Develop Reference

Someone can help me check do i convert correctly the Chandelier Exit indicator from pine script code to python?

These pine script code is from trading view and write by everget. This indicator is Chandelier Exit. I am doing back test by using this indicator so I need convert this pine script code to python in order to get the buy and sell signal.
I am using GBPJPY data from 31/12/2019 to 1/1/2022, 4hrs charts. But the buysignal and sellsignal total just have 6 signal, i feel like something wrong with the result. Therefore, I am thinking do I convert correctly the code from pine script to python?
Python code :
# ATR calculation
atr_period = 2
h, l, c_prev,c = df['high'], df['low'], df['close'].shift(1),df['close']
tr = np.max([h - l, (c_prev - h).abs(), (c_prev - l).abs()], axis=0)
df['atr'] = pd.Series(tr).rolling(2).mean().bfill().values
# CE calculation
df['c1'] = pd.Series(h).rolling(2).max().bfill().values # Get the max value within two row
df['c2'] = pd.Series(l).rolling(2).min().bfill().values # Get the min value within two row
df['cel'] = df['c1'] - df['atr']*4
df['ces'] = df['c2'] + df['atr']*4
sh = df['ces'].to_numpy(dtype='float') # Convert to array
lo = df['cel'].to_numpy(dtype='float')
sh_prev = (df['ces'].shift(1)).to_numpy(dtype='float') # Shift downward to get the previous data in ces,cel and close
lo_prev = (df['cel'].shift(1)).to_numpy(dtype='float')
c_prev = (df['close'].shift(1)).to_numpy(dtype='float')
shnew = np.empty(3126, dtype=float) # Create new array to save new data
lonew = np.empty(3126, dtype=float)
for i in range(len(df['close'])): # Compare the previous close to previous cel
if c_prev[i] > lo_prev[i]:
lonew[i] = max(lo[i], lo_prev[i])
else:
lonew[i] = lo[i]
for i in range(len(df['close'])):# Compare the previous close to previous ces
if c_prev[i] < sh_prev[i]:
shnew[i] = min(sh[i], sh_prev[i])
else:
shnew[i] = sh[i]
# Create array for direction
# Follow the CE indicator to identify the direction
direc = np.empty(3126, dtype=int)
direc[3125] = 1 # Put a initial data into the last array
for i in range(len(df['close'])): # Compare the close to CE to get the direction
if c[i] > shnew[i]:
direc[i] = 1
elif c[i] < lonew[i]:
direc[i] = -1
else:
direc[i] = direc[i-1]
dfdir = pd.DataFrame(direc,columns=['direc'],index=df.index) # Convert the direction to dataframe
df = pd.merge(df, dfdir,left_index=True,right_index=True) # Merge the direction into ori dataframe
df['direc_prev'] = df['direc'].shift(1) # Shift downward to get the previous data
df['buysignal'] = np.logical_and(df['direc'] == 1,df['direc_prev'] == -1) # Get the buy signal (long)
df['sellsignal'] = np.logical_and(df['direc'] == -1,df['direc_prev'] == 1) # Get the sell signal (short)
Pine script code in trading view:
//#version=4
// Copyright (c) 2019-present, Alex Orekhov (everget)
// Chandelier Exit script may be freely distributed under the terms of the GPL-3.0 license.
study("Chandelier Exit", shorttitle="CE", overlay=true)
length = input(title="ATR Period", type=input.integer, defval=22)
mult = input(title="ATR Multiplier", type=input.float, step=0.1, defval=3.0)
showLabels = input(title="Show Buy/Sell Labels ?", type=input.bool, defval=true)
useClose = input(title="Use Close Price for Extremums ?", type=input.bool, defval=true)
highlightState = input(title="Highlight State ?", type=input.bool, defval=true)
atr = mult * atr(length)
longStop = (useClose ? highest(close, length) : highest(length)) - atr
// if useClose = false
// longstop = highest(length) - atr (want this)
// else
// longstop = highest(close, length)
longStopPrev = nz(longStop[1], longStop)
// nz(x,y) = if previous value is valid then take x else y
longStop := close[1] > longStopPrev ? max(longStop, longStopPrev) : longStop
// if close[1] > longStopPrev
// longstop = max(longStop, longStopPrev)
// else
// longstop = longstop
shortStop = (useClose ? lowest(close, length) : lowest(length)) + atr
shortStopPrev = nz(shortStop[1], shortStop)
shortStop := close[1] < shortStopPrev ? min(shortStop, shortStopPrev) : shortStop
var int dir = 1
dir := close > shortStopPrev ? 1 : close < longStopPrev ? -1 : dir
// if close > shortStopPrev
// dir = 1
// elseif close < longStopPrev
// dir = -1
//else
// dir = dir[-1]
var color longColor = color.green
var color shortColor = color.red
buySignal = dir == 1 and dir[1] == -1
// and = 1 & 1 > 1
plotshape(buySignal and showLabels ? longStop : na, title="Buy Label", text="Buy", location=location.absolute, style=shape.labelup, size=size.tiny, color=longColor, textcolor=color.white, transp=0)
sellSignal = dir == -1 and dir[1] == 1
plotshape(sellSignal and showLabels ? shortStop : na, title="Sell Label", text="Sell", location=location.absolute, style=shape.labeldown, size=size.tiny, color=shortColor, textcolor=color.white, transp=0)

How to derive scipy's global minimum for a specific equation with trust region reflective algorithm

A new data set was created by adding random variables from a specific data set. We want to solve the problem of matching two inconsistent data sets. So I tried to solve the optimization problem using scipy's minimum_squares function. The code and data used are as follows.
from numba import jit, prange
from scipy.optimize import minimize,least_squares
import scipy.io as sio
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import basinhopping
def Data_loader(name,normalize):
global X
global Y
arr = sio.loadmat(name)
X = arr['X']
Y = X
R = np.array([[1.0171, 0.0249, 0.1269], [0.1865, 0.9383, -0.3441], [0.0172, 0.5278, 1.0347]])
# R = np.random.rand(3,3)
X = np.random.rand(1)*np.dot(X,np.transpose(R))+1
X = X*0.1
X = X[0:len(X) - 20]
Y = Y[19:len(Y)]
if normalize==True :
n = X.shape[0]
m = Y.shape[0]
x_d = np.mean(X, axis=0)
y_d = np.mean(Y, axis=0)
x = X - np.tile(x_d, [n, 1])
y = Y - np.tile(y_d, [m, 1])
x_scale = np.sqrt(np.sum(np.sum(np.power(X,2),axis=0))/n)
y_scale = np.sqrt(np.sum(np.sum(np.power(Y, 2), axis=0))/m)
x = x / x_scale
y = y / y_scale
else :
x=X
y=Y
return x,y,x_scale,y_scale,x_d,y_d
X_data,Y_data,x_scale,y_scale,x_d,y_d = Data_loader(name='cpd_data3D_face.mat',normalize=True)
#jit(nopython=True, parallel=True)
def P_cal(X,Y,sigma2,outlier):
N = X.shape[0]
M = Y.shape[0]
D = X.shape[1]
P = np.zeros(shape = (N,M),dtype=np.float64)
Np = np.zeros(shape = (N,M),dtype=np.float64)
Pmn = np.zeros(shape = (N,M),dtype=np.float64)
ksig = -2.0 * sigma2
outlier_tmp = (outlier*M*((-ksig*3.1415926535)**(0.5*D)))/((1-outlier)*N)
for n in prange(0,N):
sp=np.zeros(shape = (1,),dtype=np.float64)
for m in prange(0,M):
razn=0
for d in prange(0,D):
diff = X[n,d] - Y[m,d]
diff = diff*diff
razn += diff
P[n,m] = np.exp(razn/ksig)
sp = P.sum(axis=1)[n] + outlier_tmp
for m in prange(0,M):
Np[n,m] = P[n,m]/sp
razn = 0
for d in prange(0,D):
diff = X[n,d] - Y[m,d]
diff = diff*diff
razn += diff
Pmn[n,m] = (P[n,m]/sp) * razn
return Pmn, Np
def model(xin):
global T
N = X_data.shape[0]
M = Y_data.shape[0]
D = X_data.shape[1]
B = np.array([[xin[0], xin[1], xin[2]], [xin[3], xin[4], xin[5]], [xin[6], xin[7], xin[8]]])
t = np.array([xin[9], xin[10], xin[11]])
sigma2 = xin[12]
outlier = 0.6
T = np.dot(Y_data,np.transpose(B)) + np.tile(np.transpose(t), [M, 1])
Pmn, Np = P_cal(X_data, T, sigma2, outlier)
Q = ((1 / sigma2) * (np.sum(Pmn))) + (((np.sum(Np) * D) / 2) * np.log(sigma2))
return Q
def opt_function(xin):
Q= model(xin)
print(Q)
Q= 10000+Q
return Q
def plot_function(X, Y, T) :
X_x = X[1:X.shape[0], 0]
X_y = X[1:X.shape[0], 1]
X_z = X[1:X.shape[0], 2]
Y_x = Y[1:Y.shape[0], 0]
Y_y = Y[1:Y.shape[0], 1]
Y_z = Y[1:Y.shape[0], 2]
T_x = T[1:T.shape[0], 0]
T_y = T[1:T.shape[0], 1]
T_z = T[1:T.shape[0], 2]
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X_x, X_y, X_z)
ax.scatter(Y_x, Y_y, Y_z)
ax.set_title('before')
fig2 = plt.figure(figsize=(10, 10))
ax = fig2.add_subplot(111, projection='3d')
ax.scatter(X_x, X_y, X_z)
ax.scatter(T_x, T_y, T_z)
ax.set_title('after')
plt.show()
def transformer_data(T,Y, xin, x_scale, y_scale, x_d, y_d):
M = Y.shape[0]
Trans_R = np.array([xin[0], xin[1], xin[2], xin[3], xin[4], xin[5], xin[6], xin[7], xin[8]])
Trans_R = Trans_R.reshape((3, 3))
Trans_t = np.array([xin[9], xin[10], xin[11]])
Trans_s = 1*(x_scale/y_scale)
Trans_t = (x_scale*np.transpose(Trans_t)) + np.transpose(x_d) - Trans_s*(Trans_R#np.transpose(y_d))
Trans_T = T * x_scale + np.tile(x_d, [M, 1])
return Trans_T
x0 = np.array([1,0,0,0,1,0,0,0,1,0,0,0,0.0004])
Optimum = least_squares(opt_function,x0,jac='2-point',method='trf',xtol=1e-15,ftol=1e-15,gtol=1e-15,verbose=2)
print(Optimum)
Transformer_T = transformer_data(T,Y,Optimum.x,x_scale,y_scale,x_d,y_d)
plot_function(X,Y,Transformer_T)
[cpd_data3D_face.mat]
-1.26805102825165 0.703154087066650 0.250609993934631
-1.24199450016022 0.766805052757263 0.647247254848480
-1.23889672756195 1.06739985942841 -0.188671588897705
-1.21881270408630 0.807438790798187 1.16736936569214
-1.18697428703308 0.626793265342712 -0.182836949825287
-1.16291773319244 0.822238981723785 1.83739590644836
-1.14834547042847 0.493062376976013 0.635148286819458
-1.13797473907471 0.331227302551270 0.219468995928764
-1.13209247589111 1.01279878616333 -0.797961413860321
-1.11951982975006 0.473790675401688 1.03894352912903
-1.06242597103119 0.390692919492722 1.47615230083466
-1.04522788524628 0.358604818582535 0.543326914310455
-1.04382777214050 0.319657772779465 0.706707119941711
-1.03485214710236 0.254935413599014 0.932691574096680
-1.01772892475128 0.258871674537659 1.21425902843475
-1.01639711856842 0.200742736458778 -0.224685385823250
-1.01449811458588 0.528465926647186 -0.686351001262665
-0.983937680721283 0.189417555928230 1.00790750980377
-0.983821272850037 0.745310366153717 -1.11274755001068
-0.978638589382172 0.299691230058670 0.629687130451202
-0.922493815422058 0.150767520070076 0.828504979610443
-0.909300625324249 0.283628404140472 0.559927582740784
-0.906097352504730 0.933652937412262 -1.29045069217682
-0.891153812408447 0.289580315351486 0.480834931135178
-0.889832675457001 0.129670828580856 0.226587772369385
-0.879191935062408 0.845484137535095 2.29874420166016
-0.854662895202637 0.191300705075264 0.728563904762268
-0.837804734706879 0.327339112758637 1.77278757095337
-0.818458557128906 0.118494018912315 1.31940019130707
