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Contents

DATA Analysis

The purpose of this Notebook is to perform the data Analysis, that is comparing the outputs generated from the previous notebooks for all the different samples.

Packages installation (since Aug 2021)

#pip install -U wxPython

Libraries

import numpy 
import math
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import cm
import pandas as pd
from glob import glob
from functools import reduce
import ipywidgets as widgets
from ipywidgets import interact, interactive, fixed, interact_manual
import re
from itertools import cycle
import wx



#%matplotlib notebook

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-1-51e0e51b5ca4> in <module>
     11 import re
     12 from itertools import cycle
---> 13 import wx
     14 
     15 

ModuleNotFoundError: No module named 'wx'

1. Data import and merging

1.1 XP-Ramp

spl = 'ASW'

XP_Ramp_df = pd.read_csv('..\DATA\DATA-Processing\PAC\XP_list_test.csv')
XP_Ramp_df_I = XP_Ramp_df.set_index('Date')

XP_Ramp_df_I.head(5)

	Sample	20K	25K	30K	40K	50K	55K	60K	65K	70K	...	137K	138K	140K	142K	145K	150K	155K	160K	180K	200K
Date
2020_09_15	ASW	[1]	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	[3]	NaN	NaN	[4]	NaN	NaN	NaN	NaN
2020_09_16	ASW	[1, 2]	NaN	NaN	[3, 4, 5]	NaN	NaN	[6, 7, 8]	NaN	NaN	...	NaN	NaN	[21, 22, 23]	NaN	NaN	[24, 25, 26]	NaN	NaN	[27, 28, 29]	[30, 31]
2020_09_17	ASW	[1, 2]	NaN	NaN	[3, 4, 5]	NaN	NaN	[6, 7, 8]	NaN	NaN	...	NaN	NaN	[21, 22, 23]	NaN	NaN	[24, 25, 26]	NaN	NaN	[27, 28]	NaN
2020_09_21	ASW	[1, 2]	NaN	NaN	[3, 4, 5]	NaN	NaN	[6, 7, 8]	NaN	NaN	...	NaN	NaN	[21, 22, 23]	NaN	NaN	[24, 25, 26]	NaN	NaN	[27, 28]	NaN
2020_09_28	ASW	[1, 2]	NaN	[3, 4, 5, 6]	[7, 8, 9, 10]	[11, 12, 13, 14]	NaN	[15, 16, 17, 18]	NaN	[19, 20, 21, 22]	...	NaN	NaN	[59, 60, 61, 62]	NaN	[63, 64, 65, 66]	[67, 68, 69, 70, 71, 72, 73, 74]	[75, 76, 77, 78]	[79, 80]	NaN	NaN

5 rows × 31 columns

1.2 DR1 - DR2 - DR3 all dates

DR1 - DR2 - DR3 are merged together

1.2.1 Import

#DR1

DR1_Allscans = glob('..\DATA\DATA-Processing\PAC\XP_1-1/Samples/*/Data/DR/DR1_*_All-scans.csv')

# DR2

DR2_Allscans = glob('..\DATA\DATA-Processing\PAC\XP_1-1/Samples/*/Data/DR/DR2_*_All-scans.csv')

# DR3

DR3_Allscans = glob("..\DATA\DATA-Processing\PAC\XP_1-1/Samples/*\Data\DR\DR3_*_A.csv")

Sanity Check

DR1_Allscans

['..\\DATA\\DATA-Processing\\PAC\\XP_1-1/Samples\\2020_09_15\\Data\\DR\\DR1_2020_09_15_All-scans.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1/Samples\\2020_09_16\\Data\\DR\\DR1_2020_09_16_All-scans.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1/Samples\\2020_09_17\\Data\\DR\\DR1_2020_09_17_All-scans.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1/Samples\\2020_09_21\\Data\\DR\\DR1_2020_09_21_All-scans.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1/Samples\\2020_09_28\\Data\\DR\\DR1_2020_09_28_All-scans.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1/Samples\\2020_10_14\\Data\\DR\\DR1_2020_10_14_All-scans.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1/Samples\\2020_10_22\\Data\\DR\\DR1_2020_10_22_All-scans.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1/Samples\\2020_11_16\\Data\\DR\\DR1_2020_11_16_All-scans.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1/Samples\\2020_11_19\\Data\\DR\\DR1_2020_11_19_All-scans.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1/Samples\\2020_11_23\\Data\\DR\\DR1_2020_11_23_All-scans.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1/Samples\\2021_05_13\\Data\\DR\\DR1_2021_05_13_All-scans.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1/Samples\\2021_05_24\\Data\\DR\\DR1_2021_05_24_All-scans.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1/Samples\\2021_07_13\\Data\\DR\\DR1_2021_07_13_All-scans.csv']

#DR2_Allscans

#DR3_Allscans

1.2.2 Merging

DR1

All_data_frame = []

for items in DR1_Allscans:

    df = pd.read_csv(items)
    df_1 = df.T.iloc[1:].T
    
    All_data_frame.append(df_1) 
    
    del df
    del df_1
    
    DR1_Allscans_full = reduce(lambda left,right: pd.merge(left,right, on=['Wavenumber'],how='outer'), All_data_frame)
    
    

DR1_Allscans_full

	Wavenumber	ASW_2020_09_15_1	ASW_2020_09_15_2	ASW_2020_09_15_3	ASW_2020_09_15_4	ASW_2020_09_16_1	ASW_2020_09_16_2	ASW_2020_09_16_3	ASW_2020_09_16_4	ASW_2020_09_16_5	...	ASW_2021_07_13_17	ASW_2021_07_13_18	ASW_2021_07_13_19	ASW_2021_07_13_20	ASW_2021_07_13_21	ASW_2021_07_13_22	ASW_2021_07_13_23	ASW_2021_07_13_24	ASW_2021_07_13_25	ASW_2021_07_13_26
0	799.8442	0.070200	0.086859	0.080537	0.055492	0.068765	0.070958	0.079536	0.080457	0.083105	...	0.112301	0.112165	0.111290	0.114546	0.111352	0.112160	0.114865	0.117401	0.118798	0.112614
1	800.3264	0.069613	0.085440	0.084133	0.045511	0.066157	0.068055	0.073433	0.073509	0.076351	...	0.101721	0.100925	0.100979	0.103694	0.101778	0.100835	0.104572	0.107281	0.107575	0.111304
2	800.8085	0.069002	0.081987	0.086396	0.034734	0.063596	0.065323	0.067591	0.067142	0.069968	...	0.090586	0.090152	0.090122	0.092359	0.091307	0.089453	0.093991	0.096408	0.096146	0.109683
3	801.2906	0.069099	0.078084	0.083552	0.036551	0.063784	0.066162	0.069236	0.069118	0.071792	...	0.089263	0.090480	0.089502	0.090657	0.089994	0.088894	0.093375	0.095749	0.095276	0.107832
4	801.7727	0.068733	0.076453	0.081117	0.039316	0.064503	0.067363	0.070878	0.071425	0.073453	...	0.094123	0.094984	0.094239	0.095002	0.094698	0.094322	0.098065	0.100489	0.100310	0.107623
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6634	3998.2570	-0.017155	-0.016794	-0.016967	-0.006420	-0.017952	-0.016452	-0.014667	-0.014350	-0.013844	...	-0.019303	-0.018898	-0.018461	-0.015902	-0.016764	-0.017420	-0.018147	-0.019271	-0.019650	-0.019098
6635	3998.7390	-0.017126	-0.017087	-0.017292	-0.006350	-0.018364	-0.016865	-0.014762	-0.014372	-0.013879	...	-0.019276	-0.018882	-0.018438	-0.015903	-0.016772	-0.017392	-0.018119	-0.019247	-0.019640	-0.019025
6636	3999.2210	-0.017211	-0.017489	-0.017849	-0.006541	-0.018844	-0.017376	-0.015208	-0.014730	-0.014253	...	-0.019218	-0.018809	-0.018348	-0.015880	-0.016743	-0.017337	-0.018042	-0.019167	-0.019563	-0.018901
6637	3999.7030	-0.017211	-0.016742	-0.017107	-0.006789	-0.018146	-0.016685	-0.014652	-0.014254	-0.013813	...	-0.019852	-0.019407	-0.018991	-0.016492	-0.017368	-0.018002	-0.018668	-0.019787	-0.020182	-0.019602
6638	4000.1850	-0.017199	-0.016014	-0.016281	-0.006947	-0.017547	-0.016091	-0.014062	-0.013732	-0.013332	...	-0.020471	-0.019999	-0.019634	-0.017090	-0.017998	-0.018664	-0.019310	-0.020400	-0.020810	-0.020218

6639 rows × 2041 columns

#DR1_Allscans_full.to_csv('..\DATA\DATA-Processing\PAC\DR1_full.csv')

DR2

All_data_frame = []

for items in DR2_Allscans:

    df = pd.read_csv(items)
    df_1 = df.T.iloc[1:].T
    
    All_data_frame.append(df_1) 
    
    del df
    del df_1
    
    DR2_Allscans_full = reduce(lambda left,right: pd.merge(left,right, on=['Wavenumber'],how='outer'), All_data_frame)
    
    

#DR2_Allscans_full.to_csv('D:\DATA-Processing\PAC\DR2_full.csv')

DR2_Allscans_full


#DR2_Allscans = glob("D:\PhD-WS\Projects\PAC\XP_1-1\DATA\*\Data\DR\DR2*All-scans.csv")

	Wavenumber	ASW_2020_09_15_1	ASW_2020_09_15_2	ASW_2020_09_15_3	ASW_2020_09_15_4	ASW_2020_09_16_1	ASW_2020_09_16_2	ASW_2020_09_16_3	ASW_2020_09_16_4	ASW_2020_09_16_5	...	ASW_2021_07_13_17	ASW_2021_07_13_18	ASW_2021_07_13_19	ASW_2021_07_13_20	ASW_2021_07_13_21	ASW_2021_07_13_22	ASW_2021_07_13_23	ASW_2021_07_13_24	ASW_2021_07_13_25	ASW_2021_07_13_26
0	799.8442	0.060549	0.076188	0.071446	0.047759	0.061813	0.062677	0.070377	0.071031	0.073033	...	0.093934	0.093203	0.092399	0.099772	0.096810	0.097544	0.100107	0.103579	0.104936	0.101043
1	800.3264	0.059965	0.074772	0.075044	0.037780	0.059208	0.059777	0.064277	0.064086	0.066281	...	0.083360	0.081969	0.082094	0.088924	0.087240	0.086223	0.089819	0.093463	0.093718	0.099738
2	800.8085	0.059358	0.071323	0.077309	0.027005	0.056651	0.057048	0.058439	0.057722	0.059902	...	0.072232	0.071203	0.071244	0.077594	0.076774	0.074845	0.079243	0.082596	0.082294	0.098120
3	801.2906	0.059458	0.067423	0.074469	0.028824	0.056841	0.057890	0.060087	0.059701	0.061729	...	0.070915	0.071538	0.070631	0.075896	0.075466	0.074291	0.078632	0.081942	0.081429	0.096272
4	801.7727	0.059095	0.065795	0.072036	0.031592	0.057564	0.059094	0.061732	0.062012	0.063394	...	0.075783	0.076050	0.075375	0.080245	0.080174	0.079724	0.083327	0.086687	0.086468	0.096067
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6634	3998.2570	0.000151	0.000688	0.000876	0.000556	0.000884	0.000916	0.000533	0.000411	0.000489	...	0.001156	0.001089	0.001162	0.001180	0.001225	0.001234	0.001153	0.001119	0.001150	0.001113
6635	3998.7390	0.000183	0.000399	0.000554	0.000626	0.000476	0.000507	0.000442	0.000392	0.000458	...	0.001186	0.001108	0.001187	0.001181	0.001220	0.001265	0.001183	0.001146	0.001162	0.001189
6636	3999.2210	0.000101	0.000000	0.000000	0.000436	0.000000	0.000000	0.000000	0.000039	0.000088	...	0.001247	0.001184	0.001280	0.001206	0.001251	0.001322	0.001264	0.001229	0.001242	0.001314
6637	3999.7030	0.000105	0.000750	0.000746	0.000190	0.000703	0.000696	0.000560	0.000518	0.000532	...	0.000616	0.000588	0.000641	0.000596	0.000628	0.000660	0.000640	0.000611	0.000625	0.000614
6638	4000.1850	0.000120	0.001481	0.001575	0.000033	0.001306	0.001295	0.001154	0.001045	0.001017	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000

