def main():
from pyphare.cpp import cpp_lib
cpp = cpp_lib()
from pyphare.pharesee.run import Run
from pyphare.pharesee.hierarchy import flat_finest_field
config()
Simulator(gv.sim).run()
if cpp.mpi_rank() == 0:
vphi, t, phi, a, k = phase_speed(".", 0.01, 1000)
r = Run(".")
t = get_times_from_h5("EM_B.h5")
fig, ax = plt.subplots(figsize=(9, 5), nrows=1)
B = r.GetB(t[int(len(t) / 2)])
by, xby = flat_finest_field(B, "By")
ax.plot(xby, by, label="t = 500", alpha=0.6)
sorted_patches = sorted(B.patch_levels[1].patches,
key=lambda p: p.box.lower[0])
x0 = sorted_patches[0].patch_datas["By"].x[0]
x1 = sorted_patches[-1].patch_datas["By"].x[-1]
B = r.GetB(t[-1])
by, xby = flat_finest_field(B, "By")
ax.plot(xby, by, label="t = 1000", alpha=0.6)
ax.plot(xby,
wave(xby, 0.01, 2 * np.pi / 1000., 2 * np.pi / 1000 * 500),
color="k",
ls="--",
label="T=500 (theory)")
B = r.GetB(t[0])
by, xby = flat_finest_field(B, "By")
ax.plot(xby, by, label="t = 0", color="k")
ax.set_xlabel("x")
ax.set_ylabel(r"$B_y$")
ax.legend(ncol=4, loc="upper center")
ax.set_ylim((-0.012, 0.013))
ax.set_title(r"$V_\phi = {:6.4f}$".format(vphi.mean()))
ax.axvspan(x0, x1, alpha=0.2)
fig.tight_layout()
fig.savefig("alfven_wave.png", dpi=200)
assert np.mean(np.abs(vphi - 1) < 5e-2)
def energies(path, kkind="iso"):
"""
This loops over all times of a given run
and return the magnetic and kinetic energy
as a function of time.
"""
r = Run(path)
times = get_times_from_h5(r.path+"/EM_B.h5")
Bnrj = np.zeros_like(times)
K = np.zeros_like(times)
for it,t in enumerate(times):
B = r.GetB(t)
protons = r.GetParticles(t, "protons")
Bnrj[it] = mag_energy(B)
K[it] = total_kinetic(protons,kind=kkind)
return r, Bnrj, K, times
def growth_b_right_hand(run_path):
file = os.path.join(run_path, "EM_B.h5")
times = get_times_from_h5(file)
r = Run(run_path)
first_mode = np.array([])
from scipy.optimize import curve_fit
for time in times:
B_hier = r.GetB(time, merged=True, interp="linear")
by_interpolator, xyz_finest = B_hier["By"]
bz_interpolator, xyz_finest = B_hier["Bz"]
# remove the last point so that "x" is periodic wo. last point = first point
x = xyz_finest[0][:-1]
by = by_interpolator(x)
bz = by_interpolator(x)
# get the mode 1, as it is the most unstable in a box of length 33
mode1 = np.absolute(np.fft.fft(by-1j*bz)[1])
first_mode = np.append(first_mode, mode1)
popt, pcov = curve_fit(yaebx, times, first_mode, p0=[0.08, 0.09])
# now the signal is stripped from its exponential part
damped_mode=first_mode*yaebx(times, 1/popt[0], -popt[1])
# find the omega for which "damped_mode" is the largest :
# this term is twice the one it should be because "mode1" resulting from
# an absolute value, this (cosx)^2 = cos(2x) then appears at the 2nd
# harmonoic (hence the factor 0.5 to get "omega")
# the factor "+1" is because we remove the DC component, so the value
# given by argmax has also to miss this value
omegas = np.fabs(np.fft.fft(damped_mode).real)
