def plot_hist(data):
if config.get('outputting', 'plot'):
pdf, bins, patches = plt.hist(data[300], arange(data[300].min(),
data[300].max(), 0.05), normed=True, facecolor='green', alpha=0.75)
#n, bins, patches = plt.hist(x, 50, normed=1, facecolor='green', alpha=0.75)
if config.get('outputting', 'txt'):
s= numpy.vstack((pdf,bins[0:len(bins)-1])).transpose()
plt.savetxt(pname+'_distribution' + analysisParams['txtext'], s,
fmt='%f %f')
def plot_by_parts(na270, na300, na450, na640):
if config.get('outputting', 'txt'):
plt.savetxt('na270.' + analysisParams['txtext'], na270, fmt='%0.6f %f')
plt.savetxt('na300.' + analysisParams['txtext'], na300, fmt='%0.6f %f')
plt.savetxt('na450.' + analysisParams['txtext'], na450, fmt='%0.6f %f')
plt.savetxt('na640.' + analysisParams['txtext'], na640, fmt='%0.6f %f')
if config.get('outputting', 'plot'):
#plot hardcoded settings
plt.subplots_adjust(top=0.95,hspace=0.4)
plt.subplot(221)
plot_vector(plt, '270', na270)
plt.subplot(222)
plot_vector(plt, '300', na300)
plt.subplot(223)
plot_vector(plt, '450', na450)
plt.subplot(224)
plot_vector(plt, '640', na640, 'sampling blocks')
plt.savefig(pname + '.' + analysisParams['figext'])
ax.errorbar(24*(kitt[0]-gtime[0]),kitt[1],kitt[2]/2.0,label='Kitt Peak',fmt='o',color='red',ecolor='red',ms=0,elinewidth=4,capsize=6)
plt.xlim(min(hours),max(hours))
plt.ylim(min(pwvsnow1),24)
plt.xlabel('Time from start (hr)')
plt.ylabel('PWVs (mm)')
if night == 'n20161204':
leg = ax.legend(loc='best',prop={'size':18})
ax.fill_between(hours,medPWVs + 3*pwvstd_hi,medPWVs-3*pwvstd_lo,facecolor='gray')
plt.subplots_adjust(bottom=0.19)
plt.text(1,20, night)
plt.savefig('paperplots/outputs/CAMALGPS_pwvnow_%s.eps' %night)
# Save PWVs
plt.savetxt('pwvs/PWVnow_%s_noA2.txt' %night, zip(gtime,pwvsnow1, pwvsnow2),header='time(JD) PWVSnow1 (mm) PWVSnow2')
# Plot against e/o
#--------------------------------------
# CAMAL VS GPS
ilap = np.where(((kitt[0]-gtime[0]) < (gtime[-1]-gtime[0])) & ((kitt[0]-gtime[0]) > 0))[0]
pwv2 = interpolate.splev(kitt[0],(knots,PWVcoeffs,3))
ilap3 = np.where(((azam[0]-gtime[0]) < (gtime[-1]-gtime[0])) & ((azam[0]-gtime[0]) > 0))[0]
pwv3 = interpolate.splev(azam[0],(knots,PWVcoeffs,3))
plt.figure(100)
plt.plot(pwv2[ilap],kitt[1][ilap],'bo',label='Kitt Peak')
plt.plot(pwv3[ilap3],azam[1][ilap3],'g+',label='Amado')
plt.plot([0,20],[0,20],'k--')
#plt.legend()
def extract_percentile(mydir="20160826",
myname="PTF16fgz.npy",
plot=True,
write=True,
maxperc=30):
'''
Test to extract only the spaxels with higher signal to noise.
