Dimensionality reduction (mixing the rational and irrational)

Aaaand for good measure I implemented the r2 output as well.


----------begin_max5_patcher----------
11266.3oc68k1bikbkcet5eEbX3O3QS0bx8k4SVK1xQXoYlvRNT3nGGU.Rhh
M5FDfF.r5pzDZ9s6yIW.AHwCHA46Qhpc2REKVOr7t4MuKm6x6l+6ey6N+x4e
d7xyO6e5ru6r28t+8u4cuKcIdg2U92u67aG84qlNZY5sc9rw+z7K+gyee9kV
M9yqRW9tyVn9sild08SGsZx7Y0W+tQqt56mL6lOrX7Uqx2FiIbg88mokgKDu
+rPj+TotPb1+mxmY98qlNd0pub237G37yW+RStNcy.A7spnudSlc+sSlgORh
.kObw7WzVWMQOiWTVekE36N+iSlN9SiWrjT9Cu62c9n6taiK+tM9Hjq7CySe
Qg2u9RSlkujb8kVL9SSped65qNZA3Jq.K49EoE44e1YN+gul4WOdwr6mj9lx
W7u8MURJsSLazsiWd2nqxeXtgUe4G3y1DmUJL4+Rl96f18.mFa02Lc9U+33D
WUTu376FOaxr6VLd43Yqx6la9xWO9iite5pO7w4yVsbxeMQBRr+sqW+iERbm
uHWDI5+WuXxnoqWA2rXx0ymQhXqsBd45s66NSZSqJ6lKlz6X1n61wGFBLfuz
wKtDKx6Wd4nEbm5xoo6fp9hqlOe51uz5O2zwebU4kuaxrYOhKtZ9cc+hKlby
2umO6kywKd699tSuxxOb+r7q9AHTr5CKG8os41qFMcZQec6u9OOZ1jaGsZ7p
I4s.kX8KNd1HrP+9kWsX9zoasdyuxm1wqbMDxuZ7OM45Uee5Fsov.d6StqJD
c95c4qmby3kq19ZqFcyxsuxxUeIyz23R2eYQI9CqFe6cvbyidCP8XxxUK+94
+zxxarJnsIC3AidapTuowust99LBtsgvUmM8roa9J6vDnUj0Iy+UzP6gaZBr
CyfueCSgOwbnyt4McWlD2gYQU8E9aey2T+k22+7j+5zytYw76u6.rEmHwHLp
KLVoSqkV7Gk2He+YV4icS7r3QtN4Qp2Zdz8+0IsI0.wkny6sViF+Q5iu+Lin
MtykilcC4P0+dxrU6gY4eFLK8ql.0zwyZieY7WXB5f26k3OhnUAvG9dQbJb5
JNkLCU+eso2E7WDgHkTD73OZKkrjF3hci+SJMMy2d7e5lOFeFltbuN7wuEdy
N.6S6RHsrJ8EjyojdiR57RK3eJQrMIsONc9n8nL5kOCQM4qCK5Wcl7PrHaFL
pw6t.ZhRiQnL5nSqfoceit+NDGRc5xgf4pk2e6gzBkIFgQEtHJzvWkV3bJKr
cgKp6C6Ud8oq8plXQUwnAjEYNcYQMnmULEsS8LYqXnNjhl8jVQqEbAxNkh5G
fAd2W4JZU4nATQye5xh9z3Oe2hy9O8Q4Y+J7SUa7JcPdgQF0gfMf+XbFXLWJ
aDY9dXTgSW0sF.GkwVtSrQxPOgMJ9UM1HkNwhFRrQAwW4XiTA2.aOJH+J2jc
ULZ.YQpupwF45VMSpC8idl9zUOaqDBzTZA.2II1rq7BPC5hmcl.ZJq.AyyHq
.wSHblxfXfwYFrekazxM31rb+bAlYlUMTnLC9SWKW+isZa241gscsx2K11sl
SWNTRP5t4+z+YHL8u8ugE6eeS7KubGnxi1WprjU9LLbKO0xgh0KuPCSQwnwF
iZeL5gvjUzKgrXNgCpa4+2EqZiE4j6vtspeBpy3OcEhZHrWet1tCZMAz1upi
6MDECcMAztuxi6E1dFX.RZ+W4PHqhQCHKJ7UcbuESQCZMAzwuxqIPPqF3X0L
huxUzpxQCmhlQ9ykf0J7pgJZMyacstS2ulZ+lbqIoW+ym6RN1MXGQ2K4Mdkq
le6simscGGltIytd7m6a9SlFZKHLmOAQTqW2hMcxu1hg3dF.jEuTFxCca6zI
y5pGJSqU956lSsb98Ktptiut2EOSt0R85wKWMY15tO961HofmAlRS6UGMcP7
hmIZgNzEJdfni3QQGCG+v2Lc3FT9Q3nniAieP6tsQGzU1.RGhl4GwgbeotJa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.a3qFu3C4AhylKfONewsiRTtae7xKuYKBP89s9wCDvN5zgij
miugKGuXeBL1T2sDDWXCBmTZj3OVWz89ydZut1B6cq1BRzBSqAwMUOp.c83E
S9DLn7owOnHswrt5Czf0UyuOWYbU2bN1bGr27hpmJY51uplJbBqpYhuwpZJ+
WmpZcMX7tc7nY+Ke72sVr6vyFOo2dAarJmKXve7Zuh8Mj8v6Aa2mdayVc+x7
xaeyKOoUjXv5Xt8rc4NBLZ+kAl2uLv79kAl2fMv7tawjYGpU4j4mJIcdHVl6
U2mz.kaZr64zabuNMRJ8FzTmSpk5cL3xNpGcjCMh2TxS2YF38y9g4SNDmJCK
QFR+k6Xejj6luHdF7E8I0rTr.d2m3L8yzST5OkG2cWd1zy9qSuZ53QKNpouo
PdbyNQ9m58oaVk8zU.5u6aO7yLRTle9qLkV3tOdXrjlS6GFqUKFO51yTsYwg
CLhGCP20KOmHx25gz1qeu8qL+rq29Ktv8YW3AWL+eAUS81ux9ymd6W1bsKex
6a9hqygdJFlzMSqzsUlH4fVlYUykMSNnoeW1bY.j9AmNju86KxlaOD0fVtJU
ysKizL3zgrY5XvJuqzbhHm1roMkraiaxAZyp4Zoor+buVZ2Nd4xQ2L9I4lc8
DTXm4223uPY2coQTwGCIt0SsDgZmYgUs6rv1iqU29VqFo9Be+uVkuQq00OHw
6ttMhgXeULvq06Aft62yxRpRiJhcspJg5t+kUJIbOJ3jymrZ7sKqg2c+zo7U
mNYY5c9d9iYy4O+3jE4q8wEI82cvfjQaOUKPc+Ual+gyjGliJUgKDNiIDwuE
kNq1zlfxlodaaVwPaCnik6MiVMF3caYMuyBPYMMnc79NzPjwiwZPOV.NFa+z
IWMduqakqjFH8EBM.jCMG7m.mQFVaak9riUsXfW0cUl6kSme2381pHBcdfXt
i5Waj6q90xP7Tt909215WKC+rp90q1ZVgtGaFJOb8HcRkREvehBS.WrYsmN0
fVOjKekcNb2YWOY4cSG8k8gkvI23rry5N7Zc6DGu8B07KEnu4CztPtZO9vuT
d9eo77+R44Gtxy+vfO5efC9ny9ur7pQSGsfpzmIOzLIWjpTuN6DP5cuvAezI
bEjZopZJatB0QS9u5ixpcBOwww6axs2e6gGVlU9hMwW7l9rEGNwOg1NTeu3y
PqR9iNK55gCJR4I8wDIvPr3f0f0kzbzwPdtF1CUcUdJetOwAXHwczDSonCo5
