def plot_learning_curves(num_points,
X_train,
Y_train,
X_test,
Y_test,
positive_class=1,
negative_class=0):
train_set_sizes = [len(X_train) / k for k in range(num_points + 1, 0, -1)]
test_errors = []
training_errors = []
for training_set_size in train_set_sizes:
model = train(X_train, Y_train, training_set_size)
test_error = evaluate(model, X_test, Y_test, positive_class,
negative_class)
training_error = evaluate(model, X_train, Y_train, positive_class,
negative_class)
test_errors.append(test_error)
training_errors.append(training_error)
plt.plot(train_set_sizes,
training_errors,
'bs-',
label='Training accuracy')
plt.plot(train_set_sizes, test_errors, 'g^-', label='Test accuracy')
plt.ylabel('Accuracy')
plt.xlabel('Number of training samples')
plt.title('Augmented Logistic Regression Learning Curve')
plt.legend(loc='lower right')
plt.savefig('../Figures/accuracyPlotAugmented.png', dpi=100)
pylab.show()
def plot_sdss(cat_path):
for catfile in find_files(cat_path, "*merged+sdss.txt"):
# for now ignore the channel 2 files
if catfile.split('/')[-1].split('_')[1] != '1':
continue
print("\nreading catalog: {}".format(catfile))
df = pd.read_table(catfile, sep=' ')
# get rid of negative flux sources, if any
df = df[df.flux > 0]
# convert to magnitudes
mags = spz_jy_to_mags(df.flux * 1e-3, 1)
# print counts per magnitude bin
for i in range(10, 15):
sc = ((df.cl == 3) & (mags > i) & (mags < i + 1)).sum()
xc = ((df.xsc == 1) & (mags > i) & (mags < i + 1)).sum()
msg = "{}th to {}th mag: {} SDSS galaxy sources, {} 2MASS XSC sources"
print(msg.format(i, i + 1, sc, xc))
# print number of sources agreed upon
agree = ((df.xsc == 1) & (df.cl == 3)).sum()
disagree = ((df.xsc == 1) & (df.cl == 6)).sum()
na = ((df.xsc == 1) & (df.cl == 0)).sum()
msg = "{} 2MASS XSC sources classified as galaxies by SDSS"
print(msg.format(agree))
msg = "{} 2MASS XSC sources classified as stars by SDSS"
print(msg.format(disagree))
msg = "{} 2MASS XSC sources not matched to SDSS"
print(msg.format(na))
# plot normed histograms of 2MASS XSC and SDSS galaxy magnitudes
xsc_gals = (mags > 10) & (mags < 15) & (df.xsc == 1)
sdss_gals = (mags > 10) & (mags < 15) & (df.cl == 3)
# mags[xsc_gals].hist(label='2MASS XSC', normed=True)
# mags[sdss_gals].hist(label='SDSS galaxies', normed=True)
plt.hist([mags[xsc_gals].values, mags[sdss_gals].values],
bins=5,
label=['2MASS', 'SDSS'])
plt.xlabel('IRAC1 [mag]')
plt.ylabel('Number Count')
reg = catfile.split('/')[-1].split('_')[0]
plt.title('{} Extended Sources / Galaxies'.format(reg))
plt.legend(loc=2)
name = '{}_2mass_xsc_vs_sdss_hist.png'.format(reg)
outpath = '/'.join(catfile.split('/')[:-1] + [name])
plt.savefig(outpath, dpi=100)
plt.close()
print("created file: {}".format(outpath))
def plot_sdss(cat_path):
for catfile in find_files(cat_path, "*merged+sdss.txt"):
# for now ignore the channel 2 files
if catfile.split('/')[-1].split('_')[1] != '1':
continue
print("\nreading catalog: {}".format(catfile))
df = pd.read_table(catfile, sep=' ')
# get rid of negative flux sources, if any
df = df[df.flux > 0]
# convert to magnitudes
mags = spz_jy_to_mags(df.flux*1e-3, 1)
# print counts per magnitude bin
for i in range(10,15):
sc = ((df.cl == 3) & (mags > i) & (mags < i+1)).sum()
xc = ((df.xsc == 1) & (mags > i) & (mags < i+1)).sum()
msg = "{}th to {}th mag: {} SDSS galaxy sources, {} 2MASS XSC sources"
print(msg.format(i, i+1, sc, xc))
# print number of sources agreed upon
agree = ((df.xsc == 1) & (df.cl == 3)).sum()
disagree = ((df.xsc == 1) & (df.cl == 6)).sum()
na = ((df.xsc == 1) & (df.cl == 0)).sum()
msg = "{} 2MASS XSC sources classified as galaxies by SDSS"
print(msg.format(agree))
msg = "{} 2MASS XSC sources classified as stars by SDSS"
print(msg.format(disagree))
msg = "{} 2MASS XSC sources not matched to SDSS"
print(msg.format(na))
# plot normed histograms of 2MASS XSC and SDSS galaxy magnitudes
xsc_gals = (mags > 10) & (mags < 15) & (df.xsc == 1)
sdss_gals = (mags > 10) & (mags < 15) & (df.cl == 3)
# mags[xsc_gals].hist(label='2MASS XSC', normed=True)
# mags[sdss_gals].hist(label='SDSS galaxies', normed=True)
plt.hist([mags[xsc_gals].values, mags[sdss_gals].values],
bins=5, label=['2MASS', 'SDSS'])
plt.xlabel('IRAC1 [mag]')
plt.ylabel('Number Count')
reg = catfile.split('/')[-1].split('_')[0]
plt.title('{} Extended Sources / Galaxies'.format(reg))
plt.legend(loc=2)
name = '{}_2mass_xsc_vs_sdss_hist.png'.format(reg)
outpath = '/'.join(catfile.split('/')[:-1]+[name])
plt.savefig(outpath, dpi=100)
plt.close()
print("created file: {}".format(outpath))
def submit_time_histogram(arr):
"""
Use Matplotlib to plot a normalized histogram of submit times
"""
from math import ceil, log
try:
import matplotlib.mlab as mlab
from prettyplotlib import plt
except ImportError:
print(
'You must have Matplotlib and Prettyplotlib installed to plot a histogram.'
