# Path: smtk/smoothing/konno_ohmachi.py
# def konnoOhmachiSmoothingWindow(frequencies, center_frequency, bandwidth=40.0,
#                                 normalize=False):
# def calculateSmoothingMatrix(frequencies, bandwidth=40.0, normalize=False):
# def konnoOhmachiSmoothing(spectra, frequencies, bandwidth=40, count=1,
#                   enforce_no_matrix=False, max_memory_usage=512,
#                   normalize=False):
#     def _check_params(self, params):
#     def apply_smoothing(self, spectra, frequencies):
#     def __call__(self, spectra, frequencies):
# class KonnoOhmachi(BaseSpectralSmoother):
# 
# Path: smtk/sm_utils.py
# def get_time_vector(time_step, number_steps):
#     """
#     General SMTK utils
#     """
#     return np.cumsum(time_step * np.ones(number_steps, dtype=float)) - time_step
# 
# def _save_image(filename, fig, format='png', dpi=300, **kwargs):  # noqa
#     """
#     Saves the matplotlib figure `fig` to `filename`. Wrapper around `fig.savefig`
#     with `dpi=300` by default and `format` inferred from `filename` extension
#     or, if no extension is found, set as "png".
#     If filename is empty this function does nothing and return
# 
#     :param str filename: str, the file path
#     :param figure: a :class:`matplotlib.figure.Figure` (e.g. via
#         `matplotlib.pyplot.figure()`)
#     :param format: string, the image format. Default: 'png'. This argument is
#         ignored if `filename` has a file extension, as `format` will be set
#         equal to the extension without leading dot.
#     :param str kwargs: additional keyword arguments to pass to `fig.savefig`.
#         For details, see:
#         https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.savefig.html
#     """
#     if not filename:
#         return
# 
#     name, ext = os.path.splitext(filename)
#     if ext:
#         format = ext[1:]  # noqa
#     else:
#         filename = name + '.' + format
# 
#     fig.savefig(filename, dpi=dpi, format=format, **kwargs)
# 
# def nextpow2(nval):
#     m_f = np.log2(nval)
#     m_i = np.ceil(m_f)
#     return int(2.0 ** m_i)


import numpy as np
import matplotlib.pyplot as plt
import smtk.response_spectrum as rsp
from math import pi
from scipy.integrate import cumtrapz
from scipy import constants
from smtk.smoothing import konno_ohmachi
from smtk.sm_utils import get_time_vector, _save_image, nextpow2
        husid, time_vector = get_husid(acceleration[idx], time_step)
    return ARIAS_FACTOR * husid[-1]


def plot_husid(acceleration, time_step, start_level=0., end_level=1.0,
               figure_size=(7, 5), filename=None, filetype="png", dpi=300):
    """
    Creates a Husid plot for the record
    :param tuple figure_size:
        Size of the output figure (Width, Height)
    :param str filename:
        Name of the file to export
    :param str filetype:
        Type of file for export
    :param int dpi:
        FIgure resolution in dots per inch.
    """
    plt.figure(figsize=figure_size)
    husid, time_vector = get_husid(acceleration, time_step)
    husid_norm = husid / husid[-1]
    idx = np.where(np.logical_and(husid_norm >= start_level,
                                  husid_norm <= end_level))[0]
    plt.plot(time_vector, husid_norm, "b-", linewidth=2.0,
             label="Original Record")
    plt.plot(time_vector[idx], husid_norm[idx], "r-", linewidth=2.0,
             label="%5.3f - %5.3f Arias" % (start_level, end_level))
    plt.xlabel("Time (s)", fontsize=14)
    plt.ylabel("Fraction of Arias Intensity", fontsize=14)
    plt.title("Husid Plot")
    plt.legend(loc=4, fontsize=14)

# Path: smtk/smoothing/konno_ohmachi.py
# def konnoOhmachiSmoothingWindow(frequencies, center_frequency, bandwidth=40.0,
#                                 normalize=False):
# def calculateSmoothingMatrix(frequencies, bandwidth=40.0, normalize=False):
# def konnoOhmachiSmoothing(spectra, frequencies, bandwidth=40, count=1,
#                   enforce_no_matrix=False, max_memory_usage=512,
#                   normalize=False):
#     def _check_params(self, params):
#     def apply_smoothing(self, spectra, frequencies):
#     def __call__(self, spectra, frequencies):
# class KonnoOhmachi(BaseSpectralSmoother):
# 
# Path: smtk/sm_utils.py
# def get_time_vector(time_step, number_steps):
#     """
#     General SMTK utils
#     """
#     return np.cumsum(time_step * np.ones(number_steps, dtype=float)) - time_step
# 
# def _save_image(filename, fig, format='png', dpi=300, **kwargs):  # noqa
#     """
#     Saves the matplotlib figure `fig` to `filename`. Wrapper around `fig.savefig`
#     with `dpi=300` by default and `format` inferred from `filename` extension
#     or, if no extension is found, set as "png".
#     If filename is empty this function does nothing and return
# 
#     :param str filename: str, the file path
#     :param figure: a :class:`matplotlib.figure.Figure` (e.g. via
#         `matplotlib.pyplot.figure()`)
#     :param format: string, the image format. Default: 'png'. This argument is
#         ignored if `filename` has a file extension, as `format` will be set
#         equal to the extension without leading dot.
#     :param str kwargs: additional keyword arguments to pass to `fig.savefig`.
#         For details, see:
#         https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.savefig.html
#     """
#     if not filename:
#         return
# 
#     name, ext = os.path.splitext(filename)
#     if ext:
#         format = ext[1:]  # noqa
#     else:
#         filename = name + '.' + format
# 
#     fig.savefig(filename, dpi=dpi, format=format, **kwargs)
# 
# def nextpow2(nval):
#     m_f = np.log2(nval)
#     m_i = np.ceil(m_f)
#     return int(2.0 ** m_i)


# Path: smtk/intensity_measures.py
import numpy as np
import matplotlib.pyplot as plt
import smtk.response_spectrum as rsp
from math import pi
from scipy.integrate import cumtrapz
from scipy import constants
from smtk.smoothing import konno_ohmachi
from smtk.sm_utils import get_time_vector, _save_image, nextpow2

        husid, time_vector = get_husid(acceleration[idx], time_step)
    return ARIAS_FACTOR * husid[-1]


def plot_husid(acceleration, time_step, start_level=0., end_level=1.0,
               figure_size=(7, 5), filename=None, filetype="png", dpi=300):
    """
    Creates a Husid plot for the record
    :param tuple figure_size:
        Size of the output figure (Width, Height)
    :param str filename:
        Name of the file to export
    :param str filetype:
        Type of file for export
    :param int dpi:
        FIgure resolution in dots per inch.
    """
    plt.figure(figsize=figure_size)
    husid, time_vector = get_husid(acceleration, time_step)
    husid_norm = husid / husid[-1]
    idx = np.where(np.logical_and(husid_norm >= start_level,
                                  husid_norm <= end_level))[0]
    plt.plot(time_vector, husid_norm, "b-", linewidth=2.0,
             label="Original Record")
    plt.plot(time_vector[idx], husid_norm[idx], "r-", linewidth=2.0,
             label="%5.3f - %5.3f Arias" % (start_level, end_level))
    plt.xlabel("Time (s)", fontsize=14)
    plt.ylabel("Fraction of Arias Intensity", fontsize=14)
    plt.title("Husid Plot")
    plt.legend(loc=4, fontsize=14)

    _save_image(filename, plt.gcf(), filetype, dpi)