Smoothing Script¶
import os
import sys
import numpy as np
import pandas as pd
sys.path.insert(0, '/home/cabsel/gfa/')
from gfapy.curve_fit import curve_fitter
mainDir = '/home/cabsel/gfa/'
inputDir = os.path.join(mainDir, 'inputfiles')
Read GFA model from Excel¶
expt_data = pd.read_excel(os.path.join(inputDir, 'smoothed_data_medpH.xlsx'), sheet_name=['vcd', 'titer', 'frac', 'q_prod_matched'])
expt_data['VCD'] = (expt_data.pop('vcd').rename(columns={'VCD_1e6cells_mL': 'VCD (1E6 VC/mL)',
'Time_days': 'Time (WD)',
'fit_VCD_1e6cells_mL': 'fit_VCD (1E6 VC/mL)'}).
set_index('Time (WD)'))
expt_data['Titer'] = (expt_data.pop('titer').rename(columns={'Titer_g_L': 'Titer (g/L)',
'Time_days': 'Time (WD)',
'fit_Titer_g_L': 'fit_Titer (g/L)'}).
set_index('Time (WD)'))
expt_data['Fractions'] = (expt_data.pop('frac').rename(columns={'Time_days': 'Time (WD)',
'G0FplusGlcNAc': 'G0F+GlcNAc',
'G0FplusGlcNac': 'G0F+GlcNAc',
'G0F_GlcNac': 'G0F-GlcNAc',
'G0F_GlcNAc': 'G0F-GlcNAc',
'G0F_GlcNac': 'G0F-GlcNAc',
'G0_GlcNAc': 'G0-GlcNAc',
'G0_GlcNac': 'G0-GlcNAc',
'G1prime': 'G1a/b',
'Man7prime': 'Man7'}).
assign(**{'G1Fa/b': lambda x: x['G1F']+x['G1Fprime']}).
drop(columns=['G1F', 'G1Fprime']).
set_index('Time (WD)'))
expt_data['q_prod'] = (expt_data.pop('q_prod_matched').
rename(columns={'Spec_prod_pg_cells_day': 'Spec Prod (pg/cells/day)',
'Time_days': 'Time (WD)',
'Time_windows_days': 'Time window (days)'}).
set_index('Time (WD)'))
expt_data['Concentrations'] = expt_data['Fractions'].mul(expt_data['Titer']['Titer (g/L)'], axis=0).dropna()
for k, v in expt_data.items():
expt_data[k] = expt_data[k].sort_index()
Smooth data and record all necessary variables in new dictionary (Trial and error to find correct function)¶
smoothed_data = expt_data.copy()
retrieve_timepoints = np.arange(expt_data['VCD'].index.min(), expt_data['VCD'].index.max()+1, 1)
VCD¶
fit_vcd = curve_fitter()
fit_vcd.ingest_data(expt_data['VCD'].reset_index(), x_col='Time (WD)')
Choose a model from below keys or "Custom:"
['Polynomial', 'Exponential', 'Power', 'Logarithmic', 'Fourier', 'Gaussian', 'Weibull', 'Hill-type', 'Sigmoidal']
Logistic: A / (exp(B * x) + C * exp(-D * x))
vcd_col = 'VCD (1E6 VC/mL)'
fit_vcd.fit_jupyter(vcd_col)
smoothed_data['VCD']['fit_'+vcd_col] = fit_vcd.current_stats['fitted'].copy()
smoothed_data['VCD']['diff_'+vcd_col] = fit_vcd.current_stats['deriv'].copy()
display(smoothed_data['VCD'])
| VCD (1E6 VC/mL) | fit_VCD (1E6 VC/mL) | diff_VCD (1E6 VC/mL) | |
|---|---|---|---|
| Time (WD) | |||
| 1 | 0.784138 | 0.812027 | 0.435732 |
| 2 | 1.265309 | 1.374260 | 0.705857 |
| 3 | 2.100869 | 2.256844 | 1.073020 |
| 4 | 3.601440 | 3.532264 | 1.472161 |
| 5 | 5.214883 | 5.156431 | 1.735699 |
| 6 | 7.192178 | 6.893591 | 1.675505 |
| 7 | 8.165785 | 8.391412 | 1.275581 |
| 8 | 9.280572 | 9.396539 | 0.731072 |
| 9 | 9.786835 | 9.877230 | 0.252563 |
| 10 | 9.869698 | 9.953180 | -0.074785 |
| 11 | 9.952553 | 9.773722 | -0.264768 |
| 12 | 9.787621 | 9.454708 | -0.361261 |
| 13 | 9.111461 | 9.069461 | -0.402502 |
| 14 | 8.353707 | 8.659642 | -0.413579 |
