def setup_axes(fig, header):
from mpl_toolkits.axes_grid import make_axes_locatable
ax0 = pywcsgrid2.subplot(111, wcs=header)
divider = make_axes_locatable(ax0)
gh1 = pywcsgrid2.GridHelperSimple(wcs=header, axis_nums=[0, 2])
ax_v = divider.new_vertical(1.5, pad=0.1, sharex=ax0,
axes_class=pywcsgrid2.Axes,
grid_helper=gh1)
fig.add_axes(ax_v)
gh2 = pywcsgrid2.GridHelperSimple(wcs=header, axis_nums=[2, 1])
ax_h = divider.new_horizontal(1.5, pad=0.1, sharey=ax0,
axes_class=pywcsgrid2.Axes,
grid_helper=gh2)
fig.add_axes(ax_h)
ax_h.axis["left"].toggle(label=False, ticklabels=False)
ax_v.axis["bottom"].toggle(label=False, ticklabels=False)
return ax0, ax_v, ax_h
def setup_axes():
ax = pywcsgrid2.subplot(111, header=co_header)
#add colorbar axes
divider = make_axes_locatable(ax)
cax = divider.new_horizontal("5%", pad=0.1, axes_class=Axes)
fig.add_axes(cax)
return ax, cax
def plot_map(image,coords,title='title',cm ='RdPu'):
"""
Usage: plot_map(pixel_map,WCS_Coords)
Input: 2D pixel Map, WCS object of the Map
Output: Plot of The Image with a WCS Compass
"""
ax1 = pywcsgrid2.subplot(111, wcs=coords)
im = ax1.imshow(image,origin='low',cmap=cm)
ax1.add_compass(loc=5,color='black')
ax1.set_title(title)
plt.colorbar(im)
def show(self, filename=None):
# taken from http://matplotlib.sourceforge.net/users/usetex.html
import pywcsgrid2
from matplotlib.gridspec import GridSpec
self.imshow_args = dict(interpolation='nearest', origin='lower')
self.counts_norm = mpl.colors.LogNorm(vmin=self.counts_min,
vmax=self.counts_max)
self.norm_res = mpl.colors.Normalize(vmin=-5, vmax=5)
# first, divide the plot in 4x4
self.fig = P.figure(self.fignum, self.figsize)
P.clf()
gs = GridSpec(4, 4)
gs.update(wspace=1.5, hspace=1)
self.ax_model = pywcsgrid2.subplot(gs[0:2, 0:2], header=self.h)
self.ax_counts = pywcsgrid2.subplot(gs[0:2, 2:4], header=self.h)
self.ax_res = pywcsgrid2.subplot(gs[2:4, 0:2], header=self.h)
# then divide the 4th space in two.
self.ax_pvals = P.subplot(gs[2, 2:4])
self.ax_pvals.yaxis.set_major_locator(MaxNLocator(3))
self.ax_resplot = P.subplot(gs[3, 2:4])
self.ax_resplot.yaxis.set_major_locator(MaxNLocator(3))
self.model_plot()
self.counts_plot()
self.resids_plot()
self.hist_plot()
for ax in [self.ax_model, self.ax_counts, self.ax_res]:
ax.axis[:].set_zorder(100)
ROIMapPlotter.overlay_region(self.roi, ax, self.h,
**self.overlay_kwargs)
if self.extra_overlay is not None: self.extra_overlay(ax)
if self.title is not None: self.fig.suptitle(self.title)
if filename is not None: P.savefig(filename)
def plot_skyreg(header, data, **kwargs):
""" Plot sky region defined by header and data
header : FITS header
data : Data array
"""
kwargs.setdefault('cmap','binary')
fig = plt.figure()
ax = pywcsgrid2.subplot(111, header=header)
ax.set_ticklabel_type("dms")
im = ax.imshow(data, origin="center", **kwargs)
ax.grid()
ax.add_compass(loc=1,coord='fk5')
ax.add_compass(loc=4,coord='gal')
return ax, im
def plot_skyreg(header, data, **kwargs):
""" Plot sky region defined by header and data
header : FITS header
data : Data array
"""
kwargs.setdefault('cmap', 'binary')
fig = plt.figure()
ax = pywcsgrid2.subplot(111, header=header)
ax.set_ticklabel_type("dms")
im = ax.imshow(data, origin="center", **kwargs)
ax.grid()
ax.add_compass(loc=1, coord='fk5')
ax.add_compass(loc=4, coord='gal')
return ax, im
def show(self, filename=None, axes=None, cax=None):
import pylab as P
import pywcsgrid2
from mpl_toolkits.axes_grid1.axes_divider import make_axes_locatable
d = self.pf[0].data
if axes is None:
fig = P.figure(self.fignum, self.figsize)
P.clf()
axes = pywcsgrid2.subplot(111, header=self.header)
else:
fig = axes.get_figure()
self.axes = ax = axes
im = ax.imshow(d,
interpolation=self.interpolation,
origin="lower",
**self.imshow_kwargs())
ax.axis[:].set_zorder(100)
if self.show_colorbar:
if cax is None:
# add colorbar axes
divider = make_axes_locatable(ax)
self.cax = cax = divider.new_horizontal("5%",
pad="2%",
axes_class=Axes)
fig.add_axes(cax)
self.cbar = P.colorbar(im, cax=cax)
else:
# See comment for ROISmoothedSources's colobar code.
self.cax = cax
self.cbar = cax.colorbar(im)
if self.title is not None:
ax.set_title(self.title)
ROIMapPlotter.overlay_region(self.roi, ax, self.header,
**self.overlay_kwargs)
self._additional_plotting()
if self.extra_overlay is not None: self.extra_overlay(ax)
tight_layout(fig)
if filename is not None: P.savefig(filename)
def setup_sky(header, subplt=111, delta=100, title=None, **kwargs):
'''Setup a nice sky plot that handles images'''
import pywcsgrid2 # slow as shit
ax = pywcsgrid2.subplot(subplt, header=header)
fig = pylab.gcf()
fig.add_axes(ax)
ax.set_display_coord_system('fk5')
ax.set_ticklabel_type('absdeg', 'absdeg')
ax.set_aspect('equal')
ax.locator_params(axis='x', nbins=4)
ax.grid()
ax.set_xlim(-delta, delta + header['naxis1'])
ax.set_ylim(-delta, delta + header['naxis2'])
if title is not None:
pylab.title(title)
return ax
def setup_sky(header, subplt=111, delta=100, title=None, **kwargs):
'''Setup a nice sky plot that handles images'''
import pywcsgrid2 # slow as shit
ax = pywcsgrid2.subplot(subplt, header=header)
fig = pylab.gcf()
fig.add_axes(ax)
ax.set_display_coord_system('fk5')
ax.set_ticklabel_type('absdeg', 'absdeg')
ax.set_aspect('equal')
ax.locator_params(axis='x', nbins=4)
ax.grid()
ax.set_xlim(-delta,delta+header['naxis1'])
ax.set_ylim(-delta,delta+header['naxis2'])
if title is not None:
pylab.title(title)
return ax
def points_inside_reg(pyfits_obj, reg_name, ra_points, dec_points, plot=False):
'''
Takes an open fits file (pyfits_obj), a reg file (reg_name), ra and dec
array, and returns the indices of the ra dec array inside each shape in
the region file. Will also plot...
