def plot_power_spectrum(image, title=None, filename_prefix=None,
filename_suffix='.png', show=False, savedir='./'):
'''
Plots power spectrum derived from a fourier transform of the image.
'''
# import external modules
import numpy as np
from agpy import azimuthalAverage as radial_average
from scipy import fftpack
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.axes_grid1 import axes
from matplotlib import cm
if 0:
plt.close(); plt.clf()
plt.imshow(image)
plt.show()
image[np.isnan(image)] = 1e10
# Determine power spectrum
# -------------------------------------------------------------------------
# Take the fourier transform of the image.
#F1 = fftpack.fft2(np.ma.array(image, mask=np.isnan(image)))
F1 = fftpack.fft2(image)
# Now shift the quadrants around so that low spatial frequencies are in
# the center of the 2D fourier transformed image.
F2 = fftpack.fftshift(F1)
# Calculate a 2D power spectrum
psd2D = np.abs(F2)**2
if 0:
plt.close(); plt.clf()
plt.imshow(psd2D)
plt.show()
power_spectrum = radial_average(psd2D, interpnan=True)
# Write frequency in arcmin
freq = fftpack.fftfreq(len(power_spectrum))
freq *= 5.0
# Simulate power spectrum for white noise
noise_image = np.random.normal(scale=0.1, size=image.shape)
F1 = fftpack.fft2(noise_image)
# Now shift the quadrants around so that low spatial frequencies are in
# the center of the 2D fourier transformed image.
F2 = fftpack.fftshift(F1)
# Calculate a 2D power spectrum
psd2D_noise = np.abs(F2)**2
power_spectrum_noise = radial_average(psd2D_noise, interpnan=True)
# Plot power spectrum 1D
# -------------------------------------------------------------------------
# Set up plot aesthetics
plt.clf()
plt.rcdefaults()
colormap = plt.cm.gist_ncar
#color_cycle = [colormap(i) for i in np.linspace(0, 0.9, len(flux_list))]
font_scale = 12
params = {#'backend': .pdf',
'axes.labelsize': font_scale,
'axes.titlesize': font_scale,
'text.fontsize': font_scale,
'legend.fontsize': font_scale * 3 / 4.0,
'xtick.labelsize': font_scale,
'ytick.labelsize': font_scale,
'font.weight': 500,
'axes.labelweight': 500,
'text.usetex': False,
'figure.figsize': (5, 5),
#'axes.color_cycle': color_cycle # colors of different plots
}
plt.rcParams.update(params)
# Create figure instance
fig = plt.figure()
nrows = 1; ncols = 1; ngrids = 1;
axes = axes(fig, (1,1,1),
nrows_ncols=(nrows, ncols),
ngrids=ngrids,
axes_pad=0.25,
aspect=False,
label_mode='L',
share_all=True,
#cbar_mode='single',
cbar_pad=0.1,
cbar_size=0.2,
)
ax = axes[0]
ax.plot(freq, power_spectrum / np.nanmax(power_spectrum),
color='k',
linestyle='-',
linewidth=1.5,
drawstyle='steps-mid',
label='Data Residuals')
ax.plot(freq, power_spectrum_noise / np.nanmax(power_spectrum_noise),
color='r',
linestyle='-',
linewidth=0.4,
drawstyle='steps-mid',
label='White Noise Residuals')
#ax.set_xscale('log')
ax.legend(loc='best')
#ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlabel('Spatial Frequency [1/arcmin]')
ax.set_ylabel('Normalized Power Spectrum')
ax.set_xlim(0, 0.4)
if title is not None:
fig.suptitle(title, fontsize=font_scale)
if filename_prefix is not None:
plt.savefig(savedir + filename_prefix + '_1D' + filename_suffix,
bbox_inches='tight')
if show:
plt.show()
# Plot power spectrum image
# -------------------------
# Create figure instance
fig = plt.figure()
nrows = 1; ncols = 1; ngrids = 1;
axes = axes(fig, (1,1,1),
nrows_ncols=(nrows, ncols),