)
# Use Sturges' formula for number of bins: k = ceiling(log2 n + 1)
k = ceil(log(len(arr), 2) + 1)
n, bins, patches = plt.hist(arr,
k,
normed=1,
facecolor='green',
alpha=0.75)
# throw a PDF plot on top of it
#y = mlab.normpdf(bins, np.mean(arr), np.std(arr))
#l = plt.plot(bins, y, 'r--', linewidth=1)
# Get a Bayesian confidence interval for mean, variance, standard deviation
dmean, dvar, dsd = bayes_mvs(deltas)
# drop a line in at the mean for fun
plt.axvline(dmean[0], color='blue', alpha=0.5)
plt.axvspan(dmean[1][0], dmean[1][1], color='blue', alpha=0.5)
plt.axvline(np.median(deltas), color='y', alpha=0.5)
# Caclulate a Kernel Density Estimate
density = gaussian_kde(deltas)
xs = np.arange(0., np.max(deltas), 0.1)
density.covariance_factor = lambda: .25
density._compute_covariance()
plt.plot(xs, density(xs), color='m')
#FIXME: come up with better legend names
#plt.legend(('Normal Curve', 'Mean', 'Median', 'KDE'))
plt.legend(('Mean', 'Median', 'KDE'))
plt.xlabel('Submit Times (in Seconds)')
plt.ylabel('Probability')
plt.title('Histogram of Worker submit times')
plt.grid(True)
plt.show()
def layerActivations(model, data, labels):
print('Visualizing activations with tSNE...')
if not os.path.exists(cc.cfg['plots']['layer_activations_dir']):
os.makedirs(cc.cfg['plots']['layer_activations_dir'])
numLabels = cc.cfg['plots']['layer_activations_label_cap']
data = data[:cc.cfg['plots']['layer_activations_points_cap']]
labels = labels[:numLabels,:cc.cfg['plots']['layer_activations_points_cap']]
subplotCols = numLabels
subplotRows = len(model.layers)-1
subplotIdx = 1
plt.figure(figsize=(5*subplotCols,5*subplotRows))
for i in range(1,len(model.layers)):
print('Running tSNE for layer {}/{}'.format(i+1,len(model.layers)))
func = K.function([model.layers[0].input], [model.layers[i].output])
out = func([data])[0]
tsneModel = TSNE(n_components = 2, random_state = 0)
tsneOut = tsneModel.fit_transform(out).T
# labeledTsneOut = np.hstack((tsneOut, labels[0].reshape(-1,1)))
for j in range(numLabels):
plt.subplot(subplotRows, subplotCols, subplotIdx)
plt.title('{} / {}'.format(model.layers[i].name,cc.exp['params']['data']['labels'][j]))
plt.scatter(tsneOut[0],tsneOut[1],c=labels[j],cmap = 'plasma')
subplotIdx += 1
# tsneDF = pd.DataFrame(labeledTsneOut, columns = ('a', 'b', 'c'))
# plot = tsneDF.plot.scatter(x = 'a', y = 'b', c = 'c', cmap = 'plasma')
plt.tight_layout()
plt.savefig('{}/activations.png'.format(cc.cfg['plots']['layer_activations_dir']))
plt.close()
print('...done')
def histograms(modelLogger):
if not cc.cfg['plots']['histograms']:
return
if not os.path.exists(cc.cfg['plots']['histograms_dir']):
os.makedirs(cc.cfg['plots']['histograms_dir'])
cntEpochs = len(modelLogger.epochLogs)
cntLayers = len(modelLogger.epochLogs[-1]['weights'])
logVals = [
{'name':'weights','color':'blue'},
{'name':'updates','color':'red'},
{'name':'ratios','color':'green'}
]
subplotRows = len(logVals)
subplotCols = cntLayers
for x in range(cntEpochs):
subplotIdx = 1
plt.figure(figsize=(5*subplotCols,5*subplotRows))
plt.suptitle('Histograms per layer, epoch {}/{}'.format(x,cntEpochs-1), fontsize=14)
for logVal in logVals:
for i,layer in enumerate(modelLogger.epochLogs[x][logVal['name']]):
histmin = layer.min()
histmax = layer.max()
plt.subplot(subplotRows, subplotCols, subplotIdx)
plt.title('{}, {}'.format(modelLogger.model.layers[modelLogger.loggedLayers[i]].name,logVal['name']))
plt.hist(layer, range = (histmin, histmax), bins = 30, color = logVal['color'])
subplotIdx+=1
plt.tight_layout()
plt.subplots_adjust(top=0.9)
plt.savefig('{}/hist_{e:03d}.png'.format(cc.cfg['plots']['histograms_dir'], e = x))
plt.close()
def hero_stats_bar(self, thresh):
'''
Creates a bar chart of the heroes with highest
Wins / #Games played ratio
'''
# Create individual hero wins and losses
self.hero_stats()
fig, ax = plt.subplots(1, figsize=(9, 7))
# Compute the ratio of #Wins to the #Games played by that hero
# Most relevant statistic, better than W/L Ratio, better than
# just wins, all of them can be statistically insignificant
# in edge cases, but this can be the least of all
val = [
