Tables

Results

Table 1

Table 1: Estimated MTC properties as well as metrics derived from these properties.
Symbol Unit Rat
GMe1 GMe2 GMe3
MTC properties
Hill constant $a^{rel}$ - 0.569 0.712 0.649
Hill constant $b^{rel}$ - 5.97 7.15 6.80
maximal isometric CE force $F_{CE}^{max}$ N 15.5 14.1 17.0
PEE stiffness scaling factor $k_{PEE}$ N/mm2 28.7 24.1 34.8
SEE stiffness scaling factor $k_{SEE}$ N/mm2 952 1255 1050
CE optimum length $L_{CE}^{opt}$ mm 13.7 14.7 13.3
PEE slack length $L_{PEE}^0$ mm 15.1 15.6 14.5
SEE slack length $L_{SEE}^0$ mm 28.8 29.3 25.2
Calcium dynamics activation time constant $\tau_{act}$ ms 55.2 57.7 41.6
Calcium dynamics deactivation time constant $\tau_{deact}$ ms 27.1 25.3 22.4
Derived metrics
MTC length yielding maximal isometric SEE force $L_{MTC}^{opt}$ mm 46.6 47.3 42.5
maximal instantaneous CE power $P_{CE}^{max}$ mW 316 319 352
`half-rise time' $t_{HRT}$ ms 13.2 13.8 9.97
maximal CE shortening velocity $v_{CE}^{max}$ mm/s 144 147 140
CE velocity at $P_{CE}^{max}$ $v_{CE}^{opt}$ mm/s 54.1 57.8 53.8

Supplementary material

Table S1

Code 
#%% Load packages & set directories
import os, sys
import numpy as np
import pandas as pd
from great_tables import GT, style, loc
from pathlib import Path

# Set directories
cwd = Path.cwd()
baseDir = cwd.parent
dataDir = os.path.join(baseDir,'data')
funcDir = os.path.join(baseDir,'analysis','functions')
sys.path.append(str(funcDir))

import stats , stimulation

#%% Set-up
exp = 'SSC_PA'
muscles = ['GMe1', 'GMe2', 'GMe3']

#%% Compute data values
# Motion parameters
if exp == 'SSC_PA':
    cf = np.array([1, 2, 3, 4, 5, 3, 3, 3, 3, 5, 4, 2, 1])
elif exp == 'SSC_PB':
    cf = np.array([1, 1.5, 2, 2.5, 3, 2, 2, 2, 2, 3, 2.5, 1.5, 1])

fts = np.array([0.50, 0.50, 0.50, 0.50, 0.50, 0.80, 0.65, 0.35, 0.20, 0.80, 0.65, 0.35, 0.20])
tShort = fts / cf * 1e3
tLeng = (1 - fts) / cf * 1e3

# Stimulation duration trial 1
iSuperscript = 1  
iTrial = 1
durStim1 = np.empty((len(muscles),len(cf)))
for iMus, mus in enumerate(muscles):      
    filepaths = [os.path.join(dataDir,mus,'dataExp',exp,f'{mus}_{exp}{iCond:02d}_{iTrial:01d}.csv') for iCond in range(1,14)]
    durStim  = stimulation.get_stim_dur(filepaths)
    durStim1[iMus,:] = [x*1e3 for x in durStim] # to ms

durStim1_str = [] 
for iCond in range(1,len(cf)+1):
    same,diff,i = stats.analyse_3similar(durStim1[:,iCond-1],1)
    if i == True:
        durStim1_str.append(stats.str_round(same,2))
    else:
        durStim1_str.append(stats.str_round(same,2)+f'<sup>{iSuperscript}</sup>')
        #print(f'{iSuperscript}: Cond {iCond:02d}, GMe{i+1} stimDuration = {diff:0.0f} ms')
        iSuperscript +=1
        
