new_model_1.md

import os
import pandas as pd 
import numpy as np
import gudhi
import json
import classifier
import region
import sklearn.metrics as metrics
from sklearn.cluster import DBSCAN

from importlib import reload
reload(classifier)
reload(region)

<module 'region' from 'D:\\PROJECT\\seminconductor\\code\\data2\\region.py'>

def make_data_dictionary(basic_path='./'):
    file_list=os.listdir(basic_path)
    result_dict={'good':[],'bad':[]}
    for file_name in file_list:
        if 'Fault' in file_name:
            work_list=result_dict['bad']
        else:
            work_list=result_dict['good']
        data_frame=pd.read_csv(os.path.join(basic_path,file_name))
        data_frame=data_frame.fillna(0)
        for col_i in range(1,len(data_frame.columns)):
            work_list.append(list(data_frame.iloc[:,col_i]))
    return result_dict

basic_path='./FDC_DATA/tcmp'
data_dict=make_data_dictionary(basic_path)

cl=classifier.Classifier(dim=5,delay=2)
cl.set_db_params(eps=0.2,min_samples=5)

train_data=data_dict['good'][:100]

test_data=data_dict['good'][100:]

cl.train(train_data)

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 ...
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 ...
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[[0.00000e+00 1.78825e-03]
 [0.00000e+00 1.77950e-03]
 [0.00000e+00 1.71125e-03]
 ...
 [0.00000e+00 6.75000e-06]
 [0.00000e+00 6.25000e-06]
 [0.00000e+00 2.25000e-06]]
[[0.         2.22919275]
 [0.         2.2291865 ]
 [0.         2.22913025]
 [0.         2.22910525]
 [0.         2.229049  ]]
[[0.         0.978396  ]
 [0.         0.978371  ]
 [0.         0.978146  ]
 [0.         0.978121  ]
 [0.         0.857635  ]
 [0.         0.73572425]
 [0.         0.735218  ]
 [0.         0.734593  ]
 [0.         0.734449  ]
 [0.         0.64242475]]
[[0.         2.61645225]
 [0.         2.61638825]
 [0.         2.61632425]
 [0.         2.61631525]
 [0.         2.61630625]]
[[0.         5.49922875]
 [0.         5.4992225 ]
 [0.         5.49921625]
 [0.         5.499034  ]
 [0.         5.499025  ]]

test_label=list(np.zeros(len(test_data)))+list(np.ones(len(data_dict['bad'])))
test_data=test_data+data_dict['bad']

predict_label=cl.predict(test_data)

result=metrics.confusion_matrix(test_label,predict_label)

result

array([[175,  81],
       [  2,  24]], dtype=int64)

result=metrics.classification_report(test_label,predict_label)

print(result)

              precision    recall  f1-score   support

         0.0       1.00      0.62      0.77       330
         1.0       0.17      1.00      0.30        26

    accuracy                           0.65       356
   macro avg       0.59      0.81      0.53       356
weighted avg       0.94      0.65      0.73       356

np.array(test_data[0])

array([0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   ,
       2.032, 4.95 , 4.938, 4.951, 4.909, 4.942, 4.95 , 4.95 , 4.939,
       4.932, 4.923, 4.931, 4.942, 4.944, 4.929, 4.95 , 4.974, 4.934,
       4.961, 4.952, 4.963, 4.894, 4.896, 4.931, 4.923, 4.915, 4.882,
       4.867, 4.862, 4.862, 4.835, 4.83 , 4.842, 4.852, 4.858, 4.862,
       4.938, 4.938, 4.913, 4.915, 4.909, 4.896, 4.925, 4.928, 4.921,
       4.944, 4.892, 4.924, 4.93 , 4.936, 4.927, 4.923, 4.934, 4.936,
       4.938, 4.939, 4.928, 4.919, 4.916, 4.925, 4.921, 4.923, 4.935,
       4.928, 4.919, 4.931, 4.94 , 4.933, 4.936, 4.931, 4.929, 0.   ,
       0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   , 0.   ])

cl._arrange_persistences([test_data[0]])

