def demo_grid_with_each_cbar(fig):
"""
A grid of 2x2 images. Each image has its own colobar.
"""
grid = AxesGrid(
F,
133, # similar to subplot(122)
nrows_ncols=(2, 2),
axes_pad=0.1,
label_mode="1",
share_all=True,
cbar_location="top",
cbar_mode="each",
cbar_size="7%",
cbar_pad="2%",
)
Z, extent = get_demo_image()
for i in range(4):
im = grid[i].imshow(Z, extent=extent, interpolation="nearest")
grid.cbar_axes[i].colorbar(im)
# This affects all axes because we set share_all = True.
grid.axes_llc.set_xticks([-2, 0, 2])
grid.axes_llc.set_yticks([-2, 0, 2])
def display_kernels(kgrid, n1, n2):
# default: n1=12(mag), n2=17(Re)
fig = figure(1, figsize=(12, 8))
grid = AxesGrid(fig, (0.1, 0.15, 0.9, 0.58),
nrows_ncols=(n2, n1),
axes_pad=0.0,
share_all=True,
aspect=False)
#grid.set_aspect(0.6)
for i in arange(n1):
for j in range(n2)[::-1]:
kernel = maximum(kgrid.kernels[(i, j)].kernel, 1.e-50)
if kgrid.kernels[(i, j)].completeness < 0:
kernel = kernel * 0.
else:
#nf = 1.0/max(kernel.ravel())
kernel = kernel * kgrid.kernels[(i, j)].completeness
#grid[(n2-j-1)*n1+i].
grid[(n2 - j - 1) * n1 + i].imshow(kernel.swapaxes(0, 1),
origin='lower',
vmax=0.002,
aspect=0.4)
#grid[(n2-j-1)*n1+i].xscale=1.5
#grid[(n2-j-1)*n1+i].yscale=1.0
grid[(n2 - j - 1) * n1 + i].get_aspect()
grid.axes_llc.set_xticks([])
grid.axes_llc.set_yticks([])
draw()
def AnnotateS1Activity(exp, image, scale=0, cols=2, colorbar=True,
normalize=True):
"""Plot the S1 activation for a given image.
This shows the image in the background, with the activation plotted on top.
There is one plot for each orientation band.
:param image: Path to image on disk, or index of image in experiment.
:param int scale: Index of scale band to use.
:param bool colorbar: Whether to show colorbar with plot.
"""
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid import AxesGrid # import must be delayed
s1 = GetActivity(exp, image, Layer.S1)[scale]
image = _ScaleImage(exp, GetActivity(exp, image, Layer.IMAGE), scale)
params = exp.extractor.model.params
left = bottom = params.s1_kwidth/2
scale_factor = params.s1_sampling
top = bottom + s1.shape[-2] * scale_factor
right = left + s1.shape[-1] * scale_factor
rows = int(np.ceil(len(s1) / float(cols)))
grid = AxesGrid(plt.gcf(), 111,
nrows_ncols = (rows, cols),
axes_pad = 0.5,
share_all = True,
label_mode = "L", # XXX value can be "L" or "1" -- what is this?
cbar_location = "right",
cbar_mode = "single",
)
vmin = s1.min()
vmax = s1.max()
for i in range(len(s1)):
plt.sca(grid[i])
plt.imshow(image, cmap=plt.cm.gray)
plt.imshow(s1[i], vmin=vmin, vmax=vmax, alpha=.5,
extent=(left,right,top,bottom), cmap=plt.cm.RdBu_r)
plt.xticks(())
plt.yticks(())
if colorbar:
img = grid[0].images[-1]
grid.cbar_axes[0].colorbar(img)
for cax in grid.cbar_axes:
cax.toggle_label(True)
def demo_simple_grid(fig):
"""
A grid of 2x2 images with 0.05 inch pad between images and only
the lower-left axes is labeld.
"""
grid = AxesGrid(
fig,
131, # similar to subplot(131)
nrows_ncols=(2, 2),
axes_pad=0.05,
label_mode="1",
)
Z, extent = get_demo_image()
for i in range(4):
im = grid[i].imshow(Z, extent=extent, interpolation="nearest")
