def interpgridnodes(Pk=5.0, kappa=np.inf, struct="pp", sampling=1):
# 1: Extract the "spectra from the grid nodes
grid_name = "[NII]/[SII]+;[OIII]/[SII]+"
grid = pyqz.get_grid(
grid_name, Pk=Pk, struct=struct, kappa=kappa, coeffs=pyqz.diagnostics[grid_name]["coeffs"], sampling=sampling
)
grid_nodes = grid[0][
:, [grid[1].index("LogQ"), grid[1].index("Tot[O]+12"), grid[1].index("Mix_x"), grid[1].index("Mix_y")]
]
# 2: Now, interpolate and checks the output
interp_qs = pyqz.interp_qz(
"LogQ",
[grid_nodes[:, -2], grid_nodes[:, -1]],
grid_name,
coeffs=pyqz.diagnostics[grid_name]["coeffs"],
Pk=Pk,
kappa=kappa,
struct=struct,
sampling=sampling,
)
interp_zs = pyqz.interp_qz(
"Tot[O]+12",
[grid_nodes[:, -2], grid_nodes[:, -1]],
grid_name,
coeffs=pyqz.diagnostics[grid_name]["coeffs"],
Pk=Pk,
kappa=kappa,
struct=struct,
sampling=sampling,
)
return np.all(np.round(interp_qs, 2) == grid_nodes[:, 0]) and np.all(np.round(interp_zs, 3) == grid_nodes[:, 1])
def plot_grid(ratios,
coeffs=[[1, 0], [0, 1]],
Pk=5.0,
kappa=np.inf,
struct='pp',
sampling=1,
color_mode='Tot[O]+12',
figname=None,
show_plot=True,
data=None,
interp_data=None):
'''
Creates the diagram of a given line ratio grid for rapid inspection, including wraps
(fold-over regions), and of certain line ratios, if "data" is specified.
:Args:
ratios: string
The line ratios defining the grid, e.g. '[NII]/[SII]+;[OIII]/Hb'
coeffs: list of list [default: [[1,0],[0,1]] ]
The different coefficients with which to mix the line ratios.
The size of each sub-list must be equal to the number of line ratios
involved. Used for projected 3D diagnostic grids.
Pk: float [default: 5.0]
MAPPINGS model pressure. This value must match an existing grid file.
kappa: float [default: np.inf
The kappa value. This value must match an existing grid file.
struct: string [default: 'pp']
spherical ('sph') or plane-parallel ('pp') HII regions. This value
must match an existing reference grid file.
sampling: int [default: 1]
Use a resampled grid ?
color_mode: string [default: 'Tot[O]+12']
Color the grid according to 'Tot[O]+12', 'gas[O]+12' or 'LogQ'.
figname: string [default: None]
'path+name+format' to save the Figure to.
show_plot: bool [default: True]
Do you want to display the Figure ?
data: list of numpy array [default: None]
List of Arrays of the line ratio values. One array per line ratio.
interp_data: numpy array [default: 'linear']
interpolated line ratios (via pyqz.interp_qz)
'''
# 0) Let's get the data in question
[grid, grid_cols, metadata] = pyqz.get_grid(ratios,
coeffs=coeffs,
Pk=Pk,
kappa=kappa,
struct=struct,
sampling=sampling)