(k, self.wstat[k] / float(self.dstat[k] + self.wstat[k]))
for k in self.wstat
if self.wstat[k] / float(self.dstat[k] + self.wstat[k]) >= thresh
]
plt.title('Hero ID vs. Win Ratio (Matches from 01/23 - 02/24)')
plt.xlabel('Hero ID')
plt.ylabel('Win Ratio')
ax.set_xlim([0, len(val)])
ann = [round(k[1], 2) for k in val]
# Extract the xticklabels
xtl = [k[0] for k in val]
# Extract the individual values to be plotted
val = [k[1] for k in val]
ppl.bar(ax,
np.arange(len(val)),
val,
annotate=ann,
xticklabels=xtl,
grid='y',
color=ppl.colors.set2[2])
fig.savefig('../Figures/HIDvs#Wins#Games.png')
plt.show()
plt.clf()
for x in range(count):
data[locat].append(value)
print("data")
for i in data:
print(i[:10])
print("data")
#np.random.seed(10)
#data = np.random.randn(8, 4)
u_labels = ['1e-7 mid','1e-7 end','1e-6 mid','1e-6 end','1e-5 mid','1e-5 end','1e-4 mid','1e-4 end','0.001 mid','0.001 end','0.005 mid','0.005 end','0.01 mid','0.01 end',\
'0.05 mid', '0.05 end','0.1 mid','0.1 end']
fig, ax = plt.subplots()
ax.set_xticklabels(u_labels)
ax.set_xticks(u_labels)
ax.set_xlabel('Initial Mutation Rates', fontsize=9)
ax.set_ylabel('End Proliferation Rates', fontsize=9)
i = 0
print(ax.get_xticklabels())
ppl.boxplot(ax, data) #,xticklabels=u_labels)
plt.title("Distribution of End Proliferation Rates by Initial Mutation Rate",
fontsize=9)
fig.savefig(opt.output + '_boxplot.png')
print(opt.output + "DONE")
#ppl.hist(ax,data)
#fig.savefig('histogram_prettyplotlib_default.png')
alphas = data[2]
eps = data[0]
delta = 0.1
string = [r'$\tau\delta$ = %.2f' % float(delta*t) for t in taus]
#string2 = [r'$\tau$ = '+str(data[3][i]) for i in range(5)]
maxmmse = np.max(eps)
#maxf = np.max(data[1])
def f(x):
ppl.plot(alphas,eps[:,x]/maxmmse,label=string[x],ax=ax1)
# ax2.plot(data[2],data[1][:,x]/maxf,label=string2[x])
#plt.close()
#plt.figure()
#ax1 = plt.gcf().add_subplot(1,1,1)
fig, ax1 = ppl.subplots(1)
plt.title(r'MMSE for a Reconstruction Task with Adaptive Neurons')
#ax2 = plt.gcf().add_subplot(1,2,2)
#plt.title(r'Firing rate of adaptive code')
map(f,range(0,10,2))
ppl.legend(ax1)
#ax2.legend()
ax1.set_xlabel(r'$\alpha$')
ax1.set_ylabel(r'MMSE')
#ax2.set_xlabel(r'$\alpha$')
#ax2.set_ylabel(r'Firing Rate')
#plt.xlabel(r'Tuning width $\alpha$')
#plt.ylabel(r'MMSE')
plt.show()
#plt.savefig("mmse_discrimination.png",dpi=600)
plt.savefig("figure_5_8.png",dpi=600)
plt.savefig("figure_5_8.eps")
y = eta**2/(2*gamma)
print nd, " of ",len(deltas)
for nphi,phi in enumerate(phis):
fun = lambda x: f(x,gamma,eta,phi,alpha,thetas,tau,delta)
y = opt.fsolve(fun, y)
epss[nd,nphi] = y/(eta**2/(2*gamma))
ls[nd,nphi] = lambdabar(thetas,gamma,eta,phi,alpha,tau,delta)
strings = [r'$\tau\delta$ = '+str(i*tau) for i in deltas]
#f = lambda i : plt.plot(alphas,epss[i,:],label=strings[i])
#map(f,range(len(deltas)))
fig, ax = ppl.subplots(1,figsize=(7,7))
ppl.plot(ls[0,:],epss[0,:],label = strings[0], ax=ax)
ppl.plot(ls[1,:],epss[1,:],label = strings[1], ax=ax)
ppl.plot(ls[2,:],epss[2,:],label = strings[2], ax=ax)
ppl.plot(ls[3,:],epss[3,:],label = strings[3], ax=ax)
ppl.plot(ls[4,:],epss[4,:],label = strings[4], ax=ax)
np.savez("../data/mean_field_ratedist.npz", eps = epss, ls=ls, phis = phis, deltas = deltas, tau = tau)
ax.set_xlim([0.0,25.0])
ax.set_xlabel(r'$\lambda$ in spikes/sec')
ax.set_ylabel(r'MMSE/$\epsilon_0$')
plt.title("Rate-Distortion Curve for Adaptive Neurons")
ppl.legend(ax)
plt.savefig("rate_distortion_mf_alpha.png")
plt.savefig("rate_distortion_mf_alpha.eps")
plt.show()
observation = pm.Poisson("obs", lambda_, value=datos, observed=True)
model = pm.Model([observation, lambda_1, lambda_2, tau])
mcmc = pm.MCMC(model)
mcmc.sample(50000, 10000, 1)
lambda_1_samples = mcmc.trace('lambda_1')[:]
lambda_2_samples = mcmc.trace('lambda_2')[:]
tau_samples = mcmc.trace('tau')[:]
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, ncols=1)
plt.rc('font', **{'family': 'DejaVu Sans'})
plt.subplot(311)
plt.title(u'''Distribución posterior de las variables
$\lambda_1,\;\lambda_2,\;tau$''')
plt.hist(lambda_1_samples, histtype="stepfilled", bins=30, alpha=0.85,
