Why values is not normalized in certain array?

Why values is not normalized in certain array? - python

I have an array with certain values that I want normalize from 0 to 1. When I try to do it with minmax_scale function it returns the same set.
[2.80756379e-01 9.47215085e-01 2.98665545e-01 2.71729701e-01
9.53844447e-01 4.09155122e-01 7.73782687e-01 4.04866838e-01...]
If I slice it to 200 values (set has 400 values) function returns normalized values:
[0.28024599 0.94726674 0.29817026 0.2712117 0.95390169 0.40875301
0.77368808 0.40446111 0.0427401 0.98420903...]
if I generate for example 600 test values, it works fine.
[0. 0.00166945 0.0033389 0.00500835 0.0066778 … 0.9933222 0.99499165
0.9966611 0.99833055 1.]
So I assume that something wrong with my input, but it doesn’t raise any error that I could understand what is happening and why values haven’t been normalized.
So if someone could shed light on this and explain what I am doing wrong.
import numpy as np
from sklearn.preprocessing import minmax_scale
x = [3.4983787694629807, 6.8238036546277545, 3.5877400241270765, 3.4533384061573176, 6.856882141805892, 4.139049184316268, 5.958429090197188, 4.11765195576695, 2.314295679864955, 7.007979076325537, 4.591575092528983, 6.500165465269498, 2.8914665615540556, 3.543340276730918, 6.561891009978831, 3.6289541084867656, 3.5071777128342223, 7.056680192241884, 3.4788374494994114, 3.4464924916748862, 7.014214072992434, 3.4916155718006134, 3.5360046270365486, 7.086001703906676, 3.612050358553576, 3.896165015866581, 6.09922605707944, 3.3460547945703287, 3.910944920634269, 5.971772677577327, 5.178500485785331, 6.6090028576236675, 2.841807831193129, 5.149220347017544, 4.735677896132761, 3.8354921976610226, 7.038466835269509, 3.587747218864995, 3.555693227090981, 6.954475444783193, 3.437365707196104, 3.4325883202929894, 7.020900371706621, 3.4368379490007386, 3.475833945795732, 6.8688992156695585, 3.583554218078379, 3.520770727926182, 6.92133265995129, 4.044292336171232, 5.602564848428526, 4.411557629422499, 2.45924872382912, 7.086402867274765, 4.7372741724391965, 6.374350060657951, 2.6542178607167273, 3.508325152205329, 6.461025102348145, 3.6812426559272033, 3.5307800915694383, 7.066468458976694, 3.493276512558132, 3.4568759587746243, 7.0176506092750595, 3.478361485227409, 3.5201937331883713, 7.080979349338214, 3.5795505246747212, 3.8031183328096256, 6.245609213027018, 3.4165971380697018, 2.1723738636075023, 6.126159977852875, 5.013068437451677, 6.904030395006403, 2.7075477902159992, 4.895577020527206, 5.020958429692195, 3.9029736137362567, 7.005502917949908, 3.5789396294713756, 3.5676476014611818, 6.929131913377069, 3.4472779998622487, 3.4308686427915935, 7.018771879565203, 3.432009619429056, 3.457840956435269, 6.905877831610167, 3.5755167560389163, 3.5618219034900966, 6.97358473363722, 3.9600759412714175, 5.260824000465443, 4.692774833624142, 2.602129482124068, 7.035148649249085, 4.8915972720677825, 6.2369642563916825, 2.3878063740977975, 3.4610250988299063, 6.344643530169994, 3.745077609377894, 3.557344813692928, 7.0753891572282885, 3.5084174545346944, 3.4687392098349816, 7.018578929608326, 3.4657417965161157, 3.504674479158238, 7.073344198878297, 3.5505846727930717, 3.728275930579232, 6.374512212337401, 3.473738663253011, 2.455000320545692, 6.271460848569801, 4.853259564210921, 7.060300163815499, 2.5675976179261544, 4.625519502757723, 5.34193581659188, 3.9796698149770413, 6.961928522596542, 3.553928370489732, 3.577760192959362, 6.897549440276217, 3.4618068657264773, 3.4328376059178547, 7.019069392542759, 3.430955309222881, 3.4444356247624213, 6.935848551405397, 3.5648686589574554, 3.5823723667040626, 7.0144769584373465, 3.885706919437764, 4.947869760566333, 4.958988699542243, 2.740882010237675, 6.844670575188505, 5.052957262016582, 6.08929327319099, 2.1012153486394873, 3.4006432713737014, 6.211570326896137, 3.823951658625252, 3.5871290770943087, 7.082486008656476, 3.524032336877465, 3.481547522774735, 7.017048732257519, 3.454184925027273, 3.4896816808661573, 7.064127863850646, 3.5247589190924855, 3.6675470038864804, 6.486998043891378, 3.5179550925822936, 2.7150317741174828, 6.406130756762303, 4.700905963998837, 7.0786411289315705, 2.424024165788486, 4.340976405808333, 5.68871874308043, 4.066399458564197, 6.906772355480829, 3.5069849107646553, 3.584982183669687, 6.8586979941775175, 3.4809101839812273, 3.4385861350276734, 7.021776708035995, 3.433539453335249, 3.435646282061486, 6.959814531248965, 3.5525938762287192, 3.5880328691774226, 7.04501753482764, 3.8203341873417145, 4.672129393281222, 5.208396760624669, 2.873447040265478, 6.517042667648001, 5.219450800175008, 5.932960908282111, 4.071807320428156, 3.32681370883367, 6.060786017704781, 3.922140073470534, 3.6205701108946693, 7.086704780969265, 3.5398647239459082, 3.4949072952266786, 7.012978275363125, 3.4442572447205126, 3.4754567104312315, 7.054260858195003, 3.501860081901249, 3.6176350355084987, 6.584272251688416, 3.550139011669634, 2.9441212137915196, 6.528971419532436, 4.557509169353122, 6.971230117604573, 2.2788693414108083, 4.044384223064448, 6.045075381817579, 4.16380702973696, 6.8392444953122915, 3.4322587777495, 3.588015280869961, 6.811444662244569, 3.504571583212776, 3.448038447176144, 7.0267448030100095, 3.4394438861946495, 3.4313813610293606, 6.978663067682745, 3.539410445197603, 3.5836697468529928, 7.066336743389743, 3.763013423804524, 4.436678180031973, 5.439712464353549, 2.9978564390796802, 6.06598407921124, 5.38892971887227, 5.769885855777033, 4.7305353792164615, 3.2396389326858333, 5.891487403995336, 4.044576070907139, 3.658273834104114, 7.086908629948878, 3.5554434237079002, 3.508553789136474, 7.006154398318428, 3.4366257440898904, 3.462343969119664, 7.044561860711188, 3.4818481341401735, 3.576035272214573, 6.667629115969775, 3.5715246418069957, 3.1369130300148993, 6.639150212621939, 4.424195703276443, 6.759495560732593, 2.134069612887043, 3.7386525746914123, 6.388620586154337, 4.272297018941626, 6.7587843489580965, 3.324494111518356, 3.5853639214122595, 6.754568772053236, 3.5329516319997514, 3.4610002607293953, 7.033692354375017, 3.4481972797191154, 3.4313661148467935, 6.993158859550646, 3.5257969569299723, 3.5731476929367916, 7.07964197799761, 3.712767027132367, 4.2405670182736745, 5.652147101651347, 3.112331986409608, 5.515185072672031, 5.559088312172956, 5.602219157421699, 5.362655122965983, 3.1396940731308915, 5.703149958832117, 4.196548195898355, 3.700992351744346, 7.081897811873345, 3.569845801358458, 3.5223021303817146, 6.996232651966579, 3.4319885789163944, 3.450813363928631, 7.035729967483905, 3.464829989158296, 3.5409776462526583, 6.738403993391335, 3.583589535854453, 3.2912893051822354, 6.7362012208338795, 4.301697490919809, 6.470822564075378, 3.659773459738205, 2.0974903963829354, 6.692161174433521, 4.3919729995424195, 6.665106087684514, 3.1798600950044777, 3.5754169970262786, 6.686782075167818, 3.566547285071714, 3.47722596516465, 7.042206860585631, 3.459238171506453, 3.435134332592052, 7.003939734793729, 3.512049727586488, 3.559270908983708, 7.086176708003556, 3.6686375261134057, 4.080241002256502, 5.84537422915953, 3.215380734121881, 4.89528857023289, 5.727559855711503, 5.432265203060416, 5.934141424807943, 3.027998394822877, 5.495590110107973, 4.3831413048066254, 3.7495930218165427, 7.070434254364885, 3.5814467824887473, 3.535972176884382, 6.982737736524654, 3.43098967364103, 3.441422724579513, 7.028339316715845, 3.451013794888052, 3.5113341674962717, 6.797932388511367, 3.587942414572208, 3.4081496049032274, 6.82001018784346, 4.190357260509626, 6.134736264429092, 3.967315538885193, 2.2419098299323275, 6.926226800256952, 4.522587633421751, 6.558238517691441, 2.9967711785804605, 3.556550927861764, 6.606754741200447, 3.6063414285217674, 3.496491069547794, 7.05174661557621, 3.471997346906242, 3.4420693538840723, 7.011514159064076, 3.4983614083674994, 3.5438804663925714, 7.0871842215570755, 3.629734022986171, 3.9507935039967084, 6.019481038594121, 3.3058784855515917, 4.240129283449884, 5.892015134812656, 5.262390436269602, 6.413168227114006, 2.905958254165465, 5.269023998291438, 4.608362850137262, 3.8050205047007704, 7.051271296451422, 3.5877015700240324, 3.5493020337345564, 6.965064567979088, 3.434139087088657, 3.4347436167612146, 7.022835521591056, 3.4406465746514856, 3.4864976824344196, 6.847515844073905, 3.58620796770146, 3.4908273530643696, 6.890786354350282, 4.090156575701794, 5.77929297599503, 4.2665273948822655, 2.387152222353216, 7.062521670699347, 4.663509517553511, 6.438556051230168, 2.776539669077205, 3.52724382177749, 6.513147765418067, 3.6539307251684234, 3.5186578165204065, 7.061643896805739, 3.4859770491431257, 3.4514785509234778, 7.016259943745782, 3.4849078166839633, 3.528054396333257]
xa = np.array(x)
q = minmax_scale(xa)
# Not normalized
print(q)
d = minmax_scale(xa[:200])
# Normalized
print(d)
za = np.arange(600)
z = minmax_scale(za)
# Normalized
print(z)

