Per A Brodtkorb
9 years ago
parent
6d7beed94b
commit
c585c8087a
@ -1,551 +0,0 @@
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import warnings
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from graphutil import cltext
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from plotbackend import plotbackend
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from time import gmtime, strftime
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import numpy as np
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from scipy.integrate.quadrature import cumtrapz # @UnresolvedImport
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from scipy.interpolate import griddata
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from scipy import integrate
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__all__ = ['PlotData', 'AxisLabels']
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def empty_copy(obj):
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class Empty(obj.__class__):
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def __init__(self):
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pass
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newcopy = Empty()
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newcopy.__class__ = obj.__class__
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return newcopy
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def _set_seed(iseed):
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if iseed is not None:
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try:
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np.random.set_state(iseed)
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except:
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np.random.seed(iseed)
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def now():
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"""Return current date and time as a string."""
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return strftime("%a, %d %b %Y %H:%M:%S", gmtime())
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class PlotData(object):
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"""Container class for data objects in WAFO.
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Member variables
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----------------
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data : array_like
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args : vector for 1D, list of vectors for 2D, 3D, ...
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labels : AxisLabels
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children : list of PlotData objects
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Member methods
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--------------
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plot :
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copy :
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Example
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-------
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>>> import numpy as np
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>>> x = np.arange(-2, 2, 0.2)
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# Plot 2 objects in one call
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>>> d2 = PlotData(np.sin(x), x, xlab='x', ylab='sin', title='sinus')
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>>> h = d2.plot()
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Plot with confidence interval
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>>> d3 = PlotData(np.sin(x),x)
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>>> d3.children = [PlotData(np.vstack([np.sin(x)*0.9, np.sin(x)*1.2]).T,x)]
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>>> d3.plot_args_children=[':r']
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>>> h = d3.plot()
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See also
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--------
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specdata,
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covdata
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"""
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def __init__(self, data=None, args=None, *args2, **kwds):
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self.data = data
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self.args = args
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self.date = now()
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self.plotter = kwds.pop('plotter', None)
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self.children = None
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self.plot_args_children = kwds.pop('plot_args_children', [])
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self.plot_kwds_children = kwds.pop('plot_kwds_children', {})
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self.plot_args = kwds.pop('plot_args', [])
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self.plot_kwds = kwds.pop('plot_kwds', {})
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self.labels = AxisLabels(**kwds)
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if not self.plotter:
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self.setplotter(kwds.get('plotmethod', None))
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def plot(self, *args, **kwds):
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axis = kwds.pop('axis', None)
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if axis is None:
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axis = plotbackend.gca()
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tmp = None
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plotflag = kwds.get('plotflag', None)
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if not plotflag and self.children is not None:
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plotbackend.hold('on')
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tmp = []
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child_args = kwds.pop(
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'plot_args_children', tuple(self.plot_args_children))
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child_kwds = dict(self.plot_kwds_children).copy()
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child_kwds.update(kwds.pop('plot_kwds_children', {}))
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child_kwds['axis'] = axis
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for child in self.children:
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tmp1 = child.plot(*child_args, **child_kwds)
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if tmp1 is not None:
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tmp.append(tmp1)
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if len(tmp) == 0:
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tmp = None
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main_args = args if len(args) else tuple(self.plot_args)
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main_kwds = dict(self.plot_kwds).copy()
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main_kwds.update(kwds)
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main_kwds['axis'] = axis
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tmp2 = self.plotter.plot(self, *main_args, **main_kwds)
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return tmp2, tmp
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def eval_points(self, *args, **kwds):
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'''
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>>> x = np.linspace(0,5,20)
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>>> d = PlotData(np.sin(x),x)
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>>> xi = np.linspace(0,5,60)
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>>> di = PlotData(d.eval_points(xi, method='cubic'),xi)
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>>> h = d.plot('.')
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>>> hi = di.plot()
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'''
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if isinstance(self.args, (list, tuple)): # Multidimensional data
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ndim = len(self.args)
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if ndim < 2:
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msg = '''Unable to determine plotter-type, because len(self.args)<2.
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If the data is 1D, then self.args should be a vector!
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If the data is 2D, then length(self.args) should be 2.
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If the data is 3D, then length(self.args) should be 3.
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Unless you fix this, the plot methods will not work!'''
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warnings.warn(msg)
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else:
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return griddata(self.args, self.data.ravel(), *args, **kwds)
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else: # One dimensional data
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return griddata((self.args,), self.data, *args, **kwds)
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def integrate(self, a, b, **kwds):
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'''
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>>> x = np.linspace(0,5,60)
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>>> d = PlotData(np.sin(x), x)
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>>> d.integrate(0,np.pi/2)
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0.99940054759302188
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'''
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method = kwds.pop('method', 'trapz')
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fun = getattr(integrate, method)
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if isinstance(self.args, (list, tuple)): # Multidimensional data
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ndim = len(self.args)
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if ndim < 2:
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msg = '''Unable to determine plotter-type, because len(self.args)<2.