normed=True)
plt.xlim([150000,250000])
plt.xlabel("valor de $\lambda_1$")
plt.subplot(312)
#ax.set_autoscaley_on(False)
plt.hist(lambda_2_samples, histtype="stepfilled", bins=30, alpha=0.85,
normed=True)
plt.xlim([150000,250000])
plt.xlabel("valor de $\lambda_2$")
plt.tick_params(axis="both", which="mayor", labelsize=4)
plt.subplot(313)
for a, b in zip(midd, data):
new_data.append(a)
new_data.append(b)
print(new_data[:1])
u_labels = [
'1e-7', '1e-7 end', '1e-6', '1e-6 end', '1e-5', '1e-5 end', '1e-4',
'1e-4 end', '0.001', '0.001 end', '0.005', '0.005 end', '0.01', '0.01 end'
]
fig, ax = plt.subplots()
ax.set_xticklabels(u_labels, rotation='vertical', fontsize=9)
#ax.set_xticks(u_labels)
ax.set_yscale('log')
ax.set_xlabel('Initial Mutation Rates', fontsize=9)
ax.set_ylabel(opt.ty + ' Proliferation Rates', fontsize=9)
i = 0
print(ax.get_xticklabels())
ppl.boxplot(ax, new_data) #,xticklabels=u_labels)
plt.title("Distribution of " + opt.ty +
" proliferation rates categorised by initial mutation rate",
fontsize=9)
fig.savefig(opt.output + 'pro_boxplot.png')
print(opt.output + "DONE")
#ppl.hist(ax,data)
#fig.savefig('histogram_prettyplotlib_default.png')
ax1.plot(times,s,'r',label = 'True Sate')
#m = np.average(particles,weights=weights,axis=1)
#st = np.std(particles,weights=weights,axis=1)
#ext = (0.0,dt*timewindow,code.neurons[-1].theta,code.neurons[0].theta)
#plt.imshow(rates.T,extent=ext,cmap = cm.gist_yarg,aspect = 'auto',interpolation ='nearest')
thetas = [code.neurons[i].theta for i in sptrain]
ax1.plot(times[sptimes],thetas,'yo',label='Observed Spikes')
ax1.plot(times,m,'b',label='Posterior Mean')
ax1.plot(times,m-st,'gray',times,m+st,'gray')
#ax2 = plt.gcf().add_subplot(1,2,2)
#ax2.plot(times,s)
plt.xlabel('Time (in seconds)')
plt.ylabel('Space (in cm)')
plt.legend()
plt.title('State Estimation in a Diffusion System')
if plotting:
#matplotlib.rcParams['font.size']=10
if gaussian:
fig, (ax1,ax2) = ppl.subplots(1,2,figsize = (12,6))
else:
fig, ax2 = ppl.subplots(1)
times = np.arange(0.0,dt*timewindow,dt)
if gaussian:
if sum(sum(spsg)) !=0:
(ts,neurs) = np.where(spsg == 1)
for ftype, fsize in size.items():
sys.stderr.write('{} files contain {} bytes over {} files\n'.format(ftype.upper(), sum(fsize), len(files[ftype])))
###
# To upload to the correct spot on S3
# gzip *.paths
# s3cmd put --acl-public *.paths.gz s3://aws-publicdatasets/common-crawl/crawl-data/CC-MAIN-YYYY-WW/
###
# Plot
for ftype, fsize in size.items():
if not fsize:
continue
plt.hist(fsize, bins=50)
plt.xlabel('Size (bytes)')
plt.ylabel('Count')
plt.title('Distribution for {}'.format(ftype.upper()))
plt.savefig(prefix + '{}_dist.pdf'.format(ftype))
#plt.show(block=True)
plt.close()
###
# Find missing WAT / WET files
warc = set([x.strip() for x in open(prefix + 'warc.paths').readlines()])
wat = [x.strip() for x in open(prefix + 'wat.paths').readlines()]
wat = set([x.replace('.warc.wat.', '.warc.').replace('/wat/', '/warc/') for x in wat])
# Work out the missing files and segments
missing = sorted(warc - wat)
missing_segments = defaultdict(list)
for fn in missing:
start, suffix = fn.split('/warc/')
segment = start.split('/')[-1]
def run_xsc_phot(bcdphot_out_path, mosaic_path):
replaced = {}
for cat in find_files(bcdphot_out_path, "*_combined_hdr_catalog.txt"):
print("\n======================================================")
print("\nadjusting photometry in: {}".format(cat.split('/')[-1]))
print("------------------------------------------------------")
outpath = cat.replace('combined_hdr_catalog.txt','2mass_xsc.tbl')
# retrieve 2mass data if file doesn't already exist (from previous run)
if not os.path.isfile(outpath):
# get url and retrieve data
url = query_2mass_xsc_polygon(*get_region_corners(cat))
print("\ndownloading 2MASS photometry from: {}".format(url))
text = urllib2.urlopen(url).read()
# write to disk
with open(outpath, 'w') as f:
f.write(text)
print("\ncreated file: {}".format(outpath))
# read back in as recarray
print("\nreading: {}".format(outpath))
names = open(outpath).read().split('\n')[76].split('|')[1:-1]