Elxo83k+HNRQ09mblY1K8DizK+4vIJpXq.lwuNLGonxm0Yz2o3QJpNWc+dQB
RepeHhdz8Tkx6OtNwa+cSkz8V2MUu5sDibOm0Ne01RLJcptKtbX.GtMXj643
z4qs1fQ17HtTNni.jVqb9vRExlGPLxAc.Oj2UTu8zQyC.D4f9.3JadP4IGzA
cUq8cyP1kIxifFFrNLoQZXPkMasmeFRZHMlnZROcPGvVx5Xy5MmNzMqmNr6K
sauvLnzg7n7q8+WN3.B6oNVVS9rG2YeZoSy01h+bmUsxs9wJcqBnrNm4CUIh
6ZYJ1a45zO6ko+zZYtNYy6t00RamZmXGqS+9WmgSrsyFVmFq93Wmw2l04Gue
1UISEGtnx6RJUIdHP6C8Tfuwor4iFQC2LaNtcSmb0Ot4haqrR77mbCzt8puG
e2H3obxJVySud9siljrRF1jOuAK0Tt5nqu9t4SlspD9kL2zEJSzXMgfyoDAe
.W58ouc9RNsPYjZg2IkdW5kzkWx60NSv5BAsQjeIS8kDNqxK0dkVp7oWxleI
oRmNUFbBdOSuhqz5GQgwZUrgvDAids6jiTR.NSVsW4fROIowxH57fRhZsGqC
1VE4ZjX1mPPmikCoyzSshWO1aZKFMCRGmsNaX6ggrqVbz4ZqCG2M+3Mpe7tZ
9zomMc98WSMke68K9D.WbvU+NaGQozcDsYSmsZiSdDcfRUUkqhOb8nUidLvn
0CQEuYMjnxa6oIT4GG+ksydy4eZzz6GutwqjBqJpkFcvJh4deBZnAQ.JhQnQ
Vtji1EhdSzJRFAvkBdiV4rQn76KuKqynT.Efvkx0MuDztLAm0XkvoR9RBkPJ
CFYvoB90HF2Ed0BwK6h3UNmQXDVsEFtzoucuNpBvVFHeWhDfzbznsL86wPHe
Ir4BcdgQpbX2OcIuw37VsVFgPfOcIq2YrJgxiOLSvFscIv5FbGx0DtVncUWz
tWKfwm.XxzlWh1AWOngMSmEr0X9RZrN.woiXSxVdWduxprxPzHRDuv3T3egq
g2mIuWnLVuBWM.L7Ve9RBQvgURzGahz0cQ5zxsTFggeeLjY6XCErSKnQHNjo
yrLkzFUFgRWkYB3e4sboWdWvYf.tiUwfTUtjEe2.6QvEs0uJowXfLYPoz5Vn
cSWztFtpBZe5jApHua3ZAbEK7+nyWBLWGD2g3M7Xm2ILXC.3E.Y.A47xwP+h
gPTaP3O4sPMfSHTX6Inhl7GTIMQdVUQap9lX71NUVgpIXMfHBfSj+10drwCN
XPWUvfXsfbOxlCYkUnDBNO.7DkUhGd7BQW.uyX5nQJ8tvU.6AZNhhXji5nVK
9jvOeSJqtNId.x.HtfwvHnuxlu2Q6G5fBuPgaIBvZiQoU.yQ9RvfhxCTCPJ2
TdWPRO8O0X2nbIgFafhnDVn7lpUqnF7FK1s0wVHdemh7NHxBxll.yVLnMfHf
o.VCLfVrIBqQfrnAiBWV5fJQDlf7ZYQPBh2Z98oosxhAWqAupIKB99rJFT7g
rVz.SPgVH8PmjNDYfMQILyXsYqxvIuwG8AiWH70UC1aLtnfaGEZWyKpfExXT
4qtFfnLzAguBgq9A87DXCB85JwCIOvYbPPD1CZg3icKzfcXn5.at1BKUIAmA
R1QtijoAEVJPdvGgsxBM.gAGjaDwXz6zEcSxDvkwliLyHTQkClfkZ7NKeUJ5
6KBCkfu0joFonaNOr.CnkvvAPAW37BqG9C8PLnrYH46yQPWVQQVVB9I0Ag7j
yWcjBijPl.Naql3wkrvUczYCRcgyGf4cZKKR4vln9tctBWL7XsjBuksei2Bc
NuVELEcSaxHM0KfCUc8RPM.aQvZcQuvA6OPV.5E.OP9cgkmG6ezNkPlULbJq
fOS279YZi36z6JLagceexTVVHgGVvfICYZ7+yJdvNILvCQd3WxEyN8UTE.6R
v9uN6CFB3f5wpFhb.OX1GLhsQEfPG91Kl4wWM2ZrHJyntMXMc5fMYy.RgL.q
hWeHdno8.XqobCAWGbJIsQ.Sy4KAAKHIkrXWbtAYJQJbJrBK9WwqCvGQZ1WT
zVnIVrynoY2fsIhuSOrPARQO0f7TE+93NF.hAuBXFJfXv1..bASDVaY6OBrK
NGhVLpjUzO3c.WXLR8rVfmOwLz6MDPJZA3qwFLJn7CqwxlH8t7uhaqFBIfcQ
nSINCL2AKOPO.xxv+U5Rf.gAUfJK.emI4ACt2.Pgi8AC.8jtDh+EVzQ.Ev8b
dQa3RDdugtMB4rFAM7Ip0fU47sQ7c6e0x6EORrS8gSx5FDaBfJgudYQZvAaH
JD1qp5FyBsR3YCBZDNcQLBnGkPMGtujEM+H71ZQDt90XjTdFiDbZ.oOWSV4k
c5dE2tHHJ3PBL+h0.XSNRFUwvLDCfXrE120P9JqXBf3.9rOBIqbHHvKJvAiH
SvWmonQKHvdl0AP3Ub1DFH7oA781lvEH6z6pjNQgkAnspKPZ0jH.vIOWLEWO
VvdCfUCC3ElL7o5zfUCoBWVvBBPPzlHHASMGNCvXvy4TBb2WhECaSAHmBTAX
6v1lglNcuxD+.aZdt8aiERELGEMQHq68vXW.WFb4RLUB5eGpoZdpGacEvvfW
pIDo55AeoP8EXVAghfpxBMHjAGnalhAUSFZTcG5JrwKMTdGdipXsYB8vRA2v
B0CDKvbN8bgMjxkr.EGDUrPaubEP23s.HwlppBPLRv9.+.VhEbZLLXCQLXbs
YhW0o2UXfGF+ftOffnyBp.s.BQExsDXS5JfECrVQGfRgHESVLP3SPrAg2Bm9
wj7FwXAeQHVxjYqzGjgMBz1vXIXyYgKDEHrYg+GDUUM4cU0k2U3QjvNfoZXb
wJKDui1HPfqvfc9NZotL7lRY7rpA13gCFnvBLKdcwGbRmvCevPrIymivXK7z
ELDbwZDDZZskvn0MYrQ0k2U.IGlvMH.M.0M6OGRnQnHCcTvkx2QMyW.PdxzC
Gx1azVF0kANDv6IGACXyHrUX1EjObAj+tfsUH8.eAJWdaLPEUlw.X4U1VVC5
z6J19TvIJvJgEQl2vfvALSajBKYFnCZdLHDDPdF+.1x.dSefAzIKoJIvnWkz
4LvQT7uBaAFgfIHoFX.D0CDtJryBesMQ7cF+pCF0YNhBTOxVti.OrAl5oAAY
8RzJNQHCL30KY4r9QvXTVCYAN7iDLPvm+tDL+craTcXusDoC7bwODtAHB4ln
9NcvBcd3dEROf4TPkEAtdrZvOwpn3TBFAghg0wyk5BJSXQh4VmIYt3ShFYAh
dQj.zJ6FDCCcbA09BRZ.lzXg.GLi1n8lt8vp3CdqhF0qAyQ32VlgHDxeUj.l
fX5m.vcewMOz4rPDPykcAx.sy.9IDRfZPANOwpZQLYTKs.FMkGQr1YrMM4kU
0kWVXoANwYdm.Z1r0RfxzyLmAZ.lEx5mFBif4vSSA6LJLX6DJrNZ7SkuB3vv
