def fig_power(po, pos=(0,4, 4,8)):
ax = py.subplot(gs[pos[2]:pos[3], pos[0]:pos[1]])
set_axis(ax, -0.1, 1.2, letter= letters[po])
if po>5: py.xlabel('Power ($mV^{2}$)', fontsize = fsize-4)
for ll in range(2,4):
py.fill_between(range(2),-2,2,facecolor='grey', alpha=0.3)
mean_csd = np.mean(csd_col[ll], axis=0)
std_csd = np.std(csd_col[ll], axis=0)/np.sqrt(len(nazwy))
py.plot(mean_csd, np.linspace(-30,30,64), label = labels[ll%2], linewidth = 2, color = colors[ll%2])
py.fill_betweenx(np.linspace(-30,30,64), mean_csd - std_csd, mean_csd + std_csd, color=colors[ll%2], alpha = 0.3)
py.axvline(0, linewidth = 0.5, linestyle = '--', color = 'grey')
nzw.append('average csd')
if po<5: py.yticks([-3,0, 10], tiky)
else: py.yticks([-6,0, 6], tiky)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
if ele_typ==20: py.ylim(-15, 10)
else: py.ylim(-4, 25)
def fig_mua(pos, num, gs, df, version='cor'):
ax = py.subplot(gs[pos[2]:pos[3], pos[0]:pos[1]])
set_axis(ax, -0.05, 1.1, letter=num)
# py.title('MUA average frequency/phase')
nazwy = ['022', '027', '128', '114']#, '116'
miti= -1
csd_col = np.zeros((2,len(nazwy),64))
for i,key in enumerate(nazwy):
hist_layer = list(df[int(key)][4:36].values)
mit_pos = sl.all_indices(hist_layer, 'mit')
sp_mtrx = np.load(loaddir+'saved_csd/spike_cor'+ str(key)+'.npy')
sp_freq = np.load(loaddir+'saved_csd/spike_freq'+ str(key)+'.npy')
min_mit = mit_pos[miti]
frqs = np.linspace(0, 558, 51)
frq_max = frqs[list(np.argmax(sp_freq[0, :, :], axis=1))]
if min_mit<mit_pos[miti]: min_mit = mit_pos[miti]
csd_col[1, i, 31-min_mit:63-min_mit] = sp_mtrx[0]
csd_col[0, i, 31-min_mit:63-min_mit] = frq_max
i=0
if version == 'cor':
mean_csd = np.mean(csd_col[1], axis=0)
std_csd = np.std(csd_col[1], axis=0)/(len(nazwy))**(1/2)
else:
mean_csd = np.mean(csd_col[0], axis=0)
std_csd = np.std(csd_col[0], axis=0)/(len(nazwy))**(1/2)
py.plot(mean_csd+i, np.linspace(-31,32,64), color = 'black', marker = 'o')
py.fill_betweenx(np.linspace(-31,32,64), mean_csd - std_csd, mean_csd + std_csd, color='black', alpha = 0.5)
py.yticks([-12, -5, -1, 5], ['glom.', 'EPL', 'mitral', 'grn'])
py.ylim(-15, 15)
py.axvline(0, ls ='--', color = 'grey')
py.axvline(-0.5, ls ='--', color = 'grey')
py.axvline(0.5, ls ='--', color = 'grey')
if version == 'cor':
py.xlim(-1, 1)
py.xlabel('MUA histogram-HFO correlation', fontsize = fsize-4)
else:
py.xlim(1,200)
py.xlabel('MUA histogram frequency', fontsize=fsize-4)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
def figprof(pos, let, lowpass=0.1, highpass=8, title=''):
nazwy = ['022', '027', '114', '116', '117', '127', '128', '129']
ax = py.subplot(gs[pos[2]:pos[3], pos[0]:pos[1]])
set_axis(ax, -0.05, 1.05, letter=let)
miti = -1
csd_col = np.zeros((len(nazwy), 64))
for i, key in enumerate(nazwy):
hist_layer = list(df[int(key)][4:36].values)
mit_pos = sl.all_indices(hist_layer, 'mit')
y = np.load(loaddir + 'CSD' + str(key) + '.npy')
Fss = 1394
[b, a] = butter(3., [lowpass / (Fss / 2.0), highpass / (Fss / 2.0)],
btype='bandpass')
y = filtfilt(b, a, y)
pol = int(len(y[0]) / 2) + 1
y = y[:, pol]
y = y / np.max(abs(y + 1e-10)) * .3
csd_col[i, 31 - mit_pos[miti]:63 - mit_pos[miti]] = y
y2 = csd_col[i]
py.plot(y2 + i + 1, np.linspace(-30, 30, 64), color='grey')
py.fill_betweenx(np.linspace(-30, 30, 64),
i + 1 + y2,
i + 1,
where=y2 + i + 1 >= i + 1,
color='red',
alpha=0.5)
py.fill_betweenx(np.linspace(-30, 30, 64),
i + 1 + y2,
i + 1,
where=y2 + i + 1 <= i + 1,
color='blue',
alpha=0.5)
py.yticks([-12, -5, -1, 5], ['glom.', 'EPL', 'mitral', 'grn'])
py.ylim(-15, 15)
py.plot([0, 10], [-1, -1], color='grey', alpha=0.3, lw=10)
py.xlabel('Rats')
py.xlim(0.5, 8.5)
py.title(title, fontsize=20)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
def fig_phase(po, pos=(0,4, 4,8)):
ax = py.subplot(gs[pos[2]:pos[3], pos[0]:pos[1]])
set_axis(ax, -0.1, 1.2, letter= letters[po])
for ll in range(2):
py.fill_between(range(300),-2,2,facecolor='grey', alpha=0.3)
mean_csd = np.mean(csd_col[ll], axis=0)
std_csd = np.std(csd_col[ll], axis=0)/np.sqrt(len(nazwy))
py.plot(mean_csd, np.linspace(-30,30,64), label = labels[ll], linewidth = 2, color = colors[ll])
py.fill_betweenx(np.linspace(-30,30,64), mean_csd - std_csd, mean_csd + std_csd, color=colors[ll], alpha = 0.3)
py.axvline(0, linewidth = 0.5, linestyle = '--', color = 'grey')
if po<5: py.yticks([-3,0, 10], tiky)
else: py.yticks([-6,0, 6], tiky)
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.spines['left'].set_visible(False)
if ele_typ==20: py.ylim(-15, 10)
else: py.ylim(-4, 25)
xL=['0',r'$\frac{\pi}{2}$',r'$\pi$', r'$\frac{3\pi}{2}$']
py.xlim(0,290)
py.xticks([0, 90, 180, 270], xL)
if po<5: ax.legend(loc='center', bbox_to_anchor=(-.1, 1.1), ncol=2,
frameon = True, fontsize = 16)
if po>5: py.xlabel('Relative phase', fontsize = fsize-4)
cnt = 1
for cl in range(nclusters):
v_inds = clusters[cl][1]
t_inds = clusters[cl][0]
signals = [np.mean(np.mean(np.array(s)[:, :, v_inds], axis=0),
axis=1).squeeze() for s in data]
pl.subplot(nclusters, 2, cnt)
# The time plot figure only makes sense if it's not DICS_epochs
if mask_fname.find('DICSepochs') < 0:
for c in range(len(colors)):
pl.plot(times, signals[c], color=colors[c])
pl.xlabel('Time (s)')
pl.ylabel('Activation')
pl.legend(groups, loc='best')
pl.plot([0, 0], pl.ylim(), ':k')
