def plot_planck_vs_2mass(
av_k09,
av_pl,
filename=None,
av_error=None,
contour_plot=True,
levels=10,
plot_median=True,
limits=None,
labels=['', ''],
scale=('linear', 'linear'),
title='',
fit_labels=['', ''],
gridsize=(100, 100),
):
# import external modules
import numpy as np
import math
import matplotlib.pyplot as plt
import matplotlib
from matplotlib import cm
from astroML.plotting import scatter_contour
from mpl_toolkits.axes_grid1.axes_grid import AxesGrid
import myplotting as myplt
# set up plot aesthetics
# ----------------------
plt.close
plt.clf()
# color map
myplt.set_color_cycle(num_colors=3)
# Create figure instance
fig = plt.figure(figsize=(3.6, 3.6))
axes = AxesGrid(
fig,
(1, 1, 1),
nrows_ncols=(1, 1),
ngrids=1,
axes_pad=0.25,
aspect=False,
label_mode='L',
share_all=True,
#cbar_mode='single',
cbar_pad=0.1,
cbar_size=0.2,
)
# Drop the NaNs from the images
if type(av_error) is float or av_error is None:
indices = np.where((av_pl == av_pl) &\
(av_k09 == av_k09)
)
av_pl = av_pl[indices]
av_k09 = av_k09[indices]
# Create plot
ax = axes[0]
if limits is None:
xmin = np.min(av_k09)
ymin = np.min(av_k09)
xmax = np.max(av_pl)
ymax = np.max(av_pl)
xscalar = 0.15 * xmax
yscalar = 0.15 * ymax
limits = [
xmin - xscalar, xmax + xscalar, ymin - yscalar, ymax + yscalar
]
if contour_plot:
contour_range = ((limits[0], limits[1]), (limits[2], limits[3]))
cmap = myplt.truncate_colormap(plt.cm.binary, 0.2, 1, 1000)
l1 = myplt.scatter_contour(
av_k09,
av_pl,
threshold=3,
log_counts=1,
levels=levels,
ax=ax,
histogram2d_args=dict(bins=30, range=contour_range),
plot_args=dict(marker='o',
linestyle='none',
color='black',
alpha=0.3,
markersize=2),
contour_args=dict(
#cmap=plt.cm.binary,
cmap=cmap,
#cmap=cmap,
),
)
else:
image = ax.errorbar(nhi_nonans.ravel(),
av_nonans.ravel(),
yerr=(av_error_nonans.ravel()),
alpha=0.2,
color='k',
marker='^',
ecolor='k',
linestyle='None',
markersize=3)
# Plot sensitivies
#av_limit = np.median(av_errors[0])
#ax.axvline(av_limit, color='k', linestyle='--')
# Plot 1 to 1 pline
if 1:
x_fit = np.linspace(-5, 200, 1000)
p, V = \
np.polyfit(av_k09, av_pl, deg=1,
#w=np.abs(1.0/av_error_nonans.ravel()),
cov=True
)
y_poly_fit = p[0] * x_fit + p[1]
if 1:
ax.plot(x_fit,
y_poly_fit,
color='r',
linestyle='dashed',
linewidth=2,
label=\
'Poly fit: \n' + \
fit_labels[0] + ' = ' + '{0:.1f}'.format(p[0]) + \
r'$\times$ ' + fit_labels[1] + \
' + {0:.1f} mag'.format(p[1]),
alpha=0.7,
)
if 0:
from scipy.interpolate import UnivariateSpline
spl = UnivariateSpline(av_k09, av_pl)
ax.plot(x_fit, spl(x_fit), linewidth=3, alpha=0.5)
if 0:
print('Bootstrapping...')
# bootstrap conf intervals
import scipy as sp
bootindex = sp.random.random_integers
nboot = 20
x_fit = av_k09
y_fit = p[0] * x_fit + p[1]
r = av_pl - y_fit
for i in xrange(
nboot): # loop over n bootstrap samples from the resids
pc = sp.polyfit(av_k09, y_fit + r[bootindex(0,
len(r) - 1, len(r))],
1)
ax.plot(x_fit,
sp.polyval(pc, x_fit),
'k-',
linewidth=2,
alpha=1.0 / float(nboot))
# Annotations
anno_xpos = 0.95
ax.set_xscale(scale[0], nonposx='clip')
ax.set_yscale(scale[1], nonposy='clip')
ax.set_xlim(limits[0], limits[1])
ax.set_ylim(limits[2], limits[3])
# Adjust asthetics
#ax.set_xlabel(r'$A_{V,{\rm K+09}}$ [mag]')
#ax.set_ylabel(r'$A_{V,{\rm Planck}}$ [mag]')
ax.set_xlabel(labels[0])
ax.set_ylabel(labels[1])
ax.set_title(title)
ax.legend(loc='best')
if filename is not None:
plt.savefig(filename)
import matplotlib.patches as mpatches
from mpl_toolkits.axes_grid1.axes_grid import AxesGrid
from matplotlib.offsetbox import AnchoredText
fig = plt.figure(1, figsize=(8, 5))
fig.clf()
def add_at(ax, t, loc=2):
fp = dict(size=8)
_at = AnchoredText(t, loc=loc, prop=fp)
ax.add_artist(_at)
return _at
grid = AxesGrid(fig, 111, (3, 5), label_mode="1", share_all=True)
grid[0].set_autoscale_on(False)
x1, y1 = 0.3, 0.3
x2, y2 = 0.7, 0.7
def demo_con_style(ax, connectionstyle, label=None):
if label is None:
label = connectionstyle
x1, y1 = 0.3, 0.2
x2, y2 = 0.8, 0.6
ax.plot([x1, x2], [y1, y2], ".")