-0.813382029533386 0.144385874271393 -0.574904143810272
-0.806623399257660 0.0640521720051765 1.12445175647736
-0.806076943874359 0.179906263947487 0.362056881189346
-0.794836580753326 1.03445231914520 -1.48189103603363
-0.791478812694550 0.239165067672730 0.551705360412598
-0.790624439716339 0.193809807300568 0.438241928815842
-0.787293195724487 0.173615008592606 0.588202476501465
-0.780423700809479 0.399599254131317 -1.30588448047638
-0.775464117527008 -0.00958730466663837 -0.174502417445183
-0.767135858535767 0.172049850225449 0.568943262100220
-0.763602852821350 0.184372812509537 0.565283775329590
-0.762090682983398 0.196064323186874 0.504386007785797
-0.760000824928284 0.188478738069534 0.540778219699860
-0.755698919296265 0.209235891699791 0.508155345916748
-0.746246218681335 0.0437246896326542 0.911044776439667
-0.704111039638519 0.711774110794067 -1.47775316238403
-0.656321167945862 0.119024306535721 -1.07423388957977
-0.650056242942810 0.0959970802068710 0.784161567687988
-0.644072175025940 -0.00454827398061752 -0.505615711212158
-0.602208197116852 0.0996995121240616 0.379885524511337
-0.600404739379883 0.0790802314877510 0.659766137599945
-0.600255548954010 0.106224447488785 0.449432760477066
-0.598033308982849 0.120790801942348 0.452769994735718
-0.596876800060272 0.102229975163937 0.541571617126465
-0.595354616641998 0.0876417979598045 0.651539027690888
-0.594505965709686 0.0156574435532093 -0.975354671478272
-0.593853473663330 0.0964264348149300 0.647029995918274
-0.593455016613007 0.889857470989227 -1.61020958423615
-0.592627882957459 0.101232483983040 0.321590214967728
-0.573011338710785 -0.0138365831226110 -0.755739450454712
-0.556460380554199 -0.00661153765395284 0.212208628654480
-0.543379187583923 0.113693088293076 -1.46306395530701
-0.519296109676361 -0.0184836350381374 -1.20552349090576
-0.489446550607681 0.0686619207262993 0.649044036865234
-0.489271312952042 -0.0751322507858276 -0.480372875928879
-0.488831192255020 0.0591186434030533 0.653709352016449
-0.484343349933624 0.0449181720614433 0.664057910442352
-0.477669984102249 0.0742370337247849 0.549954950809479
-0.464891046285629 -0.0795564651489258 -0.776580691337585
-0.459231346845627 0.543196737766266 -1.60204350948334
-0.457071840763092 0.0801357030868530 0.468159794807434
-0.453608423471451 0.0624924153089523 0.466883003711700
-0.453008860349655 0.0603442415595055 0.380578845739365
-0.450547099113464 -0.137127891182899 -0.103212133049965
-0.449798971414566 0.0621318705379963 0.336125582456589
-0.414782941341400 -0.0911946594715118 -0.962513506412506
-0.404184430837631 -0.105692356824875 -0.845229566097260
-0.401245862245560 0.0562914907932282 0.555313587188721
-0.400264382362366 0.0315503329038620 0.797742426395416
-0.399596065282822 0.0716907083988190 0.495797544717789
-0.397346645593643 0.0558350458741188 0.493942171335220
-0.396078765392303 0.800601005554199 -1.71100533008575
-0.395203620195389 0.0524718016386032 0.630690515041351
-0.395150482654572 0.0250202100723982 0.647774577140808
-0.394768565893173 0.0421436578035355 0.636364042758942
-0.394747585058212 -0.106679096817970 -0.805121719837189
-0.392148017883301 -0.118094518780708 0.929876744747162
-0.385803490877152 -0.149832293391228 -0.273493885993958
-0.364510416984558 0.881731152534485 2.62541174888611
-0.360802918672562 -0.143175244331360 -0.298475503921509
-0.360712468624115 -0.165030494332314 -0.683113873004913
-0.354962378740311 -0.158224657177925 -0.731766164302826
-0.351477324962616 -0.153869569301605 -0.548111081123352
-0.350320786237717 -0.155996680259705 1.20539724826813
-0.347673654556274 0.0197845026850700 0.360736131668091
-0.343419760465622 0.0446846485137939 0.414639592170715
-0.341843873262405 0.0630866438150406 0.510752856731415
-0.341169834136963 0.0492043718695641 0.502054572105408
-0.337658882141113 0.0351469479501247 0.561841547489166
-0.337361693382263 -0.133780807256699 -0.763273239135742
-0.336719214916229 0.0249884724617004 0.577858805656433
-0.336661010980606 0.0514110624790192 0.550272345542908
-0.335812389850616 -0.0870144963264465 0.227063819766045
-0.334714591503143 -0.117261730134487 -0.773931920528412
-0.334714591503143 -0.117261730134487 -0.768631875514984
-0.327723145484924 0.200987175107002 1.97596061229706
-0.321362793445587 -0.147063419222832 -1.27225828170776
-0.319569408893585 -0.0633241608738899 -1.53213012218475
-0.313458234071732 -0.168399006128311 -0.838223099708557
-0.307326167821884 -0.179740279912949 -0.134934157133102
-0.301624149084091 -0.0517277717590332 1.45116829872131
-0.296892076730728 0.0488278791308403 0.533883988857269
-0.296568334102631 -0.140135616064072 -1.04919373989105
-0.295273929834366 0.0496075637638569 0.512347042560577
-0.293163061141968 -0.172096326947212 -0.910953700542450
-0.284124016761780 0.0332795977592468 0.505501687526703
-0.282951623201370 0.0313751697540283 0.540355861186981
-0.269329160451889 0.0239753406494856 0.553192079067230
-0.267992317676544 -0.181957572698593 -0.160023182630539
-0.267015933990479 -0.177904888987541 -0.321233123540878
-0.265541255474091 -0.245476022362709 -0.144584536552429
-0.262671560049057 -0.202741548418999 -0.0718050673604012
-0.261472642421722 0.0231583528220654 0.492826491594315
-0.248518571257591 -0.191129758954048 -0.211526885628700
-0.243474200367928 -0.315041571855545 -0.0893090441823006
-0.230440512299538 -0.228538274765015 -0.0157361794263124
-0.228599637746811 -0.249311119318008 -0.0108560575172305
-0.226721569895744 -0.278264611959457 -0.213387131690979
-0.224844038486481 -0.332080185413361 -0.176096573472023
-0.223889231681824 0.318416297435761 -1.66368579864502
-0.214929193258286 -0.266498714685440 -0.676058530807495
-0.210813075304031 -0.223832860589027 -0.255875170230866
-0.207094073295593 -0.281632810831070 -0.595061719417572
-0.204060018062592 -0.0362701974809170 0.500299334526062
-0.199752956628799 -0.219154447317123 -0.731922388076782
-0.198813900351524 -0.259246885776520 -0.518303036689758
-0.197991833090782 -0.199447497725487 -0.743696689605713
-0.197991833090782 -0.199447497725487 -0.738389372825623
-0.197726234793663 -0.252106904983521 -0.839765965938568
-0.193233251571655 -0.370242148637772 -0.0653356760740280
-0.190501421689987 -0.284990489482880 0.0164594296365976
-0.189626261591911 -0.382326304912567 -0.151752114295959
-0.188193947076797 -0.00516866426914930 0.692332863807678
-0.187763944268227 -0.247889831662178 -0.938535273075104
-0.186157137155533 -0.312341511249542 -0.215669736266136
-0.184836089611053 -0.161518707871437 0.312147319316864
-0.175839409232140 -0.193330869078636 -1.04541945457459
-0.169654235243797 -0.245794147253037 -0.369660794734955
-0.165510952472687 -0.0105156786739826 0.591866850852966
-0.163967385888100 0.744191050529480 -1.78527390956879
-0.163914248347282 -0.269649654626846 -0.244318157434464
-0.159129753708839 -0.366783618927002 -0.183630362153053
-0.153681874275208 -0.152272656559944 0.826754629611969
-0.140536934137344 -0.201197594404221 1.15596377849579
-0.133895203471184 -0.233975455164909 -1.29469370841980
-0.132924452424049 -0.456851601600647 -0.132863938808441
-0.130474045872688 -0.456304907798767 -0.0475655905902386
-0.118909314274788 -0.346768766641617 -0.195299670100212
-0.117933601140976 -0.151625871658325 -1.55685293674469
-0.115535654127598 -0.404653489589691 0.0411310084164143
-0.113641686737537 -0.379890978336334 0.0627729669213295
-0.108262777328491 -0.130481123924255 0.724745750427246
-0.0882218107581139 -0.447865873575211 -0.161400064826012
-0.0852775499224663 -0.299838036298752 -0.391834944486618
-0.0817874670028687 -0.306500911712647 -0.520046114921570
-0.0744881182909012 -0.330223441123962 -0.562329113483429
-0.0678362473845482 -0.302734911441803 -0.270571678876877
-0.0670988634228706 -0.528554797172546 -0.130210265517235
-0.0566540732979775 -0.283123373985291 -0.943625330924988
-0.0483675263822079 -0.303127199411392 -0.828308999538422
-0.0482353903353214 -0.329624116420746 -0.647095203399658
-0.0457053110003471 -0.543731153011322 -0.0194005407392979
-0.0454770512878895 -0.506025552749634 0.0706802755594254
-0.0451158955693245 -0.352757841348648 -0.214075580239296
-0.0422411337494850 -0.172186344861984 0.619287312030792
-0.0417529530823231 -0.456273496150970 0.111095674335957
-0.0337586030364037 -0.300846278667450 0.405985265970230
-0.0330427736043930 -0.261952579021454 -0.736106753349304
-0.0330427736043930 -0.261952579021454 -0.730799376964569
-0.0326184481382370 -0.289149373769760 -0.717697024345398
-0.0299390628933907 -0.481825888156891 -0.177739560604095
-0.0276746246963739 -0.431670397520065 -0.168245419859886
0.0121531123295426 -0.549932301044464 -0.00995034258812666
0.0121531123295426 -0.541036725044251 -0.122998714447021
0.0121531123295426 -0.512953639030457 0.0811826586723328
0.0121531123295426 -0.486913204193115 -0.174502417445183
0.0121531123295426 -0.465853661298752 0.124627687036991
0.0121531123295426 -0.426710635423660 -0.205699533224106
0.0121531123295426 -0.366242557764053 -0.241559505462646
0.0121531123295426 -0.336180478334427 -0.658293366432190
0.0121531123295426 -0.331316232681274 -0.596917092800140
0.0121531123295426 -0.318112850189209 0.404608368873596
0.0121531123295426 -0.316532135009766 -0.281554996967316
0.0121531123295426 -0.302485466003418 -0.736324012279511
0.0121531123295426 -0.296682208776474 -0.830315709114075
0.0121531123295426 -0.292714387178421 -0.534635245800018
0.0121531123295426 -0.277532249689102 -0.937846839427948
0.0121531123295426 -0.276381313800812 -0.391942352056503
0.0121531123295426 -0.261135667562485 -0.756581723690033
0.0121531123295426 -0.261135667562485 -0.751281678676605
0.0121531123295426 -0.259597063064575 -1.31272983551025
0.0121531123295426 -0.216226130723953 -1.03725826740265
0.0121531123295426 -0.211855024099350 0.920858681201935
0.0121531123295426 -0.198895767331123 -1.54220521450043
0.0121531123295426 -0.190407738089561 1.15325403213501
0.0121531123295426 -0.184148013591766 0.634032607078552
0.0121531123295426 -0.182100847363472 0.750254631042481
0.0121531123295426 -0.0808928310871124 1.42999994754791
0.0121531123295426 0.176368087530136 2.03060126304626
0.0121531123295426 0.222836643457413 -1.69991946220398
0.0121531123295426 0.750662505626679 -1.84563207626343
0.0121531123295426 0.938862383365631 2.70322513580322