pl.fill_betweenx(pl.ylim(), times[np.min(t_inds)],
times[np.max(t_inds)], color='grey', alpha=0.3)
cnt += 1
# now, work on the mean over time barplot or regression, depending on result
pl.subplot(nclusters, 2, cnt)
my_test = mask_fname.split('_')[0]
if my_test.find('anova') >= 0 or my_test.find('nvVSper') >= 0 or \
my_test.find('nvVSrem') >= 0 or my_test.find('perVSrem') >= 0:
signals = [np.mean(np.array(s)[:, :, v_inds], axis=2) for s in data]
signals = [np.mean(s[:, t_inds], axis=1) for s in signals]
ybars = [np.mean(s) for s in signals]
y_sd = [np.std(s)/np.sqrt(len(s)) for s in signals]
pl.bar(np.arange(len(ybars)), ybars, 0.35, color=colors,
ecolor='k', yerr=y_sd, align='center')
pl.xticks(range(len(groups)), groups)
pl.xlim([-.2, 2.5])
def foo(): pylab.fill_betweenx(fill_mtotal, fill_min_mchirp, fill_max_mchirp, edgecolor='0.6', facecolor='0.6') #pylab.plot(big_data[0], big_data[1], ',k', markersize=0.01) pylab.plot(small_data[0], small_data[1], ',k')
def make_figure(data, wavelength, beam, bins=50):
uvcts = np.concatenate([
data[spw]['uvdist'][~data[spw]['flag'].any(axis=(0, 1))]
for spw in data
]).ravel()
uvwts = np.concatenate([
data[spw]['weight'].mean(axis=0)[~data[spw]['flag'].any(axis=(0, 1))]
for spw in data
]).ravel()
beam_to_bl = (wavelength / beam).to(u.m, u.dimensionless_angles())
pl.figure(figsize=(8, 4))
ax1 = pl.subplot(1, 2, 1)
_ = pl.hist(uvcts, bins=bins)
_ = pl.xlabel('Baseline Length (m)')
_ = pl.ylabel("Number of Visibilities")
yl = pl.ylim()
pl.fill_betweenx(yl,
beam_to_bl[0].value,
beam_to_bl[1].value,
zorder=-5,
color='orange',
alpha=0.5)
pl.fill_betweenx(yl,
np.percentile(uvcts, 25),
np.percentile(uvcts, 75),
zorder=-5,
color='red',
alpha=0.25)
pl.ylim(yl)
ax1t = ax1.secondary_xaxis(
'top',
functions=(lambda x: x / 1e3 / wavelength.to(u.m).value,
lambda x: x / 1e3 / wavelength.to(u.m).value))
ax1t.set_xlabel("Baseline Length (k$\lambda$)")
#ax1t.set_ticks(np.linspace(1000,100000,10))
ax2 = pl.subplot(1, 2, 2)
_ = pl.hist(uvcts, weights=uvwts, bins=bins, density=True)
_ = pl.xlabel('Baseline Length (m)')
_ = pl.ylabel("Fractional Weight")
def forward(x):
return (wavelength.to(u.m) / (x * u.arcsec)).to(
u.m, u.dimensionless_angles()).value
def inverse(x):
return (wavelength.to(u.m) / (x * u.m)).to(
u.arcsec, u.dimensionless_angles()).value
ax2t = ax2.secondary_xaxis('top', functions=(forward, inverse))
ax2t.set_xlabel("Angular size $\lambda/D$ (arcsec)")
if ax2.get_xlim()[1] > 1000:
ax2t.set_ticks([10, 1, 0.5, 0.4, 0.3, 0.2, 0.1])
elif ax2.get_xlim()[1] > 600:
ax2t.set_ticks([10, 2, 1, 0.6, 0.5, 0.4, 0.3])
else:
ax2t.set_ticks([10, 2, 1, 0.8, 0.7, 0.6, 0.2])
yl = pl.ylim()
pl.fill_betweenx(yl,
beam_to_bl[0].value,
beam_to_bl[1].value,
zorder=-5,
color='orange',
alpha=0.5)
#pl.fill_betweenx(yl, np.percentile(uvcts, 25), np.percentile(uvcts, 75), zorder=-5, color='red', alpha=0.25)
pl.ylim(yl)
#pl.subplots_adjust(wspace=0.3)
pl.tight_layout()
print(
f"25th pctile={forward(np.percentile(uvcts, 25))}, 75th pctile={forward(np.percentile(uvcts, 75))}"
)
return (forward(np.percentile(uvcts, [1, 5, 10, 25, 50, 75, 90, 95, 99])),
scipy.stats.percentileofscore(uvcts, beam_to_bl[0].value),
scipy.stats.percentileofscore(uvcts, beam_to_bl[1].value))
def plots_hsc_paper(theta, xip, xip_s8p, xip_s8m):
# plot xiplus, ximoins
shear_color = 'k'
atmo_color = 'b'
non_linear_color = 'r'
xip_dic = pickle.load(open('xip_30_sec.pkl', 'rb'))
#print(xip_dic['xip_atm'])
g = 0.2 * 1e3
theta_atm = xip_dic['r'] * 60.
#xip_atmo = (1./8.) * (xip_dic['xip_atm']**2 / g**4)
xip_atmo = (1. / 8.) * (xip_dic['xip_atm_squared'] / g**4)
#plt.plot(theta_atm, xip_atmo * theta_atm * 1e4, )
#plt.show()
#francis = (xip_dic['xip_atm'] / (200 * 200) )**2 /16
#print(francis - xip_atmo)
#plt.plot(theta_atm, theta_atm * francis * 1e4)
#plt.xscale('log')
#plt.show()
plt.figure(figsize=(12, 7))
plt.subplots_adjust(wspace=0.0, hspace=0., top=0.99, left=0.05, right=0.99)
XLIM = [2, 300]
SUBPLOTS_plus = [1, 2, 3, 4, 5, 6, 7, 9, 10, 13]
KEY_plus = ['14', '13', '12', '11', '24', '23', '22', '34', '33', '44']
HMAX = 4.5
HMIN = -0.1
#YLIM = [[-0.49, 2.5], [-0.49, 2.5], [-0.49, 2.5], [-0.49, 2.5],
# [-0.49, 2.8], [-0.49, 2.8], [-0.49, 2.8],
# [-0.49, 4.9], [-0.49, 4.9],
# [-0.49, 5.4]]
YLIM = [[HMIN, HMAX], [HMIN, HMAX], [HMIN, HMAX], [HMIN,
HMAX], [HMIN, HMAX],
[HMIN, HMAX], [HMIN, HMAX], [HMIN, HMAX], [HMIN, HMAX],
[HMIN, HMAX]]
LEGEND = [
'1,4', '1,3', '1,2', '1,1', '2,4', '2,3', '2,2', '3,4', '3,3', '4,4'
]
for i in range(len(SUBPLOTS_plus)):
key = KEY_plus[i]
plt.subplot(4, 4, SUBPLOTS_plus[i])
if i == 1:
LABEL = 'Contribution from astrometric residuals (simulation LSST like)'
else:
LABEL = None
plt.plot(theta_atm,
xip_atmo * theta_atm * 1e4,
atmo_color,
lw=2,
label=LABEL)
plt.text(165,
3.8,
LEGEND[i],
color='black',
bbox=dict(facecolor='none',
edgecolor='black',
boxstyle='round'),
fontsize=10)
if i == 1:
LABEL = 'Cosmic shear signal with DES Y1 cosmology'
else:
LABEL = None
plt.plot(theta * 60.,
theta * 60. * xip[key] * 1e4,
shear_color,
label=LABEL)
if i == 1:
LABEL = '$\pm$ 1$\sigma$ on $S_8$ from cosmic shear DES Y1 analysis'
else:
LABEL = None
plt.fill_between(theta * 60,
theta * 60. * xip_s8m[key] * 1e4,
theta * 60. * xip_s8p[key] * 1e4,
alpha=0.5,
color=shear_color,
label=LABEL)
#shear_bias_xipm = 1e-5
#shear_bias_xipm_des = 1e-5 / 25.