BjfFkxwh.8bHh.MAISzlu.LSwzxAqz3RsI02kSVEixlNLf0cYNsnvzlwnifK
BawY7NvgNvKBjuvQULS7Zra3X9FPDqv9W5RPDDuEV9J3GHa6hgA.8WECQHuy
BDCvfD8pyRrzjWVcmdYoLBLSlLMVLu.nTH5HAA8XJnFfqSGnJIPjoJwoiULg
y6vpzTB5C.DfQcCCCTWP8aoWbCryKHT67WOXILKxJrDZSvQ2cPrRXJTjBiN3
JALnn4.nNxf0p41C1B4wIOqHXIwWvMezPQCaMKj1jPCz1g2o5knAV.8Cq8Zn
gfWAIR9kSwwlndU27dP8QhlEza4NRuQDdCDLKKHlTJGyjGb.TXzXs34pxyT2
TzigLAzb8TnJVM4DXT5NFKV4RzsADivmB95Zi22oe1TrBFByBL+rfIeRA.1D
XHAFLKxpfUCdHjuHj+rxG1IHn.ID8yndfpMhcTh2rp3iCfOgEVlXUXtM63PA
6+vT.Tpf0o1xcit6nXcD8tzRMoRvmfhXnEIrx5pXuGFXfnBbPUy0gkRZApcq
hEMAvcYQOgNhyT.PCawvMgiYFIXqIwOxbVP.d11RPe2NZA2Jn.LPvmynDdZI
d2Qoc2QIc2QobeZIbeboaah3cchtzx7Nx7zSg5L5xHrXCmP.GuqTVGOg2Xom
UrUUDkTFllFfkiYqMC3TkVKDcYwDJ+hwmDAZYTqyEdvFXN7Yjbh1rW56j5A9
JuflHBBSwzNh9CBmX2EfQz4TAvzuS3DdgOG6RBhBPQ6XL1YcVSJ2go7MP7Qk
MHfpGKRXjTlEufVhANmYJxAquMpuyvYgHKDbABM.MNV..pw9.y4p0WQNZoq.
FXjFV1K1KAM.wHQxWTVl.aBPjwybAh2ewZObDicNHgKCEyw3NhsBXCCdZZBj
ityvYQbcIPtZObNkQgD8zQEhCBAWjCFGBSAlJPnuAEtLB8TpJIvTVgrbbITR
BXIf7CL9TD6IXLh3mEzo.LkgCxZewj7zDNASmdZYrQBV5Y55vU+5gCIX2Pst
hBQqA9XArdDIZIpJVwGcBlADXJ.SgCHAg1Qs1Bo5YUUXjVpRUgLLLAB3GB+s
I3X5NdV5VLxZpYK41BRyTMFlG04UCc2BClDi.Yr7R.jO896wVgNKOPmV.N.K
dEH+7kHnO3PB3If2jroKDDGPnBQMfans7HXTcS5PlzQR06DUmkPbFNkPXt9b
bPRASTOQexp9UpFbjgn.5fYYKGjMbZFITUCSrW1IaTQSBIMqhObHcFXsJLrt
OMELqoyTESLRHPCfokg1mSSAbo.kWK8hVJ6.BvEVO8LcSpRnKb+IWEHVM2RH
UTMFVZY1NKITQS.OzttuFUBznYNIngKmnIEVio6rmA6kLGEbhHUHBFGNSaO7
tTL2.QHfGPkvmUDcou.sDxH1b+akpraJ0vLNbcgQCPF.OPxxprVPNV0Db2bL
FtlfmY5zKqfAe3oXMrgWBAETCvd.2TvvQscJLj1.LqPIXbgz.YBAS7itBaPR
C8ZFILdWwZAFg4SGeepRsCEr5DQvdr7uah5ccWEeftjIv.gssNAWrbBDCLhB
oTwOfHQjcq5qkJLv5swBrfveKuIeBdIChp1dHX+J4Jfjrr9si.digj491phe
20iEDfhh..tUsNZdoiIVHvBRUjdwdgk85ginIJx3DaKLWafmzRI+nVgGXmo9
RQvQDzLYY3N3U0ZxgcO5+FFTaq5TltSZLCCBXSDrQYVWUX3QGxkveUseMPfc
v+JvPCySkjyBe9fLAqLVrWxj7yZeDYbVpZRVQ.8.JfBNjWufPHVA5rU2XdWM
c5k0wBADXUt4VY1hlko61wpEWqfMhMD9fXlBL0TKGYJXitTBYKjJ44AMqVDh
ROeIKCDzS.01Zc0A5FmkYAkEOsIIG6W0M7jsS2rvOnlAiyR3WqoCcDxzpAX6
kRmPCdzhBTKqQ5kMXCCi0.bY5ZBPtQI8UKnv7OBQfkGDQaV.+AKUNVVYl6nl
H8NcyxtaRRLvpRhqgsFn1gvCYukjctPUNX4AQl3KExWlwdFINlZcRLHLRlvY
O6toR7GJ1vhorvVa2I7tXLkfOAgllH8tbxRyHobJAWnYGp.yQfUtF2SBILm8
oTYFfCUDfc12ulx.NDhkfkaHmXIxgY0S3FQIC3Z5dPyLUxlkpn7vxeAEHnN4
aiu2oOVBAjMOAB7u5kBTshguAnjdUsqxX1irrJa9BveFdsfP1Yhvb07CqX77
rsQ70BLaY+Jv.1skfa0LUtr3Ld5BqIpuSerrsuXgAfbRIx4..1.EVOSeZY4P
DWv1eHU9lR8BjDbOm3ftZYug4GGWKLIGE6JBl07XLgan1zKX4Simfs0nXS2I
LlIPTB77H3yhgRX6.9Uf1EVEEiaL+0valWP9eMZZ7YRcsnuVdMJ2DSENVTJ5
bJUqNntB3GZUsNVPC.RQz.ZaEC21oKV3+l4.Kvdnn.8SCfrRF9Ay.UwijGpu
rhJZXsur8GIzeVeRZZu52xB8GCAETxXLE.A3Fr0YpM6HcKZ3MUwfOah56zEK
1.MzxrME8PIybrd3.NBQ4luBSMEcz6z09QDHcrDfuiIytvmwqCOc.3ByjbgO
SDvPRBhcxJ9.r4xtE.tsZxAqMtGYdVWSIw6JqllgoECXLDxVIqRNvtXCzAM1
RN9X60QsvHquZwaJbZxj5yJ3qqPmw6Qwr+IK0ZWAXGL5dDZrns1Pv0o+Ul2P
uhE0Fa34nqgyVNkPfAOB2LayCA4x9KvylsIGcsgowDgOxtNac64jpsITZ8Em
c36AQ7DfIMXJtz0hxTZ0XAdAdolH9NcuBiZJSNm0wRO0.KHJVoDFoTIquv9t
kZxAlqQWEVoNkyZ9dUUHW.WPRDYMPCVVPVHevZJYDA6K.GN3Tz6VardU2vJ0
jPjrSPqXawcDfX7oXKpDepAnREzqnCyTaBX.L2Z0nvnPE7IAKX5BxFtaEguT
V1GWM3.DDKauPZUpIZuy3XoYQnvx9HyUJleBdoBjYRzo3lWvjG.76gfsVeJl
4IQpM3JjEqv.9lPL19R88AdAZvgoFpfQhRUrlUH9M7M1Ds2cthYoiBzxhuzr
Y31HX1qiriaKk0fMrMaiUFHUsMiIDL3FNfvEKNcgYRHcgnSYsEKsQA1XXEcg
gXe4RLssFVeDlbnlBFw0oGVl9JOahFQoaHjLaLzZCyWbVDgM8NfP3ILPQA8V
TRnuP+UQKN4bxpBLSprOpMlROsvrSAgIXCPVTLXbI.UCtm5Pa02w45twgBII
d5Au3VjtXgQD1pj0lAkfzrrqOrrz1YaR.fULvt.fdHxWJE5M1FUhZ+gB.SQP
l.eCShSovPBVgQkBAFnZSgsSWrQFOD1bYpUKOGBFVdpT+jCChULwHXnT.0.Q
PkQ6YyMyFo1T1znLepfeBVyphpAPTHXdlfm1B0CsJ9+YJ+ZK8GttihkQNy0P
fk4qXifcBAa2o0gmf8AlUf.SdWMOSIMarCvDNUPCvzByplXJk7AFafGWq.wZ