# This only affects axes in first column and second row as share_all = False.
grid.axes_llc.set_xticks([-2, 0, 2])
grid.axes_llc.set_yticks([-2, 0, 2])
def demo_grid_with_single_cbar(fig):
"""
A grid of 2x2 images with a single colobar
"""
grid = AxesGrid(
fig,
132, # similar to subplot(132)
nrows_ncols=(2, 2),
axes_pad=0.0,
share_all=True,
label_mode="L",
cbar_mode="single",
)
Z, extent = get_demo_image()
for i in range(4):
im = grid[i].imshow(Z, extent=extent, interpolation="nearest")
plt.colorbar(im, cax=grid.cbar_axes[0])
grid.cbar_axes[0].colorbar(im)
# This affects all axes as share_all = True.
grid.axes_llc.set_xticks([-2, 0, 2])
grid.axes_llc.set_yticks([-2, 0, 2])
def plot(net, n, p):
classname = net.__class__.__name__
axes.set_xticks([])
axes.set_yticks([])
divider = make_axes_locatable(axes)
subaxes = divider.new_vertical(1.0, pad=0.4, sharex=axes)
fig.add_axes(subaxes)
subaxes.set_xticks([])
subaxes.yaxis.set_major_locator(matplotlib.ticker.MaxNLocator(2))
subaxes.yaxis.set_ticks_position('right')
subaxes.set_ylabel('Distortion')
subaxes.set_xlabel('Time')
Y = net.distortion[::1]
X = np.arange(len(Y)) / float(len(Y) - 1)
subaxes.plot(X, Y)
if classname == 'NG':
plt.title('Neural Gas', fontsize=20)
elif classname == 'SOM':
plt.title('Self-Organizing Map', fontsize=20)
elif classname == 'DSOM':
plt.title('Dynamic Self-Organizing Map', fontsize=20)
axes.axis([0, 1, 0, 1])
axes.set_aspect(1)
bounds = divider.locate(0, 0).bounds
grid = AxesGrid(fig,
bounds,
nrows_ncols=(n, n),
axes_pad=0.05,
label_mode="1")
for row in range(n):
for col in range(n):
index = row * n + col
Z = net.codebook[row, col].reshape(p, p)
im = grid[index].imshow(Z,
interpolation='nearest',
vmin=0,
vmax=1,
cmap=plt.cm.hot)
grid[index].set_yticks([])
grid[index].set_xticks([])
classname = net.__class__.__name__
if classname == 'NG':
axes.text(
0.5,
-0.01,
r'$\lambda_i = %.3f,\lambda_f = %.3f, \varepsilon_i=%.3f, \varepsilon_f=%.3f$'
% (net.sigma_i, net.sigma_f, net.lrate_i, net.lrate_f),
fontsize=16,
horizontalalignment='center',
verticalalignment='top',
transform=axes.transAxes)
if classname == 'SOM':
axes.text(
0.5,
-0.01,
r'$\sigma_i = %.3f,\sigma_f = %.3f, \varepsilon_i=%.3f, \varepsilon_f=%.3f$'
% (net.sigma_i, net.sigma_f, net.lrate_i, net.lrate_f),
fontsize=16,
horizontalalignment='center',
verticalalignment='top',
transform=axes.transAxes)
elif classname == 'DSOM':
axes.text(0.5,
-0.01,
r'$elasticity = %.2f$, $\varepsilon = %.3f$' %
(net.elasticity, net.lrate),
fontsize=16,
horizontalalignment='center',
verticalalignment='top',
transform=axes.transAxes)
# This affects all axes as share_all = True.
grid.axes_llc.set_xticks([-2, 0, 2])
grid.axes_llc.set_yticks([-2, 0, 2])
"""
fig = plt.figure()
#fig.set_figheight(5)
#fig.set_figwidth(10)
#fig.subplots_adjust(right = 0.95)
grid = AxesGrid(
fig,
111, # similar to subplot(132)
nrows_ncols=(1, 2),
aspect=True,
axes_pad=1,
share_all=False,
label_mode="all",
cbar_location="right",
cbar_mode="single",
cbar_pad="2%")
maxL = 5
Np = 5
ps = np.linspace(0.01, 0.99, Np)
lines1 = []
lines2 = []
for p in ps:
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid import AxesGrid
from demo_image import get_demo_image
F = plt.figure(1, (5.5, 3.5))
grid = AxesGrid(
F,
111, # similar to subplot(111)
nrows_ncols=(1, 3),
axes_pad=0.1,
add_all=True,
label_mode="L",
)
Z, extent = get_demo_image() # demo image
im1 = Z
im2 = Z[:, :10]
im3 = Z[:, 10:]
vmin, vmax = Z.min(), Z.max()
for i, im in enumerate([im1, im2, im3]):
ax = grid[i]
ax.imshow(im,
origin="lower",
vmin=vmin,
vmax=vmax,
interpolation="nearest")
plt.draw()
plt.show()
import os
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid import AxesGrid
from scikits.image.io import MultiImage
from scikits.image import data_dir
# Load the multi-layer image
fname = os.path.join(data_dir, 'multipage.tif')
img = MultiImage(fname)
# Create an image grid
fig = plt.figure()
grid = AxesGrid(fig,
rect=(1, 1, 1),
nrows_ncols=(1, 2),
axes_pad=0.1)
# Plot the layers on the image grid
for i, frame in enumerate(img):
grid[i].imshow(frame, cmap=plt.cm.gray)
grid[i].set_xlabel('Frame %s' % i)
grid[i].set_xticks([])
grid[i].set_yticks([])
plt.show()
def Show2dArrayList(xs,
annotations=None,
normalize=True,
colorbar=False,
colorbars=False,
cols=None,
center_zero=True,
axes=None,
figure=None,
show=True,
title=None,
titles=None,
**args):
"""Display a list of 2-D arrays using matplotlib.
