Plotting Data and Running Stats (Python)

#Plotting a single participant...............Code is Altered for subject 15 FYI
import matplotlib.pyplot as plt

# Sample data for one subject
data = {
    'Subject ID': 15,
    'Condition': ['DL MIX', 'BFDL MIX', 'DL DO', 'BFDL DO', 'BLS', 'BF BLS'],
    'Symmetry Score': [14.05435897, 4.351373613, 13.5520292, 1.506611732, 12.03191529, 3.825738298],
}

# Define custom colors with alternating light blue and pink
custom_colors = ['#ADD8E6', '#FFC0CB']  # Light Blue, Pink
#custom_colors = ['#555555', '#D3D3D3']  # Dark Grey, Light Grey
# Create a bar chart for the subject
bars = plt.bar(data['Condition'], data['Symmetry Score'])

# Find and annotate the maximum value for each bar
for bar, score in zip(bars, data['Symmetry Score']):
    plt.text(bar.get_x() + bar.get_width() / 2, score, f'{score:.2f}', ha='center', va='bottom', fontsize=10)

# Set the title and axis labels
plt.title(f"Subject {data['Subject ID']}")
plt.xlabel("Condition")
plt.ylabel("Symmetry Score")

# Apply alternating custom colors to the bars
for i, bar in enumerate(bars):
    bar.set_color(custom_colors[i % len(custom_colors)])

# Show the bar chart with the legend
plt.show()

#--------------------------------------------------------------------------------#
#plotting all participants
import matplotlib.pyplot as plt

# Sample data for multiple subjects
data = {
    'Subject ID': [1, 2, 3, 4, 8],
    'Condition': [
        ['DL MIX', 'BFDL MIX', 'DL DO', 'BFDL DO', 'BLS', 'BF BLS'],
        ['DL MIX', 'BFDL MIX', 'DL DO', 'BFDL DO', 'BLS', 'BF BLS'],
        ['DL MIX', 'BFDL MIX', 'DL DO', 'BFDL DO', 'BLS', 'BF BLS'],
        ['DL MIX', 'BFDL MIX', 'DL DO', 'BFDL DO', 'BLS', 'BF BLS'],
        ['DL MIX', 'BFDL MIX', 'DL DO', 'BFDL DO', 'BLS', 'BF BLS']
    ],
    'Symmetry Score': [
        [13.86592791, 10.38887725, 19.87305564, 15.78628316, 19.07562003, 7.037254481],#subject 1
        [16.14866232, 3.426328834, 10.15692823, 5.84922281, 19.43922051, 5.887243622],#subject 2
        [8.752265998, 2.425071431, 9.411225892, 5.68946334, 13.25010756, 8.029965731],#subject 3
        [14.88508052, 5.447533475, 16.9390042, 5.906674834, 19.30511447, 4.449974859],#subject 4
        [, , , , , ],#subject 5
        [, , , , , ],#subject 6
        [, , , , , ],#subject 7
        [14.05435897, 4.351373613, 13.5520292, 1.506611732, 12.03191529, 3.825738298] #subject 8
        [, , , , , ],#subject 9
        [, , , , , ],#subject 10
        [, , , , , ],#subject 11
        [, , , , , ],#subject 12
        [, , , , , ] #subject 13
    ]
}

custom_colors = ['#ADD8E6', '#FFC0CB']  # Light Blue, Pink

# Create a 2x2 subplot
fig, axes = plt.subplots(3, 2, figsize=(10, 8))

for i, ax in enumerate(axes.flat):
    subject_data = data['Symmetry Score'][i]
    subject_conditions = data['Condition'][i]
    subject_id = data['Subject ID'][i]

    # Create a bar chart for the subject
    bars = ax.bar(subject_conditions, subject_data)

    # Find and annotate the maximum value for each bar
    for bar, score in zip(bars, subject_data):
        ax.text(bar.get_x() + bar.get_width() / 2, score, f'{score:.2f}', ha='center', va='bottom', fontsize=10)

    # Set the title and axis labels for each subplot
    ax.set_title(f"Subject {subject_id}")
    ax.set_xlabel("Condition")
    ax.set_ylabel("VGRF Symmetry Score")

    # Apply alternating custom colors to the bars
    for j, bar in enumerate(bars):
        bar.set_color(custom_colors[j % len(custom_colors)])