def cMapFirTir(tirValues=None,
firValues=None,
wcs=None,
wcsMinMax=None,
raCenter=None,
decCenter=None,
fluxImage=None,
plotTitle=None,
saveName=None):
"""
Make color maps of the TIR and FIR
Parameters:
-----------
tirValues: array_like
2D array of the total infrared fluxes.
firValues: array_like
2D array of the far infrared fluxes.
wcs: WCS
The WCS of the object.
wcsMinMax: array_like
The limits (colMin,colMax,rowMin,rowMax) of the colormap spaxels.
fluxImage: array_like
The continuum fluxes to overplot contours.
ra/decCenter: float
RA and Dec of the galaxy's center.
plotTitle: str
The title of the plot.
saveName: str
The path and/or name of the file to save the colormaps.
"""
matplotlib.rcParams['xtick.labelsize'] = 6
matplotlib.rcParams['ytick.labelsize'] = 6
matplotlib.rcParams['axes.labelsize'] = 6
fig = plt.figure()
# Grid helper
grid_helper = pywcsgrid2.GridHelper(wcs=wcs)
# Setup the grid for plotting.
grid = AxesGrid(fig,
111,
nrows_ncols=(1, 2),
axes_pad=(0.45, 0.07),
cbar_mode='each',
cbar_location='right',
cbar_pad=0,
axes_class=(pywcsgrid2.Axes,
dict(grid_helper=grid_helper)),
share_all=True)
for ii in range(2):
# Get the axis.
ax = grid[ii]
# Set background color
ax.patch.set_facecolor('black')
# Create the colormap.
cmap = matplotlib.cm.rainbow
cmap.set_bad('black', 1.) #Set masked pixels to be black.
# Plot the image.
if ii == 0:
image = firValues
label = r'FIR [W m$^{-2}$]'
if ii == 1:
image = tirValues
label = r'TIR [W m$^{-2}$]'
#im = ax.contourf(image,cmap=cmap)
im = ax.imshow(image, cmap=cmap)
# Label the flux inside the subplot.
at = AnchoredText(label, loc=2, prop=dict(size=5))
ax.add_artist(at)
# Flip the axis per convention.
ax.invert_yaxis()
# Mark the center of the galaxy.
ax['fk5'].plot(raCenter,
decCenter,
markeredgewidth=.9,
marker='+',
color='k',
ms=7)
# Mark flux contours and continuum contours.
ax.contour(fluxImage, linewidths=0.85, alpha=0.8, colors='black')
# Make the tickmarks black.
ax.tick_params(axis='both', colors='black', width=0)
# Make a colorbar for each image.
cax1 = grid.cbar_axes[ii]
cbar1 = cax1.colorbar(im, format='%.2e')
cbar1.ax.tick_params(labelsize=4)
# Set figure title.
fig.text(0.5, 0.82, plotTitle, ha='center', fontsize=10)
# Save the plot to PDF
pp = PdfPages(saveName)
pp.savefig(fig, bbox_inches='tight')
pp.close()
plt.close()
def cMapPdrParams4Fluxes(pdrParams=None,
wcs=None,
wcsMinMax=None,
raCenter=None,
decCenter=None,
objectName=None,
plotTitle=None,
saveFileName=None,
cmapMinMax=None):
"""
Plot the PDR parameters which have been "corrected" to
more believable values.
Parameters:
-----------
pdrParams: array_like
Array containing the fitted PDR Toolbox parameters: (chi2,nH,G0)
wcs: WCS
Coordinate information.
wcsMinMax: array_like
Min/max pixel values of each property. This
is used to spatially crop the WCS.
raCenter/decCenter: float
The center of the object in degrees.
objectName: str
Name of the object.
plotTitle: str
Title of the entire plot.
saveFileName: str
Pathway and name of file to save the PDF.