#plt.plot(theta*60.,
# theta * 60. * shear_bias_xipm * 1e4, atmo_color+'--')
#plt.plot(theta*60.,
# theta * 60. * shear_bias_xipm_des * 1e4, atmo_color+'-.')
# non linear scale
dic = pickle.load(open('xip_xim_real.pkl', 'rb'))
ang = dic['xip'][(dic['xip']['BIN1'] == int(key[0]))
& (dic['xip']['BIN2'] == int(key[1]))]['ANG']
mask = dic['xip'][(dic['xip']['BIN1'] == int(key[0]))
& (dic['xip']['BIN2'] == int(key[1]))]['USED']
nlscale = ang[~mask]
if i == 1:
LABEL = 'Scale removed from DES Y1 cosmic shear analysis'
else:
LABEL = None
plt.fill_betweenx(YLIM[i], [0, 0], [nlscale[-1], nlscale[-1]],
color=non_linear_color,
alpha=0.2,
label=LABEL)
#atmo scale:
#plt.fill_betweenx(YLIM[i], [nlscale[-1],nlscale[-1]], [50, 50], color=atmo_color, alpha=0.2)
if HMIN <= 0:
plt.plot(XLIM, np.zeros(2), 'k', alpha=0.5)
plt.xlim(XLIM[0], XLIM[1])
plt.ylim(YLIM[i][0], YLIM[i][1])
plt.xscale('log')
if SUBPLOTS_plus[i] not in [4, 7, 10, 13]:
plt.xticks([], [])
else:
plt.xlabel('$\\theta$ (arcmin)', fontsize=12)
if SUBPLOTS_plus[i] not in [1, 5, 9, 13]:
plt.yticks([], [])
else:
plt.ylabel('$\\theta \\xi_{+}$ / $10^{-4}$', fontsize=12)
#leg = plt.legend(handlelength=0, handletextpad=0,
# loc=1, fancybox=True, fontsize=8)
#for item in leg.legendHandles:
# item.set_visible(False)
if i == 1:
plt.legend(bbox_to_anchor=(0.9, -2.3),
loc=2,
borderaxespad=0.,
fontsize=12)
plt.ylabel(r"$\sigma(H_0)$ [km/s/Mpc]", fontsize=15, labelpad=10)
plt.gca().tick_params(axis='both', which='major', labelsize=16, size=7.,
width=1.5, pad=8., right=True, direction='in')
plt.gca().tick_params(axis='both', which='minor', labelsize=18, size=4.,
width=1.5, pad=8., right=True, direction='in')
plt.gca().yaxis.set_major_locator(ticker.MultipleLocator(1.0))
plt.gca().yaxis.set_minor_locator(ticker.MultipleLocator(0.2))
#plt.gca().xaxis.set_major_locator(ticker.MultipleLocator(2.0))
#plt.gca().xaxis.set_minor_locator(ticker.MultipleLocator(0.5))
plt.xticks(ticks=[])
#plt.xticks(ticks=np.arange(len(expts), dtype=int), labels=labels, rotation=45, size=10)
plt.fill_betweenx([0., 3.], [-1.,-1.], [0.5, 0.5], color='gray', alpha=0.1, zorder=50, lw=0)
plt.fill_betweenx([0., 3.], [3.5,3.5], [6.5, 6.5], color='gray', alpha=0.1, zorder=50, lw=0)
plt.fill_betweenx([0., 3.], [9.5,9.5], [12.5, 12.5], color='gray', alpha=0.1, zorder=50, lw=0)
# Labels
plt.text(0.0, 1.9, r" CMB + LSS", color='k', fontsize=11, rotation=90, ha='center')
plt.text(2.0, 1.9, r" + DESI", color='gray', fontsize=11, rotation=90, ha='center')
plt.text(5.0, 1.9, r" + HIRAX high-z", color='#D92E1C', fontsize=11, rotation=90, ha='center')
plt.text(8.0, 1.9, r" + HIRAX", color='#E6773D', fontsize=11, rotation=90, ha='center')
plt.text(11., 1.9, r" + Stage 2", color='#3465a4', fontsize=11, rotation=90, ha='center')
plt.text(14.5, 1.9, r" + CV-lim. low-z", color='#64a164', fontsize=11, rotation=90, ha='center')
#plt.legend(loc='upper left')
plt.tight_layout()
try:
plt.savefig(figname)
# Get values
kpar_min = 2. * np.pi / (rnu * dnutot)
kpar_max = 1. / cosmo['sigma_nl'] * np.ones(zz.shape)
kperp_min_int = 2. * np.pi * Dmin / (r * l)
kperp_max_int = 2. * np.pi * Dmax / (r * l)
kperp_min_sd = 2. * np.pi / np.sqrt(r**2. * Sarea)
kperp_max_sd = 2. * np.pi * Ddish / (r * l) # 16.*np.log(2.) / 1.22
kperp_max_sd2 = np.sqrt(16. * np.log(2.)) * Ddish / (r * l)
# Set-up plots
P.subplot(111)
# Interferom. transverse
P.plot(kperp_min_int, zz, lw=1.8, color='#1619A1')
P.plot(kperp_max_int, zz, lw=1.8, color='#1619A1')
P.fill_betweenx(zz, kperp_min_int, kperp_max_int, color='#B1C9FD', alpha=1.)
P.annotate("Int.",
xy=(0.9, 0.8),
xytext=(0., 0.),
fontsize='xx-large',
textcoords='offset points',
ha='center',
va='center')
# Single-dish transverse
P.plot(kperp_min_sd, zz, lw=1.8, color='#CC0000')
P.plot(kperp_max_sd, zz, lw=1.8, color='#CC0000', ls='dashed')
#ax.plot(kperp_max_sd2, zz, lw=1.8, color='g')
P.fill_betweenx(zz, kperp_min_sd, kperp_max_sd, color='#F09B9B', alpha=0.7)
cnt = 1
for cl in range(nclusters):
v_inds = res['clusters'][cl][1]
t_inds = res['clusters'][cl][0]
signals = [np.mean(np.mean(np.array(s)[:, :, v_inds], axis=0),
axis=1).squeeze() for s in data]
pl.subplot(nclusters, 2, cnt)
# The time plot figure only makes sense if it's not DICS_epochs
if mask_fname.find('DICSepochs') < 0:
for c in range(len(colors)):
pl.plot(stc.times, signals[c], color=colors[c])
pl.xlabel('Time (s)')
pl.ylabel('Activation')
pl.legend(groups, loc='best')
pl.plot([0, 0], pl.ylim(), ':k')
pl.fill_betweenx(pl.ylim(), stc.times[np.min(t_inds)],
stc.times[np.max(t_inds)], color='grey', alpha=0.3)
cnt += 1
# now, work on the mean over time barplot or regression, depending on result
pl.subplot(nclusters, 2, cnt)
my_test = mask_fname.split('_')[0]
if my_test.find('anova') >= 0 or my_test.find('VS') > 0:
signals = [np.mean(np.array(s)[:, :, v_inds], axis=2) for s in data]
signals = [np.mean(s[:, t_inds], axis=1) for s in signals]
ybars = [np.mean(s) for s in signals]
y_sd = [np.std(s)/np.sqrt(len(s)) for s in signals]
pl.bar(np.arange(len(ybars)), ybars, 0.35, color=colors,
ecolor='k', yerr=y_sd, align='center')
pl.xticks(range(len(groups)), groups)
pl.xlim([-.2, 2.5])
# running post-hoc tests
P.plot(kmax_int, z, 'r-', lw=1.2)
P.plot(kmin_sd, z, 'b-', lw=1.2, label="SKAMID Dish")
#P.plot(k_rsd, z, 'b-', lw=1.5)
P.plot(kmax_sd, z, 'b-', lw=1.2)
P.plot(kmax2, z, 'b--', lw=1.2, label="SKAMID Dish (RSD evol.)")