inds - cumulative
'''
if plot == True:
ax = pywcsgrid2.subplot(111, header=pyfits_obj[0].header)
ax.grid()
ax.imshow(pyfits_obj[0].data, cmap=cm.gray, origin="lower")
# load projection, reg shapes, and get them in python
proj = wcs.Projection(pyfits_obj[0].header)
r = pyregion.open(reg_name).as_imagecoord(header=pyfits_obj[0].header)
patch_list, artist_list = r.get_mpl_patches_texts()
radec = np.column_stack((ra_points, dec_points))
# get masks of ra,dec in each shape
masks = []
for p in patch_list[::-1]:
if plot == True: ax.add_patch(p)
verts_xy = p.get_xy()
pixels = (verts_xy[:, 0], verts_xy[:, 1])
ra_v, dec_v = proj.toworld(pixels)
verts = np.column_stack((ra_v, dec_v))
masks.append(nxutils.points_inside_poly(radec, verts))
# subtract masks from outside to inside to not over count.
inds = []
for i in range(len(masks) - 1):
isolated_mask = (masks[i] - masks[i + 1])
if len(isolated_mask) > 0:
within = np.nonzero(isolated_mask)[0]
inds.append(within)
if plot == True:
ax["fk5"].plot(ra_points[within],
dec_points[within],
'.',
alpha=0.2,
label='%i' % i,
color=cols[i])
for t in artist_list:
ax.add_artist(t)
ax.add_compass(loc=1)
ax.legend()
return inds
def draw_cel(center, major_axes):
header = equ_header(center, major_axes)
l,b=center.l(),center.b()
cel_ax=pywcsgrid2.subplot(122, header=header)
cel_ax.set_ticklabel_type("absdeg")
cel_ax.imshow(np.zeros((IMAGE_SIZE,IMAGE_SIZE)), origin="lower", cmap=plt.cm.gray_r)
cel_ax.grid()
# add floating galacitc axes on top of equatorial axes
cel_ax.axis["l=%.2f" % l] = cel_ax["gal"].new_floating_axis(0, l)
cel_ax.axis["b=%.2f" % b] = cel_ax["gal"].new_floating_axis(1, b)
cel_ax.add_compass(loc=1)
return cel_ax
def draw_gal(center, major_axes):
ra,dec=center.ra(),center.dec()
header=gal_header(center, major_axes)
global gal_ax
gal_ax=pywcsgrid2.subplot(121, header=header)
gal_ax.set_ticklabel_type("absdeg")
gal_ax.imshow(np.zeros((IMAGE_SIZE,IMAGE_SIZE)), origin="lower", cmap=plt.cm.gray_r)
gal_ax.grid()
# add floating equatorial axes on top of galactic axes
gal_ax.axis["ra=%.2f" % ra] = gal_ax["fk5"].new_floating_axis(0, ra)
gal_ax.axis["dec=%.2f" % dec] = gal_ax["fk5"].new_floating_axis(1, dec)
gal_ax.add_compass(loc=1)
return gal_ax
def setup_axes(fig, header):
ax_t = pywcsgrid2.subplot(111, header=header)
#gh = pywcsgrid2.GridHelper(wcs=header)
#gh.locator_params(nbins=4)
g = axes_grid.ImageGrid(fig, 111,
nrows_ncols=(ny,nx),
ngrids=None, direction='row',
axes_pad=0.0,
add_all=True,
share_all=True,
aspect=True,
label_mode='L',
cbar_mode=None)
return g, ax_t
def points_inside_reg(pyfits_obj,reg_name,ra_points,dec_points,plot=False):
'''
Takes an open fits file (pyfits_obj), a reg file (reg_name), ra and dec
array, and returns the indices of the ra dec array inside each shape in
the region file. Will also plot...
inds - cumulative
'''
if plot == True:
ax=pywcsgrid2.subplot(111, header=pyfits_obj[0].header)
ax.grid()
ax.imshow(pyfits_obj[0].data, cmap=cm.gray, origin="lower")
# load projection, reg shapes, and get them in python
proj = wcs.Projection(pyfits_obj[0].header)
r = pyregion.open(reg_name).as_imagecoord(header=pyfits_obj[0].header)
patch_list, artist_list = r.get_mpl_patches_texts()
radec = np.column_stack((ra_points,dec_points))
# get masks of ra,dec in each shape
masks = []
for p in patch_list[::-1]:
if plot == True: ax.add_patch(p)
verts_xy = p.get_xy()
pixels = (verts_xy[:,0],verts_xy[:,1])
ra_v,dec_v = proj.toworld(pixels)
verts = np.column_stack((ra_v,dec_v))
masks.append( nxutils.points_inside_poly(radec, verts))
# subtract masks from outside to inside to not over count.
inds = []
for i in range(len(masks)-1):
isolated_mask = (masks[i]-masks[i+1])
if len(isolated_mask)>0:
within = np.nonzero(isolated_mask)[0]
inds.append(within)
if plot == True:
ax["fk5"].plot(ra_points[within],dec_points[within],'.',alpha=0.2,label='%i'%i,color=cols[i])
for t in artist_list:
ax.add_artist(t)
ax.add_compass(loc=1)
ax.legend()
return inds
def plot_maps(coords,fn,cm='coolwarm',norm='log',fs=(20,10),**kwargs):
"""
Usage: plot_maps(maps=[list_of_maps],titles=[list_of_titles],fn=filename(put f for not saving),norm='log',fs=figsize,coords=wcs,cm=colormap)
Input: 2D pixel Maps, WCS object of the Maps
Output: Plot all Subplots of The Images with WCS Compass
"""
plt.rcParams['figure.figsize'] = fs
nm=len(kwargs['maps'])
a=[[]]*nm
for i,m in enumerate(kwargs['maps']):
vmax=m.max()
a[i]=(pywcsgrid2.subplot(1,nm,i,wcs=coords))
a[i].grid()
if norm=='log':
im=a[i].imshow(m,origin='low',cmap=cm,norm=LogNorm(),aspect=1.)
else:
im=a[i].imshow(m,origin='low',cmap=cm,aspect=1.)
a[i].set_title(kwargs['titles'][i])
a[i].add_compass(loc=5,color='black')
ains = inset_axes(a[i], width='2%', height='37%', loc=1)
cb=plt.colorbar(im,cax=ains)
plt.tight_layout()
if (fn !='f'):
plt.savefig(fn,bbox_inches='tight')
pointing_pix_X = 4.096000000e03
pointing_pix_Y = 4.097000000e03
pointing_Dec = 1.96615555556e01
del_RA = -1.111111061e-07
del_Dec = 1.111111061e-07
core_A = [5427.45, 5624.77]
blobs_x_A = [core_A[0], 5420.59, 5385.71]
blobs_y_A = [core_A[1], 5619.15, 5626.8]
core_B = [3089.05, 2511.30]
jet_B = [3078.38, 2501.19]
blobs_x_B = [core_B[0], jet_B[0]]
blobs_y_B = [core_B[1], jet_B[1]]
fig2 = plt.figure(figsize=(15, 6))
axA = pywcsgrid2.subplot(121, header=h44, aspect=1, adjustable="box-forced")
cA38 = axA.contourf(d38, levels=levs38, colors=cols38)
cA44 = axA.contour(d44, levels=levs44, colors=cols44)
plt.xlim((core_A[0]-63.13, core_A[0]+12.63))
plt.ylim((core_A[1]-37.88, core_A[1]+37.88))
axA.set_title("Image A")
#plt.clabel(cA44, cA44.levels[::2])#, inline=True, fmt=fmt, fontsize=10)
axB = pywcsgrid2.subplot(122, header=h44, aspect=1, adjustable="box-forced")
cB38 = axB.contourf(d38, levels=levs38, colors=cols38)
cB44 = axB.contour(d44, levels=levs44, colors=cols44)
plt.xlim((core_B[0]-50.50, core_B[0]+25.25))
plt.ylim((core_B[1]-50.50, core_B[1]+25.25))
axB.add_beam_size(b44[0]/pxl_scl, b44[1]/pxl_scl, 0.02, loc=3, frameon=True)
axB.add_beam_size(b38[0]/pxl_scl, b38[1]/pxl_scl, -5.55, loc=2, frameon=True)
axB.add_size_bar(5./pxl_scl, label='5 mas', loc=1)
# Get SDSS r image
pix, hdr, im = None, None, None
xid = SDSS.query_region(pos)
if xid is not None:
one_match = table.Table(xid[0])
im = SDSS.get_images(matches=one_match, band='r')
pix = im[0][0].data
hdr = im[0][0].header
# Plot K2 image
# Set up the GridHelper to merge the axes
grid_helper = pywcsgrid2.GridHelper(wcs=w2)
# Plot the pixel stamp as usual, except with the WCS
ax1 = pywcsgrid2.subplot(441,
grid_helper=grid_helper,
aspect=1,
adjustable="box-forced")
ax1.matshow(coadd, origin="lower", cmap=cmap, norm=colors.LogNorm())
if pix is not None:
median = np.median(pix)
stdev = np.std(pix)
levels = np.linspace(median + stdev, np.max(pix), 20)
#grey = plt.cm.Greys(0.3)
ax1[hdr].contour(pix, cmap=plt.cm.Greys, levels=levels)