cmapMinMax: array_like
Min/max values of n and G
"""
matplotlib.rcParams['xtick.labelsize'] = 3
matplotlib.rcParams['ytick.labelsize'] = 3
matplotlib.rcParams['axes.labelsize'] = 3
# Shape of the 2D spatial array.
nCols, nRows = pdrParams[0, :, :].shape[0], pdrParams[0, :, :].shape[1]
fig = plt.figure(figsize=(6, 7))
# Grid helper
grid_helper = pywcsgrid2.GridHelper(wcs=wcs)
# Setup the grid for plotting.
nrows_ncols = (4, 3)
grid = AxesGrid(fig,
111,
nrows_ncols=nrows_ncols,
axes_pad=(0.35, 0.07),
cbar_mode='each',
cbar_location='right',
cbar_pad=0,
axes_class=(pywcsgrid2.Axes,
dict(grid_helper=grid_helper)),
share_all=True)
for count in range(len(pdrParams)):
# Get the axis.
ax = grid[count]
# Create the colormap.
cmap = matplotlib.cm.rainbow
cmap.set_bad('black', 1.) # Set masked pixels to be black.
# Get the slice to plot
image = pdrParams[count, :, :]
# Plot the image
if cmapMinMax != None:
if count in [1, 4, 7, 10]:
vMin, vMax = cmapMinMax[0], cmapMinMax[1]
im = ax.imshow(image, cmap=cmap, vmin=vMin, vmax=vMax)
elif count in [2, 5, 8, 11]:
vMin, vMax = cmapMinMax[2], cmapMinMax[3]
im = ax.imshow(image, cmap=cmap, vmin=vMin, vmax=vMax)
else:
im = ax.imshow(image, cmap=cmap)
else:
im = ax.imshow(image, cmap=cmap)
# Label the ratio used inside the subplot.
labelList = [
'$X^2_{t-63-145-158}$', '$n$', '$G_0$', '$X^2_{Corr158}$',
'$n_{corr158}$', '$G_{0,corr158}$', '$X^2_{corr63}$',
'$n_{corr63}$', '$G_{0,corr63}$', '$X^2_{corr158-63}$',
'$n_{corr158-63}$', '$G_{0,corr158-63}$'
]
at = AnchoredText(labelList[count], loc=2, prop=dict(size=4))
ax.add_artist(at)
# Label the property average
ave = '{:.2f}'.format(np.nanmean(image))
med = '{:.2f}'.format(np.nanmedian(image))
at1 = AnchoredText('mean: ' + ave, loc=4, prop=dict(size=3))
at2 = AnchoredText(med, loc=3, prop=dict(size=3))
ax.add_artist(at1)
ax.add_artist(at2)
# Flip the axis per convention.
ax.invert_yaxis()
# Mark the center of the galaxy.
ax['fk5'].plot(raCenter,
decCenter,
markeredgewidth=.6,
marker='+',
color='k',
ms=5)
# Make the tickmarks black.
ax.tick_params(axis='both', colors='black', width=0)
# Make a colorbar for each image.
cax1 = grid.cbar_axes[count]
if any([cmapMinMax == None, count in [0, 3, 6, 9]]):
cbTickValues = np.linspace(np.amin(image),
np.amax(image),
5,
endpoint=True).tolist()
else:
cbTickValues = np.linspace(vMin, vMax, 5, endpoint=True).tolist()
cbar1 = cax1.colorbar(im, ticks=cbTickValues) #,format='%.2e')
if count in [0, 3, 6, 9, 8, 11]:
cbar1.ax.set_yticklabels(
[str('{:.2f}'.format(x)) for x in cbTickValues])
else:
cbar1.ax.set_yticklabels([str(int(x)) for x in cbTickValues])
cbar1.ax.tick_params(labelsize=4)
# Set figure title.
fig.text(0.5, 0.9, plotTitle, ha='center', fontsize=10)
# -------------------- #
# Save the plot to PDF #
# -------------------- #
pp = PdfPages(saveFileName)
pp.savefig(fig, bbox_inches='tight')
pp.close()
plt.close()
f_xray = pyfits.open(xray_name)
header_xray = f_xray[0].header
# the second image
radio_name="radio_21cm.fits"
f_radio = pyfits.open(radio_name)
header_radio = f_radio[0].header
# grid helper to be used.
grid_helper = pywcsgrid2.GridHelperSky(wcs=header_xray)
# AxesGrid to display tow images side-by-side
fig = plt.figure(1, (6,3.5))
grid = AxesGrid(fig, (0.15, 0.15, 0.8, 0.75), nrows_ncols=(1, 2),
axes_pad=0.1, share_all=True,
axes_class=(pywcsgrid2.Axes, dict(grid_helper=grid_helper)))
ax1 = grid[0]
#from mpl_toolkits.axes_grid1.angle_helper import LocatorDMS
#from pywcsgrid2.axes_wcs import FormatterDMSDelta
#ax1.get_grid_helper().set_ticklabel_mode("delta", center_pixel=(50, 50))
ax1.set_ticklabel_type("delta",
center_pixel=(146, 139))
ax1.locator_params(nbins=3)
# use imshow for a simply image display.
def plot_av_vs_beta_grid(plot_dict,
filename=None, levels=7,
limits=None, poly_fit=False, contour=True,
scale=['linear','linear']):
''' Plots a heat map of likelihoodelation values as a function of velocity
width and velocity center.