kk = np.logspace(-4., 3., 1000)
pk = cosmo['pk_nobao'](kk) * (1. + cosmo['fbao'](kk))
P.plot(kk, 0.3 * np.log10(pk / np.max(pk)) + 2.6, 'k-', lw=2., alpha=0.4)
#P.plot(kk, 10.*cosmo['fbao'](kk) + 2., 'k-', lw=2., alpha=0.4)
# RSD region
P.fill_betweenx(z,
2e-2 * np.ones(z.shape),
4e-1 * np.ones(z.shape),
color='k',
alpha=0.07)
P.fill_betweenx(z, kmin_int, kmax_int, color='r', alpha=0.1)
P.fill_betweenx(z, kmin_sd, kmax_sd, color='b', alpha=0.1)
P.legend(loc='upper right', prop={'size': 'large'})
P.xscale('log')
P.xlim((1e-4, 1e3))
P.ylim((0., 3.))
P.xlabel("k [Mpc$^{-1}$]", fontdict={'fontsize': '18'})
P.ylabel("z", fontdict={'fontsize': '18'})
import numpy as np
import bump_calculator
import pylab as plt
plt.ioff()
data = np.loadtxt('SN2007af_r.dat')
results = bump_calculator.get_bump_flux(data[:, 0], np.exp(-data[:, 1]),
np.exp(-data[:, 1]) * data[:, 2])
x_pred = np.arange(-30, 100, 0.1)
y_pred, y_pred_var = results.gp_model._raw_predict(x_pred[:, None])
y_pred = y_pred.squeeze()
y_pred_var = y_pred_var.squeeze()
plt.scatter(results.x, results.y_norm, s=10)
plt.errorbar(results.x, results.y_norm, yerr=results.y_norm_err,
fmt='+', color='b')
plt.plot(x_pred, y_pred, color='k')
plt.fill_between(x_pred, y_pred - np.sqrt(y_pred_var),
y_pred + np.sqrt(y_pred_var), color='b', alpha=0.1)
plt.axvline(results.bump_time, color='b')
plt.fill_betweenx(np.arange(*plt.ylim()),
results.bump_time - 3 * results.bump_time_err,
results.bump_time + 3 * results.bump_time_err,
alpha=0.1)
plt.xlabel('$\mathrm{Time}~(days)$')
plt.ylabel('$\mathrm{Flux}$')
plt.show()
def plot_coef_corr_seismic(offsets, trace_synt_ar_int, seismicData, T,
tmp_time_max, data_KK, int_time1, int_time2,
data_short, sgyname):
"""
Draws coef. correlation betweem real and synthetic seismic data
Parameters
----------
Input:
offsets - array of offsets (m)
trace_synt_ar_int - synthetic data interpolate on seismic grid
seismicData - seismic data class
T - time axs for seismic
tmp_time_max - picking of max amplitude near explored horizon
data_KK - array with correlation coefficients
int_time1 - upper time near explored horizon
int_time2 - lower time near explored horizon
data_short - interval real data
sgy_name - name of current segy
Output:
subplot figure with synthetic and real data, color shows the correlation coefficient between them
"""
#допилить граничные значения
#T_plot = np.arange(int_time1, int_time2, seismicData.dt)
fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(35, 12))
plt.sca(axs[0])
for ii in range(trace_synt_ar_int.shape[0]):
plt.plot(
trace_synt_ar_int[ii, :] / np.max(trace_synt_ar_int[ii, :]).T +
ii + 1,
T,
'k',
alpha=1,
zorder=1,
lw=1)
x = trace_synt_ar_int[ii, :] / np.max(
trace_synt_ar_int[ii, :]).T + ii + 1
T_tmp_plot = np.arange(int_time1, int_time2, seismicData.dt / 4)
y = T_tmp_plot
x2 = np.interp(T_tmp_plot, T, x)
plt.fill_betweenx(y, ii + 1, x2, where=(x2 > ii + 1), color='k')
#plt.plot(ii+1,pick[ii],'c.',alpha=1,zorder=1,lw=1,markersize = 10)
x1 = np.arange(1, ii + 1)
plt.xticks(
x1,
(np.round(offsets / 1000, decimals=1)),
fontsize=10,
)
plt.gca().invert_yaxis()
plt.xlabel('Offset,km')
plt.ylabel('T, ms')
plt.title('Synthetic')
plt.plot(tmp_time_max, '.r', markersize=25)
plt.imshow(
np.zeros_like(data_KK[:, :len(offsets)]),
alpha=1,
extent=[1, len(offsets) + 1, int_time2, int_time1],
aspect='auto',
cmap='Pastel1_r',
vmin=0,
vmax=0,
)
plt.sca(axs[1])
for ii in range(data_short.shape[0]):
plt.plot(data_short[ii, :] / np.max(data_short[ii, :]).T + ii + 1,
T,
'k',
alpha=1,
zorder=1,
lw=1)
x = data_short[ii, :] / np.max(data_short[ii, :]).T + ii + 1
T_tmp_plot = np.arange(int_time1, int_time2, seismicData.dt / 4)
y = T_tmp_plot
x2 = np.interp(T_tmp_plot, T, x)
plt.fill_betweenx(y, ii + 1, x2, where=(x2 > ii + 1), color='k')
#plt.plot(ii+1,pick[ii],'c.',alpha=1,zorder=1,lw=1,markersize = 10)
x1 = np.arange(1, ii + 1)
plt.xticks(
x1,
(np.round(offsets[:-2] / 1000, decimals=1)),
fontsize=10,
)
plt.gca().invert_yaxis()
plt.xlabel('Offset,km')
plt.ylabel('T, ms')
plt.title(sgyname)
im = plt.imshow(
data_KK[:, :len(offsets) + 1],
alpha=1,
extent=[1, len(offsets) + 1, int_time2, int_time1],
aspect='auto',
cmap='rainbow',
vmin=0,
vmax=1,
)
divider = make_axes_locatable(axs[1])
cax = divider.append_axes("right", size="5%", pad=0.05)
cbar = plt.colorbar(im, cax=cax)
cbar.set_label('Coef. correlation', rotation=270)
return
import pylab as pl
inds_t, inds_v = [(clusters[cluster_ind]) for ii, cluster_ind in
enumerate(good_cluster_inds)][0] # first cluster
times = np.arange(X[0].shape[1]) * tstep * 1e3
pl.clf()
colors = ['y', 'b', 'g', 'purple']
for ii, (condition, color, eve_id) in enumerate(
zip(X, colors, ['l_aud', 'r_aud', 'l_vis', 'r_vis'])):
# extract time course at cluster vertices
condition = condition[:, :, inds_v]
# normally we would normalize values across subjects but
# here we use data from the same subject so we're good to just
# create average time series across subjects and vertices.
mean_tc = condition.mean(axis=2).mean(axis=0)
std_tc = condition.std(axis=2).std(axis=0)
pl.plot(times, mean_tc.T, color=color, label=eve_id)
pl.fill_between(times, mean_tc + std_tc, mean_tc - std_tc, color='gray',
alpha=0.5, label='')
pl.xlabel('Time (ms)')
pl.ylabel('Activation (F-values)')
pl.xlim(times[[0, -1]])
pl.fill_betweenx(np.arange(*pl.ylim()), times[inds_t[0]],
times[inds_t[-1]], color='orange', alpha=0.3)
pl.legend()
pl.title('Interaction between stimulus-modality and location.')
