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Updated iterative_fitter.py to fix slopes to 3, stop after minimal change in best objective. ALso added last results from iteration_fitter.py

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pull/1/head
titusquah 5 years ago
parent 762f0764a5
commit 4fa69a9b44
5 changed files with 116 additions and 61 deletions
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100
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41
docs/Examples/iterative_fitter.py
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@ -13,7 +13,7 @@ def mod_lin_param_df(lp_df, input_val, mini_species, mini_lin_param):
species_list = 'Nd,Pr,Ce,La,Dy,Sm,Y'.split(',')
pitzer_param_list = ['beta0', 'beta1']
lin_param_list = ['slope', 'intercept']
lin_param_list = ['intercept']
meas_pitzer_param_df = pd.read_csv("../../data/csvs/may_pitzer_params.csv")
pitzer_params_filename = "../../data/jsons/min_h0_pitzer_params.txt"
with open(pitzer_params_filename) as file:
@ -27,6 +27,9 @@ exp_data = labeled_data.drop(labeled_data.columns[0], axis=1)
xml_file = "PC88A_HCL_NdPrCeLaDySmY_w_pitzer.xml"
lin_param_df = pd.read_csv("../../data/csvs"
"/zeroes_removed_min_h0_pitzer_lin_params.csv")
new_lin_param_df = lin_param_df.copy()
for ind, row in lin_param_df.iterrows():
new_lin_param_df.at[ind, 'slope'] = 3
estimator_params = {'exp_data': exp_data,
'phases_xml_filename': xml_file,
'phase_names': ['HCl_electrolyte', 'PC88A_liquid'],
@ -68,12 +71,12 @@ for species, complex_name in zip(species_list,
'independent_params': '(HA)2(org)_h0'}
dependant_params_dict['{0}_h0'.format(complex_name)] = inner_dict
estimator.update_xml(pitzer_params_dict)
estimator.set_custom_objects_dict({'lin_param_df': lin_param_df})
estimator.set_custom_objects_dict({'lin_param_df': new_lin_param_df})
estimator.set_dependant_params_dict(dependant_params_dict)
estimator.update_xml(ext_h0_dict,
dependant_params_dict=dependant_params_dict)
eps = 1e-4
mini_eps = 1e-8
eps = 1e-8
mini_eps = 1e-4
pitzer_guess_dict = {'species': [],
'beta0': [],
'beta1': []}
@ -85,7 +88,7 @@ for species in species_list:
pitzer_guess_dict[param].append(value)
pitzer_guess_df = pd.DataFrame(pitzer_guess_dict)
ext_h0_guess = ext_h0_dict['(HA)2(org)_h0']['input_value']
lin_guess_df = lin_param_df.copy()
lin_guess_df = new_lin_param_df.copy()
ignore_list = []
optimizer = 'scipy_minimize'
@ -94,13 +97,15 @@ output_dict = {'iter': [0],
'rel_diff': [1e20],
'best_ext_h0': [1e20]}
for species in species_list:
output_dict['{0}_slope'.format(species)] = [1e20]
output_dict['{0}_intercept'.format(species)] = [1e20]
for lin_param in lin_param_list:
output_dict['{0}_{1}'.format(species, lin_param)] = [1e20]
for pitzer_param in pitzer_param_list:
output_dict['{0}_{1}'.format(species, pitzer_param)] = [1e20]
i = 0
rel_diff = 1000
while rel_diff > 1e-4:
obj_diff1 = 1000
obj_diff2 = 1000
while obj_diff1 > eps and obj_diff2 > eps:
i += 1
print(i)
best_obj = 1e20
@ -181,13 +186,12 @@ while rel_diff > 1e-4:
species, pitzer_param)].append(value)
estimator.update_custom_objects_dict(info_dict)
estimator.update_xml(opt_dict)
pitzer_guess_dict = {'species': [],
'beta0': [],
'beta1': [],
}
lin_guess_dict = {'species': [],
'slope': [],
'intercept': []}
pitzer_guess_dict = {'species': []}
for pitzer_param in pitzer_param_list:
pitzer_guess_dict[pitzer_param] = []
lin_guess_dict = {'species': []}
for lin_param in lin_param_list:
lin_guess_dict[lin_param] = []
for species in species_list:
