def __init__(self, rooms):
self.rooms = rooms
self.mapSize = (MiniMap.mapImage.width - 2 * MiniMap.mapBorder[x],
MiniMap.mapImage.height - 2 * MiniMap.mapBorder[y])
self.xGridCoords = []
self.yGridCoords = []
for room in self.rooms:
if self.xGridCoords.count(room.gridCoord[x]) == 0:
self.xGridCoords.append(room.gridCoord[x])
if self.yGridCoords.count(room.gridCoord[y]) == 0:
self.yGridCoords.append(room.gridCoord[y])
self.xGridCoords.sort()
self.yGridCoords.sort()
self.minimums = [min(self.xGridCoords), min(self.yGridCoords)]
self.maximums = [max(self.xGridCoords), max(self.yGridCoords)]
self.roomCount = (abs(self.maximums[x]) + abs(self.minimums[x]) + 1,
abs(self.maximums[y]) + abs(self.minimums[y]) + 1)
self.roomSize = min(
MiniMap.spaceRatio * self.mapSize[x] /
((MiniMap.spaceRatio + 1) * self.roomCount[x] - 1),
MiniMap.spaceRatio * self.mapSize[y] /
((MiniMap.spaceRatio + 1) * self.roomCount[y] - 1))
self.spaceSize = min(self.roomSize / MiniMap.spaceRatio,
self.roomSize / MiniMap.spaceRatio)
self.shift = [
median(self.xGridCoords) * (self.roomSize + self.spaceSize),
median(self.yGridCoords) * (self.roomSize + self.spaceSize)
]
def __init__(self, rooms):
self.rooms = rooms
self.mapSize = (MiniMap.mapImage.width - 2 * MiniMap.mapBorder[x], MiniMap.mapImage.height - 2 * MiniMap.mapBorder[y])
self.xGridCoords = []
self.yGridCoords = []
for room in self.rooms:
if self.xGridCoords.count(room.gridCoord[x]) == 0:
self.xGridCoords.append(room.gridCoord[x])
if self.yGridCoords.count(room.gridCoord[y]) == 0:
self.yGridCoords.append(room.gridCoord[y])
self.xGridCoords.sort()
self.yGridCoords.sort()
self.minimums = [min(self.xGridCoords), min(self.yGridCoords)]
self.maximums = [max(self.xGridCoords), max(self.yGridCoords)]
self.roomCount = (abs(self.maximums[x]) + abs(self.minimums[x]) + 1,
abs(self.maximums[y]) + abs(self.minimums[y]) + 1)
self.roomSize = min(MiniMap.spaceRatio * self.mapSize[x] / ((MiniMap.spaceRatio + 1) * self.roomCount[x] - 1),
MiniMap.spaceRatio * self.mapSize[y] / ((MiniMap.spaceRatio + 1) * self.roomCount[y] - 1))
self.spaceSize = min(self.roomSize / MiniMap.spaceRatio,
self.roomSize / MiniMap.spaceRatio)
self.shift = [median(self.xGridCoords) * (self.roomSize + self.spaceSize),
median(self.yGridCoords) * (self.roomSize + self.spaceSize)]
def test_median():
''' Test the weighted median
'''
w = np.repeat(1., 100)
data = np.arange(100)
print np.median(data)
print UT.median(data, weights=w)
if np.median(data) != UT.median(data, weights=w):
raise ValueError
return None
def test_SFMS_highz(run, T, nsnap=15, lit='lee', nsnap0=15, downsampled='14'):
''' Compare the best-fit SFMS parameters from ABC to literature at high z
'''
if lit == 'lee':
lit_label = 'Lee et al. (2015)'
# median ABC theta
abcout = abcee.readABC(run, T)
theta_med = [UT.median(abcout['theta'][:, i], weights=abcout['w'][:]) for i in range(len(abcout['theta'][0]))]
subcat_sim = abcee.model(run, theta_med, nsnap0=nsnap0, downsampled=downsampled)
m_arr = np.arange(8., 12.1, 0.1)
sfr_abc = Obvs.SSFR_SFMS(m_arr, UT.z_nsnap(nsnap), theta_SFMS=subcat_sim['theta_sfms']) + m_arr
sfr_obv = Obvs.SSFR_SFMS_obvs(m_arr, UT.z_nsnap(nsnap), lit=lit) + m_arr
fig = plt.figure()
sub = fig.add_subplot(111)
sub.fill_between(m_arr, sfr_abc-0.3, sfr_abc+0.3, color='b', alpha=0.25, linewidth=0, edgecolor=None,
label=r'ABC $\theta_{median}$')
sub.plot(m_arr, sfr_obv+0.3, ls='--', c='k', label=lit_label)
sub.plot(m_arr, sfr_obv-0.3, ls='--', c='k')
sub.set_xlim([8., 12.])