pl.show()
def plotit():
import astropy.stats
import pylab as pl
pl.rcParams['axes.prop_cycle'] = pl.cycler('color', [
'#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b',
'#e377c2', '#7f7f7f', '#bcbd22', '#17becf'
])
pl.rcParams['figure.figsize'] = (12, 8)
pl.rcParams['figure.dpi'] = 75
pl.rcParams['savefig.dpi'] = 150
pl.rcParams['axes.titlesize'] = 40
pl.rcParams['axes.labelsize'] = 24
pl.rcParams['xtick.labelsize'] = 20
pl.rcParams['ytick.labelsize'] = 20
pl.figure(1).clf()
pl.figure(2).clf()
pl.figure(3).clf()
pl.figure(4).clf()
pl.figure(5, figsize=(12, 8), dpi=75).clf()
fig9 = pl.figure(9)
fig9.clf()
for ii, imname in enumerate(f for f in files if 'column' in f.lower()):
print("Plotting {0}".format(imname))
data = files[imname]['file'].data
mask = files[imname]['mask']
brickdata = brick_files[imname]['file'].data
# https://github.com/astropy/astropy/pull/5232 for ignore_nan
lo = astropy.stats.mad_std(data, ignore_nan=True)
hi = np.nanmax(data)
bins = np.logspace(np.log10(lo), np.log10(hi), 100)
pl.figure(1)
pl.subplot(2, 4, ii + 1)
brickweights = np.ones(np.isfinite(brickdata).sum(),
dtype='float') / np.isfinite(
brickdata).sum() * np.isfinite(data).sum()
bH, bL, bP = pl.hist(
brickdata[np.isfinite(brickdata)],
bins=bins,
log=True,
alpha=0.5,
histtype='step',
#weights=brickweights,
bottom=1.1,
color='b',
zorder=-1)
#weights = np.ones(np.isfinite(data).sum(), dtype='float')/np.isfinite(data).sum()
H, L, P = pl.hist(
data[np.isfinite(data) & mask],
bins=bins,
log=True,
alpha=0.5,
color='k',
#normed=True,
histtype='step')
# Lada threshold, approximately (116 Msun/pc^2)
pl.vlines(5e21, 1.1, H.max(), label='Lada+ 2010 Threshold')
#pl.hist(tbl[imname], bins=bins, log=True, alpha=0.5)
pl.xlim(np.min([bL.min(), L.min()]), L.max())
pl.semilogx()
pl.yscale('log', nonposy='clip')
ax2 = pl.gca().twinx()
ax2.plot(np.sort(tbl[imname]),
np.arange(len(tbl), dtype='float') / len(tbl),
'k-',
linewidth=3,
alpha=0.5,
zorder=10)
ax2.set_xlim(L.min(), L.max())
ax2.set_ylim(0, 1)
pl.title(imname, fontsize=12)
pl.figure(2)
pl.subplot(2, 4, ii + 1)
pl.imshow(data, cmap='gray_r')
pl.contour(data,
levels=np.percentile(tbl[imname],
[5, 10, 25, 50, 75, 90, 95]))
pl.contour(mask, levels=[0.5])
pl.title(imname)
sorted_col = u.Quantity(np.sort(data[mask & (data > 0)]), u.cm**-2)
cumul = np.cumsum(sorted_col)[::-1]
pixarea_cm2 = (files[imname]['pixarea'] * distance**2)
cum_mass = (cumul * pixarea_cm2 * 2.8 * u.Da).to(
u.M_sun, u.dimensionless_angles())
pl.figure(3)
pl.plot(sorted_col, cum_mass, label=names[imname])
pl.legend(loc='best')
pl.xlim(1e21, 2e25)
pl.gca().set_xscale('log')
pl.gca().set_yscale('log')
pl.figure(4)
assert cum_mass.min() > 0.1 * u.M_sun
pl.plot(sorted_col - 5e22 * u.cm**-2,
cum_mass - (5e22 * u.cm**-2 * pixarea_cm2 * 2.8 * u.Da).to(
u.M_sun, u.dimensionless_angles()),
label=names[imname])
pl.legend(loc='best')
pl.xlim(1e21, 2e25)
pl.gca().set_xscale('log')
pl.gca().set_yscale('log')
pl.figure(1)
pl.tight_layout()
pl.savefig(paths.fpath("flux_histograms_with_core_location_CDF.pdf"),
bbox_inches='tight')
pl.figure(3)
pl.vlines(5e21,
0.5,
1e6,
color='k',
linestyle='--',
linewidth=1,
label='Lada+ 2010 Threshold')
pl.tight_layout()
pl.legend(loc='best')
pl.savefig(paths.fpath("mass_cdf_histograms.pdf"), bbox_inches='tight')
pl.figure(4)
pl.vlines(5e21,
0.5,
1e6,
color='k',
linestyle='--',
linewidth=1,
label='Lada+ 2010 Threshold')
pl.legend(loc='best')
pl.tight_layout()
pl.savefig(paths.fpath("mass_cdf_histograms_bgsubd.pdf"),
bbox_inches='tight')
pl.figure(5).clf()
for imname, color in [('HerschelColumn25', 'k'), ('HerschelColumn36', 'g'),
('SharcHTemColumn', 'b'), ('ScubaHTemColumn', 'r')]:
pl.plot(
np.sort(tbl[imname]),
np.arange(len(tbl), dtype='float') / len(tbl),
linestyle='-',
linewidth=4,
alpha=1,
zorder=10,
color=color,
label=names[imname],
)
pl.xscale('log')
pl.xlim(1e21, 2e25)
pl.ylim(0, 1)
fb1 = pl.fill_betweenx(
y=np.arange(len(tbl), dtype='float') / len(tbl),
x1=np.sort(nan_to_val(tbl['Sharc20Column'], 1e26)),
x2=np.sort(nan_to_val(tbl['Sharc50Column'], 1e26)),
alpha=0.5,
label='SHARC 20-50 K',
edgecolor='b',
facecolor='none',
zorder=-10,
hatch='//',
linewidth=2,
)
fb2 = pl.fill_betweenx(
y=np.arange(len(tbl), dtype='float') / len(tbl),
x1=np.sort(nan_to_val(tbl['Scuba20Column'], 1e26)),
x2=np.sort(nan_to_val(tbl['Scuba50Column'], 1e26)),
alpha=0.5,
label='SCUBA 20-50 K',
edgecolor='r',
facecolor='none',
zorder=-10,
hatch='\\\\',
linewidth=2,
)
pl.figure(5)
pl.vlines(5e21,
0,
1,
color='k',
linestyle='--',
linewidth=1,
label='Lada+ 2010 Threshold')
pl.vlines(2e23,
0,
1,
color='k',
linestyle=':',
label='Krumholz+ 2008 Threshold')
pl.legend(loc='best', fontsize=20)
pl.tight_layout()
pl.xlabel("Column Density [$N(\mathrm{H}_2)$ cm$^{-2}$]", fontsize=24)
pl.ylabel("Cumulative fraction\nof cores at column $<N$", fontsize=24)
ax1 = pl.gca()
pl.draw()
ax2 = ax1.twiny()
print("ax1 xlims: {0}".format(ax1.get_xlim()))
pl.draw()
ax2.set_xlim(np.array(ax1.get_xlim()) * (2.8 * u.Da).to(u.g).value)
print("ax2 xlims: {0}".format(ax2.get_xlim()))
ax2.set_xscale('log')
ax2.set_xlabel("Column Density [g cm$^{-2}$]")
pl.draw()
pl.savefig(paths.fpath("core_background_column_cdf.pdf"),
bbox_inches='tight')
pl.figure(6).clf()
ax1 = pl.gca()
imname = 'ScubaHTemColumn'