'''
def get_header(myfile):
header = ""
with open(myfile) as f:
ls = f.readlines()
for li in ls:
if (li.strip().startswith("#")):
header += li.replace("#", "")
return header
header = get_header("/scr2/sedmdrp/redux/%s/%s" %
(mydir, myname.replace(".npy", "_SEDM.txt")))
#Extraction parameters (chosen spaxels)
E, meta = np.load("/scr2/sedmdrp/redux/%s/%s" % (mydir, myname))
#Extraction spectra
Es = np.load("/scr2/sedmdrp/redux/%s/sp_%s" % (mydir, myname))
#EsA = np.load("/scr2/sedmdrp/redux/%s/sp_A_%s"%(mydir, myname))
#EsB = np.load("/scr2/sedmdrp/redux/%s/sp_B_%s"%(mydir, myname))
std = np.load("/scr2/sedmdrp/redux/%s/std-correction.npy" % mydir)
dome = np.load("/scr2/sedmdrp/redux/%s/dome.npy" % mydir)
mask = ~np.isnan(std[0]["correction"])
waves = std[0]["nm"][mask]
wmask = (waves > 500) * (waves < 900)
for percent in np.arange(00, maxperc, 10):
Aflux = []
stdA = []
for spax in Es[0]['object_spaxel_ids_A']:
try:
l1, f1 = E[spax].get_flambda()
lmask = (l1 > 500) * (l1 < 900)
Aflux.append(np.nansum(f1[lmask]))
stdA.append(np.std(f1[mask]))
except:
pass
maskA = np.array(Aflux) > np.percentile(Aflux, percent)
Bflux = []
stdB = []
for spax in Es[0]['object_spaxel_ids_B']:
try:
l1, f1 = E[spax].get_flambda()
lmask = (l1 > 500) * (l1 < 900)
Bflux.append(-1 * np.nansum(f1[lmask]))
stdB.append(np.std(f1[mask]))
except:
pass
maskB = np.array(Bflux) > np.percentile(Bflux, percent)
medstdA = np.median(stdA)
medstdB = np.median(stdB)
Aspec = []
Bspec = []
for spax in Es[0]['object_spaxel_ids_A'][maskA]:
try:
l1, f1 = E[spax].get_flambda()
d1, df1 = dome[0][spax].get_flambda()
mask_outliers = np.repeat(
True, len(l1)) #np.abs(f1 - np.median(f1))<8*medstdA
specInt = interpolate.interp1d(l1[mask_outliers],
f1[mask_outliers],
kind="linear",
bounds_error=False)
#f1 = f1/df1
plt.plot(waves, specInt(waves), "r-", alpha=0.05)
Aspec.append(specInt(waves))
except IOError:
pass
for spax in Es[0]['object_spaxel_ids_B'][maskB]:
try:
l2, f2 = E[spax].get_flambda()
mask_outliers = np.abs(f1 - np.median(f1)) < 4 * medstdB
specInt = interpolate.interp1d(l2[mask_outliers],
f2[mask_outliers],
kind="linear",
bounds_error=False)
d2, df2 = dome[0][spax].get_flambda()
#f1 = f1/df1
plt.plot(waves, specInt(waves), "b-", alpha=0.05)
Bspec.append(specInt(waves))
except:
pass
Aspec = np.array(Aspec)
Bspec = np.array(Bspec)
sp = interpolate.interp1d(waves,
std[0]["correction"][mask],
kind="linear",
bounds_error=False)
#spec = spec / np.max(spec) * np.max(np.nanmedian(Aspec, axis=0))
correction = sp(waves)
specA = interpolate.interp1d(waves,
np.nansum(Aspec, axis=0),
kind="linear",
bounds_error=False)
specB = interpolate.interp1d(waves,
np.nansum(Bspec, axis=0),
kind="linear",
bounds_error=False)
specSum = (specA(waves) - specB(waves))
specSum = specSum / np.max(specSum[wmask]) * np.max(
np.nanmedian(Aspec, axis=0))
spec = (specA(waves) - specB(waves)) * (correction
) #/np.median(correction))
spec = spec / np.max(spec[wmask]) * np.max(np.nanmedian(Aspec, axis=0))
writemask = (waves > 400) * (waves < 950)
if (write):
plt.savetxt("/home/nblago/classifications/%s/%d_%s" %
(mydir, percent, myname.replace(".npy", ".txt")),
np.array([
np.array(waves[writemask][::-1]) * 10,
spec[writemask][::-1]
]).T,
fmt="%.1f %.4e",
header=header)
if (plot):
plt.plot(waves,
np.nanmedian(Aspec, axis=0),
"r-",
lw=2,
label="Median A")
plt.plot(waves,
np.nansum(Aspec, axis=0) *
np.max(np.nanmedian(Aspec, axis=0)) /