ngrids=ngrids,
axes_pad=0.25,
aspect=False,
label_mode='L',
share_all=True,
#cbar_mode='single',
cbar_pad=0.1,
cbar_size=0.2,
)
ax = axes[0]
extent = [- image.shape[0] / 2.0, + image.shape[0] / 2.0,
- image.shape[1] / 2.0, + image.shape[1] / 2.0]
ax.imshow(psd2D,
origin='lower',
cmap=cm.gist_heat,
norm=matplotlib.colors.LogNorm(),
extent=extent
)
#ax.set_xscale('log')
#ax.legend(loc='center right')
#ax.set_yscale('log')
ax.set_xlabel('Spatial Frequency in Right Ascension')
ax.set_ylabel('Spatial Frequency in Declination')
ax.set_xlim(-20, 20)
ax.set_ylim(-20, 20)
if title is not None:
fig.suptitle(title, fontsize=font_scale)
if filename_prefix is not None:
plt.savefig(savedir + filename_prefix + '_2D' + filename_suffix,
bbox_inches='tight')
if show:
plt.show()
def plot_avmod_vs_av(avmod_images, av_images, av_errors=None, limits=None,
fit=True, savedir='./', filename=None, show=True,
scale=('linear','linear'), title = '', gridsize=(100,100), std=None):
# Import external modules
import numpy as np
import math
import pyfits as fits
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.axes_grid1 import axes
from myscience.krumholz09 import calc_T_cnm
from matplotlib import cm
n = int(np.ceil(len(av_images)**0.5))
if n**2 - n > len(av_images):
nrows = n - 1
ncols = n
y_scaling = 1.0 - 1.0 / n
else:
nrows, ncols = n, n
y_scaling = 1.0
# Set up plot aesthetics
plt.clf()
plt.rcdefaults()
colormap = plt.cm.gist_ncar
#color_cycle = [colormap(i) for i in np.linspace(0, 0.9, len(flux_list))]
font_scale = 12
params = {#'backend': .pdf',
'axes.labelsize': font_scale,
'axes.titlesize': font_scale,
'text.fontsize': font_scale,
'legend.fontsize': font_scale * 3 / 4.0,
'xtick.labelsize': font_scale,
'ytick.labelsize': font_scale,
'font.weight': 500,
'axes.labelweight': 500,
'text.usetex': True,
'figure.figsize': (5, 5 * y_scaling),
#'axes.color_cycle': color_cycle # colors of different plots
}
plt.rcParams.update(params)
# Create figure instance
fig = plt.figure()
axes = axes(fig, (1,1,1),
nrows_ncols=(nrows, ncols),
ngrids=len(av_images),
axes_pad=0.25,
aspect=False,
label_mode='L',
share_all=True,
#cbar_mode='single',
cbar_pad=0.1,
cbar_size=0.2,
)
# Cycle through lists
for i in xrange(len(av_images)):
av = av_images[i]
avmod = avmod_images[i]
av_error = av_errors[i]
# Drop the NaNs from the images
if type(av_error) is float:
indices = np.where((av == av) &\
(avmod == avmod)
)
if type(av_error) is np.ndarray or \
type(av_error) is np.ma.core.MaskedArray or \
type(avmod_error) is np.ndarray or \
type(avmod_error) is np.ma.core.MaskedArray:
indices = np.where((av == av) &\
(avmod == avmod) &\
(av_error == av_error)
)
av_nonans = av[indices]
avmod_nonans = avmod[indices]
# Fix error data types
if type(av_error) is np.ndarray:
av_error_nonans = av_error[indices]
else:
av_error_nonans = np.array(av_error[indices])
# Create plot
ax = axes[i]
if 1:
image = ax.errorbar(av_nonans.ravel(),
avmod_nonans.ravel(),
xerr=(av_error_nonans.ravel()),
alpha=0.2,
color='k',
marker='^',
ecolor='k',
linestyle='None',
markersize=3
)
if 0:
image = ax.hexbin(av_nonans.ravel(),
avmod_nonans.ravel(),
#norm=matplotlib.colors.LogNorm(),
mincnt=1,
yscale=scale[1],
xscale=scale[0],
gridsize=gridsize,
cmap=cm.Greys,
#cmap=cm.gist_stern,
)
cb = ax.cax.colorbar(image,)
# Write label to colorbar
cb.set_label_text('Bin Counts',)
# Plot sensitivies
av_limit = np.median(av_errors[0])
