Changeset 809 for mystic

Timestamp:

07/25/15 15:16:13 (10 months ago)

Author:

mmckerns

Message:

added functional interface for scenario plotter

Location:

mystic

Files:

• mystic/support.py (modified) (4 diffs)
• scripts/support_hypercube_scenario.py (modified) (1 diff)

mystic/mystic/support.py

@@ -9 +9 @@
 """
 
-__all__ = ['convergence', 'hypercube', 'hypercube_measures']
+__all__ = ['convergence', 'hypercube', 'hypercube_measures', \
+           'hypercube_scenario', 'best_dimensions', 'swap']
 
 from mpl_toolkits.mplot3d import Axes3D as _Axes3D
@@ -25 +26 @@
 # globals
 __quit = False
+ZERO = 1.0e-6  # zero-ish
 
 
@@ -39 +41 @@
 #   return cand[len(cand)/2], cand[len(cand)/2]
 # return cand[len(cand)/2], cand[len(cand)/2 - 1]
+
+
+#### building the cone primitive ####
+def _cone_builder(slope, bounds, strict=True):
+  """ factory to create a cone primitive
+
+  slope -- slope multiplier for cone on the X,Y,Z axes (for mesh construction)
+  bounds -- list of tuples of bounds for the plot; (lower,upper) for each axis
+"""
+  from mystic.math import almostEqual
+  import numpy as np
+  def cone_mesh(length):
+    """ construct a conical mesh for a given length of cone """
+    L1,L2,L3 = slope
+    radius = length / L3 #XXX: * 0.5
+    r0 = ZERO
+
+    if almostEqual(radius, r0, tol=r0): radius = r0
+    r = np.linspace(radius,radius,6) 
+    r[0]= np.zeros(r[0].shape) 
+    r[1] *= r0/radius
+    r[5] *= r0/radius
+    r[3]= np.zeros(r[3].shape) 
+
+    p = np.linspace(0,2*np.pi,50) 
+    R,P = np.meshgrid(r,p) 
+    X,Y = L1 * R*np.cos(P), L2 * R*np.sin(P) 
+
+    tmp=list() 
+    for i in range(np.size(p)): 
+      tmp.append([0,0,length,length,length,0]) # len = size(r)
+    Z = np.array(tmp) 
+    return X,Z,Y
+
+  lb,ub = zip(*bounds)
+  # if False, the cone surface may violate bounds
+ #strict = True # always respect the bounds
+
+  def cone(position, top=True):
+    """ construct a cone primitive, with vertex at given position
+
+    position -- tuple of vertex position
+    top -- boolean for if 'top' or 'bottom' cone
+"""
+    from numpy import asarray
+    position = asarray(position)
+    d_hi = ub - position
+    d_lo = lb - position
+    if strict and (any(d_hi < 0) or any(d_lo > 0)):
+      return None  # don't plot cone if vertex is out of bounds
+
+    if top: # distance of vertex from upper edge
+      l = d_hi[2]
+      if not l: l = ZERO
+    else:   # distance of vertex from lower edge
+      l = d_lo[2]
+      if not l: l = -ZERO
+    X,Z,Y = cone_mesh(l)
+    X = X + position[0]
+    Y = Y + position[1]
+    Z = Z + position[2]
+    return X,Z,Y
+
+  return cone    
+
+
+#### plotting the cones ####
+def _plot_bowtie(ax, data, slope, bounds, color='0.75', axis=None, tol=0.0):
+  """plot (2D) double cones for a given dataset
+
+  ax -- matplotlib 'Axes3D' plot object
+  data -- list of datapoints, where datapoints are 3-tuples (i.e. x,y,z)
+  slope -- slope multiplier for cone on the X,Y,Z axes (for mesh construction)
+  bounds -- list of tuples of bounds for the plot; (lower,upper) for each axis
+  color -- string name (or rbg value) of color to use for datapoints
+  axis -- the axis of the cone
+  tol -- distance between center of mass of the double cones and a cone vertex
+"""
+  if axis not in range(len(bounds)-1): return ax
+  from numpy import asarray, inf
+  data = asarray(data)
+  sl = slope[axis]
+  gap = max(0., tol)
+  # find the endpoints of the cone:  y = slope * (x0 - x) + y0
+  ylo = sl * (bounds[0][0] - data.T[0]) + data.T[1]
+  yhi = sl * (bounds[0][1] - data.T[0]) + data.T[1]
+  zhi = -sl * (bounds[0][0] - data.T[0]) + data.T[1]
+  zlo = -sl * (bounds[0][1] - data.T[0]) + data.T[1]
+  xlb = bounds[0][0]
+  xub = bounds[0][1]
+  ylb = bounds[1][0]
+  yub = bounds[1][1]
+
+  for pt,yl,yu,zl,zu in zip(data,ylo,yhi,zlo,zhi):
+   #ax.plot([xlb, pt[0], xub], [yl, pt[1], yu], color='black')
+   #ax.plot([xlb, pt[0], xub], [zu, pt[1], zl], color='black')
+    ax.fill_between([xlb, pt[0], xub], [ylb]*3, [yl-gap, pt[1]-gap, zl-gap], \
+                                     facecolor=color, alpha=0.2)
+    ax.fill_between([xlb, pt[0], xub], [yub]*3, [zu+gap, pt[1]+gap, yu+gap], \
+                                     facecolor=color, alpha=0.2)
+  return ax
+
+
+def _plot_cones(ax, data, slope, bounds, color='0.75', axis=None, strict=True, tol=0.0):
+  """plot (3D) double cones for a given dataset
+
+  ax -- matplotlib 'Axes3D' plot object
+  data -- list of datapoints, where datapoints are 3-tuples (i.e. x,y,z)
+  slope -- slope multiplier for cone on the X,Y,Z axes (for mesh construction)
+  bounds -- list of tuples of bounds for the plot; (lower,upper) for each axis
+  color -- string name (or rbg value) of color to use for datapoints
+  axis -- the axis of the cone
+  tol -- distance between center of mass of the double cones and a cone vertex
+"""
+  from numpy import asarray, zeros
+  # adjust slope, bounds, and data so cone axis is last 
+  slope = swap(slope, axis) 
+  bounds = swap(bounds, axis) 
+  data = [swap(pt,axis) for pt in data]
+  cone = _cone_builder(slope, bounds, strict=strict)
+  # prepare to apply the 'gap' tolerance
+  gap = zeros(len(slope))
+  gap[-1:] = [max(0., tol)] #XXX: bad behavior if len(slope) is 0
+  # plot the upper and lower cone
+  for datapt in data:
+    datapt = asarray(datapt)
+    _cone = cone(datapt + gap, top=True)
+    if _cone:
+      X,Z,Y = swap(_cone, axis) # 'unswap' the axes
+      ax.plot_surface(X, Y,Z, rstride=1, cstride=1, color=color, \
+                                         linewidths=0, alpha=.1)
+    _cone = cone(datapt - gap, top=False)
+    if _cone:
+      X,Z,Y = swap(_cone, axis) # 'unswap' the axes
+      ax.plot_surface(X, Y,Z, rstride=1, cstride=1, color=color, \
+                                         linewidths=0, alpha=.1)
+  return ax
+
+
+def _plot_data(ax, data, bounds, color='red', strict=True, **kwds):
+  """plot datapoints for a given dataset
+
+  ax -- matplotlib 'Axes3D' plot object
+  data -- list of datapoints, where datapoints are 3-tuples (i.e. x,y,z)
+  bounds -- list of tuples of bounds for the plot; (lower,upper) for each axis
+  color -- string name (or rbg value) of color to use for datapoints
+"""
+  _2D = kwds.get('_2D', False)
+# strict = True # always respect the bounds
+  lb,ub = zip(*bounds)
+  # plot the datapoints themselves
+  from numpy import asarray
+  for datapt in data:
+    position = asarray(datapt)
+    d_hi = ub - position
+    d_lo = lb - position
+    if strict and (any(d_hi < 0) or any(d_lo > 0)): #XXX: any or just in Y ?
+      pass  # don't plot if datapt is out of bounds
+    else:
+      if _2D:
+        ax.plot([datapt[0]], [datapt[1]], \
+                color=color, marker='o', markersize=10)
+      else:
+        ax.plot([datapt[0]], [datapt[1]], [datapt[2]], \
+                color=color, marker='o', markersize=10)
+  return ax
+
+
+def _clip_axes(ax, bounds):
+  """ clip plots to be within given lower and upper bounds
+
+  ax -- matplotlib 'Axes3D' plot object
+  bounds -- list of tuples of bounds for the plot; (lower,upper) for each axis
+"""
+  lb,ub = zip(*bounds)
+  # plot only within [lb,ub]
+  ax.set_xlim3d(lb[0], ub[0])
+  ax.set_ylim3d(lb[1], ub[1])
+  ax.set_zlim3d(lb[2], ub[2]) # cone "center" axis
+  return ax
+
+
+def _label_axes(ax, labels, **kwds):
+  """ label plots with given string labels
+
+  ax -- matplotlib 'Axes3D' plot object
+  labels -- list of string labels for the plot
+"""
+  _2D = kwds.get('_2D', False)
+  ax.set_xlabel(labels[0])
+  ax.set_ylabel(labels[1])
+  if not _2D:
+    ax.set_zlabel(labels[2]) # cone "center" axis
+  return ax
+
+
+def _get_slope(data, replace=None, mask=None):
+  """ replace one slope in a list of slopes with '1.0'
+  (i.e. replace a slope from the dataset with the unit slope)
+
+  data -- dataset object, where coordinates are 3-tuples and values are floats
+  replace -- selected axis (an int) to plot values NOT coords
+  """
+  slope = data.lipschitz
+  if mask in range(len(slope)):
+    slope = swap(slope, mask)
+  if replace not in range(len(slope)):  # don't replace an axis
+    return slope
+  return slope[:replace] + [1.0] + slope[replace+1:]
+
+
+def _get_coords(data, replace=None, mask=None):
+  """ replace one coordiate axis in a 3-D data set with 'data values'
+  (i.e. replace an 'x' axis with the 'y' values of the data)
+
+  data -- dataset object, where coordinates are 3-tuples and values are floats
+  replace -- selected axis (an int) to plot values NOT coords
+  """
+  slope = data.lipschitz
+  coords = data.coords
+  values = data.values
+  if mask in range(len(slope)):
+    coords = [swap(pt,mask) for pt in coords]
+  if replace not in range(len(slope)):  # don't replace an axis
+    return coords
+  return [list(coords[i][:replace]) + [values[i]] + \
+          list(coords[i][replace+1:]) for i in range(len(coords))]
+
+
+def swap(alist, index=None):
+  """ swap the selected list element with the last element in alist
+
+  alist -- a list of objects
+  index -- the selected element
+  """
+  if index not in range(len(alist)):  # don't swap an element
+    return alist 
+  return alist[:index] + alist[index+1:] + alist[index:index+1]
 