omega = 0.5*(omegas[1:omegas.size//2].argmax()+1)*2*np.pi/times[-1]
print(omegas[0:6])
print(omegas[1:omegas.size//2].argmax())
print(2*np.pi/times[-1])
print(0.5*(omegas[1:omegas.size//2].argmax()+1)*2*np.pi/times[-1])
return times, first_mode, popt[0], popt[1], damped_mode, omega
def phase_speed(run_path, ampl, xmax):
from scipy.signal import medfilt
from scipy.optimize import curve_fit
import os
time = get_times_from_h5(os.path.join(run_path, "EM_B.h5"))
r = Run(run_path)
phase = np.zeros_like(time)
amplitude = np.zeros_like(time)
wave_vec = np.zeros_like(time)
for it, t in enumerate(time):
B = r.GetB(t)
by, xby = finest_field(B, "By")
a, k, phi = curve_fit(wave, xby, by, p0=(ampl, 2 * np.pi / xmax, 0))[0]
phase[it] = phi
amplitude[it] = a
wave_vec[it] = k
vphi = medfilt(np.gradient(phase, time) / wave_vec, kernel_size=7)
return vphi, time, phase, amplitude, wave_vec
def make_figure():
from scipy.optimize import curve_fit
from pyphare.pharesee.hierarchy import finest_field
rwT = Run("./withTagging")
rNoRef = Run("./noRefinement")
plot_time = 11
v = 2
BwT = rwT.GetB(plot_time)
BNoRef = rNoRef.GetB(plot_time)
JwT = rwT.GetJ(plot_time)
JNoRef = rNoRef.GetJ(plot_time)
bywT, xbywT = finest_field(BwT, "By")
byNoRef, xbyNoRef = finest_field(BNoRef, "By")
jzwT, xjzwT = finest_field(JwT, "Jz")
jzNoRef, xjzNoRef = finest_field(JNoRef, "Jz")
fig, axarr = plt.subplots(nrows=3, figsize=(8, 8))
def S(x, x0, l):
return 0.5 * (1 + np.tanh((x - x0) / l))
def by(x):
L = 200
v1 = -1
v2 = 1
return v1 + (v2 - v1) * (S(x, L * 0.25, 1) - S(x, L * 0.75, 1))
wT0 = 150.
ax0, ax1, ax2 = axarr
ax0.plot(xbywT, bywT)
ax0.plot(xbyNoRef, byNoRef, color="k", alpha=0.6)
ax0.plot(xbyNoRef, by(xbyNoRef), ls='--')
ax1.plot(xbywT, bywT)
ax1.plot(xbyNoRef, byNoRef, color='k')
ax1.set_xlim((wT0, 195))
ax1.set_ylim((-1.5, 2))
ax2.plot(xjzwT, jzwT)
ax2.plot(xjzNoRef, jzNoRef, color='k')
ax2.set_xlim((wT0, 195))
ax2.set_ylim((-1.5, 0.5))
# draw level patches
for ilvl, level in BwT.levels().items():
for patch in level.patches:
dx = patch.layout.dl[0]
x0 = patch.origin[0]
x1 = (patch.box.upper[0] + 1) * patch.layout.dl[0]
for ax in (ax1, ax2, ax0):
ax.axvspan(x0,
x1,
color='b',
ec='k',
alpha=0.2,
ymin=ilvl / 4,
ymax=(ilvl + 1) / 4)
from pyphare.pharesee.plotting import zoom_effect
zoom_effect(ax0, ax1, wT0, 195)
for ax in (ax0, ax1, ax2):
ax.axvline(wT0 + plot_time * v, color="r")
# select data around the rightward TD
idx = np.where((xbywT > 150) & (xbywT < 190))
xx = xbywT[idx]
bby = bywT[idx]
# now we will fit by_fit to the data
# and we expect to find x0=172 and L=1
# or close enough
def by_fit(x, x0, L):
v1 = 1
v2 = -1
return v1 + (v2 - v1) * S(x, x0, L)
popt, pcov = curve_fit(by_fit, xx, bby, p0=(150, 1))
x0, L = popt
if np.abs(L - 1) > 0.5:
raise RuntimeError(f"L (={L}) too far from 1.O")
if np.abs(x0 - (150 + plot_time * v)) > 0.5:
raise RuntimeError(f"x0 (={x0}) too far from 172")
def finest_field_plot(run_path, qty, **kwargs):
