translated.py

import numpy as np
import pandas as pd
from scipy.signal import butter, filtfilt
from scipy.interpolate import interp1d
from scipy.signal import find_peaks
from numpy.linalg import norm, det

# Placeholder for import_horse function
def import_horse(file_path):
    # This function should be implemented to import data from the given file path.
    # Assuming the file is in a format that can be read as a DataFrame, like CSV.
    # Replace this with actual implementation.
    df = pd.read_csv(file_path)
    return df.to_numpy().T  # Transposed to match MATLAB data structure

# Function to find the angle
def findangle(x1, y1, l1, x2, y2, l2, x3, y3, l3, frame, L, spotcheck, color, name, directionUsed=None):
    angle3 = []
    angle = np.array([x1, y1, x2, y2, x3, y3]).T
    valid = (l1 > L) & (l2 > L) & (l3 > L)
    angle = angle[valid, :]
    goodframes = frame[valid]

    P0 = angle[:, 2:4]
    P1 = angle[:, 0:2]
    P2 = angle[:, 4:6]

    for i in range(len(goodframes)):
        n1 = (P2[i, :] - P0[i, :]) / norm(P2[i, :] - P0[i, :])
        n2 = (P1[i, :] - P0[i, :]) / norm(P1[i, :] - P0[i, :])
        if directionUsed is not None:
            angle3.append(np.arctan2(det([n2, n1]), np.dot(n2, n1)))
        else:
            angle3.append(np.arctan2(norm(det([n2, n1])), np.dot(n1, n2)))

    angle3 = pd.Series(angle3).rolling(window=5).median().to_numpy()
    return angle3, goodframes

# Function to find the floor
def floorfind(HTy, HMy, HHy):
    datas = [HTy, HMy, HHy]
    floory, floorx = [], []
    for data in datas:
        floory.append(np.min(data[:len(data)//3]))
        floory.append(np.min(data[len(data)//3:2*len(data)//3]))
        floory.append(np.min(data[2*len(data)//3:]))
        floorx.append(np.argmin(data[:len(data)//3]))
        floorx.append(np.argmin(data[len(data)//3:2*len(data)//3]) + len(data)//3)
        floorx.append(np.argmin(data[2*len(data)//3:]) + 2*len(data)//3)
    floory = np.array(floory).reshape(-1)
    floorx = np.array(floorx).reshape(-1)
    P = np.polyfit(floorx, floory, 1)
    return P

# Function to extract Y data features
def pulloutYstuff(data, frames, spotcheck, color, name):
    pks, locs, w, p = find_peaks(data, height=4, distance=25)
    p = p['peak_heights']
    TFp = np.abs(p - np.mean(p)) < 2 * np.std(p)  # Remove outliers based on prominence
    TFw = np.abs(w - np.mean(w)) < 2 * np.std(w)  # Remove outliers based on width
    TF = TFp & TFw
    mp = np.mean(p[TF])
    stdp = np.std(p[TF])
    mw = np.mean(w[TF])
    stdw = np.std(w[TF])
    return mw, mp, stdw, stdp

# Function for finding stride-to-off (FSTO)
def fsto(data, frames, directionfactor, spotcheck, graphfactor, color, name):
    dxdf = np.gradient(data) / np.gradient(frames)
    dxdf, rframes = remove_outliers(np.column_stack((dxdf, frames)), 25)
    pks, locs, _, _ = find_peaks(np.abs(dxdf), height=1, distance=25)

    # Further implementation needed based on MATLAB code
    # Placeholder return values
    dutyfactor, stridelength, numcycles, strides = 0, 0, 0, np.array([])
    dutyfactorStd, stridelengthStd, byEye = 0, 0, '-'

    return dutyfactor, stridelength, numcycles, strides, dutyfactorStd, stridelengthStd, byEye

def avgforstride(data, f2, strides, stridesXstart, spotcheck, color, name):
    """
    Averages stride data.
    """
    # Placeholder implementation. Adjust as per specific requirements.
    avg_stride = sum(data) / len(data)
    return avg_stride

def remove_outliers(data, window):
    """
    Removes outliers from data based on a moving average method.
    """
    # Placeholder implementation. Adjust as per specific requirements.
    filtered_data = [d for d in data if abs(d - sum(data) / len(data)) < window]
    return filtered_data

def basiccmooth(datax, datay, dataf, d1):
    """
    Smoothes coordinate data.
    """
    # Placeholder implementation. Adjust as per specific requirements.
    smoothed_datax = [sum(datax[max(i - d1, 0):min(i + d1 + 1, len(datax))]) / (2 * d1 + 1) for i in range(len(datax))]
    smoothed_datay = [sum(datay[max(i - d1, 0):min(i + d1 + 1, len(datay))]) / (2 * d1 + 1) for i in range(len(datay))]
    return smoothed_datax, smoothed_datay

# Reading the existing Python script
script_path = '/mnt/data/horse_analysis_script.py'
with open(script_path, 'r') as file:
    existing_script = file.read()

# Adding the new function implementations to the script
new_functions = "\n\n".join([avgforstride.__doc__, remove_outliers.__doc__, basiccmooth.__doc__])
updated_script = existing_script + "\n\n# New Function Implementations\n" + new_functions

# Saving the updated script
updated_script_path = '/mnt/data/updated_horse_analysis_script.py'
with open(updated_script_path, 'w') as file:
    file.write(updated_script)

script_content = inspect.getsource(import_horse) + "\n" + \
                 inspect.getsource(findangle) + "\n" + \
                 inspect.getsource(floorfind) + "\n" + \
                 inspect.getsource(pulloutYstuff) + "\n" + \
                 inspect.getsource(fsto) + "\n" + \
                 inspect.getsource(avgforstride) + "\n" + \
                 inspect.getsource(basiccmooth) + "\n" + \
                 inspect.getsource(main)

with open('/mnt/data/horse_analysis_script.py', 'w') as file:
    file.write(script_content)

'/mnt/data/horse_analysis_script.py'