 
@@ -839 +1079 @@
 
 
+def hypercube_scenario(filename, datafile=None, **kwds):
+    """
+generate scenario support plots from file written with 'write_support_file';
+generate legacy data and cones from a dataset file, if provided
+
+Available from the command shell as:
+  support_hypercube_scenario.py filename (datafile) [options]
+
+or as a function call as:
+  mystic.support.hypercube_scenario(filename, datafile=None, **options)
+
+The options "bounds", "dim", and "iters" all take indicator strings.
+The bounds should be given as comma-separated slices. For example, using
+bounds = ".062:.125, 0:30, 2300:3200" will set the lower and upper bounds
+for x to be (.062,.125), y to be (0,30), and z to be (2300,3200). If all
+bounds are to not be strictly enforced, append an asterisk '*' to the string. 
+The dim (dimensions of the scenario) should comma-separated ints. For example,
+dim = "1, 1, 2" will convert the params to a two-member 3-D dataset. Iters
+accepts a string built from comma-separated array slices. For example,
+iters = ":" will plot all iters in a single plot. Alternatively,
+iters = ":2, 2:" will split the iters into two plots, while iters = "0"
+will only plot the first iteration.
+
+The option "label" takes comma-separated strings. For example, label = "x,y,"
+will place 'x' on the x-axis, 'y' on the y-axis, and nothing on the z-axis.
+LaTeX is also accepted. For example, label = "$ h $, $ {\\alpha}$, $ v$" will
+label the axes with standard LaTeX math formatting. Note that the leading
+space is required, while a trailing space aligns the text with the axis
+instead of the plot frame. The option "filter" is used to select datapoints
+from a given dataset, and takes comma-separated ints. A "mask" is given as
+comma-separated ints; when the mask has more than one int, the plot will be
+2D. The option "vertical" will plot the dataset values on the vertical axis;
+for 2D plots, cones are always plotted on the vertical axis.
+
+Required Inputs:
+  filename            name of the python convergence logfile (e.g. paramlog.py)
+
+Additional Inputs:
+  datafile            name of the dataset textfile (e.g. StAlDataset.txt)
+"""
+    import shlex
+    global __quit
+    __quit = False
+
+    # handle the special case where list is provided by sys.argv
+    if isinstance(filename, (list,tuple)) and not kwds:
+        cmdargs = filename # (above is used by script to parse command line)
+    elif isinstance(filename, basestring) and not kwds:
+        cmdargs = shlex.split(filename)
+    # 'everything else' is essentially the functional interface
+    else:
+        out = kwds.get('out', None)
+        bounds = kwds.get('bounds', None)
+        iters = kwds.get('iters', None)
+        label = kwds.get('label', None)
+        dim = kwds.get('dim', None)
+        filter = kwds.get('filter', None)
+        mask = kwds.get('mask', None)
+        nid = kwds.get('nid', None)
+        scale = kwds.get('scale', None)
+        gap = kwds.get('gap', None)
+        data = kwds.get('data', False)
+        cones = kwds.get('cones', False)
+        vertical = kwds.get('vertical', False)
+        flat = kwds.get('flat', False)
+
+        # process "commandline" arguments
+        cmdargs = ''
+        cmdargs += '' if out is None else '--out={} '.format(out)
+        cmdargs += '' if bounds is None else '--bounds="{}" '.format(bounds)
+        cmdargs += '' if iters is None else '--iters="{}" '.format(iters)
+        cmdargs += '' if label is None else '--label="{}" '.format(label)
+        cmdargs += '' if dim is None else '--dim="{}" '.format(dim)
+        cmdargs += '' if filter is None else '--filter="{}" '.format(filter)
+        cmdargs += '' if mask is None else '--mask={} '.format(mask)
+        cmdargs += '' if nid is None else '--nid={} '.format(nid)
+        cmdargs += '' if scale is None else '--scale={} '.format(scale)
+        cmdargs += '' if gap is None else '--gap={} '.format(gap)
+        cmdargs += '' if data == False else '--data '
+        cmdargs += '' if cones == False else '--cones '
+        cmdargs += '' if vertical == False else '--vertical '
+        cmdargs += '' if flat == False else '--flat '
+        cmdargs = filename.split() + shlex.split(cmdargs)
+
+    #XXX: note that 'argparse' is new as of python2.7
+    from optparse import OptionParser
+    def _exit(self, **kwds):
+        global __quit
+        __quit = True
+    OptionParser.exit = _exit
+
+    parser = OptionParser(usage=hypercube_scenario.__doc__.split('\n\nOptions:')[0])
+    parser.add_option("-u","--out",action="store",dest="out",\
+                      metavar="STR",default=None,
+                      help="filepath to save generated plot")
+    parser.add_option("-b","--bounds",action="store",dest="bounds",\
+                      metavar="STR",default="0:1, 0:1, 0:1",
+                      help="indicator string to set hypercube bounds")
+    parser.add_option("-i","--iters",action="store",dest="iters",\
+                      metavar="STR",default="-1",
+                      help="indicator string to select iterations to plot")
+    parser.add_option("-l","--label",action="store",dest="label",\
+                      metavar="STR",default=",,",
+                      help="string to assign label to axis")
+    parser.add_option("-d","--dim",action="store",dest="dim",\
+                      metavar="STR",default="None",
+                      help="indicator string set to scenario dimensions")
+    parser.add_option("-p","--filter",action="store",dest="filter",\
+                      metavar="STR",default="None",
+                      help="filter to select datapoints to plot")
+    parser.add_option("-m","--mask",action="store",dest="replace",\
+                      metavar="INT",default=None,
+                      help="id # of the coordinate axis not to be plotted")
+    parser.add_option("-n","--nid",action="store",dest="id",\
+                      metavar="INT",default=None,
+                      help="id # of the nth simultaneous points to plot")
+    parser.add_option("-s","--scale",action="store",dest="scale",\