    # Rotate x-axis labels by 45 degrees
    ax.tick_params(axis='x', rotation=45)

    # Set the y-limit to 22
    ax.set_ylim(0, 22)
    
# Add a legend to indicate colors
legend_labels = ['Trials without BF', 'Trials with BF']
legend_colors = custom_colors  # Use the same colors
legend_patches = [plt.Line2D([0], [0], marker='s', color='w', label=label, markersize=10, markerfacecolor=color) for label, color in zip(legend_labels, legend_colors)]
plt.legend(handles=legend_patches, loc="upper right", bbox_to_anchor=(1.55, 1))

# Adjust layout and display the subplots
plt.tight_layout()
plt.show()

#--------------------------------------------------------------------------------#
#plotting boxplots for all subjects to compare trials across all subjects
import matplotlib.pyplot as plt

# Sample data PLEASE NOTE: each line in the symmetry score list contains variables that are related to the trail name.
# For example, the first line contains SI scores for all subjects (in order i.e., subject 1,2,3, ...) in trial DL_MIX. This repeats for lines 1-6 because there
# are 6 trials. The length of each line will increase when more participants data are collected. 
data = {
    'Subject ID': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
    'Condition': [
        'DL MIX', 'BFDL MIX', 'DL DO', 'BFDL DO', 'BLS', 'BF BLS'
    ],
    'Symmetry Score': [
        [13.86592791, 16.14866232, 8.752265998, 14.88508052, 5.102426758, 15.26732963, 26.6302286, 14.05435897, 13.68019889, 5.28867734, 11.15978143, 8.594839153, 15.73516416], #DL MIX The last vertical column in this list constains subject 15 data. adjust once 12-14 data is analyzed 
        [10.38887725, 3.426328834, 2.425071431, 5.447533475, 0.5520707055, 9.541955889, 16.99936995, 4.351373613, 7.062977584, 6.091701634, 2.22955217, 4.079451706, 7.184366328], #BFDL_MIX
        [19.87305564, 10.15692823, 9.411225892, 16.9390042, 2.762998409, 10.83198789, 25.6220238, 13.5520292, 7.699453266, 10.07335348, 10.27673564, 10.10591426, 14.26679376], #DL_DO
        [15.78628316, 5.84922281, 5.68946334, 5.906674834, 1.858043482, 6.771647328, 10.81643791, 1.506611732, 1.439435071, 10.87089412, 0.0942286124, 0.01196061182, 8.321148114], #BFDL_DO
        [19.07562003, 19.43922051, 13.25010756, 19.30511447, 7.214669004, 14.4114713, 24.16483912, 12.03191529, 17.95442357, 12.55540582, 18.92893114, 11.0404162, 17.91631248], #BLS
        [7.037254481, 5.887243622, 8.029965731, 4.449974859, 0.4219410047, 2.26422687, 5.739420329, 3.825738298, 7.033868895, 9.488338202, 0.000989997723, 2.751912568, 6.402860311] #BF_BLS
    ]
}

# Custom colors for the boxes
box_colors = ['lightblue', 'pink', 'lightblue', 'pink', 'lightblue', 'pink']

# Create a box plot for comparing trials across all subjects
plt.figure(figsize=(10, 6))
boxplot = plt.boxplot(data['Symmetry Score'], labels=data['Condition'], vert=True, patch_artist=True)

# Set custom colors for boxes
for box, color in zip(boxplot['boxes'], box_colors):
    box.set(color='black', linewidth=2)
    box.set(facecolor=color)

# Set labels and title
plt.xlabel("Condition")
plt.ylabel("VGRF Symmetry Score")
plt.title("VGRF Symmetry Scores Across Conditions")

# Add a legend for boxplot colors outside the graph
legend_labels = ['Trials without BF', 'Trials with BF']
legend_colors = ['lightblue', 'pink']
legend_patches = [plt.Line2D([0], [0], marker='s', color='w', label=label, markersize=10, markerfacecolor=color) for label, color in zip(legend_labels, legend_colors)]
plt.legend(handles=legend_patches, loc="upper right", bbox_to_anchor=(1.25, 1))

# Show the box plot
plt.xticks(rotation=45)
plt.show()


#----------------------------------#
import matplotlib.pyplot as plt

# Sample data for one subject
data = {
    'Subject ID': 1,
    'Condition': ['BLS Slow'],
    'Symmetry Score': [15.95868738]
}

