def test_loess_2d():
"""
Usage example for loess_2d
"""
n = 200
x = np.random.uniform(-1,1,n)
y = np.random.uniform(-1,1,n)
z = x**2 - y**2
sigz = 0.2
zran = np.random.normal(z, sigz)
zout, wout = loess_2d(x, y, zran)
plt.clf()
plt.subplot(131)
plot_velfield(x, y, z)
plt.title("Underlying Function")
plt.subplot(132)
plot_velfield(x, y, zran)
plt.title("With Noise Added")
plt.subplot(133)
plot_velfield(x, y, zout)
plt.title("LOESS Recovery")
plt.pause(1)
def plot_folded_cones(datasets, pas=None, dpa=22.5, use_err_max=True,
use_sn_min=False):
""" Make azimuthal plots in radial sections. """
# General setting for the plots
colors = ("k", "w", "g", "w")
symbols = ("o", "s", "^", None)
ecolor = ("0.7", "0.7", "0.7", "1.0")
ylims = [[3200, 5100], [0, 950], [-.3, .3], [-.2,.4]]
xlims = [0, 360]
ylabels = [r"$V_{\rm{LOS}}$ (km/s)", r"$\sigma_{\rm{LOS}}$ (km/s)",
r"$h_3$", r"$h_4$"]
mec = ("k", "r")
names = ["rad_folded_vel", "rad_folded_sigma", "rad_folded_h3",
"rad_folded_h4"]
sn_min = [10, 10, 15, 15, 0]
err_max = [100, 80, 0.1, 0.1]
fs = _large_fig_settings()
frac_loess = 0.3
##########################################################################
# Set the default radius
# Set the default position angles
if pas is None:
pas = np.linspace(0, 180, 5)[:-1] + pa0
##########################################################################
for mm in range(4):
fig = plt.figure(2, figsize=(fs["width"], fs["height"]))
for j,pa in enumerate(pas):
###############################################################
# Initialize axes
# ax1 = plt.subplot(len(pas), 1, len(pas)-j)
ax1 = plt.subplot(len(pas), 1, j + 1)
ax1.minorticks_on()
ax1.set_xlim(0, 40)
ax2 = ax1.twiny()
ax2.set_xlim(0, 40 / re)
ax2.minorticks_on()
#################################################################
for i, d in enumerate(datasets):
x, y, r, theta = d[:,:4].T
moment = d[:,np.arange(4,12,2)[mm]].T
error = np.clip(d[:,np.arange(5,13,2)[mm]].T, 0, 1000)
sn = d[:,12]
if i == 3:
idx = np.isfinite(moment)
x = x[idx]
y = y[idx]
r = r[idx]
theta = theta[idx]
moment = moment[idx]
error = error[idx]
sn = sn[idx]
loess = ll.loess_2d(x, y, moment, frac=frac_loess)
else:
loess = moment
data = np.column_stack((theta, moment, error, loess, r, sn))
data = data[np.argsort(data[:,0])]
datam = np.copy(data)
datam[:,0] -= 360.