0.0519808456301689 -0.431670397520065 -0.168245419859886
0.0542452894151211 -0.481825888156891 -0.177739560604095
0.0569246746599674 -0.289149373769760 -0.717697024345398
0.0573489926755428 -0.261952579021454 -0.736106753349304
0.0573489926755428 -0.261952579021454 -0.730799376964569
0.0580648221075535 -0.300846278667450 0.405985265970230
0.0660591870546341 -0.456273496150970 0.111095674335957
0.0665473639965057 -0.172186344861984 0.619287312030792
0.0694221258163452 -0.352757841348648 -0.214075580239296
0.0697832852602005 -0.506025552749634 0.0706802755594254
0.0700115412473679 -0.543731153011322 -0.0194005407392979
0.0725416243076325 -0.329624116420746 -0.647095203399658
0.0726737603545189 -0.303127199411392 -0.828308999538422
0.0809603035449982 -0.283123373985291 -0.943625330924988
0.0914050936698914 -0.528554797172546 -0.130210265517235
0.0921424329280853 -0.302734911441803 -0.270571678876877
0.0987943112850189 -0.330223441123962 -0.562329113483429
0.106093667447567 -0.306500911712647 -0.520046114921570
0.109583787620068 -0.299838036298752 -0.391834944486618
0.112528048455715 -0.447865873575211 -0.161400064826012
0.132569059729576 -0.130481123924255 0.724745750427246
0.137947961688042 -0.379890978336334 0.0627729669213295
0.139841854572296 -0.404653489589691 0.0411310084164143
0.142239809036255 -0.151625871658325 -1.55685293674469
0.143215626478195 -0.346768766641617 -0.195299670100212
0.154780268669128 -0.456304907798767 -0.0475655905902386
0.157230705022812 -0.456851601600647 -0.132863938808441
0.158201426267624 -0.233975455164909 -1.29469370841980
0.164843082427979 -0.201197594404221 1.15596377849579
0.177988156676292 -0.152272656559944 0.826754629611969
0.183435946702957 -0.366783618927002 -0.183630362153053
0.188220471143723 -0.269649654626846 -0.244318157434464
0.188273563981056 0.744191050529480 -1.78527390956879
0.189817219972610 -0.0105156786739826 0.591866850852966
0.193960443139076 -0.245794147253037 -0.369660794734955
0.200145587325096 -0.193330869078636 -1.04541945457459
0.209142416715622 -0.161518707871437 0.312147319316864
0.210463374853134 -0.312341511249542 -0.215669736266136
0.212070122361183 -0.247889831662178 -0.938535273075104
0.212500244379044 -0.00516866426914930 0.692332863807678
0.213932514190674 -0.382326304912567 -0.151752114295959
0.214807629585266 -0.284990489482880 0.0164594296365976
0.217539504170418 -0.370242148637772 -0.0653356760740280
0.222032532095909 -0.252106904983521 -0.839765965938568
0.222298085689545 -0.199447497725487 -0.743696689605713
0.222298085689545 -0.199447497725487 -0.738389372825623
0.223120197653770 -0.259246885776520 -0.518303036689758
0.224059239029884 -0.219154447317123 -0.731922388076782
0.228366270661354 -0.0362701974809170 0.500299334526062
0.231400310993195 -0.281632810831070 -0.595061719417572
0.235119298100472 -0.223832860589027 -0.255875170230866
0.239235371351242 -0.266498714685440 -0.676058530807495
0.248195409774780 0.318416297435761 -1.66368579864502
0.249150261282921 -0.332080185413361 -0.176096573472023
0.251027762889862 -0.278264611959457 -0.213387131690979
0.252905875444412 -0.249311119318008 -0.0108560575172305
0.254746764898300 -0.228538274765015 -0.0157361794263124
0.267780452966690 -0.315041571855545 -0.0893090441823006
0.272824734449387 -0.191129758954048 -0.211526885628700
0.285778939723969 0.0231583528220654 0.492826491594315
0.286977857351303 -0.202741548418999 -0.0718050673604012
0.289847403764725 -0.245476022362709 -0.144584536552429
0.291322171688080 -0.177904888987541 -0.321233123540878
0.292298495769501 -0.181957572698593 -0.160023182630539
0.293635338544846 0.0239753406494856 0.553192079067230
0.307257831096649 0.0313751697540283 0.540355861186981
0.308430194854736 0.0332795977592468 0.505501687526703
0.317469328641892 -0.172096326947212 -0.910953700542450
0.319580137729645 0.0496075637638569 0.512347042560577
0.320874541997910 -0.140135616064072 -1.04919373989105
0.321198403835297 0.0488278791308403 0.533883988857269
0.325930356979370 -0.0517277717590332 1.45116829872131
0.331632345914841 -0.179740279912949 -0.134934157133102
0.337764531373978 -0.168399006128311 -0.838223099708557
0.343875676393509 -0.0633241608738899 -1.53213012218475
0.345669031143189 -0.147063419222832 -1.27225828170776
0.352029412984848 0.200987175107002 1.97596061229706
0.359020859003067 -0.117261730134487 -0.773931920528412
0.359020859003067 -0.117261730134487 -0.768631875514984
0.360118597745895 -0.0870144963264465 0.227063819766045
0.360967248678207 0.0514110624790192 0.550272345542908
0.361025452613831 0.0249884724617004 0.577858805656433
0.361667960882187 -0.133780807256699 -0.763273239135742
0.361965119838715 0.0351469479501247 0.561841547489166
0.365476071834564 0.0492043718695641 0.502054572105408
0.366150081157684 0.0630866438150406 0.510752856731415
0.367725998163223 0.0446846485137939 0.414639592170715
0.371979862451553 0.0197845026850700 0.360736131668091
0.374627053737640 -0.155996680259705 1.20539724826813
0.375783562660217 -0.153869569301605 -0.548111081123352
0.379268586635590 -0.158224657177925 -0.731766164302826
0.385018706321716 -0.165030494332314 -0.683113873004913
0.385109037160873 -0.143175244331360 -0.298475503921509
0.388816595077515 0.881731152534485 2.62541174888611
0.410109728574753 -0.149832293391228 -0.273493885993958
0.416454315185547 -0.118094518780708 0.929876744747162
0.419053852558136 -0.106679096817970 -0.805121719837189
0.419074833393097 0.0421436578035355 0.636364042758942
0.419456660747528 0.0250202100723982 0.647774577140808
0.419509917497635 0.0524718016386032 0.630690515041351
0.420385032892227 0.800601005554199 -1.71100533008575
0.421652972698212 0.0558350458741188 0.493942171335220
0.423902332782745 0.0716907083988190 0.495797544717789
0.424570620059967 0.0315503329038620 0.797742426395416
0.425552159547806 0.0562914907932282 0.555313587188721
0.428490787744522 -0.105692356824875 -0.845229566097260
0.439089149236679 -0.0911946594715118 -0.962513506412506
0.474105179309845 0.0621318705379963 0.336125582456589
0.474853336811066 -0.137127891182899 -0.103212133049965
0.477315068244934 0.0603442415595055 0.380578845739365
0.477914631366730 0.0624924153089523 0.466883003711700
0.481377989053726 0.0801357030868530 0.468159794807434
0.483537673950195 0.543196737766266 -1.60204350948334
0.489197254180908 -0.0795564651489258 -0.776580691337585
0.501976311206818 0.0742370337247849 0.549954950809479
0.508649528026581 0.0449181720614433 0.664057910442352
0.513137459754944 0.0591186434030533 0.653709352016449
0.513577580451965 -0.0751322507858276 -0.480372875928879
0.513752877712250 0.0686619207262993 0.649044036865234
0.543602466583252 -0.0184836350381374 -1.20552349090576
0.567685365676880 0.113693088293076 -1.46306395530701
0.580766677856445 -0.00661153765395284 0.212208628654480
0.597317516803742 -0.0138365831226110 -0.755739450454712
0.616934120655060 0.101232483983040 0.321590214967728
0.617761254310608 0.889857470989227 -1.61020958423615
0.618159651756287 0.0964264348149300 0.647029995918274
0.618812203407288 0.0156574435532093 -0.975354671478272
0.619660854339600 0.0876417979598045 0.651539027690888
0.621182978153229 0.102229975163937 0.541571617126465
0.622339546680450 0.120790801942348 0.452769994735718
0.624561727046967 0.106224447488785 0.449432760477066
0.624710798263550 0.0790802314877510 0.659766137599945
0.626514434814453 0.0996995121240616 0.379885524511337
0.668378472328186 -0.00454827398061752 -0.505615711212158
0.674362480640411 0.0959970802068710 0.784161567687988
0.680627405643463 0.119024306535721 -1.07423388957977
0.728417158126831 0.711774110794067 -1.47775316238403
0.770552396774292 0.0437246896326542 0.911044776439667
0.780005037784576 0.209235891699791 0.508155345916748
0.784307062625885 0.188478738069534 0.540778219699860
0.786396920681000 0.196064323186874 0.504386007785797
0.787909030914307 0.184372812509537 0.565283775329590
0.791442155838013 0.172049850225449 0.568943262100220
0.799770295619965 -0.00958730466663837 -0.174502417445183
0.804729878902435 0.399599254131317 -1.30588448047638
0.811599373817444 0.173615008592606 0.588202476501465
0.814930796623230 0.193809807300568 0.438241928815842
0.815785050392151 0.239165067672730 0.551705360412598
0.819142878055573 1.03445231914520 -1.48189103603363
0.830383181571960 0.179906263947487 0.362056881189346
0.830929577350617 0.0640521720051765 1.12445175647736
0.837688326835632 0.144385874271393 -0.574904143810272
0.842764794826508 0.118494018912315 1.31940019130707
0.862110912799835 0.327339112758637 1.77278757095337
0.878969132900238 0.191300705075264 0.728563904762268
0.903498232364655 0.845484137535095 2.29874420166016
0.914138853549957 0.129670828580856 0.226587772369385
0.915460050106049 0.289580315351486 0.480834931135178
0.930403351783752 0.933652937412262 -1.29045069217682
0.933606922626495 0.283628404140472 0.559927582740784
0.946800053119659 0.150767520070076 0.828504979610443
1.00294494628906 0.299691230058670 0.629687130451202
1.00812780857086 0.745310366153717 -1.11274755001068
1.00824403762817 0.189417555928230 1.00790750980377
1.03880441188812 0.528465926647186 -0.686351001262665
1.04070341587067 0.200742736458778 -0.224685385823250
1.04203522205353 0.258871674537659 1.21425902843475
1.05915844440460 0.254935413599014 0.932691574096680
1.06813418865204 0.319657772779465 0.706707119941711
1.06953442096710 0.358604818582535 0.543326914310455
1.08673226833344 0.390692919492722 1.47615230083466
1.14382600784302 0.473790675401688 1.03894352912903
1.15639877319336 1.01279878616333 -0.797961413860321
1.16228091716766 0.331227302551270 0.219468995928764
1.17265176773071 0.493062376976013 0.635148286819458
1.18722403049469 0.822238981723785 1.83739590644836
1.21128082275391 0.626793265342712 -0.182836949825287
1.24311912059784 0.807438790798187 1.16736936569214
1.26320302486420 1.06739985942841 -0.188671588897705
1.26630091667175 0.766805052757263 0.647247254848480
1.29235732555389 0.703154087066650 0.250609993934631
[problem in code]
That global optimization solution has a value of around -8000. However, in that code it finds a solution up to about -3000 and then exits. I tried to use the trust region reflective algorithm because the formula to be optimized is non-linear, and as a result, it failed to find the global minimum.
