def plot_ima_spectra(
start,
finish,
species=["H", "O", "O2"],
colorbar=True,
ax=None,
blocks=None,
check_times=True,
return_val=None,
verbose=False,
check_overlap=True,
vmin=2,
vmax=7.0,
raise_all_errors=False,
cmap=None,
norm=None,
die_on_empty_blocks=False,
accept_new_tables=True,
inverted=True,
**kwargs
):
""" Plot IMA spectra from start - finish, .
blocks is a list of data blocks to work from, if this is not specified then we'll read them
species names: [only those with x will function]
heavy (16, 85, 272) x
sumions (85, 32)
CO2 (16, 85, 272) x
E (96, 1)
Horig (16, 85, 272) x
tmptime (1, 272)
H (16, 85, 272)
dE (1, 85)
sumorigions (85, 32)
O (16, 85, 272) x
Hsp (16, 85, 272) x
mass (50, 272)
alpha (16, 85, 272) x
O2 (16, 85, 272) x
He (16, 85, 272) x
for blocks read with dataset='fion', we'll add an 'f' before
CO2, O2, O2plus, O, Horig, He, H
automagically
"""
if not blocks:
blocks = read_ima(start, finish, verbose=verbose, **kwargs)
if ax is None:
ax = plt.gca()
plt.sca(ax)
ax.set_axis_bgcolor("lightgrey")
if not cmap:
cmap = matplotlib.cm.Greys_r
# cmap.set_under('white')
if not norm:
norm = plt.Normalize(vmin, vmax, clip=True)
ims = []
last_finish = -9e99
if blocks:
if "fH" in list(blocks[0].keys()): # blocks were read with dataset='fion'
if isinstance(species, str):
if species in ("O", "H", "He", "alpha", "Horig", "O2", "O2plus", "CO2"):
species = "f" + species
else:
new_species = []
for s in species:
if s in ("O", "H", "He", "alpha", "Horig", "O2", "O2plus", "CO2"):
new_species.append("f" + s)
species = new_species
# if isinstance(species, basestring):
# if species[0] == 'f':
# label = species[1:] + r' flux \n / $cm^{-2}s^{-1}$'
# else:
# label = species + '****'
# else:
# label = ''
# for s in species:
# if s[0] == 'f':
# label += s[1:] + ', '
# else:
# label += s
# label += r' flux \n/ $cm^{-2}s^{-1}$'
label = r"Flux / $cm^{-2}s^{-1}$"
# Make sure we set the ylimits correctly by tracking min and max energies
min_energy = 60000.0
max_energy = -10.0
for b in blocks:
# min(abs()) to account for negative values in E table
extent = [
celsius.matlabtime_to_spiceet(b["tmptime"][0]),
celsius.matlabtime_to_spiceet(b["tmptime"][-1]),
min(abs(b["E"])),
max(b["E"]),
]
# Account for the varying size of the Energy table:
if b["sumions"].shape[0] == 96:
extent[2] = b["E"][-1]
extent[3] = b["E"][0]
elif b["sumions"].shape[0] == 85: # revised energy table
extent[2] = b["E"][-11]
extent[3] = b["E"][0]
if extent[2] < 0.0:
raise ValueError("Energies should be positive - unrecognised energy table?")
else:
if accept_new_tables:
extent[2] = np.min(np.abs(b["E"]))
extent[3] = b["E"][0]
print("New table:", extent[2], extent[3], b["E"][-1], b["E"][0], b["sumions"].shape[0])
else:
raise ValueError(
"Unrecognised energy table: E: %e - %e in %d steps?"
