/// <summary>
/// perform direct fft
/// </summary>
/// <param name="data">data for fft</param>
/// <param name="start_pos">start pos</param>
/// <param name="end_pos">end pos, end_pos - start_pos must be a power of 2</param>
/// <param name="window">window function</param>
/// <returns>complex array of size N / 2 + 1 of fft data</returns>
public static Complex[] perform_fft(double[] data, int start_pos, int end_pos, int window)
{
if ((start_pos < 0) || (end_pos > data.Length) || (start_pos >= end_pos))
{
throw new BrainFlowException((int)CustomExitCodes.INVALID_ARGUMENTS_ERROR);
}
int len = end_pos - start_pos;
if ((len & (len - 1)) != 0)
{
throw new BrainFlowException((int)CustomExitCodes.INVALID_ARGUMENTS_ERROR);
}
double[] data_to_process = new double[len];
Array.Copy(data, start_pos, data_to_process, 0, len);
double[] temp_re = new double[len / 2 + 1];
double[] temp_im = new double[len / 2 + 1];
Complex[] output = new Complex[len / 2 + 1];
int res = DataHandlerLibrary.perform_fft(data_to_process, len, window, temp_re, temp_im);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
for (int i = 0; i < len / 2 + 1; i++)
{
output[i] = new Complex(temp_re[i], temp_im[i]);
}
return(output);
}
/// <summary>
/// calculate log PSD
/// </summary>
/// <param name="data">data for log PSD</param>
/// <param name="start_pos">start pos</param>
/// <param name="end_pos">end pos, end_pos - start_pos must be a power of 2</param>
/// <param name="sampling_rate">sampling rate</param>
/// <param name="window">window function</param>
/// <returns>Tuple of ampls and freqs arrays of size N / 2 + 1</returns>
public static Tuple <double[], double[]> get_log_psd(double[] data, int start_pos, int end_pos, int sampling_rate, int window)
{
if ((start_pos < 0) || (end_pos > data.Length) || (start_pos >= end_pos))
{
throw new BrainFlowException((int)CustomExitCodes.INVALID_ARGUMENTS_ERROR);
}
int len = end_pos - start_pos;
if ((len & (len - 1)) != 0)
{
throw new BrainFlowException((int)CustomExitCodes.INVALID_ARGUMENTS_ERROR);
}
double[] data_to_process = new double[len];
Array.Copy(data, start_pos, data_to_process, 0, len);
double[] temp_ampls = new double[len / 2 + 1];
double[] temp_freqs = new double[len / 2 + 1];
int res = DataHandlerLibrary.get_log_psd(data_to_process, len, sampling_rate, window, temp_ampls, temp_freqs);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
Tuple <double[], double[]> return_data = new Tuple <double[], double[]>(temp_ampls, temp_freqs);
return(return_data);
}
/// <summary>
/// read data from file, data will be transposed back to original format
/// </summary>
/// <param name="file_name"></param>
/// <returns></returns>
public static double[,] read_file(string file_name)
{
int[] num_elements = new int[1];
int res = DataHandlerLibrary.get_num_elements_in_file(file_name, num_elements);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
double[] data_arr = new double[num_elements[0]];
int[] num_rows = new int[1];
int[] num_cols = new int[1];
res = DataHandlerLibrary.read_file(data_arr, num_rows, num_cols, file_name, num_elements[0]);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
double[,] result = new double[num_rows[0], num_cols[0]];
for (int i = 0; i < num_rows[0]; i++)
{
for (int j = 0; j < num_cols[0]; j++)
{
result[i, j] = data_arr[i * num_cols[0] + j];
}
}
return(result);
}
/// <summary>
/// perform wavelet transform
/// </summary>
/// <param name="data">data for wavelet transform</param>
/// <param name="wavelet">db1..db15,haar,sym2..sym10,coif1..coif5,bior1.1,bior1.3,bior1.5,bior2.2,bior2.4,bior2.6,bior2.8,bior3.1,bior3.3,bior3.5 ,bior3.7,bior3.9,bior4.4,bior5.5,bior6.8</param>
/// <param name="decomposition_level">decomposition level</param>
/// <returns>tuple of wavelet coeffs in format [A(J) D(J) D(J-1) ..... D(1)] where J is decomposition level, A - app coeffs, D - detailed coeffs, and array with lengths for each block</returns>
public static Tuple <double[], int[]> perform_wavelet_transform(double[] data, string wavelet, int decomposition_level)
{
double[] wavelet_coeffs = new double[data.Length + 2 * (40 + 1)];
int[] lengths = new int[decomposition_level + 1];
int res = DataHandlerLibrary.perform_wavelet_transform(data, data.Length, wavelet, decomposition_level, wavelet_coeffs, lengths);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
int total_length = 0;
foreach (int val in lengths)
{
total_length += val;
}
double[] truncated = new double[total_length];
for (int i = 0; i < total_length; i++)
{
truncated[i] = wavelet_coeffs[i];