Titer¶
fit_titer = curve_fitter()
fit_titer.ingest_data(expt_data['Titer'].reset_index(), x_col='Time (WD)')
Choose a model from below keys or "Custom:"
['Polynomial', 'Exponential', 'Power', 'Logarithmic', 'Fourier', 'Gaussian', 'Weibull', 'Hill-type', 'Sigmoidal']
titer_col = 'Titer (g/L)'
fit_titer.fit_jupyter(titer_col)
smoothed_data['Titer']['fit_'+titer_col] = fit_titer.current_stats['fitted'].copy()
smoothed_data['Titer']['diff_'+titer_col] = fit_titer.current_stats['deriv'].copy()
display(smoothed_data['Titer'])
| Titer (g/L) | fit_Titer (g/L) | diff_Titer (g/L) | |
|---|---|---|---|
| Time (WD) | |||
| 1 | 0.038894 | 0.000785 | 0.002834 |
| 2 | 0.062556 | 0.009576 | 0.017251 |
| 3 | 0.100022 | 0.041069 | 0.048891 |
| 4 | 0.163121 | 0.113737 | 0.099474 |
| 5 | 0.269600 | 0.245077 | 0.165003 |
| 6 | 0.429320 | 0.445240 | 0.234872 |
| 7 | 0.660026 | 0.711372 | 0.294462 |
| 8 | 1.038621 | 1.026431 | 0.331127 |
| 9 | 1.348201 | 1.364166 | 0.339716 |
| 10 | 1.699189 | 1.697561 | 0.323568 |
| 11 | 2.026516 | 2.005844 | 0.291056 |
| 12 | 2.340039 | 2.277226 | 0.251123 |
| 13 | 2.440604 | 2.507908 | 0.210547 |
| 14 | 2.706803 | 2.699458 | 0.173308 |
Glycoform Concentrations¶
smoothed_data['Fit_Concentrations'] = (pd.DataFrame(0, index=retrieve_timepoints, columns=smoothed_data['Concentrations'].columns).
rename_axis(index='Time (WD)'))
smoothed_data['Diff_Concentrations'] = (pd.DataFrame(0, index=retrieve_timepoints, columns=smoothed_data['Concentrations'].columns).
rename_axis(index='Time (WD)'))
fit_fracs = curve_fitter()
fit_fracs.ingest_data(expt_data['Concentrations'].reset_index(), x_col='Time (WD)')
Choose a model from below keys or "Custom:"
['Polynomial', 'Exponential', 'Power', 'Logarithmic', 'Fourier', 'Gaussian', 'Weibull', 'Hill-type', 'Sigmoidal']
Man7¶
frac_col = 'Man7'
fit_fracs.fit_jupyter(frac_col)
smoothed_data['Fit_Concentrations'][frac_col] = fit_fracs.get_fitmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
smoothed_data['Diff_Concentrations'][frac_col] = fit_fracs.get_diffmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
8.77715414835226e-8*exp(-1.04979682849682*x)/(2.51606683299321e-5 + exp(-1.04979682849682*x))**2
Man6¶
frac_col = 'Man6'
fit_fracs.fit_jupyter(frac_col)
smoothed_data['Fit_Concentrations'][frac_col] = fit_fracs.get_fitmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
smoothed_data['Diff_Concentrations'][frac_col] = fit_fracs.get_diffmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
1.53674532320441e-5/(x**3.63015880132957*(x**(-2.63015880132957) + 0.000730149475676811)**2)
Man5¶
frac_col = 'Man5'
fit_fracs.fit_jupyter(frac_col)
smoothed_data['Fit_Concentrations'][frac_col] = fit_fracs.get_fitmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
smoothed_data['Diff_Concentrations'][frac_col] = fit_fracs.get_diffmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
7.82512472492164e-5/(x**3.57898807718615*(x**(-2.57898807718615) + 1.48099279331054e-5)**2)
G0-GlcNAc¶
frac_col = 'G0-GlcNAc'
fit_fracs.fit_jupyter(frac_col)
