def plot_all_average_velocity_and_variance(data_path: str,
yaml_path: str,
search_parameters: dict = {}):
sns.set(style="white", context="talk")
os.chdir("E:/")
history = processing.get_master_yaml(yaml_path)
timesteps = processing.get_parameter_range("dt",
history,
exclude={"dt": 1.0})
fig = plotting.multiple_timescale_plot(
search_parameters,
break_time_step=40,
metric=statistics.calculate_avg_vel,
parameter="dt",
parameter_range=timesteps,
history=history,
include_traj=False,
data_path=data_path,
)
fig2 = plotting.multiple_timescale_plot(
search_parameters,
break_time_step=40,
metric=statistics.calculate_variance,
parameter="dt",
parameter_range=timesteps,
history=history,
include_traj=False,
data_path=data_path,
)
plt.show()
return
def plot_avg_vel(
ax,
search_parameters: dict,
scalarMap=None,
logx=True,
data_path: str = "Experiments/Data.nosync/",
yaml_path: str = "Experiments/positive_phi_no_of_clusters",
end_time_step: int = -1,
):
"""Plots average velocity of particles on log scale, colours lines according to
number of clusters in the inital condition
"""
if scalarMap is None:
cm = plt.get_cmap("coolwarm")
cNorm = mpl.colors.DivergingNorm(vmin=0, vcenter=2, vmax=4)
scalarMap = mpl.cm.ScalarMappable(norm=cNorm, cmap=cm)
history = get_master_yaml(yaml_path)
list_of_names = match_parameters(search_parameters, history)
print(list_of_names)
cycle = plt.rcParams["axes.prop_cycle"].by_key()["color"]
for file_name in list_of_names:
simulation_parameters = history[file_name]
t, x, v = load_traj_data(file_name, data_path)
cluster_count = _get_number_of_clusters(
simulation_parameters["initial_dist_x"])
if logx:
ax.semilogx(
t,
v.mean(axis=1),
color=cycle[cluster_count -
1], # simulation_parameters["gamma"]),
label=f"{cluster_count} Clusters",
alpha=0.1,
zorder=1,
)
else:
ax.plot(
t,
v.mean(axis=1),
color=cycle[cluster_count -
1], # simulation_parameters["gamma"]),
label=f"{cluster_count} Clusters",
alpha=0.1,
zorder=1,
)
# plt.tight_layout()
return ax
def plot_convergence_from_clusters(
ax,
search_parameters: dict,
yaml_path: str,
data_path: str = "Experiments/Data.nosync/",
logx=True,
):
history = get_master_yaml(yaml_path)
file_names = match_parameters(search_parameters, history)
cycle = plt.rcParams["axes.prop_cycle"].by_key()["color"]
for file_name in file_names:
print(file_name)
simulation_parameters = history[file_name]
t, x, v = load_traj_data(file_name, data_path)
error = calculate_l1_convergence(t, x, v, plot_hist=False)
cluster_count = _get_number_of_clusters(
simulation_parameters["initial_dist_x"])
print(cluster_count)
cluster_label = f"{cluster_count} cluster{'' if cluster_count==1 else 's'}"
if logx:
ax.semilogx(
t,
error,
label=cluster_label,
color=cycle[cluster_count - 1],
alpha=0.25,
)
else:
ax.plot(
t,
error,
label=cluster_label,
color=cycle[cluster_count - 1],
alpha=0.25,
)
ax.set(xlabel="Time", ylabel=r"$\ell^1$ Error")
handles, labels = ax.get_legend_handles_labels()
# sort both labels and handles by labels
labels, handles = zip(*sorted(zip(labels, handles), key=lambda t: t[0]))
ax.legend(handles, labels)
plt.tight_layout()
return ax
def plot_ODE_solution(data_path: str,
yaml_path: str,
search_parameters: dict = {},
**kwargs):
os.chdir("E:/")
history = processing.get_master_yaml(yaml_path)
fig, ax = plt.subplots()
timestep_range = processing.get_parameter_range("dt", history, **kwargs)
for timestep in timestep_range:
search_parameters["dt"] = timestep
file_names = processing.match_parameters(search_parameters, history,
**kwargs)
for file_name in file_names:
t, x, v = processing.load_traj_data(file_name, data_path)
# print(len(t))
if file_name == file_names[0]:
sum_avg_vel = np.zeros(len(v[:, 0]))
print(file_name)
# sum_avg_vel += v.mean(axis=1)
ax.plot(t, v.mean(axis=1), "r--", alpha=0.1)
def first_moment_ode(t, M):
return G.step(M) - M
sol = solve_ivp(
first_moment_ode,
(t.min(), t.max()),
[v[0].mean()],
t_eval=t.ravel(),
rtol=10**-9,
atol=10**-9,
)
t = sol.t
M = sol.y
ax.plot(t, M.ravel(), label="Numerical Sol")
ax.plot(t, sum_avg_vel / len(file_names))
ax.legend()
plt.show()
return
def phi_one_convergence(time_ax="linear"):
# sns.set(style="white", context="paper")
sns.set_style("ticks")
fig1, ax1 = plt.subplots(figsize=(12, 6))
cm = plt.get_cmap("coolwarm")
cNorm = colors.DivergingNorm(vmin=-25, vcenter=0.0, vmax=25)
scalarMap = mplcm.ScalarMappable(norm=cNorm, cmap=cm)
yaml_path = "./Experiments/phi_one_convergence"
history = get_master_yaml(yaml_path)
file_names = match_parameters({}, history)
dt = 0.01
print(yaml_path)
for file_name in file_names:
x, v = load_traj_data(file_name, data_path="./Experiments/Data/")
t = np.arange(0, len(v) * dt, dt)
if time_ax == "linear":
ax1.plot(
t[:int(20 / dt)],
v[:int(20 / dt)].mean(axis=1),
color=scalarMap.to_rgba(np.sign(v[0].mean()) * v[0].var()),
)
else:
ax1.semilogx(
t[:int(20 / dt)],
v[:int(20 / dt)].mean(axis=1),
color=scalarMap.to_rgba(np.sign(v[0].mean()) * v[0].var()),
)
ax1.plot([0, 20], [1, 1], "k--", alpha=0.25)
ax1.plot([0, 20], [-1, -1], "k--", alpha=0.25)
ax1.set(xlabel="Time", ylabel=r"Average Velocity, $M^N(t)$")
plt.subplots_adjust(top=0.905,
bottom=0.135,
left=0.115,
right=0.925,
hspace=0.2,
wspace=0.2)
plt.show()
return
def avg_vel(
ax,
scalarMap,
file_path: str = "Experiments/Data/",
yaml_path: str = "Experiments/vary_large_gamma_local",
search_parameters: dict = {
"particle_count": 450,
"G": "Step",
"scaling": "Local",
"phi": "Gamma",
"initial_dist_x": "two_clusters_2N_N",
"initial_dist_v": "2N_N_cluster_const",
"T_end": 100,
"dt": 0.01,
},
):
list_of_names = []
print(yaml_path)
history = get_master_yaml(yaml_path)
list_of_names = match_parameters(search_parameters, history)
for file_name in list_of_names:
simulation_parameters = history[file_name]
t, x, v = load_traj_data(file_name, data_path=file_path)
if t is None:
t = np.arange(0,
len(x) * simulation_parameters["dt"],
simulation_parameters["dt"])
if simulation_parameters["gamma"] >= 0.05:
ax.semilogx(
t,
v.mean(axis=1),
color=scalarMap.to_rgba(simulation_parameters["gamma"]),
label="{:.2f}".format(simulation_parameters["gamma"]),
# alpha=0.75,
)
return ax
def run_timestep_experiment_low_gamma_high_particles():
timesteps = np.logspace(start=0, stop=-4, num=15)
test_params = {
"particle_count": 20 * [501],
"gamma": [0.01],
"G": ["Smooth"],
"scaling": ["Local"],
"D": [0.25],
"phi": ["Gamma"],
"initial_dist_x": ["two_clusters_2N_N"],
"initial_dist_v": ["2N_N_cluster_const"],
"T_end": [200.0],
"dt": timesteps.tolist(),
"option": ["numba"],
}
os.chdir("E:/")
history = processing.get_master_yaml(yaml_path="timestep_experiments")
fn = "LowGammaLoweringTimestepHighParticles"
processing.run_experiment(test_params, history, experiment_name=fn)
print(
"Running reduced timestep with gamma =0.01 and N=500 --- does non-uniformity persist?"