YBOz4zF1YrrG8ZyGqq6FKF7jTCbqTgRl5jPZfynZn8E00v.A4.DmDcVo3mJl
LOV3GJWWy7.a1BcNo4q+fofzoi1ZD9JVwJlZN5inotQuKmrRKwQESOnAZeod
Xf5Y0vYWDTpuiNk8U5ugPIyUAJvnlnXOTaJ5wtTjhPtyVy8Me3RnOaVMmRZF
RszB7TwhL2VIk8cWTVZHQldj6bxZHCF1esv+CBaqhrLk3dboXsjxZlyIQpud
qsjLKEKhZhKBiutC4jreWYcLV2i0QFOHVlbs0D0q1SBDnwFXB.hO074kZzFn
xxGCih..2Z3Egrd8ocfoUiaP9ZiqyhXZngJXJofjf4dNkVDvnKtrYLVrOdCr
rVsQ8cWTV1KOrxiFeAgihCVZ1lFDfS1+B1gYqUvjTBGYY4KBWi3fXCOkClg4
TB1SXnVU4KHxy1ni37XEQJFLQ.AIu3wsy18ybt.L91Kyi410iU16VL9p0yNY
W+ebl7so1LFxb64oHLeH0AfoWvNAFdRStmSY9JDZ3YHb2O2fh2lGfxUsMYxK
imarm+RN+BrhyG1Cr9tO+B1bRGty0oN83eZ3y6uk8MHKbEaFI4tlygGyh9AY
oW2CsgGlcg6YeEQ.8jw5nogi5j8MyB2d0aOhUutW2xeXVDtOA6cMh.d5XI7n
1v0uMa3kiYfCeXCjE0wt8EveEac2T2qa4dO1uXmTt9+R4V3Hdln26yFsTcDp
+t9iw7Od1dG0GE9Q.BDoLpDYVpz9Haq..U5vF71d7Ru0RVDeaLq+sms2o9gI
O0Of.+S8ggn9Nrz+9Vxg2lk7uZ+6xNcdIqY2VxG1MBUNh.Sjo4k8KZS9Mx2M
OVdd3HdYmCz3jobW.F44SdnSylgzl5NFi9kXiSne6NIh1+RNuMODKY2ayR9.
x0UU4cIWKaA4R2B1w2N4586615cctI+h7ceTGqXulh0kc49WrNZdaVwOblX7
q3YhQCKcM.pwdgHXCA9.Wv1eSJEOy.thgSRGyErH6zwrL7hbLGrmj9kgfbZE
2+9kC5ST2xkoSV+qL6imnluJaxCvJ9zzobUMdmNk0gWhTsW+FlnnCDYIO2.s
cEYIG0ZufHK2Iu3XBnz7JBPgmYeCB.Eu7DUCuHw2+Z3tvoN.k5RueAn3dt54
oursG79Y1viGIvElwSGEvwsFEvcdlm615cc6jsG2j57oVov+zSAWiNejLsmW
S5L630r4jP+zW6Ada8Hge2CVz1YAhMmR0OOV.c5YKmemO5.w0.QF0Ads2ZVP
v1BKH52GKnbrjoElK7aNvIYKAWVJ6305XVfdbTt5.Td5rBsy4N5wcuNjtRpv
Gu76US5kOlhVKNHeEzHd7MuKMB+ErxJ74xSqg7th3CJFLd5q02KkGKy1kj8V
GvFm+8St95xoD5K6tqEsrK5j8fDyiMP0kYL+frRSI+tgU51BV8k7Za27PmK8
NbqYRtmBN0SqUfWmOcd16KZb4iburwuz+pusbqDpWOILg7U7d85o4HiMYWOp
5k6US6WwdYc4jMeuj8h+pC5xH1K9Fcudx7og+cC2qdQ1Hzz8x2K7PaS5WFcu
cuNjrgWsWzDxzISoEVVMOtEFBoBe7zW5kR1lVfNjJz8KmEYZBFakh5g6k7U5
doaRLy1C2qj.j9.2pdwH91GqQcIOK6q6zgXeL6AuXC3aejQ00ZJ1GxDxVtUr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.uZhsuwYV442BF5j5mYSIu0aF+g6uZz91.Fc0U3KdKlpVYKi
jtzLQJOM6yWai041aD7wlpLWFi5PYjSag2uG8gFOc7sO994C7AKmehffCh9z
uYrowa2le1k3CmN8919SyGvnxA1ju7PuGDB1h.a9gYwYSetOLYF0VdXrcnip
57ctJWjOoP19KnEI006hcHr1n.z1hqg0BoAuvlGgXgmRfOVb0U26b7+74eK+
MMHRqabdNVE9fgq+k6FO6r+znYKO6OM91IWNe50muAGcK1CG6B4moaNYBxCq
w.G4eo0dcVW9hUH9iiVsZeJDbA7wQUyyOD9vlGTkeW5nprmHn+0e8pOj9Xhi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.ASutfOJSHCsB5+782d49IlcYs4oNMG.R6ecxUqNLu5
YsS2qj4hwKGu5jCswe5p42Mtc3FOj0ZdZ5VljT60JwqJS9OO5x1WJMpmuKIm
CZeoWWTH3pKOH.5cDxWmdJeEQB8mmeCCko2fytmMEd5a3KmKYlxnozTJqRet
j9ec6AJrzqH68uL5Si+37E29ez6b3iG8n3BaOs3tdzhe7amM4lue02lxFvQJ
22ZhlWm0hiLOpcjhByZSA90EyYcYcBo6eOjCN4AD268jVz64eqOKw3Oj1JFu
PHJOgo8wW0dik7wUmXuo0TrG7h1CHN8PIVdxuzeY+pMZ+zVPpihQb4zwvkYt
.f+l4y+wy6IiS+vcSl8iGa4xvprb7tjV0bH2Z1SdWa38uynca317Dw2GpkQ5
sq272NrCgFtkOwLaCeltTId+V+rGxCb6zzqn82dtRFONwuGxKWunl7i4Xx9K
r0ddqAKMkPINLD5cpRs1Ctcscw5N+9xMhHZJchRjG0okeyG50ndtczmcbJAb
bFiBaG2huHK9xpmyVq+GsQ1e.C4BNMTAZOJOk1pha6Wih+x9deXqQy6tIrmC
W8ECSLrtAMd5usGaU9Pnb3M9ze6UzE+KoO01V6nscw7vo33R51F8zWumWi7T
932L89weq7XakNouTK5GrgE2zxUeRfKl+SyNZJ7f1V6QJ729kQGOAJ3YuP1H
IOYXx5Nb19q6eB72uX73mAEl3dqSEGOeAU99m39ivx1rUiNZxKcFvWNLfDx7
4hr2ESGuZ8MM9+b70GM84WibStN9GQwiQeSe+uGOc57e53YgJatoj7g0mmdP
pzdnlYyFEhvPr.TeMt.Rku32Oe5yPDYcJjr.wT4PDn2aas6tewcSedfG7q6l
Niz5xmQC7.d2aO.BUiHCLRYEkysPEOlCC845Zw3qFO4S6uxc6RlYcfIOrlDa
1eo8CwkCQghEM6w2wy2rXwQfuzovPZNp2K290QJZ43YWu7TyCxCL49W2amsY
rvWNJKiZS4P.jmdgxdU1Y0n6NTmbuQxn9CiVM+r+.iB87d69ub03Od+zoqNt
BrvGZk7IulaMVcl5iG28oOtwFCVa9f9im7o4yERd1Hob8KSMunFn0TOFeuJ3
CYfM50pNZd.p2qVOWMd4o0yKvOwr47kisM.Rmqf9bZ6zk8EnbWpRSS4cnwm2
fsW3b.GdfLJJiQgqbfrpLkZs6Dh7wFdbqu3Mzm+cSVr5Km8e8l4OK047iNWp
Bd76b4cksvzDe7a9aey+O.Lb6pU.
-----------end_max5_patcher-----------