# Plot the SDSS image rotated into the same frame as the pixel stamp
ax2 = pywcsgrid2.subplot(445,
grid_helper=grid_helper,
aspect=1,
adjustable="box-forced",
sharex=ax1,
def plot_av_images(cloud_dict, title=None, boxes=False, savedir='./',
filename=None, show=True, rh2_limits=None, hi_sd_limits=None):
# Import external modules
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
from mpl_toolkits.axes_grid1 import ImageGrid
import pyfits as pf
import matplotlib.pyplot as plt
import pywcsgrid2
import pywcs
from pylab import cm # colormaps
from matplotlib.patches import Polygon
from matplotlib.gridspec import GridSpec
# 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 = 20
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': True,
'figure.figsize': (20, 10),
'figure.titlesize': font_scale
#'axes.color_cycle': color_cycle # colors of different plots
}
plt.rcParams.update(params)
fig = pywcsgrid2.plt.figure()
fig.subplots_adjust(hspace=0.1)
gs1 = GridSpec(1, 2)
#gs1.update(left=0.02, right=0.6, bottom=0.02, top=0.98, wspace=0.00,
# hspace=0.12)
#grid_helper = pywcsgrid2.GridHelper(wcs=cloud_dict[cloud]['av_header'])
grid_pos = 0
cloud_list = []
cloud_dict = {'taurus':cloud_dict['taurus'],
'california':cloud_dict['california']}
for i, cloud in enumerate(cloud_dict):
av_image = cloud_dict[cloud]['av_data']
av_header = cloud_dict[cloud]['av_header']
ax = pywcsgrid2.subplot(gs1[grid_pos],
header=av_header)
# create axes
cmap = cm.pink # colormap
cmap.set_bad(color='w')
# show the image
vmin, vmax = cloud_dict[cloud]['color_scale_limits']
im = ax.imshow(av_image,
interpolation='nearest',
origin='lower',
cmap=cmap,
vmin=vmin,
vmax=vmax,
#norm=matplotlib.colors.LogNorm()
)
# Set limits
limits = cloud_dict[cloud]['limit_pixels']
ax.set_xlim([limits[0][0], limits[1][0]])
ax.set_ylim([limits[0][1], limits[1][1]])
# Asthetics
ax.set_display_coord_system("fk5")
ax.set_ticklabel_type("hms", "dms")
if i != 0:
ax.set_xlabel('')
else:
ax.set_xlabel('Right Ascension (J2000)',)
ax.set_xlabel('Right Ascension (J2000)',)
ax.set_ylabel('Declination (J2000)',)
ax.annotate(cloud.capitalize(),
xy=[0.02, 0.9],
textcoords='axes fraction',
xycoords='axes fraction',
fontsize=font_scale * 1.5,
color='w')
# Convert sky to pix coordinates
cores = cloud_dict[cloud]['cores']
'''
# Colorbar
if cloud == 'perseus':
cb = fig.colorbar(im, ax=ax)
# Write label to colorbar
cb.set_label(r'A$_V$ (Mag)',)
else:
cb = fig.colorbar(im, ax=ax)
# Write label to colorbar
cb.set_label(r'A$_V$ (Mag)',)
'''
for core in cores:
if core in cloud_dict[cloud]['plot_cores']:
scale = 1.1
linewidth=3
linestyle='solid'
else:
scale = 0.8
linewidth=1
linestyle='dashed'
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=(5,10),
textcoords='offset points',
fontsize=font_scale*scale,
#arrowprops=dict(facecolor='w'),
color='w')
if boxes:
vertices = np.copy(cores[core]['box_vertices_rotated'])
#[:, ::-1]
rect = ax.add_patch(Polygon(
vertices[:, ::-1],
facecolor='none',
edgecolor=anno_color,
linewidth=linewidth,
linestyle=linestyle))
cloud_list.append(cloud)
grid_pos += 1
#plt.tight_layout()
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:
fig.show()
def remove_field_overlaps(photfiles,drzs,footprints,base_fits=m31,plot=False,fake=False,matched_only=False,qc=True):
'''
This only works for field 3,4,5,9,10,11,15,16,17. It can be adapted for
all fields though, 3 is now the top left field, 9 and 15 are the left most
fields on the next rows.
footprints is a region file from exposure_coverage.csv
pre-processing:
1. grep UVIS exposure_coverage.csv > poop
2. grep F336W poop > poop1
3. grep 550 poop1 > poop2
4. Then some hard coding in the file:
I decided to update the exposure footprints file since full bricks are
on their way...
the problem is that chip 1 vs chip 2 flip their orientation on fields 1,2,3
from those of 4,5,6 such that chip 1 of field 3 overlaps with chip 1 of
field 4 while chip 2 of field 4 and 5 overlap with chip 1 of 5 and 6.
I swapped the chip values of 1,2,3,7,8,9,13,14,15 so it's consistent with
4,5,6,10,11,12,16,17,18.
5. Then I cut out all fields BUT 3,4,5,9,10,11,15,16,17
Hacked adaptability for all.fake files. If .fake files are coming in, just
say they are photfiles and set the fake kwarg to True.
For all.fake: need to run twice (ug). Once with qc=True and once with qc=False.
adding more fields? Check the if statements and the [i-*]s.
'''
clrs = discrete_colors(len(photfiles))
if plot == True:
ax=pywcsgrid2.subplot(111, header=f_header)
ax.grid()
ax.imshow(f_data, cmap=cm.gray, origin="lower")
r = pyregion.open(footprints).as_imagecoord(header=f_header)
# a is the left chip of the field (see # 4 in comment)
r1a = pyregion.ShapeList([rr for rr in r if re.search('550-1',rr.attr[1].get("tag"))])
# b is the right chip of the field (see # 4 in comment)
r1b = pyregion.ShapeList([rr for rr in r if re.search('550-2',rr.attr[1].get("tag"))])
patch_list1a, artist_list1a = r1a.get_mpl_patches_texts()
patch_list1b, artist_list1b = r1b.get_mpl_patches_texts(fixed_color)
if plot == True:
for p in patch_list1a + patch_list1b:
ax.add_patch(p)
for i in range(len(photfiles)):
inds1,inds2 = '',''
print 'reading',photfiles[i]
if fake==True:
if qc == False:
x,y,mag1,mag2,xxx,yyy,qc_ind1,qc_ind2 = read_all_fake(photfiles[i])
else:
x,y,mag1,mag2,xxx,yyy,qc_ind1,qc_ind2 = read_all_fake(photfiles[i],mag1_cut=999.,mag2_cut=999.)
else: x,y,mag1,mag2,qc_ind1,qc_ind2,matches = read_phot(photfiles[i])
# HACK!!! This is to only use the phot stars found in both filters (with qc)
if matched_only == True:
qc_ind1 = matches
qc_ind2 = matches
d = pyfits.open(drzs[i])
# get out of image coords
ra,dec = XY2radec(d,x,y)
# currect for chip gap and edges:
within = in_chips(ra,dec,patch_list1a[i],patch_list1b[i])
# field 3 is the chosen one to sit on top:
if re.search('F03',photfiles[i]):
inds1 = match_2inds(within,qc_ind1)
inds2 = match_2inds(within,qc_ind2)
if qc == False:
inds1 = within
inds2 = within
Mag1 = mag1[inds1]
Mag2 = mag2[inds2]
Ra1 = ra[inds1]
Dec1 = dec[inds1]
Ra2 = ra[inds2]
Dec2 = dec[inds2]
# top row will only overlap with one to the right
elif re.search('F0',photfiles[i]) or re.search('F05',photfiles[i]) or re.search('F06',photfiles[i]):
not_overlapped = poly_over_under(ra,dec,patch_list1b[i-1],patch_list1a[i])
inds1 = match_3inds(within,qc_ind1,not_overlapped)
inds2 = match_3inds(within,qc_ind2,not_overlapped)
if qc == False:
inds1 = match_2inds(within,not_overlapped)
inds2 = match_2inds(within,not_overlapped)
# next two rows will overlap above and to the right
else:
# correct for overlap by field in above row
# by left chip
top_a_a = poly_over_under(ra,dec,patch_list1a[i-4],patch_list1a[i])
top_a_b = poly_over_under(ra,dec,patch_list1a[i-4],patch_list1b[i])
top_b_b = poly_over_under(ra,dec,patch_list1b[i-4],patch_list1b[i])
tops = list(set(top_a_a) & set(top_a_b) & set(top_b_b))