)
def run_timestep_experiment_phi_uniform():
timesteps = np.logspace(start=0, stop=-4, num=15)
test_params = {
"particle_count":
10 * [99, 501], # (3 * np.arange(8, 150, 16)).tolist(),
"G": ["Smooth"],
"scaling": ["Local"],
"D": [0.25],
"phi": ["Uniform"],
"initial_dist_x": ["two_clusters_2N_N"],
"initial_dist_v": ["2N_N_cluster_const"],
"T_end": [200.0],
"dt": timesteps.tolist(),
"option": ["numba"],
}
os.chdir("E:/")
history = processing.get_master_yaml(yaml_path="timestep_experiments")
fn = "UniformInteractionLoweringTimestep"
processing.run_experiment(test_params, history, experiment_name=fn)
print(
"Running reduced timestep with phi=1 --- post-process to check against gamma =0.5"
)
import particle.processing as processing
particles = 100
test_params = {
"particle_count":
100 * [particles], # (3 * np.arange(8, 150, 16)).tolist(),
# "gamma": (np.arange(0.2, 0.5, 0.05)).tolist(),
"G": ["Smooth"],
"scaling": ["Local"],
"D": [1.0],
"phi": ["Gamma"],
"gamma": [0.1],
"initial_dist_x": ["uniform_dn"],
"initial_dist_v": ["pos_normal_dn"],
"T_end": [2000.0],
"dt": [0.01],
"option": ["numba"],
}
history = processing.get_master_yaml(yaml_path="experiments_ran")
# fn = (
# f"""{test_params["initial_dist_v"][0]}_"""
# f"""vel_{test_params["scaling"][0]}_G{test_params["G"][0]}_"""
# f"""T{int(test_params["T_end"][0])}_noise_report_Galpha"""
# )
fn = f"""PSMean0"""
processing.run_experiment(test_params, history, experiment_name=fn)
"initial_dist_x": "one_cluster",
"initial_dist_v": "pos_normal_dn",
"T_end": 2000.0,
# "dt": 0.015,
}
os.chdir("/Volumes/Extreme SSD/InteractingParticleSystems/noisysystem_temp")
final_plot_time = 5000000
yaml_path = (
"./Experiments/one_cluster_vary_noise_scale_dt_100_runs_larger_gamma_long_run"
)
data_path = "./Experiments/Data.nosync/"
history = get_master_yaml(yaml_path)
fig, [ax1, ax2] = plt.subplots(1, 2, figsize=(15, 5), sharex=True)
# Create colour bar and scale
cm = plt.get_cmap("coolwarm")
cNorm = colors.DivergingNorm(vmin=0, vcenter=0.25, vmax=0.5)
scalarMap = mplcm.ScalarMappable(norm=cNorm, cmap=cm)
# Set tick locations
cbar = fig.colorbar(scalarMap, ticks=np.arange(0, 0.5, 0.05))
# For each matching desired parameters, calculate the l1 error and plot
for diffusion in np.arange(0.05, 0.5, 0.05).tolist():
search_parameters["D"] = diffusion
file_names = match_parameters(search_parameters, history)
def plot_averaged_convergence_from_clusters(ax,
search_parameters: dict,
yaml_path: str,
data_path: str,
logx=True):
history = get_master_yaml(yaml_path)
for initial_dist_x in [
"one_cluster",
"two_clusters",
"three_clusters",
"four_clusters",
]:
search_parameters["initial_dist_x"] = initial_dist_x
file_names = match_parameters(search_parameters, history)
cycle = plt.rcParams["axes.prop_cycle"].by_key()["color"]
for idx, file_name in enumerate(file_names):
print(file_name)
simulation_parameters = history[file_name]
cluster_count = _get_number_of_clusters(
simulation_parameters["initial_dist_x"])
cluster_label = f"{cluster_count} cluster{'' if cluster_count==1 else 's'}"