:param xs: Input arrays.
:type xs: iterable of 2D ndarray
:param annotations: Small images to show with each array visualization.
:type annotations: list of 2D ndarray
:param bool normalize: Whether to use the same colormap range for all
subplots.
:param bool colorbar: Whether to show the meaning of the colormap as an extra
subplot (implies *normalize* = True). Implies ``colorbars`` is False.
:param bool colorbars: Whether to show a different colorbar for each subplot.
:param int cols: Number of subplot columns to use.
:param bool center_zero: When normalizing a range that spans zero, make sure
zero is in the center of the colormap range.
:param figure: The matplotlib figure into which to plot.
:param bool show: Whether to show the figure on screen after it is drawn.
:param str title: String to display above the set of plots.
:param titles: Title string to include above each plotted array.
:type titles: list of str
:param vmin: Minimum value for colormap range.
:param vmax: Maximum value for colormap range.
:param mapper: Function to map locations in the foreground to corresponding
locations in the background. (Required if *bg* is set.)
Any remaining keyword arguments will be passed to
:func:`matplotlib.pyplot.imshow` for each 2D array.
"""
if 'vmin' in args and 'vmax' in args:
vmin = args['vmin']
vmax = args['vmax']
elif colorbar or normalize:
vmin = min([x.min() for x in xs])
vmax = max([x.max() for x in xs])
if center_zero and vmin < 0 and vmax > 0:
vmax = max(abs(vmin), vmax)
vmin = -vmax
args = misc.MergeDict(args, vmin=vmin, vmax=vmax)
if annotations == None:
annotations = [None] * len(xs)
else:
assert len(annotations) == len(xs), \
"Got %d arrays, but %d annotations (these numbers should match)" % \
(len(xs), len(annotations))
assert 'mapper' in args, "Using annotations requires a 'mapper'."
if titles == None:
titles = [""] * len(xs)
else:
assert len(titles) == len(xs), \
"Got %d arrays, but %d title strings (these numbers should match)" % \
(len(xs), len(titles))
# Compute rows & cols
max_plots = 64
if len(xs) > max_plots:
xs = xs[:max_plots]
if cols == None:
if len(xs) <= 16:
cols = min(4, len(xs))
else:
cols = min(8, len(xs))
rows = int(math.ceil(len(xs) / float(cols)))
# Create the image grid
if figure == None:
figure = pyplot.gcf()
figure.clf()
if colorbar:
grid_args = dict(cbar_location="right", cbar_mode="single")
elif colorbars:
grid_args = dict(cbar_location="right",
cbar_mode="each",
cbar_pad="2%")
else:
grid_args = {}
grid = AxesGrid(
figure,
111,
nrows_ncols=(rows, cols),
axes_pad=0.5,
share_all=True,
label_mode="L", # XXX value can be "L" or "1" -- what is this?
**grid_args)
if title is not None:
figure.suptitle(title)
# Add all subplots
for i in range(len(xs)):
Show2dArray(xs[i],
annotation=annotations[i],
axes=grid[i],
show=False,
title=titles[i],
**args)
# Add the colorbar(s)
if colorbar:
img = grid[0].images[-1]
grid.cbar_axes[0].colorbar(img)
for cax in grid.cbar_axes:
cax.toggle_label(True)
elif colorbars:
for i in range(len(xs)):
img = grid[i].images[-1]
ca = grid.cbar_axes[i]
ca.colorbar(img)
ca.toggle_label(True)
if hasattr(figure, 'show') and show:
figure.show()
def klfYoung():
dbfile = '/u/jlu/data/gc/database/stars.sqlite'
# Create a connection to the database file
connection = sqlite.connect(dbfile)
# Create a cursor object
cur = connection.cursor()
# Get info on the stars
cur.execute('select * from stars where young = "T" and cuspPaper = "T"')
rows = cur.fetchall()
starCnt = len(rows)
starName = []
x = []
y = []
kp = []
Ak = []
starInField = []
# Loop through each star and pull out the relevant information
for ss in range(starCnt):
record = rows[ss]
name = record[0]