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If the data is 1D, then self.args should be a vector!
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If the data is 2D, then length(self.args) should be 2.
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If the data is 3D, then length(self.args) should be 3.
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Unless you fix this, the plot methods will not work!'''
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warnings.warn(msg)
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else:
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return griddata(self.args, self.data.ravel(), **kwds)
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else: # One dimensional data
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x = self.args
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ix = np.flatnonzero((a < x) & (x < b))
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xi = np.hstack((a, x.take(ix), b))
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fi = np.hstack(
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(self.eval_points(a), self.data.take(ix), self.eval_points(b)))
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return fun(fi, xi, **kwds)
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def show(self):
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self.plotter.show()
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def copy(self):
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newcopy = empty_copy(self)
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newcopy.__dict__.update(self.__dict__)
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return newcopy
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def setplotter(self, plotmethod=None):
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"""Set plotter based on the data type data_1d, data_2d, data_3d or
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data_nd."""
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if isinstance(self.args, (list, tuple)): # Multidimensional data
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ndim = len(self.args)
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if ndim < 2:
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msg = '''Unable to determine plotter-type, because len(self.args)<2.
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If the data is 1D, then self.args should be a vector!
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If the data is 2D, then length(self.args) should be 2.
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If the data is 3D, then length(self.args) should be 3.
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Unless you fix this, the plot methods will not work!'''
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warnings.warn(msg)
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elif ndim == 2:
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self.plotter = Plotter_2d(plotmethod)
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else:
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warnings.warn('Plotter method not implemented for ndim>2')
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else: # One dimensional data
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self.plotter = Plotter_1d(plotmethod)
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class AxisLabels:
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def __init__(self, title='', xlab='', ylab='', zlab='', **kwds):
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self.title = title
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self.xlab = xlab
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self.ylab = ylab
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self.zlab = zlab
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def __repr__(self):
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return self.__str__()
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def __str__(self):
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return '%s\n%s\n%s\n%s\n' % (self.title, self.xlab, self.ylab,
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self.zlab)
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def copy(self):
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newcopy = empty_copy(self)
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newcopy.__dict__.update(self.__dict__)
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return newcopy
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def labelfig(self, axis=None):
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if axis is None:
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axis = plotbackend.gca()
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try:
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h1 = axis.set_title(self.title)
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h2 = axis.set_xlabel(self.xlab)
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h3 = axis.set_ylabel(self.ylab)
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# h4 = plotbackend.zlabel(self.zlab)
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return h1, h2, h3
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except:
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pass
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class Plotter_1d(object):
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"""
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Parameters
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----------
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plotmethod : string
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defining type of plot. Options are:
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bar : bar plot with rectangles
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barh : horizontal bar plot with rectangles
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loglog : plot with log scaling on the *x* and *y* axis
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semilogx : plot with log scaling on the *x* axis
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semilogy : plot with log scaling on the *y* axis
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plot : Plot lines and/or markers (default)
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stem : Stem plot
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step : stair-step plot
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scatter : scatter plot
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"""
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def __init__(self, plotmethod='plot'):
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self.plotfun = None
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if plotmethod is None:
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plotmethod = 'plot'
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self.plotmethod = plotmethod
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self.plotbackend = plotbackend
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# try:
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# self.plotfun = getattr(plotbackend, plotmethod)
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# except:
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# pass
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def show(self):
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plotbackend.show()
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def plot(self, wdata, *args, **kwds):
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axis = kwds.pop('axis', None)
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if axis is None:
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axis = plotbackend.gca()
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plotflag = kwds.pop('plotflag', False)
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if plotflag:
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h1 = self._plot(axis, plotflag, wdata, *args, **kwds)