---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

<ipython-input-19-a4172ffacf01> in <module>
----> 1 cl._arrange_persistences([test_data[0]])


D:\PROJECT\seminconductor\code\data2\classifier.py in _arrange_persistences(self, persistences)
    153         data_dict_by_betti={}
    154         for persistence in persistences:
--> 155             for betti, cord in persistence:
    156                 if betti not in data_dict_by_betti.keys():
    157                     data_dict_by_betti[betti]=[]


TypeError: cannot unpack non-iterable float object

cl.predict(test_data)

---------------------------------------------------------------------------

KeyboardInterrupt                         Traceback (most recent call last)

<ipython-input-37-af896e8ef74a> in <module>
----> 1 cl.predict(test_data)


D:\PROJECT\seminconductor\code\data2\classifier.py in predict(self, data_set)
     54         result=[]
     55         for persistence in data_set:
---> 56             persistences=self._make_persistence([persistence])
     57             self.predict_persistences=persistences
     58             ###predict data set


D:\PROJECT\seminconductor\code\data2\classifier.py in _make_persistence(self, data_set)
    130         """
    131         func=partial(self.__get_persistence,self.te)
--> 132         persistences=list(map(func,data_set))
    133         return persistences
    134         pass


D:\PROJECT\seminconductor\code\data2\classifier.py in __get_persistence(self, te, data)
    118         embedding=te(data)
    119         simplex=self.simplex(embedding)
--> 120         tree=simplex.create_simplex_tree()
    121         persistence=tree.persistence()
    122         return persistence


KeyboardInterrupt: 

predict_label

[1]

cl.region_dict.keys()

dict_keys([4, 3, 2, 1, 0])

for k in cl.region_dict.keys():
    for j in cl.region_dict[k].region_dict.keys():
        plt.plot(cl.region_dict[k].region_dict[j].polygon.bounds)
#cl.region_dict[4].region_dict[3].polygon

import matplotlib.pyplot as plt

for j in cl.region_dict[2].region_dict.keys():
    print(cl.region_dict[2].region_dict[j].polygon.bounds)

(26.592092858722324, 26.986913664122362, 28.151272565096676, 28.32176971937116)
(29.4073447940647, 29.409512379062043, 29.688932542907875, 29.689063405592627)
(23.461031813027763, 23.461062271187636, 23.676784076412925, 23.676819915089773)
(1.7025806451612304e-05, 1.7382819357480712e-05, 0.008544261226079583, 0.008545454003915844)
(17.57001482622495, 17.57006389107601, 17.73596596898105, 17.736009539235543)
(18.221285624315342, 18.221286749999166, 18.498546272179013, 18.498546745185173)
(12.112494077409883, 12.112494205192508, 12.3604476124449, 12.360449668071825)
(11.673730081013424, 11.673732343353201, 11.80542677007692, 11.805483308686133)
(24.30744902818071, 24.307449071460923, 24.663716300127575, 24.663720446532867)
(6.096040365402301, 6.09604797061559, 6.147959625010088, 6.147960384038374)
(26.40111309397806, 26.598162905127566, 26.79956619926277, 26.818298531199005)

cl.region_dict[0].region_dict[0].polygon

cl=classifier.Classifier(dim=5,delay=2)
cl.set_db_params(eps=0.2,min_samples=5)
cl.train(train_data)