Parameters
----------
cloud : cloudpy.Cloud
If provided, properties taken from cloud.props.
'''
# Import external modules
import numpy as np
import matplotlib.pyplot as plt
import myplotting as myplt
from mpl_toolkits.axes_grid1.axes_grid import AxesGrid
# Set up plot aesthetics
# ----------------------
plt.close;plt.clf()
font_scale = 9
params = {
'figure.figsize': (3.6, 8),
#'figure.titlesize': font_scale,
}
plt.rcParams.update(params)
# Create figure instance
fig = plt.figure()
axes = AxesGrid(fig, (1,1,1),
nrows_ncols=(3, 1),
ngrids=3,
axes_pad=0.1,
aspect=False,
label_mode='L',
share_all=True)
for i, cloud_name in enumerate(plot_dict):
x = plot_dict[cloud_name]['av']
y = plot_dict[cloud_name]['beta']
ax = axes[i]
if 'log' not in scale:
ax.locator_params(nbins = 6)
# Drop the NaNs from the images
indices = np.where((x == x) &\
(y == y)
)
x_nonans = x[indices]
y_nonans = y[indices]
ax = axes[i]
if contour:
if i == 0:
if limits is None:
xmin = np.min(x_nonans)
ymin = np.min(y_nonans)
xmax = np.max(x_nonans)
ymax = np.max(y_nonans)
xscalar = 0.15 * xmax
yscalar = 0.15 * ymax
limits = [xmin - xscalar, xmax + xscalar,
ymin - yscalar, ymax + yscalar]
contour_range = ((limits[0], limits[1]),
(limits[2], limits[3]))
cmap = myplt.truncate_colormap(plt.cm.binary, 0.2, 1, 1000)
l1 = myplt.scatter_contour(x_nonans.ravel(),
y_nonans.ravel(),
threshold=2,
log_counts=1,
levels=levels,
ax=ax,
#errors=x_error_nonans.ravel(),
histogram2d_args=dict(bins=40,
range=contour_range),
plot_args=dict(marker='o',
linestyle='none',
color='black',
alpha=0.3,
markersize=2),
contour_args=dict(
#cmap=plt.cm.binary,
cmap=cmap,
#cmap=cmap,
),
)
else:
image = ax.errorbar(
x_nonans.ravel()[::100],
y_nonans.ravel()[::100],
yerr=(x_error_nonans.ravel()[::100]),
alpha=0.2,
color='k',
marker='^',
ecolor='k',
linestyle='None',
markersize=3
)
c_cycle = myplt.set_color_cycle(num_colors=2, cmap_limits=[0.5, 0.7])
if poly_fit:
from scipy.optimize import curve_fit
p = np.polyfit(x_nonans, y_nonans, 1)
x_fit = np.linspace(-10, 100, 100)
y_poly_fit = p[0] * x_fit + p[1]
ax.plot(x_fit,
y_poly_fit,
color=c_cycle[1],
linestyle='-',
linewidth=2,
label=\
'Polynomial fit: \n' + \
r'$\beta$ = {0:.2f}'.format(p[0] * 100.0) + \
r'$\frac{{A_V}}{{100 \rm mag}}$ + {0:.2f}'.format(p[1]) + \
r'',
alpha=0.7,
)
# Annotations
#anno_xpos = 0.95
ax.set_xscale(scale[0], nonposx = 'clip')
ax.set_yscale(scale[1], nonposy = 'clip')
ax.set_xlim(limits[0],limits[1])
ax.set_ylim(limits[2],limits[3])
# Adjust asthetics
ax.set_ylabel(r'$\beta$')
ax.set_xlabel(r'$A_V$ [mag]')
if 1:
loc = 'lower right'
elif i == 0:
loc = 'upper left'
else:
loc = 'lower left'
ax.legend(loc=loc)
ax.annotate(cloud_name.capitalize(),
#xytext=(0.96, 0.9),
xy=(0.96, 0.96),
textcoords='axes fraction',
xycoords='axes fraction',
size=10,
color='k',
bbox=dict(boxstyle='square',
facecolor='w',
alpha=1),
horizontalalignment='right',
verticalalignment='top',
)
if filename is not None:
plt.draw()
plt.savefig(filename, bbox_inches='tight', dpi=400)
def plot_spectra(hi_spectrum,
hi_vel_axis,
hi_std_spectrum=None,
co_spectrum=None,
co_vel_axis=None,
gauss_fits=None,
limits=None,
filename='',
vel_range=None,
comp_num=None):
''' Plots a heat map of likelihoodelation values as a function of velocity
width and velocity center.
Parameters
----------
cloud : cloudpy.Cloud
If provided, properties taken from cloud.props.
