def pc_plot_extcenwid(tempcen, cenwid, binval, plotsdir="Plots", pcatype="<unknown>", maxp=25, prefix=""):
"""
Saves a few output png files of the PCA analysis for the target centroid/width definition
"""
npc = tempcen.shape[1]
pages, npp = get_dimen(npc,maxp=maxp)
x0=binval*np.arange(cenwid.shape[0])
# First calculate the min and max values for the plotting axes, to make sure they are all the same
ymin, ymax = None, None
"""
for i in xrange(npc):
w = np.where(tempcen[:,i]!=-999999.9)
if np.size(w[0]) == 0: continue
medv = np.median(tempcen[:,i][w])
madv = 1.4826*np.median(np.abs(medv-tempcen[:,i][w]))
vmin, vmax = medv-3.0*madv, medv+3.0*madv
tymin = min(vmin,np.min(cenwid[:,i]))
tymax = max(vmax,np.max(cenwid[:,i]))
if ymin is None: ymin = tymin
else:
if tymin < ymin: ymin = tymin
if ymax is None: ymax = tymax
else:
if tymax > ymax: ymax = tymax
"""
w = np.where(tempcen!=-999999.9)
if np.size(w[0]) != 0:
medv = np.median(tempcen[w])
madv = 1.4826*np.median(np.abs(medv-tempcen[w]))
vmin, vmax = medv-3.0*madv, medv+3.0*madv
tymin = min(vmin,np.min(cenwid))
tymax = max(vmax,np.max(cenwid))
if ymin is None: ymin = tymin
else:
if tymin < ymin: ymin = tymin
if ymax is None: ymax = tymax
else:
if tymax > ymax: ymax = tymax
# Check that ymin and ymax are set, if not, return without plotting
if ymin is None or ymax is None:
msgs.warn("{0:s} fits were not plotted".format(pcatype))
return
# Generate the plots
ndone=0
for i in xrange(len(pages)):
f, axes = plt.subplots(pages[i][1], pages[i][0])
ipx, ipy = 0, 0
for j in xrange(npp[i]):
if pages[i][1] == 1: ind = (ipx)
elif pages[i][0] == 1: ind = (ipy)
else: ind = (ipy,ipx)
w = np.where(tempcen[:,ndone]!=-999999.9)
if np.size(w[0]) != 0:
rowplt = binval*(0.5 + np.arange(tempcen.shape[0]))
axes[ind].plot(rowplt[w],tempcen[:,ndone][w],'bx')
axes[ind].plot(x0,cenwid[:,ndone],'r-')
axes[ind].axis([0,binval*tempcen.shape[0],ymin,ymax])
axes[ind].set_title("Order {0:d}".format(1+ndone))
ipx += 1
if ipx == pages[i][0]:
ipx = 0
ipy += 1
ndone += 1
# Delete the unnecessary axes
for j in xrange(npp[i],axes.size):
if pages[i][1] == 1: ind = (ipx)
elif pages[i][0] == 1: ind = (ipy)
else: ind = (ipy,ipx)
f.delaxes(axes[ind])
if ipx == pages[i][0]:
ipx = 0
ipy += 1
# Save the figure
if pages[i][1] == 1 or pages[i][0] == 1: ypngsiz = 11.0/axes.size
else: ypngsiz = 11.0*axes.shape[0]/axes.shape[1]
f.set_size_inches(11.0, ypngsiz)
f.tight_layout()
if prefix != "":
f.savefig("{0:s}/{1:s}_PCA_{2:s}_page-{3:d}.png".format(plotsdir,prefix,pcatype,i+1), dpi=200, orientation='landscape')
else:
f.savefig("{0:s}/PCA_{1:s}_page-{2:d}.png".format(plotsdir,pcatype,i+1), dpi=200, orientation='landscape')
