def test_sir_lin_pca_strat():
NUM_STEPS = 70
SIR_PCA_ITER_STEPS = 1 #Number of steps between each recomputation of PCA Templates.
'PCA Strategy Parameters'
SIR_PCA_TRAJ_STEPS = 1 #Number of steps our sample trajectories should run.
SIR_PCA_NUM_TRAJ = 100 #Number of sample trajectories we should use for the PCA routine.
SIR_LIN_ITER_STEPS = 1
#
SIR_PCA_LIFE_SPAN = 3
sir_pca = SIR_UnitBox(delta=0.5)
sir_plot = Plot()
points = [[0.79,0.19,0], [0.79, 0.2,0], [0.8,0.19,0], [0.8,0.2,0], [0.79,0.195,0], [0.8,0.195,0], [0.795,0.19,0], [0.795,0.2,0]]
trajs = [Traj(sir_pca, point, NUM_STEPS) for point in points]
pca_strat = MultiStrategy(LinStrat(sir_pca, iter_steps=SIR_LIN_ITER_STEPS), \
DelayedPCAStrat(sir_pca, traj_steps=SIR_PCA_TRAJ_STEPS, num_trajs=SIR_PCA_NUM_TRAJ, life_span=SIR_PCA_LIFE_SPAN))
sir_pca_reach = ReachSet(sir_pca)
sir_flow_pca = sir_pca_reach.computeReachSet(NUM_STEPS, tempstrat=pca_strat)
sir_plot.add(sir_flow_pca, "SIR_LinApp&PCA")
'Add trajectories'
for traj in trajs:
sir_plot.add(traj)
# sir_plot.plot2DPhase(0,1,separate=False, plotvertices=True)
sir_plot.plot2DPhase(1,2,separate=False, plotvertices=True)
sir_plot.plot2DPhase(0,2,separate=False, plotvertices=True)
Timer.generate_stats()
def test_ani_pca_lin_VDP():
NUM_STEPS = 70
VDP_LIN_ITER_STEPS = 1 #Number of steps between each recomputation of LinApp Templates.
VDP_PCA_ITER_STEPS = 1 #Number of steps between each recomputation of PCA Templates.
'PCA Strategy Parameters'
VDP_PCA_TRAJ_STEPS = 2 #Number of steps our sample trajectories should run.
VDP_PCA_NUM_TRAJ = 200 #Number of sample trajectories we should use for the PCA routine.
VDP_LIN_DELAY = 2
VDP_PCA_DELAY = 5
model = VanDerPol(delta=0.08)
unit_model = VanDerPol_UnitBox(delta=0.08)
lin_1 = LinStrat(unit_model, iter_steps=VDP_LIN_ITER_STEPS)
lin_2 = LinStrat(unit_model, iter_steps=VDP_LIN_ITER_STEPS + VDP_LIN_DELAY)
pca_1 = PCAStrat(unit_model,
traj_steps=VDP_PCA_TRAJ_STEPS,
num_trajs=VDP_PCA_NUM_TRAJ,
iter_steps=VDP_PCA_ITER_STEPS)
pca_2 = PCAStrat(unit_model,
traj_steps=VDP_PCA_TRAJ_STEPS,
num_trajs=VDP_PCA_NUM_TRAJ,
iter_steps=VDP_PCA_ITER_STEPS + VDP_PCA_DELAY)
lin_strat = MultiStrategy(lin_1, lin_2, pca_1, pca_2)
inputs = ExperimentInput(unit_model, strat=lin_strat, label="VDP Kaa")
vdp_pca = Animation(inputs)
vdp_pca.execute(NUM_STEPS)
vdp_pca.animate(0, 1, lin_1, lin_2)
#vdp_pca.animate(0,1, lin_2)
#vdp_pca.animate(0,1, pca_2)
Timer.generate_stats()
def test_pca_lin_Rossler():
NUM_STEPS = 5
ROSS_PCA_ITER_STEPS = 1 #Number of steps between each recomputation of PCA Templates.
'PCA Strategy Parameters'
ROSS_PCA_TRAJ_STEPS = 1 #Number of steps our sample trajectories should run.
