def plot_vel_map_panels(vel_list, header=None, contour_image=None,
limits=None, contours=None, filename=None, show=False,
vel_vlimits=[None,None], vscale='linear'):
# Import external modules
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
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 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.BrBG
cmap = plt.cm.winter
#
ngrids = len(vel_list)
nrows, ncols = myplt.get_square_grid_sides(ngrids)
nrows, ncols = 1, len(vel_list)
nrows_ncols = (nrows, ncols)
# Create figure instance
fig = plt.figure(figsize=(3 * ncols + 1, 3 * nrows + 1))
#grid_helper = wcs.GridHelper(wcs=header)
axes = AxesGrid(fig, (1,1,1),
nrows_ncols=nrows_ncols,
ngrids=ngrids,
cbar_mode="single",
cbar_location='right',
cbar_pad="2%",
cbar_size='7%',
axes_pad=0.3,
#axes_class=(wcs.Axes,
# dict(grid_helper=grid_helper)),
axes_class=(wcs.Axes,
dict(header=header)),
aspect=True,
label_mode='L',
share_all=True)
for i in xrange(ngrids):
# create axes
ax = axes[i]
if vscale == 'log':
norm = matplotlib.colors.LogNorm()
vel_list[i][vel_list[i] <= 0] = np.nan
if 0 in vel_vlimits:
vel_vlimits = [None, None]
else:
norm = None
# show the image
im = ax.imshow(vel_list[i],
#interpolation='nearest',
origin='lower',
cmap=cmap,
vmin=vel_vlimits[0],
vmax=vel_vlimits[1],
norm=norm,
#norm=matplotlib.colors.LogNorm()
)
# Asthetics
ax.set_display_coord_system("gal")
#ax.set_ticklabel_type("hms", "dms")
ax.set_ticklabel_type("absdeg", "absdeg")
#ax.set_xlabel('Right Ascension [J2000]',)
#ax.set_ylabel('Declination [J2000]',)
ax.set_xlabel(r'$l$ [deg]',)
ax.set_ylabel(r'$b$ [deg]',)
ax.tick_params(axis='xy', which='major', colors='w')
ax.locator_params(nbins=4)
#ax.tick_params(colors='w')
# 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='galactic')
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='r')
# Write label to colorbar
cb.set_label_text(r'Velocity [km/s]',)
if filename is not None:
plt.savefig(filename, bbox_inches='tight')
if show:
plt.show()
def plot_diffuseLOS_map(header=None,
av_image=None,
df=None,
core_dict=None,
limits=None,
filename=None,
vlimits=(None, None),
contours=None,
parameter='phi_cnm',
models=['krumholz', 'sternberg']):
# Import external modules
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import AxesGrid
import pywcsgrid2 as wcs
from matplotlib.patches import Polygon
import matplotlib.patheffects as PathEffects
import myplotting as myplt
# Set up plot aesthetics
# ----------------------
#plt.close;plt.clf()
# Color map
cmap = plt.cm.copper
# Color cycle, grabs colors from cmap
color_cycle = [cmap(i) for i in np.linspace(0, 0.8, 2)]
font_scale = 9
# Create figure instance
fig = plt.figure(figsize=(3.6, 10))
if 0:
parameters = []
#if 'krumholz' in models:
# parameters.append('phi_cnm', 'alphaG', 'hi_transition', 'n_H', 'T_H']
parameters = [
'fraction_diffuse',
]
ngrids = len(parameters)
nrows_ncols = (ngrids, 1)
axesgrid = AxesGrid(fig, (1, 1, 1),
nrows_ncols=nrows_ncols,
ngrids=ngrids,
cbar_mode="each",
cbar_location='top',
cbar_pad="2%",
cbar_size='6%',
axes_pad=0.1,
axes_class=(wcs.Axes, dict(header=header)),
aspect=True,
label_mode='L',
share_all=False)
# ------------------
# Av image
# ------------------
for i in xrange(ngrids):
# create axes
ax = axesgrid[i]
# show the image
X, Y = np.meshgrid(np.arange(av_image.shape[1]),
np.arange(av_image.shape[0]))
im = ax.contour(
X,
Y,
av_image,
origin='lower',
levels=contours,
cmap=myplt.truncate_colormap(
plt.cm.binary,
minval=0.3,
)
#vmin=vlimits[0],