datap = np.copy(data)
datap[:,0] += 360
data = np.vstack((datam, data, datap))
###############################################################
# Select conic regions
idx1 = np.argwhere((data[:,0] > pa - dpa) &
(data[:,0] < pa + dpa)).ravel().tolist()
idx2 = np.argwhere((data[:,0] > pa - dpa + 180) &
(data[:,0] < pa + dpa + 180)).ravel().tolist()
#==========================================================
# Include central points of our dataset in all subplots
if i in [0,3]:
idx1 += np.argwhere((data[:,0] > pa - 90) &
(data[:,0] < pa + 90) &
(data[:,4] < 8)).ravel().tolist()
idx2 += np.argwhere((data[:,0] > pa - 90 + 180) &
(data[:,0] < pa + 90 + 180) &
(data[:,4] < 4)).ravel().tolist()
#==========================================================
idx1 = np.unique(idx1)
idx2 = np.unique(idx2)
#=============================================================
# S/N cut for our dataset
if i == 0:
if use_sn_min:
idxsn = np.where(data[:,5] > sn_min[mm])
idx1 = np.intersect1d(idx1, idxsn)
idx2 = np.intersect1d(idx2, idxsn)
if use_err_max:
idx_err = np.where(data[:,2] < err_max[mm])
idx1 = np.intersect1d(idx1, idx_err)
idx2 = np.intersect1d(idx2, idx_err)
#=============================================================
##############################################################
for k, idx in enumerate([idx1, idx2]):
if not len(idx):
continue
subset = data[idx]
subset = subset[np.argsort(subset[:,4])]
ax1.errorbar(subset[:,4], subset[:,1], yerr=subset[:,2],
fmt=symbols[i], ecolor=ecolor[i], c=colors[k],
mec=mec[k], ms=9, zorder=-i, mew=1.2)
ax1.set_ylim(ylims[mm])
ax1.set_ylabel(ylabels[mm])
if i == 3:
ax1.plot(subset[:,4], subset[:,3], "-{0}".format(mec[k]))
if mm > 1:
ax1.axhline(y=0, ls="--", c="k")
# if j == 0:
if j == len(pas) - 1:
ax1.set_xlabel("$R$ (kpc)")
else:
ax1.xaxis.set_major_formatter(plt.NullFormatter())
# if j == len(pas) -1:
if j == 0:
ax2.set_xlabel("$ R / R_e$")
else:
ax2.xaxis.set_major_formatter(plt.NullFormatter())
ax1.annotate("PA={0:.1f}$\pm${1:.1f}$^{{\\rm o}}$".format(pa,
dpa), xy=(0.17, 0.8), xycoords='axes fraction',
fontsize=12, horizontalalignment='center',
verticalalignment='bottom',
color="k")
ax1.annotate("PA={0:.1f}$\pm${1:.1f}$^{{\\rm o}}$".format(
pa+180, dpa), xy=(0.17, 0.68),
xycoords='axes fraction',
fontsize=12, horizontalalignment='center',
verticalalignment='bottom',
color="r")
plt.subplots_adjust(left=fs["left"], right=fs["right"],
bottom=fs["bottom"], top=fs["top"],
hspace=fs["hspace"])
plt.savefig(os.path.join(figures_dir, "{0}.png".format(names[mm])))
plt.clf()
return
def plot_folded_rings(datasets, radius=None):
""" Make azimuthal plots in radial sections. """
# General setting for the plots
symbols = ("o", "s", "^")
ylims = [[3600, 4200], [100, 700], [-.3, .3], [-.3,.3]]
xlims = [0, 360]
ylabels = [r"$V_{\rm{LOS}}$ (km/s)", r"$\sigma_{\rm{LOS}}$ (km/s)",
r"$h_3$", r"$h_4$"]
mec = "0.7" # Grayscale level for bars and symbol edge colors
names = ["pa_folded_vel", "pa_folded_sigma", "pa_folded_h3",
"pa_folded_h4"]
sn_min = [10, 10, 10, 10]
sn = np.loadtxt(intable, usecols=(14,))
sn = np.vstack((sn, sn, sn))
fs = _large_fig_settings()
frac_loess = 0.2
##########################################################################
# Set the default radius
if radius is None:
radius = np.linspace(0,40,5)
##########################################################################
for mm in range(4):
fig = plt.figure(2, figsize=(fs["width"], fs["height"]))
for j in range(len(radius) - 1):
###############################################################
# Initialize axes
ax = plt.subplot(len(radius)-1, 1, len(radius)-1-j)
ax.minorticks_on()
ax.set_xlim(pa0 - 90, pa0 + 90)
ax2 = ax.twiny()
ax2.set_xlim(pa0 + 90 + 180, pa0 - 90 + 180)
ax2.minorticks_on()
#################################################################
rmin = radius[j]
rmax = radius[j+1]
for i, d in enumerate(datasets):
x, y, r, theta = d[:,:4].T
moment = d[:,np.arange(4,12,2)[mm]].T
error = np.clip(d[:,np.arange(5,13,2)[mm]].T, 0, 1000)
sn = d[:,12]
if i == 0:
loess = ll.loess_2d(x, y, moment, frac=frac_loess)
else:
loess = moment
data = np.column_stack((theta, moment, error, loess, r, sn))
data = data[np.argsort(data[:,0])]
datam = np.copy(data)
datam[:,0] -= 360.