ROSS_PCA_NUM_TRAJ = 200 #Number of sample trajectories we should use for the PCA routine.
rossler_pca = Rossler_UnitBox(delta=0.5)
rossler_plot = Plot()
points = [[0.05,4.95,0.05], [0.1,4.95,0.05], [0.05,5,0.05], [0.1,5,0.05], [0.05,4.95,0.05], [0.05,4.95,0.1], [0.1,4.95,0.1], [0.1,5,0.1]]
trajs = [Traj(rossler_pca, point, NUM_STEPS) for point in points]
pca_strat = PCALinStrat(rossler_pca, traj_steps=ROSS_PCA_TRAJ_STEPS, num_trajs=ROSS_PCA_NUM_TRAJ, iter_steps=ROSS_PCA_ITER_STEPS)
ross_pca_reach = ReachSet(rossler_pca)
ross_flow_pca = ross_pca_reach.computeReachSet(NUM_STEPS, tempstrat=pca_strat)
rossler_plot.add(ross_flow_pca, "SIR_LinApp&PCA")
'Add trajectories'
for traj in trajs:
rossler_plot.add(traj)
rossler_plot.plot2DPhase(0,1,separate=True, plotvertices=True)
Timer.generate_stats()
def test_lin_HarOsc():
NUM_STEPS = 5
model = HarOsc()
#trajs = generate_traj(model, 10, 200)
mod_reach = ReachSet(model)
#mod_flow = mod_reach.computeReachSet()
sir_plot = Plot()
#mod_flow = mod_reach.computeReachSet(NUM_STEPS)
SIR_LIN_ITER_STEPS = 1 #Number of steps between each recomputation of PCA Templates.
lin_strat = LinStrat(model, iter_steps=SIR_LIN_ITER_STEPS)
mod_lin_flow = mod_reach.computeReachSet(NUM_STEPS, tempstrat=lin_strat, transmode=BundleMode.AFO)
trajs = [Traj(model, point, steps=NUM_STEPS) for point in product([-5,-4],[0,1])]
'Generaste the trajectories and add them to the plot.'
sir_plot.add(mod_lin_flow, "HarOsc LINAPP")
for t in trajs:
sir_plot.add(t)
sir_plot.plot2DPhase(0,1, separate=True, plotvertices=True)
Timer.generate_stats()
def test_pca_VDP():
NUM_STEPS = 3
model = VanDerPol(delta=0.08)
unit_model = VanDerPol_UnitBox(delta=0.08)
VDP_PCA_ITER_STEPS = 1 #Number of steps between each recomputation of PCA Templates.
'PCA Strategy Parameters'
VDP_PCA_TRAJ_STEPS = 5 #Number of steps our sample trajectories should run.
VDP_PCA_NUM_TRAJ = 50 #Number of sample trajectories we should use for the PCA routine.
VDP_PCA_DELAY = 5
pca_dirs = GeneratedPCADirs(model, VDP_PCA_NUM_TRAJ, NUM_STEPS)
pca_strat = MultiStrategy(PCAStrat(unit_model, traj_steps=VDP_PCA_TRAJ_STEPS, num_trajs=VDP_PCA_NUM_TRAJ, iter_steps=VDP_PCA_ITER_STEPS, pca_dirs=pca_dirs), \
PCAStrat(unit_model, traj_steps=VDP_PCA_TRAJ_STEPS, num_trajs=VDP_PCA_NUM_TRAJ, iter_steps=VDP_PCA_ITER_STEPS+VDP_PCA_DELAY, pca_dirs=pca_dirs), \
PCAStrat(unit_model, traj_steps=VDP_PCA_TRAJ_STEPS, num_trajs=VDP_PCA_NUM_TRAJ, iter_steps=VDP_PCA_ITER_STEPS+2*VDP_PCA_DELAY, pca_dirs=pca_dirs))
inputs = [
ExperimentInput(model, label="VDP Sapo"),
ExperimentInput(unit_model, strat=pca_strat, label="VDP Kaa PCA")
]
vdp_pca = PhasePlotExperiment(inputs)
vdp_pca.execute(NUM_STEPS)
vdp_pca.plot_results(0, 1)
Timer.generate_stats()
def test_pca_HarOsc():
NUM_STEPS = 4
model = HarOsc()
#trajs = generate_traj(model, 10, 200)
mod_reach = ReachSet(model)
#mod_flow = mod_reach.computeReachSet()
sir_plot = Plot()
SIR_PCA_ITER_STEPS = 1 #Number of steps between each recomputation of PCA Templates.