6639 rows × 1876 columns

DR2_Allscans_full

	Wavenumber	ASW_2020_09_15_1	ASW_2020_09_15_2	ASW_2020_09_15_3	ASW_2020_09_15_4	ASW_2020_09_16_1	ASW_2020_09_16_2	ASW_2020_09_16_3	ASW_2020_09_16_4	ASW_2020_09_16_5	...	ASW_2021_07_13_17	ASW_2021_07_13_18	ASW_2021_07_13_19	ASW_2021_07_13_20	ASW_2021_07_13_21	ASW_2021_07_13_22	ASW_2021_07_13_23	ASW_2021_07_13_24	ASW_2021_07_13_25	ASW_2021_07_13_26
0	799.8442	0.060549	0.076188	0.071446	0.047759	0.061813	0.062677	0.070377	0.071031	0.073033	...	0.093934	0.093203	0.092399	0.099772	0.096810	0.097544	0.100107	0.103579	0.104936	0.101043
1	800.3264	0.059965	0.074772	0.075044	0.037780	0.059208	0.059777	0.064277	0.064086	0.066281	...	0.083360	0.081969	0.082094	0.088924	0.087240	0.086223	0.089819	0.093463	0.093718	0.099738
2	800.8085	0.059358	0.071323	0.077309	0.027005	0.056651	0.057048	0.058439	0.057722	0.059902	...	0.072232	0.071203	0.071244	0.077594	0.076774	0.074845	0.079243	0.082596	0.082294	0.098120
3	801.2906	0.059458	0.067423	0.074469	0.028824	0.056841	0.057890	0.060087	0.059701	0.061729	...	0.070915	0.071538	0.070631	0.075896	0.075466	0.074291	0.078632	0.081942	0.081429	0.096272
4	801.7727	0.059095	0.065795	0.072036	0.031592	0.057564	0.059094	0.061732	0.062012	0.063394	...	0.075783	0.076050	0.075375	0.080245	0.080174	0.079724	0.083327	0.086687	0.086468	0.096067
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
6634	3998.2570	0.000151	0.000688	0.000876	0.000556	0.000884	0.000916	0.000533	0.000411	0.000489	...	0.001156	0.001089	0.001162	0.001180	0.001225	0.001234	0.001153	0.001119	0.001150	0.001113
6635	3998.7390	0.000183	0.000399	0.000554	0.000626	0.000476	0.000507	0.000442	0.000392	0.000458	...	0.001186	0.001108	0.001187	0.001181	0.001220	0.001265	0.001183	0.001146	0.001162	0.001189
6636	3999.2210	0.000101	0.000000	0.000000	0.000436	0.000000	0.000000	0.000000	0.000039	0.000088	...	0.001247	0.001184	0.001280	0.001206	0.001251	0.001322	0.001264	0.001229	0.001242	0.001314
6637	3999.7030	0.000105	0.000750	0.000746	0.000190	0.000703	0.000696	0.000560	0.000518	0.000532	...	0.000616	0.000588	0.000641	0.000596	0.000628	0.000660	0.000640	0.000611	0.000625	0.000614
6638	4000.1850	0.000120	0.001481	0.001575	0.000033	0.001306	0.001295	0.001154	0.001045	0.001017	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000

6639 rows × 1876 columns

print(DR2_Allscans_full['Wavenumber'])

0        799.8442
1        800.3264
2        800.8085
3        801.2906
4        801.7727
          ...    
6634    3998.2570
6635    3998.7390
6636    3999.2210
6637    3999.7030
6638    4000.1850
Name: Wavenumber, Length: 6639, dtype: float64

\(\color{red}{\text{Problem with Row number (should be 6639)!}}\)

DR3

All_data_frame = []

#Wavenumber = 

for items in DR3_Allscans:

    df = pd.read_csv(items)
    df_1 = df.T.iloc[1:].T

    
    All_data_frame.append(df_1) 
    
    DR3_Allscans_full = reduce(lambda left,right: pd.merge(left,right, on=['Wavenumber'],how='outer'), All_data_frame)

DR3_Allscans_full

	Wavenumber	ASW_2020_09_15_1	ASW_2020_09_15_2	ASW_2020_09_15_3	ASW_2020_09_15_4	ASW_2020_09_16_1	ASW_2020_09_16_2	ASW_2020_09_16_3	ASW_2020_09_16_4	ASW_2020_09_16_5	...	ASW_2021_07_13_17	ASW_2021_07_13_18	ASW_2021_07_13_19	ASW_2021_07_13_20	ASW_2021_07_13_21	ASW_2021_07_13_22	ASW_2021_07_13_23	ASW_2021_07_13_24	ASW_2021_07_13_25	ASW_2021_07_13_26
0	2800.178	-0.000187	0.000075	0.000050	9.007620e-19	-0.000259	-0.000271	-0.000180	-0.000149	-0.000381	...	0.000134	0.000101	0.000235	0.000275	0.000239	0.000220	0.000243	0.000233	0.000244	0.001587
1	2800.660	-0.000137	0.000142	0.000364	3.145895e-04	-0.000121	-0.000137	0.000113	0.000146	-0.000087	...	0.000020	-0.000026	0.000106	0.000145	0.000109	0.000093	0.000105	0.000098	0.000109	0.001548
2	2801.142	-0.000201	-0.000020	0.000000	9.304404e-05	-0.000172	-0.000191	-0.000143	-0.000134	-0.000362	...	0.000304	0.000252	0.000380	0.000414	0.000382	0.000361	0.000364	0.000374	0.000384	0.001655
3	2801.624	-0.000130	0.000175	0.000488	4.249901e-04	-0.000048	-0.000069	0.000192	0.000211	-0.000008	...	0.000201	0.000159	0.000272	0.000309	0.000273	0.000247	0.000250	0.000267	0.000272	0.001589
4	2802.106	-0.000186	0.000049	0.000172	2.575800e-04	-0.000066	-0.000087	-0.000006	-0.000010	-0.000224	...	0.000465	0.000433	0.000532	0.000573	0.000525	0.000505	0.000505	0.000521	0.000534	0.001676
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
2485	3998.257	0.000156	0.000714	0.000909	5.772753e-04	0.000921	0.000955	0.000556	0.000428	0.000510	...	0.001123	0.001057	0.001128	0.001146	0.001190	0.001198	0.001120	0.001087	0.001117	0.001081
2486	3998.739	0.000190	0.000414	0.000575	6.505353e-04	0.000497	0.000529	0.000461	0.000409	0.000478	...	0.001152	0.001075	0.001153	0.001147	0.001184	0.001228	0.001149	0.001113	0.001129	0.001154
2487	3999.221	0.000105	0.000000	0.000000	4.531809e-04	0.000000	0.000000	0.000000	0.000040	0.000092	...	0.001211	0.001150	0.001243	0.001171	0.001214	0.001283	0.001227	0.001193	0.001207	0.001276
2488	3999.703	0.000109	0.000779	0.000774	1.974239e-04	0.000733	0.000726	0.000584	0.000540	0.000554	...	0.000598	0.000571	0.000622	0.000579	0.000609	0.000641	0.000622	0.000593	0.000607	0.000596
2489	4000.185	0.000125	0.001538	0.001636	3.402556e-05	0.001362	0.001350	0.001203	0.001089	0.001061	...	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000

2490 rows × 1853 columns

Row number –> OK :) (2490)

#DR3_Allscans_full.to_csv('D:\DATA-Processing\PAC\DR3_full.csv')

1.3 Data_annex

data_anex = glob("..\DATA\DATA-Processing\PAC\XP_1-1\Samples\*\Data\DR\**_data_annex.csv")

data_anex

['..\\DATA\\DATA-Processing\\PAC\\XP_1-1\\Samples\\2020_09_15\\Data\\DR\\2020_09_15_data_annex.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1\\Samples\\2020_09_16\\Data\\DR\\2020_09_16_data_annex.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1\\Samples\\2020_09_17\\Data\\DR\\2020_09_17_data_annex.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1\\Samples\\2020_09_21\\Data\\DR\\2020_09_21_data_annex.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1\\Samples\\2020_09_28\\Data\\DR\\2020_09_28_data_annex.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1\\Samples\\2020_10_22\\Data\\DR\\2020_10_22_data_annex.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1\\Samples\\2020_11_16\\Data\\DR\\2020_11_16_data_annex.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1\\Samples\\2020_11_19\\Data\\DR\\2020_11_19_data_annex.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1\\Samples\\2020_11_23\\Data\\DR\\2020_11_23_data_annex.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1\\Samples\\2021_05_13\\Data\\DR\\2021_05_13_data_annex.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1\\Samples\\2021_05_24\\Data\\DR\\2021_05_24_data_annex.csv',
 '..\\DATA\\DATA-Processing\\PAC\\XP_1-1\\Samples\\2021_07_13\\Data\\DR\\2021_07_13_data_annex.csv']

1.2.2 Merging

concat, not append

All_data_frame = []

for items in data_anex:

    df = pd.read_csv(items)
    #print(df)
    #df_1 = df.T.iloc[1:].T
    
    All_data_frame.append(df) 
    
print(All_data_frame)
    
    #del df
    #del df_1
    
data_anex_full = pd.concat(All_data_frame)
    
    

[               Name      min1  index1      min2  index2      min3  index3  \
0  ASW_2020_09_15_1 -0.017293    6630 -0.009476    4293 -0.009151    4147   
1  ASW_2020_09_15_2 -0.017489    6636 -0.008995    4161 -0.009001    4132   
2  ASW_2020_09_15_3 -0.017849    6636 -0.009715    4151 -0.009664    4132   
3  ASW_2020_09_15_4 -0.006971    6630 -0.004298    4149 -0.004222    4120   

       min4  index4      min5  ...    maxA0w  maxBi      maxB     maxBw  \
0  0.001203    2482  0.001967  ...  3270.249   2936  0.008422  2215.361   
1  0.002430    2480  0.003482  ...  3246.143   2976  0.012062  2234.646   
2  0.001595    2463  0.003063  ...  3233.125   2974  0.012331  2233.682   
3  0.002220    2473  0.002968  ...  3228.304   2976  0.006884  2234.646   

   maxCi      maxC     maxCw       Int_A     Int_C     Int_N_A  
0   1777  0.022221  1656.579   96.292003  6.703636  100.000000  
1   1754  0.021662  1645.490  111.470194  7.622334  115.762670  
2   1691  0.021226  1615.116  112.359014  8.424437  116.685717  
3   1574  0.013023  1558.708   53.700867  4.010565   55.768771  