pitzer_guess_dict['species'].append(species)
lin_guess_dict['species'].append(species)
@ -221,6 +225,7 @@ while rel_diff > 1e-4:
ignore_list.append(lin_str)
pitzer_guess_df = pd.DataFrame(pitzer_guess_dict)
lin_guess_df = pd.DataFrame(lin_guess_dict)
ext_h0_guess = best_ext_h0
output_dict['best_ext_h0'].append(best_ext_h0)
output_dict['best_obj'].append(best_obj)
@ -232,4 +237,8 @@ while rel_diff > 1e-4:
del(output_dict['rel_diff'][-1])
output_dict['rel_diff'].append(rel_diff)
output_df = pd.DataFrame(output_dict)
output_df.to_csv('outputs/iterative_fitter_output.csv')
output_df.to_csv('outputs/iterative_fitter_output1.csv')
obj_diff1 = np.abs(output_dict['best_obj'][-1]-output_dict['best_obj'][-2])
if i > 2:
obj_diff2 = np.abs(
output_dict['best_obj'][-1] - output_dict['best_obj'][-3])

9
docs/Examples/iterative_fitter_monitor.py
Unescape View File

@ -27,7 +27,8 @@ for parameter in parameters:
ax.set_xlabel('iteration')
ax.set_ylabel('Value')
plt.legend()
plt.show()
plt.tight_layout()
plt.savefig('outputs/{0}.png'.format(parameter))
fig, ax = plt.subplots()
ax.set_title('best_obj_value')
ax.plot(df['iter'].values[1:],
@ -36,7 +37,8 @@ ax.plot(df['iter'].values[1:],
marker='o')
ax.set_xlabel('iteration')
ax.set_ylabel('Value')
plt.show()
plt.tight_layout()
plt.savefig('outputs/best_obj.png')
fig, ax = plt.subplots()
ax.set_title('rel_diff')
ax.plot(df['iter'].values[1:],
@ -45,4 +47,5 @@ ax.plot(df['iter'].values[1:],
marker='o')
ax.set_xlabel('iteration')
ax.set_ylabel('Value')
plt.show()
plt.tight_layout()
plt.savefig('outputs/rel_diff.png')

24
docs/Examples/outputs/iterative_fitter_output.csv
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@ -0,0 +1,24 @@
,iter,best_obj,rel_diff,best_ext_h0,Nd_slope,Nd_intercept,Nd_beta0,Nd_beta1,Pr_slope,Pr_intercept,Pr_beta0,Pr_beta1,Ce_slope,Ce_intercept,Ce_beta0,Ce_beta1,La_slope,La_intercept,La_beta0,La_beta1,Dy_slope,Dy_intercept,Dy_beta0,Dy_beta1,Sm_slope,Sm_intercept,Sm_beta0,Sm_beta1,Y_slope,Y_intercept,Y_beta0,Y_beta1
0,0,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20,1e+20
1,1,0.00016134227249800634,28.000000000000057,-1376877.7655517121,2.997016104344558,-798273.2701355412,0.8974067641732357,9.428749928658126,2.9952636557673946,-812187.4113891785,0.5879108865959323,5.448324456659969,3.000763765252656,-797785.6715641882,0.6005000281672793,4.910000049022438,3.0029250992523053,-797753.1250426786,0.5930012908436624,5.277035454314173,3.005188887840825,-797963.3947393347,0.6129503793970729,5.359935242349485,3.0049141894588356,-788597.673243324,0.5977991609874977,5.278978873136207,3.004211003525449,-808858.5407597149,0.9656358640797775,8.51418935794808
2,2,8.491196928368016e-05,3.753743510814017,-1375785.2564991405,2.9970374674100313,-798274.3744081341,0.8974068120162436,9.42874961145886,2.994978971888568,-812110.2180829814,0.5879107200993189,5.448316407663497,2.9947405016045665,-813706.4008379887,0.45247610268275573,0.4910000049022438,2.9955784444500653,-811485.5788168659,0.9744013695120132,11.536623868407073,3.0051886646689425,-797963.3849246526,0.6129504080166187,5.359935711747232,2.998286291636726,-796410.7377837397,0.6989623741537213,6.874571631031283,2.9980686905189895,-816110.2339357884,0.8864580596701681,9.871827648349246