'PCA Strategy Parameters'
SIR_PCA_TRAJ_STEPS = 2 #Number of steps our sample trajectories should run.
SIR_PCA_NUM_TRAJ = 100 #Number of sample trajectories we should use for the PCA routine.
pca_strat = PCAStrat(model, traj_steps=SIR_PCA_TRAJ_STEPS, num_trajs=SIR_PCA_NUM_TRAJ, iter_steps=SIR_PCA_ITER_STEPS)
mod_pca_flow = mod_reach.computeReachSet(NUM_STEPS, tempstrat=[pca_strat], transmode=BundleMode.AFO)
#trajs = generate_traj(model, 10, 200)
'Generaste the trajectories and add them to the plot.'
sir_plot.add(mod_pca_flow, "HarOsc PCA")
sir_plot.plot2DPhase(0,1, separate=True, plotvertices=True)
Timer.generate_stats()
def test_pca_lin_VDP():
NUM_STEPS = 70
VDP_LIN_ITER_STEPS = 1 #Number of steps between each recomputation of LinApp Templates.
VDP_PCA_ITER_STEPS = 1 #Number of steps between each recomputation of PCA Templates.
'PCA Strategy Parameters'
VDP_PCA_TRAJ_STEPS = 2 #Number of steps our sample trajectories should run.
VDP_PCA_NUM_TRAJ = 200 #Number of sample trajectories we should use for the PCA routine.
VDP_LIN_DELAY = 2
VDP_PCA_DELAY = 5
model = VanDerPol(delta=0.08)
unit_model = VanDerPol_UnitBox(delta=0.08)
lin_strat = MultiStrategy(LinStrat(unit_model, iter_steps=VDP_LIN_ITER_STEPS), \
LinStrat(unit_model, iter_steps=VDP_LIN_ITER_STEPS+VDP_LIN_DELAY), \
PCAStrat(unit_model, traj_steps=VDP_PCA_TRAJ_STEPS, num_trajs=VDP_PCA_NUM_TRAJ, iter_steps=VDP_PCA_ITER_STEPS), \
PCAStrat(unit_model, traj_steps=VDP_PCA_TRAJ_STEPS, num_trajs=VDP_PCA_NUM_TRAJ, iter_steps=VDP_PCA_ITER_STEPS+VDP_PCA_DELAY))
inputs = [
ExperimentInput(model, label="VDP Sapo"),
ExperimentInput(unit_model, strat=lin_strat, label="VDP Kaa")
]
vdp_pca = PhasePlotExperiment(inputs)
vdp_pca.execute(NUM_STEPS)
vdp_pca.plot_results(0, 1)
Timer.generate_stats()
def test_sir_pca_strat():
#Compute Sapo's version.3
sir_pca = SIR_UnitBox(delta=0.5)
sir = SIR(delta=0.5)
sir_reach = ReachSet(sir)
sir_flow = sir_reach.computeReachSet(NUM_STEPS)
sir_plot = Plot()
sir_plot.add(sir_flow, "SIR SAPO")
for i in range(ITER_SPREAD, ITER_SPREAD + 1):
print(
colored("Generating PCA with Iterative Step Size: {}".format(i),
"white",
attrs=['reverse', 'blink']))
sir_pca_reach = ReachSet(sir_pca)
sir_flow_pca = sir_pca_reach.computeReachSet(
NUM_STEPS,
tempstrat=PCAStrat(sir_pca, iter_steps=i),
transmode=BundleMode.AFO)
sir_plot.add(sir_flow_pca, "SIR_PCA_{}".format(i))
sir_plot.plot2DPhase(0, 1, separate=False, plotvertices=True)
#sir_plot.plot(0,1,2)
Timer.generate_stats()
"""
def test_sir_lin_strat():
#Compute Sapo's version.