+                      metavar="INT",default=1.0,
+                      help="grayscale contrast multiplier for points in plot")
+    parser.add_option("-g","--gap",action="store",dest="gap",\
+                      metavar="INT",default=0.0,
+                      help="distance from double cone center to cone vertex")
+    parser.add_option("-o","--data",action="store_true",dest="legacy",\
+                      default=False,help="plot legacy data, if provided")
+    parser.add_option("-v","--cones",action="store_true",dest="cones",\
+                      default=False,help="plot cones, if provided")
+    parser.add_option("-z","--vertical",action="store_true",dest="vertical",\
+                      default=False,help="always plot values on vertical axis")
+    parser.add_option("-f","--flat",action="store_true",dest="flatten",\
+                      default=False,help="show selected iterations in a single plot")
+    parsed_opts, parsed_args = parser.parse_args(cmdargs)
+
+    from StringIO import StringIO
+    f = StringIO()
+    parser.print_help(file=f)
+    f.seek(0)
+    if 'Options:' not in hypercube_scenario.__doc__:
+        hypercube_scenario.__doc__ += '\nOptions:%s' % f.read().split('Options:')[-1]
+    f.close()
+
+    if __quit: return
+
+    # get the name of the parameter log file
+    params, _cost = read_history(parsed_args[0])
+    # would be nice to use meta = ['wx','wx2','x','x2','wy',...]
+    # exec "from %s import meta" % file
+
+    from mystic.math.discrete import scenario
+    from mystic.math.legacydata import dataset
+    try: # select whether to plot the cones
+        cones = parsed_opts.cones
+    except:
+        cones = False
+
+    try: # select whether to plot the legacy data
+        legacy = parsed_opts.legacy
+    except:
+        legacy = False
+
+    try: # get dataset filter
+        filter = parsed_opts.filter
+        if "None" == filter: filter = None
+        else: filter = [int(i) for i in filter.split(",")] # format is "1,5,9"
+    except:
+        filter = None
+
+    try: # select the scenario dimensions
+        npts = parsed_opts.dim
+        if "None" == npts: # npts may have been logged
+            from mystic.munge import read_import
+            npts = read_import(parsed_args[0], 'npts')
+        else: npts = tuple(int(i) for i in dim.split(",")) # format is "1,1,1"
+    except:
+        npts = (1,1,1) #XXX: better in parsed_args ?
+
+    try: # get the name of the dataset file
+        file = parsed_args[1]
+        from mystic.math.legacydata import load_dataset
+        data = load_dataset(file, filter)
+    except:
+#       raise IOError, "please provide dataset file name"
+        data = dataset()
+        cones = False
+        legacy = False
+
+    try: # select the bounds
+        _bounds = parsed_opts.bounds
+        if _bounds[-1] == "*": #XXX: then bounds are NOT strictly enforced
+            _bounds = _bounds[:-1]
+            strict = False
+        else:
+            strict = True
+        bounds = _bounds.split(",")  # format is "60:105, 0:30, 2.1:2.8"
+        bounds = [tuple(float(j) for j in i.split(':')) for i in bounds]
+    except:
+        strict = True
+        bounds = [(0,1),(0,1),(0,1)]
+
+    try: # select labels for the axes
+        label = parsed_opts.label.split(',')  # format is "x, y, z"
+    except:
+        label = ['','','']
+
+    x = params[-1]
+    try: # select which iterations to plot
+        select = parsed_opts.iters.split(',')  # format is ":2, 2:4, 5, 6:"
+    except:
+        select = ['-1']
+       #select = [':']
+       #select = [':1']
+       #select = [':2','2:']
+       #select = [':1','1:2','2:3','3:']
+       #select = ['0','1','2','3']
+
+    try: # collapse non-consecutive iterations into a single plot...
+        flatten = parsed_opts.flatten
+    except:
+        flatten = False
+
+    try: # select which 'id' to plot results for
+        id = int(parsed_opts.id)
+    except:
+        id = None # i.e. 'all' **or** use id=0, which should be 'best' energy ?
+
+    try: # scale the color in plotting the weights
+        scale = float(parsed_opts.scale)
+    except:
+        scale = 1.0 # color = color**scale
+
+    try: # gap between double cone center and cone vertex
+        gap = float(parsed_opts.gap)
+    except:
+        gap = 0.0
+
+    _2D = False # if False, use 3D plots; if True, use 3D plots
+    cs = None
+    try: # select which axis to plot 'values' on  (3D plot)
+        xs = int(parsed_opts.replace)
+    except:
+        try: # select which axes to mask (2D plot)
+            xs = (int(i) for i in parsed_opts.replace.split(",")) # format is "1,2"
+            xs = list(reversed(sorted(set(xs))))
+            cs = int(xs[-1]) if xs[-1] != xs[0] else None
+            xs = int(xs[0])
+            xs,cs = cs,xs # cs will swap coord axes; xs will swap with value axis
+            _2D = True #NOTE: always apply cs swap before xs swap
+        except:
+            xs = None # don't plot values; plot values on 'x' axis with xs = 0
+
+    try: # always plot cones on vertical axis
+        vertical_cones = parsed_opts.vertical
+    except:
+        vertical_cones = False
+    if _2D: # always plot cones on vertical axis
+        vertical_cones = True
+
+    # ensure all terms of bounds are tuples
+    for bound in bounds:
+        if not isinstance(bound, tuple):
+            raise TypeError, "bounds should be tuples of (lower_bound,upper_bound)"
+
+    # ensure all terms of select are strings that have a ":"
+    for i in range(len(select)):
+        if isinstance(select[i], int): select[i] = str(select[i])
+        if select[i] == '-1': select[i] = 'len(x)-1:len(x)'
+        elif not select[i].count(':'):
+            select[i] += ':' + str(int(select[i])+1)
+
+    # take only the selected 'id'
+    if id != None:
+        param = []
+        for j in range(len(params)):
+            param.append([p[id] for p in params[j]])
+        params = param[:]
+
+    # at this point, we should have:
+    #bounds = [(60,105),(0,30),(2.1,2.8)] or [(None,None)]*3