3,3,8.491196925751041e-05,1.3872557586710772,-1376877.1540548366,2.981636126077384,-815701.8403939944,0.9757586265135513,7.972840020817166,2.9919947944629675,-814690.7699676664,0.6543020425532663,6.401935572967909,2.9912895029379314,-812768.7207378447,0.4524742361735793,0.4909988885665818,2.8816137399778095,-1165713.786906667,0.6673287483674718,11.13954713731451,3.0051888954051678,-797963.3977338726,0.6129504256882,5.359936001703055,2.998286456943957,-796410.7468898736,0.6989623710221783,6.8745716509720465,2.9980701639611307,-816110.3144777927,0.8864580604329536,9.87182766313202
4,4,8.491196864453183e-05,3.124350706237914,-1376877.152537996,3.0030651668677755,-788714.7627699226,0.8876477500590181,7.1795656837403845,2.9492981846969997,-866396.0136278999,0.06543020425532663,14.481702424339913,2.9915899054971504,-812784.8309966518,0.4524742264332296,0.49099888634665106,2.802400719850797,-1217568.8940603866,0.734765523062068,16.501184782487442,3.005188678309654,-797963.3880226224,0.6129504505231561,5.359936408928357,2.9982864229571073,-796410.7471635938,0.6989623610327844,6.874571712665922,2.998069983411216,-816110.5901289525,0.8864580599623918,9.871827432740531
5,5,8.491196857946013e-05,1.1315146871019859,-1376877.1544992805,3.009261826624499,-790493.4807183052,0.8876820541130862,7.180366876068199,2.95493306423167,-941679.8410781198,0.06542242940021062,14.596387046646438,2.9939929242661925,-813370.2300667212,0.4524750816922801,0.49099914533270717,2.7412268066767798,-1181700.5928732348,0.0734765523062068,17.778208795501687,3.0051889032028973,-797963.4006334972,0.6129504699710735,5.359936728023126,2.9982864523758734,-796410.7494542947,0.6989623571542753,6.874571712665922,2.9980699791830183,-816110.5900394547,0.8864580599623918,9.871827432586276
6,6,8.491196857868175e-05,0.8212092953360018,-1376877.1544988493,3.018448578935926,-756591.4774984489,0.8847071856293566,7.127732469249149,2.946746698619443,-914521.2680772808,0.0655303675034554,17.87160024357031,2.9951696006679125,-813508.1242683842,0.45247514275357914,0.4909991719996952,2.8979917114047042,-880424.1009942711,0.07397534024322364,14.445668342341632,3.005188689731846,-797963.3909449822,0.6129504919765936,5.359937088895021,2.998286417872815,-796410.7494542947,0.6989623397580044,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
7,7,8.491196857967895e-05,2.8535135475763593,-1376877.1544988335,2.9803570928818193,-831169.3940393181,2.3855150593783625,0.9943792524787087,2.927591618356134,-906967.4787334907,0.06534292291155007,16.133908724056262,3.0029738570325497,-795932.5448638537,0.4524823795863963,0.4909954629866391,2.9609895390962344,-881255.3332121583,0.07326400481624591,14.648731908806054,3.005188911656549,-797963.4035243823,0.6129505135552498,5.3599374429477535,2.998286452518918,-796410.7494542947,0.6989623360131538,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
8,8,8.491196857967895e-05,0.5074295633771292,-1376877.1544988335,3.046988319365659,-695733.7822299139,2.118879762861775,0.9832916930997248,2.9452585522682813,-902617.5883506279,0.06531634264974066,14.535540289940167,3.001724622774424,-796341.6549504208,0.45078352007925804,0.49034083082370367,2.9722238052604713,-851355.1697072648,0.07317245680858252,14.005551062985573,3.0051886986863527,-797963.3937399577,0.6129505334265856,5.359937768806609,2.998286418340077,-796410.7494542947,0.6989623190362244,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
9,9,8.491196857967895e-05,0.8233569178526139,-1376877.1544988335,2.9825936071777024,-834718.8445277695,2.265417073982851,0.994361984624478,2.960695251829361,-883765.0750479599,0.06535572997952777,14.395940812001363,3.0011755877522677,-797620.9536781705,0.451768694057885,0.4905988771385775,3.01363818781501,-765715.3192727776,0.07374063593564315,19.03231059683384,3.0051889209677576,-797963.4064770396,0.6129505574921508,5.359938163641345,2.998286452651492,-796410.7494542947,0.6989623152735008,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