ax.axvline(av_limit, color='k', linestyle='--')
# Plot 1 to 1 pline
ax.plot((0, 10),
(0, 10),
color='0.5',
linewidth=3,
alpha=0.5)
if std is not None:
ax.fill_between((-std, 10),
(0, 10 + std),
(-2*std, 10 - std),
color='0.2',
alpha=0.2,
edgecolor='none'
)
# Annotations
anno_xpos = 0.95
ax.set_xscale(scale[0], nonposx = 'clip')
ax.set_yscale(scale[1], nonposy = 'clip')
if limits is not None:
ax.set_xlim(limits[0],limits[1])
ax.set_ylim(limits[2],limits[3])
# Adjust asthetics
ax.set_xlabel(r'$A_V$ Data (mag)')
ax.set_ylabel(r'$A_V$ Model (mag)')
#ax.set_title(core_names[i])
if title is not None:
fig.suptitle(title, fontsize=font_scale*1.5)
if filename is not None:
plt.savefig(savedir + filename, bbox_inches='tight')
if show:
fig.show()
def plot_av_model(av_image=None, header=None, contour_image=None,
av_model=None, hi_velocity_axis=None, vel_range=None, hi_spectrum=None,
co_spectrum=None, co_velocity_axis=None, co_scaling=50,
hi_limits=None, cores=None, results=None, title=None, limits=None,
contours=None, boxes=False, savedir='./', filename=None, show=True,
plot_residuals=True):
# Import external modules
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
from mpl_toolkits.axes_grid1 import axes
import pyfits as fits
import matplotlib.pyplot as plt
import pywcsgrid2 as wcs
import pywcs
from pylab import cm # colormaps
from matplotlib.patches import Polygon
# Set up plot aesthetics
plt.clf()
plt.close()
plt.rcdefaults()
colormap = plt.cm.gist_ncar
#color_cycle = [colormap(i) for i in np.linspace(0, 0.9, len(flux_list))]
font_scale = 12
if plot_residuals:
figsize = (17,8)
else:
figsize = (13,10)
params = {#'backend': .pdf',
'axes.labelsize': font_scale,
'axes.titlesize': font_scale,
'text.fontsize': font_scale,
'legend.fontsize': font_scale*3/4,
'xtick.labelsize': font_scale,
'ytick.labelsize': font_scale,
'font.weight': 500,
'axes.labelweight': 500,
'text.usetex': False,
'figure.figsize': figsize,
'figure.titlesize': font_scale
#'axes.color_cycle': color_cycle # colors of different plots
}
plt.rcParams.update(params)
# Create figure instance
fig = plt.figure()
#==========================================================================
# Av maps
#==========================================================================
if plot_residuals:
nrows_ncols=(1,3)
ngrids=3
else:
nrows_ncols=(1,2)
ngrids=2
axes = axes(fig, (2,1,1),
nrows_ncols=nrows_ncols,
ngrids=ngrids,
cbar_mode='each',
cbar_location='right',
cbar_pad="2%",
cbar_size='3%',
axes_pad=0.4,
axes_class=(wcs.Axes,
dict(header=header)),
aspect=True,
label_mode='L',
share_all=True)
# ------------------
# Av model
# ------------------
# create axes
ax = axes[0]
cmap = cm.Greys # colormap
cmap = cm.gnuplot # colormap
cmap.set_bad(color='w')
# show the image
vmax = np.max((np.max(av_image[av_image == av_image]),
np.max(av_model[av_model == av_model])))
vmax = 1.4
im = ax.imshow(av_model,
interpolation='nearest',origin='lower',
cmap=cmap,
vmin=0,
vmax=vmax,
#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.set_title(r'Model $A_V$')
# colorbar
cb = ax.cax.colorbar(im)
# plot limits
if limits is not None:
ax.set_xlim(limits[0],limits[2])
ax.set_ylim(limits[1],limits[3])
# Plot Av contours
if contour_image is not None:
ax.contour(contour_image, levels=contours, colors='r')
# 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))
# ------------------
# 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=0,
vmax=vmax,