"""
plot the given quantity (qty) at 'run_path' with only the finest data
* run_path : the path of the run
* qty : ['Bx', 'By', 'Bz', 'Ex', 'Ey', 'Ez', 'Fx', 'Fy', 'Fz', 'Vx', 'Vy', 'Vz', 'rho']
kwargs:
* ax : the handle for the fig axes
* time : time (that should be in the time_stamps of the run)
* interp : the type of interpolation for the interpolator
* draw_style : steps-mid ou default... only for 1d
* title : (str) title of the plot
* xlabel, ylabel
* xlim, ylim
* filename : (str) if exists, save plot to figure under that name
return value : fig,ax
"""
import os
from pyphare.pharesee.hierarchy import get_times_from_h5
from pyphare.pharesee.run import Run
from mpl_toolkits.axes_grid1 import make_axes_locatable
import pyphare.core.gridlayout as gridlayout
r = Run(run_path)
time = kwargs.get("time", None)
dim = r.GetDl('finest', time).shape[0]
interp = kwargs.get("interp", "nearest")
domain = r.GetDomainSize()
if qty in ['Bx', 'By', 'Bz']:
file = os.path.join(run_path, "EM_B.h5")
if time is None:
times = get_times_from_h5(file)
time = times[0]
interpolator, finest_coords = r.GetB(time, merged=True,\
interp=interp)[qty]
elif qty in ['Ex', 'Ey', 'Ez']:
file = os.path.join(run_path, "EM_E.h5")
if time is None:
times = get_times_from_h5(file)
time = times[0]
interpolator, finest_coords = r.GetE(time, merged=True,\
interp=interp)[qty]
elif qty in ['Vx', 'Vy', 'Vz']:
file = os.path.join(run_path, "ions_bulkVelocity.h5")
if time is None:
times = get_times_from_h5(file)
time = times[0]
interpolator, finest_coords = r.GetVi(time, merged=True,\
interp=interp)[qty]
elif qty == 'rho':
file = os.path.join(run_path, "ions_density.h5")
if time is None:
times = get_times_from_h5(file)
time = times[0]
interpolator, finest_coords = r.GetNi(time, merged=True,\
interp=interp)[qty]
elif qty in ("Jx", "Jy", "Jz"):
file = os.path.join(run_path, "EM_B.h5")
if time is None:
times = get_times_from_h5(file)
time = times[0]
interpolator, finest_coords = r.GetJ(time, merged=True,\
interp=interp)[qty]
else:
# ___ TODO : should also include the files for a given population
raise ValueError(
"qty should be in ['Bx', 'By', 'Bz', 'Ex', 'Ey', 'Ez', 'Fx', 'Fy', 'Fz', 'Vx', 'Vy', 'Vz', 'rho']"
)
if "ax" not in kwargs:
fig, ax = plt.subplots()
else:
ax = kwargs["ax"]
fig = ax.figure
if dim == 1:
drawstyle = kwargs.get("drawstyle", "steps-mid")
ax.plot(finest_coords[0], interpolator(finest_coords[0]),\
drawstyle=drawstyle)
elif dim == 2:
x = finest_coords[0]
y = finest_coords[1]
dx = x[1] - x[0]
dy = y[1] - y[0]
# pcolormesh considers DATA_ij to be the center of the pixel
# and X,Y are the corners so XY need to be made 1 value larger
# and shifted around DATA_ij
x -= dx / 2
x = np.append(x, x[-1] + dx)
y -= dy / 2
y = np.append(y, y[-1] + dy)
X, Y = np.meshgrid(x, y)
DATA = interpolator(X, Y)
vmin = kwargs.get("vmin", np.nanmin(DATA))
vmax = kwargs.get("vmax", np.nanmax(DATA))
cmap = kwargs.get("cmap", 'Spectral_r')