You're probably making some wrong assumptions here, I can show you:
>> import numpy as np
>> from sklearn.preprocessing import minmax_scale
>> x = [3.4983787694629807, 6.8238036546277545, 3.5877400241270765, 3.4533384061573176, 6.856882141805892, 4.139049184316268, 5.958429090197188, 4.11765195576695, 2.314295679864955, 7.007979076325537, 4.591575092528983, 6.500165465269498, 2.8914665615540556, 3.543340276730918, 6.561891009978831, 3.6289541084867656, 3.5071777128342223, 7.056680192241884, 3.4788374494994114, 3.4464924916748862, 7.014214072992434, 3.4916155718006134, 3.5360046270365486, 7.086001703906676, 3.612050358553576, 3.896165015866581, 6.09922605707944, 3.3460547945703287, 3.910944920634269, 5.971772677577327, 5.178500485785331, 6.6090028576236675, 2.841807831193129, 5.149220347017544, 4.735677896132761, 3.8354921976610226, 7.038466835269509, 3.587747218864995, 3.555693227090981, 6.954475444783193, 3.437365707196104, 3.4325883202929894, 7.020900371706621, 3.4368379490007386, 3.475833945795732, 6.8688992156695585, 3.583554218078379, 3.520770727926182, 6.92133265995129, 4.044292336171232, 5.602564848428526, 4.411557629422499, 2.45924872382912, 7.086402867274765, 4.7372741724391965, 6.374350060657951, 2.6542178607167273, 3.508325152205329, 6.461025102348145, 3.6812426559272033, 3.5307800915694383, 7.066468458976694, 3.493276512558132, 3.4568759587746243, 7.0176506092750595, 3.478361485227409, 3.5201937331883713, 7.080979349338214, 3.5795505246747212, 3.8031183328096256, 6.245609213027018, 3.4165971380697018, 2.1723738636075023, 6.126159977852875, 5.013068437451677, 6.904030395006403, 2.7075477902159992, 4.895577020527206, 5.020958429692195, 3.9029736137362567, 7.005502917949908, 3.5789396294713756, 3.5676476014611818, 6.929131913377069, 3.4472779998622487, 3.4308686427915935, 7.018771879565203, 3.432009619429056, 3.457840956435269, 6.905877831610167, 3.5755167560389163, 3.5618219034900966, 6.97358473363722, 3.9600759412714175, 5.260824000465443, 4.692774833624142, 2.602129482124068, 7.035148649249085, 4.8915972720677825, 6.2369642563916825, 2.3878063740977975, 3.4610250988299063, 6.344643530169994, 3.745077609377894, 3.557344813692928, 7.0753891572282885, 3.5084174545346944, 3.4687392098349816, 7.018578929608326, 3.4657417965161157, 3.504674479158238, 7.073344198878297, 3.5505846727930717, 3.728275930579232, 6.374512212337401, 3.473738663253011, 2.455000320545692, 6.271460848569801, 4.853259564210921, 7.060300163815499, 2.5675976179261544, 4.625519502757723, 5.34193581659188, 3.9796698149770413, 6.961928522596542, 3.553928370489732, 3.577760192959362, 6.897549440276217, 3.4618068657264773, 3.4328376059178547, 7.019069392542759, 3.430955309222881, 3.4444356247624213, 6.935848551405397, 3.5648686589574554, 3.5823723667040626, 7.0144769584373465, 3.885706919437764, 4.947869760566333, 4.958988699542243, 2.740882010237675, 6.844670575188505, 5.052957262016582, 6.08929327319099, 2.1012153486394873, 3.4006432713737014, 6.211570326896137, 3.823951658625252, 3.5871290770943087, 7.082486008656476, 3.524032336877465, 3.481547522774735, 7.017048732257519, 3.454184925027273, 3.4896816808661573, 7.064127863850646, 3.5247589190924855, 3.6675470038864804, 6.486998043891378, 3.5179550925822936, 2.7150317741174828, 6.406130756762303, 4.700905963998837, 7.0786411289315705, 2.424024165788486, 4.340976405808333, 5.68871874308043, 4.066399458564197, 6.906772355480829, 3.5069849107646553, 3.584982183669687, 6.8586979941775175, 3.4809101839812273, 3.4385861350276734, 7.021776708035995, 3.433539453335249, 3.435646282061486, 6.959814531248965, 3.5525938762287192, 3.5880328691774226, 7.04501753482764, 3.8203341873417145, 4.672129393281222, 5.208396760624669, 2.873447040265478, 6.517042667648001, 5.219450800175008, 5.932960908282111, 4.071807320428156, 3.32681370883367, 6.060786017704781, 3.922140073470534, 3.6205701108946693, 7.086704780969265, 3.5398647239459082, 3.4949072952266786, 7.012978275363125, 3.4442572447205126, 3.4754567104312315, 7.054260858195003, 3.501860081901249, 3.6176350355084987, 6.584272251688416, 3.550139011669634, 2.9441212137915196, 6.528971419532436, 4.557509169353122, 6.971230117604573, 2.2788693414108083, 4.044384223064448, 6.045075381817579, 4.16380702973696, 6.8392444953122915, 3.4322587777495, 3.588015280869961, 6.811444662244569, 3.504571583212776, 3.448038447176144, 7.0267448030100095, 3.4394438861946495, 3.4313813610293606, 6.978663067682745, 3.539410445197603, 3.5836697468529928, 7.066336743389743, 3.763013423804524, 4.436678180031973, 5.439712464353549, 2.9978564390796802, 6.06598407921124, 5.38892971887227, 5.769885855777033, 4.7305353792164615, 3.2396389326858333, 5.891487403995336, 4.044576070907139, 3.658273834104114, 7.086908629948878, 3.5554434237079002, 3.508553789136474, 7.006154398318428, 3.4366257440898904, 3.462343969119664, 7.044561860711188, 3.4818481341401735, 3.576035272214573, 6.667629115969775, 3.5715246418069957, 3.1369130300148993, 6.639150212621939, 4.424195703276443, 6.759495560732593, 2.134069612887043, 3.7386525746914123, 6.388620586154337, 4.272297018941626, 6.7587843489580965, 3.324494111518356, 3.5853639214122595, 6.754568772053236, 3.5329516319997514, 3.4610002607293953, 7.033692354375017, 3.4481972797191154, 3.4313661148467935, 6.993158859550646, 3.5257969569299723, 3.5731476929367916, 7.07964197799761, 3.712767027132367, 4.2405670182736745, 5.652147101651347, 3.112331986409608, 5.515185072672031, 5.559088312172956, 5.602219157421699, 5.362655122965983, 3.1396940731308915, 5.703149958832117, 4.196548195898355, 3.700992351744346, 7.081897811873345, 3.569845801358458, 3.5223021303817146, 6.996232651966579, 3.4319885789163944, 3.450813363928631, 7.035729967483905, 3.464829989158296, 3.5409776462526583, 6.738403993391335, 3.583589535854453, 3.2912893051822354, 6.7362012208338795, 4.301697490919809, 6.470822564075378, 3.659773459738205, 2.0974903963829354, 6.692161174433521, 4.3919729995424195, 6.665106087684514, 3.1798600950044777, 3.5754169970262786, 6.686782075167818, 3.566547285071714, 3.47722596516465, 7.042206860585631, 3.459238171506453, 3.435134332592052, 7.003939734793729, 3.512049727586488, 3.559270908983708, 7.086176708003556, 3.6686375261134057, 4.080241002256502, 5.84537422915953, 3.215380734121881, 4.89528857023289, 5.727559855711503, 5.432265203060416, 5.934141424807943, 3.027998394822877, 5.495590110107973, 4.3831413048066254, 3.7495930218165427, 7.070434254364885, 3.5814467824887473, 3.535972176884382, 6.982737736524654, 3.43098967364103, 3.441422724579513, 7.028339316715845, 3.451013794888052, 3.5113341674962717, 6.797932388511367, 3.587942414572208, 3.4081496049032274, 6.82001018784346, 4.190357260509626, 6.134736264429092, 3.967315538885193, 2.2419098299323275, 6.926226800256952, 4.522587633421751, 6.558238517691441, 2.9967711785804605, 3.556550927861764, 6.606754741200447, 3.6063414285217674, 3.496491069547794, 7.05174661557621, 3.471997346906242, 3.4420693538840723, 7.011514159064076, 3.4983614083674994, 3.5438804663925714, 7.0871842215570755, 3.629734022986171, 3.9507935039967084, 6.019481038594121, 3.3058784855515917, 4.240129283449884, 5.892015134812656, 5.262390436269602, 6.413168227114006, 2.905958254165465, 5.269023998291438, 4.608362850137262, 3.8050205047007704, 7.051271296451422, 3.5877015700240324, 3.5493020337345564, 6.965064567979088, 3.434139087088657, 3.4347436167612146, 7.022835521591056, 3.4406465746514856, 3.4864976824344196, 6.847515844073905, 3.58620796770146, 3.4908273530643696, 6.890786354350282, 4.090156575701794, 5.77929297599503, 4.2665273948822655, 2.387152222353216, 7.062521670699347, 4.663509517553511, 6.438556051230168, 2.776539669077205, 3.52724382177749, 6.513147765418067, 3.6539307251684234, 3.5186578165204065, 7.061643896805739, 3.4859770491431257, 3.4514785509234778, 7.016259943745782, 3.4849078166839633, 3.528054396333257]
>> xa = np.array(x)
>> q = minmax_scale(xa)
>> x_set = set(x)
>> q_set = set(q)
>> x_set.intersection(q_set) == set()
True
As you can see, there's no intersection, it's definitely not returning the same set...