#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.set_title(r'Observed $A_V$')
# colorbar
cb = ax.cax.colorbar(im)
#cmap.set_bad(color='c')
# plot limits
if limits is not None:
ax.set_xlim(limits[0],limits[2])
ax.set_ylim(limits[1],limits[3])
# 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)',)
# ------------------
# Av residuals
# ------------------
if plot_residuals:
ax = axes[2]
#cmap = cm.Greys # colormap
# show the image
vmax = np.max((np.max(av_image[av_image == av_image]),
np.max(av_model[av_model == av_model])))
vmax = 1.4
im = ax.imshow(av_image - av_model,
interpolation='nearest',origin='lower',
cmap=cmap,
vmin=-0.25,
vmax=0.5,
#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.set_title(r'Residual $A_V$')
# colorbar
cb = ax.cax.colorbar(im)
#cmap.set_bad(color='w')
# plot limits
if limits is not None:
ax.set_xlim(limits[0],limits[2])
ax.set_ylim(limits[1],limits[3])
# ==========================================================================
# HI Spectrum
# ==========================================================================
vel_range = np.asarray(results['hi_velocity_range'][:2])
# create axes
ax = fig.add_subplot(2,1,2)
ax.plot(hi_velocity_axis,
hi_spectrum,
color='k',
drawstyle = 'steps-mid',
label='HI',
)
if co_spectrum is not None:
ax.plot(co_velocity_axis,
co_spectrum*co_scaling,
color='r',
drawstyle='steps-mid',
label=r'CO x ' + str(co_scaling),
)
ax.legend(loc='upper right')
# Plot velocity range
if vel_range.ndim == 1:
ax.axvspan(vel_range[0], vel_range[1], color='k', alpha=0.3)
elif vel_range.ndim == 2:
for i in xrange(0, vel_range.shape[0]):
ax.axvspan(vel_range[i, 0], vel_range[i, 1], color='k', alpha=0.3)
# Plot center
ax.axvline(results['co_center']['value'], color='k', linestyle='--', )
if hi_limits is not None:
ax.set_xlim(hi_limits[0], hi_limits[1])
ax.set_ylim(hi_limits[2], hi_limits[3])
ax.set_xlabel('Velocity (km/s)')
ax.set_ylabel(r'T$_b$ (K)')
# Plot results
if results is not None:
av_thres = results['av_threshold']['value']
co_thres = results['co_threshold']['value']
if av_thres > 15:
av_thres = None
text = ''
text += r'N$_{\rm pix}$ = ' + \
'{0:.0f}'.format(results['npix'])
text += '\n'
if av_thres is not None:
text += r'$A_V$ threshold = ' + \
'{0:.1f} mag'.format(av_thres)
else:
text += r'$A_V$ threshold = ' + \
'{0:s}'.format(av_thres)
text += '\n'
text += r'CO threshold = ' + \
'{0:.1f} K km/s'.format(co_thres)
text += '\n'
text += r'DGR = {0:.2f} '.format(results['dust2gas_ratio']['value']) + \
r'$\times$ 10$^{-20}$ (cm$^2$ mag$^1$)'
text += '\n'
if vel_range.ndim == 1:
text += r'Velocity range = ' + \
'{0:.1f} to {1:.1f} km/s'.format(vel_range[0], vel_range[1])
text += '\n'
text += r'$\chi^2$ / $\nu$ = {0:.1f}'.format(results['chisq'])
ax.annotate(text,
xytext=(0.03, 0.95),
xy=(0.03, 0.95),
textcoords='axes fraction',
xycoords='axes fraction',
color='k',
fontsize=font_scale*0.75,
bbox=dict(boxstyle='round',
facecolor='w',
alpha=0.8),
horizontalalignment='left',
verticalalignment='top',
)
if title is not None:
fig.suptitle(title, fontsize=font_scale)
if filename is not None:
plt.savefig(savedir + filename, bbox_inches='tight')
if show:
plt.show()
def plot_power_spectrum(image,
title=None,
filename_prefix=None,
filename_suffix='.png',
show=False,
savedir='./'):
'''
Plots power spectrum derived from a fourier transform of the image.