# correct for overlap by field to the left, F09 is the left-most.
if re.search('F09',photfiles[i]) or re.search('F15',photfiles[i]):
inds1 = list(set(qc_ind1) & set(within) & set(tops))
inds2 = list(set(qc_ind2) & set(within) & set(tops))
else:
top_left_b_a = poly_over_under(ra,dec,patch_list1b[i-5],patch_list1a[i])
the_left = poly_over_under(ra,dec,patch_list1b[i-1],patch_list1a[i])
inds1 = list(set(qc_ind1) & set(within) & set(tops) & set(top_left_b_a) & set(the_left))
inds2 = list(set(qc_ind2) & set(within) & set(tops) & set(top_left_b_a) & set(the_left))
if qc == False:
inds1 = list(set(within) & set(tops) & set(top_left_b_a) & set(the_left))
inds2 = list(set(within) & set(tops) & set(top_left_b_a) & set(the_left))
if plot==True: ax['fk5'].plot(ra[inds1],dec[inds1],'o',ms=4,mec=clrs[i],color=clrs[i])
Mag1 = np.append(Mag1,mag1[inds1])
Mag2 = np.append(Mag2,mag2[inds2])
Ra1 = np.append(Ra1,ra[inds1])
Dec1 = np.append(Dec1,dec[inds1])
Ra2 = np.append(Ra2,ra[inds2])
Dec2 = np.append(Dec2,dec[inds2])
return Ra1,Dec1,Ra2,Dec2,Mag1,Mag2
import pyfits
import matplotlib.pyplot as plt
f = pyfits.open("data/lmc.fits")
d, h = f[0].data, f[0].header
plt.figure(1, [6,3.5])
plt.subplot(121)
plt.imshow(d, origin="low", vmin=0, vmax=2000,
cmap=plt.cm.gray_r)
plt.title("Original MPL")
import pywcsgrid2
pywcsgrid2.subplot(122, header=h)
plt.imshow(d, origin="low", vmin=0, vmax=2000,
cmap=plt.cm.gray_r)
plt.title("pywcsgrid2")
plt.subplots_adjust(left=0.1, right=0.95, top=0.95, wspace=0.5)
plt.show()
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import pyregion
try:
from astropy.io import fits as pyfits
except ImportError:
import pyfits
# read in the image
cluster_name = "a383_mosaic_065mas_acs_wfc3ir_total_drz_20110825.fits"
f_cluster = pyfits.open(cluster_name)
try:
import pywcsgrid2
ax = pywcsgrid2.subplot(111, header=f_cluster[0].header)
except ImportError:
ax = plt.subplot(111)
ax.imshow(f_cluster[0].data,
cmap=cm.gray,
vmin=0.,
vmax=0.00038,
origin="lower")
reg_name = "abell383_wfc3ir_outline.reg"
r = pyregion.open(reg_name).as_imagecoord(header=f_cluster[0].header)
from pyregion.mpl_helper import properties_func_default
def plot_av_images(cloud_dict,
title=None,
boxes=False,
savedir='./',
filename=None,
show=True,
rh2_limits=None,
hi_sd_limits=None):
# Import external modules
import matplotlib.pyplot as plt
import matplotlib
import numpy as np
from mpl_toolkits.axes_grid1 import ImageGrid
import pyfits as pf
import matplotlib.pyplot as plt
import pywcsgrid2
import pywcs
from pylab import cm # colormaps
from matplotlib.patches import Polygon
from matplotlib.gridspec import GridSpec
# 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 = 20
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': True,
'figure.figsize': (19, 20),
'figure.titlesize': font_scale
#'axes.color_cycle': color_cycle # colors of different plots
}
plt.rcParams.update(params)
fig = pywcsgrid2.plt.figure()
fig.subplots_adjust(hspace=0.1)
gs1 = GridSpec(3, 1)
gs1.update(left=0.02,
right=0.6,
bottom=0.02,
top=0.98,
wspace=0.00,
hspace=0.12)
#grid_helper = pywcsgrid2.GridHelper(wcs=cloud_dict[cloud]['av_header'])
grid_pos = 0
cloud_list = []
for i, cloud in enumerate(cloud_dict):
av_image = cloud_dict[cloud]['av_data']
av_header = cloud_dict[cloud]['av_header']
ax = pywcsgrid2.subplot(gs1[grid_pos], header=av_header)
# create axes
cmap = cm.pink # colormap
cmap.set_bad(color='w')
# show the image
vmin, vmax = cloud_dict[cloud]['color_scale_limits']
im = ax.imshow(
av_image,
interpolation='nearest',
origin='lower',
cmap=cmap,
vmin=vmin,
vmax=vmax,
#norm=matplotlib.colors.LogNorm()
)
# Set limits
limits = cloud_dict[cloud]['limit_pixels']
ax.set_xlim([limits[0][0], limits[1][0]])
ax.set_ylim([limits[0][1], limits[1][1]])
# Asthetics
ax.set_display_coord_system("fk5")
ax.set_ticklabel_type("hms", "dms")
if i != 0:
ax.set_xlabel('')
else:
ax.set_xlabel('Right Ascension (J2000)', )
ax.set_xlabel('Right Ascension (J2000)', )
ax.set_ylabel('Declination (J2000)', )
ax.annotate(cloud.capitalize(),
xy=[0.02, 0.9],
textcoords='axes fraction',
xycoords='axes fraction',
fontsize=font_scale * 1.5,
color='w')
# Convert sky to pix coordinates
cores = cloud_dict[cloud]['cores']
# Colorbar
if cloud == 'perseus':
cb = fig.colorbar(im, ax=ax)
# Write label to colorbar
cb.set_label(r'A$_V$ (Mag)', )
else:
cb = fig.colorbar(im, ax=ax)
# Write label to colorbar
cb.set_label(r'A$_V$ (Mag)', )
for core in cores:
if core in cloud_dict[cloud]['plot_cores']:
scale = 1.1
linewidth = 3
linestyle = 'solid'
else:
scale = 0.8
linewidth = 1
linestyle = 'dashed'
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, 10),
textcoords='offset points',
fontsize=font_scale * scale,
#arrowprops=dict(facecolor='w'),
color='w')
if boxes:
vertices = np.copy(cores[core]['box_vertices_rotated'])
#[:, ::-1]
rect = ax.add_patch(
Polygon(vertices[:, ::-1],
facecolor='none',
edgecolor=anno_color,
linewidth=linewidth,
linestyle=linestyle))
cloud_list.append(cloud)
grid_pos += 1
# Plot RH2 and HISD vs. HSD
plot_rh2_hi_vs_h(cloud_dict,
fig,
font_scale=font_scale,
rh2_limits=rh2_limits,
cloud_list=cloud_list,
hi_sd_limits=hi_sd_limits)
#plt.tight_layout()
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:
fig.show()
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import pyregion
try:
from astropy.io import fits as pyfits
except ImportError:
import pyfits
# read in the image
xray_name= "pspc_skyview.fits"
f_xray = pyfits.open(xray_name)
try:
import pywcsgrid2
ax=pywcsgrid2.subplot(111, header=f_xray[0].header)
except ImportError:
ax=plt.subplot(111)
ax.imshow(f_xray[0].data, cmap=cm.gray, vmin=0., vmax=0.00038, origin="lower")
reg_name = "test.reg"
r = pyregion.open(reg_name).as_imagecoord(header=f_xray[0].header)
from pyregion.mpl_helper import properties_func_default
# Use custom function for patch attribute
def fixed_color(shape, saved_attrs):
attr_list, attr_dict = saved_attrs
attr_dict["color"] = "red"
image = image[600:1400, 200:1400] * 60.
# Scale input image
bottom, top = 0., 150.
data = (((top - bottom) * (image - image.min())) / (image.max() - image.min())) + bottom
# Determine image size
tam = data.shape
# Define plot size
xsize = 15
ysize = xsize * tam[0] / tam[1]
# Setup plot and custom axes
fig = plt.figure(figsize=(ysize, xsize))
ax = pywcsgrid2.subplot(111, header=hdr)
# Select image colormap
cmap = cm.get_cmap('gray_r')
# Setup axes ticks and labels
ax.locator_params(axis='x', nbins=6)
ax.locator_params(axis='y', nbins=14)
ax.set_xlabel('RIGHT ASCENSION')
ax.set_ylabel('DECLINATION')
# Display background image
im = ax.imshow(data, cmap=cmap, vmax=7.)