print("fig6: Plotting {0}".format(imname))
data = files[imname]['file'].data
mask = files[imname]['mask']
brickdata = brick_files[imname]['file'].data
lo = astropy.stats.mad_std(brickdata, ignore_nan=True)
hi = np.nanmax(data)
bins = np.logspace(np.log10(lo) - 0.5, np.log10(hi), 100)
bH, bL, bP = ax1.hist(brickdata[np.isfinite(brickdata)],
bins=bins,
log=True,
alpha=0.5,
histtype='step',
bottom=0.1,
linewidth=2,
color='b',
zorder=-1)
#weights = np.ones(np.isfinite(data).sum(), dtype='float')/np.isfinite(data).sum()
H, L, P = ax1.hist(
data[np.isfinite(data) & mask],
bins=bins,
log=True,
alpha=0.5,
color='k',
linewidth=2,
#normed=True,
histtype='step')
# Lada threshold, approximately (116 Msun/pc^2)
ax1.vlines(5e21, 1.1, H.max(), label='Lada+ 2010 Threshold')
ax1.vlines(2e23,
0.1,
H.max() * 2,
color='k',
linestyle=':',
label='Krumholz+ 2008 Threshold')
#ax1.hist(tbl[imname], bins=bins, log=True, alpha=0.5)
ax1.set_xlim(np.min([bL.min(), L.min()]), L.max())
ax1.set_ylim(0.5, np.max([bH.max(), H.max()]) * 1.1)
ax1.semilogx()
ax1.set_yscale('log', nonposy='clip')
ax1.set_xlabel("Column Density [N(H$_2$) cm$^{-2}$]")
ax1.set_ylabel("Number of pixels")
ax3 = ax1.twiny()
print("ax1 xlims: {0}".format(ax1.get_xlim()))
pl.draw()
ax3.set_xlim(np.array(ax1.get_xlim()) * (2.8 * u.Da).to(u.g).value)
ax3lims = ax3.get_xlim()
print("ax2 xlims: {0}".format(ax2.get_xlim()))
ax3.set_xscale('log')
ax3.set_xlabel("Column Density [g cm$^{-2}$]")
pl.draw()
pl.savefig(paths.fpath("compare_brick_sgrb2_colPDF_nofractions.pdf"),
bbox_inches='tight')
ax2 = ax1.twinx()
ax2.plot(np.sort(tbl[imname]),
np.arange(len(tbl), dtype='float') / len(tbl),
'k-',
linewidth=3,
alpha=0.5,
zorder=10)
ax2.set_xlim(L.min(), L.max())
ax2.set_ylim(0, 1)
ax2.set_ylabel("Fraction of point sources below N(H$_2$)")
ax3.set_xlim(ax3lims)
ax2.set_xlim(L.min(), L.max())
pl.savefig(paths.fpath("compare_brick_sgrb2_colPDF.pdf"),
bbox_inches='tight')
ax9 = fig9.gca()
ax9.loglog(np.sort(tbl[imname]),
1 - np.arange(len(tbl), dtype='float') / len(tbl),
'k-',
linewidth=3,
alpha=0.5,
zorder=10,
label='Stars')
ax9.set_ylabel("Fraction of point sources above N(H$_2$)")
ax9.set_xlabel("Column Density [N(H$_2$) cm$^{-2}$]")
fig9.savefig(paths.fpath("cdf_of_point_sources.pdf"), bbox_inches='tight')
ax9.set_ylabel("Fraction above N(H$_2$)")
dmask = np.isfinite(data) & mask
ax9.loglog(np.sort(data[dmask]),
1 - np.arange(dmask.sum(), dtype='float') / dmask.sum(),
alpha=0.5,
color='b',
linewidth=2,
label='Gas')
fig9.legend(loc='best')
fig9.savefig(paths.fpath("cdf_of_point_sources_and_column.pdf"),
bbox_inches='tight')
nn11_pc = (u.Quantity(tbl['nn11'], u.arcsec) * distance).to(
u.pc, u.dimensionless_angles())
nn = 11
nn11_msunpersqpc_starcentered = ((nn - 1) * mass_represented_by_a_source /
(np.pi * (nn11_pc)**2)).to(u.M_sun /
u.pc**2)
herschelsurfdens = (u.Quantity(tbl['HerschelColumn25']).to(u.cm**-2) *
2.8 * u.Da).to(u.M_sun / u.pc**2)
tbl.add_column(table.Column(name='HerschelSurfDens',
data=herschelsurfdens))
tbl.add_column(
table.Column(name='nn11_starcentered_surfdens',
data=nn11_msunpersqpc_starcentered))
assert herschelsurfdens.min() < 10**5 * u.M_sun / u.pc**2
assert herschelsurfdens.max() > 10**3 * u.M_sun / u.pc**2
fig7 = pl.figure(7, figsize=(10, 10))
fig7.clf()
ax7 = fig7.gca()
ax7.loglog(herschelsurfdens,
nn11_msunpersqpc_starcentered,
'k.',
alpha=0.7,
markeredgecolor=(0, 0, 0, 0.5))
lims = ax7.axis()
# 5/3 slope
#ax7.loglog([1e3,1e6], [3e0, 3e5], 'k--')
ax7.set_ylabel(
"Source-centered NN11 Surface Density\n$\Sigma_*$ [M$_\odot$ pc$^{-2}$]"
)
ax7.set_xlabel("Gas Surface Density $\Sigma_{gas}$ [M$_\odot$ pc$^{-2}$]")
ax7.axis(lims)
# Arrows showing the shift if you subtract off the "most aggressive plausible"
# uniform foreground value
bg_5e22 = (5e22 * u.cm**-2 * 2.8 * u.Da).to(u.M_sun / u.pc**2)
ax7.arrow(3e3,
1.1,
-bg_5e22.value,
0,
head_width=0.1,
head_length=0.033 * (3e3 - bg_5e22.value),
color='k')
ax7.arrow(1e4,
1.1,
-bg_5e22.value,
0,
head_width=0.1,
head_length=0.033 * (1e4 - bg_5e22.value),
color='k')
ax7.arrow(3e4,
1.1,
-bg_5e22.value,
0,
head_width=0.1,
head_length=0.033 * (3e4 - bg_5e22.value),
color='k')
ax7.axis([1e3, 1e5, 1e0, 1e5])
fig7.savefig(paths.fpath(
"stellar_vs_gas_column_density_starcentered_herschel_nomodels_nolocal.png"
),
bbox_inches='tight')
fig7.savefig(paths.fpath(
"stellar_vs_gas_column_density_starcentered_herschel_nomodels_nolocal.pdf"
),
bbox_inches='tight')
monr2_lowerline = np.array([0.1, 1e5])**2.67 / (100**2.67) * 2.5
monr2_fill = ax7.fill_between([0.1, 1e5],
monr2_lowerline,
monr2_lowerline * 10,
alpha=0.5,
color='green',
label='Mon R2')
oph_lowerline = np.array([0.1, 1e5])**1.87 / (100**1.87) * 1.5
oph_fill = ax7.fill_between([0.1, 1e5],
oph_lowerline,
oph_lowerline * 10,
color='blue',
alpha=0.5,
label='Ophiuchus')
local_plotobjs = [monr2_fill, oph_fill]
T = ax7.text(2.3e3,
9e3,
"Mon R2",
color='k',
rotation=50,
fontsize=18,
verticalalignment='bottom',
horizontalalignment='center')
local_plotobjs.append(T)
T = ax7.text(2.5e3,
4.5e2,
"Ophiuchus",
color='k',
rotation=38,
fontsize=18,
verticalalignment='bottom',
horizontalalignment='center')
local_plotobjs.append(T)
#ax7.plot([0.1, 1e5], np.array([0.1, 1e5])**1.87/(1e4**1.87)*(1e4**(5/3.)/1e5), 'b:', linewidth=3, alpha=0.5)
oph_scalefactor = 50.