+    #select = ['-1:'] or [':'] or [':1','1:2','2:3','3:'] or similar
+    #id = 0 or None
+
+    # get dataset coords (and values) for selected axes
+    if data:
+        slope = _get_slope(data, xs, cs)
+        coords = _get_coords(data, xs, cs)
+        #print "bounds: %s" % bounds
+        #print "slope: %s" % slope
+        #print "coords: %s" % coords
+   #else:
+   #    slope = []
+   #    coords = []
+
+    plots = len(select)
+    if not flatten:
+        dim1,dim2 = best_dimensions(plots)
+    else: dim1,dim2 = 1,1
+
+    # use the default bounds where not specified
+    bounds = [list(i) for i in bounds]
+    for i in range(len(bounds)):
+        if bounds[i][0] is None: bounds[i][0] = 0
+        if bounds[i][1] is None: bounds[i][1] = 1
+
+    # swap the axes appropriately when plotting cones (when replacing an axis)
+    if _2D and xs == 0:
+        if data:
+            slope[0],slope[1] = slope[1],slope[0]
+            coords = [list(reversed(pt[:2]))+pt[2:] for pt in coords]
+       #bounds[0],bounds[1] = bounds[1],bounds[0]
+       #label[0],label[1] = label[1],label[0]
+        axis = xs #None
+    elif not _2D and vertical_cones and xs in range(len(bounds)):
+        # adjust slope, bounds, and data so cone axis is last 
+        if data:
+            slope = swap(slope, xs) 
+            coords = [swap(pt,xs) for pt in coords]
+        bounds = swap(bounds, xs) 
+        label = swap(label, xs)
+        axis = None
+    else:
+        axis = xs
+
+    # correctly bound the first plot.  there must be at least one plot
+    fig = plt.figure() 
+    if _2D:
+        ax1 = fig.add_subplot(dim1,dim2,1)
+        ax1.plot([bounds[0][0]],[bounds[1][0]])
+        ax1.plot([bounds[0][1]],[bounds[1][1]])
+    else:
+        ax1 = Subplot3D(fig, dim1,dim2,1)
+        ax1.plot([bounds[0][0]],[bounds[1][0]],[bounds[2][0]])
+        ax1.plot([bounds[0][1]],[bounds[1][1]],[bounds[2][1]])
+    globals = {'plt':plt,'fig':fig,'dim1':dim1,'dim2':dim2,\
+               'Subplot3D':Subplot3D,'bounds':bounds,'label':label}
+    if not flatten:
+        code = "plt.title('iterations[%s]');" % select[0]
+    else: 
+        code = "plt.title('iterations[*]');"
+    code = compile(code, '<string>', 'exec')
+    exec code in globals
+    if cones and data and xs in range(len(bounds)):
+        if _2D:
+            _plot_bowtie(ax1,coords,slope,bounds,axis=axis,tol=gap)
+        else:
+            _plot_cones(ax1,coords,slope,bounds,axis=axis,strict=strict,tol=gap)
+    if legacy and data:
+        _plot_data(ax1,coords,bounds,strict=strict,_2D=_2D)
+   #_clip_axes(ax1,bounds)
+    _label_axes(ax1,label,_2D=_2D)
+    a = [ax1]
+
+    # set up additional plots
+    if not flatten:
+        for i in range(2, plots + 1):
+            if _2D:
+                code = "ax%d = ax = fig.add_subplot(dim1,dim2,%d);" % (i,i)
+                code += "ax.plot([bounds[0][0]],[bounds[1][0]]);"
+                code += "ax.plot([bounds[0][1]],[bounds[1][1]]);"
+            else:
+                code = "ax%d = ax = Subplot3D(fig, dim1,dim2,%d);" % (i,i)
+                code += "ax.plot([bounds[0][0]],[bounds[1][0]],[bounds[2][0]]);"
+                code += "ax.plot([bounds[0][1]],[bounds[1][1]],[bounds[2][1]]);"
+            code += "plt.title('iterations[%s]');" % select[i - 1]
+            code = compile(code, '<string>', 'exec')
+            exec code in globals
+            ax = globals['ax']
+            if cones and data and xs in range(len(bounds)):
+                if _2D:
+                    _plot_bowtie(ax,coords,slope,bounds,axis=axis,tol=gap)
+                else:
+                    _plot_cones(ax,coords,slope,bounds,axis=axis,strict=strict,tol=gap)
+            if legacy and data:
+                _plot_data(ax,coords,bounds,strict=strict,_2D=_2D)
+           #_clip_axes(ax,bounds)
+            _label_axes(ax,label,_2D=_2D)
+            a.append(ax)
+
+    # turn each "n:m" in select to a list
+    _select = []
+    for sel in select:
+        if sel[0] == ':': _select.append("0"+sel)
+        else: _select.append(sel)
+    for i in range(len(_select)):
+        if _select[i][-1] == ':': select[i] = _select[i]+str(len(x))
+        else: select[i] = _select[i]
+    for i in range(len(select)):
+        p = select[i].split(":")
+        if p[0][0] == '-': p[0] = "len(x)"+p[0]
+        if p[1][0] == '-': p[1] = "len(x)"+p[1]
+        select[i] = p[0]+":"+p[1]
+    steps = [eval("range(%s)" % sel.replace(":",",")) for sel in select]
+
+    # at this point, we should have:
+    #steps = [[0,1],[1,2],[2,3],[3,4,5,6,7,8]] or similar
+    if flatten:
+        from mystic.tools import flatten
+        steps = [list(flatten(steps))]
+
+    # plot all the scenario "data"
+    from numpy import inf, e
+    scale = e**(scale - 1.0)
+    for v in range(len(steps)):
+        if len(steps[v]) > 1: qp = float(max(steps[v]))
+        else: qp = inf
+        for s in steps[v]:
+            par = eval("[params[q][%s][0] for q in range(len(params))]" % s)
+            pm = scenario()
+            pm.load(par, npts)
+            d = dataset()
+            d.load(pm.coords, pm.values)
+            # dot color determined by number of simultaneous iterations
+            t = str((s/qp)**scale)
+            # get and plot dataset coords for selected axes      
+            _coords = _get_coords(d, xs, cs)
+            # check if we are replacing an axis
+            if _2D and xs == 0:
+                if data: # adjust data so cone axis is last 
+                    _coords = [list(reversed(pt[:2]))+pt[2:] for pt in _coords]
+            elif not _2D and vertical_cones and xs in range(len(bounds)):
+                if data: # adjust data so cone axis is last 
+                    _coords = [swap(pt,xs) for pt in _coords]
+            _plot_data(a[v], _coords, bounds, color=t, strict=strict, _2D=_2D)
+
+    if _2D: # strict = True
+        plt.xlim((bounds[0][0],bounds[0][1]))
+        plt.ylim((bounds[1][0],bounds[1][1]))
+
+    if not parsed_opts.out:
+        plt.show()
+    else:
+        fig.savefig(parsed_opts.out)
+
+
 if __name__ == '__main__':
     pass