% (b["E"][-1], b["E"][-1], b["sumions"].shape[0])
)
if extent[2] < min_energy:
min_energy = extent[2]
if extent[3] > max_energy:
max_energy = extent[3]
if check_times:
spacing = 86400.0 * np.mean(np.diff(b["tmptime"]))
if spacing > 15.0:
if raise_all_errors:
raise ValueError("Resolution not good? Mean spacing = %fs " % spacing)
else:
plt.annotate(
"Resolution warning:\nmean spacing = %.2fs @ %s "
% (spacing, celsius.utcstr(np.median(b["tmptime"]))),
(0.5, 0.5),
xycoords="axes fraction",
color="red",
ha="center",
)
if not isinstance(species, str):
# produce the MxNx3 array for to up 3 values of species (in R, G, B)
img = np.zeros((b[species[0]].shape[1], b[species[0]].shape[2], 3)) + 1.0
for i, s in enumerate(species):
im = np.sum(b[s], 0).astype(np.float64)
# im[im < 0.01] += 0.1e-10
if inverted:
im = 1.0 - norm(np.log10(im))
for j in (0, 1, 2):
if j != i:
img[..., j] *= im
tot = np.sum(1.0 - img)
else:
img[..., i] *= norm(np.log10(im))
tot = np.sum(img)
else:
img = np.sum(b[species], 0)
img[img < 0.01] += 0.1e-10
img = np.log10(img)
tot = np.sum(img)
if verbose:
print("Total scaled: %e" % tot)
# print 'Fraction good = %2.0f%%' % (np.float(np.sum(np.isfinite(img))) / (img.size) * 100.)
# print 'Min, mean, max = ', np.min(img), np.mean(img), np.max(img)
if check_overlap and (extent[0] < last_finish):
raise ValueError("Blocks overlap: Last finish = %f, this start = %f" % (last_finish, extent[0]))
if FUDGE_FIX_HANS_IMA_TIME_BUG:
# print extent, last_finish
if abs(extent[0] - last_finish) < 20.0: # if there's 20s or less between blocks
if verbose:
print("\tFudging extent: Adjusting start by %fs" % (extent[0] - last_finish))
extent[0] = last_finish # squeeze them together
if inverted:
name = cmap.name
if name[-2:] == "_r":
name = name[:-2]
else:
name = name + "_r"
cmap = getattr(plt.cm, name)
if extent[1] < start:
# if verbose:
print("Dumping block (B)", start - extent[1])
continue
if extent[0] > finish:
print("Dumping block (A)", extent[0] - finish)
continue
ims.append(plt.imshow(img, extent=extent, origin="upper", interpolation="nearest", cmap=cmap, norm=norm))
last_finish = extent[1]
if ims:
plt.xlim(start, finish)
plt.ylim(min_energy, max_energy)
cbar_im = ims[0]
else:
plt.ylim(10.0, 60000) # guess
# invisible image for using with colorbar
cbar_im = plt.imshow(np.zeros((1, 1)), cmap=cmap, norm=norm, visible=False)
plt.yscale("log")
celsius.ylabel("E / eV")
if colorbar:
## make_colorbar_cax is doing something weird to following colorbars...
# if not isinstance(species, basestring):
# img = np.zeros((64,len(species),3)) + 1.
# cax = celsius.make_colorbar_cax(width=0.03)
# for i, s in enumerate(species):
# for j in (0,1,2):
# if j != i:
# img[:,i,j] = np.linspace(0., 1., 64)
# # plt.sca(cax)
# plt.imshow(img, extent=(0, 3, vmin, vmax), interpolation='nearest', aspect='auto')
# plt.xticks([0.5, 1.5, 2.5], label.split(', '))
# plt.xlim(0., 3.)
# cax.yaxis.tick_right()
# cax.xaxis.tick_top()
# plt.yticks = [2,3,4,5,6]
# cax.yaxis.set_label_position('right')
# plt.ylabel(r'$Flux / cm^{-2}s^{-1}$')
# # plt.sca(ax)
# else:
ticks = np.arange(int(vmin), int(vmax) + 1, dtype=int)
plt.colorbar(cbar_im, cax=celsius.make_colorbar_cax(), ticks=ticks).set_label(label)
if return_val:
if return_val == "IMAGES":
del blocks
return ims
elif return_val == "BLOCKS":
return blocks
else:
print("Unrecognised return_value = " + str(return_value))
del blocks
del ims
return
def plot_els_spectra(
start,
finish,
sector="SUM",
colorbar=True,
ax=None,
blocks=None,
return_val=None,
verbose=False,
check_times=True,
vmin=None,
vmax=None,
cmap=None,
norm=None,
die_on_empty_blocks=False,
**kwargs
):
"""
Plot ASPERA/ELS spectra from start - finish.
sector = int from 0 - 15 to select single sector to plot.
sector = 'SUM' to integrate over all
sector = 'MEAN' to average over all
Shapes of the stuff in each block (215 = time!)