10,10,8.491196857967895e-05,0.4773109299773383,-1376877.1544988335,3.0188840683355007,-758130.1305298645,2.1690523176501006,0.9984406194127493,2.982890660585592,-840275.2594169229,0.0654841686928967,14.704161508382992,2.997539444125265,-805146.4436967466,0.450156356171543,0.4900762819026168,2.9884659287162063,-823730.0588083962,0.07331458114972399,16.339393531863305,3.0051887064580316,-797963.3965185066,0.6129505756465488,5.359938461354856,2.998286418810594,-796410.7494542947,0.6989622987206754,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
11,11,8.491196857967895e-05,1.3118334901996995,-1376877.1544988335,3.0139001564181234,-777482.0137288059,2.4891695692571334,1.0067452500133052,2.7082813102477377,-1340320.351525344,0.06570193477294409,15.318347397454074,2.9937062166470865,-811848.1861685036,0.3807242781267447,0.4571366236203172,3.0321484162541092,-734038.497344836,0.07278743339509186,15.771531203484976,3.0051889316605727,-797963.4095808832,0.6129506027939315,5.359938906721338,2.9982864527868296,-796410.7494542947,0.6989622949357988,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
12,12,8.491196857967895e-05,0.6176079935219678,-1376877.1544988335,2.9991637169323253,-799348.0533050542,2.0606574395422745,0.9999146041973166,2.645965929040643,-1371545.8507318976,0.06530562967326357,14.087654782739659,2.994388651142047,-812440.2720371439,0.3797153647239341,0.45689994044316656,2.976360040153504,-826947.2829702377,0.0721742508966242,13.950564590026154,3.005188713573002,-797963.3993561977,0.6129506195016742,5.3599391806962355,2.9982864192709067,-796410.7494542947,0.6989622787849055,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
13,13,8.491196857967895e-05,0.447458797489282,-1376877.1544988335,2.9980180983111584,-800782.7580580596,2.384966567099209,1.0089359468087284,2.624593032594632,-1360513.8600727562,0.0652016206426237,13.974111290564698,2.9921196188008023,-811824.6368088808,0.37971475358406565,0.4568995049435289,2.9835754249798847,-836109.188415324,0.07001616810133855,16.854824585869906,3.0051889446809152,-797963.4129476986,0.6129506507011362,5.359939692544431,2.998286452925373,-796410.7494542947,0.6989622749758465,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
14,14,8.491196857967895e-05,0.348360677031186,-1376877.1544988335,2.9982081321172895,-800795.2294772209,2.384966526824247,1.0089359542883563,2.666975566695788,-1499994.1414862953,0.06545352162164432,15.242130244485235,2.9921792830474856,-811827.830457512,0.3797147346864944,0.45689949537118524,3.0165968899178837,-760698.0568963296,0.06972550438458386,17.353271986524845,3.005188720514459,-797963.4023398461,0.6129506661375982,5.359939945648347,2.9982864196902987,-796410.7494542947,0.6989622591872565,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
15,15,8.491196857967895e-05,0.8542573291619304,-1376877.1544988335,2.998712505758874,-802387.4877053623,1.5206330649921567,0.9981622444785841,2.713156877040639,-1478192.0473444732,0.06274057809944926,13.092934019657042,2.994891728361129,-812682.5967323158,0.38018937518269785,0.4567780326119338,3.03009818160027,-757243.0989114699,0.0690109050871497,13.154321394861364,3.005188961745495,-797963.4167757028,0.6129507031227088,5.359940552366342,2.9982864530536806,-796410.7494542947,0.6989622553597238,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