# Stimulation duration trial 2
iSuperscript = 1  
iTrial = 2
durStim2 = np.empty((len(muscles),len(cf)))
for iMus, mus in enumerate(muscles):   
    filepaths = [os.path.join(dataDir,mus,'dataExp',exp,f'{mus}_{exp}{iCond:02d}_{iTrial:01d}.csv') for iCond in range(1,14)]
    durStim  = stimulation.get_stim_dur(filepaths)
    durStim2[iMus,:] = [x*1e3 for x in durStim] # to ms

durStim2_str = [] 
for iCond in range(1,len(cf)+1):
    same,diff,i = stats.analyse_3similar(durStim2[:,iCond-1],1)
    if i == True:
        durStim2_str.append(stats.str_round(same,2))
    else:
        durStim2_str.append(stats.str_round(same,2)+f'<sup>{iSuperscript}</sup>')
        #print(f'{iSuperscript}: Cond {iCond:02d}, GMe{i+1} stimDuration = {diff:0.0f} ms')
        iSuperscript +=1
        
# AMPO of the rats:
AMPO = []
for mus in ['GMe1', 'GMe2', 'GMe3']:
    fileName = mus+'_dataAMPO'
    df = pd.read_excel(dataDir+'/'+mus+'/'+fileName+'.xlsx')
    ampoData = df.to_numpy()
    
    if exp == 'SSC_PA':
        t1 = np.mean(ampoData[0:3,5:],0)
        t2 = np.mean(ampoData[3:6,5:],0)
    elif exp == 'SSC_PB':
        t1 = np.mean(ampoData[6:9,5:],0)
        t2 = np.mean(ampoData[9:12,5:],0)
    t1 = [stats.str_round(x,2) for x in t1]
    t2 = [stats.str_round(x,2) for x in t2]
    
    AMPO.append(t1)
    AMPO.append(t2)
AMPO = np.array(AMPO)

#%%
# First 4 rows are calculated values; rest are placeholders (NaN)
data_values = np.full((12, len(cf)), np.nan, dtype=object)
data_values[0] = [stats.str_round(x,2) for x in cf]
data_values[1] = [stats.str_round(x,2) for x in fts]
data_values[2] = [stats.str_round(x,3) for x in tShort]
data_values[3] = [stats.str_round(x,3) for x in tLeng]
data_values[4] = durStim1_str
data_values[5] = durStim2_str
data_values[6:] = AMPO

# === Create DataFrame ===
descriptions = [
    'Cycle frequency', 'FTS', 'MTC shortening time', 'MTC lengthening time',
    'Trial 1', 'Trial 2',
    'Trial 1', 'Trial 2',
    'Trial 1', 'Trial 2',
    'Trial 1', 'Trial 2',
]

units = ['Hz', '-', 'ms', 'ms', 'ms', 'ms', 'mW', 'mW', 'mW', 'mW', 'mW', 'mW']
types = ['SSC parameters'] * 2 + ['MTC shortening and lengthening times'] * 2 + ['Stimulation durations'] * 2 + ['Measured AMPO of rat 1'] * 2 + ['Measured AMPO of rat 2'] * 2 + ['Measured AMPO of rat 3'] * 2
conds = [str(i) for i in range(1, 14)]

df = pd.DataFrame(
    data=np.column_stack([types, descriptions, units, data_values]),
    columns=['type', 'Description', 'Unit'] + conds
)

#%% TeX table
from great_tables import GT
from gt_tex import make_latex, insert_rows, fix_reference, replace_latex_table_cell, delete_rows, replace_superscripts

df_tex = df.copy()
df_tex = df_tex.drop('type', axis=1)

gt_table = (GT(df_tex)
    #.tab_stub(rowname_col="description", groupname_col="type")
    .cols_align(align='center') 
    .cols_align(align='left', columns=['Description'])
    .cols_label(Description='')
)