[4 rows x 31 columns],                  Name      min1  index1      min2  index2      min3  index3  \
0    ASW_2020_09_16_1 -0.018844    6636 -0.008464    4235 -0.008369    4139   
1    ASW_2020_09_16_2 -0.017376    6636 -0.006804    4235 -0.006714    4139   
2    ASW_2020_09_16_3 -0.015208    6636 -0.005247    4197 -0.005165    4130   
3    ASW_2020_09_16_4 -0.014749    6631 -0.004931    4197 -0.004867    4130   
4    ASW_2020_09_16_5 -0.014274    6620 -0.004424    4245 -0.004362    4130   
5    ASW_2020_09_16_6 -0.013616    6636 -0.003845    4235 -0.003754    4139   
6    ASW_2020_09_16_7 -0.013178    6632 -0.003492    4235 -0.003383    4139   
7    ASW_2020_09_16_8 -0.012907    6632 -0.003190    4235 -0.003086    4139   
8    ASW_2020_09_16_9 -0.013285    6636 -0.002969    4245 -0.002883    4139   
9   ASW_2020_09_16_10 -0.013301    6636 -0.002779    4216 -0.002666    4139   
10  ASW_2020_09_16_11 -0.013018    6636 -0.002502    4197 -0.002379    4120   
11  ASW_2020_09_16_12 -0.012113    6636 -0.002024    4149 -0.001986    4130   
12  ASW_2020_09_16_13 -0.011900    6636 -0.001360    4151 -0.001341    4132   
13  ASW_2020_09_16_14 -0.011673    6636 -0.001157    4151 -0.001115    4130   
14  ASW_2020_09_16_15 -0.011645    6636 -0.001251    4149 -0.001226    4139   
15  ASW_2020_09_16_16 -0.011338    6636 -0.000905    4168 -0.000901    4139   
16  ASW_2020_09_16_17 -0.011244    6636 -0.000821    4149 -0.000807    4139   
17  ASW_2020_09_16_18 -0.011332    6636 -0.000989    4149 -0.001025    4130   
18  ASW_2020_09_16_19 -0.010872    6636 -0.000882    4158 -0.000914    4130   
19  ASW_2020_09_16_20 -0.010809    6636 -0.000910    4149 -0.000942    4130   
20  ASW_2020_09_16_21 -0.011159    6636 -0.001222    4151 -0.001287    4103   
21  ASW_2020_09_16_22 -0.010915    6630 -0.001349    4151 -0.001461    4111   
22  ASW_2020_09_16_23 -0.010836    6630 -0.001540    4151 -0.001650    4084   
23  ASW_2020_09_16_24 -0.014164    6636 -0.006225    4149 -0.006255    4101   
24  ASW_2020_09_16_25 -0.013780    6636 -0.008598    4151 -0.008599    4122   
25  ASW_2020_09_16_26  0.002459    6636  0.004937    4151  0.004895    4053   
26  ASW_2020_09_16_27  0.007303    6636  0.007753    4533  0.008098    3959   
27  ASW_2020_09_16_28  0.007890    6332  0.008135    4545  0.008503    4139   
28  ASW_2020_09_16_29  0.008264    6332  0.008545    4563  0.008896    4139   
29  ASW_2020_09_16_30  0.008619    6331  0.008641    4562  0.009109    4084   
30  ASW_2020_09_16_31  0.008318    6636  0.008889    4543  0.009277    3959   

        min4  index4      min5  ...    maxA0w  maxBi      maxB     maxBw  \
0  -0.001418    2403 -0.000786  ...  3268.321   2945  0.009284  2219.700   
1   0.000216    2432  0.000863  ...  3264.464   2945  0.009251  2219.700   
2   0.001310    2413  0.001796  ...  3264.464   2947  0.010343  2220.665   
3   0.001626    2413  0.002152  ...  3264.464   2947  0.010512  2220.665   
4   0.002212    2413  0.002702  ...  3264.464   2947  0.010562  2220.665   
5   0.003361    2444  0.003725  ...  3255.303   2949  0.010078  2221.629   
6   0.003972    2425  0.004340  ...  3255.303   2951  0.010252  2222.593   
7   0.004323    2406  0.004697  ...  3255.303   2951  0.010345  2222.593   
8   0.004149    2415  0.004730  ...  3251.446   2959  0.011390  2226.450   
9   0.004334    2367  0.004952  ...  3251.446   2959  0.011787  2226.450   
10  0.004644    2367  0.005298  ...  3250.482   2959  0.011822  2226.450   
11  0.005265    2415  0.005871  ...  3251.446   2959  0.011693  2226.450   
12  0.005725    2367  0.006299  ...  3250.482   2980  0.012006  2236.575   
13  0.005912    2415  0.006504  ...  3250.482   2959  0.011885  2226.450   
14  0.006022    2415  0.006723  ...  3250.482   2966  0.011713  2229.825   
15  0.006207    2415  0.006852  ...  3247.107   2964  0.012078  2228.861   
16  0.006330    2415  0.006958  ...  3247.107   2964  0.012166  2228.861   
17  0.006762    2480  0.007738  ...  3246.625   2957  0.011663  2225.486   
18  0.006530    2480  0.008113  ...  3246.625   2978  0.011855  2235.610   
19  0.006549    2480  0.008185  ...  3246.143   2978  0.011966  2235.610   
20  0.006266    2415  0.007035  ...  3242.286   2966  0.012110  2229.825   
21  0.006305    2444  0.007058  ...  3232.161   2976  0.012184  2234.646   
22  0.006335    2396  0.007176  ...  3228.304   2976  0.012485  2234.646   
23  0.003633    2444  0.004862  ...  3228.304   2976  0.011491  2234.646   
24  0.001659    2484  0.002442  ...  3228.304   2978  0.008953  2235.610   
25  0.006344    2329  0.006488  ...  3227.822   2978  0.004050  2235.610   
26  0.007518    2300  0.007247  ...  3228.304   2295  0.001530  1906.319   
27  0.008035    2480  0.007282  ...  3242.286   2698  0.001674  2100.615   
28  0.008449    2480  0.007719  ...  3242.286   2698  0.001720  2100.615   
29  0.008596    2480  0.008381  ...  3228.304   2602  0.001573  2054.332   
30  0.008351    2461  0.008204  ...  3223.965   2437  0.002416  1974.781   

    maxCi      maxC     maxCw       Int_A     Int_C     Int_N_A  
0    1773  0.023557  1654.651   95.913079  6.952239  100.000000  
1    1771  0.023602  1653.686   96.271574  7.064048  100.373772  
2    1754  0.024362  1645.490   98.372535  7.669780  102.564255  
3    1754  0.024289  1645.490   98.949916  7.710412  103.166239  
4    1754  0.024293  1645.490   99.128959  7.740047  103.352911  
5    1756  0.023139  1646.454  102.427355  8.047927  106.791854  
6    1756  0.022505  1646.454  103.570509  8.057326  107.983719  
7    1756  0.022478  1646.454  103.920588  8.069566  108.348714  
8    1754  0.022548  1645.490  106.316388  8.023931  110.846602  
9    1754  0.022958  1645.490  107.623907  8.026164  112.209835  
10   1754  0.022904  1645.490  107.980853  8.037065  112.581991  
11   1754  0.020700  1645.490  108.386906  8.033410  113.005345  
12   1771  0.021517  1653.686  109.276611  8.503723  113.932962  
13   1771  0.021516  1653.686  109.502264  8.477687  114.168230  
14   1699  0.020339  1618.973  109.378492  8.007280  114.039184  
15   1769  0.020189  1652.722  109.838625  8.093852  114.518923  
16   1769  0.020213  1652.722  110.041893  8.142568  114.730852  
17   1754  0.021377  1645.490  109.763420  7.476483  114.440514  
18   1697  0.021481  1618.009  109.666245  7.569996  114.339198  
19   1697  0.021457  1618.009  109.738473  7.560006  114.414504  
20   1699  0.020884  1618.973  109.665610  8.124397  114.338537  
21   1535  0.020529  1539.905  109.891203  8.369954  114.573742  
22   1535  0.020793  1539.905  110.395286  8.440394  115.099305  
23   1697  0.019248  1618.009  108.090993  7.265889  112.696823  
24   1535  0.016275  1539.905   83.840839  5.785232   87.413354  
25   1535  0.009392  1539.905   27.229288  2.969525   28.389547  
26   1495  0.004807  1520.620    3.170572  1.399266    3.305672  
27   1495  0.006589  1520.620    2.966754  1.841489    3.093169  
28   1495  0.006580  1520.620    3.023007  1.840784    3.151819  
29   1574  0.004599  1558.708    3.025809  1.338555    3.154741  
30   1495  0.006633  1520.620    3.256800  1.924223    3.395574  

[31 rows x 31 columns],                  Name      min1  index1      min2  index2      min3  index3  \
0    ASW_2020_09_17_1 -0.041049    6638 -0.053565    4353 -0.051591    4144   
1    ASW_2020_09_17_2 -0.041270    6638 -0.053673    4355 -0.051672    4144   
2    ASW_2020_09_17_3 -0.041100    6638 -0.053635    4372 -0.051417    4148   
3    ASW_2020_09_17_4 -0.041266    6633 -0.053916    4361 -0.051628    4148   
4    ASW_2020_09_17_5 -0.041279    6633 -0.053935    4374 -0.051636    4148   
5    ASW_2020_09_17_6 -0.041531    6633 -0.054154    4374 -0.051706    4144   
6    ASW_2020_09_17_7 -0.041550    6633 -0.054193    4374 -0.051757    4146   
7    ASW_2020_09_17_8 -0.041348    6633 -0.054032    4374 -0.051564    4146   
8    ASW_2020_09_17_9 -0.041813    6638 -0.053609    4357 -0.051546    4146   
9   ASW_2020_09_17_10 -0.042010    6638 -0.053469    4338 -0.051491    4146   
10  ASW_2020_09_17_11 -0.041987    6638 -0.053413    4338 -0.051391    4146   
11  ASW_2020_09_17_12 -0.042061    6638 -0.052986    4322 -0.051464    4148   
12  ASW_2020_09_17_13 -0.042228    6638 -0.052661    4324 -0.051230    4147   
13  ASW_2020_09_17_14 -0.041972    6638 -0.052482    4295 -0.051025    4147   
14  ASW_2020_09_17_15 -0.041883    6638 -0.051754    4288 -0.050816    4144   
15  ASW_2020_09_17_16 -0.041787    6638 -0.051383    4242 -0.050580    4144   
16  ASW_2020_09_17_17 -0.041580    6638 -0.051154    4261 -0.050351    4144   
17  ASW_2020_09_17_18 -0.041343    6638 -0.050699    4230 -0.050000    4142   
18  ASW_2020_09_17_19 -0.040932    6633 -0.050375    4230 -0.049711    4142   
19  ASW_2020_09_17_20 -0.040861    6633 -0.050217    4230 -0.049580    4144   
20  ASW_2020_09_17_21 -0.040727    6633 -0.049719    4247 -0.049215    4147   
21  ASW_2020_09_17_22 -0.040232    6633 -0.049096    4228 -0.048607    4140   
22  ASW_2020_09_17_23 -0.039847    6633 -0.048639    4228 -0.048192    4132   
23  ASW_2020_09_17_24 -0.039404    6638 -0.046864    4184 -0.046810    4136   
24  ASW_2020_09_17_25 -0.034788    6638 -0.038723    4175 -0.038613    4146   
25  ASW_2020_09_17_26 -0.013245    6638 -0.012364    4213 -0.012120    4144   
26  ASW_2020_09_17_27  0.005091    6638  0.005379    4562  0.005726    4132   
27  ASW_2020_09_17_28  0.005975    6633  0.005630    4562  0.006364    4132   

        min4  index4      min5  ...    maxA0w  maxBi      maxB     maxBw  \
0  -0.026561    2483 -0.023261  ...  3293.391   2940  0.010735  2217.290   
1  -0.026568    2483 -0.023255  ...  3289.052   2940  0.010743  2217.290   
2  -0.025492    2483 -0.021956  ...  3280.374   2940  0.011222  2217.290   
3  -0.025534    2483 -0.021993  ...  3279.892   2940  0.011301  2217.290   
4  -0.025461    2483 -0.021957  ...  3276.035   2940  0.011437  2217.290   
5  -0.025538    2473 -0.022032  ...  3265.910   2959  0.013357  2226.450   
6  -0.025422    2473 -0.021894  ...  3262.053   2959  0.013781  2226.450   
7  -0.025145    2473 -0.021623  ...  3262.053   2978  0.013934  2235.610   
8  -0.024607    2473 -0.020855  ...  3258.196   2978  0.014325  2235.610   
9  -0.024534    2473 -0.020687  ...  3257.232   2978  0.014972  2235.610   
10 -0.024377    2473 -0.020547  ...  3257.232   2978  0.015035  2235.610   
11 -0.024290    2473 -0.020396  ...  3256.268   2978  0.015104  2235.610   
12 -0.024098    2482 -0.020072  ...  3256.268   2988  0.015436  2240.432   
13 -0.023904    2482 -0.019835  ...  3256.268   2988  0.015455  2240.432   
14 -0.023720    2473 -0.019773  ...  3254.339   2988  0.015600  2240.432   
15 -0.023597    2473 -0.019453  ...  3253.375   2988  0.015976  2240.432   
16 -0.023371    2482 -0.019255  ...  3252.411   2988  0.015818  2240.432   
17 -0.023107    2483 -0.019587  ...  3254.339   2978  0.015517  2235.610   
18 -0.022701    2483 -0.019206  ...  3253.375   2978  0.015520  2235.610   
19 -0.022563    2483 -0.019068  ...  3252.411   2978  0.015529  2235.610   
20 -0.022598    2473 -0.018500  ...  3252.893   2988  0.015668  2240.432   
21 -0.022263    2473 -0.017958  ...  3244.214   2988  0.015724  2240.432   
22 -0.021806    2473 -0.017448  ...  3238.911   2988  0.015824  2240.432   
23 -0.020812    2473 -0.016158  ...  3232.644   3007  0.015618  2249.592   
24 -0.015231    2473 -0.011197  ...  3230.233   3026  0.013833  2258.752   
25 -0.001047    2473  0.000863  ...  3228.304   3026  0.007350  2258.752   
26  0.006629    2425  0.006986  ...  3224.929   2353  0.001953  1934.283   
27  0.006884    2329  0.007150  ...  3241.322   2305  0.002652  1911.141   