# Define custom colors with alternating light blue and pink (hexadecimal color codes)
custom_colors = ['#ADD8E6']  # Light Blue, Pink
#custom_colors = ['#555555', '#D3D3D3']  # Dark Grey, Light Grey
# Create a bar chart for the subject
bars = plt.bar(data['Condition'], data['Symmetry Score'])

# Find and annotate the maximum value for each bar
for bar, score in zip(bars, data['Symmetry Score']):
    plt.text(bar.get_x() + bar.get_width() / 2, score, f'{score:.2f}', ha='center', va='bottom', fontsize=10)

# Set the title and axis labels
plt.title(f"Subject {data['Subject ID']}")
plt.xlabel("Condition")
plt.ylabel("Symmetry Score")

# Apply alternating custom colors to the bars
for i, bar in enumerate(bars):
    bar.set_color(custom_colors[i % len(custom_colors)])

# Show the bar chart with the legend
plt.show()



#--------------------------------------------------------------------------------------------------#
# Running an ANOVA with VGRF Data #
#--------------------------------------------------------------------------------------------------#

import pandas as pd
import numpy as np
import pingouin as pg

# Create a DataFrame from your data
data = {
    'Subject_ID': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
    'Condition': [
        'DL MIX', 'BFDL MIX', 'DL DO', 'BFDL DO', 'BLS', 'BF BLS'
    ],
    'Symmetry_Score': [
        [13.86592791, 16.14866232, 8.752265998, 14.88508052, 5.102426758, 15.26732963, 26.6302286, 14.05435897, 13.68019889, 5.28867734, 11.15978143, 8.594839153, 15.73516416],  # DL MIX
        [10.38887725, 3.426328834, 2.425071431, 5.447533475, 0.5520707055, 9.541955889, 16.99936995, 4.351373613, 7.062977584, 6.091701634, 2.22955217, 4.079451706, 7.184366328],  # BFDL_MIX
        [19.87305564, 10.15692823, 9.411225892, 16.9390042, 2.762998409, 10.83198789, 25.6220238, 13.5520292, 7.699453266, 10.07335348, 10.27673564, 10.10591426, 14.26679376],  # DL_DO
        [15.78628316, 5.84922281, 5.68946334, 5.906674834, 1.858043482, 6.771647328, 10.81643791, 1.506611732, 1.439435071, 10.87089412, 0.0942286124, 0.01196061182, 8.321148114],  # BFDL_DO
        [19.07562003, 19.43922051, 13.25010756, 19.30511447, 7.214669004, 14.4114713, 24.16483912, 12.03191529, 17.95442357, 12.55540582, 18.92893114, 11.0404162, 17.91631248],  # BLS
        [7.037254481, 5.887243622, 8.029965731, 4.449974859, 0.4219410047, 2.26422687, 5.739420329, 3.825738298, 7.033868895, 9.488338202, 0.000989997723, 2.751912568, 6.402860311]  # BF_BLS
    ]
}

# Create a list to store rows of data
rows = []

# Iterate through subjects, conditions, and symmetry scores
for i, subject_id in enumerate(data['Subject_ID']):
    for j, condition in enumerate(data['Condition']):
        symmetry_score = data['Symmetry_Score'][j][i]
        rows.append([subject_id, condition, symmetry_score])

# Create a DataFrame from the rows
df_long = pd.DataFrame(rows, columns=['Subject_ID', 'Condition', 'Symmetry_Score'])

# Conduct repeated measures ANOVA using pingouin
rm_anova = pg.rm_anova(dv='Symmetry_Score', within='Condition', subject='Subject_ID', data=df_long)

# Display the ANOVA table
print(rm_anova)


#--------------------------------------------------------------------------------------------------#
# Running a two-way mixed model ANOVA with Knee Torque Data #
#--------------------------------------------------------------------------------------------------#
import pandas as pd
import numpy as np
import pingouin as pg