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else:
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if isinstance(wdata.data, (list, tuple)):
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vals = tuple(wdata.data)
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else:
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vals = (wdata.data,)
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if isinstance(wdata.args, (list, tuple)):
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args1 = tuple((wdata.args)) + vals + args
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else:
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args1 = tuple((wdata.args,)) + vals + args
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plotfun = getattr(axis, self.plotmethod)
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h1 = plotfun(*args1, **kwds)
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h2 = wdata.labels.labelfig(axis)
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return h1, h2
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def _plot(self, axis, plotflag, wdata, *args, **kwds):
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x = wdata.args
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data = transformdata(x, wdata.data, plotflag)
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dataCI = getattr(wdata, 'dataCI', ())
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h1 = plot1d(axis, x, data, dataCI, plotflag, *args, **kwds)
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return h1
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def plot1d(axis, args, data, dataCI, plotflag, *varargin, **kwds):
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plottype = np.mod(plotflag, 10)
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if plottype == 0: # % No plotting
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return []
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elif plottype == 1:
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H = axis.plot(args, data, *varargin, **kwds)
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elif plottype == 2:
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H = axis.step(args, data, *varargin, **kwds)
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elif plottype == 3:
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H = axis.stem(args, data, *varargin, **kwds)
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elif plottype == 4:
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H = axis.errorbar(args, data,
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yerr=[dataCI[:, 0] - data, dataCI[:, 1] - data],
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*varargin, **kwds)
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elif plottype == 5:
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H = axis.bar(args, data, *varargin, **kwds)
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elif plottype == 6:
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level = 0
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if np.isfinite(level):
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H = axis.fill_between(args, data, level, *varargin, **kwds)
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else:
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H = axis.fill_between(args, data, *varargin, **kwds)
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elif plottype == 7:
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H = axis.plot(args, data, *varargin, **kwds)
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H = axis.fill_between(
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args, dataCI[:, 0], dataCI[:, 1], alpha=0.2, color='r')
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scale = plotscale(plotflag)
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logXscale = 'x' in scale
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logYscale = 'y' in scale
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logZscale = 'z' in scale
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if logXscale:
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axis.set(xscale='log')
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if logYscale:
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axis.set(yscale='log')
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if logZscale:
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axis.set(zscale='log')
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transFlag = np.mod(plotflag // 10, 10)
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logScale = logXscale or logYscale or logZscale
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if logScale or (transFlag == 5 and not logScale):
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ax = list(axis.axis())
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fmax1 = data.max()
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if transFlag == 5 and not logScale:
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ax[3] = 11 * np.log10(fmax1)
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ax[2] = ax[3] - 40
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else:
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ax[3] = 1.15 * fmax1
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ax[2] = ax[3] * 1e-4
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axis.axis(ax)
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if np.any(dataCI) and plottype < 3:
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axis.hold(True)
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plot1d(axis, args, dataCI, (), plotflag, 'r--')
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return H
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def plotscale(plotflag):
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"""Return plotscale from plotflag.
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CALL scale = plotscale(plotflag)
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plotflag = integer defining plotscale.
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Let scaleId = floor(plotflag/100).
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If scaleId < 8 then:
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0 'linear' : Linear scale on all axes.
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1 'xlog' : Log scale on x-axis.
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2 'ylog' : Log scale on y-axis.
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3 'xylog' : Log scale on xy-axis.
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4 'zlog' : Log scale on z-axis.
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5 'xzlog' : Log scale on xz-axis.
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6 'yzlog' : Log scale on yz-axis.
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7 'xyzlog' : Log scale on xyz-axis.
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otherwise
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if (mod(scaleId,10)>0) : Log scale on x-axis.
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if (mod(floor(scaleId/10),10)>0) : Log scale on y-axis.
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if (mod(floor(scaleId/100),10)>0) : Log scale on z-axis.
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scale = string defining plotscale valid options are:
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'linear', 'xlog', 'ylog', 'xylog', 'zlog', 'xzlog',
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'yzlog', 'xyzlog'
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Example
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>>> for id in range(100,701,100):
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... plotscale(id)
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'xlog'
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'ylog'
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'xylog'