    maxCi      maxC     maxCw       Int_A     Int_C     Int_N_A  
0    1835  0.030168  1684.542  135.148307  5.806995  100.000000  
1    1835  0.030455  1684.542  135.402168  6.249885  100.187839  
2    1814  0.028092  1674.418  139.668744  5.387251  103.344797  
3    1814  0.027993  1674.418  140.633365  5.855341  104.058547  
4    1814  0.027857  1674.418  141.038119  5.758875  104.358036  
5    1814  0.028235  1674.418  146.782680  7.007985  108.608597  
6    1814  0.027795  1674.418  148.618855  7.174062  109.967234  
7    1814  0.027677  1674.418  149.121309  7.204706  110.339014  
8    1814  0.024760  1674.418  152.408599  6.885394  112.771371  
9    1814  0.024572  1674.418  154.066032  7.388391  113.997752  
10   1814  0.024441  1674.418  154.444547  7.414897  114.277826  
11   1776  0.024353  1656.097  155.071749  7.140653  114.741910  
12   1776  0.024087  1656.097  156.217955  7.391837  115.590020  
13   1776  0.024028  1656.097  156.422587  7.409158  115.741432  
14   1795  0.024562  1665.257  156.144672  7.645946  115.535796  
15   1835  0.023701  1684.542  156.649678  6.152382  115.909464  
16   1835  0.023294  1684.542  156.939959  5.738137  116.124251  
17   1814  0.023472  1674.418  156.481455  6.948400  115.784991  
18   1754  0.024173  1645.490  156.748946  7.562134  115.982915  
19   1754  0.024207  1645.490  156.973342  7.568792  116.148952  
20   1795  0.024614  1665.257  156.551748  7.727592  115.837003  
21   1776  0.024433  1656.097  156.905620  7.734879  116.098842  
22   1776  0.024010  1656.097  156.991053  7.448137  116.162056  
23   1697  0.022184  1618.009  156.348744  6.354408  115.686794  
24   1699  0.019466  1618.973  128.467096  5.824587   95.056385  
25   1697  0.011157  1618.009   55.115755  3.628102   40.781684  
26   1697  0.003724  1618.009    3.576484  1.192419    2.646340  
27    578  0.004862  1078.512    2.706240  1.708501    2.002422  

[28 rows x 31 columns],                  Name      min1  index1      min2  index2      min3  index3  \
0    ASW_2020_09_21_1 -0.024190    6635 -0.021363    4408 -0.019372    4147   
1    ASW_2020_09_21_2 -0.023390    6635 -0.020446    4408 -0.018472    4147   
2    ASW_2020_09_21_3 -0.022978    6638 -0.019493    4413 -0.017551    4145   
3    ASW_2020_09_21_4 -0.022961    6638 -0.019394    4404 -0.017429    4145   
4    ASW_2020_09_21_5 -0.022826    6638 -0.019274    4404 -0.017250    4145   
5    ASW_2020_09_21_6 -0.022259    6638 -0.018912    4389 -0.016856    4147   
6    ASW_2020_09_21_7 -0.022216    6638 -0.018716    4389 -0.016632    4147   
7    ASW_2020_09_21_8 -0.022156    6638 -0.018649    4408 -0.016515    4147   
8    ASW_2020_09_21_9 -0.021014    6635 -0.018046    4389 -0.016279    4147   
9   ASW_2020_09_21_10 -0.020738    6635 -0.017794    4381 -0.015959    4141   
10  ASW_2020_09_21_11 -0.020528    6635 -0.017625    4383 -0.015784    4141   
11  ASW_2020_09_21_12 -0.020188    6634 -0.016946    4358 -0.015520    4145   
12  ASW_2020_09_21_13 -0.019821    6636 -0.016477    4329 -0.015199    4145   
13  ASW_2020_09_21_14 -0.019420    6635 -0.016119    4329 -0.014845    4145   
14  ASW_2020_09_21_15 -0.019226    6635 -0.015382    4283 -0.014569    4147   
15  ASW_2020_09_21_16 -0.018965    6638 -0.014864    4283 -0.014110    4147   
16  ASW_2020_09_21_17 -0.018702    6638 -0.014561    4283 -0.013823    4147   
17  ASW_2020_09_21_18 -0.018787    6638 -0.014501    4262 -0.013889    4147   
18  ASW_2020_09_21_19 -0.019221    6638 -0.014377    4243 -0.013883    4147   
19  ASW_2020_09_21_20 -0.019109    6638 -0.014228    4264 -0.013762    4147   
20  ASW_2020_09_21_21 -0.019282    6638 -0.014379    4233 -0.013907    4145   
21  ASW_2020_09_21_22 -0.019843    6638 -0.014471    4243 -0.014056    4145   
22  ASW_2020_09_21_23 -0.019504    6638 -0.014153    4214 -0.013797    4145   
23  ASW_2020_09_21_24 -0.018905    6638 -0.014180    4195 -0.014076    4147   
24  ASW_2020_09_21_25 -0.008469    6634 -0.004991    4191 -0.004949    4141   
25  ASW_2020_09_21_26  0.005774    6634  0.006492    4250  0.006626    4034   
26  ASW_2020_09_21_27  0.006858    6334  0.006554    4559  0.007121    4147   
27  ASW_2020_09_21_28  0.006282    6638  0.006698    4550  0.007404    4099   

        min4  index4      min5  ...    maxA0w  maxBi      maxB     maxBw  \
0  -0.004675    2482 -0.003455  ...  3277.481   2947  0.007965  2220.665   
1  -0.003923    2482 -0.002717  ...  3273.624   2947  0.007976  2220.665   
2  -0.003091    2489 -0.001809  ...  3272.660   2964  0.008282  2228.861   
3  -0.002985    2489 -0.001640  ...  3268.803   2964  0.008400  2228.861   
4  -0.002768    2489 -0.001449  ...  3267.839   2964  0.008481  2228.861   
5  -0.002739    2480 -0.001496  ...  3259.642   2974  0.009784  2233.682   
6  -0.002465    2480 -0.001256  ...  3259.642   2974  0.009957  2233.682   
7  -0.002311    2480 -0.001079  ...  3258.678   2974  0.010071  2233.682   
8  -0.001858    2480 -0.000408  ...  3254.821   2964  0.010422  2228.861   
9  -0.001698    2480 -0.000284  ...  3253.857   2974  0.010848  2233.682   
10 -0.001495    2480 -0.000022  ...  3253.857   2974  0.010910  2233.682   
11 -0.000897    2480  0.000702  ...  3254.339   2974  0.010826  2233.682   
12 -0.000607    2480  0.000983  ...  3250.482   2974  0.011229  2233.682   
13 -0.000280    2480  0.001307  ...  3250.482   2974  0.011235  2233.682   
14 -0.000096    2480  0.001628  ...  3253.857   2974  0.011074  2233.682   
15  0.000031    2480  0.001786  ...  3249.518   2976  0.011291  2234.646   
16  0.000334    2480  0.002021  ...  3249.518   2976  0.011190  2234.646   
17  0.000887    2482  0.001990  ...  3249.518   2964  0.011057  2228.861   
18  0.001161    2489  0.002158  ...  3249.518   2993  0.011005  2242.842   
19  0.001315    2489  0.002354  ...  3249.518   2993  0.011012  2242.842   
20  0.000304    2480  0.002248  ...  3249.518   2985  0.011111  2238.985   
21  0.000039    2480  0.001931  ...  3243.732   2983  0.011126  2238.021   
22  0.000187    2480  0.002116  ...  3235.536   2983  0.011147  2238.021   
23 -0.000267    2480  0.001992  ...  3230.715   2993  0.010732  2242.842   
24  0.003089    2480  0.004251  ...  3227.340   2993  0.007173  2242.842   
25  0.007346    2365  0.007449  ...  3226.376   2320  0.002181  1918.373   
26  0.007820    2365  0.007820  ...  3235.536   2320  0.001697  1918.373   
27  0.008061    2298  0.007917  ...  3235.536   2322  0.001954  1919.337   

    maxCi      maxC     maxCw       Int_A     Int_C     Int_N_A  
0    1785  0.019938  1660.436   88.680361  5.302705  100.000000  
1    1785  0.020090  1660.436   88.678660  5.579043   99.998081  
2    1771  0.019792  1653.686   90.619014  5.523779  102.186112  
3    1771  0.019782  1653.686   91.273150  5.553721  102.923746  
4    1771  0.019890  1653.686   91.534144  5.623779  103.218055  
5    1802  0.020415  1668.632   94.993549  6.029632  107.119036  
6    1802  0.020625  1668.632   96.159751  6.759778  108.434099  
7    1802  0.020583  1668.632   96.483848  6.794238  108.799566  
8    1771  0.018731  1653.686   98.075058  6.683564  110.593887  
9    1771  0.019553  1653.686   99.088457  6.906236  111.736641  
10   1771  0.019569  1653.686   99.348481  6.934031  112.029856  
11   1754  0.017284  1645.490  100.108583  5.864341  112.886982  
12   1756  0.017001  1646.454  100.848724  6.205336  113.721599  
13   1756  0.016985  1646.454  101.084908  6.179590  113.987929  
14   1754  0.018376  1645.490  101.152267  6.829697  114.063886  
15   1697  0.018297  1618.009  101.661938  6.951543  114.638616  
16   1697  0.018379  1618.009  101.918675  6.966613  114.928124  
17   1771  0.018300  1653.686  101.998480  5.952818  115.018115  
18   1771  0.018923  1653.686  102.635762  6.192575  115.736743  
19   1771  0.018730  1653.686  102.776710  6.146912  115.895683  
20   1754  0.018467  1645.490  102.469812  6.359011  115.549610  
21   1697  0.018545  1618.009  102.715825  6.870105  115.827026  
22   1697  0.018414  1618.009  102.436553  6.913507  115.512106  
23   1754  0.016847  1645.490   98.934311  6.228148  111.562819  
24   1570  0.011939  1556.779   60.646382  4.024592   68.387613  
25   1570  0.006015  1556.779    8.738037  2.052757    9.853407  
26    566  0.003866  1072.727    3.421708  1.361210    3.858473  
27   1570  0.005483  1556.779    3.997098  1.954863    4.507309  

[28 rows x 31 columns],                  Name      min1  index1      min2  index2      min3  index3  \
0    ASW_2020_09_28_1 -0.026504    6620 -0.024226    4401 -0.022407    4134   
1    ASW_2020_09_28_2 -0.025427    6636 -0.022822    4372 -0.021184    4144   
2    ASW_2020_09_28_3 -0.024675    6636 -0.021948    4401 -0.020278    4142   
3    ASW_2020_09_28_4 -0.024353    6632 -0.021656    4393 -0.019961    4142   
4    ASW_2020_09_28_5 -0.024163    6632 -0.021448    4401 -0.019691    4134   
..                ...       ...     ...       ...     ...       ...     ...   
75  ASW_2020_09_28_76  0.033360    6633  0.035411    4154  0.035383    4135   
76  ASW_2020_09_28_77  0.039784    6633  0.040950    4154  0.040848    4077   
77  ASW_2020_09_28_78  0.043527    6335  0.043778    4163  0.043654    3962   
78  ASW_2020_09_28_79  0.043639    6638  0.043704    4557  0.044042    3943   
79  ASW_2020_09_28_80  0.016138    6633  0.020018    4211  0.020286    4136   

        min4  index4      min5  ...    maxA0w  maxBi      maxB     maxBw  \
0  -0.009105    2458 -0.007995  ...  3272.178   2923  0.009211  2209.094   
1  -0.007454    2477 -0.006254  ...  3276.035   2942  0.007386  2218.254   
2  -0.006614    2477 -0.005435  ...  3276.035   2942  0.007681  2218.254   
3  -0.006287    2477 -0.005064  ...  3271.213   2923  0.007746  2209.094   
4  -0.005975    2458 -0.004768  ...  3276.035   2942  0.007936  2218.254   
..       ...     ...       ...  ...       ...    ...       ...       ...   
75  0.040743    2479  0.041975  ...  3226.858   2990  0.005160  2241.396   
76  0.043054    2479  0.043847  ...  3225.894   3040  0.003348  2265.502   
77  0.043991    2345  0.043920  ...  3224.929   2300  0.002073  1908.730   
78  0.044228    2345  0.044163  ...  3224.929   2321  0.002171  1918.855   
79  0.033108    2489  0.034497  ...  3248.072   3037  0.011578  2264.056   

    maxCi      maxC     maxCw       Int_A     Int_C     Int_N_A  
0    1780  0.022529  1658.026   86.558093  6.591012  100.000000  
1    1771  0.019989  1653.686   86.043652  5.563222   99.405669  
2    1771  0.020173  1653.686   86.876902  5.599657  100.368318  
3    1771  0.020165  1653.686   87.117734  5.607032  100.646549  
4    1771  0.020096  1653.686   87.298165  5.540918  100.855000  
..    ...       ...       ...         ...       ...         ...  
75   1675  0.008315  1607.402   40.624815  2.506664   46.933584  
76   1675  0.006200  1607.402   22.869835  1.940948   26.421371  
77    947  0.005038  1256.416    5.198979  2.124355    6.006347  
78    947  0.005697  1256.416    3.493992  2.447063    4.036586  
79   1698  0.018059  1618.491  102.387413  7.200944  118.287510  