f.clf()
del f
return
def pc_plot_extcenwid(tempcen,
cenwid,
binval,
plotsdir="Plots",
pcatype="<unknown>",
maxp=25,
prefix=""):
"""
Saves a few output png files of the PCA analysis for the target centroid/width definition
"""
npc = tempcen.shape[1]
pages, npp = get_dimen(npc, maxp=maxp)
x0 = binval * np.arange(cenwid.shape[0])
# First calculate the min and max values for the plotting axes, to make sure they are all the same
ymin, ymax = None, None
"""
for i in xrange(npc):
w = np.where(tempcen[:,i]!=-999999.9)
if np.size(w[0]) == 0: continue
medv = np.median(tempcen[:,i][w])
madv = 1.4826*np.median(np.abs(medv-tempcen[:,i][w]))
vmin, vmax = medv-3.0*madv, medv+3.0*madv
tymin = min(vmin,np.min(cenwid[:,i]))
tymax = max(vmax,np.max(cenwid[:,i]))
if ymin is None: ymin = tymin
else:
if tymin < ymin: ymin = tymin
if ymax is None: ymax = tymax
else:
if tymax > ymax: ymax = tymax
"""
w = np.where(tempcen != -999999.9)
if np.size(w[0]) != 0:
medv = np.median(tempcen[w])
madv = 1.4826 * np.median(np.abs(medv - tempcen[w]))
vmin, vmax = medv - 3.0 * madv, medv + 3.0 * madv
tymin = min(vmin, np.min(cenwid))
tymax = max(vmax, np.max(cenwid))
if ymin is None: ymin = tymin
else:
if tymin < ymin: ymin = tymin
if ymax is None: ymax = tymax
else:
if tymax > ymax: ymax = tymax
# Check that ymin and ymax are set, if not, return without plotting
if ymin is None or ymax is None:
msgs.warn("{0:s} fits were not plotted".format(pcatype))
return
# Generate the plots
ndone = 0
for i in xrange(len(pages)):
f, axes = plt.subplots(pages[i][1], pages[i][0])
ipx, ipy = 0, 0
for j in xrange(npp[i]):
if pages[i][1] == 1: ind = (ipx)
elif pages[i][0] == 1: ind = (ipy)
else: ind = (ipy, ipx)
w = np.where(tempcen[:, ndone] != -999999.9)
if np.size(w[0]) != 0:
rowplt = binval * (0.5 + np.arange(tempcen.shape[0]))
axes[ind].plot(rowplt[w], tempcen[:, ndone][w], 'bx')
axes[ind].plot(x0, cenwid[:, ndone], 'r-')
axes[ind].axis([0, binval * tempcen.shape[0], ymin, ymax])
axes[ind].set_title("Order {0:d}".format(1 + ndone))
ipx += 1
if ipx == pages[i][0]:
ipx = 0
ipy += 1
ndone += 1
# Delete the unnecessary axes
for j in xrange(npp[i], axes.size):
if pages[i][1] == 1: ind = (ipx)
elif pages[i][0] == 1: ind = (ipy)
else: ind = (ipy, ipx)
f.delaxes(axes[ind])
if ipx == pages[i][0]:
ipx = 0
ipy += 1
# Save the figure
if pages[i][1] == 1 or pages[i][0] == 1: ypngsiz = 11.0 / axes.size
else: ypngsiz = 11.0 * axes.shape[0] / axes.shape[1]
f.set_size_inches(11.0, ypngsiz)
f.tight_layout()
if prefix != "":
f.savefig("{0:s}/{1:s}_PCA_{2:s}_page-{3:d}.png".format(
plotsdir, prefix, pcatype, i + 1),
dpi=200,
orientation='landscape')
else:
f.savefig("{0:s}/PCA_{1:s}_page-{2:d}.png".format(