# Create a DataFrame from your data
data = {
    'Subject_ID': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
    'Condition': [
        'DL MIX', 'BFDL MIX', 'DL DO', 'BFDL DO', 'BLS', 'BF BLS'
    ],
    'Symmetry_Score': [
        [13.86592791, 16.14866232, 8.752265998, 14.88508052, 5.102426758, 15.26732963, 26.6302286, 14.05435897, 13.68019889, 5.28867734, 11.15978143, 8.594839153, 15.73516416],  # DL MIX
        [10.38887725, 3.426328834, 2.425071431, 5.447533475, 0.5520707055, 9.541955889, 16.99936995, 4.351373613, 7.062977584, 6.091701634, 2.22955217, 4.079451706, 7.184366328],  # BFDL_MIX
        [19.87305564, 10.15692823, 9.411225892, 16.9390042, 2.762998409, 10.83198789, 25.6220238, 13.5520292, 7.699453266, 10.07335348, 10.27673564, 10.10591426, 14.26679376],  # DL_DO
        [15.78628316, 5.84922281, 5.68946334, 5.906674834, 1.858043482, 6.771647328, 10.81643791, 1.506611732, 1.439435071, 10.87089412, 0.0942286124, 0.01196061182, 8.321148114],  # BFDL_DO
        [19.07562003, 19.43922051, 13.25010756, 19.30511447, 7.214669004, 14.4114713, 24.16483912, 12.03191529, 17.95442357, 12.55540582, 18.92893114, 11.0404162, 17.91631248],  # BLS
        [7.037254481, 5.887243622, 8.029965731, 4.449974859, 0.4219410047, 2.26422687, 5.739420329, 3.825738298, 7.033868895, 9.488338202, 0.000989997723, 2.751912568, 6.402860311]  # BF_BLS
    ],
    'Group': ['Control', 'BF', 'Control', 'BF', 'Control', 'BF', 'Control', 'BF', 'Control', 'BF', 'Control', 'BF', 'Control', 'BF']  # Add your actual group data here
}

# Create a list to store rows of data
rows = []

# Iterate through subjects, conditions, groups, and symmetry scores
for i, subject_id in enumerate(data['Subject_ID']):
    for j, condition in enumerate(data['Condition']):
        symmetry_score = data['Symmetry_Score'][j][i]
        group = data['Group'][i]
        rows.append([subject_id, condition, group, symmetry_score])

# Create a DataFrame from the rows
df_long = pd.DataFrame(rows, columns=['Subject_ID', 'Condition', 'Group', 'Symmetry_Score'])

# Conduct two-way mixed model ANOVA using pingouin
mixed_anova = pg.mixed_anova(data=df_long, dv='Symmetry_Score', within='Condition', subject='Subject_ID', between='Group')

# Display the ANOVA table
print(mixed_anova)


#------------------------------------------------------------------------------------------------------------------------------------#
# Twoway mixed model anova that includes a pairwise comparisions with a Games-Howell correction
#------------------------------------------------------------------------------------------------------------------------------------#

import pandas as pd
import numpy as np
import pingouin as pg

# Create a DataFrame from your data
data = {
    'Subject_ID': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13],
    'Condition': [
        'DL MIX', 'BFDL MIX', 'DL DO', 'BFDL DO', 'BLS', 'BF BLS'
    ],
    'Symmetry_Score': [
        [13.86592791, 16.14866232, 8.752265998, 14.88508052, 5.102426758, 15.26732963, 26.6302286, 14.05435897, 13.68019889, 5.28867734, 11.15978143, 8.594839153, 15.73516416],  # DL MIX
        [10.38887725, 3.426328834, 2.425071431, 5.447533475, 0.5520707055, 9.541955889, 16.99936995, 4.351373613, 7.062977584, 6.091701634, 2.22955217, 4.079451706, 7.184366328],  # BFDL_MIX
        [19.87305564, 10.15692823, 9.411225892, 16.9390042, 2.762998409, 10.83198789, 25.6220238, 13.5520292, 7.699453266, 10.07335348, 10.27673564, 10.10591426, 14.26679376],  # DL_DO
        [15.78628316, 5.84922281, 5.68946334, 5.906674834, 1.858043482, 6.771647328, 10.81643791, 1.506611732, 1.439435071, 10.87089412, 0.0942286124, 0.01196061182, 8.321148114],  # BFDL_DO
        [19.07562003, 19.43922051, 13.25010756, 19.30511447, 7.214669004, 14.4114713, 24.16483912, 12.03191529, 17.95442357, 12.55540582, 18.92893114, 11.0404162, 17.91631248],  # BLS
        [7.037254481, 5.887243622, 8.029965731, 4.449974859, 0.4219410047, 2.26422687, 5.739420329, 3.825738298, 7.033868895, 9.488338202, 0.000989997723, 2.751912568, 6.402860311]  # BF_BLS
    ],
    'Group': ['A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B', 'A', 'B']  # Add your actual group data here
}