L = ax7.plot([0.1, 1e5],
oph_lowerline / oph_scalefactor,
'b:',
linewidth=3,
alpha=0.5)
local_plotobjs += L
ax7.axis([1e3, 1e5, 1e0, 1e5])
fig7.savefig(paths.fpath(
"stellar_vs_gas_column_density_starcentered_herschel_nomodels.png"),
bbox_inches='tight')
fig7.savefig(paths.fpath(
"stellar_vs_gas_column_density_starcentered_herschel_nomodels.pdf"),
bbox_inches='tight')
sigma_gas = np.logspace(1, 6) * u.M_sun / u.pc**2
mdlplots = {}
for time in (0.01, 0.1, 0.74) * u.Myr:
mdlplots[(1, time)] = ax7.loglog(
sigma_gas_of_t(sigma_gas, time, alpha=1, k=0.1 / u.Myr),
gas_depletion_law(sigma_gas, time, alpha=1, k=0.1 / u.Myr),
label=time,
color='r',
linewidth=3,
alpha=0.5,
zorder=-10,
)
mdlplots[(2, time)] = ax7.loglog(sigma_gas_of_t(sigma_gas, time),
gas_depletion_law(sigma_gas, time),
label=time,
color='orange',
linewidth=3,
zorder=-5,
alpha=0.5)
ax7.axis([1e3, 1e5, 1e0, 1e5])
#ax7.plot([0.1, 1e5], np.array([0.1, 1e5])*4e-2, 'r-', linewidth=3, alpha=0.5, zorder=-10)
fig7.savefig(
paths.fpath("stellar_vs_gas_column_density_starcentered_herschel.png"),
bbox_inches='tight')
fig7.savefig(
paths.fpath("stellar_vs_gas_column_density_starcentered_herschel.pdf"),
bbox_inches='tight')
ax7.loglog(np.logspace(3, 5),
lada2017relation.sigma_star_california(
np.logspace(3, 5) * u.M_sun / u.pc**2),
linewidth=3,
color='m',
label='Lada2017_cali')
ax7.loglog(np.logspace(3, 5),
lada2017relation.sigma_star_orionA(
np.logspace(3, 5) * u.M_sun / u.pc**2),
linewidth=3,
linestyle='--',
color='m',
label='Lada2017_orionA')
ax7.loglog(np.logspace(3, 5),
lada2017relation.sigma_star_orionB(
np.logspace(3, 5) * u.M_sun / u.pc**2),
linewidth=3,
linestyle=':',
color='m',
label='Lada2017_orionB')
fig7.savefig(paths.fpath(
"stellar_vs_gas_column_density_starcentered_herschel_withLada2017.png"
),
bbox_inches='tight')
fig7.savefig(paths.fpath(
"stellar_vs_gas_column_density_starcentered_herschel_withLada2017.pdf"
),
bbox_inches='tight')
for line in mdlplots.values():
try:
for ll in line:
ll.set_visible(False)
except TypeError:
line.set_visible(False)
for obj in local_plotobjs:
obj.set_visible(False)
fig7.savefig(paths.fpath(
"stellar_vs_gas_column_density_starcentered_herschel_nomodels_withLada2017.png"
),
bbox_inches='tight')
fig7.savefig(paths.fpath(
"stellar_vs_gas_column_density_starcentered_herschel_nomodels_withLada2017.pdf"
),
bbox_inches='tight')
scubasurfdens = (u.Quantity(tbl['ScubaHTemColumn'], u.cm**-2) * 2.8 *
u.Da).to(u.M_sun / u.pc**2)
fig8 = pl.figure(8)
fig8.clf()
ax8 = fig8.gca()
ax8.loglog(scubasurfdens,
nn11_msunpersqpc_starcentered,
'k.',
alpha=0.7,
markeredgecolor=(0, 0, 0, 0.5))
lims = ax8.axis()
# 5/3 slope
#ax8.loglog([1e3,1e6], [3e0, 3e5], 'k--')
#ax8.plot([0.1, 1e5], np.array([0.1, 1e5])**1.87/(1e4**1.87)*(1e4**(5/3.)/1e5), 'b:', linewidth=3, alpha=0.5)
ax8.plot([0.1, 1e5],
oph_lowerline / oph_scalefactor,
'b:',
linewidth=3,
alpha=0.5)
ax8.axis(lims)
ax8.set_ylabel(
"Source-centered NN11 Surface Density\n$\Sigma_*$ [M$_\odot$ pc$^{-2}$]"
)
ax8.set_xlabel("SCUBA-derived Surface Density [M$_\odot$ pc$^{-2}$]")
# arrows showing the shift if you subtract off the "most aggressive plausible"
# uniform foreground value
bg_5e22 = (5e22 * u.cm**-2 * 2.8 * u.Da).to(u.M_sun / u.pc**2)
ax8.arrow(3e3,
1.1,
-bg_5e22.value,
0,
head_width=0.1,
head_length=0.033 * (3e3 - bg_5e22.value),
color='k')
ax8.arrow(1e4,
1.1,
-bg_5e22.value,
0,
head_width=0.1,
head_length=0.033 * (1e4 - bg_5e22.value),
color='k')
ax8.arrow(3e4,
1.1,
-bg_5e22.value,
0,
head_width=0.1,
head_length=0.033 * (3e4 - bg_5e22.value),
color='k')
fig8.savefig(paths.fpath(
"stellar_vs_gas_column_density_starcentered_scuba_nomodels_nolocal.png"
),
bbox_inches='tight')
fig8.savefig(paths.fpath(
"stellar_vs_gas_column_density_starcentered_scuba_nomodels_nolocal.pdf"
),
bbox_inches='tight')
monr2_lowerline = np.array([0.1, 1e5])**2.67 / (100**2.67) * 2.5
monr2_fill = ax8.fill_between([0.1, 1e5],
monr2_lowerline,
monr2_lowerline * 10,
alpha=0.5,
color='green',
label='Mon R2')
oph_lowerline = np.array([0.1, 1e5])**1.87 / (100**1.87) * 1.5
oph_fill = ax8.fill_between([0.1, 1e5],
oph_lowerline,
oph_lowerline * 10,
color='blue',
alpha=0.5,
label='Ophiuchus')
local_plotobjs = [monr2_fill, oph_fill]
monr2txt = ax8.text(2.3e3,
9e3,
"Mon R2",
color='k',
rotation=50,
fontsize=18,
verticalalignment='bottom',
horizontalalignment='center')
ophtxt = ax8.text(2.5e3,
4.5e2,
"Ophiuchus",
color='k',
rotation=38,
fontsize=18,
verticalalignment='bottom',
horizontalalignment='center')
local_plotobjs.append(monr2txt)
local_plotobjs.append(ophtxt)
ax8.axis([1e3, 1e5, 1e0, 1e5])
fig8.savefig(paths.fpath(
"stellar_vs_gas_column_density_starcentered_scuba_nomodels.png"),
bbox_inches='tight')
fig8.savefig(paths.fpath(
"stellar_vs_gas_column_density_starcentered_scuba_nomodels.pdf"),
bbox_inches='tight')
mdlplots = {}
for time in (0.01, 0.1, 0.74) * u.Myr:
mdlplots[(1, time)] = ax8.loglog(
sigma_gas_of_t(sigma_gas, time, alpha=1, k=0.1 / u.Myr),
