoatDHRApprox( material.clearCoatRoughness, dotNL );\\n\\t#else\\n\\t\\tfloat clearCoatDHR = 0.0;\\n\\t#endif\\n\\treflectedLight.directSpecular += ( 1.0 - clearCoatDHR ) * irradiance * BRDF_Specular_GGX( directLight, geometry, material.specularColor, material.specularRoughness );\\n\\treflectedLight.directDiffuse += ( 1.0 - clearCoatDHR ) * irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\\n\\t#ifndef STANDARD\\n\\t\\treflectedLight.directSpecular += irradiance * material.clearCoat * BRDF_Specular_GGX( directLight, geometry, vec3( DEFAULT_SPECULAR_COEFFICIENT ), material.clearCoatRoughness );\\n\\t#endif\\n}\\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\\n\\t#ifndef ENVMAP_TYPE_CUBE_UV\\n\\t\\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\\n\\t#endif\\n}\\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearCoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\\n\\t#ifndef STANDARD\\n\\t\\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\\n\\t\\tfloat dotNL = dotNV;\\n\\t\\tfloat clearCoatDHR = material.clearCoat * clearCoatDHRApprox( material.clearCoatRoughness, dotNL );\\n\\t#else\\n\\t\\tfloat clearCoatDHR = 0.0;\\n\\t#endif\\n\\tfloat clearCoatInv = 1.0 - clearCoatDHR;\\n\\t#if defined( ENVMAP_TYPE_CUBE_UV )\\n\\t\\tvec3 singleScattering = vec3( 0.0 );\\n\\t\\tvec3 multiScattering = vec3( 0.0 );\\n\\t\\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\\n\\t\\tBRDF_Specular_Multiscattering_Environment( geometry, material.specularColor, material.specularRoughness, singleScattering, multiScattering );\\n\\t\\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\\n\\t\\treflectedLight.indirectSpecular += clearCoatInv * radiance * singleScattering;\\n\\t\\treflectedLight.indirectDiffuse += multiScattering * cosineWeightedIrradiance;\\n\\t\\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\\n\\t#else\\n\\t\\treflectedLight.indirectSpecular += clearCoatInv * radiance * BRDF_Specular_GGX_Environment( geometry, material.specularColor, material.specularRoughness );\\n\\t#endif\\n\\t#ifndef STANDARD\\n\\t\\treflectedLight.indirectSpecular += clearCoatRadiance * material.clearCoat * BRDF_Specular_GGX_Environment( geometry, vec3( DEFAULT_SPECULAR_COEFFICIENT ), material.clearCoatRoughness );\\n\\t#endif\\n}\\n#define RE_Direct\\t\\t\\t\\tRE_Direct_Physical\\n#define RE_Direct_RectArea\\t\\tRE_Direct_RectArea_Physical\\n#define RE_IndirectDiffuse\\t\\tRE_IndirectDiffuse_Physical\\n#define RE_IndirectSpecular\\t\\tRE_IndirectSpecular_Physical\\n#define Material_BlinnShininessExponent( material ) GGXRoughnessToBlinnExponent( material.specularRoughness )\\n#define Material_ClearCoat_BlinnShininessExponent( material ) GGXRoughnessToBlinnExponent( material.clearCoatRoughness )\\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\\n\\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\\n}\";\n\nvar lights_fragment_begin = \"\\nGeometricContext geometry;\\ngeometry.position = - vViewPosition;\\ngeometry.normal = normal;\\ngeometry.viewDir = normalize( vViewPosition );\\nIncidentLight directLight;\\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\\n\\tPointLight pointLight;\\n\\t#pragma unroll_loop\\n\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\n\\t\\tpointLight = pointLights[ i ];\\n\\t\\tgetPointDirectLightIrradiance( pointLight, geometry, directLight );\\n\\t\\t#ifdef USE_SHADOWMAP\\n\\t\\tdirectLight.color *= all( bvec2( pointLight.shadow, directLight.visible ) ) ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\\n\\t\\t#endif\\n\\t\\tRE_Direct( directLight, geometry, material, reflectedLight );\\n\\t}\\n#endif\\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\\n\\tSpotLight spotLight;\\n\\t#pragma unroll_loop\\n\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\n\\t\\tspotLight = spotLights[ i ];\\n\\t\\tgetSpotDirectLightIrradiance( spotLight, geometry, directLight );\\n\\t\\t#ifdef USE_SHADOWMAP\\n\\t\\tdirectLight.color *= all( bvec2( spotLight.shadow, directLight.visible ) ) ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\n\\t\\t#endif\\n\\t\\tRE_Direct( directLight, geometry, material, reflectedLight );\\n\\t}\\n#endif\\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\\n\\tDirectionalLight directionalLight;\\n\\t#pragma unroll_loop\\n\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\n\\t\\tdirectionalLight = directionalLights[ i ];\\n\\t\\tgetDirectionalDirectLightIrradiance( directionalLight, geometry, directLight );\\n\\t\\t#ifdef USE_SHADOWMAP\\n\\t\\tdirectLight.color *= all( bvec2( directionalLight.shadow, directLight.visible ) ) ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\n\\t\\t#endif\\n\\t\\tRE_Direct( directLight, geometry, material, reflectedLight );\\n\\t}\\n#endif\\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\\n\\tRectAreaLight rectAreaLight;\\n\\t#pragma unroll_loop\\n\\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\\n\\t\\trectAreaLight = rectAreaLights[ i ];\\n\\t\\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\\n\\t}\\n#endif\\n#if defined( RE_IndirectDiffuse )\\n\\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\\n\\tirradiance += getLightProbeIrradiance( lightProbe, geometry );\\n\\t#if ( NUM_HEMI_LIGHTS > 0 )\\n\\t\\t#pragma unroll_loop\\n\\t\\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\\n\\t\\t\\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\\n\\t\\t}\\n\\t#endif\\n#endif\\n#if defined( RE_IndirectSpecular )\\n\\tvec3 radiance = vec3( 0.0 );\\n\\tvec3 clearCoatRadiance = vec3( 0.0 );\\n#endif\";\n\nvar lights_fragment_maps = \"#if defined( RE_IndirectDiffuse )\\n\\t#ifdef USE_LIGHTMAP\\n\\t\\tvec3 lightMapIrradiance = texture2D( lightMap, vUv2 ).xyz * lightMapIntensity;\\n\\t\\t#ifndef PHYSICALLY_CORRECT_LIGHTS\\n\\t\\t\\tlightMapIrradiance *= PI;\\n\\t\\t#endif\\n\\t\\tirradiance += lightMapIrradiance;\\n\\t#endif\\n\\t#if defined( USE_ENVMAP ) && defined( PHYSICAL ) && defined( ENVMAP_TYPE_CUBE_UV )\\n\\t\\tirradiance += getLightProbeIndirectIrradiance( geometry, maxMipLevel );\\n\\t#endif\\n#endif\\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\\n\\tradiance += getLightProbeIndirectRadiance( geometry, Material_BlinnShininessExponent( material ), maxMipLevel );\\n\\t#ifndef STANDARD\\n\\t\\tclearCoatRadiance += getLightProbeIndirectRadiance( geometry, Material_ClearCoat_BlinnShininessExponent( material ), maxMipLevel );\\n\\t#endif\\n#endif\";\n\nvar lights_fragment_end = \"#if defined( RE_IndirectDiffuse )\\n\\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\\n#endif\\n#if defined( RE_IndirectSpecular )\\n\\tRE_IndirectSpecular( radiance, irradiance, clearCoatRadiance, geometry, material, reflectedLight );\\n#endif\";\n\nvar logdepthbuf_fragment = \"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\n\\tgl_FragDepthEXT = log2( vFragDepth ) * logDepthBufFC * 0.5;\\n#endif\";\n\nvar logdepthbuf_pars_fragment = \"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\\n\\tuniform float logDepthBufFC;\\n\\tvarying float vFragDepth;\\n#endif\";\n\nvar logdepthbuf_pars_vertex = \"#ifdef USE_LOGDEPTHBUF\\n\\t#ifdef USE_LOGDEPTHBUF_EXT\\n\\t\\tvarying float vFragDepth;\\n\\t#else\\n\\t\\tuniform float logDepthBufFC;\\n\\t#endif\\n#endif\";\n\nvar logdepthbuf_vertex = \"#ifdef USE_LOGDEPTHBUF\\n\\t#ifdef USE_LOGDEPTHBUF_EXT\\n\\t\\tvFragDepth = 1.0 + gl_Position.w;\\n\\t#else\\n\\t\\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\\n\\t\\tgl_Position.z *= gl_Position.w;\\n\\t#endif\\n#endif\";\n\nvar map_fragment = \"#ifdef USE_MAP\\n\\tvec4 texelColor = texture2D( map, vUv );\\n\\ttexelColor = mapTexelToLinear( texelColor );\\n\\tdiffuseColor *= texelColor;\\n#endif\";\n\nvar map_pars_fragment = \"#ifdef USE_MAP\\n\\tuniform sampler2D map;\\n#endif\";\n\nvar map_particle_fragment = \"#ifdef USE_MAP\\n\\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\\n\\tvec4 mapTexel = texture2D( map, uv );\\n\\tdiffuseColor *= mapTexelToLinear( mapTexel );\\n#endif\";\n\nvar map_particle_pars_fragment = \"#ifdef USE_MAP\\n\\tuniform mat3 uvTransform;\\n\\tuniform sampler2D map;\\n#endif\";\n\nvar metalnessmap_fragment = \"float metalnessFactor = metalness;\\n#ifdef USE_METALNESSMAP\\n\\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\\n\\tmetalnessFactor *= texelMetalness.b;\\n#endif\";\n\nvar metalnessmap_pars_fragment = \"#ifdef USE_METALNESSMAP\\n\\tuniform sampler2D metalnessMap;\\n#endif\";\n\nvar morphnormal_vertex = \"#ifdef USE_MORPHNORMALS\\n\\tobjectNormal += ( morphNormal0 - normal ) * morphTargetInfluences[ 0 ];\\n\\tobjectNormal += ( morphNormal1 - normal ) * morphTargetInfluences[ 1 ];\\n\\tobjectNormal += ( morphNormal2 - normal ) * morphTargetInfluences[ 2 ];\\n\\tobjectNormal += ( morphNormal3 - normal ) * morphTargetInfluences[ 3 ];\\n#endif\";\n\nvar morphtarget_pars_vertex = \"#ifdef USE_MORPHTARGETS\\n\\t#ifndef USE_MORPHNORMALS\\n\\tuniform float morphTargetInfluences[ 8 ];\\n\\t#else\\n\\tuniform float morphTargetInfluences[ 4 ];\\n\\t#endif\\n#endif\";\n\nvar morphtarget_vertex = \"#ifdef USE_MORPHTARGETS\\n\\ttransformed += ( morphTarget0 - position ) * morphTargetInfluences[ 0 ];\\n\\ttransformed += ( morphTarget1 - position ) * morphTargetInfluences[ 1 ];\\n\\ttransformed += ( morphTarget2 - position ) * morphTargetInfluences[ 2 ];\\n\\ttransformed += ( morphTarget3 - position ) * morphTargetInfluences[ 3 ];\\n\\t#ifndef USE_MORPHNORMALS\\n\\ttransformed += ( morphTarget4 - position ) * morphTargetInfluences[ 4 ];\\n\\ttransformed += ( morphTarget5 - position ) * morphTargetInfluences[ 5 ];\\n\\ttransformed += ( morphTarget6 - position ) * morphTargetInfluences[ 6 ];\\n\\ttransformed += ( morphTarget7 - position ) * morphTargetInfluences[ 7 ];\\n\\t#endif\\n#endif\";\n\nvar normal_fragment_begin = \"#ifdef FLAT_SHADED\\n\\tvec3 fdx = vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\\n\\tvec3 fdy = vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\\n\\tvec3 normal = normalize( cross( fdx, fdy ) );\\n#else\\n\\tvec3 normal = normalize( vNormal );\\n\\t#ifdef DOUBLE_SIDED\\n\\t\\tnormal = normal * ( float( gl_FrontFacing ) * 2.0 - 1.0 );\\n\\t#endif\\n\\t#ifdef USE_TANGENT\\n\\t\\tvec3 tangent = normalize( vTangent );\\n\\t\\tvec3 bitangent = normalize( vBitangent );\\n\\t\\t#ifdef DOUBLE_SIDED\\n\\t\\t\\ttangent = tangent * ( float( gl_FrontFacing ) * 2.0 - 1.0 );\\n\\t\\t\\tbitangent = bitangent * ( float( gl_FrontFacing ) * 2.0 - 1.0 );\\n\\t\\t#endif\\n\\t#endif\\n#endif\";\n\nvar normal_fragment_maps = \"#ifdef USE_NORMALMAP\\n\\t#ifdef OBJECTSPACE_NORMALMAP\\n\\t\\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\n\\t\\t#ifdef FLIP_SIDED\\n\\t\\t\\tnormal = - normal;\\n\\t\\t#endif\\n\\t\\t#ifdef DOUBLE_SIDED\\n\\t\\t\\tnormal = normal * ( float( gl_FrontFacing ) * 2.0 - 1.0 );\\n\\t\\t#endif\\n\\t\\tnormal = normalize( normalMatrix * normal );\\n\\t#else\\n\\t\\t#ifdef USE_TANGENT\\n\\t\\t\\tmat3 vTBN = mat3( tangent, bitangent, normal );\\n\\t\\t\\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\n\\t\\t\\tmapN.xy = normalScale * mapN.xy;\\n\\t\\t\\tnormal = normalize( vTBN * mapN );\\n\\t\\t#else\\n\\t\\t\\tnormal = perturbNormal2Arb( -vViewPosition, normal );\\n\\t\\t#endif\\n\\t#endif\\n#elif defined( USE_BUMPMAP )\\n\\tnormal = perturbNormalArb( -vViewPosition, normal, dHdxy_fwd() );\\n#endif\";\n\nvar normalmap_pars_fragment = \"#ifdef USE_NORMALMAP\\n\\tuniform sampler2D normalMap;\\n\\tuniform vec2 normalScale;\\n\\t#ifdef OBJECTSPACE_NORMALMAP\\n\\t\\tuniform mat3 normalMatrix;\\n\\t#else\\n\\t\\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm ) {\\n\\t\\t\\tvec3 q0 = vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\\n\\t\\t\\tvec3 q1 = vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\\n\\t\\t\\tvec2 st0 = dFdx( vUv.st );\\n\\t\\t\\tvec2 st1 = dFdy( vUv.st );\\n\\t\\t\\tfloat scale = sign( st1.t * st0.s - st0.t * st1.s );\\n\\t\\t\\tvec3 S = normalize( ( q0 * st1.t - q1 * st0.t ) * scale );\\n\\t\\t\\tvec3 T = normalize( ( - q0 * st1.s + q1 * st0.s ) * scale );\\n\\t\\t\\tvec3 N = normalize( surf_norm );\\n\\t\\t\\tmat3 tsn = mat3( S, T, N );\\n\\t\\t\\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\\n\\t\\t\\tmapN.xy *= normalScale;\\n\\t\\t\\tmapN.xy *= ( float( gl_FrontFacing ) * 2.0 - 1.0 );\\n\\t\\t\\treturn normalize( tsn * mapN );\\n\\t\\t}\\n\\t#endif\\n#endif\";\n\nvar packing = \"vec3 packNormalToRGB( const in vec3 normal ) {\\n\\treturn normalize( normal ) * 0.5 + 0.5;\\n}\\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\\n\\treturn 2.0 * rgb.xyz - 1.0;\\n}\\nconst float PackUpscale = 256. / 255.;const float UnpackDownscale = 255. / 256.;\\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\\nconst float ShiftRight8 = 1. / 256.;\\nvec4 packDepthToRGBA( const in float v ) {\\n\\tvec4 r = vec4( fract( v * PackFactors ), v );\\n\\tr.yzw -= r.xyz * ShiftRight8;\\treturn r * PackUpscale;\\n}\\nfloat unpackRGBAToDepth( const in vec4 v ) {\\n\\treturn dot( v, UnpackFactors );\\n}\\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\\n\\treturn ( viewZ + near ) / ( near - far );\\n}\\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\\n\\treturn linearClipZ * ( near - far ) - near;\\n}\\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\\n\\treturn (( near + viewZ ) * far ) / (( far - near ) * viewZ );\\n}\\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\\n\\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\\n}\";\n\nvar premultiplied_alpha_fragment = \"#ifdef PREMULTIPLIED_ALPHA\\n\\tgl_FragColor.rgb *= gl_FragColor.a;\\n#endif\";\n\nvar project_vertex = \"vec4 mvPosition = modelViewMatrix * vec4( transformed, 1.0 );\\ngl_Position = projectionMatrix * mvPosition;\";\n\nvar dithering_fragment = \"#if defined( DITHERING )\\n\\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\\n#endif\";\n\nvar dithering_pars_fragment = \"#if defined( DITHERING )\\n\\tvec3 dithering( vec3 color ) {\\n\\t\\tfloat grid_position = rand( gl_FragCoord.xy );\\n\\t\\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\\n\\t\\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\\n\\t\\treturn color + dither_shift_RGB;\\n\\t}\\n#endif\";\n\nvar roughnessmap_fragment = \"float roughnessFactor = roughness;\\n#ifdef USE_ROUGHNESSMAP\\n\\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\\n\\troughnessFactor *= texelRoughness.g;\\n#endif\";\n\nvar roughnessmap_pars_fragment = \"#ifdef USE_ROUGHNESSMAP\\n\\tuniform sampler2D roughnessMap;\\n#endif\";\n\nvar shadowmap_pars_fragment = \"#ifdef USE_SHADOWMAP\\n\\t#if NUM_DIR_LIGHTS > 0\\n\\t\\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHTS ];\\n\\t\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHTS ];\\n\\t#endif\\n\\t#if NUM_SPOT_LIGHTS > 0\\n\\t\\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHTS ];\\n\\t\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHTS ];\\n\\t#endif\\n\\t#if NUM_POINT_LIGHTS > 0\\n\\t\\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHTS ];\\n\\t\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHTS ];\\n\\t#endif\\n\\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\\n\\t\\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\\n\\t}\\n\\tfloat texture2DShadowLerp( sampler2D depths, vec2 size, vec2 uv, float compare ) {\\n\\t\\tconst vec2 offset = vec2( 0.0, 1.0 );\\n\\t\\tvec2 texelSize = vec2( 1.0 ) / size;\\n\\t\\tvec2 centroidUV = floor( uv * size + 0.5 ) / size;\\n\\t\\tfloat lb = texture2DCompare( depths, centroidUV + texelSize * offset.xx, compare );\\n\\t\\tfloat lt = texture2DCompare( depths, centroidUV + texelSize * offset.xy, compare );\\n\\t\\tfloat rb = texture2DCompare( depths, centroidUV + texelSize * offset.yx, compare );\\n\\t\\tfloat rt = texture2DCompare( depths, centroidUV + texelSize * offset.yy, compare );\\n\\t\\tvec2 f = fract( uv * size + 0.5 );\\n\\t\\tfloat a = mix( lb, lt, f.y );\\n\\t\\tfloat b = mix( rb, rt, f.y );\\n\\t\\tfloat c = mix( a, b, f.x );\\n\\t\\treturn c;\\n\\t}\\n\\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\\n\\t\\tfloat shadow = 1.0;\\n\\t\\tshadowCoord.xyz /= shadowCoord.w;\\n\\t\\tshadowCoord.z += shadowBias;\\n\\t\\tbvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );\\n\\t\\tbool inFrustum = all( inFrustumVec );\\n\\t\\tbvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );\\n\\t\\tbool frustumTest = all( frustumTestVec );\\n\\t\\tif ( frustumTest ) {\\n\\t\\t#if defined( SHADOWMAP_TYPE_PCF )\\n\\t\\t\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\n\\t\\t\\tfloat dx0 = - texelSize.x * shadowRadius;\\n\\t\\t\\tfloat dy0 = - texelSize.y * shadowRadius;\\n\\t\\t\\tfloat dx1 = + texelSize.x * shadowRadius;\\n\\t\\t\\tfloat dy1 = + texelSize.y * shadowRadius;\\n\\t\\t\\tshadow = (\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\\n\\t\\t\\t) * ( 1.0 / 9.0 );\\n\\t\\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\\n\\t\\t\\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\\n\\t\\t\\tfloat dx0 = - texelSize.x * shadowRadius;\\n\\t\\t\\tfloat dy0 = - texelSize.y * shadowRadius;\\n\\t\\t\\tfloat dx1 = + texelSize.x * shadowRadius;\\n\\t\\t\\tfloat dy1 = + texelSize.y * shadowRadius;\\n\\t\\t\\tshadow = (\\n\\t\\t\\t\\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy, shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\\n\\t\\t\\t\\ttexture2DShadowLerp( shadowMap, shadowMapSize, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\\n\\t\\t\\t) * ( 1.0 / 9.0 );\\n\\t\\t#else\\n\\t\\t\\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\\n\\t\\t#endif\\n\\t\\t}\\n\\t\\treturn shadow;\\n\\t}\\n\\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\\n\\t\\tvec3 absV = abs( v );\\n\\t\\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\\n\\t\\tabsV *= scaleToCube;\\n\\t\\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\\n\\t\\tvec2 planar = v.xy;\\n\\t\\tfloat almostATexel = 1.5 * texelSizeY;\\n\\t\\tfloat almostOne = 1.0 - almostATexel;\\n\\t\\tif ( absV.z >= almostOne ) {\\n\\t\\t\\tif ( v.z > 0.0 )\\n\\t\\t\\t\\tplanar.x = 4.0 - v.x;\\n\\t\\t} else if ( absV.x >= almostOne ) {\\n\\t\\t\\tfloat signX = sign( v.x );\\n\\t\\t\\tplanar.x = v.z * signX + 2.0 * signX;\\n\\t\\t} else if ( absV.y >= almostOne ) {\\n\\t\\t\\tfloat signY = sign( v.y );\\n\\t\\t\\tplanar.x = v.x + 2.0 * signY + 2.0;\\n\\t\\t\\tplanar.y = v.z * signY - 2.0;\\n\\t\\t}\\n\\t\\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\\n\\t}\\n\\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\\n\\t\\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\\n\\t\\tvec3 lightToPosition = shadowCoord.xyz;\\n\\t\\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear );\\t\\tdp += shadowBias;\\n\\t\\tvec3 bd3D = normalize( lightToPosition );\\n\\t\\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT )\\n\\t\\t\\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\\n\\t\\t\\treturn (\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\\n\\t\\t\\t\\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\\n\\t\\t\\t) * ( 1.0 / 9.0 );\\n\\t\\t#else\\n\\t\\t\\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\\n\\t\\t#endif\\n\\t}\\n#endif\";\n\nvar shadowmap_pars_vertex = \"#ifdef USE_SHADOWMAP\\n\\t#if NUM_DIR_LIGHTS > 0\\n\\t\\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHTS ];\\n\\t\\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHTS ];\\n\\t#endif\\n\\t#if NUM_SPOT_LIGHTS > 0\\n\\t\\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHTS ];\\n\\t\\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHTS ];\\n\\t#endif\\n\\t#if NUM_POINT_LIGHTS > 0\\n\\t\\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHTS ];\\n\\t\\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHTS ];\\n\\t#endif\\n#endif\";\n\nvar shadowmap_vertex = \"#ifdef USE_SHADOWMAP\\n\\t#if NUM_DIR_LIGHTS > 0\\n\\t#pragma unroll_loop\\n\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\n\\t\\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * worldPosition;\\n\\t}\\n\\t#endif\\n\\t#if NUM_SPOT_LIGHTS > 0\\n\\t#pragma unroll_loop\\n\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\n\\t\\tvSpotShadowCoord[ i ] = spotShadowMatrix[ i ] * worldPosition;\\n\\t}\\n\\t#endif\\n\\t#if NUM_POINT_LIGHTS > 0\\n\\t#pragma unroll_loop\\n\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\n\\t\\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * worldPosition;\\n\\t}\\n\\t#endif\\n#endif\";\n\nvar