Here is a comparison between the same entries with and without the first frame, to see the impact it as on the r2 value.

With first frame:
Screenshot 2020-07-11 at 12.23.15 am
(slope value isn’t great, but it’s in the “right” direction, but r2 value is pretty low given the overall shape)

Without the first frame:
Screenshot 2020-07-11 at 12.23.21 am
(stronger slope value, and much higher r2, given the fit of the slope)

With first frame:
Screenshot 2020-07-11 at 12.10.12 am

Without first frame:
Screenshot 2020-07-11 at 12.23.45 am

And for a more oddball shape.

With the first frame:
Screenshot 2020-07-11 at 12.13.07 am

Without first frame:
Screenshot 2020-07-11 at 12.26.31 am

1 Like

Aaaaaaaaand here’s the code for doing it in the log (dB) domain.


----------begin_max5_patcher----------
11341.3oc68k1bikjccet5eEzLzGrGUEUtunO4YwdbDdFIEdFESnnkiJ.IQw
FcCBPC.Vc0iBoe69bxEP.R7.RP9djnZ08LEKVOr7t4MuKm6x6l+aey6N+x4e
Y7xyO6u+ru8r28t+su4cuKcIdg2U92u67aG8kqlNZY5sc9rw+37K+9yee9kV
M9KqRWdzp4WeY8p2MZ0Ue2jY27wEiuZU9KWEUWHd+YRu8BqInctfA+wq8p2e
lNxWRg2vY+eKeCyue0zwqV8S2MN+wO+70uzjqS2PPDePEC0a4r6ucxL7QRDo
7gKl+hxWkW7e+a9F9i2+xVs2c1B0uczzqte5nUSlOaOqaiIbgEKQYfqwvyek
5OlU5lzy3Ek0WYA9ty+zjoi+73EKIk+v69cmO5t613xuaiOB4Je+7zWT38qu
zjY4KIWeoEi+7j5m2t9piV.txJvRteQZQd9Wblye3qY90iWL69Iq2hR6OERJ
sSLazsiWd2nqxeXtgUe4G3y1DmUJL4+Rl9aHm8.mFa02Lc9U+v3DWUTu376F
Oaxr6VLd43Yqx6la9xWO9Site5pO9o4yVsbxeMQBRJJuiW+SERbmuHWDI5+W
uXxnoqWA2rXx0ymQhXqsBd45s6aOSZSqJ6lKlz6X1n61wGFBLfuzwKtDKx6W
d4nEbm5xoo6fp9hqlOe51uz5O2zweZU4kuaxrYOhKtZ9cc+hKlby2smO6kyw
Kd699tSuxxOd+r7q9QHTr5iKG84s41qFMcZQec6u9uLZ1jaGsZ7pI4s.kX8K
Nd1HrP+tkWsX9zoasdyuxm2wqbMDxuZ7ON45UeW5Fsov.d6StqJDc95c4qmb
y3kq19ZqFcyxsuxxU+Tlouwkt+xhR7GWM916f4lG8Ff5wjkqV9cy+wkk2XUP
aSFvCl32TodSieac88YDbaCgqNa5YS27U1gIPqHqSl+qng1C2zDXGlAe+FlB
eh4Pmcya5tLItCyhp5KjbBTLzz+7j+5zytYw76u6.rEmHwHLpKLVoSqkV7Gk
2He+YV4icS7r3QtN4Qp2Zdz8+0IsI0.wkny6sViF+Q5iu+LinMtykilcC4P0
+dxrU6gY4eFLK8ql.0zwyZieY7W.3UAu2KweDQKwW46Ewovoq3TxLT8+0ldW
veQDhTRQvi+nsTxRZfK1M9OozzLe6w+oa9X7YX5x85vG+.7lc.1m1kPZYU5K
HmSI8Fkz4kVv+ThXaRZeZ57Q6QYzKeFhZxWGVzu5L4gXQ1LXTi2cAzDkFiPY
zQmVAS69Fc+cHNj5zkCAyUKu+1CoEJSLBiJbQTnguJsv4TLzPbQceXuxqOcs
W0DKpJFMfrHyoKKpA8rhoncpmIaEC0gTzrmzJZsfKP1oTT+.Lv69JWQqJGMf
JZ9SWVzmG+k6Vb1eymjm8qvOUswqzA4EFYTGB1.9iwYfwborQj46gQENcU2Z
.bTFa4NwFIC8D1n3W0XiT5DKZHwFEDekiMREbCr8nf7qbS1UwnAjEo9pFajq
a0LoNzO5Y5SW8rsRHPSoE.bmjXytxK.MnKd1YBnorBDLOirBDOgvYJChAFmY
v9UtQK2fayx8yEXlYV0PgxL3Ocsb820pscmaG110JeuXa2ZNc4PIAo6l+i+W
gvz+5+JVr+2Zhe4k6.Udz9Rkkrxmgga4oVNTrd4EZXJJFM1XT6iQODlrhdIj
EyIbPcK++sXUarHmbG1sU8SPcF+oqPTCg85y01cPqIf19UcbugnXnqIf18Ud
buv1y.CPR6+JGBYULZ.YQgupi6sXJZPqIfN9UdMABZ0.GqlQ7UthVUNZ3TzL
xetDrVgWMTQqYdqq0c590T62jaMI85e9bWxwtA6H5dIuwqb07auc7rs63vzM
Y10i+ReyexzPaAg47IHhZ85VroS90VLD2y.fr3kxPdnaamNYVW8PYZsxWe2b
pkyuewU0c708t3YxsVpWOd4pIyV28we6FIE7LvTZZu5noChW7LQKzgtPwCDc
DOJ5X33G9loC2fxOBGEcLX7CZ2sM5ftxFP5PzL+HNj6K0UYizwvwOZV9v3GT
8EayzQXP2WZVukY0b3nCqoY5XP0WbMuu3BCJczr+E2fpuvV69LUSzgavoC4I
.czr8C2fZ+Hsq2z9hOL3zgtY5PNnzgoI5vL37CaSzgdvoiljSCCubZa30UCI
c3aFOF6e9gSN0aOJ5X33G5loC0fxOLGEcLb7ilsq6GT8Eey988CZbkd+QQGC
F+Hzb7K9AM9kf9nnigiezrcrJmafnC4QQGC29Ry5KgA0eavdTzwvwOZVuMLn
3jSYeqIbPlA0+RhNLMSGCK+nMbxC+9Ra3wrCNczN9zgU9no3nBxAmeDZJ+X9
AmN7MQGgAmNbMo2Nn947xiJOcCGcDOp7zMXzQr87iMn1OrMuuD61dZ4h0Ily
4bpnb8GyS.lONZ0pESt79U4RWsAAcbSpjalN+xQSez3EYWCxju4Ah6HmyR0x
y83AszB0e+9FtRJY54sRJe5POnzx9aTTwsKnn9HlmRhmwJ5SSmOacWFbN1GF
M6loaNXV1yzhZmKnnrgYGUcbWr9sPN4UymlGTSeaZX0rwOjcwcLOv0WfM7Ui
W7w7.wYyEvmlu31QIJ2sOd4k2rEAnd+V+3ABXGc5vwNstt+1KGuXeBL1T2sD
DWXCBmTZj3OVWz89ydZut1B6cq1BRzBSqAwMUOp.c83ES9LLn74wOnHswrt5
izf0UyuOWYbU2bN1bGr27hpmJY51uplJbBqpYhuwpZJ+WmpZcMX7tc7nY+ie
52sVr6vyFucNS.kR6g2C1tO81ls59k4k29lWdRqHwf0wb6Y6xcDXz9KCLueY
f48KCLuAaf4QyiM0JgZodGSxqi5Yo3Py7Lk7zcH5c+rue9jCwox9okgze4N1
mQ2t4KhmAeQeRMbAKnY8INS+LNAk9S44+1kmM8r+5zqlNdzhiZbTJjG2vDj+
ode5lUYOcEf9u7gC+PTDk4GHISoml6imNIo4z9oSZ0hwit8LUaVb3DT3wHVc
8xCNg7sdpk852r6JyO6Z18hKbe1EdvEy+WP0Tytqr+7oY2oov1JNgbPKtop4
h0HGzj9JaN4yR+fSGx298EYyMkfZPKRhp4lzPZFb5P1LcLXEUTZNQjSatY.T
CZwMUMWrFk8m6Eq41wKWN5lwOI4eqeD82YBjM9KT1cm6cU7wPLa8XwPn1YZ9
T81A.RWqU29VqFD6tu+Wqx2n055mT0cWX.wPruJF3058.fz86YYIUoYQvtVU
kPG2+xhId4wIZ37IqFe6xZ3R2OcJe0oSVldmum+X1b9yOMYQ9ZeZQR+cGLHY
z1SEaR2eI++u8L4g4nRU3BgyXBQ7aQoypMsInrYpr1lULz1.5X4dynUiAz1V
Vy6rBGVSCZGuuCMDY7XrFziU3gwJOcxUi265V4JoUQegPCr2PyA+Ivgvf01V