sub.set_xlabel('$\mathtt{log\;M_*}$', fontsize=25)
sub.set_ylim([-4., 3.])
sub.set_ylabel('$\mathtt{log\;SFR}$', fontsize=25)
sub.legend(loc='best')
fig.savefig(UT.fig_dir()+'SFMS.z'+str(round(UT.z_nsnap(nsnap),2))+'.'+run+'.'+lit+'.png', bbox_inches='tight')
plt.close()
return None
def test_ABC_SMHMR(run, T):#, sumstat=['smf']):
''' Compare the SMHMR the median T-th ABC particle pool with 'data'
Hardcoded for smf only
'''
# data summary statistic
subcat_dat = abcee.Data(nsnap0=15)
# median theta
abcout = abcee.readABC('test0', T)
theta_med = [UT.median(abcout['theta'][:, i], weights=abcout['w'][:]) for i in range(len(abcout['theta'][0]))]
# F( median theta)
subcat_sim = abcee.model(run, theta_med, nsnap0=15, downsampled='14')
fig = plt.figure()
sub = fig.add_subplot(111)
smhmr = Obvs.Smhmr()
# simulation
m_mid, mu_mhalo, sig_mhalo, cnts = smhmr.Calculate(subcat_sim['m.max'], subcat_sim['m.star'],
dmhalo=0.2, weights=subcat_sim['weights'])
sub.fill_between(m_mid, mu_mhalo - sig_mhalo, mu_mhalo + sig_mhalo, color='b', alpha=0.25, linewidth=0, edgecolor=None,
label='Sim.')
# data
m_mid, mu_mhalo, sig_mhalo, cnts = smhmr.Calculate(subcat_dat['m.max'], subcat_dat['m.star'], weights=subcat_dat['weights'])
sub.errorbar(m_mid, mu_mhalo, yerr=sig_mhalo, color='k', label='Data')
sub.set_xlim([10., 15.])
sub.set_xlabel('Halo Mass $(\mathcal{M}_{halo})$', fontsize=25)
sub.set_ylim([8., 12.])
sub.set_ylabel('Stellar Mass $(\mathcal{M}_*)$', fontsize=25)
sub.legend(loc='upper right')
plt.show()
return None
def test_ABCsumstat(run, T):#, sumstat=['smf']):
''' Compare the summary statistics of the median T-th ABC particle pool with data.
Hardcoded for smf only
'''
# data summary statistic
sumdata = abcee.SumData(['smf'], info=True, nsnap0=15)
# median theta
abcout = abcee.readABC('test0', T)
theta_med = [UT.median(abcout['theta'][:, i], weights=abcout['w'][:]) for i in range(len(abcout['theta'][0]))]
subcat = abcee.model(run, theta_med, nsnap0=15, downsampled='14')
sumsim = abcee.SumSim(['smf'], subcat, info=True)
fig = plt.figure()
sub = fig.add_subplot(111)
sub.plot(sumdata[0][0], sumdata[0][1], c='k', ls='--', label='Data')
sub.plot(sumsim[0][0], sumsim[0][1], c='b', label='Sim.')
sub.set_xlim([9., 12.])
sub.set_xlabel('Stellar Masses $(\mathcal{M}_*)$', fontsize=25)
sub.set_ylim([1e-6, 10**-1.75])
sub.set_yscale('log')
sub.set_ylabel('$\Phi$', fontsize=25)
sub.legend(loc='upper right')
plt.show()
return None
def select_rewrite_expression(name, exprs):
"""
Given an expression name and a list of expressions,
tries to select an expression with the highest selectivity
for use in AST re-writing.