plotsdir, pcatype, i + 1),
dpi=200,
orientation='landscape')
f.clf()
del f
return
def pc_plot(slf, inpar, ofit, maxp=25, pcadesc="", addOne=True):
"""
Saves quality control plots for a PCA analysis
"""
npc = inpar['npc']+1
pages, npp = get_dimen(npc, maxp=maxp)
x0 = inpar['x0']
ordernum = inpar['x0in']
x0fit = inpar['x0fit']
usetrc = inpar['usetrc']
hidden = inpar['hidden']
high_fit = inpar['high_fit']
nc = np.max(ordernum[usetrc])
# Loop through all pages and plot the results
ndone = 0
for i in xrange(len(pages)):
plt.clf()
f, axes = plt.subplots(pages[i][1], pages[i][0])
ipx, ipy = 0, 0
if i == 0:
if pages[i][1] == 1: ind = (0,)
elif pages[i][0] == 1: ind = (0,)
else: ind = (0,0)
axes[ind].plot(ordernum[usetrc], x0[usetrc], 'bx')
axes[ind].plot(ordernum, x0fit, 'k-')
amn, amx = np.min(x0fit), np.max(x0fit)
diff = x0[usetrc]-x0fit[usetrc]
tdiffv = np.median(diff)
mdiffv = 1.4826*np.median(np.abs(tdiffv-diff))
amn -= 2.0*mdiffv
amx += 2.0*mdiffv
mval = amn-0.15*(amx-amn)
dmin, dmax = tdiffv-2.0*mdiffv, tdiffv+2.0*mdiffv
diff = mval + diff*0.20*(amx-amn)/(dmax-dmin)
wign = np.where(np.abs(diff-np.median(diff))<4.0*1.4826*np.median(np.abs(diff-np.median(diff))))[0]
dmin, dmax = np.min(diff[wign]), np.max(diff[wign])
axes[ind].plot(ordernum[usetrc], diff, 'rx')
if addOne:
axes[ind].plot([0, nc+1], [mval,mval], 'k-')
axes[ind].axis([0, nc+1, dmin-0.5*(dmax-dmin), amx + 0.05*(amx-amn)])
else:
axes[ind].plot([0, nc], [mval, mval], 'k-')
axes[ind].axis([0, nc, dmin-0.5*(dmax-dmin), amx + 0.05*(amx-amn)])
axes[ind].set_title("Mean Value")
ipx += 1
if ipx == pages[i][0]:
ipx = 0
ipy += 1
npp[0] -= 1
for j in xrange(npp[i]):
if pages[i][1] == 1: ind = (ipx,)
elif pages[i][0] == 1: ind = (ipy,)
else: ind = (ipy, ipx)
axes[ind].plot(ordernum[usetrc], hidden[j+ndone,:], 'bx')
axes[ind].plot(ordernum, high_fit[:,j+ndone], 'k-')
vmin, vmax = np.min(hidden[j+ndone,:]), np.max(hidden[j+ndone,:])
if ofit[1+j+ndone] != -1:
cmn, cmx = np.min(high_fit[:,j+ndone]), np.max(high_fit[:,j+ndone])
diff = hidden[j+ndone,:]-high_fit[:,j+ndone][usetrc]
tdiffv = np.median(diff)
mdiffv = 1.4826*np.median(np.abs(tdiffv-diff))
cmn -= 2.0*mdiffv
cmx += 2.0*mdiffv
mval = cmn-0.15*(cmx-cmn)
dmin, dmax = tdiffv-2.0*mdiffv, tdiffv+2.0*mdiffv
#dmin, dmax = np.min(diff), np.max(diff)
diff = mval + diff*0.20*(cmx-cmn)/(dmax-dmin)
wign = np.where(np.abs(diff-np.median(diff))<4.0*1.4826*np.median(np.abs(diff-np.median(diff))))[0]
dmin, dmax = np.min(diff[wign]), np.max(diff[wign])
#vmin, vmax = np.min(hidden[j+ndone,:][wign]), np.max(hidden[j+ndone,:][wign])
axes[ind].plot(ordernum[usetrc], diff, 'rx')
axes[ind].plot([0, 1+nc], [mval, mval], 'k-')
