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@ -14,6 +14,7 @@ from numpy import (abs, amax, any, logical_and, arange, linspace, atleast_1d,
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from scipy.special import gammaln
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from scipy.special import gammaln
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import types
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import types
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import warnings
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import warnings
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from wafo import plotbackend
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try:
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try:
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import wafo.c_library as clib
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import wafo.c_library as clib
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@ -26,7 +27,7 @@ __all__ = ['JITImport', 'DotDict', 'Bunch', 'printf', 'sub_dict_select',
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'parse_kwargs', 'ecross', 'findtc', 'findtp', 'findcross',
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'parse_kwargs', 'ecross', 'findtc', 'findtp', 'findcross',
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'findextrema', 'findrfc', 'rfcfilter', 'common_shape', 'argsreduce',
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'findextrema', 'findrfc', 'rfcfilter', 'common_shape', 'argsreduce',
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'stirlerr', 'getshipchar', 'betaloge', 'gravity', 'nextpow2',
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'stirlerr', 'getshipchar', 'betaloge', 'gravity', 'nextpow2',
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'discretize', 'pol2cart', 'cart2pol', 'ndgrid', 'meshgrid']
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'discretize', 'pol2cart', 'cart2pol', 'ndgrid', 'meshgrid', 'histgrm']
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class JITImport(object):
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class JITImport(object):
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'''
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'''
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@ -1849,6 +1850,66 @@ def tranproc(x, f, x0, *xi):
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warnings.warn('Transformation of derivatives of order>4 not supported.')
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warnings.warn('Transformation of derivatives of order>4 not supported.')
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return y #y0,y1,y2,y3,y4
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return y #y0,y1,y2,y3,y4
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def histgrm(data, n=None, odd=False, scale=False, lintype='b-'):
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'''HISTGRM Plot histogram
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CALL: binwidth = histgrm(x,N,odd,scale)
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binwidth = the width of each bin
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Parameters
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-----------
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x = the data
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n = approximate number of bins wanted
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(default depending on length(x))
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odd = placement of bins (0 or 1) (default 0)
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scale = argument for scaling (default 0)
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scale = 1 yields the area 1 under the histogram
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lintype : specify color and lintype, see PLOT for possibilities.
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Example:
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R=rndgumb(2,2,1,100);
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histgrm(R,20,0,1)
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hold on
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x=linspace(-3,16,200);
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plot(x,pdfgumb(x,2,2),'r')
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hold off
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'''
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x = np.atleast_1d(data)
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if n is None:
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n = np.ceil(4 * np.sqrt(np.sqrt(len(x))))
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mn = x.min()
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mx = x.max()
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d = (mx - mn) / n * 2
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e = np.floor(np.log(d) / np.log(10));
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m = np.floor(d / 10 ** e)
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if m > 5:
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m = 5
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elif m > 2:
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m = 2
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d = m * 10 ** e
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mn = (np.floor(mn / d) - 1) * d - odd * d / 2
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mx = (np.ceil(mx / d) + 1) * d + odd * d / 2
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limits = np.arange(mn, mx, d)
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bin, limits = np.histogram(data, bins=limits, normed=scale) #, new=True)
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limits.shape = (-1, 1)
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xx = limits.repeat(3, axis=1)
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xx.shape = (-1,)
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xx = xx[1:-1]
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bin.shape = (-1, 1)
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yy = bin.repeat(3, axis=1)
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#yy[0,0] = 0.0 # pdf
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yy[:, 0] = 0.0 # histogram
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yy.shape = (-1,)
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yy = np.hstack((yy, 0.0))
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plotbackend.plotbackend.plot(xx, yy, lintype, limits, limits * 0)
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binwidth = d
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return binwidth
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def _test_find_cross():
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def _test_find_cross():
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t = findcross([0, 0, 1, -1, 1], 0)
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t = findcross([0, 0, 1, -1, 1], 0)
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