"""
# For equality check (=, !=, is), select the mode
if name[1] == "equality":
values = [e.right.value for e in exprs]
filter_using = util.mode(values)
for e in exprs:
if e.right.value == filter_using:
return e
# For ordering checks, select the median value for static
elif name[1] == "order":
is_static = name[3][1] == "static"
values = [e.right.value for e in exprs]
# For static (numeric) compares, we use median
# value to eliminate as many as possible.
# For non-numeric, we use mode
if is_static:
filter_using = util.median(values)
else:
filter_using = util.mode(values)
for e in exprs:
if e.right.value == filter_using:
return e
# For ordering checks without static values, use any
else:
return exprs[0]
def uclus_distance(self, x, y):
"""
The method to determine the distance between one cluster an another
item/cluster. The distance equals to the *average* (median) distance
from any member of one cluster to any member of the other cluster.
:param x: first cluster/item.
:param y: second cluster/item.
"""
# create a flat list of all the items in <x>
if not isinstance(x, Cluster):
x = [x]
else:
x = x.fullyflatten()
# create a flat list of all the items in <y>
if not isinstance(y, Cluster):
y = [y]
else:
y = y.fullyflatten()
distances = []
for k in x:
for l in y:
distances.append(self.distance(k, l))
return median(distances)
def func_avg(kwargs, f=None, name=None):
kwargs = dict(kwargs)
pool = kwargs.pop("pool")
n_runs = kwargs.pop("n_runs")
result = pool.map(FunctionCaller(kwargs), range(n_runs))
xs, sizes = list(zip(*result))
if f is None:
f = open("tuning.txt", "w")
xs_str = "Evals: " + str(xs)
sizes_str = "Sizes: " + str(sizes)
median_str = "Median: %.1f Average: %.1f ~ %.1f" % (util.median(
xs), util.avg(xs), util.avg([x for x in xs if util.is_finite(x)]))
avg_size_str = "AverageSize: %.1f ~ %.1f" % (
util.avg(sizes), util.avg([s for s in sizes if util.is_finite(s)]))
success_rate = len([x for x in xs if util.is_finite(x)]) / len(xs)
success_rate_str = "Success_rate: %.2f" % success_rate
report_str = [
xs_str, sizes_str, median_str, avg_size_str, success_rate_str
]
if name is not None:
name_str = "* %s" % name
report_str = [name_str] + report_str
report_str = "\n".join(report_str)
print(report_str, file=f)
f.flush()
print(report_str)
def select_rewrite_expression(name, exprs):
"""
Given an expression name and a list of expressions,
tries to select an expression with the highest selectivity
for use in AST re-writing.
"""
# For equality check (=, !=, is), select the mode
if name[1] == "equality":
values = [e.right.value for e in exprs]
filter_using = util.mode(values)
for e in exprs:
if e.right.value == filter_using:
return e
# For ordering checks, select the median value for static
elif name[1] == "order":
is_static = name[3][1] == "static"
values = [e.right.value for e in exprs]