#vmax=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=4)
# plot limits
if limits is not None:
limits_pix = myplt.convert_wcs_limits(limits, header)
ax.set_xlim(limits_pix[0], limits_pix[1])
ax.set_ylim(limits_pix[2], limits_pix[3])
# Plot cores for each cloud
# -------------------------
from matplotlib.patches import Circle
from matplotlib.collections import PatchCollection
parameter = parameters[i]
patches = get_patches(df, header)
cmap = myplt.truncate_colormap(plt.cm.copper, minval=0.1, maxval=1.0)
collection = PatchCollection(
patches,
cmap=cmap,
edgecolors='k',
linewidth=0.75,
zorder=1000,
)
# set values in collection
collection_values = []
if parameter in [
'fraction_diffuse',
]:
collection.set_array(df[parameter])
else:
for core in df['core']:
collection_values.append(core_dict[core][parameter])
collection.set_array(np.array(collection_values))
ax.add_collection(collection, )
# colorbar
#cbar = axesgrid.cbar_axes[i].colorbar(collection)
cbar = ax.cax.colorbar(collection)
if parameter == 'fraction_diffuse':
cbar.set_label_text(r'Fraction of Diffuse LOS', )
if filename is not None:
plt.savefig(filename, bbox_inches='tight', dpi=600)
def plot_nhi_maps(nhi_1, nhi_2, header=None, contour_image=None,
limits=None, contours=None, filename=None, show=False,
nhi_1_vlimits=[None,None], nhi_2_vlimits=[None,None], vscale='linear'):
# Import external modules
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
import astropy.io 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 import AxesGrid
# Import external modules
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import AxesGrid
import pywcsgrid2 as wcs
from matplotlib.patches import Polygon
import matplotlib.patheffects as PathEffects
import myplotting as myplt
# 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_r
cmap = plt.cm.gist_heat_r
# Create figure instance
fig = plt.figure(figsize=(3, 4))
if nhi_2 is not None:
nrows_ncols=(1,2)
ngrids=2
else:
nrows_ncols=(1,1)
ngrids=1
#grid_helper = wcs.GridHelper(wcs=header)
axes = AxesGrid(fig, (1,1,1),
nrows_ncols=nrows_ncols,
ngrids=ngrids,
cbar_mode="single",
cbar_location='right',
cbar_pad="3%",
cbar_size='7%',
axes_pad=0.1,
axes_class=(wcs.Axes,
#dict(grid_helper=grid_helper)),
dict(header=header)),
aspect=True,
label_mode='L',
share_all=True)
if vscale == 'log':
norm = matplotlib.colors.LogNorm()
nhi_1[nhi_1 <= 0] = np.nan
nhi_2[nhi_2 <= 0] = np.nan
if 0 in nhi_1_vlimits:
nhi_1_vlimits = [None, None]
if 0 in nhi_2_vlimits:
nhi_2_vlimits = [None, None]
else:
norm = None
# ------------------
# NHI image
# ------------------
# create axes
ax = axes[0]
# show the image
im = ax.imshow(nhi_1,
interpolation='none',
origin='lower',
cmap=cmap,
vmin=nhi_1_vlimits[0],
vmax=nhi_1_vlimits[1],
norm=norm,
#norm=matplotlib.colors.LogNorm()
)
# Asthetics
ax.set_display_coord_system("gal")
#ax.set_ticklabel_type("hms", "dms")
ax.set_ticklabel_type("absdeg", "absdeg")
#ax.set_xlabel('Right Ascension [J2000]',)
#ax.set_ylabel('Declination [J2000]',)
ax.set_xlabel('Glong',)
ax.set_ylabel('Glat',)
# colorbar
cb = ax.cax.colorbar(im)
cmap.set_bad(color='w')
#ax.tick_params(colors='w')
ax.locator_params(nbins=4)
#ax.tick_params(colors='w')
# plot limits
if limits is not None:
limits_pix = myplt.convert_wcs_limits(limits,
header,
frame='galactic')
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='r')
# Write label to colorbar
cb.set_label_text(r'$N$(H\textsc{i}) [10$^{20}$ cm$^{-2}$]',)
# ------------------
# Av image
# ------------------
if nhi_2 is not None:
# create axes
ax = axes[1]