sir_lin = SIR_UnitBox(delta=0.5)
sir = SIR()
#sir_reach = ReachSet(sir)
#sir_flow = sir_reach.computeReachSet(NUM_STEPS)
sir_plot = Plot()
#sir_plot.add(sir_flow)
for i in range(10, 11):
print(
colored(
"Generating Lin_Approx with Iterative Step Size: {}".format(i),
"white",
attrs=['reverse', 'blink']))
sir_lin_reach = ReachSet(sir_lin)
sir_flow_lin = sir_lin_reach.computeReachSet(
NUM_STEPS, LinStrat(sir_lin, iter_steps=i))
sir_plot.add(sir_flow_lin, "SIR_LIN_{}".format(i))
#sir_plot.plot(0,1,2)
sir_plot.plot2DPhase(0, 1, separate=True)
Timer.generate_stats()
"""
def test_LL():
model = LL()
mod_reach = ReachSet(model)
mod_flow = mod_reach.computeReachSet(150)
FlowPipePlotter(mod_flow).plot2DProj(3)
Timer.generate_stats()
def test_Quad():
model = Quadcopter()
mod_reach = ReachSet(model)
mod_flow = mod_reach.computeReachSet(10)
FlowPipePlotter(mod_flow).plot2DProj(2)
FlowPipePlotter(mod_flow).plot2DProj(5)
FlowPipePlotter(mod_flow).plot2DProj(13)
Timer.generate_stats()
def test_VDP():
NUM_STEPS = 1
model = VanDerPol(delta=0.08)
vdp_sapo = PhasePlotExperiment([ExperimentInput(model, label="VDP Sapo")])
vdp_sapo.execute(NUM_STEPS)
vdp_sapo.plot_results(0, 1)
Timer.generate_stats()
def test_basic2():
basic_mod = Basic2()
basic_reach = ReachSet(basic_mod)
flowpipe = basic_reach.computeReachSet(300)
basic_plot = Plot()
basic_plot.add(flowpipe)
basic_plot.plot(0)
Timer.generate_stats()
def test_rossler_phase():
model = Rossler()
mod_reach = ReachSet(model)
mod_flow = mod_reach.computeReachSet(200)
rossler_plot = Plot()
rossler_plot.add(mod_flow)
rossler_plot.plot2DPhase(0,1)
Timer.generate_stats()
def test_Quad():
model = Quadcopter()
mod_reach = ReachSet(model)
mod_flow = mod_reach.computeReachSet(3)
quad_plot = Plot()
quad_plot.add(mod_flow)
quad_plot.plot(2, 5, 13)
Timer.generate_stats()
def test_Rossler():
model = Rossler()
mod_reach = ReachSet(model)
mod_flow = mod_reach.computeReachSet(300)
rossler_plot = Plot()
rossler_plot.add(mod_flow)
rossler_plot.plot(0,1,2)
Timer.generate_stats()
def test_LV():
model = LotkaVolterra()
mod_reach = ReachSet(model)
mod_flow = mod_reach.computeReachSet(100)
plot = Plot()
plot.add(mod_flow)
plot.plot(0,1,2)
Timer.generate_stats()
def test_LL():
model = LL()
mod_reach = ReachSet(model)
mod_flow = mod_reach.computeReachSet(150)
ll_plot = Plot()
ll_plot.add(mod_flow)
ll_plot.plot(0)
Timer.generate_stats()
def test_SIR():
model = SIR()
mod_reach = ReachSet(model)
mod_flow = mod_reach.computeReachSet(200)
FlowPipePlotter(mod_flow).plot2DProj(0)
FlowPipePlotter(mod_flow).plot2DProj(1)
FlowPipePlotter(mod_flow).plot2DProj(2)
Timer.generate_stats()
def test_Phos():
model = Phosphorelay()
#unit_model = Phosphorelay_UnitBox()
mod_reach = ReachSet(model)
#mod_unit_reach = ReachSet(unit_model)
#unit_flow = mod_unit_reach.computeReachSet(200)
mod_flow = mod_reach.computeReachSet(30)
phos_plot = Plot()
phos_plot.add(mod_flow)
phos_plot.plot2DPhase(0, 1, separate=False, plotvertices=True)
Timer.generate_stats()
def test_OscPart():
model = OscPart()
#trajs = generate_traj(model, 10, 200)
mod_reach = ReachSet(model)
mod_flow = mod_reach.computeReachSet(20)
sir_plot = Plot()
#trajs = generate_traj(model, 10, 200)
'Generaste the trajectories and add them to the plot.'