# Check that we observed this star and what field it was in.
cur.execute('select field from spectra where name = "%s"' % (name))
row = cur.fetchone()
if row != None:
starInField.append(row[0])
starName.append(name)
kp.append(record[4])
x.append(record[7])
y.append(record[9])
Ak.append(record[24])
else:
#print 'We do not have data on this star???'
#starInField.append('C')
continue
starCnt = len(starName)
starName = np.array(starName)
starInField = np.array(starInField)
x = np.array(x)
y = np.array(y)
kp = np.array(kp)
Ak = np.array(Ak)
# Now get the completeness corrections for each field.
completenessFile = '/u/jlu/work/gc/imf/klf/2010_04_02/'
completenessFile += 'completeness_extinct_correct.txt'
completeness = asciidata.open(completenessFile)
fields = ['C', 'E', 'SE', 'S', 'W', 'N', 'NE', 'SW', 'NW']
compKp = completeness[0].tonumpy()
comp = {}
for ff in range(len(fields)):
field = fields[ff]
comp[field] = completeness[1 + ff].tonumpy()
# Load up the areas for each field
masksDir = '/u/jlu/work/gc/imf/klf/2010_04_02/osiris_fov/masks/'
area = {}
area['C'] = pyfits.getdata(masksDir + 'central.fits').sum() * 0.01**2
area['E'] = pyfits.getdata(masksDir + 'east.fits').sum() * 0.01**2
area['SE'] = pyfits.getdata(masksDir + 'southeast.fits').sum() * 0.01**2
area['S'] = pyfits.getdata(masksDir + 'south.fits').sum() * 0.01**2
area['W'] = pyfits.getdata(masksDir + 'west.fits').sum() * 0.01**2
area['N'] = pyfits.getdata(masksDir + 'north.fits').sum() * 0.01**2
area['NE'] = pyfits.getdata(masksDir + 'northeast.fits').sum() * 0.01**2
area['SW'] = pyfits.getdata(masksDir + 'southwest.fits').sum() * 0.01**2
area['NW'] = pyfits.getdata(masksDir + 'northwest.fits').sum() * 0.01**2
KLFs = np.zeros((len(fields), len(compKp)), dtype=float)
KLFs_ext = np.zeros((len(fields), len(compKp)), dtype=float)
KLFs_ext_cmp = np.zeros((len(fields), len(compKp)), dtype=float)
eKLFs_ext_cmp = np.zeros((len(fields), len(compKp)), dtype=float)
kp_ext = kp - Ak + 3.0
for ff in range(len(fields)):
field = fields[ff]
# Pull out all the stars in the field
sdx = np.where(starInField == field)[0]
kpInField = kp[sdx]
AkInField = Ak[sdx]
nameInField = starName[sdx]
# Set all stars to Ak = 3
kpInField_ext = kpInField - AkInField + 3.0
# Make a binned luminosity function
binSizeKp = compKp[1] - compKp[0]
perAsec2Mag = area[field] * binSizeKp
for kk in range(len(compKp)):
kp_lo = compKp[kk]
kp_hi = compKp[kk] + binSizeKp
idx1 = np.where((kpInField >= kp_lo) & (kpInField < kp_hi))[0]
KLFs[ff, kk] = len(idx1) / perAsec2Mag
idx2 = np.where((kpInField_ext >= kp_lo)
& (kpInField_ext < kp_hi))[0]
KLFs_ext[ff, kk] = len(idx2) / perAsec2Mag
KLFs_ext_cmp[ff, kk] = KLFs_ext[ff, kk] / comp[field][kk]
eKLFs_ext_cmp[ff, kk] = math.sqrt(len(idx2))
eKLFs_ext_cmp[ff, kk] /= perAsec2Mag * comp[field][kk]
idx = np.where(np.isnan(KLFs_ext_cmp[ff]) == True)[0]
for ii in idx:
KLFs_ext_cmp[ff, ii] = 0.0
# ==========
# Plot the KLF
# ==========
outputDir = '/u/jlu/work/gc/imf/klf/2010_04_02/plots/'
