initial push on ste

examples/data/loads/sine_wave.csv

@@ -0,0 +1,15 @@
+t_peaks,peaks
+0.015,0.809016994
+0.035,0.809016994
+0.05,1
+0.065,0.809016994
+0.085,0.809016994
+0.1,1
+0.115,0.809016994
+0.135,0.809016994
+0.15,1
+0.165,0.809016994
+0.185,0.809016994
+0.2,1
+0.215,0.809016994
+0.235,0.809016994

mhkit/loads/extreme/block_maxima.m

@@ -0,0 +1,44 @@
+function block_maxima = block_maxima(t, x, t_st)
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+%     Find the block maxima of a time-series.
+%
+%     The timeseries (t,x) is divided into blocks of length t_st, and the 
+%     maxima of each block is returned.
+%
+%     Parameters
+%     ----------
+%         t : array
+%             Time array
+%         x : array
+%             Global peaks time-series
+%         t_st : double
+%             Short-term period
+%
+%      Returns
+%      -------
+%         block_maxima : array
+%             Block maxima (i.e. largest peak in each block)
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+if ~isa(t,'numeric')
+    error('ERROR: t must be a double array')
+end
+if ~isa(x,'numeric')
+    error('ERROR: x must be a double array')
+end
+if ~isa(t_st,'double')
+    error('ERROR: t_st must be a double')
+end
+
+py.importlib.import_module('mhkit');
+
+t = py.numpy.array(t);
+x = py.numpy.array(x);
+
+result = py.mhkit.loads.extreme.block_maxima(t, x, t_st);
+
+block_maxima = double(result);
+
+end

mhkit/loads/extreme/global_peaks.m

@@ -0,0 +1,45 @@
+function [t_peaks,peaks] = global_peaks(t,data)
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+%     Find the global peaks of a zero-cenered response time-series.
+%
+%     The global peaks are the maxima between consecutive zero
+%     up-crossings.
+%
+%     Parameters
+%     ----------
+%         t : array
+%             Time array
+%         data: array
+%             Response time-series
+%
+%      Returns
+%      -------
+%         t_peaks : array
+%             Time array for peaks
+%         peaks : array
+%             Peak values of the response time-series
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+if ~isa(t,'numeric')
+    error('ERROR: t must be a double array')
+end
+if ~isa(data,'numeric')
+    error('ERROR: data must be a double array')
+end
+
+py.importlib.import_module('mhkit');
+
+t = py.numpy.array(t);
+data = py.numpy.array(data);
+
+result = py.mhkit.loads.extreme.global_peaks(t, data);
+
+t_peaks = double(result{1});
+peaks = double(result{2});
+
+end
+
+
+

mhkit/loads/extreme/number_of_short_term_peaks.m

@@ -0,0 +1,22 @@
+function n_st = number_of_short_term_peaks(n, t, t_st)
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+%     Estimate the number of peaks in a specified period.
+%
+%     Parameters
+%     ----------
+%         n : int
+%             Number of peaks in analyzed timeseries.
+%         t : double
+%             Length of time of analyzed timeseries.
+%         t_st : double
+%             Short-term period for which to estimate the number of peaks.
+%
+%      Returns
+%      -------
+%         n_st : double
+%             Number of peaks in short term period.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+n_st = n * t_st / t;

mhkit/loads/extreme/peaks_distribution_peaks_over_threshold.m

@@ -0,0 +1,57 @@
+function [pks, result] = peaks_distribution_peaks_over_threshold(peaks, x, method, options)
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+%     Estimate the peaks distribution by fitting a Weibull
+%     distribution to the peaks of the response.
+%
+%     To learn more about the methods that can be used in this function,
+%     refer to the scipy.stats.rv_continuous methods documentation!
+%
+%     Parameters
+%     ----------
+%         peaks : array
+%             Global peaks
+%         x : array or double
+%             Input for the statistical function/method
+%         method : str
+%             Statistical method to apply to resulting data. Options to
+%             choose from are: "pdf", "cdf", "ppf", or "sf"
+%         threshold : double or int (optional)
+%             Threshold value. Only peaks above this value will be used.
+%             Default value calculated as: `mean(x) + 1.4 * std(x)`
+%      Returns
+%      -------
+%         p : array or double
+%             Probability distribution of the peaks
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+arguments
+    peaks
+    x
+    method
+    options.threshold double
+end
+
+if ~isa(peaks,'numeric')
+    error('ERROR: peaks must be a double array')
+end
+if ~isa(method,'string')
+    error('ERROR: method must be a string')
+end
+
+py.importlib.import_module('mhkit');
+
+p = py.numpy.array(peaks);
+x = py.numpy.array(x);
+
+if exist("threshold", 'var')==1
+    result = py.mhkit.loads.extreme.peaks_distribution_peaks_over_threshold(p, options.threshold);
+else
+    result = py.mhkit.loads.extreme.peaks_distribution_peaks_over_threshold(p);
+end
+stat = py.mhkit_python_utils.scipy_stats.convert_to_array(result, x, method);
+
+pks = double(stat);
+
+end