s05XUKF3UcW0Qc4z42Mdu8hfPmm3h6n.oF49JPpLDOkKPp+ss.oxvOqJP5ps
FFk6wlgxCWORmToTA7mnvDvEaV6oSMn0SQwWYmC2c10SVd2zQ+z9vR3jabXo
YcGdstchX2dgZ9kJ.27IlVHW8De3Wp+6uT+2eo9uCW8eeXx572xIqyY+2Wd0
noiVPU5yjGZnWKRMogN6DP5cuvIqyIbEYZoJUJathuQS9u5ixTcBORqw6axs
2e6gmFiU9hMwW7l9rkANwOBvNzLp1mgVk7GcVz0CmDgxS5ygPfgXwAqooKo4
nig7fyqGpho7T9fEhSHOh6nIlRQGR0KLkS64W9QblUp8O4PYrW5wDoW9ygir
RwVALiecXNyJkOqCAtSwyrRctZ48hDj9T+Tp7n6QIk2ebc1196NIo6st6jd0
awD4dNLW9psEST5TcWb4v.NbakH2y40xWasUhr4YnnbPmwDsVj7gkJjMOARj
C5DDHuqnd6oilmvDxA8I7T17jXSNnSRoVawlgrgRjGAMLXMwQizvfJa1Z68L
jzPZND0jd5fNAmj04xzaNcnaVOcX2WZ2dgYPoC4Q4W6+T9joG1Scrrl7gasy
9zRmlqsE+4NqZka8ys3VEPYcNyGpRD20xTr2x0oe1KS+I1x7h815ZosSsSri
0oe+qyvo057CdwK6w.Oe1QZr5tl3BcyJhuMrhOc+rqRVSNbcm2kfrR7Pr3qW
WKFM6lhESvQKsvvgdNk23bf7QCQfalMGDyzIW8Catz2JsFO+YK.2ZW8c36FQ
eky1wZN90yuczjjY1vl6BavvMkqN55qua9jYqJwukR12G3yGjLDsRizIjVGC
GkIpH8hLG67pQWPqTdqQldQc9EkW3DBQHHkQqvkRQFC2u9hRcH5kZUvqsZtK
v1An9hZKb6nT3yFBQW9d5xuX3BoEWUnblTWS3SunOSsBdxCnUP1MJCFqesiq
iTfBtsVsWwoR2OowZM57ZsKp0dSvxF3HWMFy9jV5bBSHcldpo+5wtfCpBPL5
r04caOLjc0LkNWa8R4t4GuQc92UymN8royu+ZpR8auewmALlCt52YiOJktin
gd5rodbxinWWp5zbU7wqGsZzigfs1Qf2rF7U4s8zT27Ci+osySz4edzz6Gut
EujBqJpkFcvJh4trRpzAQvpLQQTTtjSD7Pm2.yAdW9RAuQqb1XPa7k2kE50J
f2nZx.WBZWlfyZrR3lNeIgR.aCFYvACHqwltKjwEhW1EwqbNivHfMHmRnSe6
dcTErFR9tDI.o4nQaYh9igP9RXyE57BiT4vte5Rdiw4sZsLBg.e5RVuyXUBX
bzKR1NuPBuMRrzRbMgqEZW0Es60BX7I.lrEzZh1AWOnkZuyB1ZLeIXPzAhSG
wljs7t7dkUACoQiHQ7BiSQC8JKdel7dAri5U3pADsPxVJtDrn6vJI5iMQ55t
HcQDamvyfK3igLaGanfcZAMBwgLclkofGFkQnzUYl.9WdKW5k2kGttgWcULH
UkKYw2MbCDbQa8qRZLFHSBmFZcKztoKZWqw2i1mNjaJx6FtV.WwFEPhNcIvb
cPbGh2v0ddmvfM..6.jADjyKGiQpwVVTafGu7VnFnRDJr8DTQS9CpjlHO1kn
MUeSLdamJqP0DrFPDAvIxe6ZO13AGLnqJXPrVPtGYygrxJTBAmG3lhxJwCOd
v8b.uyX5T9I8tvU.6AZNhhXji5nVK9jAqqIkUWmDuADA1tcxHnuxlu2Q6G5f
BuPgaIBvZiQoUZQQtEFTTdM9jfUVdWPRO8O0X2nbIgFafhnDVn7lpUqnF7FK
AVDag38cJx6fHKHaZBLawf1.hAiFrFX.sXSDVi.YQCFEtrzAUhHLA40xhfDD
u076SSakECt.9ETpxhfuOqhAEeHqEMvDTnEROzIoCQFXSTByLVa1pLbxa7Qe
v3EBec0f8FiKJ31Qg107hJXgLFU9pqAHJCcP3qP3pePOOLwfPutR7PxCbFGD
Dg8fVH9X2BMXGFpNvlqsvRURvYfjcj6HYZPgkBjG7QXqrPCPXvA4FQLF8NcQ
2jLAbYr4HyLBUT4fIXoFuyxWkh99hvPI3aMYpQJ5lyCKv.ZILbnkpBmWX8ve
nGhAkMCIeeNB5xJJxxRvOoNHjmb9piTXjDxDvYa0DOtjEtpAF6fTW37AXdm1
xhTNrIpuamqvECOgFovaY62.f8PxA.+MEcSaxHM0KfCUc8RPM.aQvZcQuvA6
OPV.5E.OP9cgkmG6ezNkPlULP.JB9zXy6moMhuSuqvrE188ISYYgDdt2BlLj
ow+Oq3A6jv.OD4geIWL6zWQU.rKA6+5rOXHfCpGqZHxA7fYevJnXDfPG91Kl
4wWM2ZrHX0ntMXMc5fMYy.RgdbSKd8g3gl1CfslxMDbcvojzFALMmuDDrfjT
xhcw4FjofDFsS4hE+q30A3iHM6KJZKzDK1Yzzrav1Dw2oGVn.onmZPdpheeb
GC.wfWALCEPLXa..tfIBqsr8GA1EmSp.kKqnev6.tvX.+Ys.Oe1bn2aHfTzB
vWiMXTP4GVikMQ5c4eE2VMDR.6hPmRbFXtCVdfd.jkg+qzk.ABCp.UV.9NSx
CFbuAfBG63F.5IcImkH.P.Ev8bdQa3RDdugtMB4L8TMf0C7IpQHyXAzFw2s+
UKuW7zcN0wOIqaPrI.pD95kEoAGrgnPXuppaLKzJgmMHnQ3zEwHfdTB0b39R
Vz7ivaqEQ35WiQR4YLRvoAj9bMYkW1o6Ub6hfnfCIv7KVCfM4HYTECyPL.hw
VXeWC4qrhI.hC3y9Hjrxgf.un.GLhLAeclhFsf.6giKFtREmMgABeZ.eusIb
AxN8tJoSTXY.Zq5BjVMIB.bxyESw0iEr2.X0v.dgICepNMX0PpvkErf.DDsI
BRvTygy.LF7H6j.28kXwv1T.xo.U.1NrsYnoS2qv3ELr.NKsyDKjJXNJZhPV
26gwt.tL3xkXpDz+NTS07.705JfgAuTSHR00C9Rg5KvrBBEAUkEZPHCNP2LE
CplLzn5NzUXiWZn7N7FUwZy7BxrZIpRt.wBLmSOWXCobIKPwAQEKz1KWAzMd
K.Rropp.DiDrOvOfkXAmFCC1PDCFWal3Uc5cEF3gwOlvqHDMSar.s.BQExsD
XS5JfECrVQGfRgHESVLP3SPrAg2Bm9wj7FwXAeQHVxjYqzGjgMBz1vXIXyYg
KDEHrYg+GDUUM4cU0k2U3QjvNfoZXbwJKDui1HbLgh48ZXt.5xvaJkwypFXi
GNXfBKvr30EevIcBO7ACwlLeNBisvSWvPvEqQPno0VBiV2jwFUWdWAjbXB2f
.z.T2r+bHgxbNBcTvkx2QMyW.PdxrLGx1azVF0kANDv6IGACXyHrUX1EjObA
j+tfsUH8.eAJWdaLPEUlw.X4U1VVC5z6J19TvIJvJgEQl2vfvALSajBKYFnC
ZdLHDDPdF+.1x.dSefAzIKoJIvnWkz4LvQT7uBaAFgfIHoFX.D0CDtJryBes
MQ7cF+pCF0YNhBTOxVti.OrAl5oAAY8RzJNQHCL30KY4T5QvXTVCYAN7iDLP
vm+tDL+cruWcXusDoC7bwODtAHB4ln9NcvBcd3dEROf4TPkEAtdrZvOwpn3T
Jx7oCgddDKWPYBKRA5P.X5J9jnQVfnWDI.sxtAwvPGWPsufjFfIMVHvAynMZ
uoaOrJ9H9pnQ8ZvbD9MSGOLeFphDvDDS+D.t6Kt4gNmEh.ZtrKPFncFvOgPB
TCJv4IVUKhIiZoEvno7Hh0NisoIurpt7xBKMvINy6DPylsVBTldl4LPCvrPV
+zPXDLGdZJXmQgAamPg0Qiep7U.GFVfDznTNVDnmCQDnIHYh17EfYJlVNXkF
WpMo9tbxpXT1zgArtKyoEEl1LFcDbQXKNi2ANzAdQf7ENphYhWicCGy2.hXE
1+RWBhf3svpfA+.YaWLL.n+pXHB4cVfX.FjnWcDZfoIur5N8xRYDXlLYZrXd
APoPzQBB5wTPM.WmNPURfHSUhSGqXBm2gUooDzG.H.i5FFFntf52Ru3FXmWP
n14udvRXVjUXIzlfit6fXkvTnHEFcvUBXPQyAPcjAqUysGrExSFcVXwRhufa
9nghF1ZVHsIgFnsCuS0KQCr.5GV60PCAuBRj7KmhiMQ8pt48f5iDMKn2xcjd
iH7FHXVVPLoTNlIO3.nvnwZwyUkmotonGCYBn45oPUrZxIvnzcLVrxknaCHF
gOE700FuuS+roXELDlEX9YAS9LI.rIvPBLXVjUAqF7PHeQH+YkOrSPPARH5m