im = ax.pcolormesh(x, y, DATA, cmap=cmap, vmin=vmin, vmax=vmax)
ax.set_aspect("equal")
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.08)
cb = plt.colorbar(im, ax=ax, cax=cax)
else:
raise ValueError("finest_field_plot not yet ready for 3d")
ax.set_title(kwargs.get("title", ""))
ax.set_xlabel(kwargs.get("xlabel", "$x c / \omega_p$"))
ax.set_ylabel(kwargs.get("ylabel", "$y c / \omega_p$"))
if "xlim" in kwargs:
ax.set_xlim(kwargs["xlim"])
if "ylim" in kwargs:
ax.set_ylim(kwargs["ylim"])
if "filename" in kwargs:
fig.savefig(kwargs["filename"])
return fig, ax
def test_restarts(self, dim, interp, simInput):
print(f"test_restarts dim/interp:{dim}/{interp}")
simput = copy.deepcopy(simInput)
for key in ["cells", "dl", "boundary_types"]:
simput[key] = [simput[key]] * dim
if "refinement" not in simput:
b0 = [[10 for i in range(dim)], [19 for i in range(dim)]]
simput["refinement_boxes"] = {"L0": {"B0": b0}}
else: # https://github.com/LLNL/SAMRAI/issues/199
# tagging can handle more than one timestep as it does not
# appear subject to regridding issues, so we make more timesteps
# to confirm simulations are still equivalent
simput["time_step_nbr"] = 10
# if restart time exists it "loads" from restart file
# otherwise just saves restart files based on timestamps
assert "restart_time" not in simput["restart_options"]
simput["interp_order"] = interp
time_step = simput["time_step"]
time_step_nbr = simput["time_step_nbr"]
restart_idx = 4
restart_time = time_step * restart_idx
timestamps = [time_step * restart_idx, time_step * time_step_nbr]
# first simulation
local_out = f"{out}/test/{dim}/{interp}/mpi_n/{cpp.mpi_size()}/id{self.ddt_test_id()}"
simput["restart_options"]["dir"] = local_out
simput["diag_options"]["options"]["dir"] = local_out
ph.global_vars.sim = None
ph.global_vars.sim = ph.Simulation(**simput)
assert "restart_time" not in ph.global_vars.sim.restart_options
model = setup_model()
dump_all_diags(model.populations, timestamps=np.array(timestamps))
Simulator(ph.global_vars.sim).run().reset()
self.register_diag_dir_for_cleanup(local_out)
diag_dir0 = local_out
# second restarted simulation
local_out = f"{local_out}_n2"
simput["diag_options"]["options"]["dir"] = local_out
simput["restart_options"]["restart_time"] = restart_time
ph.global_vars.sim = None
ph.global_vars.sim = ph.Simulation(**simput)
assert "restart_time" in ph.global_vars.sim.restart_options
model = setup_model()
dump_all_diags(model.populations, timestamps=np.array(timestamps))
Simulator(ph.global_vars.sim).run().reset()
self.register_diag_dir_for_cleanup(local_out)
diag_dir1 = local_out
def check(qty0, qty1, checker):
checks = 0
for ilvl, lvl0 in qty0.patch_levels.items():
patch_level1 = qty1.patch_levels[ilvl]
for p_idx, patch0 in enumerate(lvl0):
patch1 = patch_level1.patches[p_idx]
for pd_key, pd0 in patch0.patch_datas.items():
pd1 = patch1.patch_datas[pd_key]
self.assertNotEqual(id(pd0), id(pd1))
checker(pd0, pd1)