shadowmask_pars_fragment = \"float getShadowMask() {\\n\\tfloat shadow = 1.0;\\n\\t#ifdef USE_SHADOWMAP\\n\\t#if NUM_DIR_LIGHTS > 0\\n\\tDirectionalLight directionalLight;\\n\\t#pragma unroll_loop\\n\\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\\n\\t\\tdirectionalLight = directionalLights[ i ];\\n\\t\\tshadow *= bool( directionalLight.shadow ) ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\\n\\t}\\n\\t#endif\\n\\t#if NUM_SPOT_LIGHTS > 0\\n\\tSpotLight spotLight;\\n\\t#pragma unroll_loop\\n\\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\\n\\t\\tspotLight = spotLights[ i ];\\n\\t\\tshadow *= bool( spotLight.shadow ) ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\\n\\t}\\n\\t#endif\\n\\t#if NUM_POINT_LIGHTS > 0\\n\\tPointLight pointLight;\\n\\t#pragma unroll_loop\\n\\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\\n\\t\\tpointLight = pointLights[ i ];\\n\\t\\tshadow *= bool( pointLight.shadow ) ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\\n\\t}\\n\\t#endif\\n\\t#endif\\n\\treturn shadow;\\n}\";\n\nvar skinbase_vertex = \"#ifdef USE_SKINNING\\n\\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\\n\\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\\n\\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\\n\\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\\n#endif\";\n\nvar skinning_pars_vertex = \"#ifdef USE_SKINNING\\n\\tuniform mat4 bindMatrix;\\n\\tuniform mat4 bindMatrixInverse;\\n\\t#ifdef BONE_TEXTURE\\n\\t\\tuniform sampler2D boneTexture;\\n\\t\\tuniform int boneTextureSize;\\n\\t\\tmat4 getBoneMatrix( const in float i ) {\\n\\t\\t\\tfloat j = i * 4.0;\\n\\t\\t\\tfloat x = mod( j, float( boneTextureSize ) );\\n\\t\\t\\tfloat y = floor( j / float( boneTextureSize ) );\\n\\t\\t\\tfloat dx = 1.0 / float( boneTextureSize );\\n\\t\\t\\tfloat dy = 1.0 / float( boneTextureSize );\\n\\t\\t\\ty = dy * ( y + 0.5 );\\n\\t\\t\\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\\n\\t\\t\\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\\n\\t\\t\\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\\n\\t\\t\\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\\n\\t\\t\\tmat4 bone = mat4( v1, v2, v3, v4 );\\n\\t\\t\\treturn bone;\\n\\t\\t}\\n\\t#else\\n\\t\\tuniform mat4 boneMatrices[ MAX_BONES ];\\n\\t\\tmat4 getBoneMatrix( const in float i ) {\\n\\t\\t\\tmat4 bone = boneMatrices[ int(i) ];\\n\\t\\t\\treturn bone;\\n\\t\\t}\\n\\t#endif\\n#endif\";\n\nvar skinning_vertex = \"#ifdef USE_SKINNING\\n\\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\\n\\tvec4 skinned = vec4( 0.0 );\\n\\tskinned += boneMatX * skinVertex * skinWeight.x;\\n\\tskinned += boneMatY * skinVertex * skinWeight.y;\\n\\tskinned += boneMatZ * skinVertex * skinWeight.z;\\n\\tskinned += boneMatW * skinVertex * skinWeight.w;\\n\\ttransformed = ( bindMatrixInverse * skinned ).xyz;\\n#endif\";\n\nvar skinnormal_vertex = \"#ifdef USE_SKINNING\\n\\tmat4 skinMatrix = mat4( 0.0 );\\n\\tskinMatrix += skinWeight.x * boneMatX;\\n\\tskinMatrix += skinWeight.y * boneMatY;\\n\\tskinMatrix += skinWeight.z * boneMatZ;\\n\\tskinMatrix += skinWeight.w * boneMatW;\\n\\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\\n\\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\\n\\t#ifdef USE_TANGENT\\n\\t\\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\\n\\t#endif\\n#endif\";\n\nvar specularmap_fragment = \"float specularStrength;\\n#ifdef USE_SPECULARMAP\\n\\tvec4 texelSpecular = texture2D( specularMap, vUv );\\n\\tspecularStrength = texelSpecular.r;\\n#else\\n\\tspecularStrength = 1.0;\\n#endif\";\n\nvar specularmap_pars_fragment = \"#ifdef USE_SPECULARMAP\\n\\tuniform sampler2D specularMap;\\n#endif\";\n\nvar tonemapping_fragment = \"#if defined( TONE_MAPPING )\\n\\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\\n#endif\";\n\nvar tonemapping_pars_fragment = \"#ifndef saturate\\n\\t#define saturate(a) clamp( a, 0.0, 1.0 )\\n#endif\\nuniform float toneMappingExposure;\\nuniform float