smoothed_data['Fit_Concentrations'][frac_col] = fit_fracs.get_fitmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
smoothed_data['Diff_Concentrations'][frac_col] = fit_fracs.get_diffmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
3.20970296762035e-6/(x**3.9857986207357*(x**(-2.9857986207357) - 8.91079207537524e-5)**2)
G0F¶
frac_col = 'G0F'
fit_fracs.fit_jupyter(frac_col)
smoothed_data['Fit_Concentrations'][frac_col] = fit_fracs.get_fitmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
smoothed_data['Diff_Concentrations'][frac_col] = fit_fracs.get_diffmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
0.00106965824298514/(x**4.9728043624615*(x**(-3.9728043624615) + 9.42588445419789e-5)**2)
G1a/b¶
frac_col = 'G1a/b'
fit_fracs.fit_jupyter(frac_col)
smoothed_data['Fit_Concentrations'][frac_col] = fit_fracs.get_fitmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
smoothed_data['Diff_Concentrations'][frac_col] = fit_fracs.get_diffmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
1.81526914872763e-6*(17.2297568100532*exp(-0.514316349820139*x) + 0.0374374429383844*exp(-0.0374374429383844*x))/(exp(-0.514316349820139*x) + 0.0298504706415847*exp(-0.0374374429383844*x))**2
G1Fa/b¶
frac_col = 'G1Fa/b'
fit_fracs.fit_jupyter(frac_col)
smoothed_data['Fit_Concentrations'][frac_col] = fit_fracs.get_fitmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
smoothed_data['Diff_Concentrations'][frac_col] = fit_fracs.get_diffmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
0.00238522708675479/(x**4.03582275597052*(x**(-3.03582275597052) + 0.00176704968125774)**2)
G0¶
frac_col = 'G0'
fit_fracs.fit_jupyter(frac_col)
smoothed_data['Fit_Concentrations'][frac_col] = fit_fracs.get_fitmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
smoothed_data['Diff_Concentrations'][frac_col] = fit_fracs.get_diffmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
9.41441888399936e-5/(x**4.140653237447*(x**(-3.140653237447) + 0.000437837528509634)**2)
G0F-GlcNAc¶
frac_col = 'G0F-GlcNAc'
fit_fracs.fit_jupyter(frac_col)
smoothed_data['Fit_Concentrations'][frac_col] = fit_fracs.get_fitmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
smoothed_data['Diff_Concentrations'][frac_col] = fit_fracs.get_diffmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
1.38019843466825e-5/(x**4.28619872306324*(x**(-3.28619872306324) + 4.59783223097008e-5)**2)
G0F+GlcNAc¶
frac_col = 'G0F+GlcNAc'
fit_fracs.fit_jupyter(frac_col)
smoothed_data['Fit_Concentrations'][frac_col] = fit_fracs.get_fitmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
smoothed_data['Diff_Concentrations'][frac_col] = fit_fracs.get_diffmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
2.51813290846332e-5*exp(-0.504806299908199*x)/(0.00380914878275068 + exp(-0.504806299908199*x))**2
G2F¶
frac_col = 'G2F'
fit_fracs.fit_jupyter(frac_col)
smoothed_data['Fit_Concentrations'][frac_col] = fit_fracs.get_fitmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
smoothed_data['Diff_Concentrations'][frac_col] = fit_fracs.get_diffmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