'''
# Import external modules
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.axes_grid import AxesGrid
# Set up plot aesthetics
# ----------------------
plt.close
plt.clf()
font_scale = 9
params = {
'figure.figsize': (3.6, 3.6 / 1.618),
#'figure.titlesize': font_scale,
}
plt.rcParams.update(params)
# Create figure instance
fig = plt.figure()
axes = AxesGrid(fig, (1, 1, 1),
nrows_ncols=(1, 1),
ngrids=1,
axes_pad=0,
aspect=False,
label_mode='L',
share_all=True)
ax = axes[0]
ax.plot(hi_vel_axis,
hi_spectrum,
linewidth=1.5,
label=r'Median H$\textsc{i}$',
drawstyle='steps-mid')
if hi_std_spectrum is not None:
ax.plot(
hi_vel_axis,
hi_std_spectrum,
linewidth=1.5,
linestyle='-.',
label=r'$\sigma_{HI}$',
)
if gauss_fits is not None:
ax.plot(
hi_vel_axis,
gauss_fits[0],
alpha=0.4,
linewidth=3,
label='Fit',
)
label = 'Component'
for i, comp in enumerate(gauss_fits[1]):
if comp_num is not None and i in comp_num:
linewidth = 2
else:
linewidth = 1
ax.plot(
hi_vel_axis,
comp,
linewidth=linewidth,
linestyle='--',
color='k',
label=label,
)
label = None
if co_spectrum is not None:
co_scalar = np.nanmax(hi_spectrum) / 2.0
co_spectrum = co_spectrum / np.nanmax(co_spectrum) * co_scalar
ax.plot(co_vel_axis,
co_spectrum,
#color='k',
label=r'Median $^{12}$CO $\times$' + \
'{0:.0f}'.format(co_scalar),
drawstyle = 'steps-mid'
)
ax.axvspan(
vel_range[0],
vel_range[1],
alpha=0.3,
color='k',
)
# plot limits
if limits is not None:
ax.set_xlim(limits[0], limits[1])
ax.set_ylim(limits[2], limits[3])
ax.legend(loc='upper left')
ax.set_xlabel('Velocity [km/s]')
ax.set_ylabel(r'T$_b$ [K]')
if filename is not None:
plt.draw()
plt.savefig(filename, bbox_inches='tight', dpi=100)
def plot_nhi_image(nhi_image=None,
header=None,
contour_image=None,
av_image=None,
cores=None,
props=None,
regions=None,
title=None,
limits=None,
contours=None,
boxes=False,
filename=None,
show=True,
hi_vlimits=[None, None],
av_vlimits=[None, None],
cb_text='N(HI)'):
# Import external modules
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import pyfits as fits
import matplotlib.pyplot as plt
import myplotting as myplt
import pywcsgrid2 as wcs
import pywcs
from pylab import cm # colormaps
from matplotlib.patches import Polygon
from mpl_toolkits.axes_grid1.axes_grid import AxesGrid
# Set up plot aesthetics
plt.clf()
plt.close()
# Color map
cmap = plt.cm.gnuplot
#cmap = myplt.reverse_colormap(plt.cm.copper)
cmap = plt.cm.copper
# Create figure instance
fig = plt.figure(figsize=(7.5, 3.6 * 2))
if av_image is not None:
nrows_ncols = (2, 1)
ngrids = 2
else:
nrows_ncols = (1, 1)
ngrids = 1
axes = AxesGrid(fig, (1, 1, 1),
nrows_ncols=nrows_ncols,
ngrids=ngrids,
cbar_mode="each",
cbar_location='right',
cbar_pad="2%",
cbar_size='3%',
axes_pad=0.1,
axes_class=(wcs.Axes, dict(header=header)),
aspect=True,
label_mode='L',
share_all=True)
# ------------------
# NHI image
# ------------------
# create axes
ax = axes[0]
# plot limits
if limits is not None:
limits_pix = myplt.convert_wcs_limits(limits, header, frame='fk5')
limits_pix = np.array(limits_pix, dtype=int)
ax.set_xlim(limits_pix[0], limits_pix[1])
ax.set_ylim(limits_pix[2], limits_pix[3])
# set pixels outside of limits to be nan for correct Vscaling
nhi_image[:limits_pix[2]] = np.nan
nhi_image[limits_pix[3]:] = np.nan
nhi_image[:, :limits_pix[0]] = np.nan
nhi_image[:, limits_pix[1]:] = np.nan
# show the image
im = ax.imshow(
nhi_image,
interpolation='nearest',
origin='lower',
cmap=cmap,
vmin=hi_vlimits[0],
vmax=hi_vlimits[1],
#norm=matplotlib.colors.LogNorm()
)
# Asthetics
ax.set_display_coord_system("fk5")
ax.set_ticklabel_type("hms", "dms")
ax.set_xlabel('Right Ascension [J2000]', )
ax.set_ylabel('Declination [J2000]', )
ax.locator_params(nbins=6)
# colorbar
cb = ax.cax.colorbar(im)
cmap.set_bad(color='w')
