feat: extract trend from signal

docs-source/source/auto_examples/filter/index.rst

@@ -31,6 +31,23 @@ Examples of how to use the filter functions included in Cognite Charts.
     </div>
 
 
+.. raw:: html
+
+    <div class="sphx-glr-thumbcontainer" tooltip="Trend extraction using the Hilbert-Huang Transform">
+
+.. only:: html
+
+  .. image:: /auto_examples/filter/images/thumb/sphx_glr_plot_hilbert_huang_transform_thumb.png
+    :alt:
+
+  :ref:`sphx_glr_auto_examples_filter_plot_hilbert_huang_transform.py`
+
+.. raw:: html
+
+      <div class="sphx-glr-thumbnail-title">Trend extraction using the Hilbert-Huang Transform</div>
+    </div>
+
+
 .. raw:: html
 
     </div>
@@ -40,4 +57,5 @@ Examples of how to use the filter functions included in Cognite Charts.
    :hidden:
 
    /auto_examples/filter/plot_wavelet_filter
+   /auto_examples/filter/plot_hilbert_huang_transform
 

docs-source/source/auto_examples/filter/sg_execution_times.rst

@@ -3,12 +3,13 @@
 
 .. _sphx_glr_auto_examples_filter_sg_execution_times:
 
+
 Computation times
 =================
-**00:27.907** total execution time for **auto_examples_filter** files:
+**00:06.221** total execution time for **auto_examples_filter** files:
 
-+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_filter_plot_wavelet_filter.py` (``plot_wavelet_filter.py``) | 00:27.907 | 0.0 MB |
-+------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_auto_examples_filter_plot_trend.py` (``plot_trend.py``)                   | 00:00.000 | 0.0 MB |
-+------------------------------------------------------------------------------------------+-----------+--------+
++------------------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_auto_examples_filter_plot_hilbert_huang_transform.py` (``plot_hilbert_huang_transform.py``) | 00:06.221 | 0.0 MB |
++------------------------------------------------------------------------------------------------------------+-----------+--------+
+| :ref:`sphx_glr_auto_examples_filter_plot_wavelet_filter.py` (``plot_wavelet_filter.py``)                   | 00:00.000 | 0.0 MB |
++------------------------------------------------------------------------------------------------------------+-----------+--------+