datap = np.copy(data)
datap[:,0] += 360
data = np.vstack((datam, data, datap))
###############################################################
# Select regions
idx = np.argwhere((data[:,4] >= rmin) & (data[:,4] < rmax))
# S/N cut for our dataset
if i == 0:
idxsn = np.where(data[:,5] > sn_min[mm])
idx = np.intersect1d(idx, idxsn)
##############################################################
idx = idx.ravel()
subset = data[idx]
ax.errorbar(subset[:,0], subset[:,1], yerr=subset[:,2],
fmt=symbols[i], ecolor=mec, c="k",
mec="k", ms=9, zorder=-i, mew=1.2)
ax2.errorbar(subset[:,0], subset[:,1], yerr=subset[:,2],
fmt=symbols[i], ecolor=mec, c="w",
mec="r", ms=9, zorder=-i, mew=1.2)
if i == 0:
ax.plot(subset[:,0], subset[:,3], "-k")
ax2.plot(subset[:,0], subset[:,3], "-r")
ax.set_ylim(ylims[mm])
ax.set_ylabel(ylabels[mm])
ax2.annotate("${0:.1f} \leq R$(kpc)$<{1:.1f}$".format(rmin,
rmax), xy=(0.22, 0.15), xycoords='axes fraction',
fontsize=14, horizontalalignment='center',
verticalalignment='bottom',
bbox=dict(boxstyle="round, pad=0.3", fc="w"),
alpha=1, zorder=3)
if mm > 1:
ax.axhline(y=0, ls="--", c="k")
if j == 0:
ax.set_xlabel("PA (degree)")
else:
ax.xaxis.set_major_formatter(plt.NullFormatter())
for tl in ax2.get_xticklabels():
tl.set_color('r')
if j == len(radius) - 2:
ax2.set_xlabel("PA (degree)", color="r")
else:
ax2.xaxis.set_major_formatter(plt.NullFormatter())
ax.axvline(x=63, ls="--", c="k")
plt.subplots_adjust(left=fs["left"], right=fs["right"],
bottom=fs["bottom"], top=fs["top"],
hspace=fs["hspace"])
plt.savefig(os.path.join(figures_dir, "{0}.png".format(names[mm])))
plt.clf()
return
def make_kinematics():
""" Make maps for kinematics individually. """
# Read data values for vel, sigma, h3, h4
data = np.loadtxt(outtable, usecols=(5,7,9,11)).T
xall, yall, sn = np.loadtxt(outtable, usecols=(1,2,14,)).T
###############################################
# Details of the maps
names = [r"vel", r"sigma", r"h3", r"h4"]
cb_label = [r"V$_{\rm LOS}$ (km/s)", r"$\sigma_{\rm LOS}$ (km/s)",
r"$h_3$", r"$h_4$"]
##########################################################################
# Limits of the maps
lims = [[3750,4000], [150,500], [-0.08, 0.08], [-0.15, 0.15] ]
##########################################################################
xcb = [0.068, 0.385, 0.705] # Position of colorbars
###############################################
# Set the threshold S/N for smoothing
# Higher values than this values are not smoothed
sn_thres = [25., 25., 1000, 1000]
###############################################
# Read values of other authors
tab1a, tab1b = get_richtler()
tab2 = get_ventimiglia()
###############################################
# Loop for figures
for i, vector in enumerate(data):
print "Producing figure for {0}...".format(names[i])
good = np.where(((~np.isnan(vector)) & (sn>sn_cut)))[0]
sn_high = np.where(((~np.isnan(vector)) & (sn>=sn_thres[i])))[0]
sn_low = np.delete(good, sn_high)
vector_low = ll.loess_2d(xall[sn_low], yall[sn_low], vector[sn_low],