latex_str = make_latex(gt_table.as_latex())
add_rows = {
    0: r"  & & \multicolumn{13}{c|}{Condition}  \\ \hline",
    1: r"  \bfseries & \bfseries Unit & \bfseries 1 & \bfseries 2 & \bfseries 3 & \bfseries 4 & \bfseries 5 & \bfseries 6 & \bfseries 7 & \bfseries 8 & \bfseries 9 & \bfseries 10 & \bfseries 11 & \bfseries 12 & \bfseries 13 \\ \hline",
    2: r"  \multicolumn{15}{|l|}{\itshape SSC parameters} \\ \hline",
    5: r"  \multicolumn{15}{|l|}{\itshape MTC shortening and lengthening times} \\ \hline",
    8: r"  \multicolumn{15}{|l|}{\itshape Stimulation durations} \\ \hline",
    11: r"  \multicolumn{15}{|l|}{\itshape Measured AMPO of Rat 1} \\ \hline",
    14: r"  \multicolumn{15}{|l|}{\itshape Measured AMPO of Rat 2} \\ \hline",
    17: r"  \multicolumn{15}{|l|}{\itshape Measured AMPO of Rat 3} \\ \hline",
}
latex_str = delete_rows(latex_str, row_numbers=[0])
latex_str = insert_rows(latex_str, add_rows)

latex_str = replace_latex_table_cell(latex_str, row=8, col=0, new_text=r'Trial 1')
latex_str = replace_latex_table_cell(latex_str, row=9, col=0, new_text=r'Trial 2')
latex_str = replace_latex_table_cell(latex_str, row=10, col=0, new_text=r'Trial 1')
latex_str = replace_latex_table_cell(latex_str, row=11, col=0, new_text=r'Trial 2')
latex_str = replace_latex_table_cell(latex_str, row=12, col=0, new_text=r'Trial 1')
latex_str = replace_latex_table_cell(latex_str, row=13, col=0, new_text=r'Trial 2')

# Write to a .tex file
latex_str += (r"\break\hfill\footnotesize{"+ 
              r"\textsuperscript{1} For conditon 1: stimulation duration of rat 3 was 455 ms. "
              r"\textsuperscript{2} For conditon 9: stimulation duration of rat 3 was 23 ms. "
              r"\textsuperscript{3} For conditon 13: stimulation duration of rat 1 was 165 ms.}")

with open('supptbl-sscpa.tex', "w", encoding="utf-8") as f:
    f.write(latex_str)


#%% Great table
from great_tables import GT, md
df_gt = df.copy()

gt_table = (GT(df_gt)
    .tab_spanner(label = "Condition", columns = [f'{x}' for x in range(1,14)])
    .tab_stub(rowname_col="Description", groupname_col="type")
    .tab_style(style = style.text(style = "italic"), locations = loc.row_groups())
    .tab_source_note(
        source_note = md("<sup>1</sup> For conditon 1: stimulation duration of rat 3 was 455 ms.")
    )
    .tab_source_note(
        source_note = md("<sup>2</sup> For conditon 9: stimulation duration of rat 3 was 23 ms.")
    )
    .tab_source_note(
        source_note = md("<sup>3</sup> For conditon 13: stimulation duration of rat 1 was 165 ms.")
    )
)
gt_table
Table S1
SSC parameters, stimulation durations, and measured AMPO of experimental stretch-shortening cycles with a 4 mm MTC length excursion. Stimulation onset was set at the start of MTC shortening in all conditions.
Unit Condition
1 2 3 4 5 6 7 8 9 10 11 12 13
SSC parameters
Cycle frequency Hz 1.0 2.0 3.0 4.0 5.0 3.0 3.0 3.0 3.0 5.0 4.0 2.0 1.0
FTS - 0.50 0.50 0.50 0.50 0.50 0.80 0.65 0.35 0.20 0.80 0.65 0.35 0.20
MTC shortening and lengthening times
MTC shortening time ms 500 250 167 125 100 267 217 117 66.7 160 162 175 200
MTC lengthening time ms 500 250 167 125 100 66.7 117 217 267 40.0 87.5 325 800
Stimulation durations
Trial 1 ms 405 175 103 65 35 183 133 53 23 85 95 115 135
Trial 2 ms 4451 205 123 85 55 213 163 73 332 115 115 135 1553
Measured AMPO of rat 1
Trial 1 mW 31 51 59 58 45 74 67 38 20 82 74 43 23
Trial 2 mW 34 56 69 72 66 84 77 49 25 104 87 47 27
Measured AMPO of rat 2
Trial 1 mW 29 48 57 58 45 71 62 37 18 79 70 40 22
Trial 2 mW 30 52 65 68 64 78 72 46 24 93 79 45 24
Measured AMPO of rat 3
Trial 1 mW 34 56 65 66 48 83 72 44 22 91 82 47 25
Trial 2 mW 37 62 74 82 74 91 85 56 20 116 96 54 28
1 For conditon 1: stimulation duration of rat 3 was 455 ms.
2 For conditon 9: stimulation duration of rat 3 was 23 ms.
3 For conditon 13: stimulation duration of rat 1 was 165 ms.