examples/filter/plot_hilbert_huang_transform.py

@@ -0,0 +1,129 @@
+"""
+=====================================================================================
+Trend extraction using the Hilbert-Huang Transform
+=====================================================================================
+Example of trend extraction from non-linear, non-stationary signals using Empirical Mode Decomposition (EMD) and the
+Hilbert-Huang Transform. We generate a synthetic signal composed of:
+    * Three oscillatory signals of different but significant amplitudes
+    * Two polynomial functions or trends
+    * Data drift
+To make the case more realistic, from an industrial perspective, the timestamps are modified to make them nonuniform
+and 35% of the data points are randomly removed. Finally, Gaussian noise with a signal-to-noise ratio of 10 db is
+added to it.
+The figure below shows each of the components of the synthetic signal (except for the Gaussian noise), the resulting
+synthetic signal and the trend obtained by means of Empirical Mode Decomposition and the Hilbert-Huang method
+implemented. It can be seen that the trend reflects the general signal behaviour.
+"""
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from indsl.signals import insert_data_gaps, line, perturb_timestamp, sine_wave, white_noise
+from matplotlib.dates import DateFormatter
+from indsl.filter import hilbert_huang_transform
+
+start_date = pd.Timestamp("2023-07-14")
+end_date = pd.Timestamp("2023-07-16")
+
+# Wave 1: Small amplitude, long wave period
+wave01 = sine_wave(
+    start_date=start_date,
+    end_date=end_date,
+    sample_freq=pd.Timedelta("1m"),
+    wave_period=pd.Timedelta("6h"),
+    wave_mean=0,
+    wave_amplitude=6.5,
+    wave_phase=0,
+)
+wave01 = np.exp(wave01)
+
+# Wave 2: Large amplitude, short wave period
+wave02 = sine_wave(
+    start_date=start_date,
+    end_date=end_date,
+    sample_freq=pd.Timedelta("1m"),
+    wave_period=pd.Timedelta("1h"),
+    wave_mean=0,
+    wave_amplitude=100,
+    wave_phase=0,
+)
+
+# Wave 3: Large amplitude, short wave period
+wave03 = sine_wave(
+    start_date=start_date,
+    end_date=end_date,
+    sample_freq=pd.Timedelta("1m"),
+    wave_period=pd.Timedelta("3h"),
+    wave_mean=5,
+    wave_amplitude=35,
+    wave_phase=np.pi,
+)
+
+# Trends
+trend_01 = (
+    line(start_date=start_date, end_date=end_date, sample_freq=pd.Timedelta("1m"), slope=0.00008, intercept=1) ** 3
+)
+
+trend_02 = (
+    line(start_date=start_date, end_date=end_date, sample_freq=pd.Timedelta("1m"), slope=-0.00005, intercept=5) ** 5
+)
+
+drift = line(start_date=start_date, end_date=end_date, sample_freq=pd.Timedelta("1m"), slope=0.00005, intercept=0)
+
+signal = (
+    pd.Series(wave01)
+    + pd.Series(wave02)
+    + pd.Series(wave03)
+    + pd.Series(trend_01)
+    + pd.Series(trend_02)
+    - pd.Series(drift)
+)
+signal_w_noise = perturb_timestamp(white_noise(signal, snr_db=10))
+signal_to_detrend = insert_data_gaps(signal_w_noise, fraction=0.35)
+
+# Unpack the trend values from the tuple
+trend_rho = hilbert_huang_transform(signal_to_detrend)
+
+fig, ax = plt.subplots(3, 1, figsize=[9, 7])
+
+ax[0].plot(wave01, label="Wave 1")
+ax[0].plot(wave02, label="Wave 2")
+ax[0].plot(wave03, label="Wave 3")
+ax[0].set_title("Oscillatory components")
+ax[0].set_ylabel("Amplitude")
+ax[0].legend()
+
+ax[1].plot(trend_01, label="Polynomial 1")
+ax[1].plot(trend_02, label="Polynomial 2")
+ax[1].set_title("Trends & Drift")
+ax[1].set_ylabel("Magnitude")
+ax[1].legend()
+
+color = "tab:red"
+ax2 = ax[1].twinx()
+ax2.plot(-drift, color=color)
+ax2.set_ylabel("Drift", color=color)
+ax2.tick_params(axis="y", labelcolor=color)
+
+ax[2].plot(signal, label="Signal without noise")
+ax[2].set_title("Signal without noise")
+ax[2].set_ylabel("Magnitude")
+ax[2].set_xlabel("Date")
+
+# sphinx_gallery_thumbnail_number = 2
+fig2, axs = plt.subplots(figsize=[9, 7])
+
+# original signal
+axs.plot(signal_to_detrend.index, signal_to_detrend.values, label="Signal")
+
+# Trend extracted from the signal using rho
+axs.plot(signal_to_detrend.index, trend_rho, label="Trend of the signal")
+
+axs.set_title("Trend found using the Hilbert-Huang Transform")
+
+# Formatting x axis
+axs.xaxis.set_major_formatter(DateFormatter("%b %d, %H:%M"))
+plt.setp(axs.get_xticklabels(), rotation=45)
+axs.legend(loc="lower right")
+plt.tight_layout()
+plt.show()

indsl/filter/__init__.py

@@ -1,10 +1,11 @@
 # Copyright 2023 Cognite AS
+from .hilbert_huang_transform import hilbert_huang_transform
 from .simple_filters import status_flag_filter
 from .wavelet_filter import wavelet_filter
 
 
 TOOLBOX_NAME = "Filter"
 
-__all__ = ["wavelet_filter", "status_flag_filter"]
+__all__ = ["hilbert_huang_transform", "wavelet_filter", "status_flag_filter"]
 