sir_plot.add(mod_flow)
sir_plot.plot(0, 1, 2)
Timer.generate_stats()
def pca_lin_comp():
sir = SIR_UnitBox()
sir_reach = ReachSet(sir)
sir_flow = sir_reach.computeReachSet(NUM_STEPS)
sir_plot = Plot()
sir_plot.add(sir_flow)
sir_lin_flow = sir_reach.computeReachSet(NUM_STEPS,
LinStrat(sir, iter_steps=150))
#sir_pca_flow = sir_reach.computeReachSet(NUM_STEPS, PCAStrat(sir, iter_steps=50))
sir_plot.add(sir_lin_flow, "SIR_LIN")
#sir_plot.add(sir_pca_flow, "SIR_PCA")
sir_plot.plot(0, 1, 2)
Timer.generate_stats()
def test_SIR():
model = SIR(delta=0.5)
model_unit = SIR_UnitBox()
#trajs = generate_traj(model, 10, 200)
mod_reach = ReachSet(model)
mod_unit_reach = ReachSet(model_unit)
mod_flow = mod_reach.computeReachSet(70)
#mod_unit_flow = mod_unit_reach.computeReachSet(300)
sir_plot = Plot()
#trajs = generate_traj(model, 10, 200)
'Generaste the trajectories and add them to the plot.'
#for traj in trajs:
# sir_plot.add(traj)
sir_plot.add(mod_flow)
#sir_plot.add(mod_unit_flow)
sir_plot.plot2DPhase(0,1)
Timer.generate_stats()
def test_sir_cut_strat():
#Compute Sapo's version.
sir_cut = SIR()
sir = SIR_UnitBox()
sir_reach = ReachSet(sir)
sir_flow = sir_reach.computeReachSet(NUM_STEPS)
sir_plot = Plot()
sir_plot.add(sir_flow)
sir_cut_reach = ReachSet(sir_cut)
sir_cut_flow = sir_cut_reach.computeReachSet(NUM_STEPS, CutStrat(sir_cut))
sir_sapo_flow = sir_cut_reach.computeReachSet(NUM_STEPS)
sir_plot.add(sir_cut_flow, "SIR_CUT_150")
sir_plot.add(sir_sapo_flow, "SIR_SAPO")
sir_plot.plot(0, 1, 2)
Timer.generate_stats()
def test_ani_pca_VDP():
NUM_STEPS = 70
#model = VanDerPol(delta=0.08)
unit_model = VanDerPol_UnitBox(delta=0.08)
VDP_PCA_ITER_STEPS = 1 #Number of steps between each recomputation of PCA Templates.
'PCA Strategy Parameters'
VDP_PCA_TRAJ_STEPS = 5 #Number of steps our sample trajectories should run.
VDP_PCA_NUM_TRAJ = 100 #Number of sample trajectories we should use for the PCA routine.
VDP_PCA_DELAY = 5
pca_1 = PCAStrat(unit_model,
traj_steps=VDP_PCA_TRAJ_STEPS,
num_trajs=VDP_PCA_NUM_TRAJ,
iter_steps=VDP_PCA_ITER_STEPS)
pca_2 = PCAStrat(unit_model,
traj_steps=VDP_PCA_TRAJ_STEPS,
num_trajs=VDP_PCA_NUM_TRAJ,
iter_steps=VDP_PCA_ITER_STEPS + VDP_PCA_DELAY)
pca_3 = PCAStrat(unit_model,
traj_steps=VDP_PCA_TRAJ_STEPS,
num_trajs=VDP_PCA_NUM_TRAJ,
iter_steps=VDP_PCA_ITER_STEPS + 2 * VDP_PCA_DELAY)
pca_strat = MultiStrategy(pca_1, pca_2, pca_3)
experi_input = ExperimentInput(unit_model,
strat=pca_strat,
label="VDP Kaa PCA")
vdp_pca = Animation(experi_input)
vdp_pca.execute(NUM_STEPS)
vdp_pca.animate(0, 1, pca_1)
vdp_pca.animate(0, 1, pca_2)
vdp_pca.animate(0, 1, pca_3)
Timer.generate_stats()
def test_rossler_pca_strat():
#Compute Sapo's version.