[[29.66568178 29.69764022]
 [29.67287229 29.69967188]
 [29.67685177 29.6993754 ]
 [29.67654529 29.69818744]
 [29.6737314  29.69461809]
 [29.67022941 29.68885774]
 [29.66902402 29.66931226]
 [29.68932898 29.68950196]
 [29.69860988 29.69871008]]
[[0.00048044 0.0004834 ]
 [0.00516083 0.00516234]
 [0.00033295 0.00033445]
 [0.0064181  0.00641915]
 [0.00051658 0.00051696]
 [0.00035979 0.00036012]
 [0.00045682 0.00045699]
 [0.00034139 0.00034147]]
[[28.26000231 28.66433574]
 [28.23308997 28.62981366]
 [28.25265293 28.6423297 ]
 [28.26358163 28.64732653]
 [28.22287802 28.59493361]]
[[28.12362214 28.28271771]
 [28.20246737 28.34854169]
 [28.22288596 28.34264736]
 [28.24331267 28.35579339]
 [28.28878834 28.32550177]
 [28.34736747 28.37060971]]
[[29.65031198 29.67231595]
 [29.64189028 29.66288954]
 [29.63772899 29.65627636]
 [29.6417247  29.65098003]
 [29.66244877 29.67109105]
 [29.66762475 29.67430509]
 [29.55275997 29.55302727]]
[[28.62987882 28.63042436]
 [28.51839423 28.51881895]
 [28.59493595 28.59521404]
 [28.64412172 28.6441566 ]
 [28.61533032 28.61533127]]
[[0.00625063 0.00641369]
 [0.00613697 0.00615091]
 [0.0058989  0.00591079]
 [0.00048131 0.00049007]
 [0.00034212 0.00034972]
 [0.00515304 0.00516026]
 [0.00639327 0.00640025]
 [0.00046751 0.00047371]
 [0.00192336 0.00192948]
 [0.00031999 0.00032563]
 [0.00035355 0.00035898]
 [0.00571844 0.0057232 ]
 [0.00434055 0.0043449 ]
 [0.00685351 0.00685785]
 [0.0004007  0.00040486]
 [0.00680693 0.00681052]
 [0.00046557 0.00046864]
 [0.0004588  0.00046167]
 [0.00041191 0.00041461]
 [0.00031342 0.00031588]
 [0.00033436 0.00033677]
 [0.00032948 0.00033181]
 [0.0004822  0.0004845 ]
 [0.00683764 0.00683984]
 [0.00041573 0.00041775]
 [0.00046195 0.00046395]
 [0.00037586 0.00037764]
 [0.00032776 0.00032915]
 [0.00102049 0.00102183]
 [0.00016228 0.00016358]
 [0.00050536 0.00050667]
 [0.00045815 0.00045945]
 [0.0004611  0.00046232]
 [0.00559057 0.00559173]
 [0.00047368 0.00047472]
 [0.00515558 0.00515661]
 [0.0004609  0.00046181]
 [0.0002317  0.0002326 ]
 [0.00044252 0.00044339]
 [0.00033916 0.00033994]
 [0.00044137 0.00044194]
 [0.00032167 0.00032221]
 [0.00037011 0.00037064]
 [0.0004074  0.00040787]
 [0.0009911  0.00099156]
 [0.00020653 0.00020698]
 [0.00045271 0.00045311]
 [0.00035445 0.00035484]
 [0.00035328 0.00035361]
 [0.0075366  0.00753692]
 [0.0010191  0.00101939]
 [0.00045628 0.00045648]
 [0.00016082 0.000161  ]
 [0.00041936 0.00041954]
 [0.0001517  0.00015188]
 [0.00047211 0.00047228]
 [0.00473523 0.00473534]
 [0.00041991 0.00042002]
 [0.00033582 0.00033591]
 [0.00047911 0.00047919]
 [0.00101874 0.00101881]
 [0.00048611 0.00048618]
 [0.00048317 0.00048322]
 [0.00016982 0.00016987]
 [0.00105694 0.00105699]
 [0.00034058 0.00034061]
 [0.00044862 0.00044865]
 [0.00044852 0.00044855]
 [0.00048321 0.00048321]
 [0.00066704 0.00066704]
 [0.00048371 0.00048371]
 [0.0004315  0.0004315 ]
 [0.00047828 0.00047828]
 [0.00026907 0.00026907]]
[[27.52130954 27.82022733]
 [27.63618454 27.91217418]
 [27.62402936 27.86913791]
 [27.63003577 27.86470365]