'''
# import external modules
import numpy as np
from agpy import azimuthalAverage as radial_average
from scipy import fftpack
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.axes_grid1 import axes
from matplotlib import cm
if 0:
plt.close()
plt.clf()
plt.imshow(image)
plt.show()
image[np.isnan(image)] = 1e10
# Determine power spectrum
# -------------------------------------------------------------------------
# Take the fourier transform of the image.
#F1 = fftpack.fft2(np.ma.array(image, mask=np.isnan(image)))
F1 = fftpack.fft2(image)
# Now shift the quadrants around so that low spatial frequencies are in
# the center of the 2D fourier transformed image.
F2 = fftpack.fftshift(F1)
# Calculate a 2D power spectrum
psd2D = np.abs(F2)**2
if 0:
plt.close()
plt.clf()
plt.imshow(psd2D)
plt.show()
power_spectrum = radial_average(psd2D, interpnan=True)
# Write frequency in arcmin
freq = fftpack.fftfreq(len(power_spectrum))
freq *= 5.0
# Simulate power spectrum for white noise
noise_image = np.random.normal(scale=0.1, size=image.shape)
F1 = fftpack.fft2(noise_image)
# Now shift the quadrants around so that low spatial frequencies are in
# the center of the 2D fourier transformed image.
F2 = fftpack.fftshift(F1)
# Calculate a 2D power spectrum
psd2D_noise = np.abs(F2)**2
power_spectrum_noise = radial_average(psd2D_noise, interpnan=True)
# Plot power spectrum 1D
# -------------------------------------------------------------------------
# Set up plot aesthetics
plt.clf()
plt.rcdefaults()
colormap = plt.cm.gist_ncar
#color_cycle = [colormap(i) for i in np.linspace(0, 0.9, len(flux_list))]
font_scale = 12
params = { #'backend': .pdf',
'axes.labelsize': font_scale,
'axes.titlesize': font_scale,
'text.fontsize': font_scale,
'legend.fontsize': font_scale * 3 / 4.0,
'xtick.labelsize': font_scale,
'ytick.labelsize': font_scale,
'font.weight': 500,
'axes.labelweight': 500,
'text.usetex': False,
'figure.figsize': (5, 5),
#'axes.color_cycle': color_cycle # colors of different plots
}
plt.rcParams.update(params)
# Create figure instance
fig = plt.figure()
nrows = 1
ncols = 1
ngrids = 1
axes = axes(
fig,
(1, 1, 1),
nrows_ncols=(nrows, ncols),
ngrids=ngrids,
axes_pad=0.25,
aspect=False,
label_mode='L',
share_all=True,
#cbar_mode='single',
cbar_pad=0.1,
cbar_size=0.2,
)
ax = axes[0]
ax.plot(freq,
power_spectrum / np.nanmax(power_spectrum),
color='k',
linestyle='-',
linewidth=1.5,
drawstyle='steps-mid',
label='Data Residuals')
ax.plot(freq,
power_spectrum_noise / np.nanmax(power_spectrum_noise),
color='r',
linestyle='-',
linewidth=0.4,
drawstyle='steps-mid',
label='White Noise Residuals')
#ax.set_xscale('log')
ax.legend(loc='best')
#ax.set_xscale('log')
ax.set_yscale('log')
ax.set_xlabel('Spatial Frequency [1/arcmin]')
ax.set_ylabel('Normalized Power Spectrum')
ax.set_xlim(0, 0.4)
if title is not None:
fig.suptitle(title, fontsize=font_scale)
if filename_prefix is not None:
plt.savefig(savedir + filename_prefix + '_1D' + filename_suffix,
bbox_inches='tight')
if show:
plt.show()
# Plot power spectrum image
# -------------------------
# Create figure instance
fig = plt.figure()
nrows = 1
ncols = 1
ngrids = 1
axes = axes(
fig,
(1, 1, 1),
nrows_ncols=(nrows, ncols),
ngrids=ngrids,
axes_pad=0.25,
aspect=False,
label_mode='L',
share_all=True,
#cbar_mode='single',
cbar_pad=0.1,