0.000585155264255064/(x**3.59120437541022*(x**(-2.59120437541022) + 0.00854648647909327)**2)
A1G1F¶
frac_col = 'A1G1F'
fit_fracs.fit_jupyter(frac_col)
smoothed_data['Fit_Concentrations'][frac_col] = fit_fracs.get_fitmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
smoothed_data['Diff_Concentrations'][frac_col] = fit_fracs.get_diffmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
1.04061189355436e-5/(x**4.70728372947352*(x**(-3.70728372947352) + 0.000396022275776027)**2)
A1G2F¶
frac_col = 'A1G2F'
fit_fracs.fit_jupyter(frac_col)
smoothed_data['Fit_Concentrations'][frac_col] = fit_fracs.get_fitmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
smoothed_data['Diff_Concentrations'][frac_col] = fit_fracs.get_diffmodel(fit_fracs.current_stats['params'])(retrieve_timepoints)
0.000116773396805464/(x**3.22003059056402*(x**(-2.22003059056402) + 0.00621613842908323)**2)
Summary¶
display(smoothed_data['VCD'])
| VCD (1E6 VC/mL) | fit_VCD (1E6 VC/mL) | diff_VCD (1E6 VC/mL) | |
|---|---|---|---|
| Time (WD) | |||
| 1 | 0.784138 | 0.812027 | 0.435732 |
| 2 | 1.265309 | 1.374260 | 0.705857 |
| 3 | 2.100869 | 2.256844 | 1.073020 |
| 4 | 3.601440 | 3.532264 | 1.472161 |
| 5 | 5.214883 | 5.156431 | 1.735699 |
| 6 | 7.192178 | 6.893591 | 1.675505 |
| 7 | 8.165785 | 8.391412 | 1.275581 |
| 8 | 9.280572 | 9.396539 | 0.731072 |
| 9 | 9.786835 | 9.877230 | 0.252563 |
| 10 | 9.869698 | 9.953180 | -0.074785 |
| 11 | 9.952553 | 9.773722 | -0.264768 |
| 12 | 9.787621 | 9.454708 | -0.361261 |
| 13 | 9.111461 | 9.069461 | -0.402502 |
| 14 | 8.353707 | 8.659642 | -0.413579 |
display(smoothed_data['Titer'])
| Titer (g/L) | fit_Titer (g/L) | diff_Titer (g/L) | |
|---|---|---|---|
| Time (WD) | |||
| 1 | 0.038894 | 0.000785 | 0.002834 |
| 2 | 0.062556 | 0.009576 | 0.017251 |
| 3 | 0.100022 | 0.041069 | 0.048891 |
| 4 | 0.163121 | 0.113737 | 0.099474 |
| 5 | 0.269600 | 0.245077 | 0.165003 |
| 6 | 0.429320 | 0.445240 | 0.234872 |
| 7 | 0.660026 | 0.711372 | 0.294462 |
| 8 | 1.038621 | 1.026431 | 0.331127 |
| 9 | 1.348201 | 1.364166 | 0.339716 |
| 10 | 1.699189 | 1.697561 | 0.323568 |
| 11 | 2.026516 | 2.005844 | 0.291056 |
| 12 | 2.340039 | 2.277226 | 0.251123 |
| 13 | 2.440604 | 2.507908 | 0.210547 |
| 14 | 2.706803 | 2.699458 | 0.173308 |
display(smoothed_data['Fractions'])
| A1G1F | A1G2F | G0 | G0F | G0F+GlcNAc | G0F-GlcNAc | G0-GlcNAc | G1a/b | G2F | Man5 | Man6 | Man7 | G1Fa/b | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Time (WD) | |||||||||||||
| 5 | 0.003273 | 0.006889 | 0.017714 | 0.596477 | 0.001061 | 0.003714 | 0.000694 | 0.002173 | 0.035255 | 0.008395 | 0.001183 | 0.000395 | 0.319791 |
| 8 | 0.003273 | 0.002323 | 0.014989 | 0.737378 | 0.002281 | 0.003429 | 0.000488 | 0.001561 | 0.016277 | 0.006052 | 0.001187 | -0.000018 | 0.208716 |
| 10 | 0.002805 | 0.002952 | 0.015032 | 0.778474 | 0.002989 | 0.004449 | 0.000684 | 0.001322 | 0.012044 | 0.006597 | 0.001141 | 0.001089 | 0.170732 |