# 1) What are the bad segments in this grid ?
bad_segs = pyqz.check_grid(ratios,
coeffs=coeffs,
Pk=Pk,
kappa=kappa,
struct=struct,
sampling=sampling)
# 2) Start the plotting
fig = plt.figure(figsize=(10, 8))
gs = gridspec.GridSpec(1, 2, height_ratios=[1], width_ratios=[1, 0.05])
gs.update(left=0.14,
right=0.88,
bottom=0.14,
top=0.92,
wspace=0.1,
hspace=0.1)
ax1 = fig.add_subplot(gs[0, 0])
if not (color_mode in grid_cols):
raise Exception('color_mode unknown: %s' % color_mode)
# 2) Plot the grid points
# Let's make the distinction between the 'TRUE' MAPPINGS point,
# and those that were interpolated in a finer grid using Akima splines
pts = ax1.scatter(grid[:, grid_cols.index('Mix_x')],
grid[:, grid_cols.index('Mix_y')],
marker='o',
c=grid[:, grid_cols.index(color_mode)],
s=30,
cmap=pyqzm.pyqz_cmap_0,
edgecolor='none',
vmin=np.min(grid[:, grid_cols.index(color_mode)]),
vmax=np.max(grid[:, grid_cols.index(color_mode)]),
zorder=3)
# Now mark the "original" points with a black outline. First, which are they ?
if sampling > 1:
origin_cond = [
n for n in range(len(grid))
if (grid[n, metadata['columns'].index('LogQ')] in
metadata['resampled']['LogQ']) and (
grid[n, metadata['columns'].index('Tot[O]+12')] in
metadata['resampled']['Tot[O]+12'])
]
else:
origin_cond = [n for n in range(len(grid))]
# Now plot the black outlines.
original_pts = ax1.scatter(grid[origin_cond,
grid_cols.index('Mix_x')],
grid[origin_cond,
grid_cols.index('Mix_y')],
marker='o',
c=grid[origin_cond,
grid_cols.index(color_mode)],
s=60,
cmap=pyqzm.pyqz_cmap_0,
edgecolor='k',
facecolor='white',
vmin=np.min(grid[:,
grid_cols.index(color_mode)]),
vmax=np.max(grid[:,
grid_cols.index(color_mode)]),
zorder=5)
# 2-1) Draw the grid lines
# Check as a function of LogQ and Tot[O]+12. Where are these ?
u = grid_cols.index('LogQ')
v = grid_cols.index('Tot[O]+12')
for i in [u, v]:
# Here, 'var' plays the role of 'q' or 'z' depending on 'i'.
for var in np.unique(grid[:, i]):
# Plot the grid line
ax1.plot(grid[grid[:, i] == var][:, grid_cols.index('Mix_x')],
grid[grid[:, i] == var][:, grid_cols.index('Mix_y')],
'k-',
lw=1,
zorder=1)
# Plot the bad segments
for bad_seg in bad_segs:
ax1.plot([bad_seg[0][0], bad_seg[1][0]],
[bad_seg[0][1], bad_seg[1][1]],
'r-',
linewidth=4,
zorder=0)
# Now, also plot the data, if anything was provided !
if not (interp_data is None):
# Compute the combined "observed" ratios
data_comb = [np.zeros_like(data[0]), np.zeros_like(data[1])]
for k in range(len(ratios.split(';'))):
data_comb[0] += coeffs[0][k] * data[k]
data_comb[1] += coeffs[1][k] * data[k]
ax1.scatter(data_comb[0][interp_data == interp_data],
data_comb[1][interp_data == interp_data],
marker='s',
c=interp_data[interp_data == interp_data],
s=15,
cmap=pyqzm.pyqz_cmap_0,
edgecolor='k',
vmin=np.min(grid[:, grid_cols.index(color_mode)]),
vmax=np.max(grid[:, grid_cols.index(color_mode)]),
zorder=2,
alpha=0.35)
# Plot also the points outside the grid ?
# Which are they ? Need to check if they have a valid input first !
# mind the "~" to invert the bools ! isn't this cool ?
my_out_pts = ~np.isnan(data_comb[0]) * ~np.isnan(data_comb[1]) * \
np.isnan(interp_data)
# Don't do this if this is a test with fullgrid_x ...
ax1.scatter(data_comb[0][my_out_pts],
data_comb[1][my_out_pts],
marker='^',
facecolor='none',
edgecolor='k',
s=60)
# 3) Plot the colorbar
cb_ax = plt.subplot(gs[0, 1])
cb = Colorbar(ax=cb_ax, mappable=pts, orientation='vertical')
# Colorbar legend
cb.set_label(color_mode, labelpad=10)