16,16,8.491196857967895e-05,0.9448120605121391,-1376877.1544988335,2.997515876288025,-800762.0931049074,2.371926193409047,1.0392028818516033,2.529014996778218,-1394593.2488741723,0.06362240882017806,15.72054555069271,2.992321826257504,-811985.236014034,0.380188573336472,0.45677745562445643,3.0311603879490625,-757395.5635541482,0.06901090642373163,13.15434949309279,3.0051887279351726,-797963.4056092768,0.6129507173831404,5.359940786171395,2.9982864200995016,-796410.7494542947,0.6989622399197148,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
17,17,8.491196857967895e-05,0.06986866489189483,-1376877.1544988335,2.998185517350526,-800940.9851411111,2.3719292648415435,1.0392030732451656,2.572480079015409,-1398076.7310485386,0.06363412395715484,14.965559296480045,2.9916507242807455,-811803.1343536598,0.3801884696593834,0.4567773926702631,3.029587351548093,-757002.5991619993,0.06901085836588974,13.153841661748762,3.00518898709267,-797963.4214979863,0.6129507639240588,5.359941549628348,2.9982864531810547,-796410.7494542947,0.6989622360690536,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
18,18,8.491196857967895e-05,7.537649140057278,-1376877.1544988335,2.9139186135307895,-903717.07595184,1.1713250218349551,7.8961519078936995,2.551409491193631,-1396607.749248508,0.06364635989520172,15.028103657774764,2.992358699183809,-811883.3497541018,0.38014823465953007,0.45676064010613937,3.0225440094645197,-757527.3282072913,0.06907140877109968,16.557914647062493,3.0051887374402306,-797963.4094522469,0.6129507769959467,5.3599417639476465,2.9982864204802095,-796410.7494542947,0.6989622209402793,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
19,19,8.491196857967895e-05,0.3395564898628593,-1376877.1544988335,2.9206876794423295,-906067.0736166579,1.171453185504549,7.897253707676486,2.542473620212442,-1395291.4660299404,0.06365232727832787,15.064625151652315,2.9913152280569513,-811600.2380135104,0.3801480833041433,0.45676053922619353,3.00948706713011,-770684.9871133575,0.06894408512873036,21.578016654838063,3.0051890362364753,-797963.4285937521,0.6129508452597554,5.359942883786607,2.998286453304157,-796410.7494542947,0.6989622170705244,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
20,20,8.491196857967895e-05,0.27887232716945426,-1376877.1544988335,2.932945093248787,-893472.3867422906,1.2372118357462467,8.367708345062676,2.5681008604924935,-1424501.3718411059,0.06389293355566235,16.006743784511794,2.9915688192964494,-811676.8964187511,0.38001482584768576,0.4566967822701942,2.9966407511121345,-795742.7223232647,0.06891628915899889,21.364797187402115,3.005188756433512,-797963.4149199789,0.6129508568428819,5.359943073668666,2.9982864208411075,-796410.7494542947,0.6989622022429722,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
21,21,8.491196857967895e-05,0.07904486462976254,-1376877.1544988335,2.9192941732704183,-899595.6903217547,1.2289167121597353,8.270591970104478,2.5885541497990276,-1425201.6370605633,0.06406357430240948,16.055658203899945,2.991141414262,-812827.4608356728,0.3798926190405488,0.45663532315631505,2.993009643896257,-802640.8358981846,0.06886889954042703,20.88499046197079,3.0051892653467993,-797963.4540443348,0.6129510889764764,5.359946881486811,2.9982864534278213,-796410.7494542947,0.6989621983540872,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