# Calculate contours
contour_x = np.arange(tam[1])
def setup_axes(fig):
ax=pywcsgrid2.subplot(111,header=f[0].header)
divider=make_axes_locatable(ax)
# cax=divider.new_horizontal('5%',pad=0.05,axes_class=Axes)
# fig.add_axes(cax)
return ax #,cax
def plot_four(epic, filename, coadd, maskmap, maskheader, init, coords, sources, ap=None, campaign=4):
logging.info("Plot four %s", epic)
logging.debug(base_path)
fig = plt.figure(figsize=(8, 8))
hdu2 = fits.open(filename)
dataheader = hdu2[1].header
hdu2.close()
keysel = np.empty(13, "S6")
keysel[:] = "binary"
w2 = WCS(dataheader, colsel=[5], keysel=keysel)
# Plot DSS/SDSS image if available
dssname = "{0}/ss_finders/{1}d/fc_{1}d_dssdss2red.fits".format(base_path, epic)
sdssname = "{0}/ss_finders/{1}d/fc_{1}d_sdss (dr7)z.fits".format(base_path, epic)
if os.path.exists(dssname):
# Open image file
hdu = fits.open(dssname)
pix, hdr = hdu[0].data, hdu[0].header
hdu.close()
elif os.path.exists(sdssname):
# Open image file
hdu = fits.open(sdssname)
pix, hdr = hdu[0].data, hdu[0].header
hdu.close()
else:
pix = None
# Set up the GridHelper to merge the axes
grid_helper = pywcsgrid2.GridHelper(wcs=w2)
# Plot the pixel stamp as usual, except with the WCS
ax1 = pywcsgrid2.subplot(221, grid_helper=grid_helper, aspect=1, adjustable="box-forced")
ax1.matshow(coadd, origin="lower", cmap="Greys", norm=colors.LogNorm())
if ap is not None:
ap_circle = plt.Circle(coords, ap, color="k", fill=False, linewidth=1.5)
ax1.add_artist(ap_circle)
# ax1.axis["bottom","left","top","right"].toggle(ticklabels=False)
if pix is not None:
median = np.median(pix)
stdev = np.std(pix)
levels = np.linspace(median + stdev, np.max(pix), 5)
ax1[hdr].contour(pix, colors="r", levels=levels)
# ax1.set_ticklabel_type("delta","delta",
# dict(offset=np.float64(dataheader["1CRVL5"]),
# latitude=np.float64(dataheader["2CRVL5"])))
# Plot the DSS image rotated into the same frame as the pixel stamp
ax2 = pywcsgrid2.subplot(
222, grid_helper=grid_helper, aspect=1, adjustable="box-forced", sharex=ax1, sharey=ax1
)
ax2[hdr].imshow_affine(pix, origin="lower", cmap="Greys", norm=colors.LogNorm())
median2 = np.median(coadd)
stdev2 = np.std(coadd)
levels2 = np.linspace(median, np.max(coadd), 5)
# ax2[w2].contour(coadd,3, colors="r")
# ax2.set_ticklabel_type("delta","delta")
# ax2.axis["bottom","left","top","right"].toggle(ticklabels=False)
# Then the pixel motion across the CCD
ax3 = plt.subplot(223)
divider3 = make_axes_locatable(ax3)
cax3 = divider3.append_axes("bottom", size="5%", pad=0.35)
stamp(coadd, maskmap, ax=ax3, cmap="gray")
lcs = at.read("{}/lcs/ktwo{}-c0{}.csv".format(base_path, epic, campaign))
ax3.set_xlim(np.floor(min(lcs["x"])), np.ceil(max(lcs["x"])))
ax3.set_ylim(np.floor(min(lcs["y"])), np.ceil(max(lcs["y"])))
xyt = ax3.scatter(
lcs["x"],
lcs["y"],
c=lcs["t"],
edgecolor="none",
alpha=0.5,
vmin=np.percentile(lcs["t"], 5),
vmax=np.percentile(lcs["t"], 95),
cmap="gnuplot",
)
cbar_ticks = np.asarray(np.percentile(lcs["t"], np.arange(10, 100, 40)), int)
cbar1 = fig.colorbar(xyt, cax=cax3, ticks=cbar_ticks, orientation="horizontal")
cbar1.set_label("Time (d)", fontsize="small")
# Then sky coordinates with the object position overlaid
ax4 = plt.subplot(224)
plot_chips(ax4, campaign)
setup_k2_axes(ax4)
plt.plot(maskheader["RA_OBJ"], maskheader["DEC_OBJ"], "*", color="Purple", ms=25, alpha=0.8)
plt.setp(ax4.get_xticklabels()[::2], visible=False)
plt.setp(ax4.get_yticklabels()[::2], visible=False)
plt.suptitle("EPIC {}".format(epic)) # , fontsize="large")
# plt.tight_layout()
plt.subplots_adjust(top=0.95, hspace=0.5, wspace=0.8)
def plot(self,subplot=111,catalog=None,cmap='jet',**kwargs):
from matplotlib.colors import NoNorm, LogNorm, Normalize
kwargs_contour = { 'levels' : None, 'colors' : ['k'],
'linewidths' : None,
'origin' : 'lower' }
kwargs_imshow = { 'interpolation' : 'nearest',
'origin' : 'lower','norm' : None,
'vmin' : None, 'vmax' : None }
zscale = kwargs.get('zscale',None)
gamma = kwargs.get('gamma',0.5)
beam_size = kwargs.get('beam_size',None)
if zscale == 'pow':
kwargs_imshow['norm'] = PowerNorm(gamma=gamma)
elif zscale == 'sqrt':
kwargs_imshow['norm'] = PowerNorm(gamma=0.5)
elif zscale == 'log': kwargs_imshow['norm'] = LogNorm()
else: kwargs_imshow['norm'] = Normalize()
# ax = kwargs.get('ax',None)
# if ax is None:
ax = pywcsgrid2.subplot(subplot, header=self._wcs.to_header())
# ax = pywcsgrid2.axes(header=self._wcs.to_header())
load_ds9_cmap()
colormap = mpl.cm.get_cmap(cmap)
colormap.set_under('white')
counts = copy.copy(self._counts)
if np.any(self._roi_msk):
kwargs_imshow['vmin'] = 0.8*np.min(self._counts[~self._roi_msk.T])
counts[self._roi_msk.T] = -np.inf
# vmax = np.max(self._counts[~self._roi_msk])
# c = self._counts[~self._roi_msk]
# if logz: vmin = np.min(c[c>0])
update_dict(kwargs_imshow,kwargs)
update_dict(kwargs_contour,kwargs)
im = ax.imshow(counts.T,**kwargs_imshow)
im.set_cmap(colormap)
if kwargs_contour['levels']:
cs = ax.contour(counts.T,**kwargs_contour)
# plt.clabel(cs, fontsize=5, inline=0)
# im.set_clim(vmin=np.min(self._counts[~self._roi_msk]),
# vmax=np.max(self._counts[~self._roi_msk]))
ax.set_ticklabel_type("d", "d")
if self._axes[0]._coordsys == 'gal':
ax.set_xlabel('GLON')
ax.set_ylabel('GLAT')
else:
ax.set_xlabel('RA')
ax.set_ylabel('DEC')
xlabel = kwargs.get('xlabel',None)
ylabel = kwargs.get('ylabel',None)
if xlabel is not None: ax.set_xlabel(xlabel)
if ylabel is not None: ax.set_ylabel(ylabel)
# plt.colorbar(im,orientation='horizontal',shrink=0.7,pad=0.15,
# fraction=0.05)
ax.grid()
if catalog:
cat = Catalog.get(catalog)
kwargs_cat = {'src_color' : 'k' }
if cmap == 'ds9_b': kwargs_cat['src_color'] = 'w'
cat.plot(self,ax=ax,**kwargs_cat)
# ax.add_compass(loc=1)
# ax.set_display_coord_system("gal")
# ax.locator_params(axis="x", nbins=12)
ax.add_size_bar(1./self._axes[0]._delta, # 30' in in pixel
r"$1^{\circ}$",loc=3,color='w')
if beam_size is not None:
ax.add_beam_size(2.0*beam_size[0]/self._axes[0]._delta,
2.0*beam_size[1]/self._axes[1]._delta,
beam_size[2],beam_size[3],
patch_props={'fc' : "none", 'ec' : "w"})
self._ax = ax
return im
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import pyregion
try:
from astropy.io import fits as pyfits
except ImportError:
import pyfits
# read in the image
cluster_name="a383_mosaic_065mas_acs_wfc3ir_total_drz_20110825.fits"
f_cluster = pyfits.open(cluster_name)
try:
import pywcsgrid2
ax=pywcsgrid2.subplot(111, header=f_cluster[0].header)
except ImportError:
ax=plt.subplot(111)
ax.imshow(f_cluster[0].data, cmap=cm.gray, vmin=0., vmax=0.00038, origin="lower")
reg_name = "abell383_wfc3ir_outline.reg"
r = pyregion.open(reg_name).as_imagecoord(header=f_cluster[0].header)
from pyregion.mpl_helper import properties_func_default
# Use custom function for patch attribute
def fixed_color(shape, saved_attrs):
attr_list, attr_dict = saved_attrs
attr_dict["color"] = "red"
from astropy.io import fits as pyfits
import matplotlib.pyplot as plt
f = pyfits.open("data/lmc.fits")
d, h = f[0].data, f[0].header
plt.figure(1, [6, 3.5])
plt.subplot(121)
plt.imshow(d, origin="low", vmin=0, vmax=2000, cmap=plt.cm.gray_r)
plt.title("Original MPL")
import pywcsgrid2
pywcsgrid2.subplot(122, header=h)
plt.imshow(d, origin="low", vmin=0, vmax=2000, cmap=plt.cm.gray_r)
plt.title("pywcsgrid2")
plt.subplots_adjust(left=0.1, right=0.95, top=0.95, wspace=0.5)
plt.show()
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import pyregion
try:
from astropy.io import fits as pyfits
except ImportError:
import pyfits
# read in the image
xray_name = "pspc_skyview.fits"
f_xray = pyfits.open(xray_name)
try:
import pywcsgrid2
ax = pywcsgrid2.subplot(111, header=f_xray[0].header)
except ImportError:
ax = plt.subplot(111)
ax.imshow(f_xray[0].data, cmap=cm.gray, vmin=0., vmax=0.00038, origin="lower")
reg_name = "test.reg"
r = pyregion.open(reg_name).as_imagecoord(header=f_xray[0].header)
from pyregion.mpl_helper import properties_func_default
# Use custom function for patch attribute
def fixed_color(shape, saved_attrs):
attr_list, attr_dict = saved_attrs
bs = bs38
bpa = bpa38
filename = 'jetmedian38'
elif fitsfile == fitsfile44:
bs = bs44
bpa = bpa44
filename = 'jetmedian44'
fhdr, fdata, dmean, dstd, dmax, ddrng = read_stats(fitsfile)
cl1 = dstd*8.