Q8.UaD6nDuYUwGG.eBKrLwpvba1wgB1+go.nTAqSsk6Fc2Qw5H5cokZRkfOA
EwPKRXk0UwdOLv.QE3fplqCKkzBT6VEKZBf6p8zSKVOE.zvVLbS3XlQB1ZR7
iLmEDfmssDz2siVvsBJ.CD74LJAhGj3agbOKkS5R.Hohov1CP54p5v.RAIBm
W.ajnbIrjPD.z9U09hRmNi3cL0CEHa.qArWFhobMzDw65Dcok4cj4omB0Yzk
QXwFNg.NdWorNdBuwROqXqpHJoLLMM.KGyVaFvoJsVH5xhIT9EiOIBzxnVmK
7fMvb3yH4DsYuz2I0C7UdAMQDDlhocD8GDNwtK.inyoBfoemvI7BeN1kDDEf
h1wXry5rlTtCS4af3iJaP.UOVjvHoLKdAsDCbNyTjCVeaTemgyBQVH3BDZ.Z
br..Ti8AlyUquhbzRWALvHMrrWrWBZ.hQhjunrLA1DfHim4BDu+h0d3HF6bP
BWFJliwcDaEvFF7zzDHGcmgyh35Rfb0d3bJiBI5oiJDGDBtHGLNDlBLUfPeC
JbYD5oTURforBY43RnjDvR.4GX7oH1SvXDwOKnSAXJCGj09hI4oIbBlN8zxX
iDrzyz0gq90CGRvtgZcEEhVC7wBX8HRzRTUrhO5DLCHvT.lBGPr+MLTqsPpd
VUEFokpTUHCCSf.9gveaBNltimktEirlZ1RtsfzLUig4Qcd0P2svfIwHPFKu
D.4Su+drUnyxCzoEfCvhWAxOeIB5CNj.dB3MIa5BAwADpPTC3FZKOBFU2jNj
IcjT8NQ0YIDmgSI1mK43fjBlndh9jU8qTM3HCQAzAyxVNHa3zLRnpFlXurS1
nhlDRZVEe3P5LvZUXXceZJXVSmoJlXjPfF.SKCsOmlB3RAJuV5EsT1ADfKrd
5Y5lTkPW39StJPrZtkPpnZLrzxrcVRnhl.dncceMpDnQybRPCWNQSJrFS2YO
C1KYNJ3rWpPDLNbl1d3coXtAhP.OfJgOqH5ReAZIjQr41.KUY2TpgYb35BiF
fL.dfjkUYsfbrpI3t4XLbMAOyzoWVAC9vSwZXCuDBJnFf8.tofgiZ6TXHsAX
VgRv3BoAxDBl3GcE1fjF50LRX1zU0BLBymN99TkZGJX0Ihf8X4e2D065tJ9.
cISfABaacBtX4DHFXDERohe.QhH6V0WKUXf0aiEXAg+VdS9D7RFDUs8Pv9Ux
U.IYY8aGA7FCIy8sUE+tqGKH.EEA.bqZcz7RGSrPfEjpH8h8BK60CGQSTjwI
1VXt1.OokR9QsBOvNS8khfiHnYxxvcvqp0jC6dz+MLn1V0oLcmzXFFDvlHXi
xrtpvviNjKg+pZ+Zf.6f+UfgFlmJImE97AYBVYrXujI4m09Hx3rT0jrh.5AT
.EbHudAgPrBzYqtw7tZ5zKqiEBHvpbysxrEMKS2siUKtVAaDaH7AwLEXpoVN
xTvFcoDxVHUxyCZVsHDkd9RVFHnm.ps05pCzMNKyBJKdZSRN1upa3IamtYge
PMCFmkvuVSG5HjoUCv1KkNgF7nEEnVVizKavFFFqA3xz0DfbiR5qVPg4eDh.
KOHh1r.9CVpbrrxL2QMQ5c5lkc2jjXfUkDWCaMPsCgGxdKI6bgpbvxChLwWJ
juLi8LRbL05jXPXjLgyd1cSk3OTrgESYgs1tS3cwXJAeBBMMQ5c4jklQR4TB
tPyNTAli.qbMtmDRXN6SoxL.GpH.6rueMkAbHDKAK2PNwRjCypmvMhRFv0z8
flYpjMKUQ4gk+BJPPcx2FeuSerDBHadBD3e0KEnZECeCPI8pZWkwrGYYU17E
f+L7ZAgryDg4p4GVw34Yai3qEX1x9UfAraKA2pYpbYwY7zEVSTem9XYaewBC
.4jRjyA.rAJrdl9zxxgHtfs+Pp7Mk5EHI3dNaCc0xdCyONtVXRNJ1UDLq4wX
B2PsoWvxmFOAaqQwltSXLSfnD34QvmECkv1A7q.sKrJJF2X9qg2Luf7+Zzz3
yj5ZQes7ZTtIlJbrnTz4TpVcPcEvOzpZcrfF.jhnAz1JFtsSWrv+MyAVf8PQ
A5mF.YkL7ClAphGIOTeYEUzvZeY6ORn+r9jzzd0ukE5OFBJnjwXJ.BvMXqyT
a1Q5VzvaphAe1D02oKVrAZnkYaJ5gRl4X8vAbDhxMeElZJ5n2oq8iHP5XI.e
GSlcgOiWGd5.vElI4BelHfgjDD6jU7AXykcK.ba0jCVabOx7rtlRh2UVMMCS
KFvXHjsRVkbfcwFnCZrkb7w1qiZgQVe0h2T3zjI0mUvWWgNi2ihY+SVp0tBv
NXz8HzXQasgfqS+qLugdEKpM1vyQWCmsbdj.CdDtY1lGBxk8WfmMaSN5ZCSi
IBejcc151yIUaSnz5KN6v2Ch3I.SZvTboqEkozpwB7B7RMQ7c5dEF0TlbNqi
kdpAVPTrRILRoRVeg8cK0jCLWitJrRcJm078ppPt.tfjHxZfFrrfrP9f0TxH
B1W.Nbvon2s1X8ptgUpIgHYmfVw1h6H.w3SwVTI9TCPkJnWQGlo1Dv.Xt0pQ
gQgJ3ShONFEjMb2JBeorrOtZvAHHV1dgzpTSzdmwwRyhPgk8QlqTL+D7REHy
jnSwMufIO.32CAas9TLyShTavUHKVgA7MgXr8k56C7BzfCSMTAiDkpXMqP7a
3arIZu6bEyRGEnkEeoYyvsQvrWGYG2VJqAaXa1FqLPpZaFSHXvMb.gKVb5By
jP5BQmxZKVZiBrwvJ5BCw9xkXZaMr9HL4PMELhqSOrL8Ud1DMhR2PHY1Xn0F
lu3rHBa5c.gvSXfhB5snjPeg9qhVbx4jUEXlTYeTaLkdZgYmBBSvFfrnXv3R
.pFbO0g1puiy0ciCERR7zCdwsHcwBiHrUIqMCJAoYYWeXYosy1j..qXfcA.8
PjuTJzarMpD09CE.lhfLA9FlDmRggDrBiJEBLP0lBamtXiLdHr4xTqVdNDLr
7To9IGFDqXhQvPo.pAhfJi1ylalMRsoroQY9TA+DrlUEUCfnPv7LAOsEpGZU
7+yT90V5ObcGEKiblqg.KyWwFA6DB1tSqCOA6CLq.Al7tZdlRZ1XGfIbpfFf
oElUMwTJ4CL1.OtVAh0xDdnyoMryXYO50lOVW2MVL3IoF3VoBkL0Igz.mF1P
6KpqgABxAHNI5rRwOULYdrvOTttl4A1rE5bRyW+ASAoSGs0H7UrhUL0bzGQS
cidWNYkVhiJldPCz9R8v.0ypgytHnTeGcJ6qzeCgRlqBTfQMQwdn1TzicoHE
gbmsl6a9vkPe1rZNkzLjZoE3ohEYtsRJ66tnrzPhL8H24j0PFLr+Zg+GD1VE
YYJw83RwZIk0LmShTe8VaIYVJVD0DWDFecGxIY+tx5XrtGqiLdPrL4ZqIpWs
mDHPiMvD.Dep4yK0nMPkkOFFEA.t0vKBY85S6.SqF2f70FWmEwzPCUvTRAIA
y8bJsHfQWbYyXrXe7FXYsZi56tnrrWdXkGM9BBGEGg0rMMH.mr+ErCyVqfIo
DNxxxWDtFwAwFdJGLCyoDrmvPspxWPjmsQGw4wJhTLXh.BRdwiams6m4DHX7
sWlGntqGfs2sX7UqmRyt9+fS4Coz3Cal64oHLe7xAfo7oXkdRStmSY9JDZ3Y
Hb2O2fh2lGfxUsMCzKCBbrm+RNoDrhyG1yd8tOoD1blJty0oN83eZ3jEvx9F
jEthMijbWSTwiYQa7uMGODOLkD2y9Jh.5ICPRSCGpJ6a5Ht8p2dDqdcutk+v
TObeB16ZRC7zAf3Qsgqea1vKGnAG9XMHKpic6Kf+J15totW2x8drewNob8+k
xsvQ7LQu2mMZo5HT+c8Gi4u6gIX8d3GAHPjxnRjYoR6irsB.ToCava6AY8VK
YQ7swr9GNauyQESdNp.A9m5CCQ8cXo+8sjCuMK4e092kc57RVytsjOraDpbD
AlHSSl6Wzl7ajuad..8vgIyNGcxIS4t.Lxym7PmlMCoM0cLF8KwFmP+1clGs
+kbdadHVxt2lk7Ajqqpx6RtV1BxktEriucx0622s065bS9E469nN.ydMEqK6
x8uXcz71rhe3z23WwSeiFV5Z.Ti8BQvFB7Atfs+lTJdlAbECmjNlKXQ1oiYY
3E4XNXOI8KCA4zJt+8KGzmntkKC4r9WY1GOQMeU1jGfU7ooS4pZ7NcJmGbeO
WoZu9MLQQGHxRdBEZ6JxRNp0dAQVtSdwwDPo4UDfBOc.GD.Jd4IpFdQhu+0v