[80 rows x 31 columns],                  Name      min1  index1      min2  index2      min3  index3  \
0    ASW_2020_10_22_1  0.014819    6636  0.018631    4326  0.019603    4134   
1    ASW_2020_10_22_2 -0.022843    6636 -0.015237    4395 -0.013955    4134   
2    ASW_2020_10_22_3 -0.029230    6636 -0.020667    4268 -0.019819    4143   
3    ASW_2020_10_22_4 -0.027761    6636 -0.018797    4249 -0.018241    4144   
4    ASW_2020_10_22_5 -0.030971    6637 -0.021733    4251 -0.021147    4134   
..                ...       ...     ...       ...     ...       ...     ...   
62  ASW_2020_10_22_63 -0.026737    6637 -0.019604    4237 -0.019420    4144   
63  ASW_2020_10_22_64 -0.024439    6638 -0.017377    4237 -0.017269    4144   
64  ASW_2020_10_22_65 -0.022301    6638 -0.015511    4247 -0.015393    4144   
65  ASW_2020_10_22_66 -0.020372    6638 -0.013859    4245 -0.013783    4144   
66  ASW_2020_10_22_67 -0.013516    6636 -0.008561    4182 -0.008546    4134   

        min4  index4      min5  ...    maxA0w  maxBi      maxB     maxBw  \
0   0.031884    2459  0.032713  ...  3272.660   2955  0.009482  2224.521   
1   0.000369    2478  0.001672  ...  3272.660   2926  0.009332  2210.540   
2  -0.004564    2478 -0.003160  ...  3254.339   2974  0.010775  2233.682   
3  -0.002777    2476 -0.000697  ...  3250.482   2974  0.011963  2233.682   
4  -0.005513    2486 -0.003327  ...  3249.518   2974  0.011912  2233.682   
..       ...     ...       ...  ...       ...    ...       ...       ...   
62 -0.006983    2469 -0.005708  ...  3226.858   2993  0.008266  2242.842   
63 -0.005614    2469 -0.004496  ...  3226.376   2993  0.007831  2242.842   
64 -0.004635    2459 -0.003677  ...  3226.858   3022  0.007336  2256.824   
65 -0.003965    2469 -0.003115  ...  3226.376   2982  0.006928  2237.539   
66 -0.002468    2486 -0.000886  ...  3226.858   2974  0.004705  2233.682   

    maxCi      maxC     maxCw       Int_A     Int_C     Int_N_A  
0    1789  0.021203  1662.365   96.288989  4.550228  100.000000  
1    1789  0.022469  1662.365   95.908370  5.544767   99.604712  
2    1760  0.019923  1648.383  105.914224  7.060409  109.996195  
3    1760  0.019266  1648.383  108.987482  6.933768  113.187898  
4    1760  0.019564  1648.383  109.252891  7.234991  113.463536  
..    ...       ...       ...         ...       ...         ...  
62   1694  0.012402  1616.563   73.168245  5.227288   75.988175  
63   1694  0.011845  1616.563   68.500794  5.015416   71.140838  
64   1694  0.011156  1616.563   63.701469  4.740874   66.156546  
65   1694  0.010578  1616.563   58.925336  4.483670   61.196339  
66   1691  0.007734  1615.116   40.133157  2.805107   41.679902  

[67 rows x 31 columns],                  Name      min1  index1      min2  index2      min3  index3  \
0    ASW_2020_11_16_1 -0.010121    6626 -0.009355    4383 -0.008172    4147   
1    ASW_2020_11_16_2 -0.026869    6637 -0.023372    4383 -0.021893    4148   
2    ASW_2020_11_16_3 -0.028153    6636 -0.023769    4383 -0.022059    4147   
3    ASW_2020_11_16_4 -0.026518    6636 -0.022628    4383 -0.020908    4147   
4    ASW_2020_11_16_5 -0.026869    6636 -0.022787    4383 -0.020939    4147   
..                ...       ...     ...       ...     ...       ...     ...   
84  ASW_2020_11_16_85 -0.028089    6636 -0.023765    4212 -0.023512    4145   
85  ASW_2020_11_16_86 -0.027660    6636 -0.023311    4224 -0.023026    4145   
86  ASW_2020_11_16_87 -0.026231    6636 -0.022321    4212 -0.022064    4145   
87  ASW_2020_11_16_88 -0.026257    6636 -0.022117    4212 -0.021893    4145   
88  ASW_2020_11_16_89 -0.009999    6636 -0.007569    4154 -0.007616    4144   

        min4  index4      min5  ...    maxA0w  maxBi      maxB     maxBw  \
0   0.005017    2488  0.006147  ...  3277.481   2947  0.007971  2220.665   
1  -0.007076    2478 -0.005719  ...  3273.624   2928  0.008250  2211.504   
2  -0.007937    2488 -0.006685  ...  3262.535   2955  0.010532  2224.521   
3  -0.006668    2488 -0.005349  ...  3258.196   2955  0.010946  2224.521   
4  -0.006565    2488 -0.005277  ...  3258.196   2955  0.010908  2224.521   
..       ...     ...       ...  ...       ...    ...       ...       ...   
84 -0.008379    2479 -0.006823  ...  3226.376   2993  0.010435  2242.842   
85 -0.008107    2488 -0.006541  ...  3226.376   2993  0.010305  2242.842   
86 -0.007428    2488 -0.005895  ...  3226.376   3022  0.010178  2256.824   
87 -0.007197    2479 -0.005681  ...  3226.376   2993  0.010150  2242.842   
88 -0.003184    2363 -0.003027  ...  3223.001   2993  0.005525  2242.842   

    maxCi      maxC     maxCw       Int_A     Int_C     Int_N_A  
0    1790  0.021751  1662.847   96.870340  5.913422  100.000000  
1    1780  0.021978  1658.026   96.939281  6.078172  100.071168  
2    1779  0.022222  1657.543  103.933533  7.059747  107.291389  
3    1779  0.021728  1657.543  106.204256  7.152024  109.635474  
4    1779  0.021779  1657.543  106.420222  7.234453  109.858417  
..    ...       ...       ...         ...       ...         ...  
84   1694  0.015213  1616.563   93.859992  6.298993   96.892395  
85   1694  0.015076  1616.563   92.901573  6.225848   95.903012  
86   1694  0.014895  1616.563   91.949100  6.146797   94.919766  
87   1694  0.014744  1616.563   90.926319  6.090073   93.863941  
88   1694  0.008163  1616.563   42.185939  3.502538   43.548870  

[89 rows x 31 columns],                    Name      min1  index1      min2  index2      min3  index3  \
0      ASW_2020_11_19_1 -0.022255    6636 -0.014984    4239 -0.014844    4141   
1      ASW_2020_11_19_2 -0.021923    6636 -0.014678    4258 -0.014551    4141   
2      ASW_2020_11_19_3 -0.020756    6636 -0.013996    4247 -0.013787    4141   
3      ASW_2020_11_19_4 -0.020955    6636 -0.014144    4247 -0.013911    4141   
4      ASW_2020_11_19_5 -0.020809    6636 -0.013993    4239 -0.013722    4141   
..                  ...       ...     ...       ...     ...       ...     ...   
155  ASW_2020_11_19_156 -0.006684    6636 -0.001928    4166 -0.002012    4116   
156  ASW_2020_11_19_157 -0.005365    6636 -0.000708    4166 -0.000795    4097   
157  ASW_2020_11_19_158  0.001811    6636  0.005097    4156  0.004987    4097   
158  ASW_2020_11_19_159  0.115129    6636  0.108430    4156  0.107438    3942   
159  ASW_2020_11_19_160 -0.001314    6636  0.000191    4156 -0.000612    3951   

         min4  index4      min5  ...    maxA0w  maxBi      maxB     maxBw  \
0   -0.005657    2466 -0.004707  ...  3271.696   2943  0.009633  2218.736   
1   -0.005469    2466 -0.004570  ...  3267.356   2943  0.009603  2218.736   
2   -0.004433    2440 -0.003640  ...  3257.714   2954  0.010272  2224.039   
3   -0.004228    2440 -0.003406  ...  3257.714   2962  0.010502  2227.896   
4   -0.004041    2488 -0.003245  ...  3257.714   2960  0.010316  2226.932   
..        ...     ...       ...  ...       ...    ...       ...       ...   
155  0.001997    2485  0.002317  ...  3225.412   3003  0.004499  2247.664   
156  0.003111    2485  0.003370  ...  3225.412   2974  0.004437  2233.682   
157  0.008112    2483  0.008396  ...  3225.412   2974  0.004239  2233.682   
158  0.107005    2483  0.107452  ...  3227.340   2974  0.003423  2233.682   
159 -0.001645    2285 -0.002079  ...  3225.412   2934  0.002297  2214.397   

     maxCi      maxC     maxCw       Int_A     Int_C     Int_N_A  
0     1786  0.024570  1660.918  101.446420  7.401140  100.000000  
1     1786  0.024570  1660.918  101.462372  7.399730  100.015724  
2     1779  0.022942  1657.543  107.069815  7.809192  105.543217  
3     1779  0.022413  1657.543  108.340997  7.787821  106.796275  
4     1779  0.022392  1657.543  108.715090  7.893904  107.165034  
..     ...       ...       ...         ...       ...         ...  
155   1699  0.006401  1618.973   40.564471  2.177198   39.986104  
156   1757  0.006255  1646.937   39.437048  2.146906   38.874755  
157   1699  0.005886  1618.973   38.505910  2.184976   37.956894  
158    499  0.034891  1040.424   38.132260  3.647014   37.588572  
159   1346  0.004708  1448.784   15.318774  2.090059   15.100360  