Related

Applying a non aggregating function to a groupby pandas object

I have a dataframe (called cep) with two indexes (Cloud and Mode) and data columns. It looks like this :
The data columns are fitted to a linear function and I'm extracting the residuals to the fit in this way :
import pandas as pd
from scipy.optimize import least_squares
args = [-1, 15] # initial guess for the fit
def residuals(args, x, y):
"""
Residual with respect to a linear function
args : list with 2 arguments
x : array
y : array
"""
return args[0] * x + args[1] - y
def residual_function(df):
"""
Returns the array of the residuals
"""
return least_squares(residuals, args, loss='soft_l1', f_scale=0.5, args=(df.logP1, df.W)).fun
cep.groupby(['Cloud', 'Mode']).apply(lambda grp : residual_function(grp))
This gives the expected result :
Now is my issue : I'd like to insert those residual values each in their respective row in the original dataframe to compare them with other columns.
I checked that the returned arrays are of the right length to be inserted but so far I have no idea how to proceed.
I tried to follow tutorials, but the difference with the textbook problem here is that the function I applied does not aggregate the data. Do you have some hints?
Small sample data here :
Mode;Cloud;W;logP1
F;LMC;14,525;0,4939
F;LMC;13,4954;0,7491
F;LMC;14,5421;0,4249
F;LMC;12,033;1,0215
F;LMC;14,3422;0,5655
F;LMC;13,937;0,6072
F;LMC;13,53;0,737
F;LMC;15,2106;0,2309
F;LMC;14,0813;0,5721
F;LMC;14,5128;0,41
F;LMC;14,1059;0,5469
F;LMC;15,6032;0,1014
F;LMC;13,1088;0,8562
F;LMC;12,3528;1,0513
F;LMC;13,1629;0,8416
F;LMC;14,3114;0,4867
F;LMC;14,4013;0,498
F;LMC;13,5057;0,7131
F;LMC;14,3626;0,464
F;LMC;14,5973;0,4111
F;LMC;13,9286;0,6059
F;LMC;15,066;0,2711
F;LMC;12,7364;0,9466
F;LMC;13,3753;0,7442
F;LMC;13,9748;0,5854
F;LMC;12,8836;0,8946
F;LMC;14,4912;0,4206
F;LMC;14,4131;0,4567
F;LMC;12,183;1,1382
F;LMC;14,5492;0,3686
F;LMC;14,1482;0,5339
F;LMC;13,7062;0,7116
F;LMC;13,0731;0,8682
F;LMC;11,5609;1,353
F;LMC;13,9453;0,5551
F;LMC;14,0072;0,6715
F;LMC;13,9838;0,6021
F;LMC;13,9974;0,5562
F;LMC;14,3898;0,5069
F;LMC;14,4497;0,4433
F;LMC;14,3524;0,5064
F;LMC;12,9604;0,9134
F;LMC;12,9757;0,8548
F;LMC;14,2783;0,4927
F;LMC;13,7148;0,6758
F;LMC;14,2348;0,5142
F;LMC;12,6793;0,9415
F;LMC;14,2241;0,5738
F;LMC;14,472;0,4554
F;LMC;15,1508;0,2076
F;LMC;12,5414;1,0159
F;LMC;14,2102;0,5334
F;LMC;15,6086;0,1116
F;LMC;13,2986;0,8381
F;LMC;13,0136;0,8864
F;LMC;13,9774;0,585
F;LMC;14,4256;0,533
F;LMC;14,3582;0,4578
F;LMC;14,3258;0,4859
F;LMC;14,6646;0,3757
F;LMC;12,733;0,9901
F;LMC;14,6296;0,3839
F;LMC;14,054;0,5766
F;LMC;14,3194;0,4884
F;LMC;12,6602;0,9715
F;LMC;13,5909;0,5675
F;LMC;13,9268;0,6196
F;LMC;12,5813;0,9935
F;LMC;13,0824;0,8591
F;LMC;13,5097;0,7375
F;LMC;13,1938;0,5053
F;LMC;14,7357;0,3253
F;LMC;14,0624;0,6009
F;LMC;14,1528;0,533
F;LMC;14,6709;0,4007
F;LMC;14,2378;0,4875
F;LMC;11,951;1,2004
F;LMC;14,4555;0,4777
F;LMC;14,4001;0,4404
F;LMC;13,7707;0,6311
F;LMC;14,578;0,4175
F;LMC;15,8662;0,0159
F;LMC;14,055;0,5687
F;LMC;13,6238;0,7307
F;LMC;15,2572;0,2171
F;LMC;13,4022;0,7723
F;LMC;14,2392;0,5256
F;LMC;14,2505;0,4977
F;LMC;14,7174;0,3614
F;LMC;14,487;0,418
F;LMC;14,9309;0,3086
F;LMC;13,8352;0,6334
F;LMC;14,5598;0,41
F;LMC;14,5614;0,422
F;LMC;14,1486;0,5149
F;LMC;14,0304;0,4945
F;LMC;13,5781;0,6801
F;LMC;14,79;0,3218
F;LMC;12,376;1,0908
F;LMC;15,3215;0,2176
F;LMC;14,7264;0,3845
F;LMC;14,6276;0,4057
F;LMC;14,1712;0,5313
F;LMC;14,4153;0,483
F;LMC;12,905;0,9356
F;LMC;14,442;0,4309
F;LMC;12,8702;0,9159
F;LMC;12,8963;0,5775
F;LMC;13,8304;0,6467
F;LMC;14,4665;0,4165
F;LMC;13,0756;0,5794
F;LMC;13,841;0,6593
F;LMC;14,0924;0,5671
F;LMC;13,7546;0,6778
F;LMC;14,2828;0,5181
F;LMC;14,2424;0,5082
F;LMC;14,659;0,3989
F;LMC;13,7528;0,6768
F;LMC;13,7743;0,6368
F;LMC;13,2894;0,791
F;LMC;14,7512;0,3187
F;LMC;14,5241;0,4452
F;LMC;14,301;0,5121
F;LMC;13,334;0,7945
F;LMC;13,5052;0,7012
F;LMC;14,3664;0,4549
F;LMC;14,8614;0,3278
F;LMC;13,8612;0,582
F;LMC;14,2668;0,5158
F;LMC;14,3937;0,4457
F;LMC;14,0226;0,582
F;LMC;14,387;0,5565
F;LMC;14,3198;0,4362
F;LMC;14,4404;0,4701
F;LMC;14,2774;0,4939
F;LMC;13,7678;0,6557
F;LMC;14,3212;0,4882
F;LMC;14,6453;0,3696
F;LMC;13,9064;0,6084
F;LMC;13,5167;0,7581
F;LMC;14,1692;0,5134
F;LMC;14,6714;0,4136
F;LMC;14,4332;0,4507
F;LMC;14,705;0,3631
F;LMC;13,6728;0,496
F;LMC;15,358;0,1651
F;LMC;13,7592;0,6278
F;LMC;14,0626;0,5754
F;LMC;13,1127;0,8692
F;LMC;14,2108;0,498
F;LMC;14,4519;0,4449
F;LMC;14,0041;0,5666
F;LMC;14,157;0,5392
F;LMC;14,254;0,5245
F;LMC;15,4844;0,1838
F;LMC;14,0845;0,5626
F;LMC;13,0861;0,838
F;LMC;13,3144;0,831
F;LMC;14,2535;0,4911
F;LMC;14,0256;0,5723
F;LMC;14,3246;0,4938
F;LMC;14,4412;0,4136
F;LMC;14,1043;0,518
F;LMC;14,7512;0,3772
F;LMC;14,3982;0,5039
F;LMC;14,2701;0,5042
F;LMC;13,9166;0,5941
F;LMC;13,0324;0,837
F;LMC;13,4839;0,6331
F;LMC;13,4491;0,7443
F;LMC;14,4702;0,458
F;LMC;14,4814;0,4595
F;LMC;14,3008;0,4575
F;LMC;14,922;0,3313
F;LMC;14,6542;0,4263
F;LMC;14,5007;0,4838
F;LMC;14,4335;0,4829
F;LMC;14,4737;0,4586
F;LMC;14,2537;0,5442
F;LMC;14,038;0,5473
F;LMC;14,1413;0,5523
F;LMC;14,669;0,3505
F;LMC;12,3572;1,1033
F;LMC;13,868;0,6416
F;LMC;13,4292;0,816
F;LMC;11,6771;1,3442
F;LMC;14,5086;0,4654
F;LMC;14,3588;0,4807
F;LMC;14,6915;0,3674
F;LMC;15,6488;0,0647
F;LMC;12,4187;0,9791
F;LMC;14,1555;0,5235
F;LMC;14,5765;0,4281
F;LMC;14,3579;0,4596
F;LMC;13,0932;0,7957
F;LMC;14,4552;0,4216
F;LMC;13,2221;0,8505
F;LMC;14,4465;0,4466
F;LMC;14,2439;0,5032
F;LMC;14,9606;0,6308
F;LMC;14,4774;0,4424
F;LMC;14,1875;0,5361
F;LMC;13,3982;0,7644
F;LMC;13,0973;0,8595
F;LMC;13,8264;0,6334
F;LMC;13,9296;0,6164
F;LMC;14,5778;0,4033
F;LMC;13,579;0,726
F;LMC;14,0054;0,5779
F;LMC;14,1219;0,5451
F;LMC;14,3512;0,4808
F;LMC;14,5058;0,4199
F;LMC;14,598;0,4201
F;LMC;14,9516;0,2498
F;LMC;13,9944;0,6075
F;LMC;13,9462;0,557
F;LMC;14,2576;0,5148
F;LMC;14,9814;0,2929
F;LMC;14,3851;0,4573
F;LMC;14,3474;0,4606
F;LMC;14,4929;0,3882
F;LMC;14,5201;0,4234
F;LMC;13,7677;0,6548
F;LMC;14,3146;0,4695
F;LMC;14,2846;0,507
F;LMC;14,0967;0,5525
F;LMC;14,7976;0,3546
F;LMC;13,7497;0,6362
F;LMC;14,4647;0,4363
F;LMC;14,1924;0,5293
F;LMC;14,588;0,4089
F;LMC;13,4896;0,7329
F;LMC;14,695;0,3737
F;LMC;14,2672;0,4857
F;LMC;14,0784;0,5848
F;LMC;13,879;0,5743
F;LMC;14,2214;0,4988
F;LMC;12,922;0,8487
F;LMC;14,189;0,5238
F;LMC;13,9938;0,5713
F;LMC;14,379;0,4771
F;LMC;11,2308;1,3564
F;LMC;14,4472;0,4205
F;LMC;14,3739;0,4699
F;LMC;14,393;0,4416
F;LMC;13,9108;0,5927
F;LMC;14,0298;0,6058
F;LMC;15,1538;0,1961
F;LMC;13,0393;0,8731
F;LMC;13,7144;0,645
F;LMC;14,2682;0,487
F;LMC;14,3506;0,4927
F;LMC;14,0472;0,5619
F;LMC;15,1418;0,2506
F;LMC;13,1227;0,5998
F;LMC;13,5646;0,7193
F;LMC;14,5872;0,4357
F;LMC;14,2636;0,5007
F;LMC;13,9564;0,5599
F;LMC;12,8576;0,946
F;LMC;12,3042;1,1454
F;LMC;11,8416;1,3675
F;LMC;13,5498;0,7219
F;LMC;12,1976;1,1581
F;LMC;13,8632;0,6202
F;LMC;14,2952;0,4807
F;LMC;14,4349;0,4437
F;LMC;14,2392;0,5445
F;LMC;13,7248;0,7213
F;LMC;14,3395;0,5117
F;LMC;15,3588;0,2253
F;LMC;12,8509;0,9229
F;LMC;15,5192;0,1453
F;LMC;14,2072;0,4975
F;LMC;14,3524;0,4945
F;LMC;14,5152;0,4488
F;LMC;14,5106;0,4558
F;LMC;14,5759;0,3786
F;LMC;11,196;1,2374
F;LMC;14,3736;0,4788
F;LMC;14,1726;0,528
F;LMC;11,7899;1,1995
F;LMC;12,1062;1,1823
F;LMC;13,7113;0,6714
F;LMC;14,3512;0,4815
F;LMC;13,1016;0,8181
F;LMC;14,4968;0,562
F;LMC;12,4557;1,0671
F;LMC;14,0573;0,551
F;LMC;14,5916;0,4066
F;LMC;14,3214;0,488
F;LMC;13,5498;0,4885
F;LMC;14,4679;0,4273
F;LMC;14,2426;0,4816
F;LMC;13,5759;0,7052
F;LMC;14,0081;0,5769
F;LMC;14,0828;0,5379
F;LMC;12,4168;0,7578
F;LMC;14,1624;0,5052
F;LMC;13,8029;0,6621
F;LMC;14,1944;0,5145
F;LMC;13,7944;0,6184
F;LMC;15,0234;0,3158
F;LMC;13,0961;0,8282
F;LMC;13,976;0,5889
F;LMC;14,3236;0,4847
F;LMC;14,2618;0,4691
F;LMC;13,4528;0,7349
F;LMC;14,2846;0,507
F;LMC;14,4115;0,446
F;LMC;14,2199;0,5336
F;LMC;14,456;0,4423
F;LMC;14,2938;0,488
F;LMC;14,4109;0,4606
F;LMC;14,2599;0,497
F;LMC;13,9034;0,6384
F;LMC;13,6126;0,7075
F;LMC;14,5036;0,4218
F;LMC;14,0065;0,5741
F;LMC;14,8622;0,3404
F;LMC;14,635;0,3683
F;LMC;14,222;0,5454
F;LMC;14,1501;0,5548
F;LMC;14,0822;0,5705
F;LMC;13,5036;0,7267
F;LMC;14,5528;0,4161
F;LMC;14,3332;0,4614
F;LMC;14,1511;0,5471
F;LMC;14,6113;0,3934
F;LMC;14,2998;0,5031
F;LMC;14,1807;0,5352
F;LMC;13,5114;0,7013
F;LMC;12,2096;1,1344
F;LMC;14,3799;0,4304
F;LMC;12,4526;1,1135
F;LMC;14,5042;0,447
F;LMC;13,4594;0,7336
F;LMC;13,2066;0,8423
F;LMC;14,3734;0,4711
F;LMC;13,945;0,5953
F;LMC;12,9938;0,8969
F;LMC;13,4993;0,7034
F;LMC;13,9466;0,5678
F;LMC;14,1772;0,5077
F;LMC;13,5566;0,6949
F;LMC;14,021;0,5811
F;LMC;14,0264;0,646
F;LMC;12,0242;1,1666
F;LMC;14,3106;0,5027
F;LMC;14,9838;0,3164
F;LMC;14,1718;0,5266
F;LMC;14,2606;0,489
F;LMC;12,6479;1,0206
F;LMC;12,9768;0,8684
F;LMC;14,0837;0,5785
F;LMC;13,7944;0,6609
F;LMC;13,532;0,6911
F;LMC;14,835;0,3375
F;LMC;13,7378;0,6941
F;LMC;14,3618;0,4658
F;LMC;12,4782;1,0176
F;LMC;14,2216;0,4981
F;LMC;14,3958;0,4917
F;LMC;11,3796;1,3161
F;LMC;13,8073;0,6301
F;LMC;14,414;0,4601
F;LMC;12,4266;1,086
F;LMC;14,7974;0,3547
F;LMC;14,3369;0,5189
F;LMC;14,3202;0,4874
F;LMC;14,4614;0,4664
F;LMC;13,8344;0,6339