altElec (1, 215)
latElec (1, 215)
zmsoElec (1, 215)
dEElec (128, 1)
ymsoElec (1, 215)
xmsoElec (1, 215)
tElec (1, 215)
fElec (16, 128, 215)
longElec (1, 215)
EElec (128, 1)
New versions:
elslevels (128, 112)
elsmatrix (128, 16, 783)
elstimes (1, 783)
elssectors (1, 16)
elsleveltimes (1, 112)
elsdeltatimes (128, 112)
"""
if not blocks:
blocks = read_els(start, finish, verbose=verbose, **kwargs)
if ax is None:
ax = plt.gca()
plt.sca(ax)
ax.set_axis_bgcolor("lightgrey")
if not cmap:
cmap = Spectral_r
cmap.set_bad("white")
ims = []
last_finish = -9e99
# label = r'Flux / $cm^{-2}s^{-1}$'
label = "Counts"
# Make sure we set the ylimits correctly by tracking min and max energies
min_energy = 1e99
max_energy = -1e99
cbar_ticks = np.array((7, 8, 9, 10, 11))
# Establish function to handle the data
if isinstance(sector, str):
if sector.lower() == "mean":
process = lambda x: np.nanmean(x, 1)
# if vmin is None:
# vmin = 7.
# if vmax is None:
# vmax = 11.
elif sector.lower() == "sum":
process = lambda x: np.nansum(x, 1)
# if vmin is None:
# vmin = 7. + 1.
# if vmax is None:
# vmax = 11. + 1.
# cbar_ticks += 1
else:
raise ValueError("Unrecognized argument for sector: %s" % sector)
else:
process = lambda x: x[:, sector, :]
# if vmin is None:
# vmin = 7.
# if vmax is None:
# vmax = 11.
if vmin is None:
vmin = 0
if vmax is None:
vmax = 4
if not norm:
norm = plt.Normalize(vmin, vmax, clip=False)
norm = None
for b in blocks:
# min(abs()) to account for negative values in E table
extent = [
celsius.matlabtime_to_spiceet(np.min(b["elstimes"])),
celsius.matlabtime_to_spiceet(np.max(b["elstimes"])),
np.min(np.abs(b["elslevels"])),
np.max(b["elslevels"]),
]
if extent[2] < min_energy:
min_energy = extent[2]
if extent[3] > max_energy:
max_energy = extent[3]
print(extent)
# if check_times:
# spacing = 86400.*np.mean(np.diff(b['tElec']))
# if spacing > 15.:
# raise ValueError("Resolution not good? Mean spacing = %fs " % spacing )
img = process(b["elsmatrix"])
# img[img < 10.] = np.min(img)
img = np.log10(img)
if verbose:
print(
"Fraction good = %2.0f%%" % (np.float(np.sum(np.isfinite(img))) / (img.shape[0] * img.shape[1]) * 100.0)
)
# print 'Min, mean, max = ', np.nanmin(img), np.nanmean(img), np.nanmax(img)
if extent[0] < last_finish:
s = "Blocks overlap: Last finish = %f, this start = %f" % (last_finish, extent[0])
if check_times:
raise ValueError(s)
else:
print(s)
# e = extent[2]
# extent[2] = extent[3]
# extent[3] = e
ims.append(plt.imshow(img, extent=extent, origin="upper", interpolation="nearest", cmap=cmap, norm=norm))
last_finish = extent[1]
number_of_blocks = len(blocks)
if blocks:
plt.xlim(start, finish)
plt.ylim(min_energy, max_energy)
cbar_im = ims[0]
else:
plt.ylim(1e0, 1e4)
# need to create an empty image so that colorbar functions
cbar_im = plt.imshow(np.zeros((1, 1)), cmap=cmap, norm=norm, visible=False)
plt.ylim(min_energy, max_energy)
plt.yscale("log")
# plt.ylabel('E / eV')
print("ELS PLOT: Time is not accurate to more than ~+/- 4s for now.")
if colorbar:
if ims:
plt.colorbar(cbar_im, cax=celsius.make_colorbar_cax(), ticks=cbar_ticks).set_label(label)
if return_val:
if return_val.upper() == "IMAGES":
del blocks
return ims
elif return_val.upper() == "BLOCKS":
del ims
return blocks
else:
print("Unrecognised return_value = " + str(return_value))
del blocks
del ims
return number_of_blocks