frac=frac_loess)
vector_high = vector[sn_high]
good = np.hstack((sn_high, sn_low ))
v_loess = np.hstack((vector_high, vector_low))
v = vector[good]
vmin = lims[i][0] if lims[i][0] else v_loess.min()
vmax = lims[i][1] if lims[i][1] else v_loess.max()
fig = plt.figure(figsize=(15, 5.1))
gs = gridspec.GridSpec(1,3)
gs.update(left=0.051, right=0.985, bottom=0.11, top=0.975, hspace=0.06,
wspace=0.06)
vs = [v, v_loess, v_loess]
ylabels = [1,0,0]
contours = ["vband", "vband", "residual"]
cb_fmts=["%i","%i", "%.2f", "%.2f"]
####################################################
# Produces pannels
####################################################
for j in range(3):
ax = plt.subplot(gs[j])
norm = Normalize(vmin=vmin, vmax=vmax)
coll = PolyCollection(polygons_bins[good], array=vs[j],
cmap="cubelaw",
edgecolors='w', norm=norm, linewidths=0.4)
draw_map(fig, ax, coll)
draw_contours(contours[j], fig, ax)
plt.gca().add_patch(Rectangle((18,-36),20,10, alpha=1, zorder=10000,
color="w"))
draw_colorbar(fig, ax, coll, cblabel=cb_label[i],
cbar_pos=[xcb[j], 0.18, 0.08, 0.04],
ticks=np.linspace(vmin, vmax, 4), cb_fmt=cb_fmts[i])
xylabels(ax, y=ylabels[j])
if j > 0:
ax.set_yticklabels([])
#####################################################
# Draw long slits of other papers
#####################################################
if i > 1:
continue
bc = ["g", "g", "b", "b"]
for k, tab in enumerate([tab1a, tab1b, tab2[4:], tab2[:4]]):
norm = Normalize(vmin=vmin, vmax=vmax)
idx = np.argsort(tab[:,0])
points = np.array([tab[:,0][idx], tab[:,1][idx]]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]],
axis=1)
lc = LineCollection(segments, array=tab[:,i+2],
cmap="cubelaw", norm=norm, lw=5)
ax.add_collection(lc)
add_borders(ax, points, c=bc[k])
# plt.savefig("figs/{0}.pdf".format(names[i]))
plt.savefig("figs/{0}.png".format(names[i]))
def make_kin_summary(loess=False, contours="vband", format="png",
sn_lims=None, sn_loess=None, sn_sig=True):
""" Make maps for the Lick indices in a single panel. """
##########################################################################
# Set the limits for the S/N in case it is not defined by the user
# sn_cut is defined in the setup_n3311 file
if sn_lims == None:
sn_lims = [sn_cut] * 4
##########################################################################
# In case of LOESS smoothing, set the smoothing region (S/N < sn_loess)
if sn_loess == None:
sn_loess = [25,25,25,25]
##########################################################################
# Read data values for Lick indices
data = np.loadtxt(outtable, usecols=(5,7,9,11)).T
# Read spectra name
s = np.genfromtxt(outtable, usecols=(0,), dtype=None).tolist()
########################################################
# Read coords and S/N
xall, yall, sn = np.loadtxt(outtable, usecols=(1,2, 14)).T
##########################################################
# If using S/N / sigma instead of S/N
if sn_sig == True:
sn /= data[1] / 100.