# show the image
im = ax.imshow(nhi_2,
interpolation='none',
origin='lower',
cmap=cmap,
vmin=nhi_2_vlimits[0],
vmax=nhi_2_vlimits[1],
norm=norm
)
# Asthetics
ax.set_display_coord_system("gal")
#ax.set_ticklabel_type("hms", "dms")
ax.set_ticklabel_type("absdeg", "absdeg")
#ax.set_xlabel('Right Ascension [J2000]',)
#ax.set_ylabel('Declination [J2000]',)
ax.set_xlabel('Glong',)
ax.set_ylabel('Glat',)
ax.locator_params(nbins=4)
#ax.tick_params(colors='w')
# 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='galactic')
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'$N$(H\textsc{i}) [10$^{20}$ cm$^{-2}$]',)
if filename is not None:
plt.savefig(filename, bbox_inches='tight')
if show:
plt.show()
def plot_rad_map(header=None, contour_image=None, rad_field=None, cores=None,
cores_to_keep=None,
props=None, cloud_dict=None, regions=None, title=None, limits=None,
contours=None, boxes=False, savedir='./', filename=None, show=False,
hi_vlimits=None, vlimits=None, vscale='linear'):
# Import external modules
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
from mpl_toolkits.axes_grid1 import AxesGrid
import pyfits as pf
import matplotlib.pyplot as plt
import pywcsgrid2 as wcs
import pywcs
from pylab import cm # colormaps
from matplotlib.patches import Polygon
import matplotlib.patheffects as PathEffects
import myplotting as myplt
# Set up plot aesthetics
# ----------------------
#plt.close();plt.clf()
# Color map
cmap = plt.cm.copper
# Color cycle, grabs colors from cmap
# Create figure instance
fig = plt.figure(figsize=(7.5, 5))
if rad_field is not None:
nrows_ncols=(1,1)
ngrids=1
imagegrid = AxesGrid(fig, (1,1,1),
nrows_ncols=nrows_ncols,
ngrids=ngrids,
cbar_mode="single",
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)
# ------------------
# Av image
# ------------------
# create axes
ax = imagegrid[0]
#ax = wcs.subplot(111, header=header)
if vscale == 'log':
norm = matplotlib.colors.LogNorm()
else:
norm = None
# show the image
im = ax.imshow(rad_field,
interpolation='nearest',
origin='lower',
cmap=cmap,
norm=norm,
vmin=vlimits[0],
vmax=vlimits[1],
)
# 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_display_coord_system("gal")
#ax.set_ticklabel_type("hms", "dms")
ax.set_ticklabel_type("dms", "dms")
#ax.set_xlabel('Right Ascension [J2000]',)
#ax.set_ylabel('Declination [J2000]',)
ax.set_xlabel('Galactic Longitude [deg]',)
ax.set_ylabel('Galactic Latitude [deg]',)
ax.axis["top",].toggle(ticklabels=True)
ax.grid(color='w', linewidth=0.5)
ax.axis[:].major_ticks.set_color("w")
cmap.set_bad(color='w')
#ax.locator_params(nbins=6)
ax.locator_params(nbins=6)
# colorbar
from matplotlib.ticker import LogFormatter
formatter = LogFormatter(10, labelOnlyBase=False)
ticks = np.logspace(np.log10(vlimits[0]),
np.log10(vlimits[1]),
#np.log10(vlimits[1]) - np.log10(vlimits[0]),
5,
)
from matplotlib.ticker import LogFormatterMathtext
if 1:
cb = ax.cax.colorbar(im,
#ticks=ticks,
#format=LogFormatterMathtext(),
)
#cb.set_label_text(r'log$_{10}$[$U_{0,M83}$]',)
cb.set_label_text(r'$U\,[U_{\rm D78}$]',)
else:
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(ax)
#cax = divider.append_axes("right", size="5%", pad=0.05)
#plt.colorbar(im, cax=cax)
#cb = fig.colorbar(im, ax=ax)
#cb.ax.set_ylabel(r'$U_{0,M83}$',)
# 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='r')
if props is not None:
if 0:
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=7,
zorder=10)
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.legend(rects, core_labels, bbox_to_anchor=(1.05, 1), loc=2,