# For static (numeric) compares, we use median
# value to eliminate as many as possible.
# For non-numeric, we use mode
if is_static:
filter_using = util.median(values)
else:
filter_using = util.mode(values)
for e in exprs:
if e.right.value == filter_using:
return e
# For ordering checks without static values, use any
else:
return exprs[0]
def to_dict(self):
return {
"name": self.name,
"num_articles": len(self.articles),
"articles": self.articles,
#"scopus_bins": [b.to_dict() for b in self.histogram],
"scopus_median": median([a["scopus"] for a in self.articles])
}
def to_dict(self):
return {
"name": self.name,
"num_articles": len(self.articles),
"articles": self.articles,
#"scopus_bins": [b.to_dict() for b in self.histogram],
"scopus_median": median([a["scopus"] for a in self.articles])
}
def test_readABC(T):
''' Try reading in different ABC outputs and do basic plots
'''
abcout = abcee.readABC('test0', T)
# median theta
theta_med = [UT.median(abcout['theta'][:, i], weights=abcout['w'][:]) for i in range(len(abcout['theta'][0]))]
print theta_med
def addVehicle(self, veHistory):
time = max(veHistory.keys()) * self.timePerFrame
getRatio = lambda mvBbox: (mvBbox[3] - self.ratioErode) / (1 + abs(
mvBbox[2] - self.ratioErode)) # height / width
ratios = map(getRatio, veHistory.values())
medianRatio = util.median(ratios)
vehicle = "Moto" if medianRatio > self.ratioRef else "Automobile"
self._data.append((time, vehicle))
self.segmenter.addVehicle(vehicle)
def xor_investigate_average():
n_runs = 100
x = []
for i in range(n_runs):
result = xor_investigate(i)
print("xor(%d) = %d" % (i, result))
x.append(result)
#x = [xor_investigate() for i in range(n_runs)]
print("Average=%.2f" % util.avg(x))
print("Median=%.2f" % util.median(x))
def time_task(self):
while True:
t_send = time.time()
clock_deltas = {None: (t_send, t_send)}
for peer, request in [(peer, peer.get_time().addCallback(lambda res: (time.time(), res))) for peer in self.peers]:
try:
t_recv, response = yield request
t = .5 * (t_send + t_recv)
clock_deltas[(peer.id, peer.address, peer.port)] = (t, float(response))
except:
traceback.print_exc()
continue
self.clock_offset = util.median(mine - theirs for mine, theirs in clock_deltas.itervalues())
yield sleep(random.expovariate(1/100))
def bondStats(self, ni, nj, ij=0):
# get the residue index extents.
rlist = [atom['resSeq'] for atom in self.models[0]]
rmin = min(rlist)
rmax = max(rlist)
# build the list of distances.
d = []
for i in range(rmin, rmax + 1):
j = i + ij
ai = self.select(i, ni)
aj = self.select(j, nj)
if ai and aj:
d = d + dist(ai, aj)
# compute and return statistics on the list.
return (median(d), min(d), max(d))
def sigMstar_tduty_fid(Mhalo=12, dMhalo=0.5):
''' Figure plotting sigmaMstar at M_halo = Mhalo for different
duty cycle time (t_duty) with fiducial SFMS parameter values rather
than ABC values.
'''
# read in parameter values for randomSFH_1gyr
abcout = ABC.readABC('randomSFH_1gyr', 13)
# the median theta will be designated the fiducial parameter values
theta_fid = [UT.median(abcout['theta'][:, i], weights=abcout['w'][:])
for i in range(len(abcout['theta'][0]))]
runs = ['randomSFH_0.5gyr', 'randomSFH_1gyr', 'randomSFH_2gyr', 'randomSFH_5gyr', 'randomSFH_10gyr']
tduties = [0.5, 1., 2., 5., 10.] #hardcoded
smhmr = Obvs.Smhmr()
sigMstar_fid = []
for i_t, tduty in enumerate(tduties):
subcat_sim = ABC.model(runs[i_t], theta_fid,
nsnap0=15, sigma_smhm=0.2, downsampled='14')
isSF = np.where(subcat_sim['gclass'] == 'sf') # only SF galaxies
sig_mstar_fid = smhmr.sigma_logMstar(
subcat_sim['halo.m'][isSF], subcat_sim['m.star'][isSF],
weights=subcat_sim['weights'][isSF], Mhalo=Mhalo, dmhalo=dMhalo)
sigMstar_fid.append(sig_mstar_fid)
sigMstar_fid = np.array(sigMstar_fid)
# make figure
fig = plt.figure(figsize=(5,5))
sub = fig.add_subplot(111)
sub.scatter(tduties, sigMstar_fid)
# x-axis
sub.set_xlabel('$t_\mathrm{duty}$ [Gyr]', fontsize=20)
sub.set_xlim([0., 10.])