########################################################
# Read values of other authors
tab1a, tab1b = get_richtler()
tab2 = get_ventimiglia()
###############################################
# Details of the maps
# Name to be displayed during processing
titles = [r"velocity", r"sigma", r"h3", r"h4"]
# Tex strings to be used in labels
cb_label = [r"V$_{\rm LOS}$ [km/s]", r"$\sigma_{\rm LOS}$ [km/s]",
r"$h_3$", r"$h_4$"]
# Ranges of the plots
lims = [[3750, 4000], [200, 500], [None, None], [None, None]]
# Position of the colorbars
xcb = [0.075, 0.555]
xcb = xcb + xcb
yc1 = 0.56
yc2 = 0.085
ycb = [yc1, yc1, yc2, yc2]
# Colormap
cmap = "cubelaw"
ylabels = [1,0,1,0]
xlabels = [0,0,1,1]
cb_fmts = ["%d", "%d", "%.2f", "%.2f"]
###############################################
# Initialize figure and subplots
fig = plt.figure(figsize=(12.5, 12))
gs = gridspec.GridSpec(2,2)
gs.update(left=0.045, right=0.988, bottom=0.05, top=0.99, hspace=0.03,
wspace=0.03)
# Loop for figures
for i, vector in enumerate(data):
print "Producing figure for {0}...".format(titles[i])
good = np.where(((~np.isnan(vector)) & (sn>sn_lims[i])))[0]
if loess:
sn_high = np.where(((~np.isnan(vector)) & (sn>=sn_loess[i])))[0]
sn_low = np.delete(good, sn_high)
vector_low = ll.loess_2d(xall[sn_low], yall[sn_low],
vector[sn_low], frac=frac_loess)
vector_high = vector[sn_high]
good = np.hstack((sn_high, sn_low ))
v = np.hstack((vector_high, vector_low))
else:
v = vector[good]
mad = 1.4826 * np.median(np.abs(v - np.median(v)))
######################################################################
# Set limits according to median deviation if not defined in lims
vmin = np.median(v) - 1.5 * mad if lims[i][0] == None else lims[i][0]
vmax = np.median(v) + 1.5 * mad if lims[i][0] == None else lims[i][1]
norm = Normalize(vmin=vmin, vmax=vmax)
######################################################################
ax = plt.subplot(gs[i])
coll = PolyCollection(polygons_bins[good], array=v, cmap=cmap,
edgecolors='w', norm=norm)
draw_map(fig, ax, coll)
draw_contours(contours, fig, ax)
plt.gca().add_patch(Rectangle((18,-36),20,10, alpha=1, zorder=1000,
color="w"))
draw_colorbar(fig, ax, coll, cblabel=cb_label[i],
cbar_pos=[xcb[i], ycb[i], 0.09, 0.02],
ticks=np.linspace(vmin, vmax, 4), cb_fmt=cb_fmts[i],
labelsize=12)
xylabels(ax, y=ylabels[i], x=xlabels[i])
if i not in [0,2]:
ax.set_yticklabels([])
if i < 2:
ax.set_xticklabels([])
#####################################################
# Draw long slits of other papers
#####################################################
if i > 1:
continue
for tab in [tab1a, tab1b, tab2]:
norm = Normalize(vmin=lims[i][0], vmax=lims[i][1])
points = np.array([tab[:,0], tab[:,1]]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]],
axis=1)
lc = LineCollection(segments, array=tab[:,i+2],
cmap=cmap, norm=norm, linewidth=10, zorder=800,
edgecolor="w")
ax.add_collection(lc)
nloess = "_loess" if loess else ""
plt.savefig("figs/kinmaps{0}_{1}.{2}".format(nloess, contours, format),
dpi=100)
return
def make_kinematics():
""" Make maps for kinematics individually. """
# Read data values for vel, sigma, h3, h4
data = np.loadtxt(outtable, usecols=(5, 7, 9, 11)).T
xall, yall, sn = np.loadtxt(outtable, usecols=(
1,
2,
14,
)).T
###############################################
# Details of the maps
names = [r"vel", r"sigma", r"h3", r"h4"]
cb_label = [
r"V$_{\rm LOS}$ (km/s)", r"$\sigma_{\rm LOS}$ (km/s)", r"$h_3$",
r"$h_4$"
]
##########################################################################
# Limits of the maps
lims = [[3750, 4000], [150, 500], [-0.08, 0.08], [-0.15, 0.15]]
##########################################################################