if idx == 0:
t, x, v = load_traj_data(file_name, data_path)
t = t.flatten()
error = calculate_l1_convergence(t[t <= 10],
x[t <= 10],
v[t <= 10],
plot_hist=False)
error_store = np.zeros((len(file_names), len(error)))
error_store[idx, :] = error
else:
t, x, v = load_traj_data(file_name, data_path)
t = t.flatten()
error = calculate_l1_convergence(t[t <= 10],
x[t <= 10],
v[t <= 10],
plot_hist=False)
error_store[idx, :] = error
if logx:
t = t[t <= 10]
ax.semilogx(
t,
np.mean(error_store, axis=0),
label=cluster_label,
color=cycle[cluster_count - 1],
)
else:
t = t[t <= 10]
ax.plot(
t,
np.mean(error_store, axis=0),
label=cluster_label,
color=cycle[cluster_count - 1],
)
# ax.plot(
# t[9:], moving_average(np.mean(error_store, axis=0), n=10), "r",
# )
ax.set(xlabel="Time", ylabel=r"$\ell^1$ Error")
handles, labels = ax.get_legend_handles_labels()
# sort both labels and handles by labels
labels, handles = zip(*sorted(zip(labels, handles), key=lambda t: t[0]))
ax.legend(handles, labels)
plt.tight_layout()
return ax
def plot_averaged_avg_vel(
ax,
search_parameters: dict,
logx=True,
include_traj=True,
scalarMap=None,
data_path: str = "Experiments/Data.nosync/",
yaml_path: str = "Experiments/positive_phi_no_of_clusters",
start_time_step: int = 0,
end_time_step: int = -1,
):
"""Plots average velocity of particles on log scale, colours lines according to
number of clusters in the inital condition
"""
if scalarMap is None:
cm = plt.get_cmap("coolwarm")
cNorm = mpl.colors.DivergingNorm(vmin=1, vcenter=2, vmax=4)
scalarMap = mpl.cm.ScalarMappable(norm=cNorm, cmap=cm)
history = get_master_yaml(yaml_path)
# for initial_dist_x in [
# "one_cluster",
# "two_clusters",
# "three_clusters",
# "four_clusters",
# ]:
# search_parameters["initial_dist_x"] = initial_dist_x
list_of_names = match_parameters(search_parameters, history)
# print(list_of_names)
cycle = plt.rcParams["axes.prop_cycle"].by_key()["color"]
for idx, file_name in enumerate(list_of_names):
simulation_parameters = history[file_name]
cluster_count = _get_number_of_clusters(
simulation_parameters["initial_dist_x"])
cluster_label = f"{cluster_count} cluster{'' if cluster_count==1 else 's'}"
t, x, v = load_traj_data(file_name, data_path)
t = t[start_time_step:end_time_step]
v = v[start_time_step:end_time_step]
avg_vel = v.mean(axis=1)
cluster_count = _get_number_of_clusters(
history[file_name]["initial_dist_x"])
if include_traj and logx:
ax.semilogx(t,
avg_vel,
color=cycle[cluster_count - 1],
alpha=0.1,
zorder=1)
if include_traj and not logx:
ax.plot(t,
avg_vel,
color=cycle[cluster_count - 1],
alpha=0.1,
zorder=1)
if idx == 0:
avg_vel_store = np.zeros((len(list_of_names), len(avg_vel)))
avg_vel_store[idx, :] = avg_vel
if logx:
ax.semilogx(
t,
np.mean(avg_vel_store, axis=0),
color=cycle[cluster_count - 1], # history[file_name]["gamma"]),
label=cluster_label,
# alpha=0.5,
zorder=2,
)
else:
ax.plot(
t,
np.mean(avg_vel_store, axis=0),
color=cycle[cluster_count - 1], # history[file_name]["gamma"]),
label=cluster_label,
# alpha=0.5,
zorder=2,
)
# plt.tight_layout()
return ax