# borderaxespad=0.)
#ax.legend(rects, core_labels, bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
# ncol=5, mode="expand", borderaxespad=0.)
if filename is not None:
plt.savefig(savedir + filename, bbox_inches='tight')
if show:
fig.show()
def plot_params_map(header=None,
av_image=None,
df=None,
limits=None,
filename=None,
vlimits=(None, None),
contours=None,
parameter='phi_cnm'):
# Import external modules
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import AxesGrid
import pywcsgrid2 as wcs
from matplotlib.patches import Polygon
import matplotlib.patheffects as PathEffects
import myplotting as myplt
# Set up plot aesthetics
# ----------------------
#plt.close;plt.clf()
# Color map
cmap = plt.cm.copper
# Color cycle, grabs colors from cmap
color_cycle = [cmap(i) for i in np.linspace(0, 0.8, 2)]
font_scale = 9
# Create figure instance
fig = plt.figure(figsize=(3.6, 5))
nrows_ncols = (3, 1)
ngrids = 3
axesgrid = 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=False)
# ------------------
# Av image
# ------------------
parameters = ['phi_cnm', 'alphaG', 'hi_transition']
for i in xrange(ngrids):
# create axes
ax = axesgrid[i]
# show the image
X, Y = np.meshgrid(np.arange(av_image.shape[1]),
np.arange(av_image.shape[0]))
im = ax.contour(
X,
Y,
av_image,
origin='lower',
levels=contours,
cmap=myplt.truncate_colormap(
plt.cm.binary,
minval=0.3,
)
#vmin=vlimits[0],
#vmax=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=4)
# plot limits
if limits is not None:
limits_pix = myplt.convert_wcs_limits(limits, header)
ax.set_xlim(limits_pix[0], limits_pix[1])
ax.set_ylim(limits_pix[2], limits_pix[3])
# Plot cores for each cloud
# -------------------------
from matplotlib.patches import Circle
from matplotlib.collections import PatchCollection
parameter = parameters[i]
patches = get_patches(df, header)
cmap = myplt.truncate_colormap(plt.cm.copper, minval=0.1, maxval=1.0)
collection = PatchCollection(
patches,
cmap=cmap,
edgecolors='none',
zorder=1000,
)
collection.set_array(df[parameter])
ax.add_collection(collection, )
# colorbar
#cbar = axesgrid.cbar_axes[i].colorbar(collection)
cbar = ax.cax.colorbar(collection)
if parameter == 'phi_cnm':
cbar.set_label_text(r'$\phi_{\rm CNM}$', )
elif parameter == 'alphaG':
cbar.set_label_text(r'$\alpha G$', )
elif parameter == 'hi_transition':
cbar.set_label_text(r'$\Sigma_{\rm HI,trans}$ ' + \
r'$[M_\odot\,{\rm pc}^{-2}]$',)
if filename is not None:
plt.savefig(filename, bbox_inches='tight', dpi=100)
def plot_params_map(header=None, av_image=None, df=None, limits=None,
filename=None, vlimits=(None,None), contours=None, parameter='phi_cnm'):
# Import external modules
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import AxesGrid
import pywcsgrid2 as wcs
from matplotlib.patches import Polygon
import matplotlib.patheffects as PathEffects
import myplotting as myplt
# Set up plot aesthetics
# ----------------------
#plt.close;plt.clf()
# Color map
cmap = plt.cm.copper
# Color cycle, grabs colors from cmap
color_cycle = [cmap(i) for i in np.linspace(0, 0.8, 2)]
font_scale = 9
# Create figure instance
fig = plt.figure(figsize=(3.6, 5))
nrows_ncols=(3,1)
ngrids=3
axesgrid = 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=False)
# ------------------
# Av image
# ------------------
parameters = ['phi_cnm', 'alphaG', 'hi_transition']
for i in xrange(ngrids):
# create axes
ax = axesgrid[i]
# show the image
X, Y = np.meshgrid(np.arange(av_image.shape[1]),
np.arange(av_image.shape[0]))
im = ax.contour(X, Y, av_image,
origin='lower',
levels=contours,
cmap=myplt.truncate_colormap(plt.cm.binary, minval=0.3,)
#vmin=vlimits[0],
#vmax=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=4)
# plot limits
if limits is not None:
limits_pix = myplt.convert_wcs_limits(limits, header)
ax.set_xlim(limits_pix[0],limits_pix[1])
ax.set_ylim(limits_pix[2],limits_pix[3])
