# param.extend(y_param) # return param if __name__ == '__main__': ### conversions ### print("building a list of params(w,x,Y)...") pts = (2,2,2) param1 = [.5,.5,1,2, .25,.75,3,4, .125,.875,5,6, -1,-2,-3,-4,-5,-6,-7,-8] print("pts: %s" % str(pts)) print("params: %s" % param1) print("\nbuilding a scenario from the params...") # [store Y as 'values' OR register(F) for Y=F(X) OR points store y as 'val' ?] from mystic.math.discrete import scenario pm = scenario() pm.load(param1, pts) print("pm.wts: %s" % str(pm.wts)) print("pm.pos: %s" % str(pm.pos)) W = pm.weights X = pm.coords Y = pm.values print("pm.weights: %s" % str(W)) print("pm.coords: %s" % str(X)) print("pm.values: %s" % str(Y)) print("\nbuilding a dataset from the scenario...") # build a dataset (using X,Y) # [store W as 'weights' ?] from mystic.math.legacydata import dataset d = dataset()
# 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)
# 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