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@ -35,7 +35,7 @@ from numpy import flatnonzero as nonzero
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from wafo.stats.estimation import FitDistribution
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try:
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import vonmises_cython
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from scipy.stats.distributions import vonmises_cython
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except:
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vonmises_cython = None
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@ -461,7 +461,8 @@ class rv_frozen_old(object):
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def median(self):
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return self.dist.median(*self.args, **self.kwds)
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def mean(self):
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return self.dist.mean(*self.args,**self.kwds)
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return self.dist.mean(*self.args, **self.kwds)
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def var(self):
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return self.dist.var(*self.args, **self.kwds)
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def std(self):
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@ -678,7 +679,8 @@ def bd0(x, npr):
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def valarray(shape,value=nan,typecode=None):
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"""Return an array of all value.
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"""
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out = reshape(repeat([value],product(shape,axis=0),axis=0),shape)
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#out = reshape(repeat([value],product(shape,axis=0),axis=0),shape)
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out = ones(shape, dtype=bool) * value
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if typecode is not None:
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out = out.astype(typecode)
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if not isinstance(out, ndarray):
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@ -1318,6 +1320,10 @@ class rv_continuous(rv_generic):
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def _construct_default_doc(self, longname=None, extradoc=None):
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"""Construct instance docstring from the default template."""
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if longname is None:
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longname = 'A'
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if extradoc is None:
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extradoc = ''
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if extradoc.startswith('\n\n'):
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extradoc = extradoc[2:]
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self.__doc__ = ''.join(['%s continuous random variable.'%longname,
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@ -1694,9 +1700,36 @@ class rv_continuous(rv_generic):
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cond1 = (q > 0) & (q < 1)
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cond2 = (q==1) & cond0
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cond = cond0 & cond1
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# begin CAT patch 1: 17 MARCH 2010
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# If expr in value is an array of shape=(N,1), valarray's call to repeat()
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# replicates it N times, so output gets shape=(N,N,1). When .reshape(N,1)
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# is invoked --> exception. The following may not be robust:
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output = valarray(shape(cond),value=self.a*scale + loc)
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#initial_value = self.a * scale + loc
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#if product(shape(initial_value)) > 1: # side effects?
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# output = initial_value
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#else:
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# output = valarray(shape(cond),value=initial_value)
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# end CAT patch 1: 17 MARCH 2010
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# This line is good
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place(output,(1-cond0)+(1-cond1)*(q!=0.0), self.badvalue)
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place(output,cond2,self.b*scale + loc)
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# begin CAT patch 2: 17 MARCH 2010
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# If expr in last arg has shape(N,1), place() tries to jam an
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# array into each cell in output with cond2 == True.
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# Causes exception even when (not any(cond2)) holds.
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#WASplace(output,cond2,self.b*scale + loc)
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proxy_value = self.b * scale + loc
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if product(shape(proxy_value)) != 1:
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proxy_value = extract(cond2, proxy_value * cond2)
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place(output,cond2,proxy_value)
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# end CAT patch 2: 17 MARCH 2010
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# rest of method unchanged...
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if any(cond): #call only if at least 1 entry
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goodargs = argsreduce(cond, *((q,)+args+(scale,loc)))
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scale, loc, goodargs = goodargs[-2], goodargs[-1], goodargs[:-2]
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@ -1735,10 +1768,40 @@ class rv_continuous(rv_generic):
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cond1 = (q > 0) & (q < 1)
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cond2 = (q==1) & cond0
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cond = cond0 & cond1
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output = valarray(shape(cond),value=self.b)
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# begin CAT patch 3: 18 MARCH 2010
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# If expr in value is an array of shape=(N,1), valarray's call to the
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# repeat() method will replicate it N times, so output becomes NxNx1.
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# The subsequent call to .reshape(N,1) --> exception.
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# This statement also has the unfortunate side effect of being wrong
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# when the support of the distribution is bounded above.