# Plot cores for each cloud
# -------------------------
from matplotlib.patches import Circle
from matplotlib.collections import PatchCollection
parameter = parameters[i]
patches = get_patches(df, header)
cmap = myplt.truncate_colormap(plt.cm.copper, minval=0.1, maxval=1.0)
collection = PatchCollection(patches,
cmap=cmap,
edgecolors='none',
zorder=1000,
)
collection.set_array(df[parameter])
ax.add_collection(collection,
)
# colorbar
#cbar = axesgrid.cbar_axes[i].colorbar(collection)
cbar = ax.cax.colorbar(collection)
if parameter == 'phi_cnm':
cbar.set_label_text(r'$\phi_{\rm CNM}$',)
elif parameter == 'alphaG':
cbar.set_label_text(r'$\alpha G$',)
elif parameter == 'hi_transition':
cbar.set_label_text(r'$\Sigma_{\rm HI,trans}$ ' + \
r'$[M_\odot\,{\rm pc}^{-2}]$',)
if filename is not None:
plt.savefig(filename, bbox_inches='tight', dpi=100)
def plot_diffuseLOS_map(header=None, av_image=None, df=None, core_dict=None,
limits=None, filename=None, vlimits=(None,None), contours=None,
parameter='phi_cnm', models=['krumholz', 'sternberg']):
# Import external modules
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import AxesGrid
import pywcsgrid2 as wcs
from matplotlib.patches import Polygon
import matplotlib.patheffects as PathEffects
import myplotting as myplt
# Set up plot aesthetics
# ----------------------
#plt.close;plt.clf()
# Color map
cmap = plt.cm.copper
# Color cycle, grabs colors from cmap
color_cycle = [cmap(i) for i in np.linspace(0, 0.8, 2)]
font_scale = 9
# Create figure instance
fig = plt.figure(figsize=(3.6, 10))
if 0:
parameters = []
#if 'krumholz' in models:
# parameters.append('phi_cnm', 'alphaG', 'hi_transition', 'n_H', 'T_H']
parameters = ['fraction_diffuse',]
ngrids=len(parameters)
nrows_ncols=(ngrids,1)
axesgrid = AxesGrid(fig, (1,1,1),
nrows_ncols=nrows_ncols,
ngrids=ngrids,
cbar_mode="each",
cbar_location='top',
cbar_pad="2%",
cbar_size='6%',
axes_pad=0.1,
axes_class=(wcs.Axes,
dict(header=header)),
aspect=True,
label_mode='L',
share_all=False)
# ------------------
# Av image
# ------------------
for i in xrange(ngrids):
# create axes
ax = axesgrid[i]
# show the image
X, Y = np.meshgrid(np.arange(av_image.shape[1]),
np.arange(av_image.shape[0]))
im = ax.contour(X, Y, av_image,
origin='lower',
levels=contours,
cmap=myplt.truncate_colormap(plt.cm.binary, minval=0.3,)
#vmin=vlimits[0],
#vmax=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=4)
# plot limits
if limits is not None:
limits_pix = myplt.convert_wcs_limits(limits, header)
ax.set_xlim(limits_pix[0],limits_pix[1])
ax.set_ylim(limits_pix[2],limits_pix[3])
# Plot cores for each cloud
# -------------------------
from matplotlib.patches import Circle
from matplotlib.collections import PatchCollection
parameter = parameters[i]
patches = get_patches(df, header)
cmap = myplt.truncate_colormap(plt.cm.copper, minval=0.1, maxval=1.0)
collection = PatchCollection(patches,
cmap=cmap,
edgecolors='k',
linewidth=0.75,
zorder=1000,
)
# set values in collection
collection_values = []
if parameter in ['fraction_diffuse',]:
collection.set_array(df[parameter])
else:
for core in df['core']:
collection_values.append(core_dict[core][parameter])
collection.set_array(np.array(collection_values))
ax.add_collection(collection,
)
# colorbar
#cbar = axesgrid.cbar_axes[i].colorbar(collection)
cbar = ax.cax.colorbar(collection)
if parameter == 'fraction_diffuse':
cbar.set_label_text(r'Fraction of Diffuse LOS',)
if filename is not None:
plt.savefig(filename, bbox_inches='tight', dpi=600)
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()
def plot_cores_map(header=None,
av_image=None,
core_sample=None,
limits=None,
filename=None,
vlimits=(None, None),
region_dict=None,
plot_regions=True,
plot_names=True):
# Import external modules
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import AxesGrid
import pywcsgrid2 as wcs