n = np.arange(0, 10)
levs = cl1*2.**n
cmap = brewer2mpl.get_map('OrRd', 'sequential', 9, reverse=True)
cols = np.array(cmap.mpl_colors)
fig = plt.figure()
axA = pywcsgrid2.subplot(121, aspect=1, header=fhdr)
axB = pywcsgrid2.subplot(122, aspect=1, header=fhdr)
core_A = [5427.45, 5624.77]
core_B = [3089.05, 2511.30]
if dataset == 8.4:
imA = fdata
imB = fdata
elif dataset == 5:
imA=fdata[core_A[1]-40:core_A[1]+40, core_A[0]-60:core_A[0]+40]
imB=fdata[core_B[1]-30:core_B[1]+25, core_B[0]-30:core_B[0]+25]
axA.contour(imA, levels=levs, orogin='lower', colors='k', alpha=0.5)
c = ['#1f78b4', '#33a02c', '#fb9a99', '#e31a1c', '#fdbf6f', '#ff7f00', '#cab2d6', '#a6cee3', '#b2df8a', 'k']
for l in range(1,2):
h = demo_header()
nx, ny = h["naxis1"], h["naxis2"],
labtypes = [
("hms", {}),
("h", {}),
("dms", {}),
("absdeg", {}),
("delta", dict(offset=h["crval1"], latitude=h["crval2"])),
("arcmin", dict(offset=h["crval1"], latitude=h["crval2"])),
("manual", dict(locs=[70, 65, 60], labels=["A", "B", "C"])),
]
n = len(labtypes)
plt.figure(figsize=(5, 1 * n))
for i, (labtyp1, labtyp1_kwargs) in enumerate(labtypes):
ax = pywcsgrid2.subplot(n, 1, i + 1, header=h, frameon=False)
ax.set_aspect(1.)
ax.set_xlim(-0.5, nx - 0.5)
ax.set_ylim(-0.5, 4.5)
ax.axis["left", "right", "top"].set_visible(False)
ax.annotate(labtyp1, (0., 0), (0, 2),
xycoords="axes fraction",
textcoords="offset points")
ax.set_ticklabel1_type(labtyp1, **labtyp1_kwargs)
ax.set_default_label(labtyp1, None)
plt.show()
gh2 = ax_h.get_grid_helper()
gh2.set_ticklabel1_type("absval", scale=0.001, nbins=5)
ax_h.axis["bottom"].label.set_text(r"$v_{\mathrm{LSR}}$ [km s$^{-1}$]")
ax_v.axis["left"].label.set_text(r"$v_{\mathrm{LSR}}$ [km s$^{-1}$]")
ax_h.set(xlim=(kcut1,kcut2+1))
ax_v.set(ylim=(kcut1,kcut2+1))
fname_o = "P-V_area.eps"
savefig(fname_o, bbox_inches='tight')
#
if 0:
gh1 = pywcsgrid2.GridHelperSimple(wcs=co_header, axis_nums=[2, 0])
gh2 = pywcsgrid2.GridHelperSimple(wcs=co_header, axis_nums=[2, 1])
fig_n = figure(2)
ax1 = pywcsgrid2.subplot(211, grid_helper=gh1)
im1 = ax1.imshow(vv1.transpose(), origin='lower', aspect='auto')
ax2 = pywcsgrid2.subplot(212, grid_helper=gh2)
im2 = ax2.imshow(vv2, origin='lower', aspect='auto')
fname_o_new = 'P-V_%f_%f.eps'%(pick_x,pick_y)
ax1.set(xlim=(kcut1,kcut2+1))
ax2.set(xlim=(kcut1,kcut2+1))
cont1 = ax1.contour(vv1.transpose(), levels=arange(2.,6.,2.), colors='w',
linewidths=0.5)
cont2 = ax2.contour(vv2, levels=arange(2.,6.,2.), colors='w',
linewidths=0.5)
savefig(fname_o_new, bbox_inches='tight')
h = demo_header()
nx, ny = h["naxis1"], h["naxis2"],
labtypes = [("hms", {}),
("h", {}),
("dms", {}),
("absdeg", {}),
("delta", dict(offset=h["crval1"], latitude=h["crval2"])),
("arcmin", dict(offset=h["crval1"], latitude=h["crval2"])),
("manual", dict(locs=[70, 65, 60], labels=["A","B","C"])),
]
n = len(labtypes)
plt.figure(figsize=(5, 1*n))
for i, (labtyp1, labtyp1_kwargs) in enumerate(labtypes):
ax = pywcsgrid2.subplot(n, 1, i+1, header=h, frameon=False)
ax.set_aspect(1.)
ax.set_xlim(-0.5, nx-0.5)
ax.set_ylim(-0.5, 4.5)
ax.axis["left","right","top"].set_visible(False)
ax.annotate(labtyp1, (0.,0), (0, 2),
xycoords="axes fraction",
textcoords="offset points")
ax.set_ticklabel1_type(labtyp1, **labtyp1_kwargs)
ax.set_default_label(labtyp1, None)
plt.show()
def remove_field_overlaps(photfiles,
drzs,
footprints,
base_fits=m31,
plot=False,
fake=False,
matched_only=False,
qc=True):
'''
This only works for field 3,4,5,9,10,11,15,16,17. It can be adapted for
all fields though, 3 is now the top left field, 9 and 15 are the left most
fields on the next rows.
footprints is a region file from exposure_coverage.csv
pre-processing:
1. grep UVIS exposure_coverage.csv > poop
2. grep F336W poop > poop1
3. grep 550 poop1 > poop2
4. Then some hard coding in the file:
I decided to update the exposure footprints file since full bricks are
on their way...
the problem is that chip 1 vs chip 2 flip their orientation on fields 1,2,3
from those of 4,5,6 such that chip 1 of field 3 overlaps with chip 1 of
field 4 while chip 2 of field 4 and 5 overlap with chip 1 of 5 and 6.
I swapped the chip values of 1,2,3,7,8,9,13,14,15 so it's consistent with
4,5,6,10,11,12,16,17,18.