[Question content]
I want to find the global minimum. I'd appreciate it if you could give me some feedback to help solve the problem.

How to efficiently calculate pairwise ratios on rows using NumPy?

I'm trying to speed up my calculation of calculating pairwise values. However, I'm having trouble visualizing how to do this in NumPy without being overly verbose and explicit.
Is there a way to use vector multiplication or broadcasting to speed up this operation?
Essentially what I'm doing is iterating through each row, then through all combinations of features in each row, and then taking the ratio (followed by log transform).
import pandas as pd
import numpy as np
from collections import *
# Get data
data = {'BC0a': {'1001.2': 4122,'1010.1': 6766,'1010.2': 11734,'1018.2': 5805,'1021.2': 9504,'1026.1': 18111,'1026.2': 1350,'1029.1': 3638,'1029.2': 3967,'1048.2': 9225,'1049.2': 8670,'1057.2': 21796,'1065.1': 5117,'1065.2': 7811,'1067.1': 17563,'1067.2': 8418,'1071.1': 10088,'1071.2': 9878,'1072.1': 13159,'1072.2': 8358,'1073.1': 5368,'1073.2': 2615,'1080.2': 9397,'1086.2': 1303,'2018.1': 2414,'2018.2': 1771,'2028.2': 13710,'2031.2': 3264,'2034.1': 10264,'2034.2': 4670,'2036.1': 7219,'2050.2': 11707,'3038.1': 1388,'3038.2': 13104,'3041.1': 4085,'3041.2': 9160,'3042.1': 8629,'3042.2': 11298,'A-1501-03.A': 4083,'A-1501-03.B': 7952,'A-1505-99.A': 4714,'A-1505-99.B': 4938,'A-1507-168.B': 9238,'M-1506-106.B': 10223,'M-1506-108.A': 2704,'M-1506-108.B': 5056,'M-1506-110.A': 20939,'M-1506-110.B': 18435,'M-1507-113.B': 23343,'M-1507-118.A': 5895,'M-1507-118.B': 5758,'M-1507-119.A': 4404,'M-1507-119.B': 5842,'M-1507-120.A': 9952,'M-1507-120.B': 7825,'M-1507-124.A': 12450,'M-1507-125.B': 28041,'M-1507-126.A': 7669,'M-1507-133.B': 4964,'M-1507-134.B': 1380,'M-1507-136.A': 5557,'M-1507-147.A': 3113,'M-1507-147.B': 1340,'M-1507-154.A': 2848,'M-1507-154.B': 17416,'M-1507-155.A': 5535,'M-1507-155.B': 3792,'S-1409-41.B': 3476,'S-1409-52.B': 3584,'S-1409-57.A': 2065,'S-1410-61.B': 10758,'S-1410-62.A': 5886,'S-1410-62.B': 7680,'S-1410-67.A': 4104,'S-1410-70.A': 2970,'S-1410-70.B': 9990,'S-1410-71.A': 14024,'S-1410-71.B': 11120,'S-1410-72.A': 9835,'S-1410-72.B': 7124,'S-1410-75.A': 1693,'S-1410-75.B': 3334,'S-1410-76.A': 3849,'S-1410-76.B': 12268,'S-1410-84.A': 16988,'S-1410-84.B': 19123,'S-1504-86.A': 15359},'BC0b': {'1001.2': 5935,'1010.1': 34253,'1010.2': 8016,'1018.2': 7557,'1021.2': 25213,'1026.1': 5462,'1026.2': 742,'1029.1': 11265,'1029.2': 2133,'1048.2': 7906,'1049.2': 27375,'1057.2': 7353,'1065.1': 8505,'1065.2': 18643,'1067.1': 22754,'1067.2': 7441,'1071.1': 15505,'1071.2': 18355,'1072.1': 9349,'1072.2': 21841,'1073.1': 8474,'1073.2': 6273,'1080.2': 7820,'1086.2': 2231,'2018.1': 1978,'2018.2': 6516,'2028.2': 12559,'2031.2': 2537,'2034.1': 13301,'2034.2': 3128,'2036.1': 12757,'2050.2': 17553,'3038.1': 1286,'3038.2': 13197,'3041.1': 11141,'3041.2': 19938,'3042.1': 11437,'3042.2': 12662,'A-1501-03.A': 36185,'A-1501-03.B': 38130,'A-1505-99.A': 16054,'A-1505-99.B': 14821,'A-1507-168.B': 26276,'M-1506-106.B': 9390,'M-1506-108.A': 8822,'M-1506-108.B': 10076,'M-1506-110.A': 32208,'M-1506-110.B': 31193,'M-1507-113.B': 72979,'M-1507-118.A': 5367,'M-1507-118.B': 4684,'M-1507-119.A': 66612,'M-1507-119.B': 13348,'M-1507-120.A': 33713,'M-1507-120.B': 8025,'M-1507-124.A': 16145,'M-1507-125.B': 28633,'M-1507-126.A': 18907,'M-1507-133.B': 4885,'M-1507-134.B': 42507,'M-1507-136.A': 17102,'M-1507-147.A': 5015,'M-1507-147.B': 114078,'M-1507-154.A': 16207,'M-1507-154.B': 12175,'M-1507-155.A': 9714,'M-1507-155.B': 14034,'S-1409-41.B': 4865,'S-1409-52.B': 2994,'S-1409-57.A': 3328,'S-1410-61.B': 12545,'S-1410-62.A': 12655,'S-1410-62.B': 3591,'S-1410-67.A': 3109,'S-1410-70.A': 14745,'S-1410-70.B': 16226,'S-1410-71.A': 3979,'S-1410-71.B': 2415,'S-1410-72.A': 25956,'S-1410-72.B': 19851,'S-1410-75.A': 5027,'S-1410-75.B': 1560,'S-1410-76.A': 28781,'S-1410-76.B': 11400,'S-1410-84.A': 15048,'S-1410-84.B': 5149,'S-1504-86.A': 15101},'BC1': {'1001.2': 289306,'1010.1': 161496,'1010.2': 69964,'1018.2': 47739,'1021.2': 124848,'1026.1': 68643,'1026.2': 17652,'1029.1': 35123,'1029.2': 131421,'1048.2': 178320,'1049.2': 97477,'1057.2': 72803,'1065.1': 66952,'1065.2': 116619,'1067.1': 70103,'1067.2': 60019,'1071.1': 355549,'1071.2': 253142,'1072.1': 72188,'1072.2': 181514,'1073.1': 132905,'1073.2': 123315,'1080.2': 105312,'1086.2': 26294,'2018.1': 63484,'2018.2': 341118,'2028.2': 65058,'2031.2': 140441,'2034.1': 84939,'2034.2': 49515,'2036.1': 145002,'2050.2': 103857,'3038.1': 13941,'3038.2': 79499,'3041.1': 42726,'3041.2': 124551,'3042.1': 40899,'3042.2': 40565,'A-1501-03.A': 137056,'A-1501-03.B': 166252,'A-1505-99.A': 202502,'A-1505-99.B': 61795,'A-1507-168.B': 144826,'M-1506-106.B': 92482,'M-1506-108.A': 134468,'M-1506-108.B': 167032,'M-1506-110.A': 101216,'M-1506-110.B': 100130,'M-1507-113.B': 156389,'M-1507-118.A': 178511,'M-1507-118.B': 114049,'M-1507-119.A': 127491,'M-1507-119.B': 96218,'M-1507-120.A': 227099,'M-1507-120.B': 97021,'M-1507-124.A': 63121,'M-1507-125.B': 126164,'M-1507-126.A': 62577,'M-1507-133.B': 33532,'M-1507-134.B': 104437,'M-1507-136.A': 25218,'M-1507-147.A': 180090,'M-1507-147.B': 67312,'M-1507-154.A': 78943,'M-1507-154.B': 73775,'M-1507-155.A': 111586,'M-1507-155.B': 146237,'S-1409-41.B': 134373,'S-1409-52.B': 122611,'S-1409-57.A': 139990,'S-1410-61.B': 326334,'S-1410-62.A': 360458,'S-1410-62.B': 448642,'S-1410-67.A': 250696,'S-1410-70.A': 72425,'S-1410-70.B': 192340,'S-1410-71.A': 113476,'S-1410-71.B': 160124,'S-1410-72.A': 82178,'S-1410-72.B': 118238,'S-1410-75.A': 70348,'S-1410-75.B': 277236,'S-1410-76.A': 130693,'S-1410-76.B': 256835,'S-1410-84.A': 103621,'S-1410-84.B': 56052,'S-1504-86.A': 108613},'BC10': {'1001.2': 33754,'1010.1': 16476,'1010.2': 8935,'1018.2': 1221,'1021.2': 80753,'1026.1': 716,'1026.2': 334,'1029.1': 1008,'1029.2': 1008,'1048.2': 8115,'1049.2': 27643,'1057.2': 6681,'1065.1': 1020,'1065.2': 1269,'1067.1': 5771,'1067.2': 1800,'1071.1': 4905,'1071.2': 6513,'1072.1': 12068,'1072.2': 22154,'1073.1': 889,'1073.2': 380,'1080.2': 5208,'1086.2': 1359,'2018.1': 1577,'2018.2': 5306,'2028.2': 2675,'2031.2': 1284,'2034.1': 2081,'2034.2': 1229,'2036.1': 2548,'2050.2': 4020,'3038.1': 334,'3038.2': 943,'3041.1': 429,'3041.2': 3665,'3042.1': 845,'3042.2': 1700,'A-1501-03.A': 2357,'A-1501-03.B': 2798,'A-1505-99.A': 3528,'A-1505-99.B': 1093,'A-1507-168.B': 13760,'M-1506-106.B': 2380,'M-1506-108.A': 8673,'M-1506-108.B': 7305,'M-1506-110.A': 12250,'M-1506-110.B': 9403,'M-1507-113.B': 56811,'M-1507-118.A': 9763,'M-1507-118.B': 3591,'M-1507-119.A': 15052,'M-1507-119.B': 2156,'M-1507-120.A': 13108,'M-1507-120.B': 3872,'M-1507-124.A': 5413,'M-1507-125.B': 15676,'M-1507-126.A': 5279,'M-1507-133.B': 5271,'M-1507-134.B': 1087,'M-1507-136.A': 3641,'M-1507-147.A': 4673,'M-1507-147.B': 1316,'M-1507-154.A': 6519,'M-1507-154.B': 