class PCAResult:
def __init__(self,filename):
self.filename=filename
self.exps=[]
self.bounds=[]
self.skip61=True
self.patches=[]
for i,corner in corners.items():
self.patches.append(Rectangle(corner,xsize,ysize))
self.outline = PatchCollection(self.patches,facecolor='None')
self.readFits()
self.grid = PatchCollection(self.bounds, cmap=matplotlib.cm.jet,
alpha=1,edgecolors=None,linewidths=0)
def readFits(self):
pyfile=pyfits.open(self.filename)
self.exps=pyfile['exps'].data['exposure']
self.ccd=pyfile['exps'].header['ccd']
self.nvar=pyfile['exps'].header['nvar']
self.nx=pyfile['exps'].header['nx']
self.ny=pyfile['exps'].header['ny']
self.type=pyfile['exps'].header['type']
self.grid=[]
self.xcell=[]
self.ycell=[]
for lx,ly,ux,uy in zip(pyfile['grid'].data['lower_x'],
pyfile['grid'].data['lower_y'],
pyfile['grid'].data['upper_x'],
pyfile['grid'].data['upper_y']):
self.grid.append((lx,ly,ux,uy))
if self.skip61:self.ccd-=1
# build the bounding box of ccds
for i in range(0,self.ccd*len(self.grid)):
iccd=i/len(self.grid)+1
icell=i%len(self.grid)
xl=self.grid[icell][0]
yl=self.grid[icell][1]
xu=self.grid[icell][2]
yu=self.grid[icell][3]
if self.skip61:
if iccd==61:iccd=62
#convert to focal plane coordinates
# for center of grid
xc=(xu-xl)/2.+xl
yc=(yu-yl)/2.+yl
bx,by=toFocal(iccd,xc,yc)
self.xcell.append(bx)
self.ycell.append(by)
# for grid boudaries
bcx,bcy=toFocal(iccd,xl,yl)
bcx2,bcy2=toFocal(iccd,xu,yu)
corner=[]
corner.append(bcx)
corner.append(bcy)
self.bounds.append(Rectangle(corner,bcx2-bcx,bcy2-bcy))
self.vec=pyfile['vec'].data
self.data=pyfile['data_mr'].data
self.coeff=pyfile['coeff'].data
self.singular=pyfile['singular'].data
def getPC(self,var,pc):
p=copy.deepcopy(self.grid)
if var>=self.nvar:
print "too high"
return False
#p.set_array(self.vec[var::self.nvar][pc])
p.set_array(self.vec[var*self.nx*self.ny*self.ccd:
(var+1)*self.nx*self.ny*self.ccd][pc])
return p
def getPC2(self,ax,var1,var2,pc,scale=-np.sqrt(2)):
#p=copy.deepcopy(self.grid)
if var1>=self.nvar or var2>=self.nvar:
print "too high"
return False
p1a=scale*self.vec[var1::self.nvar,pc]
p2a=scale*self.vec[var2::self.nvar,pc]
v1=[]
v2=[]
for p1,p2 in zip(p1a,p2a):
theta=np.math.atan2(p2,p1)/2
e=np.sqrt(p1**2+p2**2)
ce1=e*np.cos(theta)
ce2=e*np.sin(theta)
v1.append(ce1)
v2.append(ce2)
return ax.quiver(self.xcell,self.ycell,v1,v2,
angles='uv',scale=1./500,
units='xy',pivot='middle',width=1,headwidth=0.,
headlength=0., headaxislength=0.,color='blue')
for i, x in enumerate(X3[::2]):
X2.append(np.mean([X3[2*i], X3[2*i + 1]]))
X2 = np.arange(4)
xshift = [.2, -.2, .2, -.2]
X2 = [x + xshift[x] for x in X2]
print X2
ax2.set_ylabel('cov ' + betastr)
ax2.set_xticks(X2)
ax2data = [data_dict[reg][0] for reg in regs2]
ax2dots = [data_dict[reg][1] for reg in regs2]
colors2 = ['magenta', 'orange', 'steelblue', 'green']
patches = []
for i, dot in enumerate(ax2dots):
circ = mpatches.Ellipse(xy = (X2[i] + .075, ax2dots[i]), width = .05, height = .05, color = 'black')
patches.append(circ)
patches = PatchCollection(patches, match_original=True, zorder=10)
patches.set_zorder(20)
ax2.set_xticks([x + .075 for x in X2])
for i in range(4):
ax2.bar(X2[i], ax2data[i], color=colors2[i], width=0.125, alpha = .8, zorder=2)
ax2.add_line(lines.Line2D(line_xs, line_ys, linewidth=1, alpha=.5, color='black'))
ax2.add_collection(patches)
ax2.set_xticklabels(regs2)
ax2.set_xlim(X2[0] - .125, X2[-1] + .4)
X1 = []
for i, x in enumerate(X2[::2]):
X1.append(np.mean([X2[2*i], X2[2*i + 1]]))
ax1.set_ylabel('cov ' + betastr)
ax1.set_xticks(X1)