}
Tuple <double[], int[]> return_data = new Tuple <double[], int[]> (truncated, lengths);
return(return_data);
}
/// <summary>
/// redirect BrainFlow's logger from stderr to file
/// </summary>
/// <param name="log_file"></param>
public static void set_log_file(string log_file)
{
int res = DataHandlerLibrary.set_log_file_data_handler(log_file);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
}
/// <summary>
/// write data to tsv file, data will be transposed
/// </summary>
/// <param name="data"></param>
/// <param name="file_name"></param>
/// <param name="file_mode"></param>
public static void write_file(double[,] data, string file_name, string file_mode)
{
int res = DataHandlerLibrary.write_file(data.Flatten(), data.Rows(), data.Columns(), file_name, file_mode);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
}
/// <summary>
/// set log level, logger is disabled by default
/// </summary>
/// <param name="log_level"></param>
private static void set_log_level(int log_level)
{
int res = DataHandlerLibrary.set_log_level_data_handler(log_level);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
}
/// <summary>
/// perform windowing
/// </summary>
/// <param name="window_function">window function</param>
/// <param name="window_len">len of the window</param>
/// <returns>array of the size specified in window_len</returns>
public static double[] get_window(int window_function, int window_len)
{
double[] window_data = new double[window_len];
int res = DataHandlerLibrary.get_window(window_function, window_len, window_data);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
return(window_data);
}
/// <summary>
/// calculate nearest power of two
/// </summary>
/// <param name="value"></param>
/// <returns>nearest power of two</returns>
public static int get_nearest_power_of_two(int value)
{
int[] output = new int[1];
int res = DataHandlerLibrary.get_nearest_power_of_two(value, output);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
return(output[0]);
}
/// <summary>
/// calc stddev
/// </summary>
/// <param name="data"></param>
/// <param name="start_pos"></param>
/// /// <param name="end_pos"></param>
/// <returns>stddev</returns>
public static double calc_stddev(double[] data, int start_pos, int end_pos)
{
double[] output = new double[1];
int res = DataHandlerLibrary.calc_stddev(data, start_pos, end_pos, output);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
return(output[0]);
}
/// <summary>
/// detrend, unlike other bindings instead in-place calculation it returns new array
/// </summary>
/// <param name="data"></param>
/// <param name="operation"></param>
/// <returns>data with removed trend</returns>
public static double[] detrend(double[] data, int operation)
{
double[] new_data = new double[data.Length];
Array.Copy(data, new_data, data.Length);
int res = DataHandlerLibrary.detrend(new_data, new_data.Length, operation);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
return(new_data);
}
/// <summary>
/// remove env noise using notch filter
/// </summary>
/// <param name="data"></param>
/// <param name="sampling_rate"></param>
/// <param name="noise_type"></param>
/// <returns>filtered data</returns>
public static double[] remove_environmental_noise(double[] data, int sampling_rate, int noise_type)
{
double[] filtered_data = new double[data.Length];
Array.Copy(data, filtered_data, data.Length);
int res = DataHandlerLibrary.remove_environmental_noise(filtered_data, data.Length, sampling_rate, noise_type);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
return(filtered_data);
}
/// <summary>
/// perform bandstop filter, unlike other bindings instead in-place calculation it returns new array
/// </summary>
/// <param name="data"></param>
/// <param name="sampling_rate"></param>
/// <param name="center_freq"></param>
/// <param name="band_width"></param>
/// <param name="order"></param>
/// <param name="filter_type"></param>
/// <param name="ripple"></param>
/// <returns>filtered data</returns>
public static double[] perform_bandstop(double[] data, int sampling_rate, double center_freq, double band_width, int order, int filter_type, double ripple)
{
double[] filtered_data = new double[data.Length];
Array.Copy(data, filtered_data, data.Length);
int res = DataHandlerLibrary.perform_bandstop(filtered_data, data.Length, sampling_rate, center_freq, band_width, order, filter_type, ripple);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
return(filtered_data);
}
/// <summary>
/// calculate band power
/// </summary>