checks += 1
return checks
def check_particles(qty0, qty1):
return check(
qty0, qty1,
lambda pd0, pd1: self.assertEqual(pd0.dataset, pd1.dataset))
def check_field(qty0, qty1):
return check(
qty0, qty1, lambda pd0, pd1: np.testing.assert_equal(
pd0.dataset[:], pd1.dataset[:]))
def count_levels_and_patches(qty):
n_levels = len(qty.patch_levels)
n_patches = 0
for ilvl, lvl in qty.patch_levels.items():
n_patches += len(qty.patch_levels[ilvl].patches)
return n_levels, n_patches
n_quantities_per_patch = 20
pops = model.populations
for time in timestamps:
checks = 0
run0 = Run(diag_dir0)
run1 = Run(diag_dir1)
checks += check_particles(run0.GetParticles(time, pops),
run1.GetParticles(time, pops))
checks += check_field(run0.GetB(time), run1.GetB(time))
checks += check_field(run0.GetE(time), run1.GetE(time))
checks += check_field(run0.GetNi(time), run1.GetNi(time))
checks += check_field(run0.GetVi(time), run1.GetVi(time))
for pop in pops:
checks += check_field(run0.GetFlux(time, pop),
run1.GetFlux(time, pop))
checks += check_field(run0.GetN(time, pop),
run1.GetN(time, pop))
n_levels, n_patches = count_levels_and_patches(run0.GetB(time))
self.assertEqual(n_levels, 2) # at least 2 levels
self.assertGreaterEqual(n_patches,
n_levels) # at least one patch per level
self.assertEqual(checks, n_quantities_per_patch * n_patches)
def main():
import os
import subprocess
import glob
import shlex
for interp_order in (1, 2, 3):
config(interp_order)
Simulator(gv.sim).run()
if cpp.mpi_rank() == 0:
from pyphare.pharein.global_vars import sim
from pyphare.pharesee.run import Run
dt = 10 * sim.time_step
nt = sim.final_time / dt + 1
times = dt * np.arange(nt)
r = Run("shock_{}".format(interp_order))
for it, t in enumerate(times):
fig, ax = plt.subplots()
B = r.GetB(t, merged=True)
title = "interp order {} - t = {:06.3f}".format(
interp_order, t)
x = B["By"][1][0]
By = B["By"][0]
ax.plot(x, By(x), color="k")
ax.set_title(title)
ax.set_ylim((-0.2, 5))
ax.set_xlim((0, 250))
fig.savefig("shock_{}/shock_By_{}_{:04d}.png".format(
interp_order, interp_order, it))
plt.close(fig)
cmd = shlex.split(
"ffmpeg -r 10 -y -pattern_type glob -i 'shock_{}/shock_By_{}_*.png' -c:v libx264 -crf 0 shock_interp{}.mp4"
.format(interp_order, interp_order, interp_order))
subprocess.call(cmd)
gv.sim = None
pngs = glob.glob("shock*/*.png")
for png in pngs:
os.remove(png)
if cpp.mpi_rank() == 0:
from pyphare.pharein.global_vars import sim
from pyphare.pharesee.run import Run
t = 30
runs = [Run(f"shock_{i+1}") for i in range(3)]
fig, ax = plt.subplots()
colors = ["k", "r", "b"]
for r, color, interp_order in zip(runs, colors, (1, 2, 3)):
print(r.path)
B = r.GetB(t, merged=True)
x = B["By"][1][0]
By = B["By"][0]
ax.plot(x,
By(x),
color=color,
label=f"interp order {interp_order}")
title = "interp order {} - t = {:06.3f}".format(interp_order, t)
ax.set_title(title)
ax.set_ylim((-0.2, 5))
ax.set_xlim((0, 250))
ax.legend()
fig.savefig("shock_By.png")