F;LMC;14,0452;0,5896
F;LMC;11,9134;1,161
F;LMC;14,2492;0,4891
F;LMC;14,1338;0,5139
F;LMC;14,439;0,4476
F;LMC;14,1446;0,5322
F;LMC;14,102;0,549
F;LMC;14,5043;0,4421
F;LMC;14,388;0,4511
F;LMC;12,3812;1,0331
F;LMC;14,5086;0,4294
F;LMC;13,6822;0,671
F;LMC;12,3012;1,0862
F;LMC;14,0848;0,534
F;LMC;14,3381;0,4886
F;LMC;14,5544;0,3908
F;LMC;14,216;0,5226
F;LMC;14,5028;0,4323
F;LMC;12,7769;0,9244
F;LMC;13,6262;0,6984
F;LMC;14,5276;0,4107
F;LMC;13,921;0,5835
F;LMC;14,6279;0,396
F;LMC;14,6304;0,3796
F;LMC;14,2079;0,4722
F;LMC;12,4538;1,0356
F;LMC;14,2662;0,4876
F;LMC;13,8493;0,6217
F;LMC;12,9806;0,8385
F;LMC;14,3148;0,4768
F;LMC;14,2225;0,49
F;LMC;14,3932;0,4084
F;LMC;13,6934;0,5829
F;LMC;14,1702;0,5297
F;LMC;11,7812;1,2435
F;LMC;14,2866;0,4778
F;LMC;15,2824;0,1739
F;LMC;14,451;0,4485
F;LMC;14,4842;0,4222
F;LMC;14,3422;0,449
F;LMC;14,4408;0,4435
F;LMC;12,527;1,0298
F;LMC;12,3746;1,1016
F;LMC;11,4802;1,3276
F;LMC;14,47;0,4643
F;LMC;14,1469;0,5183
F;SMC;14,423;0,4796
F;SMC;15,5626;0,2344
F;SMC;15,6889;0,236
F;SMC;15,3574;0,2926
F;SMC;15,8049;0,1015
F;SMC;12,9034;0,9993
F;SMC;14,0039;0,6867
F;SMC;15,9834;0,1812
F;SMC;15,7707;0,2028
F;SMC;15,777;0,1735
F;SMC;14,7121;0,4973
F;SMC;13,8691;0,7188
F;SMC;14,889;0,4123
F;SMC;14,5322;0,6233
F;SMC;15,6791;0,331
F;SMC;13,9406;0,7262
F;SMC;13,728;0,8514
F;SMC;15,1952;0,3583
F;SMC;16,0921;0,1397
F;SMC;15,6162;0,1532
F;SMC;15,786;0,2563
F;SMC;16,0774;0,1197
F;SMC;14,4397;0,599
F;SMC;15,8693;0,2072
F;SMC;15,6668;0,2452
F;SMC;15,1954;0,3509
F;SMC;14,1387;0,669
F;SMC;15,6928;0,2125
F;SMC;14,6266;0,5017
F;SMC;15,9557;0,1772
F;SMC;15,607;0,2501
F;SMC;15,9632;0,1629
F;SMC;15,7932;0,2325
F;SMC;15,7108;0,1534
F;SMC;13,037;0,9898
F;SMC;15,3998;0,2915
F;SMC;15,1724;0,3675
F;SMC;13,7222;0,7848
F;SMC;14,8296;0,5222
F;SMC;15,704;0,2407
F;SMC;13,5231;0,8378
F;SMC;14,4338;0,5303
F;SMC;14,6202;0,4843
F;SMC;16,2836;0,0473
F;SMC;15,6011;0,1758
F;SMC;16,0037;0,1571
F;SMC;13,9062;0,6286
F;SMC;16,0606;0,0557
F;SMC;13,2924;0,8905
F;SMC;15,9942;0,1997
F;SMC;15,7766;0,2395
F;SMC;10,8462;1,6309
F;SMC;15,956;0,1425
F;SMC;13,857;0,7079
F;SMC;15,3619;0,2696
F;SMC;14,0064;0,6903
F;SMC;15,6531;0,2602
F;SMC;14,9001;0,5001
F;SMC;14,3957;0,6156
F;SMC;15,4414;0,3174
F;SMC;15,8321;0,1822
F;SMC;16,3562;0,1385
F;SMC;15,8812;0,1651
F;SMC;15,1404;0,408
F;SMC;13,7978;0,8055
F;SMC;15,9291;0,132
F;SMC;15,0555;0,507
F;SMC;15,5766;0,2596
F;SMC;13,6006;0,8469
F;SMC;16,455;0,0629
F;SMC;15,8762;0,1072
F;SMC;16,2856;0,0768
F;SMC;15,8521;0,2129
F;SMC;15,7685;0,2374
F;SMC;16,1197;0,1043
F;SMC;16,0851;0,2333
F;SMC;15,8126;0,1777
F;SMC;14,3891;0,6065
F;SMC;14,6419;0,5446
F;SMC;15,3942;0,3101
F;SMC;15,5785;0,2494
F;SMC;15,661;0,2227
F;SMC;15,9648;0,1405
F;SMC;12,7911;1,0845
F;SMC;15,9351;0,1575
F;SMC;14,1764;0,6864
F;SMC;15,153;0,3624
F;SMC;15,9336;0,1232
F;SMC;15,0124;0,3796
F;SMC;16,1231;0,106
F;SMC;14,4362;0,5306
F;SMC;13,1883;0,8354
F;SMC;15,8972;0,1757
F;SMC;14,1612;0,7287
F;SMC;15,3792;0,2869
F;SMC;16,421;0,0329
F;SMC;14,833;0,4543
F;SMC;14,3997;0,5912
F;SMC;15,8797;0,1747
F;SMC;16,0337;0,1565
F;SMC;15,7371;0,2251
F;SMC;13,954;0,7293
F;SMC;14,1691;0,6695
F;SMC;15,6208;0,2211
F;SMC;14,3416;0,6492
F;SMC;14,6636;0,5423
F;SMC;16,0386;0,1506
F;SMC;14,6578;0,5604
F;SMC;15,6368;0,24
F;SMC;14,843;0,4738
F;SMC;14,9818;0,4869
F;SMC;12,4251;1,1641
F;SMC;15,0727;0,4671
F;SMC;14,1448;0,5949
F;SMC;15,2148;0,3644
F;SMC;15,9372;0,117
F;SMC;15,4336;0,3018
F;SMC;14,5416;0,557
F;SMC;16,4654;0,0436
F;SMC;14,934;0,5498
F;SMC;14,3896;0,695
F;SMC;15,3896;0,3492
F;SMC;15,8122;0,1602
F;SMC;13,7822;0,704
F;SMC;15,7938;0,1679
F;SMC;15,4049;0,3059
F;SMC;16,0742;0,1187
F;SMC;15,704;0,2036
F;SMC;14,9947;0,3748
F;SMC;15,1374;0,4001
F;SMC;13,2254;0,7136
F;SMC;14,3267;0,577
F;SMC;12,7772;1,0317
F;SMC;15,5302;0,3074
F;SMC;16,12;0,1395
F;SMC;15,9826;0,1873
F;SMC;15,9196;0,2025
F;SMC;15,5396;0,2888
F;SMC;14,0063;0,7543
F;SMC;14,6752;0,542
F;SMC;14,3782;0,6365
F;SMC;15,8015;0,2321
F;SMC;15,4898;0,0235
F;SMC;15,6376;0,2499
F;SMC;15,527;0,2697
F;SMC;15,2883;0,3324
F;SMC;15,1014;0,3996
F;SMC;14,435;0,5827
F;SMC;16,1522;0,0832
F;SMC;13,3787;0,8974
F;SMC;16,6258;0,0226
F;SMC;14,0421;0,8043
F;SMC;15,4764;0,2719
F;SMC;14,1377;0,6069
F;SMC;15,3654;0,3461
F;SMC;16,3063;0,0677
F;SMC;15,5912;0,2227
F;SMC;14,555;0,5143
F;SMC;16,2947;0,0824
F;SMC;15,2208;0,3488
F;SMC;16,8052;-0,0287
F;SMC;15,8592;0,1835
F;SMC;15,6349;0,2632
F;SMC;16,522;0,0581
F;SMC;15,7794;0,3351
F;SMC;16,095;0,1574
F;SMC;16,0564;0,1818
F;SMC;16,4614;0,0897
F;SMC;16,1351;0,1332
F;SMC;14,4711;0,5808
F;SMC;13,8768;0,6795
F;SMC;16,2458;0,1273
F;SMC;16,1994;0,0372
F;SMC;15,3434;0,3072
F;SMC;15,5384;0,2442
F;SMC;14,5322;0,5703
F;SMC;15,7762;0,3507
F;SMC;14,3793;0,5628
F;SMC;15,4777;0,3139
F;SMC;15,9216;0,1764
F;SMC;14,3758;0,5278
F;SMC;15,2363;0,3313
F;SMC;14,3224;0,3258
F;SMC;15,2266;0,3656
F;SMC;15,6305;0,174
F;SMC;14,046;0,7832
F;SMC;14,8704;0,507
F;SMC;16,0267;0,2357
F;SMC;16,0671;0,154
F;SMC;13,8434;0,6901
F;SMC;14,4167;0,5992
F;SMC;15,9808;0,125
F;SMC;16,0696;0,1131
F;SMC;15,166;0,166
F;SMC;14,1023;0,6447
F;SMC;13,9666;0,6979
F;SMC;15,64;0,2577
F;SMC;15,6974;0,2429
F;SMC;15,1257;0,3877
F;SMC;15,186;0,3295
F;SMC;14,87;0,4651
F;SMC;16,0943;0,1807
F;SMC;15,7421;0,1809
F;SMC;14,6085;0,5253
F;SMC;14,6912;0,4777
F;SMC;14,1322;0,71
F;SMC;15,3319;0,2937
F;SMC;14,9283;0,4639
F;SMC;15,3753;0,2732
F;SMC;15,0886;0,3989
F;SMC;15,3778;0,3028
F;SMC;16,4933;0,0274
F;SMC;14,7944;0,4336
F;SMC;13,7806;0,7397
F;SMC;14,1895;0,6325
F;SMC;15,947;0,1084
F;SMC;15,9606;0,1665
F;SMC;15,417;0,0976
F;SMC;15,2905;0,3652
F;SMC;14,7712;0,4453
F;SMC;14,6692;0,5412
F;SMC;16,1936;0,0286
F;SMC;15,6136;0,2097
F;SMC;15,8061;0,078
F;SMC;15,3243;0,3385
F;SMC;15,2366;0,3669
F;SMC;16,1653;0,0573
F;SMC;15,916;0,1591
F;SMC;15,2422;0,3216
F;SMC;12,2583;1,2107
F;SMC;15,6361;0,1766
F;SMC;16,0818;0,1771
F;SMC;15,6966;0,2147
F;SMC;16,193;0,0657
F;SMC;14,8256;0,4574
F;SMC;15,7214;0,2185
F;SMC;15,5803;0,2725
F;SMC;14,7322;0,4754
F;SMC;15,8964;0,1898
F;SMC;14,5428;0,4732
F;SMC;16,1362;0,1396
F;SMC;16,2832;0,0473
F;SMC;15,6508;0,2232
F;SMC;14,725;0,4998
F;SMC;16,1585;0,1106
F;SMC;15,2284;0,3727
F;SMC;15,1728;0,3718
F;SMC;14,5354;0,5431
F;SMC;15,8224;0,1256
F;SMC;15,5462;0,2633
F;SMC;14,942;0,455
F;SMC;16,02;0,1426
F;SMC;15,2292;0,2965
F;SMC;14,6639;0,4402
F;SMC;14,887;0,4365
F;SMC;15,8288;0,1924
F;SMC;14,4903;0,5274
F;SMC;15,9464;0,1638
F;SMC;15,8069;0,1999
F;SMC;14,9924;0,3985
F;SMC;15,6917;0,1355
F;SMC;15,5414;0,1628
F;SMC;15,6168;0,2157
F;SMC;15,8006;0,177
F;SMC;14,9294;0,4732
F;SMC;14,5272;0,599
F;SMC;15,7318;0,2691
F;SMC;14,5181;0,5782
F;SMC;15,8524;0,2074
F;SMC;13,773;0,747
F;SMC;15,7608;0,1586
F;SMC;13,947;0,688
F;SMC;14,9774;0,4224
F;SMC;14,5288;0,4912
F;SMC;12,4944;1,2355
F;SMC;13,8683;0,6944
F;SMC;15,7118;0,186
F;SMC;15,7392;0,2081
F;SMC;12,292;1,1395
F;SMC;14,7918;0,4632
F;SMC;15,4428;0,3367
F;SMC;14,7542;0,4279
F;SMC;15,2914;0,3575
F;SMC;14,7332;0,4836
F;SMC;14,566;0,5553
F;SMC;15,9406;0,1167
F;SMC;15,6304;0,2296
F;SMC;14,0478;0,7063
F;SMC;15,5402;0,2821
F;SMC;15,6019;0,2443
F;SMC;15,6554;0,1979
F;SMC;14,7736;0,1631
F;SMC;16,1684;0,119
F;SMC;14,5113;0,5073
F;SMC;15,5466;0,134
F;SMC;15,1128;0,3919
F;SMC;13,4782;0,8109
F;SMC;15,8534;0,2208
F;SMC;13,1824;0,9072
F;SMC;15,8466;0,1901
1;LMC;13,9452;0,4076
1;LMC;14,3302;0,3149
1;LMC;12,9682;0,6984
1;LMC;15,0586;0,1023
1;LMC;14,328;0,304
1;LMC;15,024;0,0882
1;LMC;14,0594;0,3924
1;LMC;17,2026;-0,5304
1;LMC;14,327;0,3192
1;LMC;13,8748;0,4361
1;LMC;17,155;-0,4783
1;LMC;14,3154;0,3197
1;LMC;14,3376;0,2943
1;LMC;14,462;0,3461
1;LMC;14,139;0,3647
1;LMC;16,764;-0,4451
1;LMC;15,1618;0,1008
1;LMC;14,2229;0,3328
1;LMC;13,8046;0,4946
1;LMC;14,4268;0,2703
1;LMC;15,5032;-0,0368
1;LMC;15,9052;-0,1647
1;LMC;13,908;0,4434
1;LMC;14,3352;0,2986
1;LMC;13,6286;0,5326
1;LMC;13,7934;0,4842
1;LMC;14,3979;0,2817
1;LMC;14,0496;0,4238
1;LMC;14,4368;0,2939
1;LMC;14,3242;0,3164
1;LMC;12,6825;0,7719
1;LMC;13,846;0,4483
1;LMC;14,5746;0,2727
1;LMC;14,5171;0,2641
1;LMC;14,9218;0,1209
1;LMC;14,2248;0,3411
1;LMC;14,3478;0,3109
1;LMC;14,0999;0,357
1;LMC;14,5558;0,2632
1;LMC;13,7602;0,4936
1;LMC;14,5354;0,2775
1;LMC;13,5663;0,5364
1;LMC;17,0694;-0,4754
1;LMC;14,2915;0,3346
1;LMC;14,7311;0,218
1;LMC;13,6888;0,5417
1;LMC;14,627;0,2133
1;LMC;13,4404;0,597
1;LMC;14,7168;0,2212
1;LMC;15,0594;0,3161
1;LMC;15,0425;0,1061
1;LMC;16,815;-0,4438
1;LMC;16,001;-0,1914
1;LMC;14,4216;0,2488
1;LMC;14,4748;0,286
1;LMC;13,8631;0,466
1;LMC;14,676;0,2098
1;LMC;14,4089;0,3046
1;LMC;14,2384;0,3559
1;LMC;14,2154;0,3397
1;LMC;14,059;0,3829
1;LMC;14,7006;0,2089
1;LMC;13,2151;0,6923
1;LMC;14,5228;0,2442
1;LMC;14,1972;0,3233
1;LMC;14,7161;0,2052
1;LMC;14,4328;0,2944
1;LMC;14,4018;0,2906
1;LMC;14,7142;0,2083
1;LMC;14,5522;0,2311
1;LMC;13,6784;0,5121
1;LMC;14,396;0,31
1;LMC;14,5408;0,2582
1;LMC;13,9204;0,4699
1;LMC;14,3842;0,308
1;LMC;13,9161;0,4451
1;LMC;14,5161;0,2751
1;LMC;16,6794;-0,4003
1;LMC;14,2213;0,3356
1;LMC;14,0804;0,3867
1;LMC;14,3438;0,2957
1;LMC;16,7434;-0,4476
1;LMC;14,4333;0,2808
1;LMC;14,3312;0,2889
1;LMC;14,504;0,247
1;LMC;13,2101;0,6412
1;LMC;13,8247;0,4442
1;LMC;13,962;0,4153
1;LMC;14,0806;0,3598