| 12 | 0.002338 | 0.002234 | 0.015394 | 0.799511 | 0.003371 | 0.005429 | 0.000900 | 0.001249 | 0.009708 | 0.007928 | 0.001109 | 0.001096 | 0.149998 |
| 14 | 0.002338 | 0.002062 | 0.015970 | 0.809785 | 0.003985 | 0.007143 | 0.001373 | 0.001215 | 0.008540 | 0.009961 | 0.001285 | 0.001295 | 0.136380 |
display(smoothed_data['Concentrations'])
| A1G1F | A1G2F | G0 | G0F | G0F+GlcNAc | G0F-GlcNAc | G0-GlcNAc | G1a/b | G2F | Man5 | Man6 | Man7 | G1Fa/b | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Time (WD) | |||||||||||||
| 5 | 0.000882 | 0.001857 | 0.004776 | 0.160811 | 0.000286 | 0.001001 | 0.000187 | 0.000586 | 0.009505 | 0.002263 | 0.000319 | 0.000106 | 0.086216 |
| 8 | 0.003399 | 0.002413 | 0.015568 | 0.765856 | 0.002369 | 0.003561 | 0.000507 | 0.001621 | 0.016906 | 0.006285 | 0.001233 | -0.000019 | 0.216777 |
| 10 | 0.004767 | 0.005016 | 0.025542 | 1.322774 | 0.005079 | 0.007560 | 0.001162 | 0.002246 | 0.020465 | 0.011210 | 0.001940 | 0.001850 | 0.290106 |
| 12 | 0.005470 | 0.005227 | 0.036023 | 1.870887 | 0.007887 | 0.012703 | 0.002105 | 0.002923 | 0.022717 | 0.018551 | 0.002594 | 0.002564 | 0.351002 |
| 14 | 0.006328 | 0.005583 | 0.043227 | 2.191928 | 0.010787 | 0.019334 | 0.003716 | 0.003288 | 0.023116 | 0.026962 | 0.003477 | 0.003504 | 0.369154 |
display(smoothed_data['Fit_Concentrations'])
| A1G1F | A1G2F | G0 | G0F | G0F+GlcNAc | G0F-GlcNAc | G0-GlcNAc | G1a/b | G2F | Man5 | Man6 | Man7 | G1Fa/b | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.000003 | 0.000052 | 0.000030 | 0.000269 | 0.000082 | 0.000004 | 0.000001 | 0.000097 | 0.000224 | 0.000030 | 0.000006 | 2.388571e-07 | 0.000784 |
| 2 | 0.000036 | 0.000238 | 0.000263 | 0.004221 | 0.000135 | 0.000041 | 0.000009 | 0.000158 | 0.001294 | 0.000181 | 0.000036 | 6.823406e-07 | 0.006352 |
| 3 | 0.000161 | 0.000563 | 0.000932 | 0.021011 | 0.000223 | 0.000155 | 0.000029 | 0.000253 | 0.003392 | 0.000516 | 0.000104 | 1.948752e-06 | 0.021022 |
| 4 | 0.000449 | 0.001006 | 0.002255 | 0.064869 | 0.000365 | 0.000398 | 0.000068 | 0.000396 | 0.006258 | 0.001083 | 0.000218 | 5.561665e-06 | 0.047231 |
| 5 | 0.000949 | 0.001534 | 0.004397 | 0.152474 | 0.000594 | 0.000825 | 0.000133 | 0.000601 | 0.009412 | 0.001924 | 0.000383 | 1.584083e-05 | 0.084312 |
| 6 | 0.001651 | 0.002109 | 0.007427 | 0.297708 | 0.000956 | 0.001490 | 0.000231 | 0.000875 | 0.012424 | 0.003078 | 0.000602 | 4.486123e-05 | 0.128615 |
| 7 | 0.002479 | 0.002695 | 0.011289 | 0.504784 | 0.001511 | 0.002447 | 0.000370 | 0.001209 | 0.015049 | 0.004577 | 0.000870 | 1.250369e-04 | 0.175136 |
| 8 | 0.003323 | 0.003266 | 0.015813 | 0.763591 | 0.002327 | 0.003740 | 0.000559 | 0.001581 | 0.017217 | 0.006453 | 0.001182 | 3.339065e-04 | 0.219469 |
| 9 | 0.004092 | 0.003804 | 0.020746 | 1.051493 | 0.003453 | 0.005403 | 0.000811 | 0.001957 | 0.018954 | 0.008733 | 0.001529 | 8.039687e-04 | 0.258881 |
| 10 | 0.004740 | 0.004297 | 0.025815 | 1.341389 | 0.004876 | 0.007455 | 0.001139 | 0.002306 | 0.020327 | 0.011445 | 0.001901 | 1.584894e-03 | 0.292310 |
| 11 | 0.005258 | 0.004742 | 0.030774 | 1.610677 | 0.006491 | 0.009900 | 0.001562 | 0.002614 | 0.021407 | 0.014611 | 0.002288 | 2.401272e-03 | 0.319840 |