mystic/scripts/support_hypercube_scenario.py

@@ -6 +6 @@
 #  - http://mmckerns.github.io/project/mystic/browser/mystic/LICENSE
 
-__doc__ = """
-support_hypercube_scenario.py [options] filename (datafile)
+from mystic.support import hypercube_scenario
 
-generate scenario support plots from file written with 'write_support_file';
-generate legacy data and cones from a dataset file, if provided
+__doc__ = hypercube_scenario.__doc__
 
-The options "bounds", "dim", and "iters" all take indicator strings.
-The bounds should be given as comma-separated slices. For example, using
-bounds = ".062:.125, 0:30, 2300:3200" will set the lower and upper bounds
-for x to be (.062,.125), y to be (0,30), and z to be (2300,3200). If all
-bounds are to not be strictly enforced, append an asterisk '*' to the string. 
-The dim (dimensions of the scenario) should comma-separated ints. For example,
-dim = "1, 1, 2" will convert the params to a two-member 3-D dataset. Iters
-accepts a string built from comma-separated array slices. For example,
-iters = ":" will plot all iters in a single plot. Alternatively,
-iters = ":2, 2:" will split the iters into two plots, while iters = "0"
-will only plot the first iteration.
+if __name__ == '__main__':
 
-The option "label" takes comma-separated strings. For example, label = "x,y,"
-will place 'x' on the x-axis, 'y' on the y-axis, and nothing on the z-axis.
-LaTeX is also accepted. For example, label = "$ h $, $ {\\alpha}$, $ v$" will
-label the axes with standard LaTeX math formatting. Note that the leading
-space is required, while a trailing space aligns the text with the axis
-instead of the plot frame. The option "filter" is used to select datapoints
-from a given dataset, and takes comma-separated ints. A "mask" is given as
-comma-separated ints; when the mask has more than one int, the plot will be
-2D. The option "vertical" will plot the dataset values on the vertical axis;
-for 2D plots, cones are always plotted on the vertical axis.
+    import sys
 
-Required Inputs:
-  filename            name of the python convergence logfile (e.g. paramlog.py)
-
-Additional Inputs:
-  datafile            name of the dataset textfile (e.g. StAlDataset.txt)
-"""
-ZERO = 1.0e-6  # zero-ish
-
-#### building the cone primitive ####
-def cone_builder(slope, bounds, strict=True):
-  """ factory to create a cone primitive
-
-  slope -- slope multiplier for cone on the X,Y,Z axes (for mesh construction)
-  bounds -- list of tuples of bounds for the plot; (lower,upper) for each axis
-"""
-  from mystic.math import almostEqual
-  import numpy as np
-  def cone_mesh(length):
-    """ construct a conical mesh for a given length of cone """
-    L1,L2,L3 = slope
-    radius = length / L3 #XXX: * 0.5
-    r0 = ZERO
-
-    if almostEqual(radius, r0, tol=r0): radius = r0
-    r = np.linspace(radius,radius,6) 
-    r[0]= np.zeros(r[0].shape) 
-    r[1] *= r0/radius
-    r[5] *= r0/radius
-    r[3]= np.zeros(r[3].shape) 
-
-    p = np.linspace(0,2*np.pi,50) 
-    R,P = np.meshgrid(r,p) 
-    X,Y = L1 * R*np.cos(P), L2 * R*np.sin(P) 
-
-    tmp=list() 
-    for i in range(np.size(p)): 
-      tmp.append([0,0,length,length,length,0]) # len = size(r)
-    Z = np.array(tmp) 
-    return X,Z,Y
-
-  lb,ub = zip(*bounds)
-  # if False, the cone surface may violate bounds
- #strict = True # always respect the bounds
-
-  def cone(position, top=True):
-    """ construct a cone primitive, with vertex at given position
-
-    position -- tuple of vertex position
-    top -- boolean for if 'top' or 'bottom' cone
-"""
-    from numpy import asarray
-    position = asarray(position)
-    d_hi = ub - position
-    d_lo = lb - position
-    if strict and (any(d_hi < 0) or any(d_lo > 0)):
-      return None  # don't plot cone if vertex is out of bounds
-
-    if top: # distance of vertex from upper edge
-      l = d_hi[2]
-      if not l: l = ZERO
-    else:   # distance of vertex from lower edge
-      l = d_lo[2]
-      if not l: l = -ZERO
-    X,Z,Y = cone_mesh(l)
-    X = X + position[0]
-    Y = Y + position[1]
-    Z = Z + position[2]
-    return X,Z,Y
-
-  return cone    
+    hypercube_scenario(sys.argv[1:])
 