5. Then I cut out all fields BUT 3,4,5,9,10,11,15,16,17
Hacked adaptability for all.fake files. If .fake files are coming in, just
say they are photfiles and set the fake kwarg to True.
For all.fake: need to run twice (ug). Once with qc=True and once with qc=False.
adding more fields? Check the if statements and the [i-*]s.
'''
clrs = discrete_colors(len(photfiles))
if plot == True:
ax = pywcsgrid2.subplot(111, header=f_header)
ax.grid()
ax.imshow(f_data, cmap=cm.gray, origin="lower")
r = pyregion.open(footprints).as_imagecoord(header=f_header)
# a is the left chip of the field (see # 4 in comment)
r1a = pyregion.ShapeList(
[rr for rr in r if re.search('550-1', rr.attr[1].get("tag"))])
# b is the right chip of the field (see # 4 in comment)
r1b = pyregion.ShapeList(
[rr for rr in r if re.search('550-2', rr.attr[1].get("tag"))])
patch_list1a, artist_list1a = r1a.get_mpl_patches_texts()
patch_list1b, artist_list1b = r1b.get_mpl_patches_texts(fixed_color)
if plot == True:
for p in patch_list1a + patch_list1b:
ax.add_patch(p)
for i in range(len(photfiles)):
inds1, inds2 = '', ''
print 'reading', photfiles[i]
if fake == True:
if qc == False:
x, y, mag1, mag2, xxx, yyy, qc_ind1, qc_ind2 = read_all_fake(
photfiles[i])
else:
x, y, mag1, mag2, xxx, yyy, qc_ind1, qc_ind2 = read_all_fake(
photfiles[i], mag1_cut=999., mag2_cut=999.)
else:
x, y, mag1, mag2, qc_ind1, qc_ind2, matches = read_phot(
photfiles[i])
# HACK!!! This is to only use the phot stars found in both filters (with qc)
if matched_only == True:
qc_ind1 = matches
qc_ind2 = matches
d = pyfits.open(drzs[i])
# get out of image coords
ra, dec = XY2radec(d, x, y)
# currect for chip gap and edges:
within = in_chips(ra, dec, patch_list1a[i], patch_list1b[i])
# field 3 is the chosen one to sit on top:
if re.search('F03', photfiles[i]):
inds1 = match_2inds(within, qc_ind1)
inds2 = match_2inds(within, qc_ind2)
if qc == False:
inds1 = within
inds2 = within
Mag1 = mag1[inds1]
Mag2 = mag2[inds2]
Ra1 = ra[inds1]
Dec1 = dec[inds1]
Ra2 = ra[inds2]
Dec2 = dec[inds2]
# top row will only overlap with one to the right
elif re.search('F0', photfiles[i]) or re.search(
'F05', photfiles[i]) or re.search('F06', photfiles[i]):
not_overlapped = poly_over_under(ra, dec, patch_list1b[i - 1],
patch_list1a[i])
inds1 = match_3inds(within, qc_ind1, not_overlapped)
inds2 = match_3inds(within, qc_ind2, not_overlapped)
if qc == False:
inds1 = match_2inds(within, not_overlapped)
inds2 = match_2inds(within, not_overlapped)
# next two rows will overlap above and to the right
else:
# correct for overlap by field in above row
# by left chip
top_a_a = poly_over_under(ra, dec, patch_list1a[i - 4],
patch_list1a[i])
top_a_b = poly_over_under(ra, dec, patch_list1a[i - 4],
patch_list1b[i])
top_b_b = poly_over_under(ra, dec, patch_list1b[i - 4],
patch_list1b[i])
tops = list(set(top_a_a) & set(top_a_b) & set(top_b_b))
# correct for overlap by field to the left, F09 is the left-most.
if re.search('F09', photfiles[i]) or re.search(
'F15', photfiles[i]):
inds1 = list(set(qc_ind1) & set(within) & set(tops))
inds2 = list(set(qc_ind2) & set(within) & set(tops))
else:
top_left_b_a = poly_over_under(ra, dec, patch_list1b[i - 5],
patch_list1a[i])
the_left = poly_over_under(ra, dec, patch_list1b[i - 1],
patch_list1a[i])
inds1 = list(
set(qc_ind1) & set(within) & set(tops) & set(top_left_b_a)
& set(the_left))
inds2 = list(
set(qc_ind2) & set(within) & set(tops) & set(top_left_b_a)
& set(the_left))
if qc == False:
inds1 = list(
set(within) & set(tops) & set(top_left_b_a)
& set(the_left))
inds2 = list(
set(within) & set(tops) & set(top_left_b_a)
& set(the_left))
if plot == True:
ax['fk5'].plot(ra[inds1],
dec[inds1],
'o',
ms=4,
mec=clrs[i],
color=clrs[i])
Mag1 = np.append(Mag1, mag1[inds1])
Mag2 = np.append(Mag2, mag2[inds2])
Ra1 = np.append(Ra1, ra[inds1])
Dec1 = np.append(Dec1, dec[inds1])
Ra2 = np.append(Ra2, ra[inds2])
Dec2 = np.append(Dec2, dec[inds2])
return Ra1, Dec1, Ra2, Dec2, Mag1, Mag2
def _make_axis(self):
if self.fig is None:
self.fig = plt.figure()
if self.cmap is None:
self.cmap = plt.cm.gray_r
if self.twin and not self.right:
if self.gs:
gs = self.gs
else:
gs = 121
self.ax = pywcsgrid2.subplot(gs, wcs=self.h1, aspect=self.aspect,adjustable='box',)
elif self.twin and self.right:
if self.gs:
gs = self.gs
else:
gs = 122
self.ax = pywcsgrid2.subplot(gs, wcs=self.h1, aspect=self.aspect,adjustable='box')
elif self.gs:
if 'PACS' in self.filename:
self.ax = plt.subplot(self.gs,projection=self.w0)
else:
self.ax = pywcsgrid2.subplot(self.gs, wcs=self.h1, aspect=self.aspect,adjustable='box')
#if self.h0['CTYPE1'] == 'GLON-CAR':
# self.ax.set_display_coord_system('fk5')
else:
self.ax = pywcsgrid2.axes(wcs=self.h1, aspect=self.aspect) #aspect=1
self.ax.set_xlim(-self.nx/4, 5*self.nx/4)
self.ax.set_ylim(-self.ny/4, 5*self.ny/4)
if self.interp is None:
if 'PACS' in self.filename:
self.im = self.ax.imshow(self.data,origin='lower',vmin=self.vmin,vmax=self.vmax,cmap=self.cmap,norm=self.norm)
else:
self.im = self.ax[self.h0].imshow_affine(self.data,origin='lower',vmin=self.vmin,vmax=self.vmax,cmap=self.cmap,norm=self.norm)
else:
self.im = self.ax[self.h0].imshow(self.data,origin='lower',vmin=self.vmin,vmax=self.vmax,cmap=self.cmap,interpolation=self.interp,norm=self.norm)
if self.xlim:
self.ax.set_xlim(self.xlim)
if self.ylim:
self.ax.set_ylim(self.ylim)
self.ax.grid(color='black', alpha=0.2, linestyle='dashed')
if self.right:
self.ax.axis['left'].major_ticklabels.set_visible(False)
#self.ax.axis['right'].major_ticklabels.set_visible(True)
self.ax.axis['right'].label.set_text(r"$\delta_{2000}$")
plt.setp(self.ax.axis['right'].label,fontsize=14)
self.ax.axis['left'].label.set_text('')
else:
try:
plt.setp(self.ax.axis['left'].label,fontsize=14)
except TypeError:
plt.setp(self.ax.yaxis.label,fontsize=14)
try:
plt.setp(self.ax.axis['bottom'].label,fontsize=14)
except TypeError:
plt.setp(self.ax.xaxis.label,fontsize=14)
return self.ax
def plot_four(epic,
filename,
coadd,
maskmap,
maskheader,
init,
coords,
sources,
ap=None,
campaign=4):
logging.info("Plot four %s", epic)
logging.debug(base_path)
fig = plt.figure(figsize=(8, 8))
hdu2 = fits.open(filename)
dataheader = hdu2[1].header