from matplotlib.patches import Polygon
import matplotlib.patheffects as PathEffects
import myplotting as myplt
# Set up plot aesthetics
# ----------------------
plt.close
plt.clf()
# Color map
cmap = plt.cm.gnuplot
cmap = plt.cm.copper
# Color cycle, grabs colors from cmap
color_cycle = [cmap(i) for i in np.linspace(0, 0.8, 2)]
font_scale = 9
# Create figure instance
fig = plt.figure(figsize=(7.5, 5))
nrows_ncols = (1, 1)
ngrids = 1
axesgrid = 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)
# ------------------
# Av image
# ------------------
# create axes
ax = axesgrid[0]
# show the image
im = ax.imshow(
av_image,
interpolation='nearest',
origin='lower',
cmap=cmap,
vmin=vlimits[0],
vmax=vlimits[1],
#norm=matplotlib.colors.LogNorm()
)
# Asthetics
ax.set_display_coord_system("gal")
#ax.set_ticklabel_type("hms", "dms")
ax.set_ticklabel_type("dms", "dms")
#ax.set_xlabel('Right Ascension [J2000]',)
#ax.set_ylabel('Declination [J2000]',)
ax.set_xlabel('Galactic Longitude [deg]', )
ax.set_ylabel('Galactic Latitude [deg]', )
ax.axis["top", ].toggle(ticklabels=True)
ax.grid(color='w', linewidth=0.5)
ax.axis[:].major_ticks.set_color("w")
ax.locator_params(nbins=6)
# colorbar
cb = ax.cax.colorbar(im)
cmap.set_bad(color='w')
cb.set_label_text(r'$A_V$ [mag]', )
# plot limits
if limits is not None:
limits = myplt.convert_wcs_limits(limits, header, frame='fk5')
ax.set_xlim(limits[0], limits[1])
ax.set_ylim(limits[2], limits[3])
# Plot cores for each cloud
# -------------------------
if plot_regions:
plot_core_regions(ax, region_dict, core_sample)
else:
plot_core_locs(ax, region_dict, core_sample, plot_names=plot_names)
if filename is not None:
plt.savefig(filename, bbox_inches='tight', dpi=300)
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_av_image(header=None, contour_image=None,
av_image=None, cores=None, props=None, regions=None, title=None,
limits=None, contours=None, boxes=False, filename=None,
limits_type='wcs',
show=True, av_vlimits=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 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))
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)
# ------------------
# Av image
# ------------------
# create axes
ax = axes[0]
# 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.axis[:].major_ticks.set_color("w")
ax.locator_params(nbins=6)
# colorbar
cb = ax.cax.colorbar(im)
cmap.set_bad(color='w')
# plot limits
if limits is not None:
if limits_type == 'wcs':
print limits
limits_pix = myplt.convert_wcs_limits(limits, header, frame='fk5')
else:
limits_pix = limits
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()
def plot_cores_map(header=None, av_image=None, core_sample=None, limits=None,
filename=None, vlimits=(None,None), region_dict=None,
plot_regions=True, plot_names=True):
# Import external modules
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import AxesGrid
import pywcsgrid2 as wcs
from matplotlib.patches import Polygon
import matplotlib.patheffects as PathEffects
import myplotting as myplt
# Set up plot aesthetics
# ----------------------
plt.close;plt.clf()
# Color map
cmap = plt.cm.gnuplot
cmap = plt.cm.copper
# Color cycle, grabs colors from cmap
color_cycle = [cmap(i) for i in np.linspace(0, 0.8, 2)]
font_scale = 9
# Create figure instance
fig = plt.figure(figsize=(7.5, 5))
nrows_ncols=(1,1)
ngrids=1
axesgrid = 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)
# ------------------
# Av image
# ------------------
# create axes
ax = axesgrid[0]
# show the image
im = ax.imshow(av_image,
interpolation='nearest',
origin='lower',
cmap=cmap,
vmin=vlimits[0],
vmax=vlimits[1],
#norm=matplotlib.colors.LogNorm()
)
# Asthetics
ax.set_display_coord_system("gal")
#ax.set_ticklabel_type("hms", "dms")
ax.set_ticklabel_type("dms", "dms")
#ax.set_xlabel('Right Ascension [J2000]',)
#ax.set_ylabel('Declination [J2000]',)