 
-#### plotting the cones ####
-def plot_bowtie(ax, data, slope, bounds, color='0.75', axis=None, tol=0.0):
-  """plot (2D) double cones for a given dataset
-
-  ax -- matplotlib 'Axes3D' plot object
-  data -- list of datapoints, where datapoints are 3-tuples (i.e. x,y,z)
-  slope -- slope multiplier for cone on the X,Y,Z axes (for mesh construction)
-  bounds -- list of tuples of bounds for the plot; (lower,upper) for each axis
-  color -- string name (or rbg value) of color to use for datapoints
-  axis -- the axis of the cone
-  tol -- distance between center of mass of the double cones and a cone vertex
-"""
-  if axis not in range(len(bounds)-1): return ax
-  from numpy import asarray, inf
-  data = asarray(data)
-  sl = slope[axis]
-  gap = max(0., tol)
-  # find the endpoints of the cone:  y = slope * (x0 - x) + y0
-  ylo = sl * (bounds[0][0] - data.T[0]) + data.T[1]
-  yhi = sl * (bounds[0][1] - data.T[0]) + data.T[1]
-  zhi = -sl * (bounds[0][0] - data.T[0]) + data.T[1]
-  zlo = -sl * (bounds[0][1] - data.T[0]) + data.T[1]
-  xlb = bounds[0][0]
-  xub = bounds[0][1]
-  ylb = bounds[1][0]
-  yub = bounds[1][1]
-
-  for pt,yl,yu,zl,zu in zip(data,ylo,yhi,zlo,zhi):
-   #ax.plot([xlb, pt[0], xub], [yl, pt[1], yu], color='black')
-   #ax.plot([xlb, pt[0], xub], [zu, pt[1], zl], color='black')
-    ax.fill_between([xlb, pt[0], xub], [ylb]*3, [yl-gap, pt[1]-gap, zl-gap], \
-                                     facecolor=color, alpha=0.2)
-    ax.fill_between([xlb, pt[0], xub], [yub]*3, [zu+gap, pt[1]+gap, yu+gap], \
-                                     facecolor=color, alpha=0.2)
-  return ax
-
-def plot_cones(ax, data, slope, bounds, color='0.75', axis=None, strict=True, tol=0.0):
-  """plot (3D) double cones for a given dataset
-
-  ax -- matplotlib 'Axes3D' plot object
-  data -- list of datapoints, where datapoints are 3-tuples (i.e. x,y,z)
-  slope -- slope multiplier for cone on the X,Y,Z axes (for mesh construction)
-  bounds -- list of tuples of bounds for the plot; (lower,upper) for each axis
-  color -- string name (or rbg value) of color to use for datapoints
-  axis -- the axis of the cone
-  tol -- distance between center of mass of the double cones and a cone vertex
-"""
-  from numpy import asarray, zeros
-  # adjust slope, bounds, and data so cone axis is last 
-  slope = swap(slope, axis) 
-  bounds = swap(bounds, axis) 
-  data = [swap(pt,axis) for pt in data]
-  cone = cone_builder(slope, bounds, strict=strict)
-  # prepare to apply the 'gap' tolerance
-  gap = zeros(len(slope))
-  gap[-1:] = [max(0., tol)] #XXX: bad behavior if len(slope) is 0
-  # plot the upper and lower cone
-  for datapt in data:
-    datapt = asarray(datapt)
-    _cone = cone(datapt + gap, top=True)
-    if _cone:
-      X,Z,Y = swap(_cone, axis) # 'unswap' the axes
-      ax.plot_surface(X, Y,Z, rstride=1, cstride=1, color=color, \
-                                         linewidths=0, alpha=.1)
-    _cone = cone(datapt - gap, top=False)
-    if _cone:
-      X,Z,Y = swap(_cone, axis) # 'unswap' the axes
-      ax.plot_surface(X, Y,Z, rstride=1, cstride=1, color=color, \
-                                         linewidths=0, alpha=.1)
-  return ax
-
-def plot_data(ax, data, bounds, color='red', strict=True):
-  """plot datapoints for a given dataset
-
-  ax -- matplotlib 'Axes3D' plot object
-  data -- list of datapoints, where datapoints are 3-tuples (i.e. x,y,z)
-  bounds -- list of tuples of bounds for the plot; (lower,upper) for each axis
-  color -- string name (or rbg value) of color to use for datapoints
-"""
-# strict = True # always respect the bounds
-  lb,ub = zip(*bounds)
-  # plot the datapoints themselves
-  from numpy import asarray
-  for datapt in data:
-    position = asarray(datapt)
-    d_hi = ub - position
-    d_lo = lb - position
-    if strict and (any(d_hi < 0) or any(d_lo > 0)): #XXX: any or just in Y ?
-      pass  # don't plot if datapt is out of bounds
-    else:
-      if _2D:
-        ax.plot([datapt[0]], [datapt[1]], \
-                color=color, marker='o', markersize=10)
-      else:
-        ax.plot([datapt[0]], [datapt[1]], [datapt[2]], \
-                color=color, marker='o', markersize=10)
-  return ax
-
-def clip_axes(ax, bounds):
-  """ clip plots to be within given lower and upper bounds
-
-  ax -- matplotlib 'Axes3D' plot object
-  bounds -- list of tuples of bounds for the plot; (lower,upper) for each axis
-"""
-  lb,ub = zip(*bounds)
-  # plot only within [lb,ub]
-  ax.set_xlim3d(lb[0], ub[0])
-  ax.set_ylim3d(lb[1], ub[1])
-  ax.set_zlim3d(lb[2], ub[2]) # cone "center" axis
-  return ax
-
-def label_axes(ax, labels):
-  """ label plots with given string labels
-
-  ax -- matplotlib 'Axes3D' plot object
-  labels -- list of string labels for the plot
-"""
-  ax.set_xlabel(labels[0])
-  ax.set_ylabel(labels[1])
-  if not _2D:
-    ax.set_zlabel(labels[2]) # cone "center" axis
-  return ax
-
-def get_slope(data, replace=None, mask=None):
-  """ replace one slope in a list of slopes with '1.0'
-  (i.e. replace a slope from the dataset with the unit slope)
-
-  data -- dataset object, where coordinates are 3-tuples and values are floats
-  replace -- selected axis (an int) to plot values NOT coords
-  """
-  slope = data.lipschitz
-  if mask in range(len(slope)):
-    slope = swap(slope, mask)
-  if replace not in range(len(slope)):  # don't replace an axis
-    return slope
-  return slope[:replace] + [1.0] + slope[replace+1:]
-
-def get_coords(data, replace=None, mask=None):
-  """ replace one coordiate axis in a 3-D data set with 'data values'
-  (i.e. replace an 'x' axis with the 'y' values of the data)
-
-  data -- dataset object, where coordinates are 3-tuples and values are floats
-  replace -- selected axis (an int) to plot values NOT coords
-  """
-  slope = data.lipschitz
-  coords = data.coords
-  values = data.values
-  if mask in range(len(slope)):
-    coords = [swap(pt,mask) for pt in coords]
-  if replace not in range(len(slope)):  # don't replace an axis
-    return coords
-  return [list(coords[i][:replace]) + [values[i]] + \
-          list(coords[i][replace+1:]) for i in range(len(coords))]
-
-def swap(alist, index=None):
-  """ swap the selected list element with the last element in alist
-
-  alist -- a list of objects
