def monogenic_local_phase(monogenic_signal, **kwargs):
"""
get the local phase of a 2D monogenic signal
:param monogenic_signal: the output of the monogenic_signal function
:param unwrap: if True then unwrap the output angle, default is False
:param kwargs: keyword arguments that will be passthrough to the unwrap function
:return: local phase in h5Data format
"""
theta = np.arctan2( np.abs(monogenic_signal[1])*np.sign(np.real(monogenic_signal[1])), monogenic_signal[0])
unwrap_output = kwargs.pop('unwrap', False)
if unwrap_output:
return unwrap(theta, **kwargs)
theta.data_attrs['UNITS'] = osh5def.OSUnits('a.u.')
return theta
def sl(*args, **kwargs):
# save meta data into a list
saved = args[0].meta2dict() if hasattr(args[0], 'meta2dict') else None
# execute user function
out = func(args[0].view(np.ndarray), *args[1:], **kwargs)
# load saved meta data into specified positions
try:
if isinstance(out, osh5def.H5Data):
out.__dict__.update(saved)
else:
out = osh5def.H5Data(out, **saved)
except:
raise TypeError('Output is not/cannot convert to H5Data')
# Update unit if necessary
if unit is not None:
out.data_attrs['UNITS'] = osh5def.OSUnits(unit)
if axes is not None:
out.axes = axes
return out
attrs={'NAME':'t', 'LONG_NAME':'time', 'UNITS':'1 / \omega_p'})
delta_k = 2*np.pi/(h5_data.axis[0].max-h5_data.axis[0].min)
nk = int(kmax/delta_k)
nz=nx_end-nx_begin+1
# here we allocate the history array
total_es = np.zeros((total_time,nk,nz))
total_em =np.zeros((total_time,nk,nz))
total_es = 0
total_em = 0
kaxis=osh5def.DataAxis(0, nk* delta_k, nk, attrs{'NAME':'k_{\perp}', 'LONG_NAME':'k_{\perp}', 'UNITS':osh5def.OSUnits('\omega_{0}/c')})
data_attrs_hfhi_es_hist = { 'UNITS': osh5def.OSUnits('a.u.'), 'NAME': 'ES modes history', 'LONG_NAME': 'hfhi_es_hist' }
data_attrs_hfhi_em_hist = { 'UNITS': osh5def.OSUnits('a.u.'), 'NAME': 'EM modes history', 'LONG_NAME': 'hfhi_em_hist' }
run_attrs = {'XMAX' : np.array( [ time_step * (total_time-1),kmax,zmax] ) ,
'XMIN' : np.array( [0, 0, zmin] ) }
i_count = 0
file_number = 0
for file_number in range(i_begin, i_end):
e1_filename = e1[file_number]
e2_filename = e2[file_number]
# print('ny=' + repr(ny))
print('time_step=' + repr(time_step))
print('total_time=' + repr(total_time))
h5_output = np.zeros((total_time, nx))
total = np.zeros((total_time,nx))
#total = 0
total2 = 0
# if rank == 0:
# total = np.zeros((total_time, nx))
xaxis=h5_data.axes[0]
taxis=osh5def.DataAxis(0, time_step * (total_time -1), total_time,
attrs={'NAME':'t', 'LONG_NAME':'time', 'UNITS':'1 / \omega_p'})
data_attrs = { 'UNITS': osh5def.OSUnits('m_e \omega_p^3'), 'NAME': 's1', 'LONG_NAME': 'S_1' }
# print(repr(xaxis.min))
# print(repr(xaxis.max))
run_attrs = {'XMAX' : np.array( [ time_step * (total_time-1),xaxis.max] ) ,
'XMIN' : np.array( [0, xaxis.min, 0] ) }
# h5_output.run_attrs['TIME'] = 0.0
# h5_output.run_attrs['UNITS'] = 'm_e /T'
i_count = 0
file_number = 0
for file_number in range(i_begin, i_end):