22,22,8.491196857967895e-05,2.8078518871500875,-1376877.1544988335,2.5130533352916404,-1308478.271342789,2.3685619028157476,1.0600650778914467,2.532808888931495,-1416725.967204896,0.06411203767956787,15.094184412115302,2.990867139013824,-812773.0935418691,0.3798925911718346,0.456635306115862,2.99518346032217,-803786.8612650471,0.06825025926005274,14.294244836372703,3.0051888907116417,-797963.4353105845,0.6129510969272831,5.359947011739686,2.9982864212214833,-796410.7494542947,0.6989621838326963,6.874571712665922,2.9980699785721763,-816110.5900320362,0.8864580599623918,9.871827432559574
1 iter best_obj rel_diff best_ext_h0 Nd_slope Nd_intercept Nd_beta0 Nd_beta1 Pr_slope Pr_intercept Pr_beta0 Pr_beta1 Ce_slope Ce_intercept Ce_beta0 Ce_beta1 La_slope La_intercept La_beta0 La_beta1 Dy_slope Dy_intercept Dy_beta0 Dy_beta1 Sm_slope Sm_intercept Sm_beta0 Sm_beta1 Y_slope Y_intercept Y_beta0 Y_beta1
2 0 0 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20 1e+20
3 1 1 0.00016134227249800634 28.000000000000057 -1376877.7655517121 2.997016104344558 -798273.2701355412 0.8974067641732357 9.428749928658126 2.9952636557673946 -812187.4113891785 0.5879108865959323 5.448324456659969 3.000763765252656 -797785.6715641882 0.6005000281672793 4.910000049022438 3.0029250992523053 -797753.1250426786 0.5930012908436624 5.277035454314173 3.005188887840825 -797963.3947393347 0.6129503793970729 5.359935242349485 3.0049141894588356 -788597.673243324 0.5977991609874977 5.278978873136207 3.004211003525449 -808858.5407597149 0.9656358640797775 8.51418935794808
4 2 2 8.491196928368016e-05 3.753743510814017 -1375785.2564991405 2.9970374674100313 -798274.3744081341 0.8974068120162436 9.42874961145886 2.994978971888568 -812110.2180829814 0.5879107200993189 5.448316407663497 2.9947405016045665 -813706.4008379887 0.45247610268275573 0.4910000049022438 2.9955784444500653 -811485.5788168659 0.9744013695120132 11.536623868407073 3.0051886646689425 -797963.3849246526 0.6129504080166187 5.359935711747232 2.998286291636726 -796410.7377837397 0.6989623741537213 6.874571631031283 2.9980686905189895 -816110.2339357884 0.8864580596701681 9.871827648349246
5 3 3 8.491196925751041e-05 1.3872557586710772 -1376877.1540548366 2.981636126077384 -815701.8403939944 0.9757586265135513 7.972840020817166 2.9919947944629675 -814690.7699676664 0.6543020425532663 6.401935572967909 2.9912895029379314 -812768.7207378447 0.4524742361735793 0.4909988885665818 2.8816137399778095 -1165713.786906667 0.6673287483674718 11.13954713731451 3.0051888954051678 -797963.3977338726 0.6129504256882 5.359936001703055 2.998286456943957 -796410.7468898736 0.6989623710221783 6.8745716509720465 2.9980701639611307 -816110.3144777927 0.8864580604329536 9.87182766313202
6 4 4 8.491196864453183e-05 3.124350706237914 -1376877.152537996 3.0030651668677755 -788714.7627699226 0.8876477500590181 7.1795656837403845 2.9492981846969997 -866396.0136278999 0.06543020425532663 14.481702424339913 2.9915899054971504 -812784.8309966518 0.4524742264332296 0.49099888634665106 2.802400719850797 -1217568.8940603866 0.734765523062068 16.501184782487442 3.005188678309654 -797963.3880226224 0.6129504505231561 5.359936408928357 2.9982864229571073 -796410.7471635938 0.6989623610327844 6.874571712665922 2.998069983411216 -816110.5901289525 0.8864580599623918 9.871827432740531