py.clf()
colors = [
'blue', 'green', 'red', 'cyan', 'magenta', 'yellow', 'purple',
'orange', 'brown'
]
legendItems = []
for ff in range(len(fields)):
plt = py.plot(compKp, KLFs_ext_cmp[ff], 'k-', color=colors[ff])
py.plot(compKp, KLFs_ext[ff], 'k--', color=colors[ff])
#py.plot(compKp, KLFs[ff], 'k-.', color=colors[ff])
legendItems.append(plt)
print 'KLF for field ' + fields[ff]
py.xlabel('Kp magnitude (A_Kp = 3)')
py.ylabel('N_stars / (arcsec^2 mag)')
py.legend(legendItems, fields, loc='upper left')
py.savefig(outputDir + 'klf_young_fields_all.png')
# Make a global KLF
KLFall = KLFs_ext_cmp.sum(axis=0)
KLFouter = KLFs_ext_cmp[1:, :].sum(axis=0)
KLFinner = KLFs_ext_cmp[0, :]
eKLFall = np.sqrt((eKLFs_ext_cmp**2).sum(axis=0))
eKLFouter = np.sqrt((eKLFs_ext_cmp[1:, :]**2).sum(axis=0))
eKLFinner = eKLFs_ext_cmp[0, :]
p_compKp, p_KLFall = histNofill.convertForPlot(compKp, KLFall)
p_compKp, p_KLFouter = histNofill.convertForPlot(compKp, KLFouter)
p_compKp, p_KLFinner = histNofill.convertForPlot(compKp, KLFinner)
py.clf()
fig = py.figure(1)
py.subplots_adjust(top=0.95, right=0.95)
grid = AxesGrid(fig,
111,
nrows_ncols=(3, 1),
axes_pad=0.12,
add_all=True,
share_all=True,
aspect=False)
grid[2].plot(p_compKp, p_KLFall, 'k-', linewidth=2)
grid[2].errorbar(compKp + 0.25, KLFall, yerr=eKLFall, fmt='k.')
grid[2].text(9.25, 25, 'KLF all')
grid[2].set_xlabel('Kp magnitude (A_Kp = 3)')
grid[1].plot(p_compKp, p_KLFouter, 'k-', linewidth=2)
grid[1].errorbar(compKp + 0.25, KLFouter, yerr=eKLFouter, fmt='k.')
grid[1].set_ylabel('N_stars / (arcsec^2 mag)')
grid[1].text(9.25, 25, 'KLF outer')
grid[0].plot(p_compKp, p_KLFinner, 'k-', linewidth=2)
grid[0].errorbar(compKp + 0.25, KLFinner, yerr=eKLFinner, fmt='k.')
grid[0].text(9.25, 25, 'KLF central')
grid[0].set_xlim(9, 16)
grid[0].set_ylim(0, 12)
grid[1].set_ylim(0, 12)
grid[2].set_ylim(0, 12)
py.savefig(outputDir + 'klf_young_fields.png')
py.clf()
py.subplots_adjust(top=0.95, right=0.95)
p1 = py.plot(p_compKp, p_KLFinner, 'k-', linewidth=2)
py.errorbar(compKp + 0.25, KLFinner, yerr=eKLFinner, fmt='k.')
p2 = py.plot(p_compKp, p_KLFouter, 'b-', linewidth=2)
py.errorbar(compKp + 0.25, KLFouter, yerr=eKLFouter, fmt='b.')
py.xlabel('Kp magnitude (A_Kp = 3)')
py.ylabel('N_stars / (arcsec^2 mag)')
py.legend((p1, p2), ('Inner', 'Outer'))
py.xlim(9, 16)
py.ylim(0, 12)
py.savefig(outputDir + 'klf_young_fields_inout.png')
# ==========
# Convert to masses
# Assume solar metallicity, A_K=3, distance = 8 kpc, age = 6 Myr
# ==========
genevaFile = '/u/jlu/work/models/geneva/iso/020/c/'
genevaFile += 'iso_c020_0680.UBVRIJHKLM'
model = asciidata.open(genevaFile)
modMass = model[1].tonumpy()
modV = model[6].tonumpy()
modVK = model[11].tonumpy()
modHK = model[15].tonumpy()
modK = modV - modVK
modH = modK + modHK
# Convert from K to Kp (Wainscoat and Cowie 1992)
modKp = modK + 0.22 * (modH - modK)
dist = 8000.0
distMod = -5.0 + 5.0 * math.log10(dist)