e2_filename = e2[file_number]
e3_filename = e3[file_number]
h5_output = np.zeros((total_time, ny))
total = np.zeros((total_time,ny))
#total = 0
total2 = 0
# if rank == 0:
# total = np.zeros((total_time, nx))
raxis=h5_data.axes[0]
dr=(raxis.max-raxis.min)/ny
xaxis=h5_data.axes[1]
taxis=osh5def.DataAxis(0, time_step * (total_time -1), total_time,
attrs={'NAME':'t', 'LONG_NAME':'time', 'UNITS':'1 / \omega_p'})
data_attrs = { 'UNITS': osh5def.OSUnits('m_e \omega_p^3'), 'NAME': 'Power', 'LONG_NAME': 'Trans. Int. Power' }
print(repr(xaxis.min))
print(repr(xaxis.max))
run_attrs = {'XMAX' : np.array( [time_step * (total_time-1), xaxis.max] ) ,
'XMIN' : np.array( [0, xaxis.min ] ) }
# h5_output.run_attrs['TIME'] = 0.0
# h5_output.run_attrs['UNITS'] = 'm_e /T'
file_number = 0
skip = 1
temp=np.zeros(ny)
for file_number in range(i_begin, i_end):
print('time_step=' + repr(time_step))
print('total_time=' + repr(total_time))
e2_plus_output = np.zeros((total_time, nx))
e2_minus_output = np.zeros((total_time, nx))
total = np.zeros((total_time,nx))
#total = 0
total2 = 0
# if rank == 0:
# total = np.zeros((total_time, nx))
xaxis=h5_data.axes[1]
taxis=osh5def.DataAxis(0, time_step * (total_time -1), total_time,
attrs={'NAME':'t', 'LONG_NAME':'time', 'UNITS':'1 / \omega_p'})
data_attrs_eplus = { 'UNITS': osh5def.OSUnits('m_e^2 c \omega_p/e'), 'NAME': 'e+', 'LONG_NAME': 'e2_+' }
data_attrs_eminus = { 'UNITS': osh5def.OSUnits('m_e^2 c \omega_p/e'), 'NAME': 'e-', 'LONG_NAME': 'e2_-' }
run_attrs = {'XMAX' : np.array( [ time_step * (total_time-1),xaxis.max] ) ,
'XMIN' : np.array( [0, xaxis.min, 0] ) }
i_count = 0
file_number = 0
for file_number in range(i_begin, i_end):
e2_filename = e2[file_number]
e3_filename = e3[file_number]
b2_filename = b2[file_number]
def q3d_to_3d(rundir, plasma_field, fileno, mode_max, x1_min, x1_max, nx1,
x2_min, x2_max, nx2, x3_min, x3_max, nx3):
from scipy import interpolate
dx1 = (x1_max - x1_min) / (nx1 - 1)
dx2 = (x2_max - x2_min) / (nx2 - 1)
dx3 = (x3_max - x3_min) / (nx3 - 1)
x1_axis = np.arange(x1_min, x1_max + dx1, dx1)
x2_axis = np.arange(x2_min, x2_max + dx2, dx2)
x3_axis = np.arange(x3_min, x3_max + dx3, dx3)
a = np.zeros((nx1, nx2, nx3), dtype=float)
filename_out = filename_3d(rundir, plasma_field, fileno)
x1 = osh5def.DataAxis(x1_min,
x1_max,
nx1,
attrs={
'NAME': 'x1',
'LONG_NAME': 'x_1',
'UNITS': 'c / \omega_0'
})
x2 = osh5def.DataAxis(x2_min,
x2_max,
nx2,
attrs={
'NAME': 'x2',
'LONG_NAME': 'x_2',
'UNITS': 'c / \omega_0'
})
x3 = osh5def.DataAxis(x3_min,
x3_max,
nx3,
attrs={
'NAME': 'x3',
'LONG_NAME': 'x_3',
'UNITS': 'c / \omega_0'
})
# More attributes associated with the data/simulation. Again no need to worry about the details.
data_attrs = {
'UNITS': osh5def.OSUnits('m_e c \omega_0 / e'),
'NAME': plasma_field,
'LONG_NAME': plasma_field
}
run_attrs = {
'NOTE': 'parameters about this simulation are stored here',
'TIME UNITS': '1/\omega_0',
'XMAX': np.array([1., 15.]),
'XMIN': np.array([0., 10.])