1;LMC;14,4793;0,2675
1;LMC;14,8813;0,1499
1;LMC;14,5757;0,2212
1;LMC;14,409;0,2996
1;LMC;13,8864;0,4335
1;LMC;14,1462;0,3252
1;LMC;13,4634;0,5562
1;LMC;14,034;0,4077
1;LMC;17,5882;-0,6029
1;LMC;13,7698;0,4653
1;LMC;14,3287;0,3083
1;LMC;13,2086;0,6234
1;LMC;13,5732;0,546
1;LMC;15,48;-0,014
1;LMC;13,1248;0,6751
1;LMC;17,1166;-0,528
1;LMC;13,9133;0,4573
1;LMC;15,0072;0,1038
1;LMC;14,1087;0,3766
1;LMC;17,1206;-0,5551
1;LMC;14,6866;0,2054
1;LMC;13,4114;0,5868
1;LMC;15,8548;-0,1511
1;LMC;12,2802;0,6877
1;LMC;17,1984;-0,5196
1;LMC;13,2713;0,6421
1;LMC;14,537;0,2466
1;LMC;15,4264;0,0006
1;LMC;15,5466;-0,0351
1;LMC;14,5549;0,3135
1;LMC;14,8506;0,1502
1;LMC;15,1214;0,0971
1;LMC;14,0284;0,3934
1;LMC;13,0608;0,6455
1;LMC;14,4624;0,2676
1;LMC;15,2442;0,0527
1;LMC;13,9045;0,4276
1;LMC;14,0536;0,3947
1;LMC;14,0503;0,3833
1;LMC;14,2145;0,3506
1;LMC;14,3653;0,2799
1;LMC;12,2534;0,6564
1;LMC;13,4538;0,5395
1;LMC;16,7458;-0,3898
1;LMC;13,799;0,4515
1;LMC;14,3382;0,2787
1;LMC;13,6368;0,5072
1;LMC;13,4912;0,5308
1;LMC;14,8163;0,1739
1;LMC;13,8256;0,4412
1;LMC;14,3908;0,2858
1;LMC;14,9267;0,0972
1;LMC;14,5064;0,2072
1;LMC;13,899;0,4303
1;LMC;14,0764;0,3825
1;LMC;14,871;0,1848
1;LMC;14,8902;0,1544
1;LMC;14,1546;0,3697
1;LMC;14,7806;0,1531
1;LMC;15,3816;0,0162
1;LMC;14,1212;0,3378
1;LMC;14,6768;0,1847
1;LMC;14,229;0,3145
1;LMC;14,3439;0,2859
1;LMC;14,5225;0,183
1;LMC;14,222;0,3029
1;LMC;14,6786;0,2644
1;LMC;14,2882;0,3067
1;LMC;17,304;-0,4965
1;LMC;13,2234;0,6359
1;LMC;14,1998;0,341
1;LMC;16,9782;-0,4488
1;SMC;14,2801;0,5215
1;SMC;16,7184;-0,1413
1;SMC;15,6902;0,0745
1;SMC;16,1686;-0,057
1;SMC;14,6436;0,3746
1;SMC;16,573;-0,1489
1;SMC;15,4925;0,1575
1;SMC;15,0159;0,3255
1;SMC;15,5657;0,1226
1;SMC;14,3219;0,4484
1;SMC;16,5712;-0,1446
1;SMC;16,1988;-0,0829
1;SMC;15,4376;0,1613
1;SMC;13,6344;0,5874
1;SMC;14,3778;0,4716
1;SMC;14,2394;0,5057
1;SMC;15,8777;0,0206
1;SMC;16,7138;-0,1735
1;SMC;15,7367;0,0683
1;SMC;14,7922;0,3067
1;SMC;17,9934;-0,5486
1;SMC;14,1358;0,5249
1;SMC;14,8562;0,3176
1;SMC;15,5588;0,1312
1;SMC;14,3;0,5272
1;SMC;15,6038;0,0537
1;SMC;14,5812;0,4347
1;SMC;14,8804;0,3115
1;SMC;14,3614;0,4934
1;SMC;16,4298;-0,0449
1;SMC;15,8712;0,0365
1;SMC;14,3527;0,5141
1;SMC;15,639;0,0993
1;SMC;14,0709;0,4997
1;SMC;16,0837;0,0029
1;SMC;14,7445;0,4165
1;SMC;16,23;-0,0246
1;SMC;15,1252;0,2608
1;SMC;16,255;-0,043
1;SMC;15,4152;0,2079
1;SMC;15,6954;0,0998
1;SMC;14,8665;0,3692
1;SMC;15,7832;0,0378
1;SMC;14,8404;-0,2293
1;SMC;15,9228;0,0104
1;SMC;16,1484;0,0015
1;SMC;15,8728;0,0054
1;SMC;14,8986;0,2908
1;SMC;16,731;-0,2169
1;SMC;15,2766;0,1077
1;SMC;15,5933;0,0706
1;SMC;14,6399;0,3879
1;SMC;16,4613;-0,0989
1;SMC;15,1788;0,1832
1;SMC;16,2002;-0,0848
1;SMC;15,0008;0,2784
1;SMC;14,7586;0,2794
1;SMC;16,3034;-0,118
1;SMC;16,4006;-0,1251
1;SMC;15,849;-0,0155
1;SMC;16,3728;-0,0437
1;SMC;13,959;0,5954
1;SMC;15,9233;0,0135
1;SMC;15,1752;0,2438
1;SMC;14,8222;0,3179
1;SMC;16,0276;0,0558
1;SMC;15,2084;0,1235
1;SMC;16,3546;-0,1292
1;SMC;14,5508;0,4422
1;SMC;15,656;0,1128
1;SMC;15,2515;0,2473
1;SMC;15,8121;0,0231
1;SMC;15,6758;0,0838
1;SMC;16,729;-0,1389
1;SMC;16,2468;-0,126
1;SMC;13,9121;0,5834
1;SMC;14,368;0,4634
1;SMC;15,7206;0,0583
1;SMC;15,6693;0,0931
1;SMC;16,2687;-0,0599
1;SMC;15,0676;0,227
1;SMC;15,5143;0,1668
1;SMC;15,7076;0,0811
1;SMC;15,566;0,0386
1;SMC;16,1032;-0,0477
1;SMC;16,2852;-0,0936
1;SMC;13,9415;0,5344
1;SMC;13,7318;0,6038
1;SMC;14,6932;0,2731
1;SMC;17,5597;-0,4531
1;SMC;15,6816;0,0183
1;SMC;16,6984;-0,0744
1;SMC;15,0062;0,2869
1;SMC;15,8423;0,0837
1;SMC;15,6786;0,1166
1;SMC;14,6876;0,3651
1;SMC;15,5642;0,1374
1;SMC;16,8114;-0,1078
1;SMC;14,795;0,2782
1;SMC;14,2601;0,4012
1;SMC;16,4018;-0,1529
1;SMC;14,9727;0,2929
1;SMC;15,5267;0,1388
1;SMC;15,0455;0,2939
1;SMC;16,1594;-0,0279
1;SMC;15,6552;0,0574
1;SMC;14,4008;0,4278
1;SMC;16,1806;-0,0993
1;SMC;15,8383;0,0532
1;SMC;15,4704;0,1488
1;SMC;16,3872;-0,0714
1;SMC;14,7915;0,3349
1;SMC;13,9011;0,5528
1;SMC;16,5788;-0,1133
1;SMC;13,9728;0,5471
1;SMC;15,8312;0,048
1;SMC;15,696;0,0947
1;SMC;16,378;-0,0909
1;SMC;15,3721;0,1404
1;SMC;14,9808;0,2511
1;SMC;15,7881;0,0277
1;SMC;15,7657;0,0796
1;SMC;15,9406;0,0803
1;SMC;15,5712;0,1499
1;SMC;15,4664;0,1231
1;SMC;16,3175;-0,0522
1;SMC;15,4929;0,1124
1;SMC;13,5586;0,3835
1;SMC;16,205;-0,0705
1;SMC;15,55;0,08
1;SMC;17,5096;-0,2768
1;SMC;15,8832;0,0417
1;SMC;17,738;-0,542
1;SMC;14,5475;0,4257
1;SMC;15,4079;0,0751
1;SMC;16,2626;0,0103
1;SMC;14,5742;0,3754
1;SMC;16,521;-0,1554
1;SMC;16,791;-0,1832
1;SMC;15,4673;0,1727
1;SMC;14,2996;0,4629
1;SMC;13,6418;0,6525
1;SMC;15,7457;0,0729
1;SMC;15,4886;0,1447
1;SMC;14,7568;0,3357
1;SMC;15,482;0,1373
1;SMC;16,1634;-0,0447
1;SMC;15,7054;0,1234
1;SMC;14,5147;0,4154
1;SMC;15,0815;0,2683
1;SMC;15,992;-0,0153
1;SMC;14,3333;0,4373
1;SMC;15,3798;0,1507
1;SMC;15,957;-0,0025
1;SMC;15,889;0,0482
1;SMC;16,3458;-0,0707
1;SMC;15,565;0,17
1;SMC;15,0304;0,273
1;SMC;14,0869;0,4998
1;SMC;14,986;0,2767
1;SMC;16,144;-0,0551
1;SMC;15,5166;0,1347
1;SMC;14,3772;0,4966
1;SMC;15,8712;0,0196
1;SMC;14,6147;0,3938
1;SMC;16,7266;-0,1534
1;SMC;15,6266;0,1039
1;SMC;14,3126;0,4288
1;SMC;15,9238;-0,016
1;SMC;16,1556;-0,0916
1;SMC;14,6832;0,3555
1;SMC;14,9996;0,3125
1;SMC;14,8072;0,313
1;SMC;17,2238;-0,2249
1;SMC;14,2168;0,4893
1;SMC;16,0782;-0,0494
1;SMC;15,9124;0,0302
1;SMC;14,6897;0,3772
1;SMC;14,8998;0,317
1;SMC;14,3068;0,4708
1;SMC;14,9732;0,2529
1;SMC;16,1034;-0,0252
1;SMC;15,2416;0,2186
1;SMC;15,9578;-0,0056
1;SMC;14,605;0,3675
1;SMC;15,3892;0,1909
1;SMC;14,1306;0,5392
1;SMC;14,2198;0,4472
1;SMC;15,9806;0,1076
1;SMC;17,3222;-0,3888
1;SMC;14,8756;0,3077
1;SMC;16,4862;-0,1431
1;SMC;15,453;0,1643
1;SMC;15,719;0,105
1;SMC;15,0462;0,2544
1;SMC;14,3558;0,4541
1;SMC;13,7118;0,6472
1;SMC;14,9858;0,3054
1;SMC;14,7582;0,3293
1;SMC;15,8872;0,0343
1;SMC;14,2318;0,4783
1;SMC;15,7902;0,1023
1;SMC;15,7548;0,0084
1;SMC;16,3536;-0,1291
1;SMC;15,7356;0,0787
1;SMC;15,0988;0,2505
1;SMC;15,007;0,1926
1;SMC;15,0572;0,2629
1;SMC;15,4202;0,1177
1;SMC;14,5873;0,4062
1;SMC;14,274;0,472
1;SMC;15,953;0,032
1;SMC;15,1688;0,1666
1;SMC;15,4486;0,1694
1;SMC;16,2714;-0,084
1;SMC;14,1066;0,444
1;SMC;14,1883;0,4876
1;SMC;14,6876;0,3783
1;SMC;16,2804;-0,0307
1;SMC;16,004;0,0296
1;SMC;15,5427;0,0665
1;SMC;15,2691;0,1932
1;SMC;15,0723;0,2626
1;SMC;16,4086;-0,135
1;SMC;16,1279;-0,0629
1;SMC;14,6822;0,3247
1;SMC;16,1232;-0,1099
1;SMC;14,3967;0,4784
1;SMC;16,1678;-0,019
1;SMC;14,3868;0,4022
1;SMC;14,738;0,3264
1;SMC;15,8982;0,0036
1;SMC;16,0884;-0,0763
1;SMC;14,7889;0,3277
1;SMC;15,5037;0,1452
1;SMC;14,9974;0,3175
1;SMC;16,1114;-0,0793
1;SMC;15,5855;0,0736
1;SMC;15,1194;0,2507
1;SMC;15,1229;0,2498
1;SMC;15,5506;0,0998
1;SMC;15,8262;0,0085
1;SMC;17,6762;-0,4719
1;SMC;15,512;0,1091
1;SMC;15,1242;0,2304
1;SMC;14,8618;0,2606
1;SMC;15,8314;-0,0355
1;SMC;13,9661;0,5273
1;SMC;15,7528;0,0473
1;SMC;15,4834;0,1461
1;SMC;16,1654;0,0084
1;SMC;17,02;-0,0819
1;SMC;15,7764;0,0479
1;SMC;15,1877;0,2523
1;SMC;15,2879;0,1914
1;SMC;16,2964;-0,0454
1;SMC;15,5908;0,1223
1;SMC;15,6662;0,0394
1;SMC;15,5124;0,1418
1;SMC;14,876;0,2962
1;SMC;16,015;-0,0057
1;SMC;14,6491;0,4071
1;SMC;16,5376;-0,1862
1;SMC;16,4474;-0,1131
1;SMC;16,0558;0,0361
1;SMC;16,6338;-0,2435
1;SMC;18,2798;-0,5471
1;SMC;15,7256;0,0648
1;SMC;16,963;-0,2991
1;SMC;15,5069;0,1115
1;SMC;15,0298;0,1803
1;SMC;16,3346;-0,1174
1;SMC;14,794;0,3238
1;SMC;14,271;0,4877
1;SMC;15,9154;0,0438
1;SMC;16,5047;-0,1339
1;SMC;16,65;-0,1978
1;SMC;14,8017;0,3421
1;SMC;15,397;0,1778
1;SMC;16,8134;-0,2104
1;SMC;14,3519;0,421
1;SMC;14,6731;0,3168
1;SMC;15,2232;0,2349
1;SMC;14,6852;0,3608
1;SMC;14,9719;0,1979
1;SMC;15,1469;0,2306
1;SMC;15,2132;0,1439
1;SMC;14,788;0,3559
1;SMC;15,638;0,131
1;SMC;15,1227;0,1846
1;SMC;15,7846;-0,0333
1;SMC;16,1864;-0,0533
1;SMC;16,4067;-0,0201
1;SMC;16,7493;-0,236
1;SMC;16,5681;-0,2147
1;SMC;15,6974;0,0783
1;SMC;16,1395;-0,074
1;SMC;14,7655;0,3273
1;SMC;14,5638;0,3947
1;SMC;16,6594;-0,1952
1;SMC;16,1283;-0,0393
1;SMC;15,9034;0,0257
1;SMC;15,8515;0,0495
1;SMC;15,0717;0,3022
1;SMC;15,3598;0,1681
1;SMC;14,4274;0,4869
1;SMC;16,2396;-0,0553
1;SMC;16,082;-0,0294
1;SMC;14,8533;0,2512
1;SMC;14,6503;0,3586
1;SMC;16,1;-0,0353
1;SMC;15,6848;0,1708
1;SMC;15,9834;0,0201
1;SMC;14,3646;0,4274
1;SMC;15,285;0,1942
1;SMC;15,1247;0,2598
1;SMC;15,7448;0,0919
1;SMC;15,6758;0,1366
1;SMC;15,0902;0,226
1;SMC;14,0126;0,5439
1;SMC;15,9319;-0,082
1;SMC;15,0558;0,2398
1;SMC;14,5532;0,4375
1;SMC;14,8176;0,3557
1;SMC;15,1869;0,2378
1;SMC;14,5042;0,3989
1;SMC;14,7118;0,2721
1;SMC;14,5803;0,3939
1;SMC;15,4836;0,1186
1;SMC;15,2548;0,2071
1;SMC;15,5388;0,1499
1;SMC;15,507;0,1285
1;SMC;13,958;0,5414
1;SMC;16,4458;-0,0405
1;SMC;15,6919;0,0892
1;SMC;14,4196;0,4557
1;SMC;15,7577;0,03
1;SMC;16,382;-0,1317
1;SMC;14,456;0,4701
1;SMC;15,5165;0,0565
1;SMC;16,198;-0,0138
1;SMC;16,1511;-0,0355
1;SMC;14,3661;0,4568
1;SMC;15,088;0,2109
1;SMC;14,3802;0,4206
1;SMC;14,7786;0,2707
1;SMC;15,2855;0,3013
1;SMC;15,3114;0,1119
1;SMC;15,43;0,1134
1;SMC;16,1082;-0,0503
1;SMC;16,2348;-0,022
1;SMC;15,9953;-0,0417
1;SMC;15,2678;0,1952
1;SMC;15,1298;0,2325
1;SMC;15,1712;0,2456
1;SMC;15,5435;0,1342
1;SMC;15,8772;0,0307