# Convert our observed magnitudes to absolute magnitudes.
# Use the differential extinction corrected individual magnitudes.
# Also use the diff. ex. corrected and completeness corrected KLF.
absKp = kp_ext - 3.0 - distMod
absKpKLF = compKp - 3.0 - distMod
p_absKpKLF = p_compKp - 3.0 - distMod
# First, calculate the indvidiual masses
modMassStar = np.zeros(len(absKp), dtype=float)
modKpStar = np.zeros(len(absKp), dtype=float)
for ii in range(len(absKp)):
idx = abs(absKp[ii] - modKp).argmin()
modMassStar[ii] = modMass[idx]
modKpStar[ii] = modKp[idx]
# Calculate the mass function
modMassIMF = np.zeros(len(absKpKLF), dtype=float)
modKpIMF = np.zeros(len(absKpKLF), dtype=float)
for ii in range(len(absKpKLF)):
idx = abs(absKpKLF[ii] - modKp).argmin()
modMassIMF[ii] = modMass[idx]
modKpIMF[ii] = modKp[idx]
# Calculate the mass function we can plot
p_modMassIMF = np.zeros(len(p_absKpKLF), dtype=float)
p_modKpIMF = np.zeros(len(p_absKpKLF), dtype=float)
for ii in range(len(p_absKpKLF)):
idx = abs(p_absKpKLF[ii] - modKp).argmin()
p_modMassIMF[ii] = modMass[idx]
p_modKpIMF[ii] = modKp[idx]
# ==========
# Plot the masses and mass functions
# ==========
py.clf()
py.plot(modMassStar, absKp, 'rs')
py.plot(modMassStar, modKpStar, 'b.')
py.xlabel('Mass (Msun)')
py.ylabel('Kp (mag)')
py.legend(('Observed Absolute', 'Model Absolute'))
py.savefig(outputDir + 'pdmf_young_indiv.png')
py.clf()
py.plot(modMassIMF, KLFall, 'ks')
py.plot(p_modMassIMF, p_KLFall, 'k-')
py.xlabel('Mass (Msun)')
py.ylabel('N')
# Completeness correction (polynomial fits)
comp = {
'C': [44.6449, -14.4162, 1.75114, -0.0923230, 0.00177092],
'E': [228.967, -71.9702, 8.43935, -0.435115, 0.00830836],
'SE': [527.679, -172.892, 21.1500, -1.14200, 0.0229464],
'S': [-293.901, 90.8734, -10.4823, 0.537018, -0.0103230],
'W': [2476.22, -795.154, 95.1204, -5.02096, 0.0986629],
'N': [334.286, -107.820, 12.8997, -0.676552, 0.0131203],
'NE': [-686.107, 224.631, -27.3240, 1.46633, -0.0293125],
'all':
[-142.858, 58.5277, -9.49619, 0.768180, -0.0309478, 0.000495177]
}
# Make a mass function
massEdges = 10**np.arange(1, 1.5, 0.05)
massBins = massEdges[:-1] + ((massEdges[1:] - massEdges[:-1]) / 2.0)
massHist = np.zeros(len(massBins), dtype=float)
p_massBins = np.zeros(len(massBins) * 2, dtype=float)
p_massHist = np.zeros(len(massBins) * 2, dtype=float)
for mm in range(len(massBins)):
m_lo = massEdges[mm]
m_hi = massEdges[mm + 1]
idx = np.where((modMassStar > m_lo) & (modMassStar <= m_hi))[0]
# Find the completeness factor for this mass bin
kpInBin = kp_ext[idx].mean()
cmpCorr = comp['all'][0] + comp['all'][1]*kpInBin + \
comp['all'][2]*kpInBin**2 + comp['all'][3]*kpInBin**3 +\
comp['all'][4]*kpInBin**4 + comp['all'][5]*kpInBin**5
print m_lo, m_hi, cmpCorr, len(idx), kpInBin
massHist[mm] = len(idx) # / cmpCorr
p_massBins[2 * mm] = m_lo
p_massBins[2 * mm + 1] = m_hi
p_massHist[2 * mm] = len(idx) # / cmpCorr
p_massHist[2 * mm + 1] = len(idx) # / cmpCorr
py.clf()
py.plot(p_massBins, p_massHist, 'k-')
py.plot(massBins, massHist, 'ks')
py.show()
data_path + 'ngc6946_cross_robust_ca.fits',
data_path + 'ngc6946_cross_robust_cs.fits',
data_path + 'ngc6946_cross_robust.fits',
]
letters = ['a', 'b', 'c', 'd', 'e']
letters = ['(' + l + ')' for l in letters]
out_fname = data_path + 'ngc6946_cross_robust_maps_grid' + '.png'
# create grid
# figure
#plt.gray()
fig = plt.figure()
grid = AxesGrid(
fig,
111,
nrows_ncols=(3, 2),
axes_pad=0.0,
share_all=True,
)
# loop on each map
for i, fname in enumerate(fname_ext):
data = np.flipud(pyfits.fitsopen(fname)[0].data.T)
if i == 0:
data /= 8.