 __cognite__ = ["wavelet_filter", "status_flag_filter"]

indsl/filter/hilbert_huang_transform.py

@@ -0,0 +1,202 @@
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+
+from scipy.signal import hilbert
+
+from indsl.ts_utils import get_timestamps
+
+
+# DROP_SENTINAL is used to get the smallest (most negative)
+# number that can be represented with a 32-bit signed integer.
+DROP_SENTINAL = np.iinfo(np.int32).min
+
+
+def _compute_hilbert_2d_spectrum(values_X, values_Z, x_boundaries):
+    """Compute 2D Hilbert spectrum.
+
+    Calculate a 2D Hilbert-Huang power distribution in sparse format.
+    This utility function generates a sparse matrix that captures a two-dimensional
+    power distribution.
+    """
+    from sparse import COO
+
+    # Calculates bin indices for values_X using the np.digitize() function.
+    # This function returns an array of indices that can be used to bin the
+    # input values_X into the bins defined by x_boundaries. Any values in
+    # values_X that are out of the range defined by x_boundaries are assigned
+    # the DROP_SENTINAL value.
+    primary_indices = np.digitize(values_X, x_boundaries) - 1
+    out_of_bounds = np.logical_or(values_X < x_boundaries[0], values_X >= x_boundaries[-1])
+    primary_indices[out_of_bounds] = DROP_SENTINAL
+
+    # Create meshgrids for time and IMF indices
+    time_indices, imf_indices = np.meshgrid(np.arange(values_X.shape[0]), np.arange(values_X.shape[1]), indexing="ij")
+
+    # Flatten arrays for COO matrix creation
+    flattened_coordinates = np.vstack([primary_indices.ravel(), time_indices.ravel(), imf_indices.ravel()])
+    exclusions = np.any(flattened_coordinates == DROP_SENTINAL, axis=0)
+
+    # Apply the exclusions mask to values_Z
+    values_Z_masked = np.ma.masked_array(values_Z, mask=exclusions.reshape(values_Z.shape)).compressed()
+
+    # Adjust coordinates for valid points
+    valid_coordinates = flattened_coordinates[:, ~exclusions]
+
+    # Create the sparse COO matrix
+    spectrum_shape = (x_boundaries.shape[0] - 1, values_X.shape[0], values_X.shape[1])
+    sparse_spectrum = COO(valid_coordinates, values_Z_masked, shape=spectrum_shape)
+
+    return sparse_spectrum
+
+
+def _calculate_histogram_bins_and_centers(dataset, margin=1e-8):
+    """Calculate histogram bins and centers.
+
+    Determine bin edges for a histogram based on the provided dataset.
+    """
+    # Determine the data range with margin
+    data_lower_bound = dataset.min() - margin
+    data_upper_bound = dataset.max() + margin
+
+    # Calculate the number of bins
+    bin_count = min(int(np.sqrt(dataset.size)), 2048)  # Assuming a bin limit of 2048
+
+    # Establish bin edges
+    bin_edges = np.linspace(data_lower_bound, data_upper_bound, bin_count + 1)
+
+    return bin_edges
+
+
+def _hilbert_spectrum(
+    instant_freq,
+    instant_amp,
+    use_sparse_format=True,
+):
+    """Hilbert Spectrum.
+
+    Calculate the Hilbert-Huang Transform (HHT) from instantaneous frequency and amplitude.
+    This transform depicts the energy of a time-series signal over both time and frequency domains. By default, the
+    output is aggregated over time and IMFs, giving only the frequency spectrum.
+    """
+    # Preliminary checks and setups. Add a dimension if necessary.
+    instant_freq = instant_freq[:, np.newaxis] if instant_freq.ndim == 1 else instant_freq
+    instant_amp = instant_amp[:, np.newaxis] if instant_amp.ndim == 1 else instant_amp
+
+    bin_edges = _calculate_histogram_bins_and_centers(instant_freq.ravel())
+
+    # Calculate the 2D Hilbert spectrum
+    spectral_data = _compute_hilbert_2d_spectrum(instant_freq, instant_amp, bin_edges)
+
+    # Calculate the final spectrum as a power spectrum and return the result in the desired format (sparse or dense)
+    final_spectra = spectral_data**2 if use_sparse_format else spectral_data.todense()
+    return final_spectra
+
+
+def hilbert_huang_transform(
+    signal: pd.Series,
+    sift_thresh: float = 1e-8,
+    max_num_imfs: Optional[int] = None,
+    error_tolerance: float = 0.05,
+    return_trend: bool = True,
+) -> pd.Series:
+    r"""Hilbert-Huang Transform.
+
+    The Hilbert-Huang Transform is a technique that combines Empirical Mode Decomposition (EMD)
+    and the Hilbert Transform to analyze non-stationary and non-linear time series data,
+    where the Hilbert transform is applied to each mode after having performed the EMD.
+    These are oscillatory modes of the full signal with well-defined instantaneous frequencies called
