def plot_list(
list_file,
directory,
extension=EXTENSION,
specific=None,
title="No Title",
scaling=SCALING,
text_offset=TEXT_OFFSET,
axis_offset=AXIS_OFFSET,
):
file = open(list_file)
# Formating for thicker figure for readability
plt.rc("axes", linewidth=2.5)
ax = plt.gca()
ax.tick_params(width=2.5, size=5)
for tick in ax.xaxis.get_major_ticks():
tick.label1.set_fontsize(19)
# tick.label1.set_fontweight('bold')
for tick in ax.yaxis.get_major_ticks():
tick.label1.set_fontsize(19)
# tick.label1.set_fontweight('bold')
obj_red = []
for line in file: # Creates a list and appends all the objects with their redshifts as nested lists.
(object, redshift) = line.split()
obj_red.append([object, float(redshift)])
obj_red.sort(key=lambda x: x[1]) # Sorts the object list by the redshift
full_list = lib_spec(directory, extension) # Now create the dictionary of spectra objects.
# The obj_red list acts as the keys to the dictionary to the spectra in the full_list dictionary.
final = full_list[obj_red[0][0].split(".")[0]]
first = full_list[obj_red[-1][0].split(".")[0]]
for spec in obj_red: # Reassigns all the redshifts to objects in the full_list.
instance = full_list[spec[0].split(".")[0]]
instance.redshift(spec[1]) # Assigning redshift
i = 0 # Keep track of object number
for spec in obj_red: # This iteration plots the spectra.
instance = full_list[spec[0].split(".")[0]]
instance.plot(i, scaling) # Plot this with the index number (for y translation) and with scaling coefficient.
x = (
first.wavlist[0] / (1 + first.z) - text_offset
) # Plots the text relative to the lowest wavelength of the spectra
y = i
text = instance.name + ", z = " + str(round(instance.z, 3))
plt.rc("font", family="serif")
plt.text(x, y, text, fontsize=13, family="Cambria")
i += 1
# Assigns the axis of the plot.
x_axis_low = first.wavlist[0] / (1 + first.z) - axis_offset
x_axis_high = final.wavlist[-1] / (1 + final.z) + 200
y_axis_high = i + 1
y_axis_low = -1
# Plot the axis and the line labels.
plt.axis([x_axis_low, x_axis_high, y_axis_low, y_axis_high])
plt.xlabel("$\mathrm{Rest \\ Wavelength \\ (\AA)}$", fontsize=25, fontweight="bold")
plt.ylabel(
"$\mathrm{Relative \\ Spectral \\ Flux \\ (erg \\ s^{-1} \\ cm^{-2} \\ \AA ^{-1}) \\ + \\ Constant}$",
fontsize=25,
fontweight="bold",
)
# Will plot a specific spectra if asked to, not main point of the code.
if specific is None:
pass
else:
plt.clf()
# Formating for thicker figure for readability
plt.rc("axes", linewidth=3)
ax = plt.gca()
ax.tick_params(width=3, size=5)
for tick in ax.xaxis.get_major_ticks():
tick.label1.set_fontsize(22)
# tick.label1.set_fontweight('bold')
for tick in ax.yaxis.get_major_ticks():
tick.label1.set_fontsize(22)
# tick.label1.set_fontweight('bold')
plt.xlabel("$\mathrm{Rest \\ Wavelength \\ (\AA)}$", fontsize=30)
plt.ylabel("$\mathrm{Relative \\ Spectral \\ Flux \\ (erg \\ s^{-1} \\ cm^{-2} \\ \AA ^{-1})}$", fontsize=30)
full_list[specific].plot(0, scaling)
y_axis_high = max(full_list[specific].data) * scaling / full_list[specific].norm + 0.9
plt.axis(
[
(full_list[specific].wavlist[0] - 50) / (1 + full_list[specific].z),
(full_list[specific].wavlist[-1] + 50) / (1 + full_list[specific].z),
-0.5,
y_axis_high - 0.3,
]
)
plt.text(
(full_list[specific].wavlist[0]) / (1 + full_list[specific].z),
y_axis_high - 0.5,
full_list[specific].name + ", $\mathrm{z} = $" + str(round(full_list[specific].z, 3)),
horizontalalignment="left",
fontsize=25,
)
