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 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