# Plot Av contours
if contour_image is not None:
ax.contour(contour_image, levels=contours, colors='r')
# Write label to colorbar
#cb.set_label_text(r'$N$(H\textsc{i}) [10$^{20}$ cm$^{-2}$]',)
if cb_text == 'N(HI)':
cb.set_label_text(r'$N$(H\textsc{i}) [10$^{20}$ cm$^{-2}$]', )
else:
cb.set_label_text(cb_text, )
# Convert sky to pix coordinates
#wcs_header = pywcs.WCS(header)
if type(cores) is dict:
for core in cores:
pix_coords = cores[core]['center_pixel']
anno_color = (0.3, 0.5, 1)
ax.scatter(pix_coords[0],
pix_coords[1],
color=anno_color,
s=200,
marker='+',
linewidths=2)
ax.annotate(core,
xy=[pix_coords[0], pix_coords[1]],
xytext=(5, 5),
textcoords='offset points',
color=anno_color)
if boxes:
rect = ax.add_patch(
Polygon(cores[core]['box_vertices'][:, ::-1],
facecolor='none',
edgecolor=anno_color))
if regions is not None:
for region in props['region_name_pos']:
vertices = np.copy(regions[region]['poly_verts']['pixel'])
rect = ax.add_patch(
Polygon(vertices[:, ::-1], facecolor='none', edgecolor='w'))
# ------------------
# Av image
# ------------------
if av_image is not None:
# create axes
ax = axes[1]
# show the image
im = ax.imshow(
av_image,
interpolation='nearest',
origin='lower',
cmap=cmap,
vmin=av_vlimits[0],
vmax=av_vlimits[1],
#norm=matplotlib.colors.LogNorm()
)
# Asthetics
ax.set_display_coord_system("fk5")
ax.set_ticklabel_type("hms", "dms")
ax.set_xlabel('Right Ascension [J2000]', )
ax.set_ylabel('Declination [J2000]', )
ax.locator_params(nbins=6)
# colorbar
cb = ax.cax.colorbar(im)
cmap.set_bad(color='w')
# plot limits
if limits is not None:
limits_pix = myplt.convert_wcs_limits(limits, header, frame='fk5')
ax.set_xlim(limits_pix[0], limits_pix[1])
ax.set_ylim(limits_pix[2], limits_pix[3])
ax.tick_params(axis='xy', which='major', colors='w')
# Plot Av contours
if contour_image is not None:
ax.contour(contour_image, levels=contours, colors='r')
# Write label to colorbar
cb.set_label_text(r'$A_V$ [mag]', )
# Convert sky to pix coordinates
#wcs_header = pywcs.WCS(header)
if type(cores) is dict:
for core in cores:
pix_coords = cores[core]['center_pixel']
anno_color = (0.3, 0.5, 1)
if boxes:
rect = ax.add_patch(
Polygon(cores[core]['box_vertices'][:, ::-1],
facecolor='none',
edgecolor='w'))
if regions is not None:
for region in props['region_name_pos']:
vertices = np.copy(regions[region]['poly_verts']['pixel'])
rect = ax.add_patch(
Polygon(vertices[:, ::-1], facecolor='none',
edgecolor='w'))
if props is not None:
for region in props['region_name_pos']:
if region == 'taurus1':
region = 'taurus 1'
if region == 'taurus2':
region = 'taurus 2'
ax.annotate(region.capitalize(),
xy=props['region_name_pos'][region]['pixel'],
xytext=(0, 0),
textcoords='offset points',
color='w',
fontsize=10,
zorder=10)
if title is not None:
fig.suptitle(title, fontsize=font_scale)
if filename is not None:
plt.savefig(filename, bbox_inches='tight')
if show:
fig.show()
def plot_hi_h2_sd(
plot_dict,
contour_image=None,
limits=None,
filename=None,
show=True,
hi_vlimits=None,
h2_vlimits=None,
av_vlimits=None,
cloud_names=('california', 'perseus', 'taurus'),
hi_contours=None,
h2_contours=None,
):
# Import external modules
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import pyfits as fits
import matplotlib.pyplot as plt
import myplotting as myplt
import pywcsgrid2
import pywcs
from pylab import cm # colormaps
from matplotlib.patches import Polygon
from mpl_toolkits.axes_grid1.axes_grid import AxesGrid
# Set up plot aesthetics
plt.clf()
plt.close()
# Colormap settings
cmap = plt.cm.copper
cmap.set_bad(color='w')
norm = matplotlib.colors.LogNorm()
# Create figure instance
figsize = (7.5, 9)
fig = pywcsgrid2.plt.figure(figsize=figsize)
colorbar_axes = [
0.05,
0.97,
0.95,
0.02,
]
map_axes = np.array([0.05, 0.69, 0.95, 0.25])