7 5 5 8.491196857946013e-05 1.1315146871019859 -1376877.1544992805 3.009261826624499 -790493.4807183052 0.8876820541130862 7.180366876068199 2.95493306423167 -941679.8410781198 0.06542242940021062 14.596387046646438 2.9939929242661925 -813370.2300667212 0.4524750816922801 0.49099914533270717 2.7412268066767798 -1181700.5928732348 0.0734765523062068 17.778208795501687 3.0051889032028973 -797963.4006334972 0.6129504699710735 5.359936728023126 2.9982864523758734 -796410.7494542947 0.6989623571542753 6.874571712665922 2.9980699791830183 -816110.5900394547 0.8864580599623918 9.871827432586276
8 6 6 8.491196857868175e-05 0.8212092953360018 -1376877.1544988493 3.018448578935926 -756591.4774984489 0.8847071856293566 7.127732469249149 2.946746698619443 -914521.2680772808 0.0655303675034554 17.87160024357031 2.9951696006679125 -813508.1242683842 0.45247514275357914 0.4909991719996952 2.8979917114047042 -880424.1009942711 0.07397534024322364 14.445668342341632 3.005188689731846 -797963.3909449822 0.6129504919765936 5.359937088895021 2.998286417872815 -796410.7494542947 0.6989623397580044 6.874571712665922 2.9980699785721763 -816110.5900320362 0.8864580599623918 9.871827432559574
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13 11 11 8.491196857967895e-05 1.3118334901996995 -1376877.1544988335 3.0139001564181234 -777482.0137288059 2.4891695692571334 1.0067452500133052 2.7082813102477377 -1340320.351525344 0.06570193477294409 15.318347397454074 2.9937062166470865 -811848.1861685036 0.3807242781267447 0.4571366236203172 3.0321484162541092 -734038.497344836 0.07278743339509186 15.771531203484976 3.0051889316605727 -797963.4095808832 0.6129506027939315 5.359938906721338 2.9982864527868296 -796410.7494542947 0.6989622949357988 6.874571712665922 2.9980699785721763 -816110.5900320362 0.8864580599623918 9.871827432559574
14 12 12 8.491196857967895e-05 0.6176079935219678 -1376877.1544988335 2.9991637169323253 -799348.0533050542 2.0606574395422745 0.9999146041973166 2.645965929040643 -1371545.8507318976 0.06530562967326357 14.087654782739659 2.994388651142047 -812440.2720371439 0.3797153647239341 0.45689994044316656 2.976360040153504 -826947.2829702377 0.0721742508966242 13.950564590026154 3.005188713573002 -797963.3993561977 0.6129506195016742 5.3599391806962355 2.9982864192709067 -796410.7494542947 0.6989622787849055 6.874571712665922 2.9980699785721763 -816110.5900320362 0.8864580599623918 9.871827432559574
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17 15 15 8.491196857967895e-05 0.8542573291619304 -1376877.1544988335 2.998712505758874 -802387.4877053623 1.5206330649921567 0.9981622444785841 2.713156877040639 -1478192.0473444732 0.06274057809944926 13.092934019657042 2.994891728361129 -812682.5967323158 0.38018937518269785 0.4567780326119338 3.03009818160027 -757243.0989114699 0.0690109050871497 13.154321394861364 3.005188961745495 -797963.4167757028 0.6129507031227088 5.359940552366342 2.9982864530536806 -796410.7494542947 0.6989622553597238 6.874571712665922 2.9980699785721763 -816110.5900320362 0.8864580599623918 9.871827432559574
18 16 16 8.491196857967895e-05 0.9448120605121391 -1376877.1544988335 2.997515876288025 -800762.0931049074 2.371926193409047 1.0392028818516033 2.529014996778218 -1394593.2488741723 0.06362240882017806 15.72054555069271 2.992321826257504 -811985.236014034 0.380188573336472 0.45677745562445643 3.0311603879490625 -757395.5635541482 0.06901090642373163 13.15434949309279 3.0051887279351726 -797963.4056092768 0.6129507173831404 5.359940786171395 2.9982864200995016 -796410.7494542947 0.6989622399197148 6.874571712665922 2.9980699785721763 -816110.5900320362 0.8864580599623918 9.871827432559574