xcb = [0.068, 0.385, 0.705] # Position of colorbars
###############################################
# Set the threshold S/N for smoothing
# Higher values than this values are not smoothed
sn_thres = [25., 25., 1000, 1000]
###############################################
# Read values of other authors
tab1a, tab1b = get_richtler()
tab2 = get_ventimiglia()
###############################################
# Loop for figures
for i, vector in enumerate(data):
print "Producing figure for {0}...".format(names[i])
good = np.where(((~np.isnan(vector)) & (sn > sn_cut)))[0]
sn_high = np.where(((~np.isnan(vector)) & (sn >= sn_thres[i])))[0]
sn_low = np.delete(good, sn_high)
vector_low = ll.loess_2d(xall[sn_low],
yall[sn_low],
vector[sn_low],
frac=frac_loess)
vector_high = vector[sn_high]
good = np.hstack((sn_high, sn_low))
v_loess = np.hstack((vector_high, vector_low))
v = vector[good]
vmin = lims[i][0] if lims[i][0] else v_loess.min()
vmax = lims[i][1] if lims[i][1] else v_loess.max()
fig = plt.figure(figsize=(15, 5.1))
gs = gridspec.GridSpec(1, 3)
gs.update(left=0.051,
right=0.985,
bottom=0.11,
top=0.975,
hspace=0.06,
wspace=0.06)
vs = [v, v_loess, v_loess]
ylabels = [1, 0, 0]
contours = ["vband", "vband", "residual"]
cb_fmts = ["%i", "%i", "%.2f", "%.2f"]
####################################################
# Produces pannels
####################################################
for j in range(3):
ax = plt.subplot(gs[j])
norm = Normalize(vmin=vmin, vmax=vmax)
coll = PolyCollection(polygons_bins[good],
array=vs[j],
cmap="cubelaw",
edgecolors='w',
norm=norm,
linewidths=0.4)
draw_map(fig, ax, coll)
draw_contours(contours[j], fig, ax)
plt.gca().add_patch(
Rectangle((18, -36), 20, 10, alpha=1, zorder=10000, color="w"))
draw_colorbar(fig,
ax,
coll,
cblabel=cb_label[i],
cbar_pos=[xcb[j], 0.18, 0.08, 0.04],
ticks=np.linspace(vmin, vmax, 4),
cb_fmt=cb_fmts[i])
xylabels(ax, y=ylabels[j])
if j > 0:
ax.set_yticklabels([])
#####################################################
# Draw long slits of other papers
#####################################################
if i > 1:
continue
bc = ["g", "g", "b", "b"]
for k, tab in enumerate([tab1a, tab1b, tab2[4:], tab2[:4]]):
norm = Normalize(vmin=vmin, vmax=vmax)
idx = np.argsort(tab[:, 0])
points = np.array([tab[:, 0][idx],
tab[:, 1][idx]]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
lc = LineCollection(segments,
array=tab[:, i + 2],
cmap="cubelaw",
norm=norm,
lw=5)
ax.add_collection(lc)
add_borders(ax, points, c=bc[k])
# plt.savefig("figs/{0}.pdf".format(names[i]))
plt.savefig("figs/{0}.png".format(names[i]))
def make_kin_summary(loess=False,
contours="vband",
format="png",
sn_lims=None,
sn_loess=None,
sn_sig=True):
""" Make maps for the Lick indices in a single panel. """
##########################################################################
# Set the limits for the S/N in case it is not defined by the user
# sn_cut is defined in the setup_n3311 file
if sn_lims == None:
sn_lims = [sn_cut] * 4
##########################################################################
# In case of LOESS smoothing, set the smoothing region (S/N < sn_loess)
if sn_loess == None:
sn_loess = [25, 25, 25, 25]
##########################################################################
# Read data values for Lick indices
data = np.loadtxt(outtable, usecols=(5, 7, 9, 11)).T
# Read spectra name
s = np.genfromtxt(outtable, usecols=(0, ), dtype=None).tolist()
########################################################
# Read coords and S/N
xall, yall, sn = np.loadtxt(outtable, usecols=(1, 2, 14)).T
##########################################################
# If using S/N / sigma instead of S/N
if sn_sig == True:
sn /= data[1] / 100.
