def pulses(xs, size=(10, 4), c=None):
c = np.linspace(0, 1, xs.shape[0]) if isinstance(c, type(None)) else c
color = cm.coolwarm_r(c)
fig = plt.figure(figsize=size)
for xi, ci in zip(xs, color):
plt.plot(xi, color=ci)
plt.title("Centered pulses")
return fig
def plot_maprmax(savefilename, plotname):
with open(savefilename, 'rb') as savefile:
bf = numpy.array(pickle.load(savefile))
samples = numpy.array(pickle.load(savefile))
bf_g15 = numpy.array(pickle.load(savefile))
samples_g15 = numpy.array(pickle.load(savefile))
bf_zero = numpy.array(pickle.load(savefile))
samples_zero = numpy.array(pickle.load(savefile))
maps = define_rcsample.MAPs()
plotthis = numpy.zeros(len(bf)) + numpy.nan
for ii, map in enumerate(maps.map()):
tmed = numpy.median(
numpy.exp(samples[ii, 3])[True -
numpy.isnan(numpy.exp(samples[ii, 3]))])
if tmed < 5.:
tmed = 0.
plotthis[ii] = tmed
bovy_plot.bovy_print()
maps.plot(plotthis,
vmin=5.,
vmax=13.,
minnstar=15,
zlabel=r'$R_{\mathrm{peak}}\,(\mathrm{kpc})$',
shrink=0.68,
cmap='coolwarm_r')
# Sequences
haloc = define_rcsample.highalphalocus()
bovy_plot.bovy_plot(haloc[:, 0],
haloc[:, 1],
'-',
color='0.75',
lw=2.5,
overplot=True)
haloc = define_rcsample.lowalphalocus()
haloc = haloc[(haloc[:, 0] > -0.55) * (haloc[:, 0] < 0.225)]
bovy_plot.bovy_plot(haloc[:, 0],
haloc[:, 1],
'-',
color='0.75',
lw=2.5,
overplot=True)
# Label
#t= pyplot.text(-0.51,0.235,r'$\mathrm{single}$',
# size=16.,color='w')
#t.set_bbox(dict(alpha=0.5,color=cm.coolwarm(0.),
# edgecolor='none'))
#t= pyplot.text(-0.475,0.195,r'$\mathrm{exponential}$',
# size=16.,color='w')
t = pyplot.text(-0.625,
0.195,
r'$R_{\mathrm{peak}} < 5\,\mathrm{kpc}$',
size=16.,
color='w')
t.set_bbox(dict(alpha=0.5, color=cm.coolwarm_r(0.), edgecolor='none'))
pyplot.tight_layout()
bovy_plot.bovy_end_print(plotname, dpi=300)
return None
def plot_maprmax(savefilename,plotname):
with open(savefilename,'rb') as savefile:
bf= numpy.array(pickle.load(savefile))
samples= numpy.array(pickle.load(savefile))
bf_g15= numpy.array(pickle.load(savefile))
samples_g15= numpy.array(pickle.load(savefile))
bf_zero= numpy.array(pickle.load(savefile))
samples_zero= numpy.array(pickle.load(savefile))
maps= define_rcsample.MAPs()
plotthis= numpy.zeros(len(bf))+numpy.nan
for ii, map in enumerate(maps.map()):
tmed= numpy.median(numpy.exp(samples[ii,3])[True-numpy.isnan(numpy.exp(samples[ii,3]))])
if tmed < 5.:
tmed= 0.
plotthis[ii]= tmed
bovy_plot.bovy_print()
maps.plot(plotthis,
vmin=5.,vmax=13.,
minnstar=15,
zlabel=r'$R_{\mathrm{peak}}\,(\mathrm{kpc})$',
shrink=0.68,cmap='coolwarm_r')
# Sequences
haloc= define_rcsample.highalphalocus()
bovy_plot.bovy_plot(haloc[:,0],haloc[:,1],'-',color='0.75',
lw=2.5,overplot=True)
haloc= define_rcsample.lowalphalocus()
haloc= haloc[(haloc[:,0] > -0.55)*(haloc[:,0] < 0.225)]
bovy_plot.bovy_plot(haloc[:,0],haloc[:,1],'-',color='0.75',
lw=2.5,overplot=True)
# Label
#t= pyplot.text(-0.51,0.235,r'$\mathrm{single}$',
# size=16.,color='w')
#t.set_bbox(dict(alpha=0.5,color=cm.coolwarm(0.),
# edgecolor='none'))
#t= pyplot.text(-0.475,0.195,r'$\mathrm{exponential}$',
# size=16.,color='w')
t= pyplot.text(-0.625,0.195,r'$R_{\mathrm{peak}} < 5\,\mathrm{kpc}$',
size=16.,color='w')
t.set_bbox(dict(alpha=0.5,color=cm.coolwarm_r(0.),
edgecolor='none'))
pyplot.tight_layout()
bovy_plot.bovy_end_print(plotname,dpi=300)
return None
def map_color(val):
color = cm.coolwarm_r(val/max_val)
return '#%02x%02x%02x' % (round(color[0]*255), round(color[1]*255), round(color[2]*255))
ssfr
) - 1 # --- correct SFR for duration (WILL NOT BE NEEDED IN FUTURE)
ssfr += 9 # convert to Gyr^-1
# --- apply mass cut
s = np.log10(mstar) > 9.
# --- add scatter plot color-coded by sSFR
norm = mpl.colors.Normalize(vmin=-0.5,
vmax=1.5) # +1 gets rid of the yellow colour
ax.scatter(VJ[s],
UV[s],
c=cm.coolwarm_r(norm(ssfr[s])),
s=1,
alpha=0.5,
zorder=1)
# --- UVJ selection region
UVJ_region = [[-1., 0.75, 1.4, 1.4], [1.2, 1.2, 1.75, 2.0]]
ax.fill_between(*UVJ_region, [xlims[1]] * len(UVJ_region[0]),
color='k',
alpha=0.1)
ax.plot(*UVJ_region, c='k', lw=1)
# --- add redshift label
# sampling frequency
sampl_freq = 200
# total nr of samples for 1h
NSAMP = sampl_freq * 60 * min_compute
comp_colors = {"Z": "orange", "N": "blue", "E": "green"}
#%% Settings
win_sizes = [30, 20, 15, 10, 5, 1]
win_size = 1
components = ("Z", "N", "E")
component = "E"
winsize_colors = [cm.coolwarm_r(x) for x in np.linspace(0, 1, len(win_sizes))]
depths = [0, 100, 200, 300, 400, 500]
# define Modus for plotting
modus = "norm_stack"
#%% 3D MFP
### COMP-ANALYSIS: norm_stack
if modus == "norm_stack":
# stack normalized beampower of all windows (one component)
with open(r"C:\Users\Philip\Desktop\MA\Data\MFP_norm_stacked.pickle",
'rb') as pickle_dict:
MFP_norm_stacked = pickle.load(pickle_dict)