# 4) Axis names, kappa value, etc ...
rats = ratios.split(';')
# Make sure the labels look pretty in ALL cases ...
labelx, labely = get_plot_labels(rats, coeffs)
ax1.set_xlabel(labelx, labelpad=10)
ax1.set_ylabel(labely, labelpad=10)
if not (kappa in [np.inf, 'inf']):
kappa_str = r'$\kappa$ = ' + str(kappa)
else:
kappa_str = r'$\kappa$ = $\infty$'
ax1.text(0.85,
0.9,
kappa_str,
horizontalalignment='left',
verticalalignment='bottom',
transform=ax1.transAxes)
ax1.grid(True)
if figname:
fig.savefig(figname, bbox_inches='tight')
if show_plot:
plt.show()
else:
plt.close()
def plot_grid(ratios, coeffs = [[1,0],[0,1]],
Pk = 5.0, kappa=np.inf, struct = 'pp', sampling = 1,
color_mode = 'Tot[O]+12', figname = None, show_plot = True,
data = None, interp_data = None):
'''
Creates the diagram of a given line ratio grid for rapid inspection, including wraps
(fold-over regions), and of certain line ratios, if "data" is specified.
:Args:
ratios: string
The line ratios defining the grid, e.g. '[NII]/[SII]+;[OIII]/Hb'
coeffs: list of list [default: [[1,0],[0,1]] ]
The different coefficients with which to mix the line ratios.
The size of each sub-list must be equal to the number of line ratios
involved. Used for projected 3D diagnostic grids.
Pk: float [default: 5.0]
MAPPINGS model pressure. This value must match an existing grid file.
kappa: float [default: np.inf
The kappa value. This value must match an existing grid file.
struct: string [default: 'pp']
spherical ('sph') or plane-parallel ('pp') HII regions. This value
must match an existing reference grid file.
sampling: int [default: 1]
Use a resampled grid ?
color_mode: string [default: 'Tot[O]+12']
Color the grid according to 'Tot[O]+12', 'gas[O]+12' or 'LogQ'.
figname: string [default: None]
'path+name+format' to save the Figure to.
show_plot: bool [default: True]
Do you want to display the Figure ?
data: list of numpy array [default: None]
List of Arrays of the line ratio values. One array per line ratio.
interp_data: numpy array [default: 'linear']
interpolated line ratios (via pyqz.interp_qz)
'''
# 0) Let's get the data in question
[grid, grid_cols, metadata] = pyqz.get_grid(ratios, coeffs=coeffs, Pk=Pk, kappa=kappa,
struct=struct, sampling = sampling)
# 1) What are the bad segments in this grid ?
bad_segs = pyqz.check_grid(ratios, coeffs=coeffs, Pk = Pk, kappa=kappa, struct=struct,
sampling = sampling)
# 2) Start the plotting
fig = plt.figure(figsize=(10,8))
gs = gridspec.GridSpec(1,2, height_ratios=[1], width_ratios=[1,0.05])
gs.update(left=0.14,right=0.88,bottom=0.14,top=0.92,
wspace=0.1,hspace=0.1)
ax1 = fig.add_subplot(gs[0,0])
if not(color_mode in grid_cols):
sys.exit('color_mode unknown: %s' % color_mode)
# 2) Plot the grid points
# Let's make the distinction between the 'TRUE' MAPPINGS point,
# and those that were interpolated in a finer grid using Akima splines
pts = ax1.scatter(grid[:,grid_cols.index('Mix_x')],
grid[:,grid_cols.index('Mix_y')],
marker='o',
c=grid[:,grid_cols.index(color_mode)],
s=30, cmap=pyqz_cmap_0, edgecolor='none',
vmin=np.min(grid[:,grid_cols.index(color_mode)]),
vmax=np.max(grid[:,grid_cols.index(color_mode)]),
zorder=3)