mhkit/loads/extreme/peaks_distribution_weibull.m

@@ -0,0 +1,46 @@
+function [pks, result] = peaks_distribution_weibull(peaks, x, method)
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+%     Estimate the peaks distribution by fitting a Weibull
+%     distribution to the peaks of the response.
+%
+%     To learn more about the methods that can be used in this function,
+%     refer to the scipy.stats.rv_continuous methods documentation!
+%
+%     Parameters
+%     ----------
+%         peaks : array
+%             Global peaks
+%         x : array or double
+%             Input for the statistical function/method
+%         method : str
+%             Statistical method to apply to resulting data. Options to
+%             choose from are: "pdf", "cdf", "ppf", or "sf"
+%
+%      Returns
+%      -------
+%         p : array or double
+%             Probability distribution of the peaks
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+if ~isa(peaks,'numeric')
+    error('ERROR: peaks must be a double array')
+end
+
+if ~isa(method,'string')
+    error('ERROR: method must be a string')
+end
+
+py.importlib.import_module('mhkit');
+
+p = py.numpy.array(peaks);
+x = py.numpy.array(x);
+
+result = py.mhkit.loads.extreme.peaks_distribution_weibull(p);
+
+stat = py.mhkit_python_utils.scipy_stats.convert_to_array(result, x, method);
+
+pks = double(stat);
+
+end

mhkit/loads/extreme/peaks_distribution_weibull_tail_fit.m

@@ -0,0 +1,45 @@
+function [pks, result] = peaks_distribution_weibull_tail_fit(peaks, x, method)
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+%     Estimate the peaks distribution by using the Weibull tail fit method.
+%
+%     To learn more about the methods that can be used in this function,
+%     refer to the scipy.stats.rv_continuous methods documentation!
+%
+%     Parameters
+%     ----------
+%         peaks : array
+%             Global peaks
+%         x : array or double
+%             Input for the statistical function/method
+%         method : str
+%             Statistical method to apply to resulting data. Options to
+%             choose from are: "pdf", "cdf", "ppf", or "sf"
+%
+%      Returns
+%      -------
+%         p : array or double
+%             Probability distribution of the peaks
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+if ~isa(peaks,'numeric')
+    error('ERROR: peaks must be a double array')
+end
+
+if ~isa(method,'string')
+    error('ERROR: method must be a string')
+end
+
+py.importlib.import_module('mhkit');
+
+p = py.numpy.array(peaks);
+x = py.numpy.array(x);
+
+result = py.mhkit.loads.extreme.peaks_distribution_weibull_tail_fit(p);
+
+stat = py.mhkit_python_utils.scipy_stats.convert_to_array(result, x, method);
+
+pks = double(stat);
+
+end

mhkit/loads/extreme/short_term_extreme.m

@@ -0,0 +1,60 @@
+function ste = short_term_extreme(t, data, t_st, type, x, method)
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+%     Estimate the peaks distribution by fitting a Weibull
+%     distribution to the peaks of the response.
+%
+%     To learn more about the methods that can be used in this function,
+%     refer to the scipy.stats.rv_continuous methods documentation!
+%
+%     Parameters
+%     ----------
+%         t : array
+%             Time array
+%         data : array
+%             Response timeseries
+%         t_st : double or int
+%             Short time period
+%         type : string
+%             Method for estimating the short-term extreme distribution
+%         x : array or double
+%             Input for the statistical function/method
+%         method : str
+%             Statistical method to apply to resulting data. Options to
+%             choose from are: "pdf", "cdf", "ppf", or "sf"
+%
+%      Returns
+%      -------
+%         ste : array or double
+%             Probability distribution of the peaks
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+if ~isa(t,'numeric')
+    error('ERROR: t must be a double array')
+end
+if ~isa(data,'numeric')
+    error('ERROR: data must be a double array')
+end
+if ~isa(t_st,'numeric')
+    error('ERROR: t_st must be a double')
+end
+if ~isa(type,'string')
+    error('ERROR: type must be a string')
+end
+if ~isa(method,'string')
+    error('ERROR: method must be a string')
+end
+
+py.importlib.import_module('mhkit');
+
+t = py.numpy.array(t);
+data = py.numpy.array(data);
+
+result = py.mhkit.loads.extreme.short_term_extreme(t, data, t_st, type);
+
+stat = py.mhkit_python_utils.scipy_stats.convert_to_array(result, x, method);
+
+ste = double(stat);
+
+end

mhkit/loads/extreme/ste_block_maxima_gev.m

@@ -0,0 +1,46 @@
+function ste = ste_block_maxima_gev(block_maxima, x, method)
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%
+%     Approximate the short-term extreme distribution using the block 
+%     maxima method and the Generalized Extreme Value distribution.
+%
+%     To learn more about the methods that can be used in this function,
+%     refer to the scipy.stats.rv_continuous methods documentation!
+%
+%     Parameters
+%     ----------
+%         block_maxima : array
+%             Block maxima (i.e. largest peak in each block).
+%         x : array
+%             Input for the statistical function/method
+%         method : str
+%             Statistical method to apply to resulting data. Options to
+%             choose from are: "pdf", "cdf", "ppf", or "sf"
+%
+%      Returns
+%      -------
+%         block_maxima : array
+%             Block maxima (i.e. largest peak in each block)
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+if ~isa(block_maxima,'numeric')
+    error('ERROR: block_maxima must be a double array')
+end
+
+if ~isa(method,'string')
+    error('ERROR: method must be a string')
+end
+
+py.importlib.import_module('mhkit');
+
+block_maxima = py.numpy.array(block_maxima);
+x = py.numpy.array(x);
+
+result = py.mhkit.loads.extreme.ste_block_maxima_gev(block_maxima);
+
+stat = py.mhkit_python_utils.scipy_stats.convert_to_array(result, x, method);
+
+ste = double(stat);
+
+end