3229,'M-1507-155.A': 5602,'M-1507-155.B': 10569,'S-1409-41.B': 2057,'S-1409-52.B': 1593,'S-1409-57.A': 13432,'S-1410-61.B': 7979,'S-1410-62.A': 5516,'S-1410-62.B': 1783,'S-1410-67.A': 6542,'S-1410-70.A': 4102,'S-1410-70.B': 6543,'S-1410-71.A': 22790,'S-1410-71.B': 12016,'S-1410-72.A': 11283,'S-1410-72.B': 1319,'S-1410-75.A': 1938,'S-1410-75.B': 2400,'S-1410-76.A': 2461,'S-1410-76.B': 10497,'S-1410-84.A': 5015,'S-1410-84.B': 1202,'S-1504-86.A': 57069},'BC100': {'1001.2': 3286,'1010.1': 10947,'1010.2': 7687,'1018.2': 2765,'1021.2': 38762,'1026.1': 33677,'1026.2': 13154,'1029.1': 5563,'1029.2': 9174,'1048.2': 13501,'1049.2': 23698,'1057.2': 3075,'1065.1': 18148,'1065.2': 24837,'1067.1': 8748,'1067.2': 11369,'1071.1': 4122,'1071.2': 41568,'1072.1': 67820,'1072.2': 42658,'1073.1': 12069,'1073.2': 38510,'1080.2': 25111,'1086.2': 3059,'2018.1': 11287,'2018.2': 46781,'2028.2': 12872,'2031.2': 43654,'2034.1': 12293,'2034.2': 4663,'2036.1': 7529,'2050.2': 26721,'3038.1': 5641,'3038.2': 27469,'3041.1': 18354,'3041.2': 24639,'3042.1': 14481,'3042.2': 19247,'A-1501-03.A': 39180,'A-1501-03.B': 58928,'A-1505-99.A': 16431,'A-1505-99.B': 17782,'A-1507-168.B': 26221,'M-1506-106.B': 2450,'M-1506-108.A': 35528,'M-1506-108.B': 45668,'M-1506-110.A': 47633,'M-1506-110.B': 76327,'M-1507-113.B': 16337,'M-1507-118.A': 2003,'M-1507-118.B': 80529,'M-1507-119.A': 33802,'M-1507-119.B': 59446,'M-1507-120.A': 29498,'M-1507-120.B': 24406,'M-1507-124.A': 2117,'M-1507-125.B': 42188,'M-1507-126.A': 12968,'M-1507-133.B': 15551,'M-1507-134.B': 72534,'M-1507-136.A': 5452,'M-1507-147.A': 311,'M-1507-147.B': 102750,'M-1507-154.A': 18811,'M-1507-154.B': 14461,'M-1507-155.A': 4477,'M-1507-155.B': 6166,'S-1409-41.B': 3421,'S-1409-52.B': 6475,'S-1409-57.A': 140259,'S-1410-61.B': 17793,'S-1410-62.A': 74388,'S-1410-62.B': 21366,'S-1410-67.A': 5405,'S-1410-70.A': 65814,'S-1410-70.B': 155655,'S-1410-71.A': 10657,'S-1410-71.B': 8941,'S-1410-72.A': 6123,'S-1410-72.B': 24783,'S-1410-75.A': 2620,'S-1410-75.B': 337,'S-1410-76.A': 45598,'S-1410-76.B': 5859,'S-1410-84.A': 9742,'S-1410-84.B': 20088,'S-1504-86.A': 5997},'BC101': {'1001.2': 12807,'1010.1': 16089,'1010.2': 28135,'1018.2': 7601,'1021.2': 29398,'1026.1': 8161,'1026.2': 2873,'1029.1': 3002,'1029.2': 23365,'1048.2': 24211,'1049.2': 12970,'1057.2': 18223,'1065.1': 5070,'1065.2': 15738,'1067.1': 11808,'1067.2': 18002,'1071.1': 10263,'1071.2': 20660,'1072.1': 30239,'1072.2': 23493,'1073.1': 9962,'1073.2': 9259,'1080.2': 57914,'1086.2': 10509,'2018.1': 8176,'2018.2': 13699,'2028.2': 23270,'2031.2': 11688,'2034.1': 7456,'2034.2': 4892,'2036.1': 5897,'2050.2': 24212,'3038.1': 822,'3038.2': 8498,'3041.1': 3023,'3041.2': 12399,'3042.1': 3497,'3042.2': 11858,'A-1501-03.A': 2538,'A-1501-03.B': 4517,'A-1505-99.A': 3186,'A-1505-99.B': 6374,'A-1507-168.B': 9800,'M-1506-106.B': 9571,'M-1506-108.A': 7972,'M-1506-108.B': 12036,'M-1506-110.A': 21647,'M-1506-110.B': 18908,'M-1507-113.B': 13387,'M-1507-118.A': 20837,'M-1507-118.B': 35744,'M-1507-119.A': 6812,'M-1507-119.B': 13414,'M-1507-120.A': 24473,'M-1507-120.B': 9791,'M-1507-124.A': 30794,'M-1507-125.B': 29891,'M-1507-126.A': 9887,'M-1507-133.B': 7909,'M-1507-134.B': 3400,'M-1507-136.A': 5674,'M-1507-147.A': 74549,'M-1507-147.B': 2853,'M-1507-154.A': 5432,'M-1507-154.B': 23947,'M-1507-155.A': 5888,'M-1507-155.B': 4172,'S-1409-41.B': 14725,'S-1409-52.B': 19069,'S-1409-57.A': 12003,'S-1410-61.B': 21601,'S-1410-62.A': 18475,'S-1410-62.B': 21780,'S-1410-67.A': 30727,'S-1410-70.A': 3549,'S-1410-70.B': 21779,'S-1410-71.A': 185412,'S-1410-71.B': 78044,'S-1410-72.A': 8771,'S-1410-72.B': 1100,'S-1410-75.A': 6739,'S-1410-75.B': 53418,'S-1410-76.A': 24381,'S-1410-76.B': 68226,'S-1410-84.A': 12141,'S-1410-84.B': 4067,'S-1504-86.A': 12996},'BC102': {'1001.2': 11219,'1010.1': 6212,'1010.2': 5608,'1018.2': 771,'1021.2': 4191,'1026.1': 1567,'1026.2': 378,'1029.1': 584,'1029.2': 534,'1048.2': 3344,'1049.2': 27804,'1057.2': 179,'1065.1': 2359,'1065.2': 6919,'1067.1': 2335,'1067.2': 301,'1071.1': 1975,'1071.2': 4508,'1072.1': 6226,'1072.2': 6786,'1073.1': 5397,'1073.2': 1984,'1080.2': 422,'1086.2': 261,'2018.1': 700,'2018.2': 298,'2028.2': 1575,'2031.2': 4126,'2034.1': 5626,'2034.2': 972,'2036.1': 773,'2050.2': 9878,'3038.1': 462,'3038.2': 1571,'3041.1': 5059,'3041.2': 3557,'3042.1': 35644,'3042.2': 9463,'A-1501-03.A': 997,'A-1501-03.B': 8698,'A-1505-99.A': 1430,'A-1505-99.B': 2856,'A-1507-168.B': 2674,'M-1506-106.B': 380,'M-1506-108.A': 6261,'M-1506-108.B': 4569,'M-1506-110.A': 3987,'M-1506-110.B': 2212,'M-1507-113.B': 391,'M-1507-118.A': 411,'M-1507-118.B': 2462,'M-1507-119.A': 2932,'M-1507-119.B': 5597,'M-1507-120.A': 8961,'M-1507-120.B': 4788,'M-1507-124.A': 2628,'M-1507-125.B': 2169,'M-1507-126.A': 3431,'M-1507-133.B': 4702,'M-1507-134.B': 667,'M-1507-136.A': 5041,'M-1507-147.A': 152,'M-1507-147.B': 819,'M-1507-154.A': 233,'M-1507-154.B': 8963,'M-1507-155.A': 2123,'M-1507-155.B': 1654,'S-1409-41.B': 1625,'S-1409-52.B': 2464,'S-1409-57.A': 3097,'S-1410-61.B': 4198,'S-1410-62.A': 4627,'S-1410-62.B': 652,'S-1410-67.A': 682,'S-1410-70.A': 1507,'S-1410-70.B': 2692,'S-1410-71.A': 8955,'S-1410-71.B': 3057,'S-1410-72.A': 5078,'S-1410-72.B': 4436,'S-1410-75.A': 2217,'S-1410-75.B': 76,'S-1410-76.A': 2578,'S-1410-76.B': 1591,'S-1410-84.A': 430,'S-1410-84.B': 3293,'S-1504-86.A': 1022},'BC103': {'1001.2': 47,'1010.1': 4121,'1010.2': 2117,'1018.2': 2568,'1021.2': 25001,'1026.1': 2212,'1026.2': 1535,'1029.1': 4580,'1029.2': 1152,'1048.2': 8221,'1049.2': 27980,'1057.2': 1380,'1065.1': 11013,'1065.2': 23179,'1067.1': 3541,'1067.2': 2241,'1071.1': 9779,'1071.2': 15474,'1072.1': 8528,'1072.2': 10340,'1073.1': 5147,'1073.2': 14658,'1080.2': 2112,'1086.2': 406,'2018.1': 5170,'2018.2': 30349,'2028.2': 6192,'2031.2': 5190,'2034.1': 12565,'2034.2': 2884,'2036.1': 6942,'2050.2': 17112,'3038.1': 540,'3038.2': 11259,'3041.1': 4713,'3041.2': 14039,'3042.1': 31265,'3042.2': 23319,'A-1501-03.A': 6818,'A-1501-03.B': 14055,'A-1505-99.A': 8326,'A-1505-99.B': 9245,'A-1507-168.B': 6074,'M-1506-106.B': 2501,'M-1506-108.A': 19348,'M-1506-108.B': 40895,'M-1506-110.A': 26179,'M-1506-110.B': 10612,'M-1507-113.B': 7493,'M-1507-118.A': 2033,'M-1507-118.B': 17832,'M-1507-119.A': 69373,'M-1507-119.B': 10119,'M-1507-120.A': 10813,'M-1507-120.B': 1515,'M-1507-124.A': 5464,'M-1507-125.B': 3609,'M-1507-126.A': 1456,'M-1507-133.B': 6704,'M-1507-134.B': 22506,'M-1507-136.A': 2103,'M-1507-147.A': 2314,'M-1507-147.B': 40975,'M-1507-154.A': 10102,'M-1507-154.B': 9181,'M-1507-155.A': 8766,'M-1507-155.B': 3702,'S-1409-41.B': 2009,'S-1409-52.B': 4647,'S-1409-57.A': 12433,'S-1410-61.B': 10995,'S-1410-62.A': 24021,'S-1410-62.B': 15828,'S-1410-67.A': 1468,'S-1410-70.A': 28608,'S-1410-70.B': 24070,'S-1410-71.A': 9840,'S-1410-71.B': 1263,'S-1410-72.A': 10484,'S-1410-72.B': 13375,'S-1410-75.A': 3502,'S-1410-75.B': 207,'S-1410-76.A': 