+    Intrinsic Mode Functions (IMFs). We calculate the hilbert spectrum of the IMFs to determine the significant ones
+    from which we extract the trend as their sum. One can select either the trend or the detrended signal as an output
+    of this function. You can read more about the Hilbert-Huang Transformation and the sifting process
+    in the given sources.
+
+    Args:
+        signal (pd.Series): Input time series signal.
+        sift_thresh (float, optional): The threshold used for sifting convergence. Defaults to 1e-8.
+        max_num_imfs (int, optional): The maximum number of oscillatory components to extract. If None, extract all IMFs. Defaults to None.
+        error_tolerance (float, optional): The tolerance for determining significant IMFs. Defaults to 0.05.
+        return_trend (bool, optional): Flag indicating whether to return the trend component of the signal. Defaults to True.
+
+    Returns:
+        pd.Series: The detrended signal if `return_trend` is False, otherwise, the trend component of the signal.
+
+    Raises:
+        ValueError: If `sift_thresh` is not a number higher than zero.
+        ValueError: If `max_num_imfs` is not an integer higher than zero.
+        ValueError: If `error_tolerance` is not higher than zero.
+        Any exceptions that may occur during Empirical Mode Decomposition (EMD).
+
+
+    References:
+        - Huang, Norden E., et al. "The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis."
+        Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences 454.1971 (1998): 903-995.
+        - Yang, Zhijing & Bingham, Chris & Ling, Bingo & Gallimore, Michael & Stewart, Paul & Zhang, Yu. (2012).
+        "Trend extraction based on Hilbert-Huang transform." 1-5. 10.1109/CSNDSP.2012.6292713.
+    """
+    from PyEMD import EMD
+
+    if sift_thresh <= 0:
+        raise ValueError("The sifting threshold must be positive")
+    if max_num_imfs is not None and max_num_imfs <= 0:
+        raise ValueError("The maximum number of IMFs must be positive")
+    if error_tolerance <= 0:
+        raise ValueError("Error tolerance must be positive")
+
+    signal_array = signal.to_numpy()
+
+    # Empirical Mode Decomposition
+    emd = EMD()
+    # If max_num_imfs is None, Python's slicing will automatically include all IMFs.
+    imfs = emd(signal_array)[:max_num_imfs]
+
+    # Compute the analytic signal for each IMF using the Hilbert transform and store them in a list
+    analytic_signals = [hilbert(imf) for imf in imfs]
+
+    # Find the total number of IMFs and the index of the last IMF
+    index_of_the_last_imf = imfs.shape[1] - 1
+    significant_imf_index_rho = len(imfs) - 1
+
+    # Compute the instantaneous frequency and amplitude of each IMF
+    phase = np.unwrap(np.angle(analytic_signals))
+    dt = np.mean(np.diff(get_timestamps(signal, "s").to_numpy()))
+    frequency = np.gradient(phase) / (2 * np.pi * dt)
+    amplitude = np.abs(analytic_signals)
+
+    # Transpose to get the right shape for the array
+    frequency_array = np.array(frequency).T
+
+    # average over time
+    avg_frequency_array = np.mean(frequency_array, axis=-1)
+
+    amplitude_array = np.array(amplitude)
+
+    # Ensure that the arrays are 1D by flattening them
+    # to avoid errors if the arrays have more than 2 dimensions
+    flat_amplitude = amplitude_array.flatten()
+    flat_avg_frequency = avg_frequency_array.flatten()
+
+    # timestamps = get_timestamps(signal, "s")
+    # sample_rate_hz = 1 / (np.mean(np.diff(timestamps.to_numpy())))
+
+    # compute the marginal Hilbert spectrum as a sum over the sample rate of the Hilbert spectrum
+    hht = _hilbert_spectrum(flat_avg_frequency, flat_amplitude)
+    # marginal_hilbert_spectrum = sample_rate_hz * np.sum(hht, axis=0)
+
+    # Loop for rho calculations
+    for i in range(index_of_the_last_imf - 1, -1, -1):
+        rho = np.sum(hht[:, i] * hht[:, i + 1]) / (np.sum(hht[:, i]) * np.sum(hht[:, i + 1]))  # type: ignore
+
+        if rho < error_tolerance:
+            break
+        else:
+            significant_imf_index_rho -= 1
+
+    trend_rho = np.sum(imfs[significant_imf_index_rho:], axis=0, dtype=np.float64)
+    trend_series_rho = pd.Series(trend_rho, index=signal.index)
+
+    # Return the trend component of the signal if return_trend is True, otherwise, return the detrended signal
+    result_rho = trend_series_rho if return_trend else signal - trend_series_rho
+
+    return result_rho