# Plots the relative lin labels. Some have extra whitespace due to closeness.
# line_label(1400,"SiIV",y_axis_high)
# line_label(1549,"CIV",y_axis_high)
# line_label(1909,"CIII",y_axis_high)
line_label(2798, "MgII", y_axis_high)
line_label(3427, "[NeV]", y_axis_high)
line_label(3729, "[OII] ", y_axis_high)
line_label(3870, " [NeIII]", y_axis_high)
line_label(4341, "H$\gamma$ ", y_axis_high)
line_label(4386, " [OIII]", y_axis_high)
line_label(4861, "H$\\beta$ ", y_axis_high)
line_label(4959, "", y_axis_high)
line_label(5007, " [OIII]", y_axis_high)
line_label(6548, "[NII] ", y_axis_high)
line_label(6563, "H$\\alpha$", y_axis_high)
line_label(6584, " [NII]", y_axis_high)
plt.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.1)
plt.show()
plt.close()
def compspectra(list_file, directory, cutoffs, regions, extension=EXTENSION):
file = open(list_file)
obj_red = []
for line in file: # Creates a list and appends all the objects with their redshifts as nested lists.
(object, redshift) = line.split()
obj_red.append([object, float(redshift)])
obj_red.sort(key=lambda x: x[1]) # Sorts the object list by the redshift
full_list = lib_spec(directory, extension) # Now create the dictionary of spectra objects.
# The obj_red list acts as the keys to the dictionary to the spectra in the full_list dictionary.
for spec in obj_red: # Reassigns all the redshifts to objects in the full_list.
instance = full_list[spec[0].split(".")[0]]
instance.redshift(spec[1]) # Assigning redshift
final = full_list[obj_red[0][0].split(".")[0]].wavlist[-1] / (1 + full_list[obj_red[0][0].split(".")[0]].z)
first = full_list[obj_red[-1][0].split(".")[0]].wavlist[0] / (1 + full_list[obj_red[-1][0].split(".")[0]].z)
interpgrid = arange(
math.floor(first), math.ceil(final) - 0.5, 0.5
) # Make the grid we are going to interpolate over.
regiondat = []
regionsigma = []
regioncount = []
regionspectralists = []
# Creating composite spectra components
for j in range(len(regions)):
comp_spectras = []
for spec in obj_red:
instance = full_list[spec[0].split(".")[0]]
if j == 0:
if instance.z > cutoffs[0]:
comp_spectras.append(
CompSpecCpnt(instance.wavlist, instance.data, interpgrid, instance.z, instance.name, regions[0])
)
else:
pass
elif j == len(regions) - 1:
if instance.z < cutoffs[j - 1]:
comp_spectras.append(
CompSpecCpnt(instance.wavlist, instance.data, interpgrid, instance.z, instance.name, regions[j])
)
else:
pass
else:
if (instance.z < cutoffs[j - 1]) and (instance.z > cutoffs[j]):
comp_spectras.append(
CompSpecCpnt(instance.wavlist, instance.data, interpgrid, instance.z, instance.name, regions[j])
)
else:
pass
compdat = [] # Flux Data
compsigma = [] # Sigma Data
countdat = [] # Number Data
# Creating Composite Spectra
for i in range(len(interpgrid)):
candidates = []
for spec in comp_spectras:
if not (spec.fluxlist[i] == 0):
candidates.append(spec.fluxlist[i])
if len(candidates) > 1:
nooutliers = stats.sigmaclip(candidates)[0]
else:
nooutliers = candidates
if len(nooutliers) > 0:
compdat.append(sum(nooutliers) / len(nooutliers))
compsigma.append(std(nooutliers))
else:
compdat.append(0)
compsigma.append(0)
countdat.append(len(nooutliers))
regiondat.append(compdat)
regionsigma.append(compsigma)
regioncount.append(countdat)
regionspectralists.append(comp_spectras)
centerregion = regions[int(math.floor(len(regions) / 2))]
comp_spectras = []
for i in range(len(regiondat)):
for j in range(len(regionspectralists[i])):
regionspectralists[i][j].fluxlist = regionspectralists[i][j].fluxlist / sum(
regiondat[i][centerregion[0] : centerregion[1]]
)
comp_spectras = comp_spectras + regionspectralists[i]
compdat = [] # Flux Data
compsigma = [] # Sigma Data
countdat = [] # Number Data
# Creating Composite Spectra
for i in range(len(interpgrid)):
candidates = []
for spec in comp_spectras:
if not (spec.fluxlist[i] == 0):
candidates.append(spec.fluxlist[i])
if len(candidates) > 1:
nooutliers = stats.sigmaclip(candidates)[0]
else:
nooutliers = candidates
if len(nooutliers) > 0:
compdat.append(sum(nooutliers) / len(nooutliers))
compsigma.append(std(nooutliers) / math.sqrt(len(nooutliers)))
else:
compdat.append(0)
compsigma.append(0)
countdat.append(len(nooutliers))
return [interpgrid, compdat, compsigma, countdat]