 [27.52980464 27.66112316]]
[[27.94148316 28.12508327]
 [28.00465628 28.18759421]
 [28.0493271  28.22424482]
 [28.07933077 28.25252772]
 [28.05270198 28.22193934]
 [27.93487122 27.97394246]
 [28.03863153 28.06224522]]
[[0.0056069  0.00583826]
 [0.00607639 0.00622779]
 [0.00301438 0.00313468]
 [0.0054798  0.00559034]
 [0.00494735 0.00505664]
 [0.00561243 0.0057175 ]
 [0.00415383 0.00425726]
 [0.00612854 0.00622082]
 [0.00370974 0.00380068]
 [0.00037921 0.00044392]
 [0.00289404 0.00295873]
 [0.00462882 0.00469127]
 [0.00605722 0.00611852]
 [0.00269394 0.00273547]
 [0.00172892 0.00177032]
 [0.0025705  0.0026097 ]
 [0.00502427 0.00506051]
 [0.00673009 0.00676537]
 [0.00093003 0.0009628 ]
 [0.00074438 0.00077704]
 [0.00036716 0.00039935]
 [0.00041014 0.00043943]
 [0.00549568 0.00552273]
 [0.00029092 0.00031768]
 [0.00028631 0.00031274]
 [0.00049479 0.00052115]
 [0.00464567 0.00466983]
 [0.00050629 0.00053044]
 [0.00493936 0.00496299]
 [0.00028034 0.00030136]
 [0.00462593 0.00464613]
 [0.00043582 0.00045526]
 [0.0028397  0.0028589 ]
 [0.00187209 0.00189123]
 [0.00039308 0.00041176]
 [0.00043765 0.00045557]
 [0.00029024 0.00030745]
 [0.00032789 0.00034348]
 [0.00586615 0.00588141]
 [0.00054664 0.00056185]
 [0.00607513 0.00609008]
 [0.00040182 0.0004162 ]
 [0.00032288 0.00033707]
 [0.00096472 0.0009789 ]
 [0.00027223 0.00028619]
 [0.00076004 0.00077359]
 [0.0010052  0.00101756]
 [0.00644479 0.006457  ]
 [0.00032355 0.00033511]
 [0.00034041 0.0003519 ]
 [0.00013103 0.00014194]
 [0.00027875 0.00028953]
 [0.00183117 0.00184193]
 [0.0003783  0.00038904]
 [0.00038299 0.00039368]
 [0.00273124 0.00274074]
 [0.00041938 0.00042856]
 [0.00146856 0.00147766]
 [0.00041473 0.00042372]
 [0.00045485 0.00046383]
 [0.00036899 0.00037759]
 [0.00041007 0.00041725]
 [0.00014229 0.0001491 ]
 [0.00044571 0.00045236]
 [0.00025251 0.00025912]
 [0.00025979 0.00026632]
 [0.00045219 0.00045836]
 [0.00034727 0.00035316]
 [0.00049098 0.00049674]
 [0.00035882 0.00036443]
 [0.00013165 0.00013714]
 [0.00029886 0.00030419]
 [0.00029497 0.00030027]
 [0.00021627 0.00022122]
 [0.00033551 0.00034009]
 [0.00053244 0.00053681]
 [0.00032865 0.00033295]
 [0.0004491  0.00045329]
 [0.00615549 0.00615957]
 [0.00114204 0.00114605]
 [0.00032966 0.00033354]
 [0.00029541 0.00029924]
 [0.00013124 0.00013502]
 [0.00019269 0.00019646]
 [0.001891   0.00189475]
 [0.00100332 0.00100706]
 [0.00040757 0.00041127]
 [0.00055071 0.0005542 ]
 [0.00636543 0.0063689 ]
 [0.00096672 0.00097016]
 [0.00040792 0.00041135]
 [0.00018735 0.00019073]
 [0.00066267 0.00066603]
 [0.00045746 0.00046076]
 [0.00471033 0.00471348]
 [0.00043078 0.00043387]
 [0.00039051 0.00039354]
 [0.00037369 0.0003766 ]
 [0.00021969 0.00022256]
 [0.00396565 0.00396848]
 [0.00015824 0.00016106]
 [0.00617604 0.00617884]
 [0.00045692 0.00045967]
 [0.00751673 0.00751946]
 [0.00045139 0.00045411]
 [0.00191333 0.00191602]
 [0.00021819 0.00022074]
 [0.00017784 0.00018031]
 [0.000411   0.00041344]
 [0.00032954 0.00033179]
 [0.00032983 0.00033207]
 [0.00039418 0.0003964 ]
