def _calculate_halpha_view(self):
for ileaf, offset in enumerate(self.dataset.block_offsets):
block = datfile_utilities.get_single_block_data(self.dataset.file, offset, self.dataset.block_shape)
# this adds the temperature and pressure to the block
block = process_data.create_data_dict(block, self.dataset.header)
# interpolate ionisation and f parameter for each block
block_ion, block_fpar = ionisation.block_interpolate_ionisation_f(block, self.dataset)
block_ne = super()._get_ne(block, block_ion)
n2 = block_ne**2 / (block_fpar * 1e16) # parameter f is interpolated in units of 1e16 cm-3
# calculate block opacity
block_kappa = (np.pi * self.dataset.units.ec**2 / (self.dataset.units.m_e * self.dataset.units.c)) * \
self.f23 * n2 * self._gaussian(block)
# integrate block along line of sight to get opacity
l_edge, r_edge = process_data.get_block_edges(ileaf, self.dataset)
opacity = super()._integrate_block(block_kappa, l_edge, r_edge)
S = self._source_function()
intensity = S * (1 - np.exp(-opacity))
if self.simulation_type == 'filament':
Ibgr = 2.2 * S
intensity += Ibgr * np.exp(-opacity)
# add integrated block to list, this preserves block index
self.integrated_block_list.append(intensity)
# merge all integrated blocks into one single 2D array
view = super()._merge_integrated_blocks()
# plot final result
super()._plot_synthetic_view(view)
def plot_2d(self):
varmin, varmax = self.dataset.get_extrema(self.var)
norm = None
if self.logscale:
norm = matplotlib.colors.LogNorm()
# iterate over blocks in dataset
for ileaf, offset in enumerate(self.dataset.block_offsets):
# retrieve x and y coordinates for each block
l_edge, r_edge = process_data.get_block_edges(ileaf, self.dataset)
# read in block data (contains all variables)
block = datfile_utilities.get_single_block_data(self.dataset.file, offset, self.dataset.block_shape)
block = process_data.create_data_dict(block, self.dataset.header)
x = np.linspace(l_edge[0], r_edge[0], self.dataset.header['block_nx'][0])
y = np.linspace(l_edge[1], r_edge[1], self.dataset.header['block_nx'][1])
im = self.ax.pcolormesh(x, y, block[self.var].T, cmap=self.cmap, vmin=varmin, vmax=varmax, norm=norm)
# logic to draw the mesh
if self.draw_mesh:
if not r_edge[0] == self.dataset.header["xmax"][0]:
self.ax.vlines(x=r_edge[0], ymin=l_edge[1], ymax=r_edge[1], color=self.mesh_color,
lw=self.mesh_linewidth, linestyle=self.mesh_linestyle, alpha=self.mesh_opacity)
if not r_edge[1] == self.dataset.header["xmax"][1]:
self.ax.hlines(y=r_edge[1], xmin=l_edge[0], xmax=r_edge[0], color=self.mesh_color,
lw=self.mesh_linewidth, linestyle=self.mesh_linestyle, alpha=self.mesh_opacity)
self.ax.set_aspect('equal')
divider = make_axes_locatable(self.ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
self.colorbar = self.fig.colorbar(im, cax=cax)
self.ax.set_title(self.var)
self.fig.tight_layout()
def plot_1d(self):
for ileaf, offset in enumerate(self.dataset.block_offsets):
l_edge, r_edge = process_data.get_block_edges(ileaf, self.dataset)
block = datfile_utilities.get_single_block_data(self.dataset.file, offset, self.dataset.block_shape)
block = process_data.create_data_dict(block, self.dataset.header)
x = np.linspace(l_edge, r_edge, self.dataset.header['block_nx'])
self.ax.plot(x, block[self.var], '-k')
self.ax.set_title(self.var)
def load_all_data(self, nbprocs=None, regriddir=None):
"""
Loads in all the data to RAM, this can take up quite some space for huge datasets.
Data will be regridded if this is not already done.
"""
if self.data_dict is not None:
return self.data_dict
if self._uniform:
data = datfile_utilities.get_uniform_data(self)
else:
data = self._regrid_data(nbprocs, regriddir)
# create dictionary containing the data
self.data_dict = process_data.create_data_dict(data, self.header,
self.adiab_constant)
return self.data_dict
def get_extrema(self, var, print_result=False):
"""
Returns the maximum and minimum value of the requested variable
:param var: variable, see ds.known_fields which are available
:param print_result: if True, prints the extrema to the console
:return: min, max of the given variable
"""
if var not in self.known_fields:
raise KeyError("variable not known: {}".format(var))
varmax = -1e99
varmin = 1e99
for offset in self.block_offsets:
block = datfile_utilities.get_single_block_data(
self.file, offset, self.block_shape)
block = process_data.create_data_dict(block, self.header)
varmax = np.maximum(varmax, np.max(block[var]))
varmin = np.minimum(varmin, np.min(block[var]))
if print_result:
print(">> minimum {}: {:2.3e} | maximum {}: {:2.3e}".format(
var, varmin, var, varmax))
return varmin, varmax
def _calculate_faraday_view(self):
for ileaf, offset in enumerate(self.dataset.block_offsets):
block = datfile_utilities.get_single_block_data(self.dataset.file, offset, self.dataset.block_shape)
# add pressure and temperature to block
block = process_data.create_data_dict(block, self.dataset.header)
block_ion, block_fpar = ionisation.block_interpolate_ionisation_f(block, self.dataset)
block_ne = super()._get_ne(block, block_ion)
prefactor = self.dataset.units.ec**3 / (2*np.pi*self.dataset.units.m_e**2 * self.dataset.units.c**4)
if self.line_of_sight == 'x':
b_key = 'b1'
elif self.line_of_sight == 'y':
b_key = 'b2'
else:
b_key = 'b3'
b_para = block[b_key] * self.dataset.units.unit_magneticfield
l_edge, r_edge = process_data.get_block_edges(ileaf, self.dataset)
fara_measure = super()._integrate_block(block_ne*b_para, l_edge, r_edge) * prefactor
self.integrated_block_list.append(fara_measure)
view = super()._merge_integrated_blocks()
super()._plot_synthetic_view(view)
self.colorbar.set_label("rad cm$^{-2}$")