da = np.recfromtxt(outpath, skip_header=80, names=names)
# write input file for xsc_phot.pro
infile_outpath = '/'.join(cat.split('/')[:-1])+'/xsc.txt'
with open(infile_outpath,'w') as w:
for i in range(da.shape[0]):
w.write("{} {} {} {}\n".format(da.designation[i], da.ra[i], da.dec[i], da.r_ext[i]))
print("\ncreated input file for xsc_phot.pro: {}".format(infile_outpath))
# locate the FITS mosaic file for xsc_phot.pro to do photometry on
reg, ch = cat.split('/')[-1].split('_')[:2]
mosaicfile = filter(lambda x: 'dirbe{}/ch{}/long/full/Combine'\
.format(reg,ch) in x, find_files(mosaic_path, '*mosaic.fits'))[0]
print("\nfound mosaic file: {}".format(mosaicfile))
# spawn IDL subprocess running xsc_phot.pro and catch stdout in file
outpath = infile_outpath.replace('xsc.txt', 'xsc_phot_out.txt')
if not os.path.isfile(outpath):
outfile = open(outpath,'w')
print("\nspawning xsc_phot.pro IDL subprocess")
cmd = "xsc_phot,'"+mosaicfile+"','"+infile_outpath+"','long'"
rc = subprocess.call(['/usr/local/itt/idl71/bin/idl','-quiet','-e',cmd],
stderr = subprocess.PIPE, stdout = outfile)
outfile.close()
# read in output to recarray
print("\nreading: {}".format(outpath))
phot = np.recfromtxt(outpath, names=['id','flux','unc','sky','skyunc'])
# make sure rows are aligned
assert (da.designation == phot.id).all()
# ignore xsc sources we got a NaN or negative flux for
bad = np.isnan(phot.flux) | (phot.flux < 0)
print("\naper.pro returned NaN or negative flux for {} sources".format(bad.sum()))
if bad.sum() > 0:
for i in phot[bad].id:
print(i)
outpath = cat.replace('combined_hdr_catalog.txt','xsc_nan_phot.csv')
with open(outpath,'w') as f:
w = csv.writer(f)
w.writerow(da.dtype.names)
w.writerows(da[bad].tolist())
print('\ncreated file: {}'.format(outpath))
phot = phot[~bad]
da = da[~bad]
# read in pipeline catalog
print("\nreading: {}".format(cat))
names = open(cat).readline().split()[1:]
c = np.recfromtxt(cat, names=names)
# loop through xsc sources and find matches in pipeline catalog
print("\nfinding records associated with XSC sources in pipeline catalog")
c_flux_total = []
n_in_aper = []
c_idx = []
coords = radec_to_coords(c.ra, c.dec)
kdt = KDT(coords)
for i in range(phot.size):
radius = da.r_ext[i]/3600.
# idx1, idx2, ds = spherematch(da.ra[i], da.dec[i],
# c.ra, c.dec, tolerance=radius)
idx, ds = spherematch2(da.ra[i], da.dec[i], c.ra, c.dec,
kdt, tolerance=radius, k=500)
# c_flux_total.append(c.flux[idx2].sum())
# n_in_aper.append(c.flux[idx2].size)
# c_idx.append(idx2.tolist())
c_flux_total.append(c.flux[idx].sum())
n_in_aper.append(ds.size)
c_idx.append(idx.tolist())
print("\nhistogram of source counts in r_ext aperture")
for i in [(i,n_in_aper.count(i)) for i in set(n_in_aper)]:
print i
# create new version of catalog file with xsc-associated entries replaced
c_idx = np.array(flatten(c_idx))
print("\nremoving {}, adding {}".format(c_idx.size, phot.size))
replaced[cat] = {'old':c_idx.size, 'new':phot.size}
replaced[cat]['hist'] = [(i,n_in_aper.count(i)) for i in set(n_in_aper)]
c = np.delete(c, c_idx)
newrows = np.rec.array([(-i, da.ra[i], da.dec[i],
phot.flux[i], phot.unc[i], 1) for i in \
range(phot.size)], dtype=c.dtype)
newcat = np.hstack((c, newrows))
# write new version of catalog to disk
fmt = ['%i']+['%0.8f']*2+['%.4e']*2+['%i']
outpath = cat.replace('catalog.txt', 'catalog_xsc_cor.txt')
np.savetxt(outpath, newcat, fmt = fmt, header = ' '.join(names))
print('\ncreated file: {}'.format(outpath))
# make plot of total old vs. new flux
plt.scatter(c_flux_total, phot.flux)
ylim = plt.gca().get_ylim()
plt.xlim(*ylim)
max_y = ylim[1]
plt.plot(ylim, ylim, 'r-')
plt.xlabel('old flux [mJy]')
plt.ylabel('new flux [mJy]')
name = ' '.join(cat.split('/')[-1].split('_')[:2])
plt.title(name)
outpath = cat.replace('combined_hdr_catalog.txt','xsc_new_vs_old_phot.png')
plt.savefig(outpath, dpi=200)
plt.close()
print('\ncreated file: {}'.format(outpath))
outfile = 'xsc_replaced.json'
json.dump(replaced, open(outfile,'w'))
print("\ncreated file: {}".format(outfile))
print("\nremoved / added")
for k,v in replaced.iteritems():
print k.split('/')[-1], v['old'], v['new']