cbar_size=0.2,
)
ax = axes[0]
extent = [
-image.shape[0] / 2.0, +image.shape[0] / 2.0, -image.shape[1] / 2.0,
+image.shape[1] / 2.0
]
ax.imshow(psd2D,
origin='lower',
cmap=cm.gist_heat,
norm=matplotlib.colors.LogNorm(),
extent=extent)
#ax.set_xscale('log')
#ax.legend(loc='center right')
#ax.set_yscale('log')
ax.set_xlabel('Spatial Frequency in Right Ascension')
ax.set_ylabel('Spatial Frequency in Declination')
ax.set_xlim(-20, 20)
ax.set_ylim(-20, 20)
if title is not None:
fig.suptitle(title, fontsize=font_scale)
if filename_prefix is not None:
plt.savefig(savedir + filename_prefix + '_2D' + filename_suffix,
bbox_inches='tight')
if show:
plt.show()
def plot_avmod_vs_av(avmod_images,
av_images,
av_errors=None,
limits=None,
fit=True,
savedir='./',
filename=None,
show=True,
scale=('linear', 'linear'),
title='',
gridsize=(100, 100),
std=None):
# Import external modules
import numpy as np
import math
import pyfits as fits
import matplotlib.pyplot as plt
import matplotlib
from mpl_toolkits.axes_grid1 import axes
from myscience.krumholz09 import calc_T_cnm
from matplotlib import cm
n = int(np.ceil(len(av_images)**0.5))
if n**2 - n > len(av_images):
nrows = n - 1
ncols = n
y_scaling = 1.0 - 1.0 / n
else:
nrows, ncols = n, n
y_scaling = 1.0
# Set up plot aesthetics
plt.clf()
plt.rcdefaults()
colormap = plt.cm.gist_ncar
#color_cycle = [colormap(i) for i in np.linspace(0, 0.9, len(flux_list))]
font_scale = 12
params = { #'backend': .pdf',
'axes.labelsize': font_scale,
'axes.titlesize': font_scale,
'text.fontsize': font_scale,
'legend.fontsize': font_scale * 3 / 4.0,
'xtick.labelsize': font_scale,
'ytick.labelsize': font_scale,
'font.weight': 500,
'axes.labelweight': 500,
'text.usetex': True,
'figure.figsize': (5, 5 * y_scaling),
#'axes.color_cycle': color_cycle # colors of different plots
}
plt.rcParams.update(params)
# Create figure instance
fig = plt.figure()
axes = axes(
fig,
(1, 1, 1),
nrows_ncols=(nrows, ncols),
ngrids=len(av_images),
axes_pad=0.25,
aspect=False,
label_mode='L',
share_all=True,
#cbar_mode='single',
cbar_pad=0.1,
cbar_size=0.2,
)
# Cycle through lists
for i in xrange(len(av_images)):
av = av_images[i]
avmod = avmod_images[i]
av_error = av_errors[i]
# Drop the NaNs from the images
if type(av_error) is float:
indices = np.where((av == av) &\
(avmod == avmod)
)
if type(av_error) is np.ndarray or \
type(av_error) is np.ma.core.MaskedArray or \
type(avmod_error) is np.ndarray or \
type(avmod_error) is np.ma.core.MaskedArray:
indices = np.where((av == av) &\
(avmod == avmod) &\
(av_error == av_error)
)
av_nonans = av[indices]
avmod_nonans = avmod[indices]
# Fix error data types
if type(av_error) is np.ndarray:
av_error_nonans = av_error[indices]
else:
av_error_nonans = np.array(av_error[indices])
# Create plot
ax = axes[i]
if 1:
image = ax.errorbar(av_nonans.ravel(),
avmod_nonans.ravel(),
xerr=(av_error_nonans.ravel()),
alpha=0.2,
color='k',
marker='^',
ecolor='k',
linestyle='None',
markersize=3)
if 0:
image = ax.hexbin(
av_nonans.ravel(),
avmod_nonans.ravel(),
#norm=matplotlib.colors.LogNorm(),
mincnt=1,
yscale=scale[1],
xscale=scale[0],
gridsize=gridsize,
cmap=cm.Greys,
#cmap=cm.gist_stern,
)
cb = ax.cax.colorbar(image, )
# Write label to colorbar
cb.set_label_text('Bin Counts', )
# Plot sensitivies
av_limit = np.median(av_errors[0])