rossler_pca = Rossler_UnitBox()
rossler = Rossler()
rossler_reach = ReachSet(rossler)
#rossler_flow = rossler_reach.computeReachSet(NUM_STEPS)
rossler_plot = Plot()
#rossler_plot.add(rossler_flow)
for i in range(3, 4):
print(
colored("Generating PCA with Iterative Step Size: {}".format(i),
"white",
attrs=['reverse', 'blink']))
rossler_pca_reach = ReachSet(rossler_pca)
rossler_flow_pca = rossler_pca_reach.computeReachSet(
NUM_STEPS, PCAStrat(rossler_pca, iter_steps=i))
rossler_plot.add(rossler_flow_pca, "Rossler_PCA_{}".format(i))
rossler_plot.plot(0, 1, 2)
Timer.generate_stats()
def test_rossler_lin_strat():
#Compute Sapo's version.
rossler_lin = Rossler_UnitBox()
rossler = Rossler()
rossler_reach = ReachSet(rossler)
rossler_flow = rossler_reach.computeReachSet(NUM_STEPS)
rossler_plot = Plot()
rossler_plot.add(rossler_flow)
for i in range(10, 11):
print(
colored("Generating LIN with Iterative Step Size: {}".format(i),
"white",
attrs=['reverse', 'blink']))
rossler_lin_reach = ReachSet(rossler_lin)
rossler_flow_lin = rossler_lin_reach.computeReachSet(
NUM_STEPS, LinStrat(rossler_lin, iter_steps=i))
rossler_plot.add(rossler_flow_lin, "Rossler_LIN_{}".format(i))
rossler_plot.plot(0, 1, 2)
Timer.generate_stats()
def test_haroscrotate():
NUM_STEPS = 4
model = HarOscRotate()
mod_reach = ReachSet(model)
mod_flow = mod_reach.computeReachSet(NUM_STEPS)
SIR_PCA_ITER_STEPS = 1 #Number of steps between each recomputation of PCA Templates.
'PCA Strategy Parameters'
SIR_PCA_TRAJ_STEPS = 1 #Number of steps our sample trajectories should run.
SIR_PCA_NUM_TRAJ = 100 #Number of sample trajectories we should use for the PCA routine.
#pca_strat = PCAStrat(model, traj_steps=SIR_PCA_TRAJ_STEPS, num_trajs=SIR_PCA_NUM_TRAJ, iter_steps=SIR_PCA_ITER_STEPS)
#mod_pca_flow = mod_reach.computeReachSet(NUM_STEPS, tempstrat=pca_strat)
vdp_plot = Plot()
vdp_plot.add(mod_flow, "HarOsc")
#vdp_plot.add(mod_pca_flow, "HarOsc PCA")
vdp_plot.plot2DPhase(0, 1)
Timer.generate_stats()
def test_lin_VDP():
NUM_STEPS = 70
VDP_LIN_ITER_STEPS = 1 #Number of steps between each recomputation of LinApp Templates.
VDP_LIN_DELAY = 1
model = VanDerPol(delta=0.08)
unit_model = VanDerPol_UnitBox(delta=0.08)
lin_strat = MultiStrategy(LinStrat(unit_model, iter_steps=VDP_LIN_ITER_STEPS), \
LinStrat(unit_model, iter_steps=VDP_LIN_ITER_STEPS+VDP_LIN_DELAY), \
LinStrat(unit_model, iter_steps=VDP_LIN_ITER_STEPS+2*VDP_LIN_DELAY))
inputs = [
ExperimentInput(model, label="VDP Sapo"),
ExperimentInput(unit_model, strat=lin_strat, label="VDP Kaa Lin")
]
vdp_pca = PhasePlotExperiment(inputs)
vdp_pca.execute(NUM_STEPS)
vdp_pca.plot_results(0, 1)
Timer.generate_stats()
def test_lv_pca_strat():
#Compute Sapo's version.
lv_pca = LotkaVolterra_UnitBox()
lv = LotkaVolterra()
lv_reach = ReachSet(lv)
lv_flow = lv_reach.computeReachSet(NUM_STEPS)
lv_plot = Plot()
lv_plot.add(lv_flow)
for i in range(1, ITER_SPREAD):
print(
colored("Generating PCA with Iterative Step Size: {}".format(2 *
i),
"white",
attrs=['reverse', 'blink']))
lv_pca_reach = ReachSet(lv_pca)
lv_flow_pca = lv_pca_reach.computeReachSet(
NUM_STEPS, PCAStrat(lv_pca, iter_steps=2 * i))
lv_plot.add(lv_flow_pca, "LV_PCA_{}".format(2 * i))
lv_plot.plot(0, 1, 2)
Timer.generate_stats()