hdu2.close()
keysel = np.empty(13, "S6")
keysel[:] = "binary"
w2 = WCS(dataheader, colsel=[5], keysel=keysel)
# Plot DSS/SDSS image if available
dssname = "{0}/ss_finders/{1}d/fc_{1}d_dssdss2red.fits".format(
base_path, epic)
sdssname = "{0}/ss_finders/{1}d/fc_{1}d_sdss (dr7)z.fits".format(
base_path, epic)
if os.path.exists(dssname):
# Open image file
hdu = fits.open(dssname)
pix, hdr = hdu[0].data, hdu[0].header
hdu.close()
elif os.path.exists(sdssname):
# Open image file
hdu = fits.open(sdssname)
pix, hdr = hdu[0].data, hdu[0].header
hdu.close()
else:
pix = None
# Set up the GridHelper to merge the axes
grid_helper = pywcsgrid2.GridHelper(wcs=w2)
# Plot the pixel stamp as usual, except with the WCS
ax1 = pywcsgrid2.subplot(221,
grid_helper=grid_helper,
aspect=1,
adjustable="box-forced")
ax1.matshow(coadd, origin="lower", cmap='Greys', norm=colors.LogNorm())
if ap is not None:
ap_circle = plt.Circle(coords,
ap,
color="k",
fill=False,
linewidth=1.5)
ax1.add_artist(ap_circle)
#ax1.axis["bottom","left","top","right"].toggle(ticklabels=False)
if pix is not None:
median = np.median(pix)
stdev = np.std(pix)
levels = np.linspace(median + stdev, np.max(pix), 5)
ax1[hdr].contour(pix, colors="r", levels=levels)
# ax1.set_ticklabel_type("delta","delta",
# dict(offset=np.float64(dataheader["1CRVL5"]),
# latitude=np.float64(dataheader["2CRVL5"])))
# Plot the DSS image rotated into the same frame as the pixel stamp
ax2 = pywcsgrid2.subplot(222,
grid_helper=grid_helper,
aspect=1,
adjustable="box-forced",
sharex=ax1,
sharey=ax1)
ax2[hdr].imshow_affine(pix,
origin="lower",
cmap='Greys',
norm=colors.LogNorm())
median2 = np.median(coadd)
stdev2 = np.std(coadd)
levels2 = np.linspace(median, np.max(coadd), 5)
#ax2[w2].contour(coadd,3, colors="r")
# ax2.set_ticklabel_type("delta","delta")
# ax2.axis["bottom","left","top","right"].toggle(ticklabels=False)
# Then the pixel motion across the CCD
ax3 = plt.subplot(223)
divider3 = make_axes_locatable(ax3)
cax3 = divider3.append_axes("bottom", size="5%", pad=0.35)
stamp(coadd, maskmap, ax=ax3, cmap="gray")
lcs = at.read("{}/lcs/ktwo{}-c0{}.csv".format(base_path, epic, campaign))
ax3.set_xlim(np.floor(min(lcs["x"])), np.ceil(max(lcs["x"])))
ax3.set_ylim(np.floor(min(lcs["y"])), np.ceil(max(lcs["y"])))
xyt = ax3.scatter(lcs["x"],
lcs["y"],
c=lcs["t"],
edgecolor="none",
alpha=0.5,
vmin=np.percentile(lcs["t"], 5),
vmax=np.percentile(lcs["t"], 95),
cmap="gnuplot")
cbar_ticks = np.asarray(np.percentile(lcs["t"], np.arange(10, 100, 40)),
int)
cbar1 = fig.colorbar(xyt,
cax=cax3,
ticks=cbar_ticks,
orientation="horizontal")
cbar1.set_label("Time (d)", fontsize="small")
# Then sky coordinates with the object position overlaid
ax4 = plt.subplot(224)
plot_chips(ax4, campaign)
setup_k2_axes(ax4)
plt.plot(maskheader["RA_OBJ"],
maskheader["DEC_OBJ"],
'*',
color="Purple",
ms=25,
alpha=0.8)
plt.setp(ax4.get_xticklabels()[::2], visible=False)
plt.setp(ax4.get_yticklabels()[::2], visible=False)
plt.suptitle("EPIC {}".format(epic)) #, fontsize="large")
#plt.tight_layout()
plt.subplots_adjust(top=0.95, hspace=0.5, wspace=0.8)
def line_stages(sname, linename, fancyname=None,
stampdir="Stamps", drange=None, sky=0,
pmax=None, psmooth=None, doublet=False):
"""
Plot all the stages in the reduction of a normal or doublet line
"""
SINGLET_TABLE = [
["", 0, 231, "Original"],
["-nc", 0, 232, "Line"],
["-nc", "CONT", 235, "Continuum"],
["-nc", "PROP", 233, "Proplyd"],
["-nc", "NEB", 236, ["", "Sky + "][sky] + "Nebula"]]
DOUBLET_TABLE = [
["", 0, 241, "Original"],
["-nc", 0, 242, "Line"],
["-nc", "CONT", 246, "Continuum"],
["-nc-dd", 0, 243, "Deconvolved"],
["-nc-dd", "PROP", 244, "Proplyd"],
["-nc-dd", "NEB", 248, "Nebula"]]
if doublet:
table = DOUBLET_TABLE
ncolumns = 4
outid = "-doublet"
else:
table = SINGLET_TABLE
ncolumns = 3
outid = "-line"
stampname = os.path.join(stampdir,
"-".join([sname, linename, "stamp"]))
fig = plt.figure(figsize=[4*ncolumns, 7])
h = None
for suff, index, win, title in table:
hdu = pyfits.open(stampname + suff + ".fits")[index]
if index != "CONT":
h = hdu.header
d = hdu.data
ax = pywcsgrid2.subplot(win, header=h)
im = ax.imshow(d, vmin=drange[0], vmax=drange[1],
origin="low", cmap=plt.cm.gray_r,
aspect="auto",
interpolation="nearest")
if index == "PROP" and pmax is not None:
if psmooth is None:
pd = d
else:
pd = ni.gaussian_filter(d.astype("d"), psmooth/FWHM)
cs = ax.contour(pd, pmax*2**(-0.5*np.arange(NPCONT)),
colors="r", alpha=0.5)
plt.clabel(cs, cs.levels[::2],
inline=True, fmt="%.0d", inline_spacing=1, fontsize=6)
ax.set_ticklabel2_type("arcsec", scale=1./3600, nbins=8)
ax.set_ticklabel1_type("absval", scale=1./1000, nbins=7)
ax.set_xlim(*kms2xpix(h, [-30.0, 60.0]))
ax.set_title(title)
ax.grid()
ax.axis["bottom"].label.set_text(
r"$V_{\mathrm{top}}\ \mathrm{(km\,s^{-1})}$")
# Make a separate axis on the second row just for the line label
# Use 10 times as many horizontal divisions
ax = plt.subplot(2, 10*ncolumns, 10*ncolumns+1, frameon=False)
ax.axis("off")
ax.text(0.0, 0.5, fancyname or linename,
fontsize="x-large",
horizontalalignment='left',
verticalalignment='center',
transform=ax.transAxes)
# And another one for the color bar
cb_ax = plt.subplot(2, 10*ncolumns, 10*ncolumns+6)
cb = plt.colorbar(im, cax=cb_ax, orientation="vertical")
cb.set_label("Intensity")
fig.subplots_adjust(bottom=0.08, top=0.95,
left=0.05, right=0.99,
wspace=0.25, hspace=0.4)
fig.savefig(stampname + outid + "-stages.pdf")
plt.close(fig)
import matplotlib.pyplot as plt
from matplotlib.patheffects import withStroke
import pywcsgrid2
import pyfits
f = pyfits.open("Hen_3-519.Ha.fits")
fig = plt.figure(figsize=(8,4))
ax = pywcsgrid2.subplot(121, wcs=f[0].header)
ax.set_default_path_effects([withStroke(foreground="w", linewidth=3)])
im = ax.imshow(f[0].data, origin="lower", cmap="gray_r")
im.set_clim(2941, 15000)
ax.set(xlim=(149.5, 290.5),
ylim=(149.5, 295.5))
ax.axis[:].toggle(all=False)
it = ax.add_inner_title(r"(a) Hen 3-519 : H$\alpha$", loc=2)
it.patch.set_ec("none")
pixels_of_30_arcmin = 0.5/60./f[0].header["CD2_2"]
ax.add_size_bar(pixels_of_30_arcmin, r"$30^{\prime\prime}$", loc=8)
ax.add_compass(loc=4)
f2 = pyfits.open("G26.47+0.02.24um.fits")