}
# Now "wrap" the numpy array into osh5def.H5Data. Note that the data and the axes are consistent and are in fortran ordering
b = osh5def.H5Data(a,
timestamp='123456',
data_attrs=data_attrs,
run_attrs=run_attrs,
axes=[x1, x2, x3])
# I am doing mode 0 outside of the loop
fname_re = filename_re(rundir, plasma_field, 0, fileno)
# DEBUG
print(fname_re)
# DEBUG
data_re = osh5io.read_h5(fname_re)
print(data_re.shape)
print(data_re.axes[1].ax.shape)
print(data_re.axes[0].ax.shape)
func_re = interpolate.interp2d(data_re.axes[1].ax,
data_re.axes[0].ax,
data_re,
kind='cubic')
for i1 in range(0, nx1):
for i2 in range(0, nx2):
for i3 in range(0, nx3):
z = x1_axis[i1]
x = x3_axis[i3]
y = x2_axis[i2]
r = np.sqrt(x * x + y * y)
# if r != 0:
# cos_th = x/r
# sin_th = y/r
# else:
# cos_th = 1
# sin_th = 0
a[i1, i2, i3] = a[i1, i2, i3] + func_re(z, r)
for i_mode in range(1, mode_max + 1):
fname_re = filename_re(rundir, plasma_field, i_mode, fileno)
fname_im = filename_im(rundir, plasma_field, i_mode, fileno)
# DEBUG
print(fname_re)
# DEBUG
if (plasma_field == 'e2' or plasma_field == 'e3'):
if (plasma_field == 'e2'):
field_comp = 'e3'
else:
field_comp = 'e2'
data_re_self = osh5io.read_h5(
filename_re(rundir, plasma_field, i_mode, fileno))
data_im_self = osh5io.read_h5(
filename_im(rundir, plasma_field, i_mode, fileno))
data_re_comp = osh5io.read_h5(
filename_re(rundir, field_comp, i_mode, fileno))
data_im_comp = osh5io.read_h5(
filename_im(rundir, field_comp, i_mode, fileno))
else:
data_re = osh5io.read_h5(
filename_re(rundir, plasma_field, i_mode, fileno))
data_im = osh5io.read_h5(
filename_im(rundir, plasma_field, i_mode, fileno))
func_re = interpolate.interp2d(data_re.axes[1].ax,
data_re.axes[0].ax,
data_re,
kind='cubic')
func_im = interpolate.interp2d(data_im.axes[1].ax,
data_im.axes[0].ax,
data_im,
kind='cubic')
for i1 in range(0, nx1):
for i2 in range(0, nx2):
for i3 in range(0, nx3):
z = x1_axis[i1]
x = x3_axis[i3]
y = x2_axis[i2]
r = np.sqrt(x * x + y * y)
if r > 0.000001:
cos_th = x / r
sin_th = y / r
else:
cos_th = 1
sin_th = 0
# start the recursion relation to evaluate cos(n*theta) and sin(n_theta)
sin_n = sin_th
cos_n = cos_th
for int_mode in range(2, i_mode + 1):
temp_s = sin_n
temp_c = cos_n
cos_n = temp_c * cos_th - temp_s * sin_th
sin_n = temp_s * cos_th + temp_c * sin_th
#
# here we perform the addition of the N-th mode
# to the data in 3D
#
a[i1, i2,
i3] = a[i1, i2, i3] + func_re(z, r) * cos_n - func_im(
z, r) * sin_n
osh5io.write_h5(b, filename=filename_out)
#total = 0
total2 = 0
xaxis = h5_data.axes[1]
taxis = osh5def.DataAxis(0,
time_step * (total_time - 1),
total_time,
attrs={
'NAME': 't',
'LONG_NAME': 'time',
'UNITS': '1 / \omega_p'
})
data_attrs = {
'UNITS': osh5def.OSUnits('m_e \omega_p^3'),
'NAME': 's1',
'LONG_NAME': 'S_1'
}
print(repr(xaxis.min))
print(repr(xaxis.max))
run_attrs = {
'XMAX': np.array([time_step * (total_time - 1), xaxis.max]),
'XMIN': np.array([0, xaxis.min])
}
# h5_output.run_attrs['TIME'] = 0.0
# h5_output.run_attrs['UNITS'] = 'm_e /T'
file_number = 0
# ******************************************************************
from h5_utilities import *
import matplotlib.pyplot as plt
import sys
# Han Wen's pyVisOS
sys.path.append('/Volumes/Lacie-5TB/codes/pyVisOS/')
def os_rwigner(real_array,xaxis):
nx=real_array.shape
nxh=(nx+1)/2
dx=(xaxis.max-xaxis.min)/nx
kmax=(np.pi)/dx
dk=(2.0*np.pi)/xaxis.max
w_data= rwigner(real_array)
kaxis=osh5def.DataAxis(0,kmax,nx_h,
attrs={'NAME':'k','LONG_NAME':'wave number', 'UNITS': '\omega_0/c'}
data_attrs = data_attrs = { 'UNITS': osh5def.OSUnits('[a.u.]'),
'NAME': 'W_{\phi}', 'LONG_NAME': 'Wigner Transform' }
run_attrs = {'XMAX' : np.array( [kmax, xaxis.max] ) ,
'XMIN' : np.array( [0, xaxis.min ] ) }
os5data_wigner=osh5def.H5Data(w_data,timestamp='x', data_attrs=data_attrs, axes=[kaxis,xaxis])