cgSc.J0kd+BPw8b0ySeYaOh+yrgGO7gKLicLrz2ZnC20XwlHn5btM158Z82x
duWoiX25.Q+YeuxeKGbcoba8ttcx1yaSc9b+T3e54HrQmOTq1yqIclc7Z1bx
0e5q8fLyhqymElhWHKPr4b994wBnyba4DP8QGovFnJnNvq8VyBB1VXAQ+9XA
kC1MsvbgeyAoIa04xRYGu1KU9Mr0g5PmGnCldPuLeuNndIKnyK+d0jd4ion0
hCxWAMhGey6RiveAqXDedC0ZHuqHtmhAim9Z88R4wxrcIYu0QTx4e2jqutbN
q9xt6ZQK6hNYe303QFn5xLlePVooj52vJcaAq9RdssadnykdGt0LI2SAm5o0
.wqymuQ68EMt7gVX13W5e02VtUB0qmDlP9Jdud8zbjwlrqGU8x8R0L1t9wGx
AMiG6E+UtWO4vzfFug6UureEZ5d46EdnsIYdit2tWGR1vq1qGdY571zBqclG
2tDgTQVd5K8RIaSKtySEU+kyhLMAsrRQ8v8R9JcuzMIlY6g6UR.RefaUuXXc
6Cqotjmk80c5PrOlohWbv4aePX00ZJ1GxDxVtU8R1Mx2JcK2JYubqLGRRW35
skk8f2KcucuZRDretUGTupO3fsEcsnWD2EgluWuXgv1PxJ5CA9X62pWNKrIu
HBQuvBcMeudoqqlx8ZrORmTSQBD6C9Wzz5c5EqC2hPQv1G9Fa9N8h822hvWn
OL15isdmdobOWKR49dYMIa8N8hWSsHQ3dpsubsgFc2ced7h5SQQ5lb9si994
KVe3Rfvelk+mo7Ec9hwed8ScQ5oK77QKt56lrZ7UqteQ9nd7KkiDqyuc90iW
L69IkbckdlONe4peZ5iqG05SIxe88WOY9eZ0nU2u7i+wwytOW3Krp9zn6mtZ
aVwk27oISmd07oYxaymVjZE0NO+pue8YnT48lluCbHXmGgnbL24JSZSEebee
33OM+Yj0ODmEXhxHEUUF6V7AHq73s+nOl5g6EevPySX35HOQyKwl.XyO1nY2
jO2vT90mAe7QcY9cyWT2S4rKc86+9UyuYwnqmTNBvEa8bv79hjzB7pI19FmD
mmeKXnSpelMk7VuY7Gt+pQ6aCXzUWgu3sXpZksL96Ryeo7jyOesMVmauQvGQ
qxLfLpCkwasEd+dzGZ7zw2936mOvGhc9IBBNz6S+lwlFkda9YWhOb5.Gb6OM
eXlJGNT9xCXePHX6Hr4GlEBN8493jYTa4gQDhNppyR5pbQ9TIY6ufVjTWuK1
gvZiBPaKtFVKjF7BadbkEdJA9XwUWcuyw+ym+s72zfHstwQPYU3CFt9Gua7r
y9Sils7r+z3amb47oWe9FbzsXObDOje9w4TPHOXHCb7BlV604p4KVg3ONZ0p
8oPvEvmFUMO+P3Cad1Z9soSWydhf9m90q9X5iINR8z11g2VFjC4r7QUikSqx
xAZPH8jtt0GZ5X3R3gOoLeTbF4P5NeOgvHepMaRGKeLdV9wlehGwTUeEqu07
GaXU390KlLZ5dzwjkSOjm9axdSh9u7m+edjhxbTcUFqXbx4kefTUR09b4HoG
+rsgP8DgyW88e.WNFNvSyN9iJWdHWZixz30cOxmaVEr0X61VlKML.yOJ1xxT
8hCT17yhc7sW5tMm6u4Xe1sv8+qe55EyuY7r+bhE0gLNGvnkw9JGKMkwtfQD
6SQ7T+lM4piTL2TrztqeqanUM9gdoVeiGT3tyunmIJ9GL4pKC9.dxckcOwKY
08iP4VKhvZ7dO82dVhx6Uh82Od13OOpSY0Csv6SY0eyFmtx6Vh8o96CqYwhx
u06j0ui9qN0Hpe+nIy9OZjpXOgW8+7zeq+Isk+3DnbdTVdjEE1G9wfPSpSJh
5+8xoSXipbDxVOhZ1G.hFMJu6XVa8S+HqEO09aexu9Ciu93zC2kmfcwqbCJY
i34VL4KWsZwzW.02qDDO832qwiWh.0iDIdUM87OL+554wdCVEazQ6KharCDL
85B9nLgLzJn+C2e6k6mX1k0lm5zb.Hs+oIWs5v7pm0NcuRlKFub7pSNzF+oq
le231ga7PVq4I2aYpUsWqDupL4+7nKaeoznd9tjbNn8kdcQgfqt7f.n2QHec
5o7UDIzed9MLTldCN6d1T3I8gubFnYJiASSorJ84R5e91CTXoWQ16eYzmG+o
4Kt8+n24vGO5QwE1dZwc8nE+vGlM4lua0GRYC3Hk6aMQyqyZwQlG0NRQgYso
.+5h4rtrNgz8uGxAm7.h68dRK587u0mkX76SaEiWHDkml093qZuwR93pSr2z
ZJ1CdQ6ADmdnDKO4W5ure0FseZKH0QwHtb5X3xLW.vey74+v48jwou+tIy9g
isbYXUVNJYRqZNPcM6IuqM792YztMbadh36C0xH810a9aG1gPC2xmXlsgOSW
pDueqe1C4AtcZ5Uz9aOWIiGm32C4kqWTS9gbLY+E1ZOu0fklRnDGFB8NUoV6
A2t1tXcmee4FQDMkNQIxiU0xu4C8ZTO2N5KNNQBNNiQgsiawWjEeY0yYq0+i
1H6OfgbAmFfAsGkmRaUws8qQweYeuOr0X.d2D1ygq9hgIFV2fFO821isJeHT
NnHe5u8J5h+kzmZaqcz1tXdPXbbIcaid5q2yqQdhh7ald+3OHO1VoS5K0h9A
aXwMsb0mD3h4+3rilBOns0djB+s+znim.E7bdHajjmBMYcGdNBn6eB72uX73
mAEl3dqSEGOKCU99m39ivx1rUiNZxKcdyWN3gDx7Yvr2ESGka8MM9+Y70GM8
4WibStN9GQwiQeSe+KimNc9Od7rPkM2TR9v5ytOHUZOTyrY4nmeHV.puFW.o
xW76mO8YHhrNERVfXpbfEz6ss1c2u3toOOvC90cSmQZc4yCBdXx6sG.gpQjA
ForhxYjnhGohg9bcsX7Uim748W4tcIyrNvjGVShM6uz9g3xgnPwhl8363YoV
r3HvW5TXHMG06ka+5HEsb7rqWdp4A4Alb+q6sy1LV3KGalQsobfCxSJQYuJ6
rZzcGpSt2HYT+gQqle1efQgdduc+WtZ7mte5zUGWAV3CsR9TdysFqNS8wi69
zG2XiAqMenBxSY07YPIOGlTt9kolWTCzZpGiuWE7gLvF8ZUGMOr16UqmqFu7
z54E3GY1b9oisM.Rmgg9bZ6zk8EnbWpRSS4cnwm2fsW3bXJdfLJJiQgqb3up
LkZs6Dh7QTdbqu3Mzm+cSVr5mN6+wMyeVpy4GctTE7324x6JagooK427u+M+
+A+MJwmA
-----------end_max5_patcher-----------
1 Like

Moving this into here, so it’s not in the “public” part of the forum, and it also relates to this discussion in this thread.

I’m thinking about doing something similar-ish, or rather, trying to create macro descriptors. So chunking a bunch of loudness descriptors/stats (perhaps even per-frame) and reducing that down to 1 or 2 dimensions, doing the same with mfccs/descriptors/stats, and bringing that down etc…

For my analysis time frame, and general use case, pitch isn’t as important, so not sure what to do on that front, but the general idea being to reduce a mixed/large descriptor space into a lower amount of dimensions, but grouped by perceptually related sub categories…

Is the idea to give the KDTree at the end the same amount of entries per thing you find important (as well as scaling them appropriately)? So if the KDTree has 6 things in it, two per L, P, and T, that it should give them equal significance in finding the nearest point?