squishy.py

import pandas as pd
import matplotlib.pyplot as plt

def plot_scaled_graph_with_values(frame_0, frame_7, data):
    """
    Plots the graph with the given data, scaling the values at frame 0 and frame 7,
    and prints the scaled values per frame.
    
    Parameters:
    - frame_0: tuple (x0, y0, z0) values at frame 0
    - frame_7: tuple (x7, y7, z7) values at frame 7
    - data: dictionary containing the data with keys "Frame", "X", "Y", and "Z"
    """
    df = pd.DataFrame(data)
    
    # Scaling factors
    scale_x = (frame_7[0] - frame_0[0]) / (df[df['Frame'] == 7]['X'].values[0] - df[df['Frame'] == 0]['X'].values[0])
    scale_y = (frame_7[1] - frame_0[1]) / (df[df['Frame'] == 7]['Y'].values[0] - df[df['Frame'] == 0]['Y'].values[0])
    scale_z = (frame_7[2] - frame_0[2]) / (df[df['Frame'] == 7]['Z'].values[0] - df[df['Frame'] == 0]['Z'].values[0])
    
    # Scaling the data
    df['X'] = frame_0[0] + scale_x * (df['X'] - df['X'][0])
    df['Y'] = frame_0[1] + scale_y * (df['Y'] - df['Y'][0])
    df['Z'] = frame_0[2] + scale_z * (df['Z'] - df['Z'][0])
    
    # Print scaled values per frame
    print("Scaled Values per Frame:")
    print(df)
    
    # Plotting the data
    fig, ax = plt.subplots(figsize=(10, 6))
    
    ax.plot(df['Frame'], df['X'], label='X', marker='o')
    ax.plot(df['Frame'], df['Y'], label='Y', marker='o')
    ax.plot(df['Frame'], df['Z'], label='Z', marker='o')
    
    ax.set_title('Frame vs X, Y, Z (Scaled)')
    ax.set_xlabel('Frame')
    ax.set_ylabel('Values')
    ax.legend()
    plt.grid(True)
    plt.show()

# Guide for creating function
data = {
    "Frame": [0, 3, 4, 5, 7],
    "X": [0, 50.6748, 34.79839, 50.6748, 45],
    "Y": [0, 1, 1, 1, 1],
    "Z": [0, 20.0071, 32.31347, 20.0071, 29]
}

# Input desired end scale by replacing the (13, 13, 13)
# (13, 13, 13) are X, Y, Z in order
plot_scaled_graph_with_values((0, 0, 0), (13, 13, 13), data)