# 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)
# 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