 [0.00135642 0.00135857]
 [0.00032194 0.00032397]
 [0.00037219 0.00037414]
 [0.0002984  0.00030027]
 [0.00030306 0.0003049 ]
 [0.0001596  0.00016141]
 [0.00037361 0.0003754 ]
 [0.00044036 0.00044213]
 [0.00014383 0.0001456 ]
 [0.00040633 0.00040807]
 [0.00011868 0.00012038]
 [0.00017653 0.00017817]
 [0.00031432 0.00031587]
 [0.0028446  0.0028461 ]
 [0.00684612 0.00684758]
 [0.0002782  0.00027952]
 [0.00036493 0.00036622]
 [0.00065851 0.00065974]
 [0.00043658 0.00043772]
 [0.00214095 0.00214204]
 [0.00031812 0.00031921]
 [0.00043177 0.00043284]
 [0.00015285 0.00015386]
 [0.00274143 0.00274242]
 [0.00132912 0.00133008]
 [0.00037669 0.00037763]
 [0.00039965 0.00040059]
 [0.00062031 0.00062115]
 [0.00043518 0.000436  ]
 [0.0005203  0.00052112]
 [0.00037722 0.00037803]
 [0.00028527 0.00028599]
 [0.00033236 0.00033305]
 [0.00015664 0.00015732]
 [0.00023016 0.00023081]
 [0.00015677 0.00015733]
 [0.00041249 0.00041305]
 [0.00047493 0.00047547]
 [0.00032889 0.0003294 ]
 [0.00014098 0.00014146]
 [0.00027641 0.00027686]
 [0.00020608 0.00020652]
 [0.00047163 0.00047207]
 [0.00042832 0.00042866]
 [0.0004175  0.00041783]
 [0.00101376 0.00101407]
 [0.00045609 0.00045637]
 [0.00368044 0.0036807 ]
 [0.00039108 0.00039132]
 [0.00345701 0.00345725]
 [0.000505   0.00050521]
 [0.00034197 0.00034217]
 [0.00105587 0.00105606]
 [0.00046193 0.00046209]
 [0.0001606  0.00016076]
 [0.00024746 0.00024761]
 [0.00045882 0.00045895]
 [0.00030924 0.00030937]
 [0.00047699 0.00047712]
 [0.00041601 0.00041613]
 [0.00034597 0.00034608]
 [0.00048296 0.00048307]
 [0.00016337 0.00016347]
 [0.00101547 0.00101556]
 [0.00480296 0.00480302]
 [0.00014757 0.00014763]
 [0.00042782 0.00042788]
 [0.00064136 0.00064141]
 [0.00037812 0.00037817]
 [0.00033523 0.00033527]
 [0.00034997 0.00035002]
 [0.00036527 0.0003653 ]
 [0.00041871 0.00041873]
 [0.000656   0.00065603]
 [0.00045384 0.00045385]
 [0.00092298 0.00092299]
 [0.00037523 0.00037524]
 [0.00074164 0.00074165]
 [0.00047715 0.00047715]
 [0.00293818 0.00293818]]
[[17.68209371 17.68231448]
 [17.68113802 17.68124688]
 [17.68220305 17.68224047]
 [17.67363372 17.67365004]
 [17.67722902 17.67723503]]
[[1.91075000e-03 5.46838107e-03]
 [4.39700000e-03 4.87113169e-03]
 [3.14250000e-03 3.59888006e-03]
 [1.29400000e-03 1.69564848e-03]
 [2.41700000e-03 2.81608517e-03]
 [1.40475000e-03 1.78408557e-03]
 [4.25000000e-03 4.62038475e-03]
 [2.50425000e-03 2.83986490e-03]
 [1.75700000e-03 2.07739098e-03]
 [4.20500000e-04 7.37124071e-04]
 [4.33500000e-04 7.49250116e-04]
 [9.38750000e-04 1.17653796e-03]
 [4.70200000e-03 4.89834003e-03]
 [1.34475000e-03 1.51887369e-03]
 [1.82525000e-03 1.99066871e-03]
 [2.22250000e-04 3.48348489e-04]
 [3.48750000e-04 4.63760239e-04]
 [5.31950000e-03 5.43026982e-03]
 [2.65500000e-04 3.75341173e-04]
 [3.21125000e-03 3.31570787e-03]
 [1.31050000e-03 1.41173666e-03]
 [3.12350000e-03 3.21902513e-03]
 [1.70450000e-03 1.79756302e-03]
 [8.05750000e-04 8.98627097e-04]
 [2.79250000e-04 3.70143334e-04]
 [2.53075000e-03 2.61696299e-03]
 [6.77000000e-04 7.62159268e-04]