/// <param name="psd">psd data returned by get_psd or get_log_psd</param>
/// <param name="start_freq">lowest frequency of band</param>
/// <param name="stop_freq">highest frequency of band</param>
/// <returns>band power</returns>
public static double get_band_power(Tuple <double[], double[]> psd, double start_freq, double stop_freq)
{
double[] band_power = new double[1];
int res = DataHandlerLibrary.get_band_power(psd.Item1, psd.Item2, psd.Item1.Length, start_freq, stop_freq, band_power);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
return(band_power[0]);
}
/// <summary>
/// perform wavelet based denoising
/// </summary>
/// <param name="data">data for denoising</param>
/// <param name="wavelet">db1..db15,haar,sym2..sym10,coif1..coif5,bior1.1,bior1.3,bior1.5,bior2.2,bior2.4,bior2.6,bior2.8,bior3.1,bior3.3,bior3.5 ,bior3.7,bior3.9,bior4.4,bior5.5,bior6.8</param>
/// <param name="decomposition_level">level of decomposition in wavelet transform</param>
/// <returns>denoised data</returns>
public static double[] perform_wavelet_denoising(double[] data, string wavelet, int decomposition_level)
{
double[] filtered_data = new double[data.Length];
Array.Copy(data, filtered_data, data.Length);
int res = DataHandlerLibrary.perform_wavelet_denoising(filtered_data, filtered_data.Length, wavelet, decomposition_level);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
return(filtered_data);
}
/// <summary>
/// perform inverse wavelet transorm
/// </summary>
/// <param name="wavelet_data">tuple returned by perform_wavelet_transform</param>
/// <param name="original_data_len">size of original data before direct wavelet transform</param>
/// <param name="wavelet">db1..db15,haar,sym2..sym10,coif1..coif5,bior1.1,bior1.3,bior1.5,bior2.2,bior2.4,bior2.6,bior2.8,bior3.1,bior3.3,bior3.5 ,bior3.7,bior3.9,bior4.4,bior5.5,bior6.8</param>
/// <param name="decomposition_level">level of decomposition</param>
/// <returns>restored data</returns>
public static double[] perform_inverse_wavelet_transform(Tuple <double[], int[]> wavelet_data, int original_data_len, string wavelet, int decomposition_level)
{
double[] original_data = new double[original_data_len];
int res = DataHandlerLibrary.perform_inverse_wavelet_transform(wavelet_data.Item1, original_data_len, wavelet, decomposition_level,
wavelet_data.Item2, original_data);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
return(original_data);
}
/// <summary>
/// perform moving average or moving median filter, unlike other bindings instead in-place calculation it returns new array
/// </summary>
/// <param name="data"></param>
/// <param name="period"></param>
/// <param name="operation"></param>
/// <returns>filered data</returns>
public static double[] perform_rolling_filter(double[] data, int period, int operation)
{
double[] filtered_data = new double[data.Length];
Array.Copy(data, filtered_data, data.Length);
int res = DataHandlerLibrary.perform_rolling_filter(filtered_data, filtered_data.Length, period, operation);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
return(filtered_data);
}
/// <summary>
/// get version
/// </summary>
/// <returns>version</returns>
/// <exception cref="BrainFlowException"></exception>
public static string get_version()
{
int[] len = new int[1];
byte[] str = new byte[64];
int res = DataHandlerLibrary.get_version_data_handler(str, len, 64);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
string version = System.Text.Encoding.UTF8.GetString(str, 0, len[0]);
return(version);
}
/// <summary>
/// calculate PSD using Welch method
/// </summary>
/// <param name="data">data for log PSD</param>
/// <param name="nfft">FFT Size</param>
/// <param name="overlap">FFT Window overlap, must be between 0 and nfft</param>
/// <param name="sampling_rate">sampling rate</param>
/// <param name="window">window function</param>
/// <returns>Tuple of ampls and freqs arrays</returns>
public static Tuple <double[], double[]> get_psd_welch(double[] data, int nfft, int overlap, int sampling_rate, int window)
{
if ((nfft & (nfft - 1)) != 0)
{
throw new BrainFlowException((int)CustomExitCodes.INVALID_ARGUMENTS_ERROR);
}
double[] temp_ampls = new double[nfft / 2 + 1];
double[] temp_freqs = new double[nfft / 2 + 1];
int res = DataHandlerLibrary.get_psd_welch(data, data.Length, nfft, overlap, sampling_rate, window, temp_ampls, temp_freqs);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
Tuple <double[], double[]> return_data = new Tuple <double[], double[]>(temp_ampls, temp_freqs);
return(return_data);
}
/// <summary>