# Create a list to store rows of data
rows = []

# Iterate through subjects, conditions, groups, and symmetry scores
for i, subject_id in enumerate(data['Subject_ID']):
    for j, condition in enumerate(data['Condition']):
        symmetry_score = data['Symmetry_Score'][j][i]
        group = data['Group'][i]
        rows.append([subject_id, condition, group, symmetry_score])

# Create a DataFrame from the rows
df_long = pd.DataFrame(rows, columns=['Subject_ID', 'Condition', 'Group', 'Symmetry_Score'])

# Conduct two-way mixed model ANOVA using pingouin
mixed_anova = pg.mixed_anova(data=df_long, dv='Symmetry_Score', within='Condition', subject='Subject_ID', between='Group', correction=True)

# Display the ANOVA table
print(mixed_anova)

# Perform pairwise comparisons with Games-Howell correction
posthoc = pg.pairwise_gameshowell(data=df_long, dv='Symmetry_Score', between='Condition', effsize='eta-square')
# Display the pairwise comparison results
print(posthoc)

#------------------------------------------------------------------------------------------------------------------------------------#
# Twoway mixed model anova that includes a pairwise comparisions with a Games-Howell correction and displays the output table as a figure
#------------------------------------------------------------------------------------------------------------------------------------#
import plotly.graph_objects as go

# Two-Way Mixed Model ANOVA Results
mixed_anova_results = {
    'Source': ['Group', 'Condition', 'Interaction'],
    'SS': [24.397002, 1416.046361, 6.389000],
    'DF1': [1, 5, 5],
    'DF2': [11, 55, 55],
    'MS': [24.397002, 283.209272, 1.277800],
    'F': [0.264983, 26.255309, 0.118460],
    'p-unc': [0.617, 1.890e-13, 0.988],
    'np2': [0.023523, 0.704740, 0.010654],
    'eps': [None, 0.548312, None],
    'Sphericity': [None, True, None],
    'W-spher': [None, 0.132779, None],
    'p-spher': [None, 0.127508, None]
}

# Pairwise Comparisons Results
pairwise_results = {
    'Comparison': ['BF BLS vs. BFDL DO', 'BF BLS vs. BFDL MIX', 'BF BLS vs. BLS', 'BF BLS vs. DL DO', 'BF BLS vs. DL MIX',
                    'BFDL DO vs. BFDL MIX', 'BFDL DO vs. BLS', 'BFDL DO vs. DL DO', 'BFDL DO vs. DL MIX',
                    'BFDL MIX vs. BLS', 'BFDL MIX vs. DL DO', 'BFDL MIX vs. DL MIX', 'BLS vs. DL DO', 'BLS vs. DL MIX', 'DL DO vs. DL MIX'],
    'Mean_A': [4.871826, 4.871826, 4.871826, 4.871826, 4.871826, 5.763235, 5.763235, 5.763235, 5.763235, 6.136972, 6.136972, 6.136972, 15.945265, 15.945265, 12.428577],
    'Mean_B': [5.763235, 6.136972, 15.945265, 12.428577, 13.012688, 6.136972, 15.945265, 12.428577, 13.012688, 15.945265, 12.428577, 13.012688, 12.428577, 13.012688, 13.012688],
    'Diff': [-0.891409, -1.265146, -11.073439, -7.556751, -8.140862, -0.373737, -10.182030, -6.665343, -7.249453, -9.808294, -6.291606, -6.875716, 3.516688, 2.932577, -0.584111],
    'SE': [1.556267, 1.445534, 1.509061, 1.796572, 1.749974, 1.793971, 1.845543, 2.087207, 2.047236, 1.753177, 2.006000, 1.964376, 2.052250, 2.011584, 2.235369],
    'T': [-0.572787, -0.875210, -7.337967, -4.206207, -4.651991, -0.208329, -5.517091, -3.193426, -3.541094, -5.594582, -3.136394, -3.500203, 1.713576, 1.457845, -0.261304],
    'DF': [11, 22.837115, 23.129224, 23.973777, 23.973777, 22.565719, 23.129224, 23.973777, 23.973777, 23.129224, 23.973777, 23.973777, 22.837115, 23.129224, 23.973777],
    'p-unc': [0.537147, 6.230411e-09, 0.692838, 0.999811, 0.021225, 0.999811, 0.002619, 0.537147, 0.999811, 2.225e-08, 5.351e-06, 1.049e-07, 0.537147, 0.692838, 0.999811],
    'Cohen_d': [0.101476, 7.638, 0.075566, 0.002619, 0.320287, 0.101476, 0.075566, 0.002619, 0.002619, 0.320287, 0.075566, 0.002619, 0.101476, 0.075566, 0.002619]
}