# ymin = np.min([(3.0*dmin-dmax)/2.0,vmin-0.1*(vmax-dmin),dmin-0.1*(vmax-dmin)])
# ymax = np.max([np.max(high_fit[:,j+ndone]),vmax+0.1*(vmax-dmin),dmax+0.1*(vmax-dmin)])
ymin = dmin-0.5*(dmax-dmin)
ymax = cmx + 0.05*(cmx-cmn)
if addOne: axes[ind].axis([0, nc+1, ymin, ymax])
else: axes[ind].axis([0, nc, ymin, ymax])
else:
if addOne: axes[ind].axis([0, nc+1, vmin-0.1*(vmax-vmin), vmax+0.1*(vmax-vmin)])
else: axes[ind].axis([0, nc, vmin-0.1*(vmax-vmin), vmax+0.1*(vmax-vmin)])
axes[ind].set_title("PC {0:d}".format(j+ndone))
ipx += 1
if ipx == pages[i][0]:
ipx = 0
ipy += 1
if i == 0: npp[0] = npp[0] + 1
# Delete the unnecessary axes
for j in xrange(npp[i], axes.size):
if pages[i][1] == 1: ind = (ipx,)
elif pages[i][0] == 1: ind = (ipy,)
else: ind = (ipy, ipx)
f.delaxes(axes[ind])
if ipx == pages[i][0]:
ipx = 0
ipy += 1
ndone += npp[i]
# Save the figure
if pages[i][1] == 1 or pages[i][0] == 1: ypngsiz = 11.0/axes.size
else: ypngsiz = 11.0*axes.shape[0]/axes.shape[1]
f.set_size_inches(11.0, ypngsiz)
if pcadesc != "":
pgtxt = ""
if len(pages) != 1:
pgtxt = ", page {0:d}/{1:d}".format(i+1, len(pages))
f.suptitle(pcadesc + pgtxt, y=1.02, size=16)
f.tight_layout()
slf._qa.savefig(dpi=200, orientation='landscape', bbox_inches='tight')
plt.close()
f.clf()
del f
return
def pc_plot(slf, inpar, ofit, maxp=25, pcadesc="", addOne=True):
"""
Saves quality control plots for a PCA analysis
"""
npc = inpar['npc'] + 1
pages, npp = get_dimen(npc, maxp=maxp)
x0 = inpar['x0']
ordernum = inpar['x0in']
x0fit = inpar['x0fit']
usetrc = inpar['usetrc']
hidden = inpar['hidden']
high_fit = inpar['high_fit']
nc = np.max(ordernum[usetrc])
# Loop through all pages and plot the results
ndone = 0
for i in xrange(len(pages)):
plt.clf()
f, axes = plt.subplots(pages[i][1], pages[i][0])
ipx, ipy = 0, 0
if i == 0:
if pages[i][1] == 1: ind = (0, )
elif pages[i][0] == 1: ind = (0, )
else: ind = (0, 0)
axes[ind].plot(ordernum[usetrc], x0[usetrc], 'bx')
axes[ind].plot(ordernum, x0fit, 'k-')
amn, amx = np.min(x0fit), np.max(x0fit)
diff = x0[usetrc] - x0fit[usetrc]
tdiffv = np.median(diff)
mdiffv = 1.4826 * np.median(np.abs(tdiffv - diff))
amn -= 2.0 * mdiffv
amx += 2.0 * mdiffv
mval = amn - 0.15 * (amx - amn)
dmin, dmax = tdiffv - 2.0 * mdiffv, tdiffv + 2.0 * mdiffv
diff = mval + diff * 0.20 * (amx - amn) / (dmax - dmin)
wign = np.where(
np.abs(diff - np.median(diff)) < 4.0 * 1.4826 *
np.median(np.abs(diff - np.median(diff))))[0]
dmin, dmax = np.min(diff[wign]), np.max(diff[wign])
axes[ind].plot(ordernum[usetrc], diff, 'rx')
if addOne:
axes[ind].plot([0, nc + 1], [mval, mval], 'k-')
axes[ind].axis([
0, nc + 1, dmin - 0.5 * (dmax - dmin),
amx + 0.05 * (amx - amn)
])