m = np.mean([i['old']/float(i['new']) for i in replaced.values()])
print("average ratio: {}".format(m))
print("\nK mag and r_ext of sources with NaN photometry:")
for i in find_files(bcdphot_out_path, "*xsc_nan_phot.csv"):
reg = i.split('/')[-1]
rec = np.recfromcsv(i)
bad_id = rec.designation.tolist()
bad_k = rec.k_m_k20fe.tolist()
bad_r_ext = rec.r_ext.tolist()
print reg
print ("\tid\t\t\tKmag\tr_ext")
if type(bad_id) is list:
seq = sorted(zip(bad_id, bad_k, bad_r_ext), key=lambda x: x[0])
for j,k,l in seq:
print("\t{}\t{}\t{}".format(j,k,l))
else:
print("\t{}\t{}\t{}".format(bad_id, bad_k, bad_r_ext))
X.append(i + 1)
Y.append(err)
incr_orthoY.append(check_orthogonality(uL[i]))
incr_ortho.append(['iSVD u={}'.format(num), X, incr_orthoY])
plt.plot(X, Y, label='iSVD u={}'.format(num))
"""
print 'Testing raw SVD => exact reconstruction'
svT = scipy.linalg.diagsvd(s, u.shape[0], vT.shape[1]).dot(vT)
for y in xrange(train.shape[0]):
for x in xrange(train.shape[1]):
colU = u[y, :]
rowV = svT[:, x]
assert np.allclose(train[y, x], single_dot(u, svT, x, y))
"""
##
plt.title('SVD reconstruction error on {}x{} matrix'.format(*train.shape))
plt.xlabel('Low rank approximation (k)')
plt.ylabel('Frobenius norm')
plt.ylim(0, max(svdY))
plt.legend(loc='best')
plt.savefig('reconstruct_fro_{}x{}.pdf'.format(*train.shape))
plt.show(block=True)
##
plt.plot(orthoX,
orthoY,
label="SVD",
color='black',
linewidth=2,
linestyle='--')
for label, X, Y in incr_ortho:
plt.plot(X, Y, label=label)
width = 0.5
font = {'family' : 'normal',
'weight' : 'normal',
'size' : 10}
# k c b m r o char
vals = [ 45174 , 48810 , 41719 , 255017 , 642090 , 3053979 , 3066135 , 1731655 , 582226 , 126045 , 6738 ]
total = float(sum(vals))
print(vals)
vals = [(i / total)*100 for i in vals]
print(vals)
colors = ['LightGray','Gray','LightSkyBlue','RoyalBlue','LightSeaGreen','MediumSeaGreen','Khaki',\
'Goldenrod','Tomato','MediumVioletRed','DarkViolet']
#plt.bar(ind, vals, width)
ppl.bar(ax, ind, vals, color=colors)
plt.xticks(ind+width/2.,\
('1e-7','1e-6','1e-5','1e-4','0.001','0.005','0.01', '0.05', '0.1', '0.2', '0.5')\
,fontsize=11)
plt.yticks(fontsize=11)
plt.title("Distribution of cells by mutation rate across resistant populations",fontsize=11)
#plt.ylim((0,100))
plt.ylabel("Percent of Total",fontsize=11)
plt.xlabel("Initial Mutation rate: u",fontsize=11)
plt.savefig(opt.filename+"_bar")
plt.clf()
###
svdY = []
u, s, vT = scipy.linalg.svd(train)
for k in xrange(1, 100):
low_s = [s[i] for i in xrange(k)] # + (min(u.shape[0], vT.shape[1]) - k) * [0]
print 'Exact SVD with low-rank approximation {}'.format(k)
svdX.append(k)
svdY.append(get_error(u, np.diag(low_s), vT, train, test))
plt.plot(svdX, svdY, label="SVD", color='black', linewidth='2', linestyle='--')
"""
print
print 'Testing incremental SVD'
for num in xrange(400, 1001, 300):
print '... with block size of {}'.format(num)
X, Y = [], []
for k in xrange(1, 91, 10):
print k
u, s, vT = incremental_SVD(train, k, num)
X.append(k)
Y.append(get_error(u, s, vT, train, test, prod_avg))
plt.plot(X, Y, label='iSVD u={}'.format(num))
##
plt.title(
'Recommendation system RMSE on {}x{} matrix'.format(*train.shape))
plt.xlabel('Low rank approximation (k)')
plt.ylabel('Root Mean Squared Error')
#plt.ylim(0, max(svdY))
plt.legend(loc='best')
plt.savefig('recommend_rmse_{}x{}.pdf'.format(*train.shape))
plt.show(block=True)
observation = pm.Poisson("obs", lambda_, value=datos, observed=True)
model = pm.Model([observation, lambda_1, lambda_2, tau])
mcmc = pm.MCMC(model)
mcmc.sample(50000, 10000, 1)
lambda_1_samples = mcmc.trace('lambda_1')[:]
lambda_2_samples = mcmc.trace('lambda_2')[:]
tau_samples = mcmc.trace('tau')[:]
fig, (ax1, ax2, ax3) = plt.subplots(nrows=3, ncols=1)
plt.rc('font', **{'family': 'DejaVu Sans'})
plt.subplot(311)
plt.title(u'''Distribución posterior de las variables
$\lambda_1,\;\lambda_2,\;tau$''')
plt.hist(lambda_1_samples,
histtype="stepfilled",
bins=30,
alpha=0.85,
normed=True)
plt.xlim([150000, 250000])
plt.xlabel("valor de $\lambda_1$")
plt.subplot(312)
#ax.set_autoscaley_on(False)
plt.hist(lambda_2_samples,