data = data**.25
data[np.isnan(data)] = 0
extent = [0., 192., 0., 192.]
im = grid[i].imshow(data, extent=extent, interpolation="nearest")
grid[i].text(10, 170, letters[i], fontsize=20, color="white")
data_zoom = data[135:155, 80:100, ]
axins = zoomed_inset_axes(grid[i], 2, loc=3) # zoom = 6
def sort_func(a, b):
ai,aj = map(int, a.split(','))
bi,bj = map(int, b.split(','))
if bi > ai or (bi == ai and bj > aj): return -1
return 1
bls.sort(cmp=sort_func)
if len(bls) == 0:
print 'No data to plot.'
sys.exit(0)
m2 = int(math.sqrt(len(bls)))
m1 = int(math.ceil(float(len(bls)) / m2))
# Generate all the plots
dmin,dmax = None, None
fig = p.figure()
grid = AxesGrid(fig,111,aspect=False,nrows_ncols=(m2,m1),axes_pad=0.,share_all=1,cbar_mode='single',cbar_location='top')
if not opts.src is None:fig.suptitle(opts.src)
for cnt, bl in enumerate(bls):
d = n.ma.concatenate(plot_x[bl], axis=0)
if opts.df: d = d[:,:-2]/2 + d[:,2:]/2 - d[:,1:-1]
if opts.dt: d = d[:-2]/2 + d[2:]/2 - d[1:-1]
if opts.fringe:
d = d.filled(0)
flags = n.where(d[:,0] != 0, 1., 0.)
gain = n.sqrt(n.average(flags**2))
ker = n.fft.ifft(flags)
d = n.fft.ifft(d, axis=0)
if not opts.clean is None:
for chan in range(d.shape[1]):
d[:,chan],info = a.deconv.clean(d[:,chan],ker,tol=opts.clean)
d[:,chan] += info['res'] / gain
def plot_face_edge(isnap=28, selected_field='h2density', sizekpc=7., cutLow=1.e-5, f_out=None, save_plot=True,
overplot_clumps=True, incut=6., field_cut='h2density', n_cell_min=8, largeNum=1.e+42,
data=None, ds=None, dd=None, leaf_clumps=None, plotClumpID=False
):
"""
Parameters
----------
isnap: int
number of snaphot select for the plot
selected_field: str
field for image plot
sizekpc: float
size of the extracted region from isnap
cutLow: float
dynamical range for the colorbar
f_out: str
output filename
save_plot: bool
print to file
overplot_clumps: bool
overplot clumps
incut: float
threshold for clump definition
field_cut: str
field out of which clumps are identified
n_cell_min: int
min cell to define a clump
largeNum: float
BS to have YT to collaborate
"""
if f_out is None:
f_out = 'test_' + selected_field + '_out' + \
str(isnap) + '_yt_unit_plot.png'
from mpl_toolkits.axes_grid import AxesGrid
import matplotlib.pyplot as plt
# prepare input data, if not already passed as arguments
#
if data is None:
data = import_fetch_gal(isnap=isnap)
#
if (ds is None) and (dd is None):
ds, dd = prepare_unigrid(data=data,
add_unit=True,
regionsize_kpc=sizekpc,
debug=False)
#
if overplot_clumps and (leaf_clumps is None):
__, leaf_clumps = ytclumpfind_H2(ds, dd, field_cut, incut,
c_max=None, step=1e+6,
N_cell_min=n_cell_min, save=False,
plot=False, saveplot=None, fold_out='./')
if overplot_clumps:
id_sorted = sorted(range(len(leaf_clumps)),
key=lambda x: np.sum(leaf_clumps[x]["density"]))
# compute inertia tensor and it principal axes
e_value, e_vectors = calculate_eigenvektoren(data=data, sizekpc=sizekpc)
# references
# los_vec = e_vectors[0,:] # face on
# los_vec = e_vectors[1,:] # edge on
# los_vec = e_vectors[2,:] # face on (again, perpendicular direction)
#
# set the camera axes along the principal axes of the inertia tensor
vec_list = [e_vectors[2, :], e_vectors[1, :]]
up_list = [e_vectors[0, :], e_vectors[0, :]]
# setup the plot
#
fig = plt.figure()