A simple solution:
arrays=df.groupby(['Mode','Cloud']).apply(lambda grp : residual_function(grp))
residuals_value=[]
[residuals_value.extend(elem.tolist()) for elem in arrays]
df["residuals"]=residuals_value

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.

Python Matrix/Vector Operations

I have an array with shape = (2, 257) and want to use each column which are vectors of shape = (2,) to create an array with shape = (2, 2) for each column.
Previously I did this by selecting each column by iterating through my input array
import numpy as np
for counter in input
x = np.array([input[0][counter], input[1][counter]])
y = np.conj(x)
y = y.T
E = x[:, None] * y
corr_matr = np.where(self.iterator == 1, E, alpha * self.altes_E[counter] + (1 - alpha) * E)
self.altes_E[counter] = corr_matr
However this is very slow and I would like to vectorize this calculation so in the end I will have an array containing my E variables for each column of my input variable. I tried to do so but I get broadcasting errors I am not able to solve.. So it will be great if someone helped me out!
Self.iterator will be removed and replaced by the first element of the new array containing all E arrays
My goal now is to have an Array which has the shape (257, 2, 2) and contains 257 2x2 corr_matr matrices. The n-th corr_matr depends on the n-1-th
Self.altes_E is this matrix I am looking for but I cant create it with my vectorized approach. Maybe you guys have an idea how I can create it vectorized without a for-loop.
Test data:
input = array([[ 3.94351315e-02+0.00000000e+00j, -1.50913336e-02+6.03795651e-04j,\n 1.99272113e-04-8.07005910e-04j, -4.67793985e-04+8.33903992e-04j,\n -2.64236148e-03+2.77521785e-05j, -1.49792915e-03+7.36359583e-04j,\n 1.50533594e-03-6.15859179e-04j, -6.54810392e-05-5.01831397e-04j,\n -1.01095434e-03-1.70553920e-04j, 5.81738784e-04+7.12800200e-04j,\n -3.11310287e-04-9.01545559e-04j, -5.86002908e-05-9.55615603e-04j,\n 1.44156235e-04+1.09251279e-03j, -4.87454341e-04+8.03194960e-05j,\n 3.78562845e-04+1.29788540e-04j, -4.87558912e-04+6.55677040e-04j,\n -4.87274113e-04-8.31101470e-04j, 8.16597471e-04+3.81774926e-04j,\n 5.89999582e-04-7.40645680e-05j, -7.03418446e-05-4.16067625e-04j,\n -1.02284759e-03+2.56541860e-04j, -7.25162530e-05-2.12897828e-04j,\n 2.86242195e-04+2.15252463e-04j, -6.97098238e-04-5.35675945e-04j,\n 4.49257188e-04+4.96744002e-04j, 2.86015111e-04+9.92285825e-05j,\n -6.63212048e-05-1.97287145e-04j, -4.96012767e-05+1.68083300e-04j,\n -3.68913382e-04-1.76126405e-04j, 3.05618600e-04-2.13305860e-05j,\n -1.22923172e-04-3.58717400e-04j, -3.92479536e-04+1.02063591e-03j,\n 6.45622389e-04-8.53094144e-04j, -3.14203107e-04+1.47936574e-04j,\n 1.54020776e-05-2.45868608e-05j, 2.78312174e-04+2.11224838e-04j,\n -1.70668244e-04+4.57545662e-04j, 1.89143085e-04-1.62612861e-04j,\n -5.05276967e-04-7.33565277e-04j, 3.87931183e-04+6.84968797e-05j,\n -3.88693353e-04+3.29574348e-04j, 1.88775042e-05+3.06450544e-04j,\n -1.02881416e-04-6.28814378e-04j, 1.50437664e-04-4.64790639e-05j,\n 6.80136794e-05+7.07755678e-04j, 4.29081846e-04-6.60769121e-06j,\n -3.89883869e-05+9.94456323e-05j, -2.88405737e-04-3.90610565e-04j,\n 2.89706554e-04-4.61313935e-04j, 8.53534820e-05+3.45993148e-04j,\n -6.48341994e-04+1.61728688e-05j, 7.08075756e-04+4.18876357e-04j,\n -2.41676738e-04-6.57686042e-04j, -4.52960231e-05+4.69549856e-04j,\n -2.98667220e-04+3.69428944e-04j, 3.09898762e-04-5.55573884e-04j,\n 3.16198618e-05-2.18262971e-04j, -2.43962041e-05+6.14800458e-04j,\n 1.22281179e-04-4.27259031e-05j, -2.02764807e-04-3.04080095e-04j,\n -2.60131161e-05-2.72507038e-04j, -3.48411552e-04-1.65106382e-04j,\n 3.60262912e-04+1.15929180e-03j, 6.44188577e-04-6.57529271e-04j,\n -6.64472633e-04+7.34788284e-05j, -2.53962823e-04-8.64577990e-05j,\n 4.12548109e-04-4.30493761e-04j, -3.89652217e-04+1.10427049e-03j,\n 2.19613546e-04-6.47350601e-04j, -3.01855256e-04-1.70512519e-04j,\n 3.52232474e-04-5.97253780e-04j, -2.46726574e-04+8.25901553e-05j,\n 5.01323354e-04+4.97268616e-04j, -3.87112186e-04+2.31773757e-04j,\n -5.51833095e-04+5.28582216e-04j, 1.52037945e-04-9.87780746e-04j,\n 1.80951699e-04-1.64293165e-04j, 9.81853960e-04+8.24736454e-04j,\n -3.08003998e-04+4.10959821e-05j, -7.18984896e-04+9.56216393e-05j,\n 9.23759625e-06-5.16799160e-04j, 3.62720586e-04-4.27438243e-04j,\n 4.54825689e-04+6.60654467e-04j, -8.45689094e-04+3.84855215e-04j,\n -2.67177134e-05-7.42870583e-04j, -1.92531972e-04-3.02859614e-04j,\n 7.90923909e-04+2.13245532e-04j, -7.46436347e-04+4.59160357e-04j,\n 2.42516064e-04+5.79452623e-04j, 2.76445921e-04-1.07193028e-03j,\n -5.72620393e-04+1.96574790e-04j, 1.34058726e-03+2.34585361e-04j,\n -3.57420647e-04-1.70451007e-04j, -2.82518121e-04+2.07459060e-04j,\n -4.28715245e-05-3.11639838e-04j, -2.31994207e-04-3.50393413e-04j,\n 1.79812526e-04+9.46405559e-06j, 6.45826858e-06-7.27244722e-05j,\n -6.54773349e-04+9.59387600e-04j, 3.84335152e-04-2.72657471e-04j,\n -4.87276832e-04-3.96541032e-04j, 6.77017368e-04+4.47778225e-04j,\n 4.45890499e-04+1.29559357e-04j, -6.51094304e-04+1.86624435e-04j,\n 3.98953747e-05-4.33256129e-04j, -2.22111375e-04-2.04651458e-04j,\n 7.55794351e-04-5.85769035e-04j, -3.94789819e-04+1.04401607e-03j,\n -1.58222133e-04+1.11208833e-05j, 9.23486664e-05-7.75775861e-04j,\n -3.43146772e-04+1.17239880e-04j, 2.84564728e-04+6.11844429e-06j,\n 4.70805410e-04+3.47810038e-04j, 1.69658330e-04-6.84370728e-06j,\n -5.22356227e-04+6.50334373e-04j, -1.73679813e-04+3.63334974e-05j,\n 4.98834691e-04-1.01761885e-03j, 5.87157344e-04+1.20334407e-04j,\n -9.03141283e-04+5.43231194e-04j, 2.12604011e-04+4.25936737e-05j,\n 1.20421975e-04+1.99248114e-04j, 1.36291525e-04-1.74378105e-04j,\n -3.22569755e-04-3.90555480e-04j, -8.56585373e-04-1.36390458e-05j,\n 8.21771053e-04+5.11195915e-04j, -1.32706058e-04-6.24514006e-05j,\n -5.38444724e-04-7.24478095e-04j, 1.28496700e-03+2.93993678e-05j,\n -6.10961533e-04+1.10329922e-04j, -3.67753624e-04+2.46454903e-04j,\n -1.97998194e-04+4.12632455e-04j, 4.80604477e-04-2.31270841e-04j,\n 1.92777568e-05-4.07627748e-04j, 2.86666234e-04+7.01966268e-05j,\n 1.15379387e-04-1.33773849e-04j, -3.17265283e-04+5.00725722e-04j,\n 1.36599939e-04-6.75427006e-04j, 6.37731351e-05+7.04635115e-04j,\n -1.96653870e-04+3.25483834e-04j, -5.86921673e-04-1.23419379e-03j,\n 3.14143574e-04+1.00360594e-04j, 3.31560079e-04+1.07206559e-03j,\n 4.92924636e-05-5.68876368e-04j, -8.41042140e-04+3.22939688e-04j,\n 5.49575067e-04-5.15098419e-05j, -3.93180240e-04-4.42074142e-05j,\n 5.60555298e-04-2.85327349e-04j, -2.38831244e-04+1.91581065e-05j,\n -7.45519046e-05+1.27549869e-04j, -5.89318659e-05-4.21902661e-04j,\n 3.75275146e-04+1.82785513e-05j, -2.34356665e-04+4.60409956e-04j,\n -1.92879655e-04+2.25686712e-05j, 2.29916609e-04-3.98052727e-04j,\n 1.14636467e-04+3.40506254e-04j, 3.66346397e-04+4.71324904e-05j,\n -3.50054041e-04-5.13589144e-04j, -2.54987713e-04+1.99572441e-04j,\n 5.45736142e-07-5.96936864e-05j, -1.24422570e-04+9.28100584e-05j,\n -3.07471848e-04-4.81938971e-05j, 8.54350904e-04+8.01221802e-05j,\n -1.36143168e-03+1.05957395e-04j, 4.67515499e-04+1.18207109e-04j,\n 2.22224539e-04-7.87005141e-04j, 2.06836573e-04+7.33415318e-04j,\n 4.11857186e-05-2.78641304e-04j, 8.46935368e-05+2.47412699e-05j,\n 9.63134514e-05-6.25919599e-04j, -1.74852058e-04+6.83190116e-04j,\n 5.17474224e-05-2.10492739e-05j, 5.02637722e-05+8.55532060e-06j,\n -1.48067521e-04+1.27482971e-04j, -3.17709988e-04-3.19972013e-04j,\n 7.93806547e-04+7.24172271e-04j, 3.71753847e-04-2.96357705e-04j,\n -7.16045744e-04-2.45445209e-04j, 1.85488700e-04-7.80975779e-05j,\n -6.33931296e-04-3.79990485e-04j, 4.23058885e-04+5.71413970e-04j,\n -1.96111954e-04-1.64805179e-04j, -2.39387453e-04+4.67926668e-04j,\n 4.48049475e-04-4.39783397e-04j, -4.57884754e-04-8.64764107e-05j,\n 2.36198689e-04-1.81618919e-04j, -3.98041496e-04+5.49284505e-04j,\n 7.71543104e-04+2.33418707e-04j, -5.37125816e-04-1.39968077e-04j,\n 1.21667266e-04+2.94272358e-05j, -4.22621149e-05-4.03145881e-04j,\n 5.15855772e-04+6.12452186e-05j, -1.75597310e-04+3.06332086e-04j,\n 9.80239412e-05-5.22006358e-04j, -3.91105404e-04+7.24365490e-04j,\n 5.41880105e-04-6.62486843e-04j, -1.44683949e-04+2.83522226e-04j,\n -1.91702886e-04+1.31554681e-04j, 5.24558737e-05-1.08982522e-04j,\n 6.74327223e-04+1.10659354e-04j, -5.79971252e-04+1.77968960e-04j,\n -2.51067236e-04-1.07853197e-04j, 3.11939498e-04-1.38577070e-04j,\n -6.04476470e-05+8.52540539e-05j, 1.34265204e-04+8.21556997e-04j,\n -1.17370999e-04-7.91857871e-04j, 2.79181388e-04-7.31803351e-04j,\n -6.90453886e-04+7.46784829e-04j, -1.16164963e-04-4.57167257e-04j,\n -4.69417951e-04-4.41735735e-04j, 1.35998487e-05+1.12007021e-03j,\n 3.29877562e-04+1.48284571e-04j, 5.83769268e-04+2.49004599e-04j,\n -7.52982150e-04-1.68075249e-03j, -3.38846404e-04+9.63951937e-04j,\n 3.55617024e-04-1.27152987e-04j, 8.13345170e-04-4.35530692e-04j,\n 2.21017421e-04+3.81957629e-04j, -2.29169840e-04+4.31749297e-04j,\n -3.49092860e-04-5.49892781e-04j, 1.47060146e-05-4.94382810e-04j,\n 8.50208905e-04+7.47663086e-05j, -1.45781683e-04+3.05060633e-04j,\n -5.68202031e-04+2.25181950e-04j, -6.70225923e-05-4.04705073e-04j,\n 5.49901664e-04+1.00974501e-03j, 1.09107837e-05-4.09919474e-04j,\n -7.08992001e-04-6.93567150e-04j, 2.38546368e-05+3.33138967e-04j,\n 6.59087722e-04+4.15920176e-04j, -9.24723091e-04+5.72433162e-04j,\n 3.16802067e-04-1.97599886e-04j, 9.59921563e-04-7.20312263e-04j,\n -2.04590275e-04-7.58383004e-04j, -9.83521376e-04+1.39303955e-03j,\n 1.00191560e-03-1.06808718e-03j, -1.44975474e-04+2.10013067e-04j,\n 1.57355072e-04-8.06148227e-05j, -9.77899528e-04+1.01124440e-03j,\n 1.40629188e-04+3.07843307e-04j, 4.58437822e-04-1.54270986e-03j,\n -3.58655124e-06+3.05346109e-04j, -8.51095471e-04+9.50186675e-04j,\n 6.49841806e-04-3.69990669e-04j, -2.42669267e-04+1.97887318e-04j,\n 1.08560919e-03-5.57172096e-04j, -1.38075404e-03+3.06128065e-04j,\n -2.99248592e-04-1.96865567e-04j, 4.34882427e-04+6.50054051e-05j,\n 1.09891678e-03+1.84768495e-04j, -7.36401037e-04-1.54611504e-03j,\n 1.95455637e-04+1.59373547e-03j, -1.65580093e-04-1.90926799e-04j,\n -1.13688576e-04+4.12506434e-04j, 1.12544155e-03+4.77414267e-04j,\n -1.31365139e-03-1.41451042e-03j, 5.79908017e-04+1.48045447e-04j,\n -4.82300426e-04+0.00000000e+00j],\n [ 3.94302788e-02+0.00000000e+00j, -1.50769610e-02+6.62688618e-04j,\n 1.63792293e-04-9.09965691e-04j, -4.20457549e-04+7.67888474e-04j,\n 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1.21452700e-03+1.36751774e-05j, -4.48800651e-04-8.19776275e-04j,\n -1.19266297e-04+6.53314340e-04j, -1.48156234e-04+2.20506208e-04j,\n -3.25802814e-04-5.00083015e-04j, 9.93835264e-04+6.10726403e-04j,\n -5.92139594e-04-3.50046309e-04j, -2.20551315e-04+2.66541088e-04j,\n -5.38101746e-04-4.33232244e-04j, 4.53140432e-05+2.67048105e-04j,\n 5.45584100e-04-4.36233554e-04j, 1.89055956e-04+4.05146241e-04j,\n 4.91230583e-04+5.80214622e-04j, -9.70303336e-04-2.07957665e-04j,\n -1.54105042e-04-1.78764985e-04j, 8.29454691e-04-9.70980630e-04j,\n 2.93041276e-05+2.99636023e-04j, 1.11761881e-04+7.06579832e-04j,\n -5.13429489e-04+1.03407490e-05j, -4.51280720e-04-3.28230045e-04j,\n 6.09306297e-04-5.08877271e-04j, 4.69052361e-04+7.90693011e-04j,\n -7.29652303e-04+1.16791352e-04j, -2.63583592e-04+2.40084959e-04j,\n 7.80339281e-05-8.09166755e-04j, 5.91733367e-04+6.46003586e-04j,\n 5.18742468e-04-3.69670376e-04j, -1.10819540e-03-2.98322967e-05j,\n 3.35961109e-04+5.08219023e-04j, -1.37905601e-04-1.35428372e-03j,\n -7.54841905e-05+3.93744885e-04j, 2.75316936e-04+2.05765280e-04j,\n -2.85925096e-04+1.42533512e-04j, -2.49244553e-04+3.44507517e-04j,\n -5.49966896e-05-1.61934566e-04j, -6.94024634e-05+3.21277311e-05j,\n 1.14427279e-03+2.14503629e-04j, -4.06256577e-04-2.42573698e-04j,\n -1.37008788e-04+6.09181906e-04j, 9.41131852e-05-8.08853747e-04j,\n -1.05763981e-04-4.56058417e-04j, 4.74122378e-04+4.69608012e-04j,\n -3.95122830e-04+1.79715038e-04j, -5.41657307e-05+2.94641051e-04j,\n -8.99698699e-04-3.08409334e-04j, 1.08313369e-03-2.52154929e-04j,\n 9.61555026e-05+1.16751009e-04j, -6.78396810e-04+3.85853518e-04j,\n 3.08973305e-04+2.04145634e-04j, -1.66564595e-04-2.23731139e-04j,\n -3.64032556e-05-5.80136045e-04j, -1.25077116e-04-1.17649763e-04j,\n 8.22473217e-04-5.10403627e-05j, 1.35846968e-04+9.04349589e-05j,\n -1.08069909e-03+3.56424784e-04j, -3.42539982e-04-4.83310914e-04j,\n 1.02127798e-03+2.27836680e-04j, -1.38094505e-04-1.76668123e-04j,\n 2.64921676e-05-2.31094625e-04j, -2.30827528e-04+4.76342119e-05j,\n 1.46863589e-07+5.09299510e-04j, 6.74095347e-04+5.26744232e-04j,\n -3.63377125e-04-9.05099052e-04j, 6.26177364e-05+1.23244300e-04j,\n 4.31863072e-05+8.96514097e-04j, -1.06310512e-03-7.14346108e-04j,\n 5.75517243e-04+2.35967322e-04j, 4.41244980e-04-2.65860383e-05j,\n 3.70694639e-04-2.71684200e-04j, -5.39649217e-04-2.89578543e-05j,\n -2.05237670e-04+3.55566693e-04j, -2.29304581e-04-1.82601791e-04j,\n -1.63278707e-04+8.28089685e-05j, 4.85237610e-04-3.27131430e-04j,\n -3.85198889e-04+5.78130634e-04j, 1.91039368e-04-6.73331290e-04j,\n 2.83290401e-04-2.36600115e-04j, 1.56155251e-04+1.35530242e-03j,\n -2.58546407e-04-1.92172329e-04j, -1.64289988e-05-1.12061403e-03j,\n -2.06761243e-04+5.39404115e-04j, 8.90146217e-04-1.10273389e-04j,\n -5.53419489e-04-8.01172163e-05j, 3.69800922e-04+1.19385537e-04j,\n -6.06753749e-04+1.74073233e-04j, 2.37762330e-04+2.21509768e-05j,\n 9.04367392e-05-1.17178154e-04j, 6.65735029e-06+4.23161474e-04j,\n -3.66355661e-04-5.69644277e-05j, 2.72752455e-04-4.36970254e-04j,\n 1.89332418e-04-1.28477791e-05j, -2.48450704e-04+3.85021186e-04j,\n -1.06569421e-04-3.41965661e-04j, -3.70428389e-04-4.26724302e-05j,\n 3.58595638e-04+5.14301117e-04j, 2.51269109e-04-2.08539926e-04j,\n 4.88939967e-06+5.71210855e-05j, 1.18578623e-04-1.05802288e-04j,\n 3.11344492e-04+1.01891390e-05j, -8.56978432e-04+1.74893270e-05j,\n 1.33232876e-03-2.98487141e-04j, -4.85805120e-04-5.94291833e-05j,\n -7.76562206e-05+8.03680105e-04j, -3.43743448e-04-6.84465578e-04j,\n 1.92205105e-05+2.78271830e-04j, -8.62407096e-05-6.06097609e-06j,\n 7.51468262e-05+6.27872523e-04j, -1.25139166e-05-7.07494831e-04j,\n -4.26531031e-05+3.33935842e-05j, -4.93983638e-05+6.62065104e-06j,\n 9.65211897e-05-1.71035129e-04j, 4.09508943e-04+1.81017834e-04j,\n -1.00836515e-03-3.78649449e-04j, -2.28902765e-04+4.29769144e-04j,\n 7.55644169e-04-6.15984149e-05j, -1.31806876e-04+1.50093610e-04j,\n 7.41534101e-04+5.25070731e-05j, -6.37489984e-04-3.17623167e-04j,\n 2.50510928e-04+4.72741634e-05j, -3.12101871e-05-5.30198666e-04j,\n -1.61833058e-04+6.04663509e-04j, 4.33069517e-04-1.76875910e-04j,\n -9.68597935e-05+2.72233313e-04j, 1.29069366e-05-6.86698274e-04j,\n -7.70844850e-04+2.59679250e-04j, 5.06334114e-04-2.08281614e-04j,\n -1.23040807e-04+4.96912636e-05j, 2.81721303e-04+2.82239777e-04j,\n -4.32856798e-04+2.85045100e-04j, -7.18904719e-05-3.42817657e-04j,\n 2.82159126e-04+4.51272457e-04j, -2.22012038e-04-7.93265681e-04j,\n 8.25420573e-05+8.53400594e-04j, -1.00706437e-04-2.98939601e-04j,\n 3.14683041e-05-2.27519261e-04j, 4.36941077e-05+1.13381143e-04j,\n -5.18682202e-04+4.48501226e-04j, 2.30610517e-04-5.52246756e-04j,\n 2.34490912e-04-1.26264973e-04j, -7.97095177e-05+3.35952614e-04j,\n -3.45318332e-05-9.44128409e-05j, -7.57633937e-04-3.43506355e-04j,\n 7.20500401e-04+3.51295162e-04j, 4.74556384e-04+6.27668922e-04j,\n -2.81101536e-04-9.78262731e-04j, 4.46874372e-04+1.24837907e-04j,\n 6.05163789e-04-2.12023275e-04j, -1.01407397e-03-5.16929477e-04j,\n -2.98164906e-04+2.33126469e-04j, -4.86355494e-04+4.33123201e-04j,\n 1.83462356e-03-7.71867376e-07j, -7.69209261e-04-7.01104884e-04j,\n -3.18641790e-05+3.73050143e-04j, 1.15613125e-04+9.09600948e-04j,\n -4.28446346e-04+7.86494004e-05j, -3.34852105e-04-3.47817544e-04j,\n 6.26099520e-04-1.68044497e-04j, 4.68369045e-04+1.42514209e-04j,\n -2.83908873e-04+7.92399750e-04j, -2.49184244e-04-2.18710226e-04j,\n -9.19001071e-05-6.04839544e-04j, 4.08264053e-04+9.93217421e-06j,\n -1.08943196e-03+3.64046481e-04j, 4.09723216e-04+8.49350013e-05j,\n 7.86130478e-04-6.09249721e-04j, -3.38645074e-04-2.27244838e-05j,\n -4.77324227e-04+6.16456017e-04j, -5.01373021e-04-9.55310274e-04j,\n 1.70573740e-04+3.42094097e-04j, 6.89184712e-04+9.91871858e-04j,\n 7.74817539e-04-2.00708515e-04j, -1.39524815e-03-9.81590142e-04j,\n 1.09255944e-03+9.77000557e-04j, -2.06580095e-04-1.44088734e-04j,\n 1.00169291e-04+1.44579525e-04j, -1.08783213e-03-8.86595885e-04j,\n -2.96315620e-04+1.88823864e-04j, 1.58771473e-03+2.66780661e-04j,\n -2.99713732e-04+3.14700680e-05j, -1.08078974e-03-6.78289646e-04j,\n 4.79116024e-04+5.81652824e-04j, -2.43603471e-04-1.88258714e-04j,\n 7.93634703e-04+9.36679925e-04j, -6.29229943e-04-1.26267628e-03j,\n 9.93210429e-05-3.33132766e-04j, 5.00382390e-05+4.41575210e-04j,\n 1.61111886e-04+1.10747772e-03j, 1.23195666e-03-1.20419700e-03j,\n -1.43246313e-03+7.18458426e-04j, 1.23366572e-04-2.18045537e-04j,\n -4.21981246e-04+5.80438962e-05j, 2.37621151e-05+1.23103317e-03j,\n 7.49466857e-04-1.79312642e-03j, 1.24531095e-04+4.69038221e-04j,\n -8.82536309e-04+0.00000000e+00j]])
self.altes_E = np.zeros((257, 2, 2), dtype = complex)
alpha = 0.8