# y-axis
sub.set_ylabel('$\sigma_{M_*}(M_\mathrm{halo} = 10^{'+str(Mhalo)+'} M_\odot)$', fontsize=20)
sub.set_ylim([0., 0.5])
fig.savefig(''.join([UT.tex_dir(), 'figs/sigMstar_tduty_fid.pdf']),
bbox_inches='tight', dpi=150)
plt.close()
return None
def angleStats(self, ni, nj, nk, ij=0, ik=0):
# get the residue index extents.
rlist = [atom['resSeq'] for atom in self.models[0]]
rmin = min(rlist)
rmax = max(rlist)
# build the list of angles.
theta = []
for i in range(rmin, rmax + 1):
j = i + ij
k = i + ik
ai = self.select(i, ni)
aj = self.select(j, nj)
ak = self.select(k, nk)
if ai and aj and ak:
theta = theta + angle(ai, aj, ak)
# compute and return statistics on the list.
return (median(theta), min(theta), max(theta))
def dihedStats(self, ni, nj, nk, nl, ij=0, ik=0, il=0):
# get the residue index extents.
rlist = [atom['resSeq'] for atom in self.models[0]]
rmin = min(rlist)
rmax = max(rlist)
# build the list of dihedrals.
omega = []
for i in range(rmin, rmax + 1):
j = i + ij
k = i + ik
l = i + il
ai = self.select(i, ni)
aj = self.select(j, nj)
ak = self.select(k, nk)
al = self.select(l, nl)
if ai and aj and ak and al:
omega = omega + dihed(ai, aj, ak, al)
# compute and return statistics on the list.
return (median(omega), min(omega), max(omega))
def time_task(self):
while True:
t_send = time.time()
clock_deltas = {None: (t_send, t_send)}
for peer, request in [
(peer,
peer.get_time().addCallback(lambda res: (time.time(), res)))
for peer in self.peers
]:
try:
t_recv, response = yield request
t = .5 * (t_send + t_recv)
clock_deltas[(peer.id, peer.address,
peer.port)] = (t, float(response))
except:
traceback.print_exc()
continue
self.clock_offset = util.median(
mine - theirs for mine, theirs in clock_deltas.itervalues())
yield sleep(random.expovariate(1 / 100))
def find_info_gain(self, X, y, attr):
'''
Find the information gain for a given attribute
'''
# Find out if its a numerical or categorical
isCategorical = type(X[1][attr]) == str
if isCategorical:
value_set = set([x[attr] for x in X])
if len(value_set) == 1:
# only one value for entire list
return None
elif len(value_set) == 2:
attribute_values = [list(value_set)[0]]
else:
attribute_values = [x for x in value_set]
else:
# calculate mean, median of the values
value_set = set([x[attr] for x in X])
if len(value_set) == 1:
return None
values = [x for x in value_set]
# calculate info gain on median and mean
attribute_values = [mean(values), median(values)]
max_info_gain = 0
val = None
X_left = None
X_right = None
Y_left = None
Y_right = None
for each in attribute_values:
x_left, x_right, y_left, y_right = partition_classes(
X, y, attr, each)
info_gain = information_gain(y, [y_left, y_right])
if info_gain > max_info_gain:
max_info_gain = info_gain
val = each
X_left, X_right, Y_left, Y_right = x_left, x_right, y_left, y_right
return isCategorical, val, max_info_gain, X_left, X_right, Y_left, Y_right
def unbalanced_countries(self):
self.sort_countries()
q1 = self.num_countries/4.0
q2 = int(q1*2)
q3 = int(q1*3)
q1 = int(q1)
quant1 = self.countries[:q1]
quant2 = self.countries[q1:q2]
quant3 = self.countries[q2:q3]
median = util.median(self.countries)
mid50 = len(quant2[-1].territories) - len(quant2[0].territories)
min_terrs = median - mid50*1.5
max_terrs = median + mid50*1.5
small = [c for c in self.countries if len(c.territories) < min_terrs]
large = [c for c in self.countries if len(c.territories) > max_terrs]
if len(self.countries[0].territories)*1.5 \