[160 rows x 31 columns],                    Name      min1  index1      min2  index2      min3  index3  \
0      ASW_2020_11_23_1 -0.048605    6636 -0.054942    4425 -0.051454    4147   
1      ASW_2020_11_23_2 -0.048993    6636 -0.054901    4425 -0.051299    4147   
2      ASW_2020_11_23_3 -0.048375    6636 -0.054771    4415 -0.051253    4146   
3      ASW_2020_11_23_4 -0.026927    6636 -0.033931    4425 -0.030514    4146   
4      ASW_2020_11_23_5 -0.047706    6636 -0.054582    4425 -0.050982    4146   
..                  ...       ...     ...       ...     ...       ...     ...   
160  ASW_2020_11_23_161 -0.023476    6634 -0.021884    4557 -0.020983    4146   
161  ASW_2020_11_23_162 -0.023096    6634 -0.021644    4537 -0.020652    4146   
162  ASW_2020_11_23_163 -0.005374    6634 -0.003921    4547 -0.003129    4146   
163  ASW_2020_11_23_164 -0.022173    6634 -0.021017    4557 -0.020003    4146   
164  ASW_2020_11_23_165 -0.021724    6634 -0.020708    4555 -0.019500    4146   

         min4  index4      min5  ...    maxA0w  maxBi      maxB     maxBw  \
0   -0.027763    2481 -0.024776  ...  3265.910   2967  0.010980  2230.307   
1   -0.027272    2489 -0.024254  ...  3265.910   2967  0.010892  2230.307   
2   -0.027716    2479 -0.024967  ...  3264.946   2967  0.012430  2230.307   
3   -0.006862    2471 -0.004306  ...  3260.607   2967  0.013197  2230.307   
4   -0.027427    2479 -0.024669  ...  3260.607   2967  0.012906  2230.307   
..        ...     ...       ...  ...       ...    ...       ...       ...   
160 -0.018501    2335 -0.018258  ...  3219.626   2359  0.002775  1937.175   
161 -0.018287    2335 -0.018074  ...  3218.662   2984  0.002813  2238.503   
162 -0.000935    2335 -0.000807  ...  3218.662   2984  0.002908  2238.503   
163 -0.017830    2335 -0.017583  ...  3218.662   2359  0.002763  1937.175   
164 -0.017328    2335 -0.017110  ...  3218.662   2359  0.002770  1937.175   

     maxCi      maxC     maxCw       Int_A     Int_C     Int_N_A  
0     1801  0.022375  1668.150  119.768412  4.997663  103.317735  
1     1801  0.022296  1668.150  120.179675  4.893321  103.672509  
2     1809  0.023067  1672.007  122.450319  5.325348  105.631271  
3     1810  0.022556  1672.489  123.960379  5.494804  106.933919  
4     1810  0.023123  1672.489  124.157452  5.862579  107.103923  
..     ...       ...       ...         ...       ...         ...  
160    588  0.005387  1083.333    6.833895  2.414610    5.895232  
161    588  0.006205  1083.333    6.540537  2.772033    5.642168  
162    559  0.006144  1069.352    6.359314  2.183563    5.485837  
163    588  0.005447  1083.333    6.032295  2.381032    5.203735  
164    559  0.005220  1069.352    5.569998  2.244073    4.804936  

[165 rows x 31 columns],                    Name      min1  index1      min2  index2      min3  index3  \
0      ASW_2021_05_13_1 -0.014722    6638 -0.004114    4151 -0.004143    4140   
1      ASW_2021_05_13_2 -0.013557    6630 -0.003223    4209 -0.003271    4140   
2      ASW_2021_05_13_3 -0.012140    6636 -0.002166    4153 -0.002128    4134   
3      ASW_2021_05_13_4 -0.011840    6636 -0.001680    4153 -0.001689    4134   
4      ASW_2021_05_13_5 -0.011791    6636 -0.001560    4153 -0.001565    4134   
..                  ...       ...     ...       ...     ...       ...     ...   
842  ASW_2021_05_13_843  0.007231    6638  0.010093    4149  0.009504    3951   
843  ASW_2021_05_13_844  0.008622    6638  0.010998    4151  0.010368    3951   
844  ASW_2021_05_13_845  0.009758    6638  0.011605    4151  0.010888    3942   
845  ASW_2021_05_13_846  0.010426    6638  0.011803    4149  0.011117    3961   
846  ASW_2021_05_13_847  0.011142    6638  0.011994    4149  0.011298    3942   

         min4  index4      min5  ...    maxA0w  maxBi      maxB     maxBw  \
0    0.002053    2392  0.002395  ...  3268.321   2928  0.008403  2211.504   
1    0.002862    2392  0.003149  ...  3268.321   2926  0.008431  2210.540   
2    0.003635    2390  0.003998  ...  3259.160   2953  0.009904  2223.557   
3    0.004012    2447  0.004475  ...  3258.196   2943  0.010262  2218.736   
4    0.004190    2388  0.004680  ...  3258.196   2972  0.010508  2232.718   
..        ...     ...       ...  ...       ...    ...       ...       ...   
842  0.008941    2286  0.008657  ...  3230.233   2974  0.003747  2233.682   
843  0.009187    2286  0.008756  ...  3233.125   2984  0.003207  2238.503   
844  0.009167    2286  0.008658  ...  3235.054   2915  0.002778  2205.237   
845  0.009026    2286  0.008403  ...  3182.021   2896  0.002373  2196.076   
846  0.008909    2286  0.008190  ...  3172.860   2838  0.002158  2168.113   

     maxCi      maxC     maxCw      Int_A     Int_C     Int_N_A  
0     1790  0.021825  1662.847  90.046648  6.326076  100.000000  
1     1790  0.021791  1662.847  90.114214  6.358682  100.075035  
2     1780  0.020271  1658.026  94.952295  6.967246  105.447896  
3     1771  0.019722  1653.686  96.240967  7.115134  106.879012  
4     1781  0.019706  1658.508  96.523813  7.165569  107.193123  
..     ...       ...       ...        ...       ...         ...  
842   1758  0.005088  1647.419  23.725832  1.953275   26.348379  
843   1758  0.004406  1647.419  17.889279  1.792270   19.866680  
844   1894  0.003936  1712.988  12.598864  1.622215   13.991486  
845   1913  0.003616  1722.148   8.090795  1.501391    8.985115  
846   1923  0.003434  1726.969   4.503201  1.384698    5.000965  

[847 rows x 31 columns],      Unnamed: 0                Name      min1  index1      min2  index2  \
0             0    ASW_2021_05_24_1 -0.002640    6636  0.011161    4153   
1             1    ASW_2021_05_24_2 -0.001645    6636  0.012008    4153   
2             2    ASW_2021_05_24_3 -0.001016    6636  0.012534    4153   
3             3    ASW_2021_05_24_4  0.000278    6636  0.014021    4151   
4             4    ASW_2021_05_24_5  0.001221    6636  0.014551    4151   
..          ...                 ...       ...     ...       ...     ...   
487         487  ASW_2021_05_24_488  0.015455    6636  0.012918    4555   
488         488  ASW_2021_05_24_489  0.015946    6636  0.013282    4555   
489         489  ASW_2021_05_24_490  0.016240    6636  0.013454    4563   
490         490  ASW_2021_05_24_491  0.016592    6636  0.013727    4546   
491         491  ASW_2021_05_24_492  0.017505    6636  0.014293    4555   

         min3  index3      min4  index4  ...     maxAw  scan_number  maxBi  \
0    0.010603    3942  0.011893    2392  ...  3267.356            1   2926   
1    0.011458    3942  0.012649    2363  ...  3267.356            2   2926   
2    0.012014    3942  0.013173    2344  ...  3268.321            3   2926   
3    0.013516    3998  0.014016    2285  ...  3259.160            4   2936   
4    0.014093    3978  0.014571    2344  ...  3258.196            5   2936   
..        ...     ...       ...     ...  ...       ...          ...    ...   
487  0.013973    4134  0.015475    2323  ...  3245.179          488   3241   
488  0.014290    4134  0.015699    2323  ...  3245.179          489   3241   
489  0.014445    4134  0.015793    2323  ...  3245.179          490   3241   
490  0.014715    4134  0.016050    2342  ...  3245.179          491   3241   
491  0.015249    4134  0.016378    2323  ...  3245.179          492   3244   

         maxB     maxBw  maxCi      maxC     maxCw       Int_A     Int_N_A  
0    0.009988  2210.540      2  0.072921  800.8085   95.599945  100.000000  
1    0.009952  2210.540      2  0.072887  800.8085   95.721654  100.127310  
2    0.010010  2210.540      2  0.073617  800.8085   95.891793  100.305280  
3    0.010640  2215.361      8  0.077077  803.7012   99.648824  104.235231  
4    0.010740  2215.361      8  0.078190  803.7012  100.622867  105.254105  
..        ...       ...    ...       ...       ...         ...         ...  
487  0.020827  2362.409      2  0.058832  800.8085    7.858836    8.220544  
488  0.020510  2362.409      2  0.060971  800.8085    7.890271    8.253426  
489  0.020154  2362.409      2  0.062315  800.8085    7.835753    8.196399  
490  0.019851  2362.409      2  0.063480  800.8085    7.833736    8.194289  
491  0.019413  2363.855      2  0.070444  800.8085    7.880451    8.243154  

[492 rows x 28 columns],     Unnamed: 0               Name      min1  index1      min2  index2  \
0            0   ASW_2021_07_13_1 -0.023247    6638 -0.016507    4321   
1            1   ASW_2021_07_13_2 -0.024986    6638 -0.017637    4321   
2            2   ASW_2021_07_13_3 -0.025060    6638 -0.017461    4311   
3            3   ASW_2021_07_13_4 -0.024484    6638 -0.016808    4323   
4            4   ASW_2021_07_13_5 -0.023746    6638 -0.016474    4325   
5            5   ASW_2021_07_13_6 -0.023772    6638 -0.016451    4333   
6            6   ASW_2021_07_13_7 -0.025788    6638 -0.017365    4246   
7            7   ASW_2021_07_13_8 -0.027713    6638 -0.018953    4196   
8            8   ASW_2021_07_13_9 -0.028306    6638 -0.019867    4227   
9            9  ASW_2021_07_13_10 -0.028047    6638 -0.019582    4198   
10          10  ASW_2021_07_13_11 -0.027592    6638 -0.019192    4198   
11          11  ASW_2021_07_13_12 -0.027410    6638 -0.019096    4227   
12          12  ASW_2021_07_13_13 -0.027203    6638 -0.018973    4227   
13          13  ASW_2021_07_13_14 -0.026585    6638 -0.018483    4198   
14          14  ASW_2021_07_13_15 -0.025243    6638 -0.017489    4179   
15          15  ASW_2021_07_13_16 -0.021303    6638 -0.013459    4227   
16          16  ASW_2021_07_13_17 -0.020471    6638 -0.012965    4227   
17          17  ASW_2021_07_13_18 -0.019999    6638 -0.012605    4227   
18          18  ASW_2021_07_13_19 -0.019634    6638 -0.012372    4198   
19          19  ASW_2021_07_13_20 -0.017090    6638 -0.011731    4179   
20          20  ASW_2021_07_13_21 -0.017998    6638 -0.012285    4198   
21          21  ASW_2021_07_13_22 -0.018664    6638 -0.012685    4198   
22          22  ASW_2021_07_13_23 -0.019310    6638 -0.013218    4198   
23          23  ASW_2021_07_13_24 -0.020400    6638 -0.014330    4198   
24          24  ASW_2021_07_13_25 -0.020810    6638 -0.014576    4198   
25          25  ASW_2021_07_13_26 -0.020218    6638 -0.016644    4390   

        min3  index3      min4  index4  ...     maxAw  scan_number  maxBi  \
0  -0.015874    4148 -0.003954    2416  ...  3271.213            1   2931   
1  -0.017018    4148 -0.004779    2464  ...  3274.106            2   2931   
2  -0.016754    4148 -0.004488    2464  ...  3274.106            3   2931   
3  -0.015983    4141 -0.003471    2464  ...  3261.089            4   2960   
4  -0.015621    4141 -0.003115    2445  ...  3256.750            5   2960   
5  -0.015600    4141 -0.003059    2445  ...  3256.750            6   2960   
6  -0.016911    4148 -0.003640    2474  ...  3255.785            7   2960   
7  -0.018817    4148 -0.005100    2436  ...  3250.964            8   2969   
8  -0.019682    4129 -0.005615    2466  ...  3244.697            9   2979   
9  -0.019390    4148 -0.005116    2466  ...  3241.804           10   2979   
10 -0.018974    4129 -0.004603    2476  ...  3238.911           11   2979   
11 -0.018885    4148 -0.004401    2485  ...  3237.947           12   2979   
12 -0.018723    4148 -0.004244    2476  ...  3236.983           13   2979   
13 -0.018245    4148 -0.003778    2485  ...  3236.018           14   2979   
14 -0.017222    4148 -0.002918    2466  ...  3233.608           15   2979   
15 -0.013217    4148  0.001125    2466  ...  3233.608           16   2979   
16 -0.012714    4148  0.001476    2485  ...  3232.644           17   2979   
17 -0.012382    4148  0.001704    2466  ...  3232.644           18   2979   
18 -0.012103    4148  0.001858    2466  ...  3231.679           19   2998   
19 -0.011503    4148  0.002747    2466  ...  3224.929           20   2998   
20 -0.012054    4148  0.002371    2485  ...  3224.929           21   2998   
21 -0.012469    4148  0.002138    2466  ...  3224.929           22   2998   
22 -0.012967    4148  0.001726    2476  ...  3224.929           23   3236   
23 -0.014095    4148  0.000553    2476  ...  3224.929           24   3236   
24 -0.014322    4148  0.000491    2476  ...  3224.929           25   3236   
25 -0.014663    4148  0.000497    2474  ...  3211.912           26   3236   