histtype="stepfilled",
bins=30,
alpha=0.85,
normed=True)
##
import sys
if __name__ == '__main__':
# Accumulate the counts fed into standard in (stdin)
counts = []
for line in sys.stdin:
topic, count = line.split()
# Shift the ones up by a tiny amount to allow them to be visible on the graph
counts.append(int(count) + 0.05)
print 'Total page view counts: {}'.format(len(counts))
# Display the hourly page view distribution on a log-log plot
# This matches our friendly and well understood Zipf distribution
fig = plt.figure(figsize=(9, 5))
ax = fig.add_subplot(1, 1, 1)
plt.plot(xrange(len(counts)), counts, linewidth=3, label='Pageviews per page')
#
ax.set_xscale('log')
ax.set_yscale('log')
#
plt.title('Log-log plot of hourly Wikipedia page view distribution')
plt.xlabel('Rank order')
plt.ylabel('Frequency')
plt.grid(color='black')
plt.legend()
#
plt.savefig('hourly_wikipedia_zipf.pdf')
plt.show(block=True)
def run_xsc_phot(bcdphot_out_path, mosaic_path):
replaced = {}
for cat in find_files(bcdphot_out_path, "*_combined_hdr_catalog.txt"):
print("\n======================================================")
print("\nadjusting photometry in: {}".format(cat.split('/')[-1]))
print("------------------------------------------------------")
outpath = cat.replace('combined_hdr_catalog.txt', '2mass_xsc.tbl')
# retrieve 2mass data if file doesn't already exist (from previous run)
if not os.path.isfile(outpath):
# get url and retrieve data
url = query_2mass_xsc_polygon(*get_region_corners(cat))
print("\ndownloading 2MASS photometry from: {}".format(url))
text = urllib2.urlopen(url).read()
# write to disk
with open(outpath, 'w') as f:
f.write(text)
print("\ncreated file: {}".format(outpath))
# read back in as recarray
print("\nreading: {}".format(outpath))
names = open(outpath).read().split('\n')[76].split('|')[1:-1]
da = np.recfromtxt(outpath, skip_header=80, names=names)
# write input file for xsc_phot.pro
infile_outpath = '/'.join(cat.split('/')[:-1]) + '/xsc.txt'
with open(infile_outpath, 'w') as w:
for i in range(da.shape[0]):
w.write("{} {} {} {}\n".format(da.designation[i], da.ra[i],
da.dec[i], da.r_ext[i]))
print(
"\ncreated input file for xsc_phot.pro: {}".format(infile_outpath))
# locate the FITS mosaic file for xsc_phot.pro to do photometry on
reg, ch = cat.split('/')[-1].split('_')[:2]
mosaicfile = filter(lambda x: 'dirbe{}/ch{}/long/full/Combine'\
.format(reg,ch) in x, find_files(mosaic_path, '*mosaic.fits'))[0]
print("\nfound mosaic file: {}".format(mosaicfile))
# spawn IDL subprocess running xsc_phot.pro and catch stdout in file
outpath = infile_outpath.replace('xsc.txt', 'xsc_phot_out.txt')
if not os.path.isfile(outpath):
outfile = open(outpath, 'w')
print("\nspawning xsc_phot.pro IDL subprocess")
cmd = "xsc_phot,'" + mosaicfile + "','" + infile_outpath + "','long'"
rc = subprocess.call(
['/usr/local/itt/idl71/bin/idl', '-quiet', '-e', cmd],
stderr=subprocess.PIPE,
stdout=outfile)
outfile.close()
# read in output to recarray
print("\nreading: {}".format(outpath))
phot = np.recfromtxt(outpath,
names=['id', 'flux', 'unc', 'sky', 'skyunc'])
# make sure rows are aligned
assert (da.designation == phot.id).all()
# ignore xsc sources we got a NaN or negative flux for
bad = np.isnan(phot.flux) | (phot.flux < 0)
print("\naper.pro returned NaN or negative flux for {} sources".format(
bad.sum()))
if bad.sum() > 0:
for i in phot[bad].id:
print(i)
outpath = cat.replace('combined_hdr_catalog.txt',
'xsc_nan_phot.csv')
with open(outpath, 'w') as f:
w = csv.writer(f)
w.writerow(da.dtype.names)
w.writerows(da[bad].tolist())
print('\ncreated file: {}'.format(outpath))
phot = phot[~bad]
da = da[~bad]
# read in pipeline catalog
print("\nreading: {}".format(cat))
names = open(cat).readline().split()[1:]
c = np.recfromtxt(cat, names=names)
# loop through xsc sources and find matches in pipeline catalog
print(
"\nfinding records associated with XSC sources in pipeline catalog"
)
c_flux_total = []
n_in_aper = []
c_idx = []
coords = radec_to_coords(c.ra, c.dec)
kdt = KDT(coords)
for i in range(phot.size):
radius = da.r_ext[i] / 3600.