Table S2

Code 
#%% Load packages & set directories
import os, sys
import numpy as np
import pandas as pd
from great_tables import GT, style, loc
from pathlib import Path

# Set directories
cwd = Path.cwd()
baseDir = cwd.parent
dataDir = os.path.join(baseDir,'data')
funcDir = os.path.join(baseDir,'analysis','functions')
sys.path.append(str(funcDir))

import stats, stimulation

#%% Set-up
exp = 'SSC_PB'
muscles = ['GMe1', 'GMe2', 'GMe3']

#%% Compute data values
# Motion parameters
if exp == 'SSC_PA':
    cf = np.array([1, 2, 3, 4, 5, 3, 3, 3, 3, 5, 4, 2, 1])
elif exp == 'SSC_PB':
    cf = np.array([1, 1.5, 2, 2.5, 3, 2, 2, 2, 2, 3, 2.5, 1.5, 1])

fts = np.array([0.50, 0.50, 0.50, 0.50, 0.50, 0.80, 0.65, 0.35, 0.20, 0.80, 0.65, 0.35, 0.20])
tShort = fts / cf * 1e3
tLeng = (1 - fts) / cf * 1e3

# Stimulation duration trial 1
iSuperscript = 1  
iTrial = 1
durStim1 = np.empty((len(muscles),len(cf)))
for iMus, mus in enumerate(muscles):      
    filepaths = [os.path.join(dataDir,mus,'dataExp',exp,f'{mus}_{exp}{iCond:02d}_{iTrial:01d}.csv') for iCond in range(1,14)]
    durStim  = stimulation.get_stim_dur(filepaths)
    durStim1[iMus,:] = [x*1e3 for x in durStim] # to ms

durStim1_str = [] 
for iCond in range(1,len(cf)+1):
    same,diff,i = stats.analyse_3similar(durStim1[:,iCond-1],1)
    if i == True:
        durStim1_str.append(stats.str_round(same,2))
    else:
        durStim1_str.append(stats.str_round(same,2)+f'<sup>{iSuperscript}</sup>')
        #print(f'{iSuperscript}: Cond {iCond:02d}, GMe{i+1} stimDuration = {diff:0.0f} ms')
        iSuperscript +=1
        
# Stimulation duration trial 2
iSuperscript = 1  
iTrial = 2
durStim2 = np.empty((len(muscles),len(cf)))
for iMus, mus in enumerate(muscles):   
    filepaths = [os.path.join(dataDir,mus,'dataExp',exp,f'{mus}_{exp}{iCond:02d}_{iTrial:01d}.csv') for iCond in range(1,14)]
    durStim  = stimulation.get_stim_dur(filepaths)
    durStim2[iMus,:] = [x*1e3 for x in durStim] # to ms

durStim2_str = [] 
for iCond in range(1,len(cf)+1):
    same,diff,i = stats.analyse_3similar(durStim2[:,iCond-1],1)
    if i == True:
        durStim2_str.append(stats.str_round(same,2))
    else:
        durStim2_str.append(stats.str_round(same,2)+f'<sup>{iSuperscript}</sup>')
        #print(f'{iSuperscript}: Cond {iCond:02d}, GMe{i+1} stimDuration = {diff:0.0f} ms')
        iSuperscript +=1
        