ax.set_xlabel('Galactic Longitude [deg]',)
ax.set_ylabel('Galactic Latitude [deg]',)
ax.axis["top",].toggle(ticklabels=True)
ax.grid(color='w', linewidth=0.5)
ax.axis[:].major_ticks.set_color("w")
ax.locator_params(nbins=6)
# colorbar
cb = ax.cax.colorbar(im)
cmap.set_bad(color='w')
cb.set_label_text(r'$A_V$ [mag]',)
# plot limits
if limits is not None:
limits = myplt.convert_wcs_limits(limits, header, frame='fk5')
ax.set_xlim(limits[0],limits[1])
ax.set_ylim(limits[2],limits[3])
# Plot cores for each cloud
# -------------------------
if plot_regions:
plot_core_regions(ax, region_dict, core_sample)
else:
plot_core_locs(ax, region_dict, core_sample, plot_names=plot_names)
if filename is not None:
plt.savefig(filename, bbox_inches='tight', dpi=300)
def plot_rad_map(header=None,
contour_image=None,
rad_field=None,
cores=None,
cores_to_keep=None,
props=None,
cloud_dict=None,
regions=None,
title=None,
limits=None,
contours=None,
boxes=False,
savedir='./',
filename=None,
show=False,
hi_vlimits=None,
vlimits=None,
vscale='linear'):
# Import external modules
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
from mpl_toolkits.axes_grid1 import AxesGrid
import pyfits as pf
import matplotlib.pyplot as plt
import pywcsgrid2 as wcs
import pywcs
from pylab import cm # colormaps
from matplotlib.patches import Polygon
import matplotlib.patheffects as PathEffects
import myplotting as myplt
# Set up plot aesthetics
# ----------------------
#plt.close();plt.clf()
# Color map
cmap = plt.cm.copper
# Color cycle, grabs colors from cmap
# Create figure instance
fig = plt.figure(figsize=(7.5, 5))
if rad_field is not None:
nrows_ncols = (1, 1)
ngrids = 1
imagegrid = AxesGrid(fig, (1, 1, 1),
nrows_ncols=nrows_ncols,
ngrids=ngrids,
cbar_mode="single",
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)
# ------------------
# Av image
# ------------------
# create axes
ax = imagegrid[0]
#ax = wcs.subplot(111, header=header)
if vscale == 'log':
norm = matplotlib.colors.LogNorm()
else:
norm = None
# show the image
im = ax.imshow(
rad_field,
interpolation='nearest',
origin='lower',
cmap=cmap,
norm=norm,
vmin=vlimits[0],
vmax=vlimits[1],
)
# 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_display_coord_system("gal")
#ax.set_ticklabel_type("hms", "dms")
ax.set_ticklabel_type("dms", "dms")
#ax.set_xlabel('Right Ascension [J2000]',)
#ax.set_ylabel('Declination [J2000]',)
ax.set_xlabel('Galactic Longitude [deg]', )
ax.set_ylabel('Galactic Latitude [deg]', )
ax.axis["top", ].toggle(ticklabels=True)
ax.grid(color='w', linewidth=0.5)
ax.axis[:].major_ticks.set_color("w")
cmap.set_bad(color='w')
#ax.locator_params(nbins=6)
ax.locator_params(nbins=6)
# colorbar
from matplotlib.ticker import LogFormatter
formatter = LogFormatter(10, labelOnlyBase=False)
ticks = np.logspace(
np.log10(vlimits[0]),
np.log10(vlimits[1]),
#np.log10(vlimits[1]) - np.log10(vlimits[0]),
5,
)
from matplotlib.ticker import LogFormatterMathtext
if 1:
cb = ax.cax.colorbar(im,
#ticks=ticks,
#format=LogFormatterMathtext(),
)
#cb.set_label_text(r'log$_{10}$[$U_{0,M83}$]',)
cb.set_label_text(r'$U\,[U_{\rm D78}$]', )
else:
from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(ax)
#cax = divider.append_axes("right", size="5%", pad=0.05)
#plt.colorbar(im, cax=cax)
#cb = fig.colorbar(im, ax=ax)
#cb.ax.set_ylabel(r'$U_{0,M83}$',)
# 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='r')
if props is not None:
if 0:
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=7,
zorder=10)
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.legend(rects, core_labels, bbox_to_anchor=(1.05, 1), loc=2,
# borderaxespad=0.)
#ax.legend(rects, core_labels, bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
# ncol=5, mode="expand", borderaxespad=0.)
if filename is not None:
plt.savefig(savedir + filename, bbox_inches='tight')
if show:
fig.show()