< len(self.countries[-1].territories):
if self.countries[0] not in small:
small.append(self.countries[0])
if self.countries[-1] not in large:
large.append(self.countries[-1])
return sorted(small), sorted(large)
def combine(result_matrices, score_scalings, membership, iteration, config_params):
"""This is the combining function, taking n result matrices and scalings"""
quantile_normalize = config_params['quantile_normalize']
for i, m in enumerate(result_matrices):
m.fix_extreme_values()
m.subtract_with_quantile(0.99)
# debug mode: print scoring matrices before combining
if ('dump_scores' in config_params['debug'] and
(iteration == 1 or (iteration % config_params['debug_freq'] == 0))):
funs = config_params['pipeline']['row-scoring']['args']['functions']
m.write_tsv_file(os.path.join(config_params['output_dir'], 'score-%s-%04d.tsv' % (funs[i]['id'], iteration)), compressed=False)
if quantile_normalize:
if len(result_matrices) > 1:
start_time = util.current_millis()
result_matrices = dm.quantile_normalize_scores(result_matrices,
score_scalings)
elapsed = util.current_millis() - start_time
logging.debug("quantile normalize in %f s.", elapsed / 1000.0)
in_matrices = [m.values for m in result_matrices]
else:
in_matrices = []
num_clusters = membership.num_clusters()
mat = result_matrices[0]
index_map = {name: index for index, name in enumerate(mat.row_names)}
# we assume matrix 0 is always the gene expression score
# we also assume that the matrices are already extreme value
# fixed
rsm = []
for cluster in range(1, num_clusters + 1):
row_members = sorted(membership.rows_for_cluster(cluster))
rsm.extend([mat.values[index_map[row], cluster - 1] for row in row_members])
scale = util.mad(rsm)
if scale == 0: # avoid that we are dividing by 0
scale = util.r_stddev(rsm)
if scale != 0:
median_rsm = util.median(rsm)
rsvalues = (mat.values - median_rsm) / scale
num_rows, num_cols = rsvalues.shape
rscores = dm.DataMatrix(num_rows, num_cols,
mat.row_names,
mat.column_names,
values=rsvalues)
rscores.fix_extreme_values()
else:
logging.warn("combiner scaling -> scale == 0 !!!")
rscores = mat
in_matrices.append(rscores.values)
if len(result_matrices) > 1:
rs_quant = util.quantile(rscores.values, 0.01)
logging.debug("RS_QUANT = %f", rs_quant)
for i in range(1, len(result_matrices)):
values = result_matrices[i].values
qqq = abs(util.quantile(values, 0.01))
if qqq == 0:
logging.warn('SPARSE SCORES - %d attempt 1: pick from sorted values', i)
qqq = sorted(values.ravel())[9]
if qqq == 0:
logging.warn('SPARSE SCORES - %d attempt 2: pick minimum value', i)
qqq = abs(values.min())
if qqq != 0:
values = values / qqq * abs(rs_quant)
else:
logging.warn('SPARSE SCORES - %d not normalizing!', i)
in_matrices.append(values)
if len(result_matrices) > 0:
start_time = util.current_millis()
# assuming same format of all matrices
combined_score = np.zeros(in_matrices[0].shape)
for i in xrange(len(in_matrices)):
combined_score += in_matrices[i] * score_scalings[i]
elapsed = util.current_millis() - start_time
logging.debug("combined score in %f s.", elapsed / 1000.0)
matrix0 = result_matrices[0] # as reference for names
return dm.DataMatrix(matrix0.num_rows, matrix0.num_columns,
matrix0.row_names, matrix0.column_names,
values=combined_score)
else:
return None
def centralize(self): self.position.x = utl.median(self.axes['x'][:]) self.position.y = utl.median(self.axes['y'][:]) self.position.z = utl.median(self.axes['z'][:])
