filter.py

import numpy as np
import pandas as pd
from geopy.distance import geodesic

def smooth(data, window=100, start_index=0):
    """
    Compute the moving average of a 1D array using a given window size.

    Parameters
    ----------
    data : 1D array
        The input data.
    window : int
        The size of the moving average window.
    
    Returns
    -------
    1D array
        The smoothed data.
    """
    pad_width = window // 2  # Number of values to pad on each side
    padded_data = np.pad(data, (pad_width, pad_width), mode='edge')  # Pad using edge values
    a = np.convolve(padded_data, np.ones(window)/window, mode='same')  # Use 'same' mode for convolution
    return np.pad(a, (start_index, 0), 'constant', constant_values=(0, 0))



def add_smoothed_cols(ddict, window=100, verbose=False, inPlace=True):
    """
    Create a new column for each accelerometer column in the data dictionary with the smoothed data.

    Parameters
    ----------
    ddict : dict
        The data dictionary to be modified.
    window : int
        The window size for the smoothing algorithm.
    verbose : bool
        Whether or not to print out the columns that are being added.
    inPlace : bool
        Whether or not to modify the dictionary in place.

    Returns
    -------
    dict
        The modified data dictionary.
    """
    if not inPlace:
        ddict = ddict.copy()
    for t_type in ddict:
        for csvf in ddict[t_type]:
            try:
                for dir in ddict[t_type][csvf]:
                    for col in ddict[t_type][csvf][dir].columns:
                        if "acc_" in col:
                            d = ddict[t_type][csvf][dir][col]
                            ddict[t_type][csvf][dir][col + "_smooth"] = smooth(d, window=window)[:len(d)]
                            if verbose:
                                print("Added", col + "_smooth")
            except TypeError:
                continue
    return ddict


def add_lat_long_meters(df):
    # convert the coordinates to meters 
    x_diffs = [geodesic((df['latitude'].iloc[0], df['longitude'].iloc[i]), (df['latitude'].iloc[i], df['longitude'].iloc[i])).meters for i in range(len(df)-1)]
    x_diffs.append(None)

    y_diffs = [geodesic((df['latitude'].iloc[i], df['longitude'].iloc[0]), (df['latitude'].iloc[i], df['longitude'].iloc[i])).meters for i in range(len(df)-1)]
    y_diffs.append(None)

    df['lat_m'] = x_diffs
    df['long_m'] = y_diffs
    df.dropna(inplace=True)

def lat_long_meters(ddict, verbose=False, inPlace=True):
    """
    Create a new column for each lat/long column in the data dictionary with the difference data.

    Parameters
    ----------
    ddict : dict
        The data dictionary to be modified.
    verbose : bool
        Whether or not to print out the columns that are being added.
    inPlace : bool
        Whether or not to modify the dictionary in place.

    Returns
    -------
    dict
        The modified data dictionary.
    """
    # takes about 5 mins! 
    if not inPlace:
        ddict = ddict.copy()
    names = ["latitude", "longitude"]
    for t_type in ddict:
        for csvf in ddict[t_type]:
            try:
                for dir in ddict[t_type][csvf]:
                    if 'latitude' in ddict[t_type][csvf][dir].columns:
                        d = ddict[t_type][csvf][dir]
                        add_lat_long_meters(d)
                        
                        if verbose:
                            print("Added Lat/Long meters to", csvf + " " + dir)
                        
            except TypeError:
                continue
    return ddict


# def add_diff_cols(ddict, verbose=False, inPlace=True):
#     """
#     Create a new column for each lat/long/elevation column in the data dictionary with the difference data.

#     Parameters
#     ----------
#     ddict : dict
#         The data dictionary to be modified.
#     verbose : bool
#         Whether or not to print out the columns that are being added.
#     inPlace : bool
#         Whether or not to modify the dictionary in place.

#     Returns
#     -------
#     dict
#         The modified data dictionary.
#     """
#     if not inPlace:
#         ddict = ddict.copy()
#     names = ["latitude", "longitude", "elevation"]
#     for t_type in ddict:
#         for csvf in ddict[t_type]:
#             try:
#                 for dir in ddict[t_type][csvf]:
#                     for col in ddict[t_type][csvf][dir].columns:
#                         if any(element in col for element in names):
#                             d = ddict[t_type][csvf][dir]

#                             # this is the only thing going on in this function
#                             # print(d.shape)
#                             # prepend a zero
#                             d["d_" + col] = np.insert((np.diff(d[col])), 0, 0)
#                             d["meters_" + col] = np.cumsum(d["d_" + col])

#                             if verbose:
#                                 print("Added", "d_" + col)
#                                 print("Added", "meters_" + col)
#             except TypeError:
#                 continue
#     return ddict


# def toMeters(data, col):
#     """
#     Convert the data from degrees to meters using the haversine metric.

#     Parameters
#     ----------
#     data : 1D array
#         The input data.
    
#     Returns
#     -------
#     1D array
#         The data in meters.
#     """
#     R = 6371000  # Radius of the Earth in meters
#     if col == "elevation":
#         return data
#     return np.deg2rad(data) * R

# def total_distance(data):
#     """
#     Compute the total distance traveled so far using the difference column (in meters)

#     If the first two values are 1, 2, then the total distance so far col is 1, 3.
#     """
#     return np.cumsum(data)