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617 lines
20 KiB
Python
617 lines
20 KiB
Python
import warnings
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from graphutil import cltext # @UnresolvedImport
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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 import interpolate
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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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'''
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Return current date and time as a string
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'''
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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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'''
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Container class for data with interpolation and plotting methods
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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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plot_args_children : list of arguments to the children plots
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plot_kwds_children : dict of keyword arguments to the children plots
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plot_args : list of arguments to the main plot
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plot_kwds : dict of keyword arguments to the main plot
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Member methods
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--------------
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copy : return a copy of object
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eval_points : interpolate data at given points and return the result
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plot : plot data on given axis and the object handles
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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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>>> h1 = d2()
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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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'''
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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 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 eval_points(self, *points, **kwds):
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'''
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Interpolate data at points
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Parameters
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----------
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points : ndarray of float, shape (..., ndim)
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Points where to interpolate data at.
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method : {'linear', 'nearest', 'cubic'}
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method : {'linear', 'nearest', 'cubic'}
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Method of interpolation. One of
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- ``nearest``: return the value at the data point closest to
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the point of interpolation.
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- ``linear``: tesselate the input point set to n-dimensional
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simplices, and interpolate linearly on each simplex.
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- ``cubic`` (1-D): return the value detemined from a cubic
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spline.
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- ``cubic`` (2-D): return the value determined from a
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piecewise cubic, continuously differentiable (C1), and
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approximately curvature-minimizing polynomial surface.
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fill_value : float, optional
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Value used to fill in for requested points outside of the
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convex hull of the input points. If not provided, then the
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default is ``nan``. This option has no effect for the
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'nearest' method.
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Examples
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--------
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>>> import numpy as np
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>>> x = np.arange(-2, 2, 0.4)
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>>> xi = np.arange(-2, 2, 0.1)
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>>> d = PlotData(np.sin(x), x, xlab='x', ylab='sin', title='sinus',
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... plot_args=['r.'])
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>>> di = PlotData(d.eval_points(xi), xi)
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>>> hi = di.plot()
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>>> h = d.plot()
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See also
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--------
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scipy.interpolate.griddata
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'''
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options = dict(method='linear')
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options.update(**kwds)
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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 = '''
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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 interpolation will not work!'''
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warnings.warn(msg)
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else:
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xi = np.meshgrid(*self.args)
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return interpolate.griddata(
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xi, self.data.ravel(), points, **options)
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else: # One dimensional data
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return interpolate.griddata(
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self.args, self.data, points, **options)
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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.dataCI = np.vstack((d.data*.9,d.data*1.1)).T
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>>> d.integrate(0,np.pi/2, return_ci=True)
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array([ 0.99940055, 0.85543644, 1.04553343])
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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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raise NotImplementedError('integration for ndim>1 not implemented')
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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
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# 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 interpolate.griddata(self.args, self.data.ravel(), **kwds)
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else: # One dimensional data
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return_ci = kwds.pop('return_ci', False)
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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),
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self.data.take(ix),
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self.eval_points(b)))
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res = fun(fi, xi, **kwds)
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if return_ci:
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return np.hstack(
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(res, fun(self.dataCI[ix, :].T, xi[1:-1], **kwds)))
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return res
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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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default_plotflag = self.plot_kwds.get('plotflag', None)
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plotflag = kwds.get('plotflag', default_plotflag)
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if not plotflag and self.children is not None:
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axis.hold('on')
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tmp = []
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child_args = kwds.pop(
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'plot_args_children',
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tuple(
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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(*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 setplotter(self, plotmethod=None):
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'''
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Set plotter based on the data type:
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data_1d, data_2d, data_3d or data_nd
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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 = '''
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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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def show(self, *args, **kwds):
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self.plotter.show(*args, **kwds)
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__call__ = plot
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interpolate = eval_points
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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' % (
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self.title, self.xlab, self.ylab, 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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h = []
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for fun, txt in zip(
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('set_title', 'set_xlabel', 'set_ylabel', 'set_ylabel'),
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(self.title, self.xlab, self.ylab, self.zlab)):
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if txt:
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if fun.startswith('set_title'):
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title0 = axis.get_title()
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if title0.lower().strip() != txt.lower().strip():
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txt = title0 + '\n' + txt
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h.append(getattr(axis, fun)(txt))
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return h
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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, *args, **kwds):
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plotbackend.show(*args, **kwds)
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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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__call__ = plot
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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(
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args,
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data,
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yerr=[
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dataCI[
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:,
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0] - data,
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dataCI[
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:,
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1] - data],
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*varargin,
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**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[
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:, 0], dataCI[
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:, 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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'''
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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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plotscale(100) % xlog
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plotscale(200) % xlog
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plotscale(1000) % ylog
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See also plotscale
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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 = [
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'linear',
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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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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])
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clvec = np.sort(CL)
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if plotflag in [1, 8, 9]:
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h = axis.contour(*args1, levels=CL, **kwds)
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# else:
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# [cs hcs] = contour3(f.x{:},f.f,CL,sym);
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if plotflag in (1, 6):
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ncl = len(clvec)
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if ncl > 12:
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ncl = 12
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warnings.warn(
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'Only the first 12 levels will be listed in table.')
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clvals = PL[:ncl] if isPL else clvec[:ncl]
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unused_axcl = cltext(
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clvals,
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percent=isPL) # print contour level text
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elif any(plotflag == [7, 9]):
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axis.clabel(h)
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else:
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axis.clabel(h)
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elif plotflag == 2:
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h = axis.mesh(*args1, **kwds)
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elif plotflag == 3:
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# shading interp % flat, faceted % surfc
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h = axis.surf(*args1, **kwds)
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elif plotflag == 4:
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h = axis.waterfall(*args1, **kwds)
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elif plotflag == 5:
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h = axis.pcolor(*args1, **kwds) # %shading interp % flat, faceted
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elif plotflag == 10:
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h = axis.contourf(*args1, **kwds)
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axis.clabel(h)
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plotbackend.colorbar(h)
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else:
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raise ValueError('unknown option for plotflag')
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# if any(plotflag==(2:5))
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# shading(shad);
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# end
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# pass
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def test_plotdata():
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plotbackend.ioff()
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x = np.arange(-2, 2, 0.4)
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xi = np.arange(-2, 2, 0.1)
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d = PlotData(np.sin(x), x, xlab='x', ylab='sin', title='sinus',
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plot_args=['r.'])
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di = PlotData(d.eval_points(xi, method='cubic'), xi)
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unused_hi = di.plot()
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unused_h = d.plot()
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d.show()
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def test_docstrings():
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import doctest
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print('Testing docstrings in %s' % __file__)
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doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE)
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def main():
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pass
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if __name__ == '__main__':
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test_docstrings()
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# test_plotdata()
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# main()
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