map_axes = np.array([0.05, 0.31, 0.95, 0.25])
# plot HI and H2 maps for each cloud
# ---------------------------------------------------------------------------
for i, cloud_name in enumerate(cloud_names):
# get image properties
# -----------------------------------------------------------------------
header = plot_dict[cloud_name]['header']
limits = plot_dict[cloud_name]['limits']
contour_image = plot_dict[cloud_name]['contour_image']
contours = plot_dict[cloud_name]['contours']
# Prep axes instance
# -----------------------------------------------------------------------
nrows_ncols = (1, 2)
ngrids = 2
# only show cbar for first row
if i == 0:
cbar_mode = "each"
else:
cbar_mode = None
axes = AxesGrid(
fig,
311 + i, #(3,1,i),
nrows_ncols=nrows_ncols,
ngrids=ngrids,
cbar_mode=cbar_mode,
cbar_location='top',
cbar_pad="5%",
cbar_size='10%',
axes_pad=0.1,
axes_class=(pywcsgrid2.Axes, dict(header=header)),
aspect=True,
label_mode='L',
share_all=True,
)
# get axes from row
ax1 = axes[0]
ax2 = axes[1]
# Plot the maps
# -----------------------------------------------------------------------
# HI surface density map
hi_sd = plot_dict[cloud_name]['hi_sd']
# H2 surface density map
h2_sd = plot_dict[cloud_name]['h2_sd']
# show the images
im_hi = ax1.imshow(
hi_sd,
interpolation='nearest',
origin='lower',
cmap=cmap,
vmin=hi_vlimits[0],
vmax=hi_vlimits[1],
#norm=matplotlib.colors.LogNorm()
)
im_h2 = ax2.imshow(
h2_sd,
interpolation='nearest',
origin='lower',
cmap=cmap,
vmin=h2_vlimits[0],
vmax=h2_vlimits[1],
#norm=matplotlib.colors.LogNorm()
)
# Asthetics
# -----------------------------------------------------------------------
for ax in (ax1, ax2):
ax.set_display_coord_system("fk5")
ax.set_ticklabel_type("hms", "dms")
ax.set_xlabel('Right Ascension [J2000]', )
ax.set_ylabel('Declination [J2000]', )
ax.locator_params(nbins=5)
# colorbar
# -----------------------------------------------------------------------
cb_hi = ax1.cax.colorbar(im_hi)
cb_h2 = ax2.cax.colorbar(im_h2)
# Write label to colorbar
cb_hi.set_label_text(r'$\Sigma_{\rm HI}$ [M$_\odot$\,pc$^{-2}$]', )
cb_h2.set_label_text(r'$\Sigma_{\rm H_2}$ [M$_\odot$\,pc$^{-2}$]', )
# plot limits
# -----------------------------------------------------------------------
if limits is not None:
limits_pix = myplt.convert_wcs_limits(limits, header, frame='fk5')
ax1.set_xlim(limits_pix[0], limits_pix[1])
ax1.set_ylim(limits_pix[2], limits_pix[3])
ax2.set_xlim(limits_pix[0], limits_pix[1])
ax2.set_ylim(limits_pix[2], limits_pix[3])
# Plot contours
# -----------------------------------------------------------------------
if hi_contours is not None:
hi_colors = myplt.get_color_cycle(
num_colors=len(hi_contours),
cmap=plt.cm.binary,
)
ax1.contour(hi_sd, levels=hi_contours, colors=hi_colors)
if h2_contours is not None:
h2_colors = myplt.get_color_cycle(
num_colors=len(h2_contours),
cmap=plt.cm.binary,
)
ax2.contour(h2_sd, levels=h2_contours, colors=h2_colors)
# Cloud names
# -----------------------------------------------------------------------
ax1.annotate(
cloud_name.capitalize(),
xytext=(0.96, 0.94),
xy=(0.96, 0.94),
textcoords='axes fraction',
xycoords='axes fraction',
size=10,
color='k',
bbox=dict(boxstyle='square', facecolor='w', alpha=1),
horizontalalignment='right',
verticalalignment='top',
)
# create new box for plot
# -----------------------------------------------------------------------
#map_axes[1] -= 0.3
map_axes[1] += 0.3
if filename is not None:
plt.savefig(filename, bbox_inches='tight')
if show:
fig.show()
def plot_h2_sd(
plot_dict,
contour_image=None,
limits=None,
filename=None,
show=True,
vlimits=None,
av_vlimits=None,
cloud_names=('california', 'perseus', 'taurus'),
):
# Import external modules
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import pyfits as fits