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#WASoutput = valarray(shape(cond),value=self.b)
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initial_value = self.b * scale + loc
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#if product(shape(initial_value)) > 1:
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# output = initial_value
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#else:
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output = valarray(shape(cond),value=initial_value)
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#end CAT patch 3: 18 MARCH 2010
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#place(output,(1-cond0)*(cond1==cond1), self.badvalue)
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place(output,(1-cond0)*(cond1==cond1)+(1-cond1)*(q!=0.0), self.badvalue)
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place(output,cond2,self.a)
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# begin CAT patch 4: 18 MARCH 2010
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# If expr in last arg has shape=(N,1), place() tries to jam a large
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# array into each cell in output where cond2==True.
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# Causes exception even if cond2 is never True.
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# This statement also has the unfortunate side effect of being wrong
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# when the support of the distribution is bounded below.
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#WASplace(output,cond2,self.a)
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proxy_value = self.a * scale + loc
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if product(shape(proxy_value)) != 1:
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proxy_value = extract(cond2, proxy_value * cond2)
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place(output, cond2, proxy_value)
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#end CAT patch 4: 18 MARCH 2010
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if any(cond): #call only if at least 1 entry
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goodargs = argsreduce(cond, *((q,)+args+(scale,loc))) #PB replace 1-q by q
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scale, loc, goodargs = goodargs[-2], goodargs[-1], goodargs[:-2]
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@ -2198,43 +2261,50 @@ class rv_continuous(rv_generic):
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def fit(self, data, *args, **kwds):
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"""
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Return maximum likelihood estimators to shape, location, and scale from data
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Return MLEs for shape, location, and scale parameters from data.
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Starting points for the fit are given by input arguments. For any
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arguments not given starting points, self._fitstart(data) is called
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to get the starting estimates.
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MLE stands for Maximum Likelihood Estimate. Starting estimates for
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the fit are given by input arguments; for any arguments not provided
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with starting estimates, ``self._fitstart(data)`` is called to generate
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such.
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You can hold some parameters fixed to specific values by passing in
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One can hold some parameters fixed to specific values by passing in
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keyword arguments f0..fn for shape paramters and floc, fscale for
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location and scale parameters.
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location and scale parameters, respectively.
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Parameters
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----------
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data : array-like
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Data to use in calculating the MLE
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args : optional
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Starting values for any shape arguments (those not specified
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will be determined by _fitstart(data))
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kwds : loc, scale
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Starting values for the location and scale parameters
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Data to use in calculating the MLEs.
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args : floats, optional
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Starting value(s) for any shape-characterizing arguments (those not
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provided will be determined by a call to ``_fitstart(data)``).
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No default value.
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kwds : floats, optional
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Starting values for the location and scale parameters; no default.
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Special keyword arguments are recognized as holding certain
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parameters fixed:
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f0..fn : hold respective shape paramters fixed
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floc : hold location parameter fixed to specified value
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f0..fn : hold respective shape parameters fixed.
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floc : hold location parameter fixed to specified value.
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fscale : hold scale parameter fixed to specified value
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method : of estimation. Options are
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'ml' : Maximum Likelihood method (default)
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'mps': Maximum Product Spacing method
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optimizer : The optimizer to use. The optimizer must take func,
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and starting position as the first two arguments,
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plus args (for extra arguments to pass to the
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function to be optimized) and disp=0 to suppress
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output as keyword arguments.
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Return
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------
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shape, loc, scale : tuple of float
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MLE estimates for any shape arguments followed by location and scale
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Returns
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-------
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shape, loc, scale : tuple of floats
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MLEs for any shape statistics, followed by those for location and
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scale.
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"""
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Narg = len(args)
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if Narg > self.numargs:
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@ -2762,10 +2832,12 @@ class fisk_gen(burr_gen):
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return burr_gen._stats(self, c, 1.0)
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def _entropy(self, c):
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return 2 - log(c)
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fisk = fisk_gen(a=0.0, name='fink', longname="A funk",
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fisk = fisk_gen(a=0.0, name='fisk', longname="Fisk",
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shapes='c', extradoc="""
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Fink distribution.
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Fisk distribution.
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Also known as the log-logistic distribution.
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Burr distribution with d=1.
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"""
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@ -5562,6 +5634,8 @@ class rv_discrete(rv_generic):
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super(rv_generic,self).__init__()
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self.fix_loc = self._fix_loc
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if badvalue is None:
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badvalue = nan
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self.badvalue = badvalue
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