findessentialmat gives different results for the same set of feature points

I am extracting matching feature points from a camera pair. I have a bit more than 200 feature points, by eye ~90% of them are really matching ...
I try to reconstruct the relative position and orientation of the two cameras, using the opencv's findessentialmat function.
A minimal running example is here:
minimal.py
import numpy as np
import cv2
DIM=(1280, 960)
K0 =np.array([[741.33782999, 0., 682.12142279],
[ 0., 742.80374714, 464.20413311],
[ 0., 0., 1. ]])
def readfile(filename):
q = []
p = []
with open(filename) as fp:
for cnt, line in enumerate(fp):
px, py, qx, qy = line.split()
p = np.append(p, [float(px), float(py)])
q = np.append(q, [float(qx), float(qy)])
n = int(len(p)/2)
p = np.reshape(p, (n, 2))
q = np.reshape(q, (n, 2))
return p, q
p12, q12 = readfile("d_temp")
retval, mask = cv2.findEssentialMat(p12, q12, K0, method=cv2.RANSAC)
NN, R, t, _ = cv2.recoverPose(retval, p12, q12, K0, mask=mask)
dst, jacobian = cv2.Rodrigues(R)
print("rotation vector")
print(np.degrees(dst))
print("translation vector")
print(t)
sample data file is here ...
(sorry for the long file, but in order of reproducibility, I think it is a good idea to provide this ...)
# d.dat
447.7982482910156 783.8470458984375 844.46875 478.64984130859375
472.2575988769531 783.9226684570312 861.17919921875 463.9976806640625
471.056884765625 785.6572265625 861.0101928710938 463.9947814941406
679.7252197265625 613.1166381835938 835.0584716796875 162.0465087890625
444.4239196777344 773.3471069335938 834.8787231445312 474.52978515625
476.6434631347656 548.7996215820312 695.3291625976562 295.1576232910156
316.2449035644531 876.6978149414062 767.7656860351562 608.9740600585938
28.56636619567871 835.704833984375 449.8716125488281 713.1991577148438
482.4017028808594 755.1663208007812 850.0111083984375 430.626220703125
471.2223205566406 795.663818359375 868.9219360351562 471.4686279296875
585.5891723632812 928.5010986328125 1024.892333984375 481.9182434082031
134.27027893066406 438.8541564941406 1023.7425537109375 448.80987548828125
560.4802856445312 782.7987060546875 934.5108642578125 399.1592102050781
437.55364990234375 783.9776611328125 835.0953979492188 485.783203125
558.8355102539062 783.1337280273438 934.3226928710938 401.5900573730469
493.6907958984375 811.2030639648438 899.3700561523438 465.0192565917969
333.5005187988281 474.5987548828125 514.41748046875 354.7676696777344
281.6152648925781 854.6431274414062 725.10009765625 614.118896484375
489.6645812988281 819.7667846679688 901.4575805664062 474.939697265625
567.0088500976562 793.2717895507812 946.3422241210938 409.54779052734375
473.9570007324219 741.4544677734375 835.43359375 427.68084716796875
263.5504455566406 593.8671875 308.0599670410156 864.5149536132812
329.52447509765625 759.8671875 982.2100219726562 759.9042358398438
223.50721740722656 877.8092041015625 697.9076538085938 710.441162109375
448.5132751464844 783.9352416992188 845.5458984375 478.6185302734375
166.29356384277344 703.2822875976562 575.96337890625 587.17431640625
195.16452026367188 645.3009033203125 548.0439453125 573.9312133789062
679.7011108398438 613.3897094726562 835.2294311523438 162.2067413330078
560.1014404296875 783.4053344726562 934.7252807617188 400.22491455078125
260.0428466796875 682.58203125 988.1619262695312 737.9358520507812
173.69033813476562 726.2459716796875 244.25367736816406 609.8120727539062
193.5665283203125 495.71881103515625 493.33575439453125 457.4891052246094
318.1491394042969 455.9480895996094 518.5922241210938 354.5550537109375
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743.0471801757812 889.39306640625 1060.811767578125 363.8262023925781
99.52837371826172 455.26544189453125 503.113525390625 550.7799682617188
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190.50099182128906 745.3849487304688 614.2245483398438 600.6458740234375
189.45762634277344 399.43841552734375 471.45098876953125 468.6031799316406
324.149169921875 851.31103515625 753.8226928710938 590.1079711914062
477.1978759765625 545.6710205078125 691.1409301757812 295.8692626953125
399.811767578125 455.1125793457031 349.0351257324219 853.0255126953125
150.71499633789062 637.7068481445312 127.8241958618164 617.8019409179688
174.24513244628906 443.05633544921875 460.3077697753906 445.8348388671875
290.4751892089844 563.4282836914062 1177.7535400390625 647.7413330078125
35.504730224609375 375.5752868652344 1132.147216796875 732.7445678710938
570.9328002929688 891.2274780273438 430.9983825683594 598.0726928710938
129.2855224609375 447.6126403808594 269.48297119140625 518.7491455078125
916.583984375 654.3966674804688 131.51922607421875 544.0574340820312
453.64923095703125 909.1268920898438 310.0630798339844 873.9638061523438
272.41595458984375 779.4100952148438 242.86692810058594 574.124267578125
145.89622497558594 426.38616943359375 839.9185791015625 446.7758483886719
353.2640075683594 649.1328125 568.7075805664062 853.6813354492188
336.7197570800781 477.26580810546875 522.921630859375 354.66552734375
465.9938659667969 766.7280883789062 1245.819091796875 328.0860595703125
603.0092163085938 918.9746704101562 1035.0767822265625 462.84014892578125
177.19642639160156 786.0979614257812 1025.5509033203125 709.3068237304688
743.3534545898438 889.3507080078125 1060.8709716796875 362.4633483886719
424.6748962402344 745.2859497070312 805.2852172851562 469.1532897949219
666.6295776367188 846.3238525390625 1186.088134765625 331.71063232421875
265.49560546875 617.9306640625 1212.5106201171875 742.3143920898438
235.90284729003906 899.5637817382812 237.75428771972656 630.4393920898438
309.32379150390625 528.5013427734375 557.26025390625 405.9731140136719
365.8267822265625 794.9527587890625 1054.2913818359375 461.01287841796875
479.682373046875 811.927978515625 887.924560546875 476.56243896484375
546.8660888671875 825.3713989257812 954.0406494140625 419.4182434082031
263.32550048828125 719.295166015625 938.8235473632812 842.3818969726562
410.2393798828125 752.4403076171875 802.4618530273438 483.38238525390625
322.66204833984375 894.9562377929688 781.970458984375 615.1595458984375
84.81143188476562 472.9810485839844 1218.040771484375 608.5279541015625
198.11459350585938 504.37188720703125 500.67315673828125 641.6554565429688
148.35653686523438 638.7088623046875 186.9049835205078 736.7140502929688
303.63336181640625 844.1828002929688 730.8097534179688 596.8998413085938
187.0508575439453 509.2354431152344 490.9916076660156 470.39593505859375
973.512939453125 734.4038696289062 730.7944946289062 682.7640991210938
317.968994140625 850.9186401367188 748.3575439453125 593.0419311523438
547.7904663085938 864.0103149414062 959.0444946289062 599.9322509765625
264.11163330078125 719.9434814453125 939.9754638671875 844.283203125
667.170166015625 850.118408203125 1074.2694091796875 365.3814392089844
480.75238037109375 556.4450073242188 699.0028686523438 298.0946960449219
264.4339294433594 696.676513671875 619.2904052734375 537.121826171875
239.04403686523438 301.0240478515625 189.99940490722656 249.11346435546875
398.0354309082031 789.0571899414062 994.8922729492188 768.0885009765625
311.0710754394531 763.4730834960938 568.8160400390625 637.556884765625
87.1875 889.8743286132812 775.7293090820312 556.4700927734375
190.0430145263672 742.0386352539062 602.095703125 597.9042358398438
227.24232482910156 776.474609375 115.55091094970703 617.3444213867188
265.21966552734375 707.8191528320312 639.8690795898438 541.1021118164062
162.44972229003906 723.4267578125 245.604736328125 615.490478515625
437.36431884765625 785.8866577148438 836.6463623046875 487.3155517578125
793.1463623046875 923.9131469726562 1126.0709228515625 637.1099243164062
206.6471405029297 476.3389892578125 264.1603698730469 445.1524353027344
903.2003173828125 625.0552978515625 897.1513061523438 67.21099853515625
256.93646240234375 801.38671875 672.7518310546875 598.4820556640625
88.26525115966797 470.6452331542969 666.8927612304688 731.1099853515625
282.2829284667969 762.2633056640625 693.4093017578125 528.4248046875
121.02236938476562 770.4916381835938 698.5695190429688 358.8071594238281
486.1945495605469 880.182861328125 650.6167602539062 584.8759765625
442.5867614746094 771.2802124023438 831.2958374023438 474.41204833984375
352.16180419921875 797.5498657226562 578.2386474609375 631.1714477539062
183.8833465576172 510.9300231933594 280.1349182128906 506.5831604003906
380.51165771484375 810.257080078125 1074.3548583984375 473.5334777832031
231.75296020507812 884.1380615234375 837.761474609375 427.0298767089844
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I run the code several times like this:
for i in `seq 10`; do sort -R d.dat > d_temp; python minimal.py; done
It means, that I use the same set of correspondet feature point point pairs but I shuffle them.
As a result I got reconstructed rotations (in this case) around the X axis between 14 and 23 degree,
around the Y axis 11 and 33 degree and around the Z axis -45 and -50 degree. The translation vector also varies a lot (x -> [-.99:0.49]. y -> [-0.11:0.15], z -> [-0.9:0] (I dont have an absolute measurement for the rotation but the translation should be in X ~42cm, Y and Z ~0cm)).
As far I understand this is due to the ransac and the 5-point algorithm. They are not deterministic (if there are more than 5 valid feature point correspondance (can you correct me?)).
But in this case how one could know which 'order' of the correspondent feature points gives the real essential matrix? Or is there any better algorithm to hunt down this problem?