14776,'S-1410-76.B': 659,'S-1410-84.A': 9886,'S-1410-84.B': 8042,'S-1504-86.A': 14472},'BC104': {'1001.2': 0,'1010.1': 35,'1010.2': 313,'1018.2': 35,'1021.2': 9,'1026.1': 288,'1026.2': 1,'1029.1': 0,'1029.2': 0,'1048.2': 12,'1049.2': 415,'1057.2': 37,'1065.1': 162,'1065.2': 241,'1067.1': 47,'1067.2': 22,'1071.1': 6,'1071.2': 0,'1072.1': 1,'1072.2': 7,'1073.1': 1,'1073.2': 0,'1080.2': 0,'1086.2': 0,'2018.1': 10,'2018.2': 129,'2028.2': 127,'2031.2': 57,'2034.1': 12,'2034.2': 0,'2036.1': 64,'2050.2': 64,'3038.1': 0,'3038.2': 449,'3041.1': 0,'3041.2': 20,'3042.1': 3,'3042.2': 7620,'A-1501-03.A': 21,'A-1501-03.B': 49,'A-1505-99.A': 17,'A-1505-99.B': 0,'A-1507-168.B': 0,'M-1506-106.B': 0,'M-1506-108.A': 767,'M-1506-108.B': 407,'M-1506-110.A': 1,'M-1506-110.B': 3,'M-1507-113.B': 2069,'M-1507-118.A': 2,'M-1507-118.B': 110,'M-1507-119.A': 3081,'M-1507-119.B': 1222,'M-1507-120.A': 1340,'M-1507-120.B': 151,'M-1507-124.A': 0,'M-1507-125.B': 1,'M-1507-126.A': 0,'M-1507-133.B': 1012,'M-1507-134.B': 8,'M-1507-136.A': 8,'M-1507-147.A': 385,'M-1507-147.B': 4,'M-1507-154.A': 205,'M-1507-154.B': 24,'M-1507-155.A': 0,'M-1507-155.B': 349,'S-1409-41.B': 3,'S-1409-52.B': 640,'S-1409-57.A': 3,'S-1410-61.B': 3,'S-1410-62.A': 226,'S-1410-62.B': 12,'S-1410-67.A': 33,'S-1410-70.A': 0,'S-1410-70.B': 2965,'S-1410-71.A': 0,'S-1410-71.B': 0,'S-1410-72.A': 4,'S-1410-72.B': 54,'S-1410-75.A': 218,'S-1410-75.B': 170,'S-1410-76.A': 99,'S-1410-76.B': 481,'S-1410-84.A': 0,'S-1410-84.B': 9,'S-1504-86.A': 0},'BC105': {'1001.2': 0,'1010.1': 3813,'1010.2': 5828,'1018.2': 232,'1021.2': 412,'1026.1': 4,'1026.2': 13,'1029.1': 95,'1029.2': 8,'1048.2': 2127,'1049.2': 9956,'1057.2': 18,'1065.1': 469,'1065.2': 2961,'1067.1': 988,'1067.2': 439,'1071.1': 14668,'1071.2': 3951,'1072.1': 415,'1072.2': 182,'1073.1': 1288,'1073.2': 74,'1080.2': 122,'1086.2': 696,'2018.1': 21,'2018.2': 173,'2028.2': 3865,'2031.2': 59,'2034.1': 1097,'2034.2': 255,'2036.1': 1710,'2050.2': 8870,'3038.1': 307,'3038.2': 445,'3041.1': 984,'3041.2': 5100,'3042.1': 1481,'3042.2': 139,'A-1501-03.A': 351,'A-1501-03.B': 1183,'A-1505-99.A': 11933,'A-1505-99.B': 4359,'A-1507-168.B': 8246,'M-1506-106.B': 980,'M-1506-108.A': 699,'M-1506-108.B': 4191,'M-1506-110.A': 3477,'M-1506-110.B': 36780,'M-1507-113.B': 8960,'M-1507-118.A': 543,'M-1507-118.B': 324,'M-1507-119.A': 7886,'M-1507-119.B': 1448,'M-1507-120.A': 15303,'M-1507-120.B': 1669,'M-1507-124.A': 9443,'M-1507-125.B': 3264,'M-1507-126.A': 430,'M-1507-133.B': 176,'M-1507-134.B': 952,'M-1507-136.A': 163,'M-1507-147.A': 310,'M-1507-147.B': 16023,'M-1507-154.A': 1068,'M-1507-154.B': 2703,'M-1507-155.A': 71589,'M-1507-155.B': 351,'S-1409-41.B': 956,'S-1409-52.B': 85,'S-1409-57.A': 168,'S-1410-61.B': 1492,'S-1410-62.A': 1215,'S-1410-62.B': 562,'S-1410-67.A': 218,'S-1410-70.A': 925,'S-1410-70.B': 643,'S-1410-71.A': 87,'S-1410-71.B': 428,'S-1410-72.A': 232,'S-1410-72.B': 973,'S-1410-75.A': 11737,'S-1410-75.B': 21,'S-1410-76.A': 1169,'S-1410-76.B': 1833,'S-1410-84.A': 108,'S-1410-84.B': 6469,'S-1504-86.A': 13818}}
X = pd.DataFrame(data) + 1
# Shape of X where rows are samples and columns are features
n,m = X.shape
index_samples = X.index
index_features = X.columns
X_values = X.values
# A = np.empty((n, m**2 - m))
# A[:] = np.nan
# Not the most efficient way but the best way to show what I'm trying to do
A = defaultdict(dict)
for row_pos, a in enumerate(X_values):
id_sample = index_samples[row_pos]
# col_pos = 0
for i in range(m):
a_i = a[i]
i_feature = index_features[i]
for j in range( m):
if i != j:
a_j = a[j]
j_feature = index_features[j]
# A[row_pos, col_pos] = a_i - a_j
A[id_sample]["{}/{}".format(i_feature, j_feature)] = np.log(a_i/a_j)
# col_pos += 1
df = pd.DataFrame(A).T
df.iloc[:5,:5]
BC0a/BC0b BC0a/BC1 BC0a/BC10 BC0a/BC100 BC0a/BC101
1001.2 -0.364454 -4.250907 -2.102548 0.226606 -1.133489
1010.1 -1.621745 -3.172429 -0.889908 -0.481099 -0.866140
1010.2 0.381012 -1.785419 0.272488 0.422915 -0.874474
1018.2 -0.263715 -2.106878 1.558403 0.741490 -0.269520
1021.2 -0.975581 -2.575287 -2.139590 -1.405648 -1.129143

You can use np.triu_indices to get all possible combinations by indexing thusly:
i_index, j_index = np.triu_indices(m, 1)
Using the fact that logarithms were invented to be able to do log(a / b) = log(a) - log(b), you can now do:
df = pd.DataFrame(data=np.log(X_values[:, i_index]) - np.log(X_values[:, j_index]),
index=X.index.copy(), columns=[f'{a}/{b}' for a, b in zip(X.columns[i_index], X.columns[j_index])])
While it's more elegant in my opinion, you can just as easily keep the original formulation of the ratio:
np.log(X_values[:, i_index] / X_values[:, j_index])
Notice that this is half of the data that your answer contains. The other half is just the inverse ratios, which appear as negatives in the log table. If you absolutely insist on computing those (even though it's quite wasteful), you can adjust i_index and j_index accordingly:
i_index = np.repeat(np.arange(m), m - 1)
j_index = np.delete(np.tile(np.arange(m), m), slice(None, None, m + 1))
Either way, your entire code can be expressed in about three lines.

Trasform a Graph from a dictionary form to a Matrix in python

{'X050412_1_0000_1_4': {'0-4': 64.9516586968339, '2-3': 47.573389284767174, '1-2': 29.414889381739993, '0-1': 20.137499912849165, '0-2': 48.36841808990657, '0-3': 65.07665932728878, '1-3': 58.326301639483376, '3-4': 42.477331382632784, '1-4': 47.380533137671634, '2-4': 18.995337464493748}, 'X050412_1_0000_1_5': {'0-4': 74.45548880223673, '2-3': 42.921786085506746, '1-2': 22.297921437434468, '0-1': 16.755486281812296, '0-2': 31.640265835324445, '0-3': 49.53140665618129, '1-3': 55.29908155494086, '3-4': 25.4227462686469, '1-4': 78.22578893907047, '2-4': 61.73419510554909}, 'X050412_1_0000_1_6': {'0-4': 53.36246109354403, '2-3': 49.125438624810265, '1-2': 26.33647295558006, '0-1': 27.165619713343553, '0-2': 53.21604990987211, '0-3': 31.972624680029007, '1-3': 34.38869991232585, '3-4': 35.126226807472506, '1-4': 33.294628738732015, '2-4': 23.60048014617499}} Suppose that I've a graph represented like this and i Want to create to convert it into matrix form.

for k,v in dataset.distances.items():
if k not in diz2:
diz2[k] = {}
for k2,v2 in v.items():
res = tuple(map(int, k2.split('-')))
if res not in diz2:
diz2[k][res] = v2
def convert(diz5):
temp_x = max([cord[0] for cord in diz5.keys()])
temp_y = max([cord[1] for cord in diz5.keys()])
res2 = [[0] * (temp_y + 1) for ele in range(temp_x + 1)]
for (i, j), val in diz5.items():
res2[i][j] = val
#print("The dictionary after creation of Matrix : " + str(res2))
return res2

How to solve warning message in Gekko due to m.connection?

I am using m.connection to estimate variables initial conditions but I am getting 12 warning messages like:
Moreover, the APM file shows:
I am not sure how to solve these messages.