np.max(np.nansum(Aspec, axis=0)),
"m--",
lw=2,
ls="--",
label="Sum A")
plt.plot(waves,
-1 * np.nanmedian(Bspec, axis=0),
"g-",
lw=2,
label="Median B")
plt.plot(waves,
-1 * np.nansum(Bspec, axis=0) *
np.max(np.nanmedian(Bspec, axis=0)) /
np.max(np.nansum(Bspec, axis=0)),
"b--",
lw=2,
ls="--",
label="Sum B")
plt.plot(
waves,
np.nansum(Aspec, axis=0) * np.max(np.nanmedian(
Aspec, axis=0)) / np.max(np.nansum(Aspec, axis=0)) -
np.nanmedian(Aspec, axis=0),
lw=1,
label="Sum - Median A")
plt.plot(waves,
-1 * np.nansum(Bspec, axis=0) *
np.max(np.nanmedian(Bspec, axis=0)) /
np.max(np.nansum(Bspec, axis=0)) +
np.nanmedian(Bspec, axis=0),
lw=1,
label="Sum - Median B")
plt.plot(waves[writemask], spec[writemask], color="orange", lw=2)
plt.plot(waves[writemask], specSum[writemask], color="cyan", lw=2)
plt.legend(loc="best", frameon=False)
plt.show()
if 1:
if os.path.isfile(BACKGROUND_FILE):
print 'reading background.txt'
background = pylab.loadtxt(BACKGROUND_FILE)
else:
print 'getting new background image for equalization'
numBackground = 5
background = pylab.zeros((768, ))
for i in range(0, numBackground):
print i
data = reader.getData()
background = background + data
background = background / numBackground
print
pylab.savetxt('background.txt', background)
delta = 500.0 - background
else:
print 'background subtraction disabled'
delta = 0
i = 0
while 1:
data = reader.getData()
if data is None:
continue
data = data + delta
if i == 0:
pylab.figure(1)
h_line, = pylab.plot(data, linewidth=2)
h_level, = pylab.plot([0], [0], 'or')
def plot_by_temp(by_temp):
plt.savetxt('by_temp.csv', by_temp, fmt='%f %f')
plot_vector(plt, 'Rg(Temperature)', by_temp, 'Temperature')
plt.savefig('by_temp.png')
"ecoli": ("BIOGRID-ORGANISM-Escherichia_coli_K12_W3110-3.5.166.tab2.txt", None),
"celegans": ("BIOGRID-ORGANISM-Caenorhabditis_elegans-3.5.166.tab2.txt", 8),
"athaliana": ("BIOGRID-ORGANISM-Arabidopsis_thaliana_Columbia-3.5.166.tab2.txt", 8)
#"homosapiens": "BIOGRID-ORGANISM-Homo_sapiens-3.5.166.tab2.txt"
}
for i, f in enumerate(files.keys()):
if os.path.isfile(f + "_fuzzy.pdf"):
continue
graph = load.build_graph_from_ppin_file("huge/" + files[f][0])
p, lb, Nb = TFD.tfd_fuzzy(graph)
plt.savetxt(f + "_fuzzy.dat", np.stack((lb, Nb), axis=1))
plt.figure(i)
plt.loglog(lb, Nb, 'o')
x = np.linspace(min(np.log(lb)), max(np.log(lb)), 100)
plt.loglog(np.exp(x), np.exp(x * p[0] + p[1]), label="Slope: {:.3f}".format(p[0]))
plt.legend()
plt.savefig(f + "_fuzzy.pdf")
for i, f in enumerate(files.keys()):
lb, Nb = np.loadtxt(f + "_fuzzy.dat", unpack = True)
if files[f][1] is not None:
end = files[f][1]
else:
end = len(lb)
("BIOGRID-ORGANISM-Caenorhabditis_elegans-3.5.166.tab2.txt", 8),
"athaliana":
("BIOGRID-ORGANISM-Arabidopsis_thaliana_Columbia-3.5.166.tab2.txt", 8)
#"homosapiens": "BIOGRID-ORGANISM-Homo_sapiens-3.5.166.tab2.txt"
}
for i, f in enumerate(files.keys()):
if os.path.isfile(f + "_greedy.pdf"):
continue
graph = load.build_graph_from_ppin_file("huge/" + files[f][0])
p, lb, Nb = TFD.tfd_greedy(graph)
plt.savetxt(f + "_greedy.dat", np.stack((lb, Nb), axis=1))
plt.figure(i)
plt.loglog(lb, Nb, 'o')
x = np.linspace(min(np.log(lb)), max(np.log(lb)), 100)
plt.loglog(np.exp(x),
np.exp(x * p[0] + p[1]),
label="Slope: {:.3f}".format(p[0]))
plt.legend()
plt.savefig(f + "_greedy.pdf")
for i, f in enumerate(files.keys()):
lb, Nb = np.loadtxt(f + "_greedy.dat", unpack=True)
if files[f][1] is not None:
end = files[f][1]