        maxB     maxBw  maxCi      maxC     maxCw       Int_A     Int_N_A  
0   0.010150  2212.950      1  0.063002  800.3264  102.978270  100.000000  
1   0.009962  2212.950      1  0.063354  800.3264  103.109427  100.127364  
2   0.010023  2212.950      1  0.064041  800.3264  103.321193  100.333006  
3   0.011388  2226.932      6  0.070443  802.7370  110.353134  107.161574  
4   0.011925  2226.932      5  0.073464  802.2549  111.998335  108.759193  
5   0.012024  2226.932      6  0.074375  802.7370  112.343960  109.094822  
6   0.011853  2226.932      6  0.074133  802.7370  114.658673  111.342590  
7   0.012560  2231.271      8  0.076707  803.7012  116.219512  112.858288  
8   0.012491  2236.093      8  0.077200  803.7012  116.285704  112.922566  
9   0.012425  2236.093      8  0.076597  803.7012  115.741609  112.394207  
10  0.012335  2236.093      8  0.077850  803.7012  115.030556  111.703718  
11  0.012174  2236.093      8  0.078411  803.7012  114.121406  110.820862  
12  0.012126  2236.093      8  0.077994  803.7012  113.180598  109.907264  
13  0.011935  2236.093      8  0.077645  803.7012  112.192297  108.947546  
14  0.012013  2236.093      8  0.077545  803.7012  111.215388  107.998890  
15  0.011903  2236.093      8  0.085764  803.7012  110.193054  107.006123  
16  0.011663  2236.093      8  0.087491  803.7012  109.234792  106.075576  
17  0.011675  2236.093      8  0.087453  803.7012  108.374365  105.240033  
18  0.011615  2245.253      8  0.086546  803.7012  107.784448  104.667177  
19  0.011730  2245.253      8  0.091422  803.7012  109.120932  105.965009  
20  0.011549  2245.253      8  0.091772  803.7012  108.956293  105.805131  
21  0.011747  2245.253      8  0.091667  803.7012  108.758884  105.613431  
22  0.311595  2359.999      8  0.094514  803.7012  109.178866  106.021267  
23  0.621625  2359.999      8  0.097058  803.7012  110.082086  106.898365  
24  0.662658  2359.999      8  0.098187  803.7012  110.049914  106.867123  
25  0.691713  2359.999      1  0.099738  800.3264  113.285420  110.009054  

[26 rows x 28 columns]]

data_anex_full

	Name	min1	index1	min2	index2	min3	index3	min4	index4	min5	...	maxBi	maxB	maxBw	maxCi	maxC	maxCw	Int_A	Int_C	Int_N_A	Unnamed: 0
0	ASW_2020_09_15_1	-0.017293	6630	-0.009476	4293	-0.009151	4147	0.001203	2482	0.001967	...	2936	0.008422	2215.361	1777	0.022221	1656.5790	96.292003	6.703636	100.000000	NaN
1	ASW_2020_09_15_2	-0.017489	6636	-0.008995	4161	-0.009001	4132	0.002430	2480	0.003482	...	2976	0.012062	2234.646	1754	0.021662	1645.4900	111.470194	7.622334	115.762670	NaN
2	ASW_2020_09_15_3	-0.017849	6636	-0.009715	4151	-0.009664	4132	0.001595	2463	0.003063	...	2974	0.012331	2233.682	1691	0.021226	1615.1160	112.359014	8.424437	116.685717	NaN
3	ASW_2020_09_15_4	-0.006971	6630	-0.004298	4149	-0.004222	4120	0.002220	2473	0.002968	...	2976	0.006884	2234.646	1574	0.013023	1558.7080	53.700867	4.010565	55.768771	NaN
0	ASW_2020_09_16_1	-0.018844	6636	-0.008464	4235	-0.008369	4139	-0.001418	2403	-0.000786	...	2945	0.009284	2219.700	1773	0.023557	1654.6510	95.913079	6.952239	100.000000	NaN
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
21	ASW_2021_07_13_22	-0.018664	6638	-0.012685	4198	-0.012469	4148	0.002138	2466	0.004063	...	2998	0.011747	2245.253	8	0.091667	803.7012	108.758884	NaN	105.613431	21.0
22	ASW_2021_07_13_23	-0.019310	6638	-0.013218	4198	-0.012967	4148	0.001726	2476	0.003659	...	3236	0.311595	2359.999	8	0.094514	803.7012	109.178866	NaN	106.021267	22.0
23	ASW_2021_07_13_24	-0.020400	6638	-0.014330	4198	-0.014095	4148	0.000553	2476	0.002548	...	3236	0.621625	2359.999	8	0.097058	803.7012	110.082086	NaN	106.898365	23.0
24	ASW_2021_07_13_25	-0.020810	6638	-0.014576	4198	-0.014322	4148	0.000491	2476	0.002494	...	3236	0.662658	2359.999	8	0.098187	803.7012	110.049914	NaN	106.867123	24.0
25	ASW_2021_07_13_26	-0.020218	6638	-0.016644	4390	-0.014663	4148	0.000497	2474	0.003447	...	3236	0.691713	2359.999	1	0.099738	800.3264	113.285420	NaN	110.009054	25.0

2017 rows × 32 columns

data_anex_full.to_csv('..\DATA\DATA-Processing\PAC\Data_Annex_full.csv')

2 Scans selection (Temperature - isotherms)

Using I python widget

I python widget list

Widgets layout

2.1 Parameter list

Temp = XP_Ramp_df_I.columns[1:].values.tolist()
Date = XP_Ramp_df_I.index.values.tolist()
Sample = XP_Ramp_df_I['Sample'].values.tolist()  

2.2 Widget selection

the purpose of the widget is to select the data we want to plot using widget

• create list

Document:

I python widget list

#Create Dropdown Box Widget
wT = widgets.SelectMultiple(
    options= Temp,
    description='Temperature:',
    disabled=False,
)

wD = widgets.SelectMultiple(
    options= Date,
    description='Date',
    disabled=False,
)

wS = widgets.SelectMultiple(
    options= Sample,
    description='Sample',
    disabled=False,
)

widgets.HBox([wS,wD, wT])



display(wT,wD,wS)

wT_L = list(wT.value)
wD_L = list(wD.value)

Sanity Check

wT_L

wD_L

['2020_09_15', '2020_09_16', '2020_09_17']

sample neme construction

use list value to extract scan number and produce file name

insert condition to supress first scan (ramp)

#date = ['2020_09_16','2020_09_17']
#temp = ['60K','80K']
#spl = 'ASW'

z = []
value_1 = []

for x in wD_L:
    for y in wT_L:

        value = XP_Ramp_df_I.loc[XP_Ramp_df_I.index == x, y].values[0]
        value_1 = re.findall(r"[-+]?\d*\.\d+|\d+", value)
    
        for items in value_1:
        
            to_plot = str('{}_{}_{}'.format(spl, x, items))
            z.append({
                
               'Name' : str(to_plot),
               'Temp' : y,
               'Date' : x, 
                
          
         
         })
             

z

[{'Name': 'ASW_2020_09_15_1', 'Temp': '20K ', 'Date': '2020_09_15'},
 {'Name': 'ASW_2020_09_16_1', 'Temp': '20K ', 'Date': '2020_09_16'},
 {'Name': 'ASW_2020_09_16_2', 'Temp': '20K ', 'Date': '2020_09_16'},
 {'Name': 'ASW_2020_09_17_1', 'Temp': '20K ', 'Date': '2020_09_17'},
 {'Name': 'ASW_2020_09_17_2', 'Temp': '20K ', 'Date': '2020_09_17'}]

dat= pd.DataFrame(z)
data_df = dat.set_index('Name')

data_df

	Temp	Date
Name
ASW_2020_09_15_1	20K	2020_09_15
ASW_2020_09_16_1	20K	2020_09_16
ASW_2020_09_16_2	20K	2020_09_16
ASW_2020_09_17_1	20K	2020_09_17
ASW_2020_09_17_2	20K	2020_09_17

1.1.4 Plot pre-formating

Temperature

Find a way to have a specific c=cmjet when we want to plot long isotherm data.

def Temp_color(row):
    if row['Temp'] == '20K ':
        return int(1)
    if row['Temp'] == '30K ':
        return int(2) 
    elif row['Temp'] == '40K':
        return int(3)
    elif row['Temp'] == '50K':
        return int(4)
    elif row['Temp'] == '60K':
        return int(5)
    elif row['Temp'] == '70K':
        return int(6)
    elif row['Temp'] == '80K':
        return int(7)
    elif row['Temp'] == '90K':
        return int(8)
    elif row['Temp'] == '100K':
        return int(9)
    elif row['Temp'] == '110K':
        return int(10)
    elif row['Temp'] == '120K':
        return int(11)
    elif row['Temp'] == '125K':
        return int(12)
    elif row['Temp'] == '130K':
        return int(13)
    elif row['Temp'] == '132K':
        return int(14)
    elif row['Temp'] == '134K':
        return int(15)
    elif row['Temp'] == '135K':
        return int(16)
    elif row['Temp'] == '136K':
        return int(17)
    elif row['Temp'] == '137K':
        return int(18)
    elif row['Temp'] == '138K':
        return int(19)
    elif row['Temp'] == '140K':
        return int(20)
    elif row['Temp'] == '145K':
        return int(21)
    elif row['Temp'] == '150K':
        return int(21)
    elif row['Temp'] == '155K':
        return int(22)
    elif row['Temp'] == '160K':
        return int(23)
    elif row['Temp'] == '180K':
        return int(24)
    elif row['Temp'] == '200K':
        return int(25)

data_df['Colour'] = data_df.apply (lambda row: Temp_color(row), axis=1)

data_df

	Temp	Date	Colour
Name
ASW_2020_09_15_1	20K	2020_09_15	1
ASW_2020_09_16_1	20K	2020_09_16	1
ASW_2020_09_16_2	20K	2020_09_16	1
ASW_2020_09_17_1	20K	2020_09_17	1
ASW_2020_09_17_2	20K	2020_09_17	1

Dates

linestyle = ['-',':','--']

6 sample max

LD = dict(zip(wD_L, linestyle))

LD 

{'2020_09_15': '-', '2020_09_16': ':', '2020_09_17': '--'}

data_df['linestyle'] = data_df['Date'].map(LD)

data_df

	Temp	Date	Colour	linestyle
Name
ASW_2020_09_15_1	20K	2020_09_15	1	-
ASW_2020_09_16_1	20K	2020_09_16	1	:
ASW_2020_09_16_2	20K	2020_09_16	1	:
ASW_2020_09_17_1	20K	2020_09_17	1	--
ASW_2020_09_17_2	20K	2020_09_17	1	--

Cycler methodology

For plotting

Seaborn library

Could try Class attribute

What do we want to do:

Vocabulary:

• Method = function associated with a class

• Attributes =

Class XP_Param is a blueprint to construct a list of sample that we want to plot based on the parameters enters with the widget.

One function should find within XP_Ramp_df_I the scan numbers associated with a peculiar date and temperature. A second function should build a string with the format spl_date_scan number and append it into a list

differences between instance variables and class variables ?

Plotting

Purpose here is to plot from DR2_all scan the scan present in dat using Temp and date in the legend and use the groupping for formatting

Find way to implement label with respect to dataframe value (input for color ?)