I am following this explanation "If pos1 or pos2 is not None, the associated var must be a GEKKO variable and the position is the (0-indexed) time-discretized index of the variable" to write m.Connection(var1,var2,pos1=None,pos2=None,node1='end',node2='end').
https://gekko.readthedocs.io/en/latest/quick_start.html#connections
Thanks in advance.
from gekko import GEKKO
import numpy as np
import matplotlib.pyplot as plt
import math as math
import pandas as pd
tm1 = [0, 0.0667,0.5,1,4, 22.61667]
mca1 = [5.68, 3.48, 3.24, 3.36, 2.96, 1.96]
tm2 = [0, 0.08333,0.5,1,4.25 , 22.8167]
mca2 = [5.68, 4.20, 4.04, 4.00, 3.76, 2.88]
tm3 = [0,0.08333,0.5,1,4.33 , 22.9500]
mca3 = [5.68, 4.64, 4.52, 4.56, 4.24, 3.72]
tm4 = [0,0.08333,0.5,1,4.0833 , 23.0833]
mca4 =[18.90,15.4,14.3,15.10,13.50, 10.90]
tm5 = [0,0.08333,0.5,1,4.5, 23.2167]
mca5 =[18.90, 15.5, 16.30, 16, 14.70, 13.00]
tm6 = [0,0.08333,0.5,1,4.6667, 23.3333 ]
mca6 = [18.90, 15.8, 11.70,15.5,12, 9.5 ]
df1=pd.DataFrame({'time':tm1,'x1':mca1})
df2=pd.DataFrame({'time':tm2,'x2':mca2})
df3=pd.DataFrame({'time':tm3,'x3':mca3})
df4=pd.DataFrame({'time':tm4,'x4':mca4})
df5=pd.DataFrame({'time':tm5,'x5':mca5})
df6=pd.DataFrame({'time':tm6,'x6':mca6})
df1.set_index('time',inplace=True)
df2.set_index('time',inplace=True)
df3.set_index('time',inplace=True)
df4.set_index('time',inplace=True)
df5.set_index('time',inplace=True)
df6.set_index('time',inplace=True)
#simulation time points
dfx = pd.DataFrame({'time':np.linspace(0,25,101)})
dfx.set_index('time',inplace=True)
#merge dataframes
dfxx=dfx.join(df1,how='outer')
dfxxx=dfxx.join(df2,how='outer')
dfxxxx=dfxxx.join(df3,how='outer')
dfxxxxx=dfxxxx.join(df4,how='outer')
dfxxxxxx=dfxxxxx.join(df5,how='outer')
df=dfxxxxxx.join(df6,how='outer')
# get True (1) or False (0) for measurement
df['meas1']=(df['x1'].values==df['x1'].values).astype(int)
df['meas2']=(df['x2'].values==df['x2'].values).astype(int)
df['meas3']=(df['x3'].values==df['x3'].values).astype(int)
df['meas4']=(df['x4'].values==df['x4'].values).astype(int)
df['meas5']=(df['x5'].values==df['x5'].values).astype(int)
df['meas6']=(df['x6'].values==df['x6'].values).astype(int)
#replace NaN with zeros
df0=df.fillna(value=0)
m = GEKKO()
m.time = df0.index.values
meas1 = m.Param(df0['meas1'].values)
meas2 = m.Param(df0['meas2'].values)
meas3 = m.Param(df0['meas3'].values)
meas4 = m.Param(df0['meas4'].values)
meas5 = m.Param(df0['meas5'].values)
meas6 = m.Param(df0['meas6'].values)
#adjustable Parameters
kf=m.FV(1.3,lb=0.01,ub=10)
ks=m.FV(1.3,lb=0.01,ub=10)
cnf01=m.FV(1.3,lb=0.01,ub=10)
cns01=m.FV(1.3,lb=0.01,ub=10)
#constrains
cnf02=m.FV(value=cnf01*0.5,lb=cnf01*0.5, ub=cnf01*0.5)
cns02=m.FV(value=cns01*0.5,lb=cns01*0.5, ub=cns01*0.5)
cnf03=m.FV(value=cnf01*0.25,lb=cnf01*0.25, ub=cnf01*0.25)
cns03=m.FV(value=cns01*0.25,lb=cns01*0.25, ub=cns01*0.25)
cnf04=m.FV(value=cnf01,lb=cnf01, ub=cnf01)
cns04=m.FV(value=cns01,lb=cns01, ub=cns01)
cnf05=m.FV(value=cnf01*0.5,lb=cnf01*0.5, ub=cnf01*0.5)
cns05=m.FV(value=cns01*0.5,lb=cns01*0.5, ub=cns01*0.5)
cnf06=m.FV(value=cnf01*0.25,lb=cnf01*0.25, ub=cnf01*0.25)
cns06=m.FV(value=cns01*0.25,lb=cns01*0.25, ub=cns01*0.25)
#Variables
c1 = m.Var(value=mca1[0])
c2 = m.Var(value=mca2[0])
c3 = m.Var(value=mca3[0])
c4 = m.Var(value=mca4[0])
c5 = m.Var(value=mca5[0])
c6 = m.Var(value=mca6[0])
cm1 = m.Param(df0['x1'].values)
cm2 = m.Param(df0['x2'].values)
cm3 = m.Param(df0['x3'].values)
cm4 = m.Param(df0['x4'].values)
cm5 = m.Param(df0['x5'].values)
cm6 = m.Param(df0['x6'].values)
m.Minimize((meas1*(c1-cm1)**2)+(meas2*(c2-cm2)**2)\
+(meas3*(c3-cm3)**2)+(meas4*(c4-cm4)**2)\
+(meas5*(c5-cm5)**2)+(meas6*(c6-cm6)**2))
cnf1=m.Var(value=cnf01,fixed_initial=False)
cns1=m.Var(value=cns01,fixed_initial=False)
cnf2=m.Var(value=cnf02,fixed_initial=False)
cns2=m.Var(value=cns02,fixed_initial=False)
cnf3=m.Var(value=cnf03,fixed_initial=False)
cns3=m.Var(value=cns03,fixed_initial=False)
cnf4=m.Var(value=cnf04,fixed_initial=False)
cns4=m.Var(value=cns04,fixed_initial=False)
cnf5=m.Var(value=cnf05,fixed_initial=False)
cns5=m.Var(value=cns05,fixed_initial=False)
cnf6=m.Var(value=cnf06,fixed_initial=False)
cns6=m.Var(value=cns06,fixed_initial=False)
#Equations
t = m.Param(value=m.time)
m.Connection(cnf1,cnf01,pos1=0,pos2=0,node1=1,node2=1)
m.Connection(cnf2,cnf02,pos1=0,pos2=0,node1=1,node2=1)
m.Connection(cnf3,cnf03,pos1=0,pos2=0,node1=1,node2=1)
m.Connection(cnf4,cnf04,pos1=0,pos2=0,node1=1,node2=1)
m.Connection(cnf5,cnf05,pos1=0,pos2=0,node1=1,node2=1)
m.Connection(cnf6,cnf06,pos1=0,pos2=0,node1=1,node2=1)
m.Connection(cns1,cns01,pos1=0,pos2=0,node1=1,node2=1)
m.Connection(cns2,cns02,pos1=0,pos2=0,node1=1,node2=1)
m.Connection(cns3,cns03,pos1=0,pos2=0,node1=1,node2=1)
m.Connection(cns4,cns04,pos1=0,pos2=0,node1=1,node2=1)
m.Connection(cns5,cns05,pos1=0,pos2=0,node1=1,node2=1)
m.Connection(cns6,cns06,pos1=0,pos2=0,node1=1,node2=1)
m.Equation(cnf1.dt()==-kf*c1*cnf1)
m.Equation(cns1.dt()==-ks*c1*cns1)
m.Equation(c1.dt()==cnf1.dt()+cns1.dt())
m.Equation(cnf2.dt()==-kf*c2*cnf2)
m.Equation(cns2.dt()==-ks*c2*cns2)
m.Equation(c2.dt()==cnf2.dt()+cns2.dt())
m.Equation(cnf3.dt()==-kf*c3*cnf3)
m.Equation(cns3.dt()==-ks*c3*cns3)
m.Equation(c3.dt()==cnf3.dt()+cns3.dt())
m.Equation(cnf4.dt()==-kf*c4*cnf4)
m.Equation(cns4.dt()==-ks*c4*cns4)
m.Equation(c4.dt()==cnf4.dt()+cns4.dt())
m.Equation(cnf5.dt()==-kf*c5*cnf5)
m.Equation(cns5.dt()==-ks*c5*cns5)
m.Equation(c5.dt()==cnf5.dt()+cns5.dt())
m.Equation(cnf6.dt()==-kf*c6*cnf6)
m.Equation(cns6.dt()==-ks*c6*cns6)
m.Equation(c6.dt()==cnf6.dt()+cns6.dt())
if True:
kf.STATUS=1
ks.STATUS=1
cnf01.STATUS=1
cns01.STATUS=1
cnf02.STATUS=1
cns02.STATUS=1
cnf03.STATUS=1
cns03.STATUS=1
cnf04.STATUS=1
cns04.STATUS=1
cnf05.STATUS=1
cns05.STATUS=1
cnf06.STATUS=1
cns06.STATUS=1
#Options
m.options.SOLVER = 1 #IPOPT solver
m.options.IMODE = 5 #Dynamic Simultaneous - estimation = MHE
m.options.EV_TYPE = 2 #absolute error
m.options.NODES = 3 #collocation nodes (2,5)
m.solve(disp=True)
m.open_folder()
print('Final SSE Objective: ' + str(m.options.objfcnval))
print('Solution')
print('cnf01 = ' + str(cnf01.value[0]))
print('cns01 = ' + str(cns01.value[0]))
print('kf = ' + str(kf.value[0]))
print('ks = ' + str(ks.value[0]))
print('cns02 = '+ str(cns02.value[0]))
print('cnf02 = '+ str(cnf02.value[0]))
print('cns03 = '+ str(cns03.value[0]))
print('cnf03 = '+ str(cnf03.value[0]))
print('cns04 = '+ str(cns04.value[0]))
print('cnf04 = '+ str(cnf04.value[0]))
print('cns05 = '+ str(cns05.value[0]))
print('cnf05 = '+ str(cnf05.value[0]))
print('cns06 = '+ str(cns06.value[0]))
print('cnf06 = '+ str(cnf06.value[0]))
plt.figure(1,figsize=(8,5))
plt.plot(m.time,c1.value,'r',label='Predicted c1')
plt.plot(m.time,c2.value,'y',label='Predicted c2')
plt.plot(m.time,c3.value,'c',label='Predicted c3')
plt.plot(m.time,c4.value,'g',label='Predicted c4')
plt.plot(m.time,c5.value,'b',label='Predicted c5')
plt.plot(m.time,c6.value,'m',label='Predicted c6')
plt.plot(tm1,mca1,'rx',label='Meas c1')
plt.plot(tm2,mca2,'yx',label='Meas c2')
plt.plot(tm3,mca3,'cx',label='Meas c3')
plt.plot(tm4,mca4,'go',label='Meas c4')
plt.plot(tm5,mca5,'bo',label='Meas c5')
plt.plot(tm6,mca6,'mo',label='Meas c6')
plt.xlabel('time (h)')
plt.ylabel('Concentration (mgCl2/L)')
plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=2)

The underlying node structure has a 1-index instead of a 0-index that is common in Python. Using pos1=1 and pos2=1 resolves the warnings.
m.Connection(cnf1,cnf01,pos1=1,pos2=1,node1=1,node2=1)
m.Connection(cnf2,cnf02,pos1=1,pos2=1,node1=1,node2=1)
m.Connection(cnf3,cnf03,pos1=1,pos2=1,node1=1,node2=1)
m.Connection(cnf4,cnf04,pos1=1,pos2=1,node1=1,node2=1)
m.Connection(cnf5,cnf05,pos1=1,pos2=1,node1=1,node2=1)
m.Connection(cnf6,cnf06,pos1=1,pos2=1,node1=1,node2=1)
Another issue is that Gekko variables shouldn't generally be used to initialize other values. I recommend setting x0=1.3 and using that float to initialize the variables. Change m.Var() to m.SV() to avoid reclassification of m.Var() as an m.FV() during the connection. The m.SV() is a promoted type of variable that is at the same level of precedence as the m.FV(). Here is a complete script although the results don't look optimal.