########################################################
# Read values of other authors
tab1a, tab1b = get_richtler()
tab2 = get_ventimiglia()
###############################################
# Details of the maps
# Name to be displayed during processing
titles = [r"velocity", r"sigma", r"h3", r"h4"]
# Tex strings to be used in labels
cb_label = [
r"V$_{\rm LOS}$ [km/s]", r"$\sigma_{\rm LOS}$ [km/s]", r"$h_3$",
r"$h_4$"
]
# Ranges of the plots
lims = [[3750, 4000], [200, 500], [None, None], [None, None]]
# Position of the colorbars
xcb = [0.075, 0.555]
xcb = xcb + xcb
yc1 = 0.56
yc2 = 0.085
ycb = [yc1, yc1, yc2, yc2]
# Colormap
cmap = "cubelaw"
ylabels = [1, 0, 1, 0]
xlabels = [0, 0, 1, 1]
cb_fmts = ["%d", "%d", "%.2f", "%.2f"]
###############################################
# Initialize figure and subplots
fig = plt.figure(figsize=(12.5, 12))
gs = gridspec.GridSpec(2, 2)
gs.update(left=0.045,
right=0.988,
bottom=0.05,
top=0.99,
hspace=0.03,
wspace=0.03)
# Loop for figures
for i, vector in enumerate(data):
print "Producing figure for {0}...".format(titles[i])
good = np.where(((~np.isnan(vector)) & (sn > sn_lims[i])))[0]
if loess:
sn_high = np.where(((~np.isnan(vector)) & (sn >= sn_loess[i])))[0]
sn_low = np.delete(good, sn_high)
vector_low = ll.loess_2d(xall[sn_low],
yall[sn_low],
vector[sn_low],
frac=frac_loess)
vector_high = vector[sn_high]
good = np.hstack((sn_high, sn_low))
v = np.hstack((vector_high, vector_low))
else:
v = vector[good]
mad = 1.4826 * np.median(np.abs(v - np.median(v)))
######################################################################
# Set limits according to median deviation if not defined in lims
vmin = np.median(v) - 1.5 * mad if lims[i][0] == None else lims[i][0]
vmax = np.median(v) + 1.5 * mad if lims[i][0] == None else lims[i][1]
norm = Normalize(vmin=vmin, vmax=vmax)
######################################################################
ax = plt.subplot(gs[i])
coll = PolyCollection(polygons_bins[good],
array=v,
cmap=cmap,
edgecolors='w',
norm=norm)
draw_map(fig, ax, coll)
draw_contours(contours, fig, ax)
plt.gca().add_patch(
Rectangle((18, -36), 20, 10, alpha=1, zorder=1000, color="w"))
draw_colorbar(fig,
ax,
coll,
cblabel=cb_label[i],
cbar_pos=[xcb[i], ycb[i], 0.09, 0.02],
ticks=np.linspace(vmin, vmax, 4),
cb_fmt=cb_fmts[i],
labelsize=12)
xylabels(ax, y=ylabels[i], x=xlabels[i])
if i not in [0, 2]:
ax.set_yticklabels([])
if i < 2:
ax.set_xticklabels([])
#####################################################
# Draw long slits of other papers
#####################################################
if i > 1:
continue
for tab in [tab1a, tab1b, tab2]:
norm = Normalize(vmin=lims[i][0], vmax=lims[i][1])
points = np.array([tab[:, 0], tab[:, 1]]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
lc = LineCollection(segments,
array=tab[:, i + 2],
cmap=cmap,
norm=norm,
linewidth=10,
zorder=800,
edgecolor="w")
ax.add_collection(lc)
nloess = "_loess" if loess else ""
plt.savefig("figs/kinmaps{0}_{1}.{2}".format(nloess, contours, format),
dpi=100)
return