else:
axes[ind].plot([0, nc], [mval, mval], 'k-')
axes[ind].axis([
0, nc, dmin - 0.5 * (dmax - dmin), amx + 0.05 * (amx - amn)
])
axes[ind].set_title("Mean Value")
ipx += 1
if ipx == pages[i][0]:
ipx = 0
ipy += 1
npp[0] -= 1
for j in xrange(npp[i]):
if pages[i][1] == 1: ind = (ipx, )
elif pages[i][0] == 1: ind = (ipy, )
else: ind = (ipy, ipx)
axes[ind].plot(ordernum[usetrc], hidden[j + ndone, :], 'bx')
axes[ind].plot(ordernum, high_fit[:, j + ndone], 'k-')
vmin, vmax = np.min(hidden[j + ndone, :]), np.max(hidden[j +
ndone, :])
if ofit[1 + j + ndone] != -1:
cmn, cmx = np.min(high_fit[:, j + ndone]), np.max(
high_fit[:, j + ndone])
diff = hidden[j + ndone, :] - high_fit[:, j + ndone][usetrc]
tdiffv = np.median(diff)
mdiffv = 1.4826 * np.median(np.abs(tdiffv - diff))
cmn -= 2.0 * mdiffv
cmx += 2.0 * mdiffv
mval = cmn - 0.15 * (cmx - cmn)
dmin, dmax = tdiffv - 2.0 * mdiffv, tdiffv + 2.0 * mdiffv
#dmin, dmax = np.min(diff), np.max(diff)
diff = mval + diff * 0.20 * (cmx - cmn) / (dmax - dmin)
wign = np.where(
np.abs(diff - np.median(diff)) < 4.0 * 1.4826 *
np.median(np.abs(diff - np.median(diff))))[0]
dmin, dmax = np.min(diff[wign]), np.max(diff[wign])
#vmin, vmax = np.min(hidden[j+ndone,:][wign]), np.max(hidden[j+ndone,:][wign])
axes[ind].plot(ordernum[usetrc], diff, 'rx')
axes[ind].plot([0, 1 + nc], [mval, mval], 'k-')
# ymin = np.min([(3.0*dmin-dmax)/2.0,vmin-0.1*(vmax-dmin),dmin-0.1*(vmax-dmin)])
# ymax = np.max([np.max(high_fit[:,j+ndone]),vmax+0.1*(vmax-dmin),dmax+0.1*(vmax-dmin)])
ymin = dmin - 0.5 * (dmax - dmin)
ymax = cmx + 0.05 * (cmx - cmn)
if addOne: axes[ind].axis([0, nc + 1, ymin, ymax])
else: axes[ind].axis([0, nc, ymin, ymax])
else:
if addOne:
axes[ind].axis([
0, nc + 1, vmin - 0.1 * (vmax - vmin),
vmax + 0.1 * (vmax - vmin)
])
else:
axes[ind].axis([
0, nc, vmin - 0.1 * (vmax - vmin),
vmax + 0.1 * (vmax - vmin)
])
axes[ind].set_title("PC {0:d}".format(j + ndone))
ipx += 1
if ipx == pages[i][0]:
ipx = 0
ipy += 1
if i == 0: npp[0] = npp[0] + 1
# Delete the unnecessary axes
for j in xrange(npp[i], axes.size):
if pages[i][1] == 1: ind = (ipx, )
elif pages[i][0] == 1: ind = (ipy, )
else: ind = (ipy, ipx)
f.delaxes(axes[ind])
if ipx == pages[i][0]:
ipx = 0
ipy += 1
ndone += npp[i]
# Save the figure
if pages[i][1] == 1 or pages[i][0] == 1: ypngsiz = 11.0 / axes.size
else: ypngsiz = 11.0 * axes.shape[0] / axes.shape[1]
f.set_size_inches(11.0, ypngsiz)
if pcadesc != "":
pgtxt = ""
if len(pages) != 1:
pgtxt = ", page {0:d}/{1:d}".format(i + 1, len(pages))
f.suptitle(pcadesc + pgtxt, y=1.02, size=16)
f.tight_layout()
slf._qa.savefig(dpi=200, orientation='landscape', bbox_inches='tight')
plt.close()
f.clf()
del f
return