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20 18 18 8.491196857967895e-05 7.537649140057278 -1376877.1544988335 2.9139186135307895 -903717.07595184 1.1713250218349551 7.8961519078936995 2.551409491193631 -1396607.749248508 0.06364635989520172 15.028103657774764 2.992358699183809 -811883.3497541018 0.38014823465953007 0.45676064010613937 3.0225440094645197 -757527.3282072913 0.06907140877109968 16.557914647062493 3.0051887374402306 -797963.4094522469 0.6129507769959467 5.3599417639476465 2.9982864204802095 -796410.7494542947 0.6989622209402793 6.874571712665922 2.9980699785721763 -816110.5900320362 0.8864580599623918 9.871827432559574
21 19 19 8.491196857967895e-05 0.3395564898628593 -1376877.1544988335 2.9206876794423295 -906067.0736166579 1.171453185504549 7.897253707676486 2.542473620212442 -1395291.4660299404 0.06365232727832787 15.064625151652315 2.9913152280569513 -811600.2380135104 0.3801480833041433 0.45676053922619353 3.00948706713011 -770684.9871133575 0.06894408512873036 21.578016654838063 3.0051890362364753 -797963.4285937521 0.6129508452597554 5.359942883786607 2.998286453304157 -796410.7494542947 0.6989622170705244 6.874571712665922 2.9980699785721763 -816110.5900320362 0.8864580599623918 9.871827432559574
22 20 20 8.491196857967895e-05 0.27887232716945426 -1376877.1544988335 2.932945093248787 -893472.3867422906 1.2372118357462467 8.367708345062676 2.5681008604924935 -1424501.3718411059 0.06389293355566235 16.006743784511794 2.9915688192964494 -811676.8964187511 0.38001482584768576 0.4566967822701942 2.9966407511121345 -795742.7223232647 0.06891628915899889 21.364797187402115 3.005188756433512 -797963.4149199789 0.6129508568428819 5.359943073668666 2.9982864208411075 -796410.7494542947 0.6989622022429722 6.874571712665922 2.9980699785721763 -816110.5900320362 0.8864580599623918 9.871827432559574
23 21 21 8.491196857967895e-05 0.07904486462976254 -1376877.1544988335 2.9192941732704183 -899595.6903217547 1.2289167121597353 8.270591970104478 2.5885541497990276 -1425201.6370605633 0.06406357430240948 16.055658203899945 2.991141414262 -812827.4608356728 0.3798926190405488 0.45663532315631505 2.993009643896257 -802640.8358981846 0.06886889954042703 20.88499046197079 3.0051892653467993 -797963.4540443348 0.6129510889764764 5.359946881486811 2.9982864534278213 -796410.7494542947 0.6989621983540872 6.874571712665922 2.9980699785721763 -816110.5900320362 0.8864580599623918 9.871827432559574
24 22 22 8.491196857967895e-05 2.8078518871500875 -1376877.1544988335 2.5130533352916404 -1308478.271342789 2.3685619028157476 1.0600650778914467 2.532808888931495 -1416725.967204896 0.06411203767956787 15.094184412115302 2.990867139013824 -812773.0935418691 0.3798925911718346 0.456635306115862 2.99518346032217 -803786.8612650471 0.06825025926005274 14.294244836372703 3.0051888907116417 -797963.4353105845 0.6129510969272831 5.359947011739686 2.9982864212214833 -796410.7494542947 0.6989621838326963 6.874571712665922 2.9980699785721763 -816110.5900320362 0.8864580599623918 9.871827432559574

3
llepe/llepe.py
Unescape View File

@ -1326,7 +1326,8 @@ class LLEPE:
filtered_data = combined_df[combined_df['label'] == label]
filtered_meas = filtered_data['meas']
filtered_pred = filtered_data['pred']
ax.scatter(filtered_meas, filtered_pred, label=label)
if len(filtered_pred) != 0:
ax.scatter(filtered_meas, filtered_pred, label=label)
if legend:
ax.legend(loc='best')

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