import matplotlib.pyplot as plt
import myplotting as myplt
import pywcsgrid2
import pywcs
from pylab import cm # colormaps
from matplotlib.patches import Polygon
from mpl_toolkits.axes_grid1.axes_grid import AxesGrid
# Set up plot aesthetics
plt.clf()
plt.close()
cmap = plt.cm.copper
norm = matplotlib.colors.LogNorm()
# Create figure instance
fig = plt.figure(figsize=(3.6, 9))
if 1:
nrows = 3
ncols = 1
nrows_ncols = (3, 1)
ngrids = 3
figsize = (3.6, 8)
fig = pywcsgrid2.plt.figure(figsize=figsize)
else:
nrows_ncols = (1, 1)
ngrids = 1
axes = AxesGrid(fig, (1, 1, 1),
nrows_ncols=nrows_ncols,
ngrids=ngrids,
cbar_mode="each",
cbar_location='right',
cbar_pad="2%",
cbar_size='3%',
axes_pad=0.1,
axes_class=(wcs.Axes,
dict(header=plot_dict['header'])),
aspect=True,
label_mode='L',
share_all=True)
colorbar_axes = [
0.05,
0.97,
0.95,
0.02,
]
map_axes = np.array([0.05, 0.69, 0.95, 0.25])
for i, cloud_name in enumerate(cloud_names):
header = plot_dict[cloud_name]['header']
limits = plot_dict[cloud_name]['limits']
contour_image = plot_dict[cloud_name]['contour_image']
contours = plot_dict[cloud_name]['contours']
#ax = pywcsgrid2.subplot(311+i, header=header)
ax = pywcsgrid2.axes(map_axes, header=header)
#ax = fig.add_subplot(311 + i, header=)
h2_sd = plot_dict[cloud_name]['h2_sd']
# create axes
# show the image
im = ax.imshow(
h2_sd,
interpolation='nearest',
origin='lower',
cmap=cmap,
vmin=vlimits[0],
vmax=vlimits[1],
#norm=matplotlib.colors.LogNorm()
)
# Asthetics
ax.set_display_coord_system("fk5")
ax.set_ticklabel_type("hms", "dms")
if i == 2:
ax.set_xlabel('Right Ascension [J2000]', )
else:
ax.set_xlabel('')
ax.set_xlabel('Right Ascension [J2000]', )
ax.set_ylabel('Declination [J2000]', )
ax.locator_params(nbins=6)
#cb_axes.colorbar(im)
#cb_axes.axis["right"].toggle(ticklabels=False)
# colorbar
#cb = ax.cax.colorbar(im)
cmap.set_bad(color='w')
# plot limits
if limits is not None:
limits_pix = myplt.convert_wcs_limits(limits, header, frame='fk5')
ax.set_xlim(limits_pix[0], limits_pix[1])
ax.set_ylim(limits_pix[2], limits_pix[3])
# Plot Av contours
if contour_image is not None:
ax.contour(contour_image, levels=contours, colors='w')
# Write label to colorbar
#cb.set_label_text(r'$\Sigma_{\rm H_2} [M_\odot$\,pc$^{-2}$]',)
if 0:
if regions is not None:
for region in props['region_name_pos']:
vertices = np.copy(regions[region]['poly_verts']['pixel'])
rect = ax.add_patch(
Polygon(vertices[:, ::-1],
facecolor='none',
edgecolor='w'))
ax.annotate(
cloud_name.capitalize(),
xytext=(0.96, 0.94),
xy=(0.96, 0.94),
textcoords='axes fraction',
xycoords='axes fraction',
size=10,
color='k',
bbox=dict(boxstyle='square', facecolor='w', alpha=1),
horizontalalignment='right',
verticalalignment='top',
)
# create new box for plot
map_axes[1] -= 0.3
#map_axes[3] -= 0.3
ax = plt.axes(colorbar_axes, )
cb = plt.colorbar(
im,
cax=ax,
orientation='horizontal',
)
cb.ax.set_xlabel(r'$\Sigma_{\rm H_2} [{\rm M_\odot}$\,pc$^{-2}$]', )
cb.ax.xaxis.set_label_position('top')
if filename is not None:
plt.savefig(filename, bbox_inches='tight')
if show:
fig.show()
header_xray = f_xray[0].header
# the second image
radio_name = "radio_21cm.fits"
f_radio = pyfits.open(radio_name)
header_radio = f_radio[0].header
# grid helper
grid_helper = pywcsgrid2.GridHelper(wcs=header_xray)
# AxesGrid to display tow images side-by-side
fig = plt.figure(1, (6, 3.5))
grid = AxesGrid(fig, (0.15, 0.15, 0.8, 0.75),
nrows_ncols=(1, 2),
axes_pad=0.1,
share_all=True,
cbar_mode="each",
cbar_location="top",
cbar_pad=0,
axes_class=(pywcsgrid2.Axes, dict(grid_helper=grid_helper)))
ax1 = grid[0]
# use imshow for a simply image display.
im = ax1.imshow(f_xray[0].data,
origin="lower",
vmin=0.,
cmap=cm.gray_r,
interpolation="nearest")
im.set_clim(4.e-05, 0.00018)
ticklocs = [6, 9, 12, 15]