| 12 | 0.005661 | 0.005139 | 0.035439 | 1.845968 | 0.008112 | 0.012721 | 0.002106 | 0.002877 | 0.022260 | 0.018254 | 0.002679 | 2.929413e-03 | 0.342132 |
| 13 | 0.005970 | 0.005490 | 0.039695 | 2.042813 | 0.009553 | 0.015883 | 0.002807 | 0.003103 | 0.022937 | 0.022393 | 0.003066 | 3.173732e-03 | 0.360037 |
| 14 | 0.006205 | 0.005799 | 0.043490 | 2.202836 | 0.010701 | 0.019336 | 0.003717 | 0.003301 | 0.023478 | 0.027047 | 0.003442 | 3.269164e-03 | 0.374388 |
display(smoothed_data['Diff_Concentrations'])
| A1G1F | A1G2F | G0 | G0F | G0F+GlcNAc | G0F-GlcNAc | G0-GlcNAc | G1a/b | G2F | Man5 | Man6 | Man7 | G1Fa/b | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0.000010 | 0.000115 | 0.000094 | 0.001069 | 0.000041 | 0.000014 | 0.000003 | 0.000048 | 0.000575 | 0.000078 | 0.000015 | 2.507334e-07 | 0.002377 |
| 2 | 0.000067 | 0.000257 | 0.000412 | 0.008373 | 0.000068 | 0.000067 | 0.000013 | 0.000076 | 0.001595 | 0.000234 | 0.000047 | 7.161719e-07 | 0.009503 |
| 3 | 0.000195 | 0.000389 | 0.000962 | 0.027620 | 0.000111 | 0.000170 | 0.000029 | 0.000117 | 0.002554 | 0.000443 | 0.000090 | 2.044594e-06 | 0.020267 |
| 4 | 0.000389 | 0.000492 | 0.001712 | 0.062965 | 0.000179 | 0.000326 | 0.000051 | 0.000172 | 0.003094 | 0.000698 | 0.000139 | 5.828847e-06 | 0.032039 |
| 5 | 0.000609 | 0.000558 | 0.002585 | 0.114683 | 0.000286 | 0.000537 | 0.000080 | 0.000239 | 0.003140 | 0.000992 | 0.000192 | 1.655038e-05 | 0.041484 |
| 6 | 0.000783 | 0.000586 | 0.003466 | 0.176578 | 0.000447 | 0.000803 | 0.000117 | 0.000307 | 0.002843 | 0.001321 | 0.000244 | 4.645938e-05 | 0.046252 |
| 7 | 0.000854 | 0.000583 | 0.004230 | 0.235860 | 0.000675 | 0.001118 | 0.000163 | 0.000359 | 0.002398 | 0.001682 | 0.000291 | 1.263242e-04 | 0.046037 |
| 8 | 0.000818 | 0.000557 | 0.004774 | 0.277831 | 0.000966 | 0.001473 | 0.000218 | 0.000379 | 0.001943 | 0.002074 | 0.000331 | 3.153108e-04 | 0.042175 |
| 9 | 0.000712 | 0.000516 | 0.005046 | 0.293292 | 0.001283 | 0.001856 | 0.000287 | 0.000366 | 0.001543 | 0.002492 | 0.000361 | 6.398031e-04 | 0.036481 |
| 10 | 0.000582 | 0.000470 | 0.005051 | 0.282654 | 0.001545 | 0.002250 | 0.000372 | 0.000330 | 0.001215 | 0.002935 | 0.000381 | 8.702574e-04 | 0.030401 |
| 11 | 0.000457 | 0.000421 | 0.004837 | 0.253702 | 0.001653 | 0.002637 | 0.000479 | 0.000285 | 0.000957 | 0.003401 | 0.000391 | 6.992117e-04 | 0.024775 |
| 12 | 0.000352 | 0.000373 | 0.004474 | 0.216192 | 0.001558 | 0.002999 | 0.000616 | 0.000243 | 0.000757 | 0.003888 | 0.000391 | 3.642221e-04 | 0.019954 |
| 13 | 0.000269 | 0.000329 | 0.004030 | 0.177821 | 0.001304 | 0.003317 | 0.000795 | 0.000210 | 0.000603 | 0.004394 | 0.000383 | 1.496321e-04 | 0.015997 |
| 14 | 0.000205 | 0.000289 | 0.003559 | 0.143035 | 0.000988 | 0.003578 | 0.001037 | 0.000187 | 0.000484 | 0.004917 | 0.000369 | 5.556953e-05 | 0.012826 |
Write to excel¶
with pd.ExcelWriter(os.path.join(inputDir, 'Lee et al', 'medpH_processed.xlsx')) as writer:
for k, v in smoothed_data.items():
v.to_excel(writer, sheet_name=k)