3 different DR

print(dat['Name'])

0    ASW_2020_09_15_1
1    ASW_2020_09_16_1
2    ASW_2020_09_16_2
3    ASW_2020_09_17_1
4    ASW_2020_09_17_2
Name: Name, dtype: object

nscan = len(list(data_df['Date'].values.tolist()))

print(nscan)

fig, ax= plt.subplots(figsize=(10,10))

#colors = sns.color_palette("coolwarm", data_df.Temp.nunique())
#ax.set_prop_cycle('color', colors)

#ax.set_prop_cycle(color =['b', 'g'])
#cc = (cycler(linestyle=['-', '--', '-.', ]))

for i in dat['Name']:
    
#DR2
    
    #x = DR2_Allscans_full.Wavenumber
    #y = DR2_Allscans_full['{}'.format(i)]

#`DR3

    x = DR3_Allscans_full.Wavenumber
    y = DR3_Allscans_full['{}'.format(i)]
    
    plt.plot(x,y, label="{}_{}".format(str(data_df.loc[i]['Date']), str(data_df.loc[i]['Temp'])), c=cm.jet(int(data_df.loc[i]['Colour'])/25), linestyle=(data_df.loc[i]['linestyle'])) 


#plt.title('{0} DR1 '.format(date))
plt.axis([3800,2800,0,0.45])
plt.xlabel('Wavenumber (cm-1)').set_fontsize(13)
plt.ylabel('Absorbance').set_fontsize(13)
#ax = fig.gca()
plt.grid()
plt.legend()


#plt.savefig('D:\PhD-WS\Projects\PAC\XP_1-1\DATA\{0}\Plots\DR1\DR1_{0}_All_scans.png'.format(date))

plt.show()

5

Max Absorbance

Import code from DR

Other plotting

# imports
%matplotlib inline

from ipywidgets import interactive
import pandas as pd
import numpy as np
# from jupyterthemes import jtplot

# Sample data
np.random.seed(123)
rows = 50
dfx = pd.DataFrame(np.random.randint(90,110,size=(rows, 1)), columns=['Variable X'])
dfy = pd.DataFrame(np.random.randint(25,68,size=(rows, 1)), columns=['Variable Y'])
dfz = pd.DataFrame(np.random.randint(60,70,size=(rows, 1)), columns=['Variable Z'])

df = pd.concat([dfx,dfy,dfz], axis = 1)
#jtplot.style()

import ipywidgets as widgets
from IPython.display import display

def multiplot(a):
    opts = df.columns.values
    df.loc[:, a].plot()

interactive_plot = interactive(multiplot, a=['Variable X', 'Variable Y', 'Variable Z'])
output = interactive_plot.children[-1]
output.layout.height = '350px'
interactive_plot

Create class with widget input as variable

Embed Naming function

Youtube tutorial on classes

class XP_Param:
    
    def found(self):
        print("input is" + self.column + self.row )
        return XP_Ramp_df_I.loc[self.row,self.column]
    

    

T1 = XP_Param()
T1.column = "60K"
T1.row = "2020_09_21"

T1.found()

input is60K2020_09_21

'[6, 7, 8]'

Name the desired sample

Purpose here is to create a function that create sample name based on previously selected parameters (date / Temperature)

Documentation

Interact function

output_Date = wD.value

# Define any function
def file_name(output_Date):
    return print(output_Date) 
    

Plotting strategy

Plotting

Data from Data_Annex

Data from DR2

Interactive Plotting

I python widget list

x = numpy.linspace(0, 2 * numpy.pi, 100)
 
fig, ax = plt.subplots()
line, = ax.plot(x, numpy.sin(x))
ax.grid(True)
 
def update(change):
    line.set_ydata(numpy.sin(change.new * x))
    fig.canvas.draw()
     

int_slider = widgets.IntSlider(
    value=1, 
    min=0, max=10, step=1,
    description='$\omega$',
    continuous_update=False
)
int_slider.observe(update, 'value')
int_slider

wx GUI creation

import wx

# Création d'un nouveau cadre, dérivé du wxPython 'Frame'.
class TestFrame(wx.Frame):
    def __init__(self, parent, ID, title):
        wx.Frame.__init__(self, parent, -1, title, pos=(0, 0), size=(500, 200))

        # À l'intérieur du cadre, créer un panneau..
        panel = wx.Panel(self, -1)

        # Créer un texte dans le panneau
        texte = wx.StaticText(panel, -1, "Bonjour tout le monde!", wx.Point(10, 5), wx.Size(-1, -1))
                
        # Créer un bouton dans le panneau
        bouton = wx.Button(panel, -1, "Cliquez-moi!",  wx.Point(10, 35), wx.Size(-1, -1))
        # lier le bouton à une fonction:
        self.Bind(wx.EVT_BUTTON, self.creerDiag, bouton)
        
    # fonction qui affiche une boîte de dialogue
    def creerDiag(self, event):
        dlg = wx.MessageDialog(self, "Merci de m'avoir cliqué, ça fait du bien.",
          "Merci!", wx.ICON_EXCLAMATION | wx.YES_NO | wx.CANCEL)
        dlg.ShowModal()
        dlg.Destroy()
        

# Chaque application wxWidgets doit avoir une classe dérivée de wx.App
class TestApp(wx.App):
    def OnInit(self):
        frame = TestFrame(None, -1, "Test")
        self.SetTopWindow(frame)
        frame.Show(True)
        return True

if __name__ == '__main__':
    app = TestApp(0) # créer une nouvelle instance de l'application
    app.MainLoop()   # lancer l'application

app = wx.App()

frame = wx.Frame(None, title='Simple application')
frame.Show()

app.MainLoop()

---------------------------------------------------------------------------
PyNoAppError                              Traceback (most recent call last)
<ipython-input-61-95e17b317317> in <module>
      1 app = wx.App()
      2 
----> 3 frame = wx.Frame(None, title='Simple application')
      4 frame.Show()
      5 

PyNoAppError: The wx.App object must be created first!

#!/usr/bin/env python

"""
ZetCode wxPython tutorial

In this example, we create a submenu and a menu
separator.

author: Jan Bodnar
website: www.zetcode.com
last modified: July 2020
"""

import wx


class Example(wx.Frame):

    def __init__(self, *args, **kwargs):
        super(Example, self).__init__(*args, **kwargs)

        self.InitUI()

    def InitUI(self):

        menubar = wx.MenuBar()

        fileMenu = wx.Menu()
        fileMenu.Append(wx.ID_NEW, '&New')
        fileMenu.Append(wx.ID_OPEN, '&Open')
        fileMenu.Append(wx.ID_SAVE, '&Save')
        fileMenu.AppendSeparator()

        imp = wx.Menu()
        imp.Append(wx.ID_ANY, 'Import newsfeed list...')
        imp.Append(wx.ID_ANY, 'Import bookmarks...')
        imp.Append(wx.ID_ANY, 'Import mail...')

        fileMenu.AppendMenu(wx.ID_ANY, 'I&mport', imp)

        qmi = wx.MenuItem(fileMenu, wx.ID_EXIT, '&Quit\tCtrl+W')
        fileMenu.AppendItem(qmi)

        self.Bind(wx.EVT_MENU, self.OnQuit, qmi)

        menubar.Append(fileMenu, '&File')
        self.SetMenuBar(menubar)

        self.SetSize((900, 600))
        self.SetTitle('Data Analysis Plotting GUI')
        self.Centre()

    def OnQuit(self, e):
        self.Close()


def main():

    app = wx.App()
    ex = Example(None)
    ex.Show()
    app.MainLoop()


if __name__ == '__main__':
    main()

<ipython-input-62-2cf284cf38ca>:39: wxPyDeprecationWarning: Call to deprecated item. Use Append instead.
  fileMenu.AppendMenu(wx.ID_ANY, 'I&mport', imp)
<ipython-input-62-2cf284cf38ca>:42: wxPyDeprecationWarning: Call to deprecated item. Use Append instead.
  fileMenu.AppendItem(qmi)

#!/usr/bin/env python


"""
ZetCode wxPython tutorial

In this example we create a Go To class
layout with wx.BoxSizer.

author: Jan Bodnar
website: www.zetcode.com
last modified: July 2020
"""

import wx

class Example(wx.Frame):

    def __init__(self, parent, title):
        super(Example, self).__init__(parent, title=title)

        self.InitUI()
        self.Centre()

    def InitUI(self):

        panel = wx.Panel(self)

        font = wx.SystemSettings.GetFont(wx.SYS_SYSTEM_FONT)

        font.SetPointSize(9)

        vbox = wx.BoxSizer(wx.VERTICAL)

        hbox1 = wx.BoxSizer(wx.HORIZONTAL)
        st1 = wx.StaticText(panel, label='Class Name')
        st1.SetFont(font)
        hbox1.Add(st1, flag=wx.RIGHT, border=8)
        tc = wx.TextCtrl(panel)
        hbox1.Add(tc, proportion=1)
        vbox.Add(hbox1, flag=wx.EXPAND|wx.LEFT|wx.RIGHT|wx.TOP, border=10)

        vbox.Add((-1, 10))

        hbox2 = wx.BoxSizer(wx.HORIZONTAL)
        st2 = wx.StaticText(panel, label='Matching Classes')
        st2.SetFont(font)
        hbox2.Add(st2)
        vbox.Add(hbox2, flag=wx.LEFT | wx.TOP, border=10)

        vbox.Add((-1, 10))

        hbox3 = wx.BoxSizer(wx.HORIZONTAL)
        tc2 = wx.TextCtrl(panel, style=wx.TE_MULTILINE)
        hbox3.Add(tc2, proportion=1, flag=wx.EXPAND)
        vbox.Add(hbox3, proportion=1, flag=wx.LEFT|wx.RIGHT|wx.EXPAND,
            border=10)

        vbox.Add((-1, 25))

        hbox4 = wx.BoxSizer(wx.HORIZONTAL)
        cb1 = wx.CheckBox(panel, label='Case Sensitive')
        cb1.SetFont(font)
        hbox4.Add(cb1)
        cb2 = wx.CheckBox(panel, label='Nested Classes')
        cb2.SetFont(font)
        hbox4.Add(cb2, flag=wx.LEFT, border=10)
        cb3 = wx.CheckBox(panel, label='Non-Project classes')
        cb3.SetFont(font)
        hbox4.Add(cb3, flag=wx.LEFT, border=10)
        vbox.Add(hbox4, flag=wx.LEFT, border=10)

        vbox.Add((-1, 25))

        hbox5 = wx.BoxSizer(wx.HORIZONTAL)
        btn1 = wx.Button(panel, label='Ok', size=(70, 30))
        hbox5.Add(btn1)
        btn2 = wx.Button(panel, label='Close', size=(70, 30))
        hbox5.Add(btn2, flag=wx.LEFT|wx.BOTTOM, border=5)
        vbox.Add(hbox5, flag=wx.ALIGN_RIGHT|wx.RIGHT, border=10)

        panel.SetSizer(vbox)


def main():

    app = wx.App()
    ex = Example(None, title='Go To Class')
    ex.Show()
    app.MainLoop()


if __name__ == '__main__':
    main()

%load_ext watermark

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
<ipython-input-63-a0d979a7c178> in <module>
----> 1 get_ipython().run_line_magic('load_ext', 'watermark')

~\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py in run_line_magic(self, magic_name, line, _stack_depth)
   2324                 kwargs['local_ns'] = sys._getframe(stack_depth).f_locals
   2325             with self.builtin_trap:
-> 2326                 result = fn(*args, **kwargs)
   2327             return result
   2328 

<decorator-gen-65> in load_ext(self, module_str)

~\Anaconda3\lib\site-packages\IPython\core\magic.py in <lambda>(f, *a, **k)
    185     # but it's overkill for just that one bit of state.
    186     def magic_deco(arg):
--> 187         call = lambda f, *a, **k: f(*a, **k)
    188 
    189         if callable(arg):

~\Anaconda3\lib\site-packages\IPython\core\magics\extension.py in load_ext(self, module_str)
     31         if not module_str:
     32             raise UsageError('Missing module name.')
---> 33         res = self.shell.extension_manager.load_extension(module_str)
     34 
     35         if res == 'already loaded':

~\Anaconda3\lib\site-packages\IPython\core\extensions.py in load_extension(self, module_str)
     78             if module_str not in sys.modules:
     79                 with prepended_to_syspath(self.ipython_extension_dir):
---> 80                     mod = import_module(module_str)
     81                     if mod.__file__.startswith(self.ipython_extension_dir):
     82                         print(("Loading extensions from {dir} is deprecated. "

~\Anaconda3\lib\importlib\__init__.py in import_module(name, package)
    125                 break
    126             level += 1
--> 127     return _bootstrap._gcd_import(name[level:], package, level)
    128 
    129 

~\Anaconda3\lib\importlib\_bootstrap.py in _gcd_import(name, package, level)

~\Anaconda3\lib\importlib\_bootstrap.py in _find_and_load(name, import_)

~\Anaconda3\lib\importlib\_bootstrap.py in _find_and_load_unlocked(name, import_)

ModuleNotFoundError: No module named 'watermark'

Package version for binder

%watermark --iversions

matplotlib: 3.2.2
ipywidgets: 7.6.3
re        : 2.2.1
wx        : 4.1.1
pandas    : 1.0.5
numpy     : 1.18.5