toneMappingWhitePoint;\\nvec3 LinearToneMapping( vec3 color ) {\\n\\treturn toneMappingExposure * color;\\n}\\nvec3 ReinhardToneMapping( vec3 color ) {\\n\\tcolor *= toneMappingExposure;\\n\\treturn saturate( color / ( vec3( 1.0 ) + color ) );\\n}\\n#define Uncharted2Helper( x ) max( ( ( x * ( 0.15 * x + 0.10 * 0.50 ) + 0.20 * 0.02 ) / ( x * ( 0.15 * x + 0.50 ) + 0.20 * 0.30 ) ) - 0.02 / 0.30, vec3( 0.0 ) )\\nvec3 Uncharted2ToneMapping( vec3 color ) {\\n\\tcolor *= toneMappingExposure;\\n\\treturn saturate( Uncharted2Helper( color ) / Uncharted2Helper( vec3( toneMappingWhitePoint ) ) );\\n}\\nvec3 OptimizedCineonToneMapping( vec3 color ) {\\n\\tcolor *= toneMappingExposure;\\n\\tcolor = max( vec3( 0.0 ), color - 0.004 );\\n\\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\\n}\\nvec3 ACESFilmicToneMapping( vec3 color ) {\\n\\tcolor *= toneMappingExposure;\\n\\treturn saturate( ( color * ( 2.51 * color + 0.03 ) ) / ( color * ( 2.43 * color + 0.59 ) + 0.14 ) );\\n}\";\n\nvar uv_pars_fragment = \"#if defined( USE_MAP ) || defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( USE_SPECULARMAP ) || defined( USE_ALPHAMAP ) || defined( USE_EMISSIVEMAP ) || defined( USE_ROUGHNESSMAP ) || defined( USE_METALNESSMAP )\\n\\tvarying vec2 vUv;\\n#endif\";\n\nvar uv_pars_vertex = \"#if defined( USE_MAP ) || defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( USE_SPECULARMAP ) || defined( USE_ALPHAMAP ) || defined( USE_EMISSIVEMAP ) || defined( USE_ROUGHNESSMAP ) || defined( USE_METALNESSMAP )\\n\\tvarying vec2 vUv;\\n\\tuniform mat3 uvTransform;\\n#endif\";\n\nvar uv_vertex = \"#if defined( USE_MAP ) || defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( USE_SPECULARMAP ) || defined( USE_ALPHAMAP ) || defined( USE_EMISSIVEMAP ) || defined( USE_ROUGHNESSMAP ) || defined( USE_METALNESSMAP )\\n\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\n#endif\";\n\nvar uv2_pars_fragment = \"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\n\\tvarying vec2 vUv2;\\n#endif\";\n\nvar uv2_pars_vertex = \"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\n\\tattribute vec2 uv2;\\n\\tvarying vec2 vUv2;\\n#endif\";\n\nvar uv2_vertex = \"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\\n\\tvUv2 = uv2;\\n#endif\";\n\nvar worldpos_vertex = \"#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP )\\n\\tvec4 worldPosition = modelMatrix * vec4( transformed, 1.0 );\\n#endif\";\n\nvar background_frag = \"uniform sampler2D t2D;\\nvarying vec2 vUv;\\nvoid main() {\\n\\tvec4 texColor = texture2D( t2D, vUv );\\n\\tgl_FragColor = mapTexelToLinear( texColor );\\n\\t#include \\n\\t#include \\n}\";\n\nvar background_vert = \"varying vec2 vUv;\\nuniform mat3 uvTransform;\\nvoid main() {\\n\\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\\n\\tgl_Position = vec4( position.xy, 1.0, 1.0 );\\n}\";\n\nvar cube_frag = \"uniform samplerCube tCube;\\nuniform float tFlip;\\nuniform float opacity;\\nvarying vec3 vWorldDirection;\\nvoid main() {\\n\\tvec4 texColor = textureCube( tCube, vec3( tFlip * vWorldDirection.x, vWorldDirection.yz ) );\\n\\tgl_FragColor = mapTexelToLinear( texColor );\\n\\tgl_FragColor.a *= opacity;\\n\\t#include \\n\\t#include \\n}\";\n\nvar cube_vert = \"varying vec3 vWorldDirection;\\n#include \\nvoid main() {\\n\\tvWorldDirection = transformDirection( position, modelMatrix );\\n\\t#include \\n\\t#include \\n\\tgl_Position.z = gl_Position.w;\\n}\";\n\nvar depth_frag = \"#if DEPTH_PACKING == 3200\\n\\tuniform float opacity;\\n#endif\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\nvoid main() {\\n\\t#include \\n\\tvec4 diffuseColor = vec4( 1.0 );\\n\\t#if DEPTH_PACKING == 3200\\n\\t\\tdiffuseColor.a = opacity;\\n\\t#endif\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#if DEPTH_PACKING == 3200\\n\\t\\tgl_FragColor = vec4( vec3( 1.0 - gl_FragCoord.z ), opacity );\\n\\t#elif DEPTH_PACKING == 3201\\n\\t\\tgl_FragColor = packDepthToRGBA( gl_FragCoord.z );\\n\\t#endif\\n}\";\n\nvar depth_vert = \"#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\nvoid main() {\\n\\t#include \\n\\t#include \\n\\t#ifdef USE_DISPLACEMENTMAP\\n\\t\\t#include \\n\\t\\t#include \\n\\t\\t#include \\n\\t#endif\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n}\";\n\nvar distanceRGBA_frag = \"#define DISTANCE\\nuniform vec3 referencePosition;\\nuniform float nearDistance;\\nuniform float farDistance;\\nvarying vec3 vWorldPosition;\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\nvoid main () {\\n\\t#include \\n\\tvec4 diffuseColor = vec4( 1.0 );\\n\\t#include \\n\\t#include \\n\\t#include \\n\\tfloat dist = length( vWorldPosition - referencePosition );\\n\\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\\n\\tdist = saturate( dist );\\n\\tgl_FragColor = packDepthToRGBA( dist );\\n}\";\n\nvar distanceRGBA_vert = \"#define DISTANCE\\nvarying vec3 vWorldPosition;\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\nvoid main() {\\n\\t#include \\n\\t#include \\n\\t#ifdef USE_DISPLACEMENTMAP\\n\\t\\t#include \\n\\t\\t#include \\n\\t\\t#include \\n\\t#endif\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\tvWorldPosition = worldPosition.xyz;\\n}\";\n\nvar equirect_frag = \"uniform sampler2D tEquirect;\\nvarying vec3 vWorldDirection;\\n#include \\nvoid main() {\\n\\tvec3 direction = normalize( vWorldDirection );\\n\\tvec2 sampleUV;\\n\\tsampleUV.y = asin( clamp( direction.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\\n\\tsampleUV.x = atan( direction.z, direction.x ) * RECIPROCAL_PI2 + 0.5;\\n\\tvec4 texColor = texture2D( tEquirect, sampleUV );\\n\\tgl_FragColor = mapTexelToLinear( texColor );\\n\\t#include \\n\\t#include \\n}\";\n\nvar equirect_vert = \"varying vec3 vWorldDirection;\\n#include \\nvoid main() {\\n\\tvWorldDirection = transformDirection( position, modelMatrix );\\n\\t#include \\n\\t#include \\n}\";\n\nvar linedashed_frag = \"uniform vec3 diffuse;\\nuniform float opacity;\\nuniform float dashSize;\\nuniform float totalSize;\\nvarying float vLineDistance;\\n#include \\n#include \\n#include \\n#include \\n#include \\nvoid main() {\\n\\t#include \\n\\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\\n\\t\\tdiscard;\\n\\t}\\n\\tvec3 outgoingLight = vec3( 0.0 );\\n\\tvec4 diffuseColor = vec4( diffuse, opacity );\\n\\t#include \\n\\t#include \\n\\toutgoingLight = diffuseColor.rgb;\\n\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n}\";\n\nvar linedashed_vert = \"uniform float scale;\\nattribute float lineDistance;\\nvarying float vLineDistance;\\n#include \\n#include \\n#include \\n#include \\n#include \\nvoid main() {\\n\\t#include \\n\\tvLineDistance = scale * lineDistance;\\n\\tvec4 mvPosition = modelViewMatrix * vec4( position, 1.0 );\\n\\tgl_Position = projectionMatrix * mvPosition;\\n\\t#include \\n\\t#include \\n\\t#include \\n}\";\n\nvar meshbasic_frag = \"uniform vec3 diffuse;\\nuniform float opacity;\\n#ifndef FLAT_SHADED\\n\\tvarying vec3 vNormal;\\n#endif\\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\nvoid main() {\\n\\t#include \\n\\tvec4 diffuseColor = vec4( diffuse, opacity );\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\\n\\t#ifdef USE_LIGHTMAP\\n\\t\\treflectedLight.indirectDiffuse += texture2D( lightMap, vUv2 ).xyz * lightMapIntensity;\\n\\t#else\\n\\t\\treflectedLight.indirectDiffuse += vec3( 1.0 );\\n\\t#endif\\n\\t#include \\n\\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\\n\\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\\n\\t#include \\n\\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n}\";\n\nvar meshbasic_vert = \"#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\n#include \\nvoid main() {\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#ifdef USE_ENVMAP\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#endif\\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n\\t#include \\n}\";\n\nvar meshlambert_frag = \"uniform vec3 diffuse;\\nuniform vec3 emissive;\\nuniform float opacity;\\nvarying vec3 vLightFront;\\nvarying vec3 vIndirectFront;\\n#ifdef DOUBLE_SIDED\\n\\tvarying vec3 vLightBack;\\n\\tvarying vec3 vIndirectBack;\\n#endif\\n#include \\n#include \\n#include