gas_depletion_law(sigma_gas, time, alpha=1, k=0.1 / u.Myr),
label=time,
color='r',
linewidth=3,
alpha=0.5,
zorder=-10,
)
mdlplots[(2, time)] = ax8.loglog(sigma_gas_of_t(sigma_gas, time),
gas_depletion_law(sigma_gas, time),
label=time,
color='orange',
linewidth=3,
zorder=-5,
alpha=0.5)
#ax8.axis(lims)
ax8.axis([1e3, 1e5, 1e0, 1e5])
fig8.savefig(
paths.fpath("stellar_vs_gas_column_density_starcentered_scuba.png"),
bbox_inches='tight')
fig8.savefig(
paths.fpath("stellar_vs_gas_column_density_starcentered_scuba.pdf"),
bbox_inches='tight')
ax8.loglog(np.logspace(3, 5),
lada2017relation.sigma_star(
np.logspace(3, 5) * u.M_sun / u.pc**2),
linewidth=3,
color='m',
label='Lada2017')
ax8.loglog(np.logspace(3, 5),
lada2017relation.sigma_star_orionA(
np.logspace(3, 5) * u.M_sun / u.pc**2),
linewidth=3,
linestyle='--',
color='m',
label='Lada2017_orionA')
ax8.loglog(np.logspace(3, 5),
lada2017relation.sigma_star_orionB(
np.logspace(3, 5) * u.M_sun / u.pc**2),
linewidth=3,
linestyle=':',
color='m',
label='Lada2017_orionB')
fig8.savefig(paths.fpath(
"stellar_vs_gas_column_density_starcentered_scuba_withLada2017.png"),
bbox_inches='tight')
fig8.savefig(paths.fpath(
"stellar_vs_gas_column_density_starcentered_scuba_withLada2017.pdf"),
bbox_inches='tight')
for line in mdlplots.values():
try:
for ll in line:
ll.set_visible(False)
except TypeError:
line.set_visible(False)
for obj in local_plotobjs:
obj.set_visible(False)
fig8.savefig(paths.fpath(
"stellar_vs_gas_column_density_starcentered_scuba_nomodels_withLada2017.png"
),
bbox_inches='tight')
fig8.savefig(paths.fpath(
"stellar_vs_gas_column_density_starcentered_scuba_nomodels_withLada2017.pdf"
),
bbox_inches='tight')
# TODO: plot the same (?) histograms for The Brick
# DONE!
tbl.write(paths.tpath("continuum_photometry_plusbackground.ipac"),
format='ascii.ipac',
overwrite=True)
def results(fiuno,fidue,fauno,fadue,fivalues,dval,lee_lambda,txtout,graphout):
"""Draws output graphs and a text file report for the point(s) examined"""
import pylab
fout=open(txtout,'w')
fout.write("~~~ Input Data Summary ~~~")
fout.write('\n')
fout.writelines("Grainsize examined:")
fout.write('\n')
fout.writelines('phi_1='+str(fiuno))
fout.write('\n')
fout.writelines('phi_2='+str(fidue))
fout.write('\n')
fout.writelines("Bedload composition:")
fout.write('\n')
fout.writelines('Fa_1='+str(fauno))
fout.write('\n')
fout.writelines('Fa_2='+str(fadue))
fout.write('\n')
fout.write('\n')
fout.writelines("~~~ Prediction Results ~~~")
fout.write('\n')
neg=[]
for i in dval:
neg.append(float(i*(-1)))
for i in fivalues:
f=i.split('|')[4:][::2]
fi=[]
for j in f:
fi.append(float(j))
pylab.plot(fi,neg,'o',color='b')
pylab.fill_betweenx(neg,fi,facecolor='orange')
pylab.axis([0.0,1.0,min(neg),max(neg)])
pylab.xlabel('F1, F2 [%]')
pylab.ylabel('Relative depth from the bed surface [mm]')
pylab.savefig(str(graphout)+'_point'+str(i[0])+'.png')
pylab.close()
east=float(i.split('|')[2])
north=float(i.split('|')[3])
cat=int(i.split('|')[1])
deltalee=float(lee_lambda[cat-1].split('|')[7])
etat=float(lee_lambda[cat-1].split('|')[8])
etab=float(lee_lambda[cat-1].split('|')[9])
fout.write('\n')
fout.writelines("Output for point #"+str(i.split('|')[0])+":")
fout.write('\n')
fout.writelines("E [mm or m]="+str(east))
fout.write('\n')
fout.writelines("N [mm or m]="+str(north))
fout.write('\n')
fout.writelines("Total heigth of the bedform [mm or m]="+str(deltalee))
fout.write('\n')
fout.writelines("Crest heigth of the bedform over the mean heigth of the reach [mm or m]="+str(etat))
fout.write('\n')
fout.writelines("Through heigth of the bedform over the mean heigth of the reach [mm or m]="+str(etab))
fout.write('\n')
fout.writelines("Prediction of vertical sorting of sediment over the bed surface for chosen grainsizes (phi_1,phi_2="+str(fiuno)+","+str(fidue)+"):")
fout.write('\n')
fout.writelines("(depth [m or mm], F1 [%], F2 [%])")
fout.write('\n')
f11=i.split('|')[4:][::2]
f22=i.split('|')[5:][::2]
m=0
for i in neg:
fout.writelines("("+str(i)+", "+str(float(f11[m]))+", "+str(float(f22[m]))+")")
fout.write('\n')
m=m+1
fout.close()
pl.clf()
colors = ['y', 'b', 'g', 'purple']
for ii, (condition, color, eve_id) in enumerate(
zip(X, colors, ['l_aud', 'r_aud', 'l_vis', 'r_vis'])):
# extract time course at cluster vertices
condition = condition[:, :, inds_v]
# normally we would normalize values across subjects but
# here we use data from the same subject so we're good to just
# create average time series across subjects and vertices.
mean_tc = condition.mean(axis=2).mean(axis=0)
std_tc = condition.std(axis=2).std(axis=0)
pl.plot(times, mean_tc.T, color=color, label=eve_id)
pl.fill_between(times,
mean_tc + std_tc,
mean_tc - std_tc,
color='gray',
alpha=0.5,
label='')
pl.xlabel('Time (ms)')
pl.ylabel('Activation (F-values)')
pl.xlim(times[[0, -1]])
pl.fill_betweenx(np.arange(*pl.ylim()),
times[inds_t[0]],
times[inds_t[-1]],
color='orange',
alpha=0.3)
pl.legend()
pl.title('Interaction between stimulus-modality and location.')
pl.show()