ax.axvline(av_limit, color='k', linestyle='--')
# Plot 1 to 1 pline
ax.plot((0, 10), (0, 10), color='0.5', linewidth=3, alpha=0.5)
if std is not None:
ax.fill_between((-std, 10), (0, 10 + std), (-2 * std, 10 - std),
color='0.2',
alpha=0.2,
edgecolor='none')
# Annotations
anno_xpos = 0.95
ax.set_xscale(scale[0], nonposx='clip')
ax.set_yscale(scale[1], nonposy='clip')
if limits is not None:
ax.set_xlim(limits[0], limits[1])
ax.set_ylim(limits[2], limits[3])
# Adjust asthetics
ax.set_xlabel(r'$A_V$ Data (mag)')
ax.set_ylabel(r'$A_V$ Model (mag)')
#ax.set_title(core_names[i])
if title is not None:
fig.suptitle(title, fontsize=font_scale * 1.5)
if filename is not None:
plt.savefig(savedir + filename, bbox_inches='tight')
if show:
fig.show()
def plot_av_model(av_image=None,
header=None,
contour_image=None,
av_model=None,
hi_velocity_axis=None,
vel_range=None,
hi_spectrum=None,
co_spectrum=None,
co_velocity_axis=None,
co_scaling=50,
hi_limits=None,
cores=None,
results=None,
title=None,
limits=None,
contours=None,
boxes=False,
savedir='./',
filename=None,
show=True,
plot_residuals=True):
# Import external modules
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
from mpl_toolkits.axes_grid1 import axes
import pyfits as fits
import matplotlib.pyplot as plt
import pywcsgrid2 as wcs
import pywcs
from pylab import cm # colormaps
from matplotlib.patches import Polygon
# Set up plot aesthetics
plt.clf()
plt.close()
plt.rcdefaults()
colormap = plt.cm.gist_ncar
#color_cycle = [colormap(i) for i in np.linspace(0, 0.9, len(flux_list))]
font_scale = 12
if plot_residuals:
figsize = (17, 8)
else:
figsize = (13, 10)
params = { #'backend': .pdf',
'axes.labelsize': font_scale,
'axes.titlesize': font_scale,
'text.fontsize': font_scale,
'legend.fontsize': font_scale * 3 / 4,
'xtick.labelsize': font_scale,
'ytick.labelsize': font_scale,
'font.weight': 500,
'axes.labelweight': 500,
'text.usetex': False,
'figure.figsize': figsize,
'figure.titlesize': font_scale
#'axes.color_cycle': color_cycle # colors of different plots
}
plt.rcParams.update(params)
# Create figure instance
fig = plt.figure()
#==========================================================================
# Av maps
#==========================================================================
if plot_residuals:
nrows_ncols = (1, 3)
ngrids = 3
else:
nrows_ncols = (1, 2)
ngrids = 2
axes = axes(fig, (2, 1, 1),
nrows_ncols=nrows_ncols,
ngrids=ngrids,
cbar_mode='each',
cbar_location='right',
cbar_pad="2%",
cbar_size='3%',
axes_pad=0.4,
axes_class=(wcs.Axes, dict(header=header)),
aspect=True,
label_mode='L',
share_all=True)
# ------------------
# Av model
# ------------------
# create axes
ax = axes[0]
cmap = cm.Greys # colormap
cmap = cm.gnuplot # colormap
cmap.set_bad(color='w')
# show the image
vmax = np.max((np.max(av_image[av_image == av_image]),
np.max(av_model[av_model == av_model])))
vmax = 1.4
im = ax.imshow(
av_model,
interpolation='nearest',
origin='lower',
cmap=cmap,
vmin=0,
vmax=vmax,
#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.set_title(r'Model $A_V$')
# colorbar
cb = ax.cax.colorbar(im)
# plot limits
if limits is not None:
ax.set_xlim(limits[0], limits[2])
ax.set_ylim(limits[1], limits[3])
# Plot Av contours
if contour_image is not None:
ax.contour(contour_image, levels=contours, colors='r')