/// perform data downsampling, it just aggregates data without applying lowpass filter
/// </summary>
/// <param name="data"></param>
/// <param name="period"></param>
/// <param name="operation"></param>
/// <returns>data after downsampling</returns>
public static double[] perform_downsampling(double[] data, int period, int operation)
{
if (period == 0)
{
throw new BrainFlowException((int)CustomExitCodes.INVALID_ARGUMENTS_ERROR);
}
if (data.Length / period <= 0)
{
throw new BrainFlowException((int)CustomExitCodes.INVALID_ARGUMENTS_ERROR);
}
double[] downsampled_data = new double[data.Length / period];
int res = DataHandlerLibrary.perform_downsampling(data, data.Length, period, operation, downsampled_data);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
return(downsampled_data);
}
/// <summary>
/// get common spatial patterns
/// </summary>
/// <param name="data">data for csp</param>
/// <param name="labels">labels for each class</param>
/// <returns>Tuple of two arrays: [n_channels x n_channels] shaped array of filters and n_channels length array of eigenvalues</returns>
public static Tuple <double[, ], double[]> get_csp(double[,,] data, double[] labels)
{
int n_epochs = data.GetLength(0);
int n_channels = data.GetLength(1);
int n_times = data.GetLength(2);
double[] temp_data1d = new double[n_epochs * n_channels * n_times];
for (int e = 0; e < n_epochs; e++)
{
for (int c = 0; c < n_channels; c++)
{
for (int t = 0; t < n_times; t++)
{
int idx = e * n_channels * n_times + c * n_times + t;
temp_data1d[idx] = data[e, c, t];
}
}
}
double[] temp_filters = new double[n_channels * n_channels];
double[] output_eigenvalues = new double[n_channels];
int res = DataHandlerLibrary.get_csp(temp_data1d, labels, n_epochs, n_channels, n_times, temp_filters, output_eigenvalues);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
double[,] output_filters = new double[n_channels, n_channels];
for (int i = 0; i < n_channels; i++)
{
for (int j = 0; j < n_channels; j++)
{
output_filters[i, j] = temp_filters[i * n_channels + j];
}
}
Tuple <double[, ], double[]> return_data = new Tuple <double[, ], double[]>(output_filters, output_eigenvalues);
return(return_data);
}
/// <summary>
/// calculate avg and stddev bandpowers across channels, bands are 1-4,4-8,8-13,13-30,30-50
/// </summary>
/// <param name="data">2d array with values</param>
/// <param name="channels">rows of data array which should be used for calculation</param>
/// <param name="sampling_rate">sampling rate</param>
/// <param name="apply_filters">apply bandpass and bandstop filters before calculation</param>
/// <returns>Tuple of avgs and stddev arrays</returns>
public static Tuple <double[], double[]> get_avg_band_powers(double[,] data, int[] channels, int sampling_rate, bool apply_filters)
{
double[] data_1d = new double[data.GetRow(0).Length *channels.Length];
for (int i = 0; i < channels.Length; i++)
{
Array.Copy(data.GetRow(channels[i]), 0, data_1d, i * data.GetRow(channels[i]).Length, data.GetRow(channels[i]).Length);
}
double[] avgs = new double[5];
double[] stddevs = new double[5];
int res = DataHandlerLibrary.get_avg_band_powers(data_1d, channels.Length, data.GetRow(0).Length, sampling_rate, (apply_filters) ? 1 : 0, avgs, stddevs);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
Tuple <double[], double[]> return_data = new Tuple <double[], double[]>(avgs, stddevs);
return(return_data);
}
/// <summary>
/// perform inverse fft
/// </summary>
/// <param name="data">data from perform_fft</param>
/// <returns>restored data</returns>
public static double[] perform_ifft(Complex[] data)
{
int len = (data.Length - 1) * 2;
double[] temp_re = new double[data.Length];
double[] temp_im = new double[data.Length];
double[] output = new double[len];
for (int i = 0; i < data.Length; i++)
{
temp_re[i] = data[i].Real;
temp_im[i] = data[i].Imaginary;
}
int res = DataHandlerLibrary.perform_ifft(temp_re, temp_im, len, output);
if (res != (int)CustomExitCodes.STATUS_OK)
{
throw new BrainFlowException(res);
}
return(output);
}
/// <summary>
/// enable BrainFlow's logger with level INFO
/// </summary>
public static void enable_board_logger()
{
DataHandlerLibrary.set_log_level((int)LogLevels.LEVEL_INFO);
}
/// <summary>
/// enable Data logger with level TRACE
/// </summary>
public static void enable_dev_data_logger()
{
DataHandlerLibrary.set_log_level((int)LogLevels.LEVEL_TRACE);
}
/// <summary>
/// disable Data logger
/// </summary>
public static void disable_data_logger()
{
DataHandlerLibrary.set_log_level((int)LogLevels.LEVEL_OFF);
}