 [2.79750000e-04 3.63874149e-04]
 [2.37225000e-03 2.45343243e-03]
 [4.75000000e-03 4.82933883e-03]
 [1.96750000e-04 2.73471434e-04]
 [2.37250000e-04 3.10279967e-04]
 [2.16750000e-04 2.84937831e-04]
 [2.42000000e-04 3.09705136e-04]
 [1.99500000e-04 2.62188033e-04]
 [5.96500000e-04 6.59021393e-04]
 [2.48150000e-03 2.54395470e-03]
 [2.01750000e-04 2.63056749e-04]
 [2.02500000e-04 2.63216166e-04]
 [4.28500000e-04 4.86916987e-04]
 [4.01300000e-03 4.06640300e-03]
 [2.07750000e-04 2.59009451e-04]
 [2.29500000e-04 2.79946373e-04]
 [2.51350000e-03 2.56353012e-03]
 [2.66750000e-04 3.16062421e-04]
 [1.50775000e-03 1.55386712e-03]
 [6.00425000e-03 6.04976107e-03]
 [2.12500000e-04 2.56341111e-04]
 [4.26250000e-04 4.69073439e-04]
 [9.95000000e-05 1.42266835e-04]
 [3.77175000e-03 3.81338476e-03]
 [5.74750000e-04 6.13744813e-04]
 [5.06500000e-04 5.44748265e-04]
 [6.00775000e-03 6.04540086e-03]
 [2.15000000e-04 2.51105264e-04]
 [9.32500000e-05 1.28770532e-04]
 [3.79500000e-04 4.14290790e-04]
 [2.84975000e-03 2.88446657e-03]
 [2.25750000e-04 2.60369824e-04]
 [4.68750000e-04 5.01180127e-04]
 [1.33050000e-03 1.36220359e-03]
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 [3.59500000e-04 3.85973159e-04]
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 [9.87500000e-05 1.16539926e-04]
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 [3.47325000e-03 3.48870873e-03]
 [4.75000000e-04 4.90431438e-04]
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 [1.97500000e-04 2.11526110e-04]
 [1.38550000e-03 1.39915223e-03]
 [2.70850000e-03 2.72187025e-03]
 [3.72750000e-04 3.86040701e-04]
 [3.78950000e-03 3.80244214e-03]
 [9.88750000e-04 1.00083027e-03]
 [6.75000000e-05 7.95185891e-05]
 [3.87500000e-04 3.98805697e-04]
 [1.23250000e-04 1.34441198e-04]
 [2.49750000e-04 2.60540443e-04]
 [5.28750000e-04 5.38661990e-04]
 [2.41750000e-04 2.51526020e-04]
 [3.43750000e-04 3.53407858e-04]
 [3.46750000e-04 3.56140760e-04]
 [1.54000000e-04 1.62118107e-04]
 [3.39500000e-04 3.47281695e-04]
 [1.38375000e-03 1.39129341e-03]
 [2.90750000e-04 2.97870891e-04]
 [2.96125000e-03 2.96803792e-03]
 [7.82500000e-05 8.49785988e-05]
 [4.99750000e-04 5.06414843e-04]
 [1.03500000e-04 1.10144049e-04]
 [5.43050000e-03 5.43713088e-03]
 [2.73325000e-03 2.73974755e-03]
 [3.42975000e-03 3.43622232e-03]
 [3.42750000e-04 3.49130236e-04]
 [1.15500000e-04 1.21658153e-04]
 [1.27500000e-04 1.33484804e-04]
 [9.60000000e-05 1.01980480e-04]
 [2.07750000e-04 2.13550497e-04]
 [1.34500000e-04 1.40004553e-04]
 [3.35500000e-04 3.40878758e-04]
 [1.78700000e-03 1.79138185e-03]
 [6.52500000e-04 6.56646263e-04]
 [3.70750000e-04 3.74893139e-04]
 [4.28000000e-04 4.32049002e-04]
 [3.31750000e-04 3.35414794e-04]
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 [5.32750000e-04 5.36321745e-04]
 [1.01250000e-04 1.04656542e-04]
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 [1.12725000e-03 1.12948504e-03]
 [1.13000000e-03 1.13221684e-03]
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 [4.11250000e-04 4.13408333e-04]