def median(self):
"""returns the mean value"""
return util.median(self.values)
def test_bench_disk_paxos(metasync, opts):
"test disk paxos"
"bencmark latency of paxos with backends"
from disk_paxos import DiskPaxosWorker
repeat = 5
client_num = [1, 2, 3, 4, 5]
backend_list = [["google"], ["dropbox"], ["onedrive"], ["box"], ["google", "dropbox", "onedrive"]]
results = [['Clients'] + [','.join(x) for x in backend_list]]
# start to test
for num in client_num:
for num_prop in range(1, num + 1):
for _ in range(repeat):
row = ['%d/%d clients' % (num_prop, num)]
for backend in backend_list:
srvs = map(services.factory, backend)
dbg.info('Test paxos for %d/%d clients and %s' % (num_prop, num, ','.join(backend)))
# initialize all disk blocks
blockList = []
for i in range(num):
path = '/diskpaxos/client%d' % i
for srv in srvs:
if not srv.exists(path):
srv.put(path, '')
else:
srv.update(path, '')
blockList.append(path)
clients = []
for i in range(num_prop):
storages = map(services.factory, backend)
worker = DiskPaxosWorker(storages, blockList[i], blockList)
clients.append(worker)
#dbg.dbg('client %d %s' % (i, worker.clientid))
for worker in clients:
worker.start()
latency = []
master_latency = None
for worker in clients:
worker.join()
latency.append(worker.latency)
if (worker.master):
assert master_latency is None
master_latency = worker.latency
for worker in clients:
worker.join()
summary = ",".join(map(str,[min(latency), max(latency), util.median(latency), master_latency]))
dbg.info("Result: %s" % summary)
row.append(summary)
results.append(row)
# tabularize
print "Item Format: min,max,median,master"
for row in results:
for e in row:
print "%s \t" % e,
print
def test_bench_paxos2(metasync, opts):
"bencmark latency of paxos with backends"
def new_index(srv, folder, prefix):
if not srv.exists(folder):
return 0
files = srv.listdir(folder)
cnt = 0
for fn in files:
if fn.startswith(prefix):
cnt += 1
return cnt
from paxos import PPaxosWorker2
repeat = 5
client_num = [1, 2, 3, 4, 5]
backend_list = [["dropbox"], ["onedrive"]]
results = [['Clients'] + [','.join(x) for x in backend_list]]
# start to test
for num in client_num:
for _ in range(repeat):
row = ['%d clients' % (num)]
for backend in backend_list:
dbg.info('Test paxos for %d clients and %s' % (num, ','.join(backend)))
srvs = map(services.factory, backend)
# init log file
prefix = 'test2-%d-%d' % (num , len(backend))
index = new_index(srvs[0], '/ppaxos', prefix)
path = '/ppaxos/%s.%d' % (prefix, index)
dbg.info(path)
for srv in srvs:
srv.init_log2(path)
clients = []
for i in range(num):
storages = map(services.factory, backend)
worker = PPaxosWorker2(storages, path)
clients.append(worker)
for worker in clients:
worker.start()
for worker in clients:
worker.join()
latency = []
master_latency = None
for worker in clients:
latency.append(worker.latency)
if (worker.master):
assert master_latency is None
master_latency = worker.latency
assert master_latency is not None
summary = ",".join(map(str,[min(latency), max(latency), util.median(latency), master_latency]))
dbg.info("Result: %s" % summary)
row.append(summary)
results.append(row)
# tabularize
print "Item Format: min,max,median,master"
for row in results:
for e in row:
print "%s \t" % e,
print
def combine(result_matrices, score_scalings, membership, quantile_normalize):
"""This is the combining function, taking n result matrices and scalings"""
for m in result_matrices:
m.fix_extreme_values()