# AMPO of the rats:
AMPO = []
for mus in ['GMe1', 'GMe2', 'GMe3']:
    fileName = mus+'_dataAMPO'
    df = pd.read_excel(dataDir+'/'+mus+'/'+fileName+'.xlsx')
    ampoData = df.to_numpy()
    
    if exp == 'SSC_PA':
        t1 = np.mean(ampoData[0:3,5:],0)
        t2 = np.mean(ampoData[3:6,5:],0)
    elif exp == 'SSC_PB':
        t1 = np.mean(ampoData[6:9,5:],0)
        t2 = np.mean(ampoData[9:12,5:],0)
    t1 = [stats.str_round(x,2) for x in t1]
    t2 = [stats.str_round(x,2) for x in t2]
    
    AMPO.append(t1)
    AMPO.append(t2)
AMPO = np.array(AMPO)

#%%
# First 4 rows are calculated values; rest are placeholders (NaN)
data_values = np.full((12, len(cf)), np.nan, dtype=object)
data_values[0] = [stats.str_round(x,2) for x in cf]
data_values[1] = [stats.str_round(x,2) for x in fts]
data_values[2] = [stats.str_round(x,3) for x in tShort]
data_values[3] = [stats.str_round(x,3) for x in tLeng]
data_values[4] = durStim1_str
data_values[5] = durStim2_str
data_values[6:] = AMPO

# === Create DataFrame ===
descriptions = [
    'Cycle frequency', 'FTS', 'MTC shortening time', 'MTC lengthening time',
    'Trial 1', 'Trial 2',
    'Trial 1', 'Trial 2',
    'Trial 1', 'Trial 2',
    'Trial 1', 'Trial 2',
]

units = ['Hz', '-', 'ms', 'ms', 'ms', 'ms', 'mW', 'mW', 'mW', 'mW', 'mW', 'mW']
types = ['SSC parameters'] * 2 + ['MTC shortening and lengthening times'] * 2 + ['Stimulation durations'] * 2 + ['Measured AMPO of rat 1'] * 2 + ['Measured AMPO of rat 2'] * 2 + ['Measured AMPO of rat 3'] * 2
conds = [str(i) for i in range(1, 14)]

df = pd.DataFrame(
    data=np.column_stack([types, descriptions, units, data_values]),
    columns=['type', 'Description', 'Unit'] + conds
)

#%% TeX table
from great_tables import GT
from gt_tex import make_latex, insert_rows, fix_reference, replace_latex_table_cell, delete_rows, replace_superscripts

df_tex = df.copy()
df_tex = df_tex.drop('type', axis=1)

gt_table = (GT(df_tex)
    #.tab_stub(rowname_col="description", groupname_col="type")
    .cols_align(align='center') 
    .cols_align(align='left', columns=['Description'])
    .cols_label(Description='')
)

latex_str = make_latex(gt_table.as_latex())
add_rows = {
    0: r"  & & \multicolumn{13}{c|}{Condition}  \\ \hline",
    1: r"  \bfseries & \bfseries Unit & \bfseries 1 & \bfseries 2 & \bfseries 3 & \bfseries 4 & \bfseries 5 & \bfseries 6 & \bfseries 7 & \bfseries 8 & \bfseries 9 & \bfseries 10 & \bfseries 11 & \bfseries 12 & \bfseries 13 \\ \hline",
    2: r"  \multicolumn{15}{|l|}{\itshape SSC parameters} \\ \hline",
    5: r"  \multicolumn{15}{|l|}{\itshape MTC shortening and lengthening times} \\ \hline",
    8: r"  \multicolumn{15}{|l|}{\itshape Stimulation durations} \\ \hline",
    11: r"  \multicolumn{15}{|l|}{\itshape Measured AMPO of Rat 1} \\ \hline",
    14: r"  \multicolumn{15}{|l|}{\itshape Measured AMPO of Rat 2} \\ \hline",
    17: r"  \multicolumn{15}{|l|}{\itshape Measured AMPO of Rat 3} \\ \hline",
}
latex_str = delete_rows(latex_str, row_numbers=[0])
latex_str = insert_rows(latex_str, add_rows)