# Create tables
mixed_anova_table = go.Figure(data=[go.Table(
    header=dict(values=list(mixed_anova_results.keys())),
    cells=dict(values=[mixed_anova_results[col] for col in mixed_anova_results.keys()])
)])

pairwise_table = go.Figure(data=[go.Table(
    header=dict(values=list(pairwise_results.keys())),
    cells=dict(values=[pairwise_results[col] for col in pairwise_results.keys()])
)])

# Display tables
mixed_anova_table.show()
pairwise_table.show()


#------------------------------------------------------------------------------------------------------------------------------------#
# Plots boxplot of symmetry scores
#------------------------------------------------------------------------------------------------------------------------------------#

import plotly.express as px
import seaborn as sns
import matplotlib.pyplot as plt

# Set the Seaborn theme
sns.set_theme()

# Create a box plot using Plotly Express
fig = px.box(df_long, x='Condition', y='Symmetry_Score', color='Group', points="all", title="Symmetry Scores Across Conditions")
fig.update_layout(
    xaxis_title='Condition',
    yaxis_title='Symmetry Score',
    boxmode='group',
    showlegend=True
)

# Show the plot
fig.show()

# Alternatively, you can use Seaborn to create a similar box plot
plt.figure(figsize=(10, 6))
sns.boxplot(data=df_long, x='Condition', y='Symmetry_Score', hue='Group', showfliers=False)
plt.title('VGRF Symmetry Scores Across Conditions')
plt.xlabel('Condition')
plt.ylabel('VGRF Symmetry Score')
plt.show()

#------------------------------------------------------------------------------------------------------------------------------------#
#Using the Excel file to run stats. 
#------------------------------------------------------------------------------------------------------------------------------------#


import pandas as pd
import pingouin as pg
import matplotlib.pyplot as plt
import seaborn as sns

# Load the Excel file
file_path = "C:\Users\jacob\Documents\Research\IMU Biofeedback Project\Stats\WVBF_STATS.xlsx"
df = pd.read_excel(file_path)

# Perform two-way mixed model ANOVA
mixed_anova = pg.mixed_anova(
    data=df, dv='VGRF Symmetry Score', within='Trial', subject='Subject ID', between='Condition'
)

# Perform pairwise comparisons with Bonferroni correction
posthoc = pg.pairwise_tukey(data=df, dv='Dependent Variable', between='Exercise', effsize='eta-square', correction=True)

# Display the results
print("Two-way Mixed Model ANOVA Results:")
print(mixed_anova)
print("\nPairwise Comparison Results:")
print(posthoc)

# Boxplot
plt.figure(figsize=(10, 6))
sns.boxplot(x='Exercise', y='Dependent Variable', hue='Condition', data=df)
plt.title('VGRF Symmetry Score Across Exercises and Conditions')
plt.xlabel('Exercise')
plt.ylabel('VGRF Symmetry Score')
plt.legend(title='Condition')
plt.show()














import matplotlib.pyplot as plt
import pandas as pd
from statsmodels.stats.multicomp import pairwise_tukeyhsd
from statsmodels.formula.api import mixedlm

# Step 2 data
step_2_data = np.array([
    [13.86592791, 10.38887725, 19.87305564, 15.78628316, 19.07562003, 7.03725448],
    [16.14866232, 3.42632883, 10.15692823, 5.84922281, 19.43922051, 5.88724362],
    [8.752266, 2.42507143, 9.41122589, 5.68946334, 13.25010756, 8.02996573],
    [14.88508052, 5.44753348, 16.9390042, 5.90667483, 19.30511447, 4.44997486],
    [5.10242676, 0.55207071, 2.76299841, 1.85804348, 7.214669, 0.421941],
    [15.26732963, 9.54195589, 10.83198789, 6.77164733, 14.4114713, 2.26422687],
    [26.6302286, 16.99936995, 25.6220238, 10.81643791, 24.16483912, 5.73942033],
    [14.05435897, 4.35137361, 13.5520292, 1.50661173, 12.03191529, 3.8257383],
    [13.68019889, 7.06297758, 7.69945327, 1.43943507, 17.95442357, 7.0338689],
    [5.28867734, 6.09170163, 10.07335348, 10.87089412, 12.55540582, 9.4883382],
    [11.15978143, 2.22955217, 10.2767356, 0.09422861, 18.92893114, 9.89997723e-04],
    [8.59483915, 4.07945171, 10.10591426, 0.01196061, 11.0404162, 2.75191257],
    [15.73516416, 7.18436633, 14.26679376, 8.32114811, 17.91631248, 6.40286031]
])