from gekko import GEKKO
import numpy as np
import matplotlib.pyplot as plt
import math as math
import pandas as pd
tm1 = [0, 0.0667,0.5,1,4, 22.61667]
mca1 = [5.68, 3.48, 3.24, 3.36, 2.96, 1.96]
tm2 = [0, 0.08333,0.5,1,4.25 , 22.8167]
mca2 = [5.68, 4.20, 4.04, 4.00, 3.76, 2.88]
tm3 = [0,0.08333,0.5,1,4.33 , 22.9500]
mca3 = [5.68, 4.64, 4.52, 4.56, 4.24, 3.72]
tm4 = [0,0.08333,0.5,1,4.0833 , 23.0833]
mca4 =[18.90,15.4,14.3,15.10,13.50, 10.90]
tm5 = [0,0.08333,0.5,1,4.5, 23.2167]
mca5 =[18.90, 15.5, 16.30, 16, 14.70, 13.00]
tm6 = [0,0.08333,0.5,1,4.6667, 23.3333 ]
mca6 = [18.90, 15.8, 11.70,15.5,12, 9.5 ]
df1=pd.DataFrame({'time':tm1,'x1':mca1})
df2=pd.DataFrame({'time':tm2,'x2':mca2})
df3=pd.DataFrame({'time':tm3,'x3':mca3})
df4=pd.DataFrame({'time':tm4,'x4':mca4})
df5=pd.DataFrame({'time':tm5,'x5':mca5})
df6=pd.DataFrame({'time':tm6,'x6':mca6})
df1.set_index('time',inplace=True)
df2.set_index('time',inplace=True)
df3.set_index('time',inplace=True)
df4.set_index('time',inplace=True)
df5.set_index('time',inplace=True)
df6.set_index('time',inplace=True)
#simulation time points
dfx = pd.DataFrame({'time':np.linspace(0,25,101)})
dfx.set_index('time',inplace=True)
#merge dataframes
dfxx=dfx.join(df1,how='outer')
dfxxx=dfxx.join(df2,how='outer')
dfxxxx=dfxxx.join(df3,how='outer')
dfxxxxx=dfxxxx.join(df4,how='outer')
dfxxxxxx=dfxxxxx.join(df5,how='outer')
df=dfxxxxxx.join(df6,how='outer')
# get True (1) or False (0) for measurement
df['meas1']=(df['x1'].values==df['x1'].values).astype(int)
df['meas2']=(df['x2'].values==df['x2'].values).astype(int)
df['meas3']=(df['x3'].values==df['x3'].values).astype(int)
df['meas4']=(df['x4'].values==df['x4'].values).astype(int)
df['meas5']=(df['x5'].values==df['x5'].values).astype(int)
df['meas6']=(df['x6'].values==df['x6'].values).astype(int)
#replace NaN with zeros
df0=df.fillna(value=0)
m = GEKKO()
m.time = df0.index.values
meas1 = m.Param(df0['meas1'].values)
meas2 = m.Param(df0['meas2'].values)
meas3 = m.Param(df0['meas3'].values)
meas4 = m.Param(df0['meas4'].values)
meas5 = m.Param(df0['meas5'].values)
meas6 = m.Param(df0['meas6'].values)
#adjustable Parameters
kf=m.FV(1.3,lb=0.01,ub=10)
ks=m.FV(1.3,lb=0.01,ub=10)
x0 = 1.3
cnf01=m.FV(x0,lb=0.01,ub=10)
cns01=m.FV(x0,lb=0.01,ub=10)
#constrains
cnf02=m.FV(value=x0*0.5,lb=x0*0.5, ub=x0*0.5)
cns02=m.FV(value=x0*0.5,lb=x0*0.5, ub=x0*0.5)
cnf03=m.FV(value=x0*0.25,lb=x0*0.25, ub=x0*0.25)
cns03=m.FV(value=x0*0.25,lb=x0*0.25, ub=x0*0.25)
cnf04=m.FV(value=x0,lb=x0, ub=x0)
cns04=m.FV(value=x0,lb=x0, ub=x0)
cnf05=m.FV(value=x0*0.5,lb=x0*0.5, ub=x0*0.5)
cns05=m.FV(value=x0*0.5,lb=x0*0.5, ub=x0*0.5)
cnf06=m.FV(value=x0*0.25,lb=x0*0.25, ub=x0*0.25)
cns06=m.FV(value=x0*0.25,lb=x0*0.25, ub=x0*0.25)
#Variables
c1 = m.SV(value=mca1[0])
c2 = m.SV(value=mca2[0])
c3 = m.SV(value=mca3[0])
c4 = m.SV(value=mca4[0])
c5 = m.SV(value=mca5[0])
c6 = m.SV(value=mca6[0])
cm1 = m.Param(df0['x1'].values)
cm2 = m.Param(df0['x2'].values)
cm3 = m.Param(df0['x3'].values)
cm4 = m.Param(df0['x4'].values)
cm5 = m.Param(df0['x5'].values)
cm6 = m.Param(df0['x6'].values)
m.Minimize((meas1*(c1-cm1)**2)+(meas2*(c2-cm2)**2)\
+(meas3*(c3-cm3)**2)+(meas4*(c4-cm4)**2)\
+(meas5*(c5-cm5)**2)+(meas6*(c6-cm6)**2))
cnf1=m.SV(value=x0,fixed_initial=False)
cns1=m.SV(value=x0,fixed_initial=False)
cnf2=m.SV(value=x0,fixed_initial=False)
cns2=m.SV(value=x0,fixed_initial=False)
cnf3=m.SV(value=x0,fixed_initial=False)
cns3=m.SV(value=x0,fixed_initial=False)
cnf4=m.SV(value=x0,fixed_initial=False)
cns4=m.SV(value=x0,fixed_initial=False)
cnf5=m.SV(value=x0,fixed_initial=False)
cns5=m.SV(value=x0,fixed_initial=False)
cnf6=m.SV(value=x0,fixed_initial=False)
cns6=m.SV(value=x0,fixed_initial=False)
#Equations
t = m.Param(value=m.time)
m.Connection(cnf1,cnf01,pos1=1,pos2=1,node1=1,node2=1)
m.Connection(cnf2,cnf02,pos1=1,pos2=1,node1=1,node2=1)
m.Connection(cnf3,cnf03,pos1=1,pos2=1,node1=1,node2=1)
m.Connection(cnf4,cnf04,pos1=1,pos2=1,node1=1,node2=1)
m.Connection(cnf5,cnf05,pos1=1,pos2=1,node1=1,node2=1)
m.Connection(cnf6,cnf06,pos1=1,pos2=1,node1=1,node2=1)
m.Connection(cns1,cns01,pos1=1,pos2=1,node1=1,node2=1)
m.Connection(cns2,cns02,pos1=1,pos2=1,node1=1,node2=1)
m.Connection(cns3,cns03,pos1=1,pos2=1,node1=1,node2=1)
m.Connection(cns4,cns04,pos1=1,pos2=1,node1=1,node2=1)
m.Connection(cns5,cns05,pos1=1,pos2=1,node1=1,node2=1)
m.Connection(cns6,cns06,pos1=1,pos2=1,node1=1,node2=1)
m.Equation(cnf1.dt()==-kf*c1*cnf1)
m.Equation(cns1.dt()==-ks*c1*cns1)
m.Equation(c1.dt()==cnf1.dt()+cns1.dt())
m.Equation(cnf2.dt()==-kf*c2*cnf2)
m.Equation(cns2.dt()==-ks*c2*cns2)
m.Equation(c2.dt()==cnf2.dt()+cns2.dt())
m.Equation(cnf3.dt()==-kf*c3*cnf3)
m.Equation(cns3.dt()==-ks*c3*cns3)
m.Equation(c3.dt()==cnf3.dt()+cns3.dt())
m.Equation(cnf4.dt()==-kf*c4*cnf4)
m.Equation(cns4.dt()==-ks*c4*cns4)
m.Equation(c4.dt()==cnf4.dt()+cns4.dt())
m.Equation(cnf5.dt()==-kf*c5*cnf5)
m.Equation(cns5.dt()==-ks*c5*cns5)
m.Equation(c5.dt()==cnf5.dt()+cns5.dt())
m.Equation(cnf6.dt()==-kf*c6*cnf6)
m.Equation(cns6.dt()==-ks*c6*cns6)
m.Equation(c6.dt()==cnf6.dt()+cns6.dt())
#Options
m.options.SOLVER = 1 # APOPT solver
m.options.IMODE = 5 # Dynamic Simultaneous - estimation = MHE
m.options.EV_TYPE = 2 # Squared error
m.options.NODES = 3 # Collocation nodes (2,5)
if True:
kf.STATUS=1
ks.STATUS=1
cnf01.STATUS=1
cns01.STATUS=1
cnf02.STATUS=1
cns02.STATUS=1
cnf03.STATUS=1
cns03.STATUS=1
cnf04.STATUS=1
cns04.STATUS=1
cnf05.STATUS=1
cns05.STATUS=1
cnf06.STATUS=1
cns06.STATUS=1
m.options.TIME_SHIFT = 0
try:
m.solve(disp=True)
except:
print("don't stop when not finding cnf01...cnf06")
#m.open_folder()
print('Final SSE Objective: ' + str(m.options.objfcnval))
print('Solution')
print('cnf01 = ' + str(cnf1.value[0]))
print('cns01 = ' + str(cns1.value[0]))
print('kf = ' + str(kf.value[0]))
print('ks = ' + str(ks.value[0]))
print('cns02 = '+ str(cns2.value[0]))
print('cnf02 = '+ str(cnf2.value[0]))
print('cns03 = '+ str(cns3.value[0]))
print('cnf03 = '+ str(cnf3.value[0]))
print('cns04 = '+ str(cns4.value[0]))
print('cnf04 = '+ str(cnf4.value[0]))
print('cns05 = '+ str(cns5.value[0]))
print('cnf05 = '+ str(cnf5.value[0]))
print('cns06 = '+ str(cns6.value[0]))
print('cnf06 = '+ str(cnf6.value[0]))
plt.figure(1,figsize=(8,5))
plt.plot(m.time,c1.value,'r',label='Predicted c1')
plt.plot(m.time,c2.value,'y',label='Predicted c2')
plt.plot(m.time,c3.value,'c',label='Predicted c3')
plt.plot(m.time,c4.value,'g',label='Predicted c4')
plt.plot(m.time,c5.value,'b',label='Predicted c5')
plt.plot(m.time,c6.value,'m',label='Predicted c6')
plt.plot(tm1,mca1,'rx',label='Meas c1')
plt.plot(tm2,mca2,'yx',label='Meas c2')
plt.plot(tm3,mca3,'cx',label='Meas c3')
plt.plot(tm4,mca4,'go',label='Meas c4')
plt.plot(tm5,mca5,'bo',label='Meas c5')
plt.plot(tm6,mca6,'mo',label='Meas c6')
plt.xlabel('time (h)')
plt.ylabel('Concentration (mgCl2/L)')
plt.legend(loc='upper center', bbox_to_anchor=(0.5, -0.15), ncol=2)
plt.show()