# idx1, idx2, ds = spherematch(da.ra[i], da.dec[i],
# c.ra, c.dec, tolerance=radius)
idx, ds = spherematch2(da.ra[i],
da.dec[i],
c.ra,
c.dec,
kdt,
tolerance=radius,
k=500)
# c_flux_total.append(c.flux[idx2].sum())
# n_in_aper.append(c.flux[idx2].size)
# c_idx.append(idx2.tolist())
c_flux_total.append(c.flux[idx].sum())
n_in_aper.append(ds.size)
c_idx.append(idx.tolist())
print("\nhistogram of source counts in r_ext aperture")
for i in [(i, n_in_aper.count(i)) for i in set(n_in_aper)]:
print i
# create new version of catalog file with xsc-associated entries replaced
c_idx = np.array(flatten(c_idx))
print("\nremoving {}, adding {}".format(c_idx.size, phot.size))
replaced[cat] = {'old': c_idx.size, 'new': phot.size}
replaced[cat]['hist'] = [(i, n_in_aper.count(i))
for i in set(n_in_aper)]
c = np.delete(c, c_idx)
newrows = np.rec.array([(-i, da.ra[i], da.dec[i],
phot.flux[i], phot.unc[i], 1) for i in \
range(phot.size)], dtype=c.dtype)
newcat = np.hstack((c, newrows))
# write new version of catalog to disk
fmt = ['%i'] + ['%0.8f'] * 2 + ['%.4e'] * 2 + ['%i']
outpath = cat.replace('catalog.txt', 'catalog_xsc_cor.txt')
np.savetxt(outpath, newcat, fmt=fmt, header=' '.join(names))
print('\ncreated file: {}'.format(outpath))
# make plot of total old vs. new flux
plt.scatter(c_flux_total, phot.flux)
ylim = plt.gca().get_ylim()
plt.xlim(*ylim)
max_y = ylim[1]
plt.plot(ylim, ylim, 'r-')
plt.xlabel('old flux [mJy]')
plt.ylabel('new flux [mJy]')
name = ' '.join(cat.split('/')[-1].split('_')[:2])
plt.title(name)
outpath = cat.replace('combined_hdr_catalog.txt',
'xsc_new_vs_old_phot.png')
plt.savefig(outpath, dpi=200)
plt.close()
print('\ncreated file: {}'.format(outpath))
outfile = 'xsc_replaced.json'
json.dump(replaced, open(outfile, 'w'))
print("\ncreated file: {}".format(outfile))
print("\nremoved / added")
for k, v in replaced.iteritems():
print k.split('/')[-1], v['old'], v['new']
m = np.mean([i['old'] / float(i['new']) for i in replaced.values()])
print("average ratio: {}".format(m))
print("\nK mag and r_ext of sources with NaN photometry:")
for i in find_files(bcdphot_out_path, "*xsc_nan_phot.csv"):
reg = i.split('/')[-1]
rec = np.recfromcsv(i)
bad_id = rec.designation.tolist()
bad_k = rec.k_m_k20fe.tolist()
bad_r_ext = rec.r_ext.tolist()
print reg
print("\tid\t\t\tKmag\tr_ext")
if type(bad_id) is list:
seq = sorted(zip(bad_id, bad_k, bad_r_ext), key=lambda x: x[0])
for j, k, l in seq:
print("\t{}\t{}\t{}".format(j, k, l))
else:
print("\t{}\t{}\t{}".format(bad_id, bad_k, bad_r_ext))
#data = np.random.randn(8, 4)
u_labels = [
'1e-7', '1e-6', '1e-5', '1e-4', '0.001', '0.005', '0.01', '0.05', '0.1'
]
fig, ax = plt.subplots()
ax.set_xticklabels(u_labels)
ax.set_xticks(u_labels)
ax.set_xlabel('Initial Mutation Rates', fontsize=9)
ax.set_ylabel('End Time', fontsize=9)
ax.set_yscale('log')
i = 0
print(ax.get_xticklabels())
ppl.boxplot(ax, data) #,xticklabels=u_labels)
t = [
"time taken to reach full size", "time taken until crash",
"time taken until recovery"
]
tt = ["pre", "crash", "post"]
plt.title("distribution of " + t[j] + " categorised by mutation rate",
fontsize=9)
fig.savefig(opt.output + tt[j] + '_boxplot.png')
print(opt.output + "DONE")
#ppl.hist(ax,data)
#fig.savefig('histogram_prettyplotlib_default.png')
X.append(i + 1)
Y.append(err)
incr_orthoY.append(check_orthogonality(uL[i]))
incr_ortho.append(['iSVD u={}'.format(num), X, incr_orthoY])
plt.plot(X, Y, label='iSVD u={}'.format(num))
"""
print 'Testing raw SVD => exact reconstruction'
svT = scipy.linalg.diagsvd(s, u.shape[0], vT.shape[1]).dot(vT)
for y in xrange(train.shape[0]):
for x in xrange(train.shape[1]):
colU = u[y, :]
rowV = svT[:, x]
assert np.allclose(train[y, x], single_dot(u, svT, x, y))
"""
##
plt.title('SVD reconstruction error on {}x{} matrix'.format(*train.shape))
plt.xlabel('Low rank approximation (k)')
plt.ylabel('Frobenius norm')
plt.ylim(0, max(svdY))
plt.legend(loc='best')
plt.savefig('reconstruct_fro_{}x{}.pdf'.format(*train.shape))
plt.show(block=True)
##
plt.plot(orthoX, orthoY, label="SVD", color='black', linewidth=2, linestyle='--')
for label, X, Y in incr_ortho:
plt.plot(X, Y, label=label)
plt.title('SVD orthogonality error on {}x{} matrix'.format(*train.shape))
plt.xlabel('Low rank approximation (k)')
plt.ylabel('Deviation from orthogonality')
plt.semilogy()
#plt.ylim(0, max(orthoY))
svdX = []
svdY = []
u, s, vT = scipy.linalg.svd(train)
for k in xrange(1, 100):
low_s = [s[i] for i in xrange(k)] # + (min(u.shape[0], vT.shape[1]) - k) * [0]
print 'Exact SVD with low-rank approximation {}'.format(k)
svdX.append(k)
svdY.append(get_error(u, np.diag(low_s), vT, train, test))
plt.plot(svdX, svdY, label="SVD", color='black', linewidth='2', linestyle='--')
"""
print
print 'Testing incremental SVD'
for num in xrange(400, 1001, 300):
print '... with block size of {}'.format(num)
X, Y = [], []
for k in xrange(1, 91, 10):
print k
u, s, vT = incremental_SVD(train, k, num)
X.append(k)
Y.append(get_error(u, s, vT, train, test, prod_avg))
plt.plot(X, Y, label='iSVD u={}'.format(num))
##
plt.title('Recommendation system RMSE on {}x{} matrix'.format(*train.shape))
plt.xlabel('Low rank approximation (k)')
plt.ylabel('Root Mean Squared Error')
#plt.ylim(0, max(svdY))
plt.legend(loc='best')
plt.savefig('recommend_rmse_{}x{}.pdf'.format(*train.shape))
plt.show(block=True)