# Step 1 data
data = {
    'Condition': ['DL MIX', 'BFDL MIX', 'DL DO', 'BFDL DO', 'BLS', 'BF BLS'],
    'Symmetry_Score': step_2_data.transpose().tolist()  # Transpose the data
}

# Convert the data to a DataFrame
df = pd.DataFrame({'Condition': np.repeat(data['Condition'], len(data['Symmetry_Score'][0])),
                   'Symmetry_Score': np.concatenate(data['Symmetry_Score'])})

# Add a new column for subject IDs
df['Subject ID'] = np.tile(np.arange(1, df.shape[0] // 2 + 1), 2)

# Rename the column to eliminate the space
df.rename(columns={'Symmetry_Score': 'Symmetry_Score'}, inplace=True)

# Perform mixed ANOVA
model = mixedlm("Symmetry_Score ~ Condition", df, groups='Subject ID')
result = model.fit()

# Perform pairwise comparisons with Tukey's HSD
posthoc = pairwise_tukeyhsd(df['Symmetry_Score'], df['Condition'])

# Display ANOVA and posthoc results
print("Mixed ANOVA Results:")
print(result.summary())

print("\nPairwise Comparisons with Tukey's HSD:")
print(posthoc)

# Display the results in a figure
fig, axes = plt.subplots(1, 2, figsize=(15, 6))

# Plot the ANOVA results
pg.plot_paired(data=df, dv='Symmetry_Score', within='Condition', between='Subject ID', ax=axes[0])

# Plot the pairwise comparisons
posthoc.plot_simultaneous(ax=axes[1])

plt.tight_layout()
plt.show()








import matplotlib.pyplot as plt
import numpy as np

# Step 2 data
step_2_data = np.array([
    [13.86592791, 10.38887725, 19.87305564, 15.78628316, 19.07562003, 7.03725448],
    [16.14866232, 3.42632883, 10.15692823, 5.84922281, 19.43922051, 5.88724362],
    [8.752266, 2.42507143, 9.41122589, 5.68946334, 13.25010756, 8.02996573],
    [14.88508052, 5.44753348, 16.9390042, 5.90667483, 19.30511447, 4.44997486],
    [5.10242676, 0.55207071, 2.76299841, 1.85804348, 7.214669, 0.421941],
    [15.26732963, 9.54195589, 10.83198789, 6.77164733, 14.4114713, 2.26422687],
    [26.6302286, 16.99936995, 25.6220238, 10.81643791, 24.16483912, 5.73942033],
    [14.05435897, 4.35137361, 13.5520292, 1.50661173, 12.03191529, 3.8257383],
    [13.68019889, 7.06297758, 7.69945327, 1.43943507, 17.95442357, 7.0338689],
    [5.28867734, 6.09170163, 10.07335348, 10.87089412, 12.55540582, 9.4883382],
    [11.15978143, 2.22955217, 10.2767356, 0.09422861, 18.92893114, 0.00098999],
    [8.59483915, 4.07945171, 10.10591426, 0.01196061, 11.0404162, 2.75191257],
    [15.73516416, 7.18436633, 14.26679376, 8.32114811, 17.91631248, 6.40286031]
])

# Step 1
# Box plot data
data = {
    'Condition': [
        'DL MIX', 'BFDL MIX', 'DL DO', 'BFDL DO', 'BLS', 'BF BLS'
    ],
    'Symmetry Score': step_2_data.transpose().tolist()  # Transpose the data
}

# Custom colors for the boxes
box_colors = ['lightblue', 'pink', 'lightblue', 'pink', 'lightblue', 'pink']

# Create a box plot for comparing trials across all subjects
plt.figure(figsize=(10, 6))
boxplot = plt.boxplot(data['Symmetry Score'], labels=data['Condition'], vert=True, patch_artist=True)