latex_str = replace_latex_table_cell(latex_str, row=8, col=0, new_text=r'Trial 1')
latex_str = replace_latex_table_cell(latex_str, row=9, col=0, new_text=r'Trial 2')
latex_str = replace_latex_table_cell(latex_str, row=10, col=0, new_text=r'Trial 1')
latex_str = replace_latex_table_cell(latex_str, row=11, col=0, new_text=r'Trial 2')
latex_str = replace_latex_table_cell(latex_str, row=12, col=0, new_text=r'Trial 1')
latex_str = replace_latex_table_cell(latex_str, row=13, col=0, new_text=r'Trial 2')

# Write to a .tex file
latex_str += (r"\break\hfill\footnotesize{"+ 
              r"\textsuperscript{1} For conditon 1: stimulation duration of rat 3 was 455 ms. "
              r"\textsuperscript{2} For conditon 9: stimulation duration of rat 3 was 23 ms. "
              r"\textsuperscript{3} For conditon 13: stimulation duration of rat 1 was 165 ms.}")

with open('supptbl-sscpb.tex', "w", encoding="utf-8") as f:
    f.write(latex_str)


#%% Great table
from great_tables import GT, md
df_gt = df.copy()

gt_table = (GT(df_gt)
    .tab_spanner(label = "Condition", columns = [f'{x}' for x in range(1,14)])
    .tab_stub(rowname_col="Description", groupname_col="type")
    .tab_style(style = style.text(style = "italic"), locations = loc.row_groups())
    .tab_source_note(
        source_note = md("<sup>1</sup> For conditon 1: stimulation duration of rat 3 was 455 ms.")
    )
    .tab_source_note(
        source_note = md("<sup>2</sup> For conditon 9: stimulation duration of rat 3 was 23 ms.")
    )
    .tab_source_note(
        source_note = md("<sup>3</sup> For conditon 13: stimulation duration of rat 1 was 165 ms.")
    )
)
gt_table
Table S2
SSC parameters, stimulation durations, and measured AMPO of experimental stretch-shortening cycles with an 8 mm MTC length excursion. Stimulation onset was set at the start of MTC shortening in all conditions.
Unit Condition
1 2 3 4 5 6 7 8 9 10 11 12 13
SSC parameters
Cycle frequency Hz 1.0 1.5 2.0 2.5 3.0 2.0 2.0 2.0 2.0 3.0 2.5 1.5 1.0
FTS - 0.50 0.50 0.50 0.50 0.50 0.80 0.65 0.35 0.20 0.80 0.65 0.35 0.20
MTC shortening and lengthening times
MTC shortening time ms 500 333 250 200 167 400 325 175 100 267 260 233 200
MTC lengthening time ms 500 333 250 200 167 100.0 175 325 400 66.7 140 433 800
Stimulation durations
Trial 1 ms 405 253 175 135 103 295 245 115 55 173 175 163 145
Trial 2 ms 455 293 205 165 123 345 275 135 751 2132 225 193 1653
Measured AMPO of rat 1
Trial 1 mW 53 67 75 80 79 94 89 55 26 113 95 54 34
Trial 2 mW 54 47 80 86 85 99 97 63 28 121 104 62 35
Measured AMPO of rat 2
Trial 1 mW 49 63 73 75 76 88 83 52 24 104 88 50 32
Trial 2 mW 50 66 76 80 81 92 86 55 27 113 95 54 33
Measured AMPO of rat 3
Trial 1 mW 57 73 81 87 88 103 95 60 28 123 101 60 37
Trial 2 mW 47 - 89 - 88 110 - - 32 129 - - 40
1 For conditon 1: stimulation duration of rat 3 was 455 ms.
2 For conditon 9: stimulation duration of rat 3 was 23 ms.
3 For conditon 13: stimulation duration of rat 1 was 165 ms.