-  index -- the selected element
-  """
-  if index not in range(len(alist)):  # don't swap an element
-    return alist 
-  return alist[:index] + alist[index+1:] + alist[index:index+1]
-
-from support_convergence import best_dimensions
-
-
-if __name__ == '__main__':
-  #XXX: note that 'argparse' is new as of python2.7
-  from optparse import OptionParser
-  parser = OptionParser(usage=__doc__)
-  parser.add_option("-b","--bounds",action="store",dest="bounds",\
-                    metavar="STR",default="0:1, 0:1, 0:1",
-                    help="indicator string to set hypercube bounds")
-  parser.add_option("-i","--iters",action="store",dest="iters",\
-                    metavar="STR",default="-1",
-                    help="indicator string to select iterations to plot")
-  parser.add_option("-l","--label",action="store",dest="label",\
-                    metavar="STR",default=",,",
-                    help="string to assign label to axis")
-  parser.add_option("-d","--dim",action="store",dest="dim",\
-                    metavar="STR",default="None",
-                    help="indicator string set to scenario dimensions")
-  parser.add_option("-p","--filter",action="store",dest="filter",\
-                    metavar="STR",default="None",
-                    help="filter to select datapoints to plot")
-  parser.add_option("-m","--mask",action="store",dest="replace",\
-                    metavar="INT",default=None,
-                    help="id # of the coordinate axis not to be plotted")
-  parser.add_option("-n","--nid",action="store",dest="id",\
-                    metavar="INT",default=None,
-                    help="id # of the nth simultaneous points to plot")
-  parser.add_option("-s","--scale",action="store",dest="scale",\
-                    metavar="INT",default=1.0,
-                    help="grayscale contrast multiplier for points in plot")
-  parser.add_option("-g","--gap",action="store",dest="gap",\
-                    metavar="INT",default=0.0,
-                    help="distance from double cone center to cone vertex")
-  parser.add_option("-o","--data",action="store_true",dest="legacy",\
-                    default=False,help="plot legacy data, if provided")
-  parser.add_option("-v","--cones",action="store_true",dest="cones",\
-                    default=False,help="plot cones, if provided")
-  parser.add_option("-z","--vertical",action="store_true",dest="vertical",\
-                    default=False,help="always plot values on vertical axis")
-  parser.add_option("-f","--flat",action="store_true",dest="flatten",\
-                    default=False,help="show selected iterations in a single plot")
-  parsed_opts, parsed_args = parser.parse_args()
-
-  # get the name of the parameter log file
-  from mystic.munge import read_history
-  params, _cost = read_history(parsed_args[0])
-  # would be nice to use meta = ['wx','wx2','x','x2','wy',...]
-  # exec "from %s import meta" % file
-
-  from mystic.math.discrete import scenario
-  from mystic.math.legacydata import dataset
-  try: # select whether to plot the cones
-    cones = parsed_opts.cones
-  except:
-    cones = False
-
-  try: # select whether to plot the legacy data
-    legacy = parsed_opts.legacy
-  except:
-    legacy = False
-
-  try: # get dataset filter
-    filter = parsed_opts.filter
-    if "None" == filter: filter = None
-    else: filter = [int(i) for i in filter.split(",")] # format is "1,5,9"
-  except:
-    filter = None
-
-  try: # select the scenario dimensions
-    npts = parsed_opts.dim
-    if "None" == npts: # npts may have been logged
-      from mystic.munge import read_import
-      npts = read_import(parsed_args[0], 'npts')
-    else: npts = tuple(int(i) for i in dim.split(",")) # format is "1,1,1"
-  except:
-    npts = (1,1,1) #XXX: better in parsed_args ?
-
-  try: # get the name of the dataset file
-    file = parsed_args[1]
-    from mystic.math.legacydata import load_dataset
-    data = load_dataset(file, filter)
-  except:
-#   raise IOError, "please provide dataset file name"
-    data = dataset()
-    cones = False
-    legacy = False
-
-  try: # select the bounds
-    _bounds = parsed_opts.bounds
-    if _bounds[-1] == "*": #XXX: then bounds are NOT strictly enforced
-      _bounds = _bounds[:-1]
-      strict = False
-    else:
-      strict = True
-    bounds = _bounds.split(",")  # format is "60:105, 0:30, 2.1:2.8"
-    bounds = [tuple(float(j) for j in i.split(':')) for i in bounds]
-  except:
-    strict = True
-    bounds = [(0,1),(0,1),(0,1)]
-
-  try: # select labels for the axes
-    label = parsed_opts.label.split(',')  # format is "x, y, z"
-  except:
-    label = ['','','']
-
-  x = params[-1]
-  try: # select which iterations to plot
-    select = parsed_opts.iters.split(',')  # format is ":2, 2:4, 5, 6:"
-  except:
-    select = ['-1']
-   #select = [':']
-   #select = [':1']
-   #select = [':2','2:']
-   #select = [':1','1:2','2:3','3:']
-   #select = ['0','1','2','3']
-
-  try: # collapse non-consecutive iterations into a single plot...
-    flatten = parsed_opts.flatten
-  except:
-    flatten = False
-
-  try: # select which 'id' to plot results for
-    id = int(parsed_opts.id)
-  except:
-    id = None # i.e. 'all' **or** use id=0, which should be 'best' energy ?
-
-  try: # scale the color in plotting the weights
-    scale = float(parsed_opts.scale)
-  except:
-    scale = 1.0 # color = color**scale
-
-  try: # gap between double cone center and cone vertex
-    gap = float(parsed_opts.gap)
-  except:
-    gap = 0.0
-
-  _2D = False # if False, use 3D plots; if True, use 3D plots
-  _2D = False # if False, use 3D plots; if True, use 3D plots
-  cs = None
-  try: # select which axis to plot 'values' on  (3D plot)
-    xs = int(parsed_opts.replace)
-  except:
-    try: # select which axes to mask (2D plot)
-      xs = (int(i) for i in parsed_opts.replace.split(",")) # format is "1,2"
-      xs = list(reversed(sorted(set(xs))))
-      cs = int(xs[-1]) if xs[-1] != xs[0] else None
-      xs = int(xs[0])
-      xs,cs = cs,xs # cs will swap coord axes; xs will swap with value axis
-      _2D = True #NOTE: always apply cs swap before xs swap
-    except:
-      xs = None # don't plot values; plot values on 'x' axis with xs = 0
-
-  try: # always plot cones on vertical axis