# Set custom colors for boxes
for box, color in zip(boxplot['boxes'], box_colors):
    box.set(color='black', linewidth=2)
    box.set(facecolor=color)

# Set labels and title
plt.xlabel("Condition")
plt.ylabel("VGRF Symmetry Score")
plt.title("VGRF Symmetry Scores Across Conditions")

# Add a legend for boxplot colors outside the graph
legend_labels = ['Trials without BF', 'Trials with BF']
legend_colors = ['lightblue', 'pink']
legend_patches = [plt.Line2D([0], [0], marker='s', color='w', label=label, markersize=10, markerfacecolor=color) for label, color in zip(legend_labels, legend_colors)]
plt.legend(handles=legend_patches, loc="upper right", bbox_to_anchor=(1.25, 1))

# Show the box plot
plt.xticks(rotation=45)
plt.show()





#--------------------------------------------------------------------------------------#
#Mixed ANOVA Results
#--------------------------------------------------------------------------------------#

import matplotlib.pyplot as plt
import pandas as pd
from statsmodels.stats.multicomp import pairwise_tukeyhsd
from statsmodels.formula.api import mixedlm

# Step 2 data
step_2_data = np.array([
    [13.86592791, 10.38887725, 19.87305564, 15.78628316, 19.07562003, 7.03725448],
    [16.14866232, 3.42632883, 10.15692823, 5.84922281, 19.43922051, 5.88724362],
    [8.752266, 2.42507143, 9.41122589, 5.68946334, 13.25010756, 8.02996573],
    [14.88508052, 5.44753348, 16.9390042, 5.90667483, 19.30511447, 4.44997486],
    [5.10242676, 0.55207071, 2.76299841, 1.85804348, 7.214669, 0.421941],
    [15.26732963, 9.54195589, 10.83198789, 6.77164733, 14.4114713, 2.26422687],
    [26.6302286, 16.99936995, 25.6220238, 10.81643791, 24.16483912, 5.73942033],
    [14.05435897, 4.35137361, 13.5520292, 1.50661173, 12.03191529, 3.8257383],
    [13.68019889, 7.06297758, 7.69945327, 1.43943507, 17.95442357, 7.0338689],
    [5.28867734, 6.09170163, 10.07335348, 10.87089412, 12.55540582, 9.4883382],
    [11.15978143, 2.22955217, 10.2767356, 0.09422861, 18.92893114, 9.89997723e-04],
    [8.59483915, 4.07945171, 10.10591426, 0.01196061, 11.0404162, 2.75191257],
    [15.73516416, 7.18436633, 14.26679376, 8.32114811, 17.91631248, 6.40286031]
])

# Step 1 data
data = {
    'Condition': ['DL MIX', 'BFDL MIX', 'DL DO', 'BFDL DO', 'BLS', 'BF BLS'],
    'Symmetry_Score': step_2_data.transpose().tolist()  # Transpose the data
}

# Convert the data to a DataFrame
df = pd.DataFrame({'Condition': np.repeat(data['Condition'], len(data['Symmetry_Score'][0])),
                   'Symmetry_Score': np.concatenate(data['Symmetry_Score'])})

# Add a new column for subject IDs
df['Subject ID'] = np.tile(np.arange(1, df.shape[0] // 2 + 1), 2)

# Rename the column to eliminate the space
df.rename(columns={'Symmetry_Score': 'Symmetry_Score'}, inplace=True)

# Perform mixed ANOVA
model = mixedlm("Symmetry_Score ~ Condition", df, groups='Subject ID')
result = model.fit()

# Perform pairwise comparisons with Tukey's HSD
posthoc = pairwise_tukeyhsd(df['Symmetry_Score'], df['Condition'])

# Display ANOVA and posthoc results
print("Mixed ANOVA Results:")
print(result.summary())

print("\nPairwise Comparisons with Tukey's HSD:")
print(posthoc)

# Display the results in a figure
fig, axes = plt.subplots(1, 2, figsize=(15, 6))

# Plot the ANOVA results
pg.qqplot(result.resid, ax=axes[0])

# Plot the pairwise comparisons
posthoc.plot_simultaneous(ax=axes[1])

plt.tight_layout()
plt.show()