# 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))
# ------------------
# 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=0,
vmax=vmax,
#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.set_title(r'Observed $A_V$')
# colorbar
cb = ax.cax.colorbar(im)
#cmap.set_bad(color='c')
# plot limits
if limits is not None:
ax.set_xlim(limits[0], limits[2])
ax.set_ylim(limits[1], limits[3])
# 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)',)
# ------------------
# Av residuals
# ------------------
if plot_residuals:
ax = axes[2]
#cmap = cm.Greys # colormap
# show the image
vmax = np.max((np.max(av_image[av_image == av_image]),
np.max(av_model[av_model == av_model])))
vmax = 1.4
im = ax.imshow(
av_image - av_model,
interpolation='nearest',
origin='lower',
cmap=cmap,
vmin=-0.25,
vmax=0.5,
#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.set_title(r'Residual $A_V$')
# colorbar
cb = ax.cax.colorbar(im)
#cmap.set_bad(color='w')
# plot limits
if limits is not None:
ax.set_xlim(limits[0], limits[2])
ax.set_ylim(limits[1], limits[3])
# ==========================================================================
# HI Spectrum
# ==========================================================================
vel_range = np.asarray(results['hi_velocity_range'][:2])
# create axes
ax = fig.add_subplot(2, 1, 2)
ax.plot(
hi_velocity_axis,
hi_spectrum,
color='k',
drawstyle='steps-mid',
label='HI',
)
if co_spectrum is not None:
ax.plot(
co_velocity_axis,
co_spectrum * co_scaling,
color='r',
drawstyle='steps-mid',
label=r'CO x ' + str(co_scaling),
)
ax.legend(loc='upper right')
# Plot velocity range
if vel_range.ndim == 1:
ax.axvspan(vel_range[0], vel_range[1], color='k', alpha=0.3)
elif vel_range.ndim == 2:
for i in xrange(0, vel_range.shape[0]):
ax.axvspan(vel_range[i, 0], vel_range[i, 1], color='k', alpha=0.3)
# Plot center
ax.axvline(
results['co_center']['value'],
color='k',
linestyle='--',
)
if hi_limits is not None:
ax.set_xlim(hi_limits[0], hi_limits[1])
ax.set_ylim(hi_limits[2], hi_limits[3])
ax.set_xlabel('Velocity (km/s)')
ax.set_ylabel(r'T$_b$ (K)')
# Plot results
if results is not None:
av_thres = results['av_threshold']['value']
co_thres = results['co_threshold']['value']
if av_thres > 15:
av_thres = None
text = ''
text += r'N$_{\rm pix}$ = ' + \
'{0:.0f}'.format(results['npix'])
text += '\n'
if av_thres is not None:
text += r'$A_V$ threshold = ' + \
'{0:.1f} mag'.format(av_thres)
else:
text += r'$A_V$ threshold = ' + \
'{0:s}'.format(av_thres)
text += '\n'
text += r'CO threshold = ' + \
'{0:.1f} K km/s'.format(co_thres)
text += '\n'
text += r'DGR = {0:.2f} '.format(results['dust2gas_ratio']['value']) + \
r'$\times$ 10$^{-20}$ (cm$^2$ mag$^1$)'
text += '\n'
if vel_range.ndim == 1:
text += r'Velocity range = ' + \
'{0:.1f} to {1:.1f} km/s'.format(vel_range[0], vel_range[1])
text += '\n'
text += r'$\chi^2$ / $\nu$ = {0:.1f}'.format(results['chisq'])
ax.annotate(
text,
xytext=(0.03, 0.95),
xy=(0.03, 0.95),
textcoords='axes fraction',
xycoords='axes fraction',
color='k',
fontsize=font_scale * 0.75,
bbox=dict(boxstyle='round', facecolor='w', alpha=0.8),
horizontalalignment='left',
verticalalignment='top',
)
if title is not None:
fig.suptitle(title, fontsize=font_scale)
if filename is not None:
plt.savefig(savedir + filename, bbox_inches='tight')
if show:
plt.show()