 [2.87250000e-04 2.89383150e-04]
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 [3.69250000e-04 3.71222122e-04]
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 [1.13250000e-04 1.14388937e-04]
 [7.22750000e-04 7.23475528e-04]
 [6.90000000e-04 6.90670906e-04]
 [4.75500000e-04 4.76123124e-04]
 [8.79500000e-04 8.80122675e-04]
 [6.02500000e-04 6.03069331e-04]
 [1.46500000e-04 1.47068380e-04]
 [1.42750000e-03 1.42804721e-03]
 [1.54000000e-04 1.54509529e-04]
 [4.27500000e-04 4.27992007e-04]
 [3.43500000e-04 3.43928780e-04]
 [5.72500000e-04 5.72815429e-04]
 [6.04475000e-03 6.04501142e-03]
 [5.74175000e-03 5.74197078e-03]
 [4.76375000e-03 4.76393989e-03]
 [6.95750000e-04 6.95872300e-04]
 [1.63000000e-04 1.63086610e-04]
 [3.49750000e-04 3.49826413e-04]
 [1.43000000e-04 1.43064118e-04]
 [1.46500000e-04 1.46557679e-04]
 [7.41500000e-04 7.41556597e-04]
 [2.19750000e-04 2.19790326e-04]
 [3.39250000e-04 3.39276407e-04]
 [1.34100000e-03 1.34102249e-03]
 [4.31500000e-04 4.31505653e-04]
 [1.65175000e-03 1.65175078e-03]]
[[0.         6.16530525]
 [0.         6.16529525]
 [0.         6.165289  ]
 [0.         6.14101125]
 [0.         6.140969  ]
 [0.         6.14094625]
 [0.         6.140484  ]]
[[0.00000e+00 1.78825e-03]
 [0.00000e+00 1.77950e-03]
 [0.00000e+00 1.71125e-03]
 [0.00000e+00 1.38375e-03]
 [0.00000e+00 1.22375e-03]
 [0.00000e+00 1.21850e-03]
 [0.00000e+00 1.18650e-03]
 [0.00000e+00 9.23750e-04]
 [0.00000e+00 7.84500e-04]
 [0.00000e+00 7.76500e-04]
 [0.00000e+00 6.15750e-04]
 [0.00000e+00 5.97750e-04]
 [0.00000e+00 5.82500e-04]
 [0.00000e+00 4.94750e-04]
 [0.00000e+00 4.69000e-04]
 [0.00000e+00 4.53250e-04]
 [0.00000e+00 4.41250e-04]
 [0.00000e+00 4.28500e-04]
 [0.00000e+00 4.12500e-04]
 [0.00000e+00 4.03500e-04]
 [0.00000e+00 3.72750e-04]
 [0.00000e+00 3.57500e-04]
 [0.00000e+00 3.53500e-04]
 [0.00000e+00 3.42500e-04]
 [0.00000e+00 3.38250e-04]
 [0.00000e+00 3.34750e-04]
 [0.00000e+00 3.33750e-04]
 [0.00000e+00 2.98500e-04]
 [0.00000e+00 2.08000e-04]
 [0.00000e+00 2.00250e-04]
 [0.00000e+00 1.97500e-04]
 [0.00000e+00 1.92750e-04]
 [0.00000e+00 1.90500e-04]
 [0.00000e+00 1.89750e-04]
 [0.00000e+00 1.88250e-04]
 [0.00000e+00 1.75250e-04]
 [0.00000e+00 1.71500e-04]
 [0.00000e+00 1.60500e-04]
 [0.00000e+00 1.56500e-04]
 [0.00000e+00 1.53750e-04]
 [0.00000e+00 1.53750e-04]
 [0.00000e+00 1.52750e-04]
 [0.00000e+00 1.51500e-04]
 [0.00000e+00 1.35250e-04]
 [0.00000e+00 1.24000e-04]
 [0.00000e+00 1.19000e-04]
 [0.00000e+00 1.11250e-04]
 [0.00000e+00 1.10750e-04]
 [0.00000e+00 1.08500e-04]
 [0.00000e+00 9.97500e-05]
 [0.00000e+00 8.65000e-05]
 [0.00000e+00 8.62500e-05]
 [0.00000e+00 7.72500e-05]
 [0.00000e+00 7.45000e-05]
 [0.00000e+00 7.17500e-05]
 [0.00000e+00 6.70000e-05]
 [0.00000e+00 5.42500e-05]
 [0.00000e+00 5.15000e-05]
 [0.00000e+00 4.15000e-05]
 [0.00000e+00 3.85000e-05]
 [0.00000e+00 3.47500e-05]
 [0.00000e+00 3.45000e-05]
 [0.00000e+00 3.32500e-05]
 [0.00000e+00 3.15000e-05]
 [0.00000e+00 1.75000e-05]
 [0.00000e+00 1.00000e-06]
 [0.00000e+00 2.50000e-07]]