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'zlog'
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'xzlog'
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'yzlog'
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'xyzlog'
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>>> plotscale(200)
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'ylog'
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>>> plotscale(300)
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'xylog'
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>>> plotscale(300)
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'xylog'
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See also
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--------
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transformdata
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"""
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scaleId = plotflag // 100
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if scaleId > 7:
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logXscaleId = np.mod(scaleId, 10) > 0
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logYscaleId = (np.mod(scaleId // 10, 10) > 0) * 2
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logZscaleId = (np.mod(scaleId // 100, 10) > 0) * 4
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scaleId = logYscaleId + logXscaleId + logZscaleId
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scales = ['linear', 'xlog', 'ylog', 'xylog',
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'zlog', 'xzlog', 'yzlog', 'xyzlog']
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return scales[scaleId]
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def transformdata(x, f, plotflag):
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transFlag = np.mod(plotflag // 10, 10)
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if transFlag == 0:
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data = f
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elif transFlag == 1:
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data = 1 - f
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elif transFlag == 2:
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data = cumtrapz(f, x)
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elif transFlag == 3:
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data = 1 - cumtrapz(f, x)
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if transFlag in (4, 5):
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if transFlag == 4:
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data = -np.log1p(-cumtrapz(f, x))
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else:
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if any(f < 0):
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raise ValueError('Invalid plotflag: Data or dataCI is '
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'negative, but must be positive')
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data = 10 * np.log10(f)
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return data
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class Plotter_2d(Plotter_1d):
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"""
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Parameters
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----------
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plotmethod : string
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defining type of plot. Options are:
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contour (default)
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contourf
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mesh
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surf
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"""
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def __init__(self, plotmethod='contour'):
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if plotmethod is None:
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plotmethod = 'contour'
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super(Plotter_2d, self).__init__(plotmethod)
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def _plot(self, axis, plotflag, wdata, *args, **kwds):
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h1 = plot2d(axis, wdata, plotflag, *args, **kwds)
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return h1
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def plot2d(axis, wdata, plotflag, *args, **kwds):
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f = wdata
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if isinstance(wdata.args, (list, tuple)):
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args1 = tuple((wdata.args)) + (wdata.data,) + args
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else:
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args1 = tuple((wdata.args,)) + (wdata.data,) + args
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if plotflag in (1, 6, 7, 8, 9):
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isPL = False
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# check if contour levels is submitted
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if hasattr(f, 'clevels') and len(f.clevels) > 0:
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CL = f.clevels
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isPL = hasattr(f, 'plevels') and f.plevels is not None
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if isPL:
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PL = f.plevels # levels defines quantile levels? 0=no 1=yes
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else:
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dmax = np.max(f.data)
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dmin = np.min(f.data)
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CL = dmax - (dmax - dmin) * \
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(1 - np.r_[0.01, 0.025, 0.05, 0.1, 0.2, 0.4, 0.5, 0.75])
|
||||
clvec = np.sort(CL)
|
||||
|
||||
if plotflag in [1, 8, 9]:
|
||||
h = axis.contour(*args1, levels=CL, **kwds)
|
||||
# else:
|
||||
# [cs hcs] = contour3(f.x{:},f.f,CL,sym);
|
||||
|
||||
if plotflag in (1, 6):
|
||||
ncl = len(clvec)
|
||||
if ncl > 12:
|
||||
ncl = 12
|
||||
warnings.warn(
|
||||
'Only the first 12 levels will be listed in table.')
|
||||
|
||||
clvals = PL[:ncl] if isPL else clvec[:ncl]
|
||||
# print(contour level text)
|
||||
unused_axcl = cltext(clvals, percent=isPL)
|
||||
elif any(plotflag == [7, 9]):
|
||||
axis.clabel(h)
|
||||
else:
|
||||
axis.clabel(h)
|
||||
elif plotflag == 2:
|
||||
h = axis.mesh(*args1, **kwds)
|
||||
elif plotflag == 3:
|
||||
# shading interp % flat, faceted % surfc
|
||||
h = axis.surf(*args1, **kwds)
|
||||
elif plotflag == 4:
|
||||
h = axis.waterfall(*args1, **kwds)
|
||||
elif plotflag == 5:
|
||||
h = axis.pcolor(*args1, **kwds) # %shading interp % flat, faceted
|
||||
elif plotflag == 10:
|
||||
h = axis.contourf(*args1, **kwds)
|
||||
axis.clabel(h)
|
||||
plotbackend.colorbar(h)
|
||||
else:
|
||||
raise ValueError('unknown option for plotflag')
|
||||
# if any(plotflag==(2:5))
|
||||
# shading(shad);
|
||||
# end
|
||||
# pass
|
||||
|
||||
|
||||
def test_eval_points():
|
||||
plotbackend.ioff()
|
||||
x = np.linspace(0, 5, 21)
|
||||
d = PlotData(np.sin(x), x)
|
||||
xi = np.linspace(0, 5, 61)
|
||||
di = PlotData(d.eval_points(xi, method='cubic'), xi)
|
||||
d.plot('.')
|
||||
di.plot()
|
||||
di.show()
|
||||
|
||||
|
||||
def test_integrate():
|
||||
x = np.linspace(0, 5, 60)
|
||||
d = PlotData(np.sin(x), x)
|
||||
print(d.integrate(0, np.pi / 2, method='simps'))
|
||||
|
||||
|
||||
def test_docstrings():
|
||||
import doctest
|
||||
doctest.testmod()
|
||||
|
||||
|
||||
def main():
|
||||
pass
|
||||
|
||||
if __name__ == '__main__':
|
||||
|
||||
# test_integrate()
|
||||
# test_eval_points()
|
||||
test_docstrings()
|
||||
# main()
|
||||
Loading…
Reference in New Issue