guess function when using built-in defined models in lmfit

I am having a problem with the guess function of lmfit. I am trying to fit some experimental data and I want to use different built in models of lmfit, but I cannot run the built in modules, only if I define the function directly.
The following code does not work, but if I comment the guess function it works.
P.S. It would be more interesting for me that the index is the first column because I will put this in a loop that will use all the same first column of the data and therefore i could put each new second column of the data as a new column in the DataFrame.
# -*- coding: utf-8 -*-
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from lmfit import Model
from lmfit.models import GaussianModel
minvalue = 3.25
maxvalue = 3.45
rawdata = pd.read_csv('datafile.txt', delim_whitespace = True, names=['XX','YY'])
#Section the data
data = rawdata[(rawdata['XX']>minvalue) & (rawdata['XX'] < maxvalue)]
#Create a DataFrame with the data
dataDataframe = pd.DataFrame()
dataDataframe[0] = data['YY']
dataDataframe = dataDataframe.set_index(data['XX'])
# Gaussian curve
def gaussian(x, amp, cen, wid):
"1-d gaussian: gaussian(x, amp, cen, wid)"
return (amp/(np.sqrt(2*np.pi)*wid)) * np.exp(-(x-cen)**2 /(2*wid**2))
result_gaussian = Model(gaussian).fit(dataDataframe[0], x=dataDataframe.index.values, amp=5, cen=5, wid=1)
mod = GaussianModel()
pars = mod.guess(dataDataframe[0], x = np.float32(dataDataframe.index.values))
out = mod.fit(dataDataframe[0], pars , x = np.float32(dataDataframe.index.values))
plt.plot(dataDataframe.index.values, dataDataframe[0],'bo')
plt.plot(dataDataframe.index.values, result_gaussian.best_fit, 'r-', label = 'Gaussian')
plt.plot(dataDataframe.index.values, out.best_fit, 'b-', label = 'Gaussian2')
plt.legend()
plt.show()
Error message I am having if I uncomment the built in modules:
File "/Users/johndoe/anaconda2/lib/python2.7/site-packages/lmfit/models.py", line 52, in guess_from_peak
cen = x[imaxy]
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices
I have tried to run the guess_from_peak from models.py and i did not have a problem it resulted in an integer.
Raw data:
1.1661899e+000 7.3414581e+002
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2.4563179e+000 4.6177853e+002
2.4632170e+000 4.5766202e+002
2.4701161e+000 4.4741263e+002
2.4770153e+000 4.4859024e+002
2.4839141e+000 4.6913116e+002
2.4908133e+000 5.0019971e+002
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2.8219693e+000 4.4581104e+002
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2.8426664e+000 4.5602731e+002
2.8495655e+000 4.6227423e+002
2.8564646e+000 4.5953806e+002
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2.8702629e+000 4.5450616e+002
2.8771617e+000 4.5531360e+002
2.8840609e+000 4.4464761e+002
2.8909600e+000 4.6128970e+002
2.8978591e+000 4.4664514e+002
2.9047582e+000 4.4719708e+002
2.9116573e+000 4.4492749e+002
2.9185562e+000 4.4260013e+002
2.9254553e+000 4.5593594e+002
2.9323545e+000 4.6237164e+002
2.9392536e+000 4.7034845e+002
2.9461527e+000 4.7368185e+002
2.9530518e+000 4.7302234e+002
2.9599507e+000 4.7327332e+002
2.9668498e+000 4.4960791e+002
2.9737489e+000 4.4319986e+002
2.9806480e+000 4.5416092e+002
2.9875472e+000 4.6674429e+002
2.9944463e+000 4.6089871e+002
3.0013452e+000 4.6334650e+002
3.0082443e+000 4.6833719e+002
3.0151434e+000 4.8842966e+002
3.0220425e+000 4.8455182e+002
3.0289416e+000 4.6504678e+002
3.0358407e+000 4.6673508e+002
3.0427399e+000 4.6887064e+002
3.0496387e+000 4.6799823e+002
3.0565379e+000 4.5299500e+002
3.0634370e+000 4.5381485e+002
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3.1048315e+000 4.6533044e+002
3.1117306e+000 4.7819165e+002
3.1186297e+000 4.9699246e+002
3.1255288e+000 4.8907956e+002
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3.1393268e+000 5.0308936e+002
3.1462259e+000 5.0668982e+002
3.1531250e+000 5.0537222e+002
3.1600242e+000 4.9574966e+002
3.1669233e+000 4.9894128e+002
3.1738222e+000 4.9885315e+002
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3.1876204e+000 5.2202740e+002
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3.2014186e+000 5.4433679e+002
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3.2290149e+000 6.0779541e+002
3.2359140e+000 6.1212280e+002
3.2428131e+000 6.5589185e+002
3.2497122e+000 7.1807507e+002
3.2566113e+000 7.5950916e+002
3.2635102e+000 8.1842242e+002
3.2704093e+000 9.1277783e+002
3.2773085e+000 1.0486207e+003
3.2842076e+000 1.3214080e+003
3.2911067e+000 1.7085295e+003
3.2980058e+000 2.4946370e+003
3.3049047e+000 4.1229609e+003
3.3118038e+000 7.1944038e+003
3.3187029e+000 1.1714122e+004
3.3256021e+000 1.5338923e+004
3.3325012e+000 1.5092694e+004
3.3394003e+000 1.1227008e+004
3.3462994e+000 6.9070176e+003
3.3531983e+000 4.0318586e+003
3.3600974e+000 2.5069387e+003
3.3669965e+000 1.7313556e+003
3.3738956e+000 1.3203175e+003
3.3807948e+000 1.0810967e+003
3.3876939e+000 9.2702356e+002
3.3945928e+000 8.2453217e+002
3.4014919e+000 7.5468195e+002
3.4083910e+000 7.1011224e+002
3.4152901e+000 6.7312701e+002
3.4221892e+000 6.2927734e+002
3.4290884e+000 6.0679126e+002
3.4359872e+000 5.8445929e+002
3.4428864e+000 5.5084033e+002
3.4497855e+000 5.2990625e+002
3.4566846e+000 5.3244171e+002
3.4635837e+000 5.3299860e+002
3.4704828e+000 5.2270801e+002
3.4773817e+000 5.0838147e+002
3.4842808e+000 4.9768036e+002
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3.7257488e+000 3.9189124e+002
3.7326479e+000 3.9534134e+002
3.7395468e+000 4.0249893e+002
3.7464459e+000 4.0382443e+002
3.7533450e+000 3.9881796e+002
3.7602441e+000 4.0283856e+002
3.7671432e+000 4.0544543e+002
3.7740424e+000 3.9527063e+002
3.7809412e+000 3.9659631e+002
3.7878404e+000 4.0054132e+002
3.7947395e+000 3.9123737e+002
3.8016386e+000 3.8058502e+002
3.8085377e+000 3.7388980e+002
3.8154368e+000 3.7337103e+002
3.8223360e+000 3.6008588e+002
3.8292348e+000 3.5135416e+002
3.8361340e+000 3.5958188e+002
3.8430331e+000 3.5756583e+002
3.8499322e+000 3.5956232e+002
3.8568313e+000 3.7803802e+002
3.8637304e+000 3.9012396e+002
3.8706293e+000 3.8674255e+002
3.8775284e+000 3.7771600e+002
3.8844275e+000 3.7648160e+002
3.8913267e+000 3.7692780e+002
3.8982258e+000 3.6927103e+002
3.9051249e+000 3.7007745e+002
3.9120238e+000 3.7482629e+002
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3.9258220e+000 3.6110025e+002
3.9327211e+000 3.6490872e+002
3.9396203e+000 3.7283734e+002
3.9465194e+000 3.7933209e+002
3.9534183e+000 3.6968182e+002
3.9603174e+000 3.5532330e+002
3.9672165e+000 3.5889478e+002
3.9741156e+000 3.6407483e+002
3.9810147e+000 3.6295535e+002
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3.9948130e+000 3.6416183e+002
4.0017118e+000 3.6089911e+002
4.0086112e+000 3.6826599e+002
4.0155101e+000 3.7570581e+002
4.0224090e+000 3.6361679e+002
4.0293083e+000 3.6003177e+002
4.0362072e+000 3.7528265e+002
4.0431066e+000 3.7368362e+002
4.0500054e+000 3.8174683e+002
4.0569048e+000 4.0386084e+002
4.0638037e+000 4.2738324e+002
4.0707026e+000 4.4587668e+002
4.0776019e+000 4.5433987e+002
4.0845008e+000 4.4404083e+002
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4.0982990e+000 3.9662262e+002
4.1051979e+000 3.7311325e+002
4.1120973e+000 3.5790594e+002
4.1189961e+000 3.4554794e+002
4.1258955e+000 3.5435367e+002
4.1327944e+000 3.7766489e+002
4.1396937e+000 3.7425708e+002
4.1465926e+000 3.5805182e+002
4.1534915e+000 3.5078519e+002
4.1603909e+000 3.5888739e+002
4.1672897e+000 3.7242688e+002
4.1741891e+000 3.7792575e+002
4.1810880e+000 3.7338031e+002
4.1879873e+000 3.6538324e+002
4.1948862e+000 3.5872525e+002
4.2017851e+000 3.4688391e+002
4.2086844e+000 3.4881918e+002
4.2155833e+000 3.4818274e+002
4.2224827e+000 3.4055273e+002
4.2293816e+000 3.3977536e+002
4.2362804e+000 3.3322891e+002
4.2431798e+000 3.3594962e+002
4.2500787e+000 3.4658536e+002
4.2569780e+000 3.4479083e+002
4.2638769e+000 3.4267456e+002
4.2707763e+000 3.4828876e+002
4.2776752e+000 3.4845041e+002
4.2845740e+000 3.3986469e+002
4.2914734e+000 3.3093433e+002
4.2983723e+000 3.3255331e+002
4.3052716e+000 3.4089511e+002
4.3121705e+000 3.4742932e+002
4.3190699e+000 3.3570422e+002
4.3259687e+000 3.2636673e+002
4.3328676e+000 3.3228806e+002
4.3397670e+000 3.5141977e+002
4.3466659e+000 3.5683167e+002
4.3535652e+000 3.4719943e+002
4.3604641e+000 3.4054718e+002
4.3673630e+000 3.2842471e+002
4.3742623e+000 3.2503146e+002
4.3811612e+000 3.3431540e+002
4.3880606e+000 3.3462808e+002
4.3949594e+000 3.3529224e+002
4.4018588e+000 3.3313510e+002
4.4087577e+000 3.4015598e+002
4.4156566e+000 3.3703552e+002
4.4225559e+000 3.3024448e+002
4.4294548e+000 3.2974786e+002

As I suggested in the comment above, coercing the pandas Series into an ndarray will fix the problem:
mod = GaussianModel()
ydata = np.array(dataDataframe[0])
xdata = np.array(dataDataframe.index.values)
pars = mod.guess(ydata, x=xdata)
out = mod.fit(ydata, pars, x=xdata)

This example works for me:
#!/usr/bin/env python
from lmfit.models import LorentzianModel
import matplotlib.pyplot as plt
import pandas as pd
dframe = pd.read_csv('peak.csv')
model = LorentzianModel()
params = model.guess(dframe['y'], x=dframe['x'])
result = model.fit(dframe['y'], params, x=dframe['x'])
print(result.fit_report())
result.plot_fit()
plt.show()
with peaks.csv of
x,y
0.000000, 0.021654
0.200000, 0.385367
0.400000, 0.193304
0.600000, 0.103481
0.800000, 0.404041
1.000000, 0.212585
1.200000, 0.253212
1.400000, -0.037306
1.600000, 0.271415
1.800000, 0.025614
2.000000, 0.066419
2.200000, -0.034347
2.400000, 0.153702
2.600000, 0.161341
2.800000, -0.097676
3.000000, -0.061880
3.200000, 0.085341
3.400000, 0.083674
3.600000, 0.190944
3.800000, 0.222168
4.000000, 0.214417
4.200000, 0.341221
4.400000, 0.634501
4.600000, 0.302566
4.800000, 0.101096
5.000000, -0.106441
5.200000, 0.567396
5.400000, 0.531899
5.600000, 0.459800
5.800000, 0.646655
6.000000, 0.662228
6.200000, 0.820844
6.400000, 0.947696
6.600000, 1.541353
6.800000, 1.763981
7.000000, 1.846081
7.200000, 2.986333
7.400000, 3.182907
7.600000, 3.786487
7.800000, 4.822287
8.000000, 5.739122
8.200000, 6.744448
8.400000, 7.295213
8.600000, 8.737766
8.800000, 9.693782
9.000000, 9.894218
9.200000, 10.193956
9.400000, 10.091519
9.600000, 9.652392
9.800000, 8.670938
10.000000, 8.004205
10.200000, 6.773599
10.400000, 6.076502
10.600000, 5.127315
10.800000, 4.303762
11.000000, 3.426006
11.200000, 2.416431
11.400000, 2.311363
11.600000, 1.748020
11.800000, 1.135594
12.000000, 0.888514
12.200000, 1.030794
12.400000, 0.543024
12.600000, 0.767751
12.800000, 0.657551
13.000000, 0.495730
13.200000, 0.447520
13.400000, 0.173839
13.600000, 0.256758
13.800000, 0.596106
14.000000, 0.065328
14.200000, 0.197267
14.400000, 0.260038
14.600000, 0.460880
14.800000, 0.335248
15.000000, 0.295977
15.200000, -0.010228
15.400000, 0.138670
15.600000, 0.192113
15.800000, 0.304371
16.000000, 0.442517
16.200000, 0.164944
16.400000, 0.001907
16.600000, 0.207504
16.800000, 0.012640
17.000000, 0.090878
17.200000, -0.222967
17.400000, 0.391717
17.600000, 0.180295
17.800000, 0.206875
18.000000, 0.240595
18.200000, -0.037437
18.400000, 0.139918
18.600000, 0.012560
18.800000, -0.053009
19.000000, 0.226069
19.200000, 0.076879
19.400000, 0.078599
19.600000, 0.016125
19.800000, -0.071217
20.000000, -0.091474

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