#
grid = AxesGrid(fig, (0.075, 0.075, 0.85, 0.85),
nrows_ncols=(1, 2),
axes_pad=0.7,
#label_mode = "L",
label_mode="1",
share_all=True,
cbar_location="right",
cbar_mode="single",
cbar_size="3%",
cbar_pad=0.03)
# plot edge on and face on
#
for iplot, los_vec, up_vec in zip(xrange(2), vec_list, up_list):
prj = yt.OffAxisProjectionPlot(ds=ds, center=[0, 0, 0], normal=los_vec, fields=selected_field, width=(4, 'kpc'), north_vector=up_vec, weight_field='density',
)
prj.set_cmap(field=selected_field, cmap='inferno')
if selected_field == 'density':
selected_unit = 'Msun/pc**3'
elif selected_field == 'h2density':
selected_unit = '1/cm**3'
else:
raise TypeError('unit not implemented for the field')
prj.set_unit(selected_field, selected_unit)
prj.set_zlim(selected_field, cutLow * dd[selected_field].max().to(selected_unit),
dd[selected_field].max().to(selected_unit))
# annotate clump if asked for
if overplot_clumps:
prj.annotate_contour(field=field_cut, ncont=1, factor=1,
clim=(incut, largeNum), plot_args={
'colors': 'white'}
) # to deal w/ stupid yt annotate_clump() bug
for ileaf in id_sorted:
_fc = np.mean(leaf_clumps[ileaf].data.fcoords[:], axis=0)
if plotClumpID:
prj.annotate_marker(_fc,
coord_system='data',
plot_args={'color': 'red', 's': 50})
prj.annotate_text(_fc,
ileaf + 1,
coord_system='data',
text_args={'color': 'red', 'size': 25},
inset_box_args={'boxstyle': 'square',
'facecolor': 'white',
'linewidth': 1.0,
'edgecolor': 'white',
'alpha': 0.})
prj.set_ylabel('kpc')
prj.set_xlabel('kpc')
prj.set_font({'family': 'Times', # 'style': 'italic',
# 'weight': 'bold',
'size': 26})
# set the plot into the grid
plot = prj.plots[selected_field]
plot.figure = fig
plot.axes = grid[iplot].axes
plot.cax = grid.cbar_axes[iplot]
if iplot == 1:
plot.axes.set_axis_off()
# plot.axes.set_label('')
# plot.axes.set_visible(False)
plot.axes.set_xlabel('')
plot.axes.set_xticks(())
plot.axes.set_xticklabels('')
# Finally, this actually redraws the plot.
prj._setup_plots()
for cax in grid.cbar_axes:
cax.toggle_label(True)
# cax.axis[cax.orientation].set_label(field_select)
if save_plot:
prj.save(f_out, mpl_kwargs={'bbox_inches': 'tight'})
print 'dump to ', f_out
return prj, plot.axes
im0 = data[..., idx].copy()
m = np.mean(data, axis=-1)
for i in xrange(data.shape[-1]):
data[..., i] -= m
im = data[..., idx]
im_fht = fht.fht(im0)
im_fht[0, 0] = 0.
# figure
fig = pl.figure()
pl.gray()
grid = AxesGrid(
fig,
111,
nrows_ncols=(3, 1),
axes_pad=0.0,
share_all=True,
# cbar_mode="each",
)
ims = grid[0].imshow(im0, interpolation="nearest")
grid[0].text(2, 4, '(a)', fontsize=20, color="white")
grid[0].set_xticks(())
grid[0].set_yticks(())
ims = grid[1].imshow(im, interpolation="nearest")
grid[1].text(2, 4, '(b)', fontsize=20, color="white")
#grid.cbar_axes[0].colorbar(ims)
ims = grid[2].imshow(im_fht, interpolation="nearest")
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid import AxesGrid
import numpy as np
im = np.arange(100)
im.shape = 10, 10
fig = plt.figure(1, (4., 4.))
grid = AxesGrid(
fig,
111, # similar to subplot(111)
nrows_ncols=(2, 2), # creates 2x2 grid of axes
axes_pad=0.1, # pad between axes in inch.
)
for i in range(4):
grid[i].imshow(im) # The AxesGrid object work as a list of axes.
plt.show()