tmp=np.array([[29.66568178, 29.69764022],
 [29.67287229, 29.69967188],
 [29.67685177, 29.6993754 ],
 [29.67654529, 29.69818744],
 [29.6737314,  29.69461809],
 [29.67022941, 29.68885774],
 [29.66902402, 29.66931226],
 [29.68932898, 29.68950196],
 [29.69860988, 29.69871008]])

tmp.sort(axis=0)

tmp

array([[29.66568178, 29.66931226],
       [29.66902402, 29.68885774],
       [29.67022941, 29.68950196],
       [29.67287229, 29.69461809],
       [29.6737314 , 29.69764022],
       [29.67654529, 29.69818744],
       [29.67685177, 29.69871008],
       [29.68932898, 29.6993754 ],
       [29.69860988, 29.69967188]])

tmp.sort(axis=1)

tmp

array([[29.66568178, 29.66931226],
       [29.66902402, 29.68885774],
       [29.67022941, 29.68950196],
       [29.67287229, 29.69461809],
       [29.6737314 , 29.69764022],
       [29.67654529, 29.69818744],
       [29.67685177, 29.69871008],
       [29.68932898, 29.6993754 ],
       [29.69860988, 29.69967188]])

rest=np.array([[1,20],[3,4],[50,6]])

print(rest.sort(axis=0))

---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

<ipython-input-137-824a4bef42ce> in <module>
----> 1 print(rest.sort(axis=0))


TypeError: 'axis' is an invalid keyword argument for sort()

rest

[[1, 20], [3, 4], [50, 6]]