# Now mark the "original" points with a black outline. First, which are they ?
if sampling > 1:
origin_cond = [n for n in range(len(grid)) if
(grid[n,metadata['columns'].index('LogQ')] in
metadata['resampled']['LogQ']) and
(grid[n,metadata['columns'].index('Tot[O]+12')] in
metadata['resampled']['Tot[O]+12'])]
else:
origin_cond = [n for n in range(len(grid))]
# Now plot the black outlines.
original_pts = ax1.scatter(grid[origin_cond, grid_cols.index('Mix_x')],
grid[origin_cond, grid_cols.index('Mix_y')],
marker='o',
c=grid[origin_cond, grid_cols.index(color_mode)],
s=60, cmap=pyqz_cmap_0, edgecolor='k', facecolor='white',
vmin=np.min(grid[:,grid_cols.index(color_mode)]),
vmax=np.max(grid[:,grid_cols.index(color_mode)]),
zorder=5)
# 2-1) Draw the grid lines
# Check as a function of LogQ and Tot[O]+12. Where are these ?
u = grid_cols.index('LogQ')
v = grid_cols.index('Tot[O]+12')
for i in [u,v]:
# Here, 'var' plays the role of 'q' or 'z' depending on 'i'.
for var in np.unique(grid[:,i]):
# Plot the grid line
ax1.plot(grid[grid[:,i]==var][:,grid_cols.index('Mix_x')],
grid[grid[:,i]==var][:,grid_cols.index('Mix_y')],
'k-', lw = 1, zorder=1)
# Plot the bad segments
for bad_seg in bad_segs:
ax1.plot([bad_seg[0][0],bad_seg[1][0]],[bad_seg[0][1],bad_seg[1][1]], 'r-',
linewidth=4, zorder=0)
# Now, also plot the data, if anything was provided !
if not(interp_data is None):
# Compute the combined "observed" ratios
data_comb = [np.zeros_like(data[0]),np.zeros_like(data[1])]
for k in range(len(ratios.split(';'))):
data_comb[0] += coeffs[0][k]*data[k]
data_comb[1] += coeffs[1][k]*data[k]
ax1.scatter(data_comb[0][interp_data == interp_data],
data_comb[1][interp_data == interp_data],
marker='s', c=interp_data[interp_data == interp_data],
s=15, cmap = pyqz_cmap_0, edgecolor='k',
vmin = np.min(grid[:,grid_cols.index(color_mode)]),
vmax = np.max(grid[:,grid_cols.index(color_mode)]),
zorder=2, alpha=0.35)
# Plot also the points outside the grid ?
# Which are they ? Need to check if they have a valid input first !
# mind the "~" to invert the bools ! isn't this cool ?
my_out_pts = ~np.isnan(data_comb[0]) * ~np.isnan(data_comb[1]) * \
np.isnan(interp_data)
# Don't do this if this is a test with fullgrid_x ...
ax1.scatter(data_comb[0][my_out_pts], data_comb[1][my_out_pts],
marker = '^', facecolor = 'none', edgecolor = 'k', s=60)
# 3) Plot the colorbar
cb_ax = plt.subplot(gs[0,1])
cb = Colorbar(ax = cb_ax, mappable = pts, orientation='vertical')
# Colorbar legend
cb.set_label(color_mode, labelpad = 10)
# 4) Axis names, kappa value, etc ...
rats = ratios.split(';')
# Make sure the labels look pretty in ALL cases ...
labelx,labely = get_plot_labels(rats,coeffs)
ax1.set_xlabel(labelx,labelpad = 10)
ax1.set_ylabel(labely,labelpad = 10)
if not(kappa in [np.inf, 'inf']) :
kappa_str = r'$\kappa$ = '+str(kappa)
else :
kappa_str = r'$\kappa$ = $\infty$'
ax1.text(0.85,0.9,kappa_str, horizontalalignment='left',verticalalignment='bottom',
transform=ax1.transAxes)
ax1.grid(True)
if figname :
fig.savefig(figname, bbox_inches='tight')
if show_plot:
plt.show()
else:
plt.close()