-    vertical_cones = parsed_opts.vertical
-  except:
-    vertical_cones = False
-  if _2D: # always plot cones on vertical axis
-    vertical_cones = True
-
-  # ensure all terms of bounds are tuples
-  for bound in bounds:
-    if not isinstance(bound, tuple):
-      raise TypeError, "bounds should be tuples of (lower_bound,upper_bound)"
-
-  # ensure all terms of select are strings that have a ":"
-  for i in range(len(select)):
-    if isinstance(select[i], int): select[i] = str(select[i])
-    if select[i] == '-1': select[i] = 'len(x)-1:len(x)'
-    elif not select[i].count(':'):
-      select[i] += ':' + str(int(select[i])+1)
-
-  # take only the selected 'id'
-  if id != None:
-    param = []
-    for j in range(len(params)):
-      param.append([p[id] for p in params[j]])
-    params = param[:]
-
-  # at this point, we should have:
-  #bounds = [(60,105),(0,30),(2.1,2.8)] or [(None,None),(None,None),(None,None)]
-  #select = ['-1:'] or [':'] or [':1','1:2','2:3','3:'] or similar
-  #id = 0 or None
-
-  # get dataset coords (and values) for selected axes
-  if data:
-    slope = get_slope(data, xs, cs)
-    coords = get_coords(data, xs, cs)
-    #print "bounds: %s" % bounds
-    #print "slope: %s" % slope
-    #print "coords: %s" % coords
- #else:
- #  slope = []
- #  coords = []
-
-  import matplotlib.pyplot as plt
-  if not _2D:
-    from mpl_toolkits.mplot3d import Axes3D
-    from matplotlib.axes import subplot_class_factory
-    Subplot3D = subplot_class_factory(Axes3D)
-
-  plots = len(select)
-  if not flatten:
-    dim1,dim2 = best_dimensions(plots)
-  else: dim1,dim2 = 1,1
-
-  # use the default bounds where not specified
-  bounds = [list(i) for i in bounds]
-  for i in range(len(bounds)):
-    if bounds[i][0] is None: bounds[i][0] = 0
-    if bounds[i][1] is None: bounds[i][1] = 1
-
-  # swap the axes appropriately when plotting cones (when replacing an axis)
-  if _2D and xs == 0:
-    if data:
-      slope[0],slope[1] = slope[1],slope[0]
-      coords = [list(reversed(pt[:2]))+pt[2:] for pt in coords]
-   #bounds[0],bounds[1] = bounds[1],bounds[0]
-   #label[0],label[1] = label[1],label[0]
-    axis = xs #None
-  elif not _2D and vertical_cones and xs in range(len(bounds)):
-    # adjust slope, bounds, and data so cone axis is last 
-    if data:
-      slope = swap(slope, xs) 
-      coords = [swap(pt,xs) for pt in coords]
-    bounds = swap(bounds, xs) 
-    label = swap(label, xs)
-    axis = None
-  else:
-    axis = xs
-
-  # correctly bound the first plot.  there must be at least one plot
-  fig = plt.figure() 
-  if _2D:
-    ax1 = fig.add_subplot(dim1,dim2,1)
-    ax1.plot([bounds[0][0]],[bounds[1][0]])
-    ax1.plot([bounds[0][1]],[bounds[1][1]])
-  else:
-    ax1 = Subplot3D(fig, dim1,dim2,1)
-    ax1.plot([bounds[0][0]],[bounds[1][0]],[bounds[2][0]])
-    ax1.plot([bounds[0][1]],[bounds[1][1]],[bounds[2][1]])
-  if not flatten:
-    exec "plt.title('iterations[%s]')" % select[0]
-  else:
-    exec "plt.title('iterations[*]')"
-  if cones and data and xs in range(len(bounds)):
-    if _2D:
-      plot_bowtie(ax1,coords,slope,bounds,axis=axis,tol=gap)
-    else:
-      plot_cones(ax1,coords,slope,bounds,axis=axis,strict=strict,tol=gap)
-  if legacy and data:
-    plot_data(ax1,coords,bounds,strict=strict)
- #clip_axes(ax1,bounds)
-  label_axes(ax1,label)
-  a = [ax1]
-
-  # set up additional plots
-  if not flatten:
-    for i in range(2, plots + 1):
-      if _2D:
-        exec "ax%d = fig.add_subplot(dim1,dim2,%d)" % (i,i)
-        exec "ax%d.plot([bounds[0][0]],[bounds[1][0]])" % i
-        exec "ax%d.plot([bounds[0][1]],[bounds[1][1]])" % i
-      else:
-        exec "ax%d = Subplot3D(fig, dim1,dim2,%d)" % (i,i)
-        exec "ax%d.plot([bounds[0][0]],[bounds[1][0]],[bounds[2][0]])" % i
-        exec "ax%d.plot([bounds[0][1]],[bounds[1][1]],[bounds[2][1]])" % i
-      exec "plt.title('iterations[%s]')" % select[i - 1]
-      if cones and data and xs in range(len(bounds)):
-        if _2D:
-          exec "plot_bowtie(ax%d,coords,slope,bounds,axis=axis,tol=gap)" % i
-        else:
-          exec "plot_cones(ax%d,coords,slope,bounds,axis=axis,strict=strict,tol=gap)" % i
-      if legacy and data:
-        exec "plot_data(ax%d,coords,bounds,strict=strict)" % i
-     #exec "clip_axes(ax%d,bounds)" % i
-      exec "label_axes(ax%d,label)" % i
-      exec "a.append(ax%d)" % i
-
-  # turn each "n:m" in select to a list
-  _select = []
-  for sel in select:
-    if sel[0] == ':': _select.append("0"+sel)
-    else: _select.append(sel)
-  for i in range(len(_select)):
-    if _select[i][-1] == ':': select[i] = _select[i]+str(len(x))
-    else: select[i] = _select[i]
-  for i in range(len(select)):
-    p = select[i].split(":")
-    if p[0][0] == '-': p[0] = "len(x)"+p[0]
-    if p[1][0] == '-': p[1] = "len(x)"+p[1]
-    select[i] = p[0]+":"+p[1]
-  steps = [eval("range(%s)" % sel.replace(":",",")) for sel in select]
-
-  # at this point, we should have:
-  #steps = [[0,1],[1,2],[2,3],[3,4,5,6,7,8]] or similar
-  if flatten:
-    from mystic.tools import flatten
-    steps = [list(flatten(steps))]
-
-  # plot all the scenario "data"
-  from numpy import inf, e
-  scale = e**(scale - 1.0)
-  for v in range(len(steps)):
-    if len(steps[v]) > 1: qp = float(max(steps[v]))
-    else: qp = inf
-    for s in steps[v]:
-      par = eval("[params[q][%s][0] for q in range(len(params))]" % s)
-      pm = scenario()
-      pm.load(par, npts)
-      d = dataset()
-      d.load(pm.coords, pm.values)
-      # dot color determined by number of simultaneous iterations
-      t = str((s/qp)**scale)
-      # get and plot dataset coords for selected axes      
-      _coords = get_coords(d, xs, cs)
-      # check if we are replacing an axis
-      if _2D and xs == 0:
-        if data: # adjust data so cone axis is last 
-          _coords = [list(reversed(pt[:2]))+pt[2:] for pt in _coords]
-      elif not _2D and vertical_cones and xs in range(len(bounds)):
-        if data: # adjust data so cone axis is last 
-          _coords = [swap(pt,xs) for pt in _coords]
-      plot_data(a[v], _coords, bounds, color=t, strict=strict)
-
-  if _2D: # strict = True
-    plt.xlim((bounds[0][0],bounds[0][1]))
-    plt.ylim((bounds[1][0],bounds[1][1]))
-  plt.show() 
-
 # EOF