if quantile_normalize:
if len(result_matrices) > 1:
start_time = util.current_millis()
result_matrices = dm.quantile_normalize_scores(result_matrices,
score_scalings)
elapsed = util.current_millis() - start_time
logging.info("quantile normalize in %f s.", elapsed / 1000.0)
in_matrices = [m.values for m in result_matrices]
else:
in_matrices = []
num_clusters = membership.num_clusters()
mat = result_matrices[0]
index_map = {name: index for index, name in enumerate(mat.row_names)}
# we assume matrix 0 is always the gene expression score
# we also assume that the matrices are already extreme value
# fixed
rsm = []
for cluster in range(1, num_clusters + 1):
row_members = sorted(membership.rows_for_cluster(cluster))
rsm.extend([mat.values[index_map[row]][cluster - 1]
for row in row_members])
scale = util.mad(rsm)
if scale == 0: # avoid that we are dividing by 0
scale = util.r_stddev(rsm)
if scale != 0:
median_rsm = util.median(rsm)
rsvalues = (mat.values - median_rsm) / scale
num_rows, num_cols = rsvalues.shape
rscores = dm.DataMatrix(num_rows, num_cols,
mat.row_names,
mat.column_names,
values=rsvalues)
rscores.fix_extreme_values()
else:
logging.warn("combiner scaling -> scale == 0 !!!")
rscores = mat
in_matrices.append(rscores.values)
if len(result_matrices) > 1:
rs_quant = util.quantile(rscores.values, 0.01)
logging.info("RS_QUANT = %f", rs_quant)
for i in range(1, len(result_matrices)):
values = result_matrices[i].values
qqq = abs(util.quantile(values, 0.01))
#print "qqq(%d) = %f" % (i, qqq)
if qqq == 0:
logging.error("very sparse score !!!")
values = values / qqq * abs(rs_quant)
in_matrices.append(values)
if len(result_matrices) > 0:
start_time = util.current_millis()
# assuming same format of all matrices
combined_score = np.zeros(in_matrices[0].shape)
for i in xrange(len(in_matrices)):
combined_score += in_matrices[i] * score_scalings[i]
elapsed = util.current_millis() - start_time
logging.info("combined score in %f s.", elapsed / 1000.0)
matrix0 = result_matrices[0] # as reference for names
return dm.DataMatrix(matrix0.num_rows, matrix0.num_columns,
matrix0.row_names, matrix0.column_names,
values=combined_score)
else:
return None
def analysis(data, settings, flat_age_matrix=[], flat_sim_matrix=[], alpha=0.03):
# value_keys = ["complete_term_jaccard", "top_term_jaccard", "top_gene_jaccard", "top_parents_jaccard"]
value_keys = ["top_gene_jaccard", "top_parents_jaccard"]
data_values = [[row._asdict()[k] for k in value_keys] for row in data]
means = [mean([x[i] for x in data_values]) for i in range(len(value_keys))]
stds = [sstdev([x[i] for x in data_values]) for i in range(len(value_keys))]
medians = [median([x[i] for x in data_values]) for i in range(len(value_keys))]
log.info("N: %s", len(data_values))
genes_found, genes_missed = len([genes[1] for row in data for genes in row.genes_found]),len([genes for row in data for genes in row.genes_missed])
log.info("genes_found: %s, genes_missed: %s", genes_found, genes_missed)
total = genes_found + genes_missed
log.info("genes_found: %s, genes_missed: %s", round(genes_found / total, 2), round(genes_missed / total, 2))
#log.info("distinct unknown genes: %s", len(unknown))
fig_num = 0
for i, value_key in enumerate(value_keys):
log.info("%s: mean=%s, std=%s, median=%s", value_key, means[i], stds[i], medians[i])
f = plt.figure(fig_num)
fig_num +=1
d = [x[i] for x in data_values]
weights = np.ones_like(d)/float(len(d))
plt.hist(d, 100, weights=weights, alpha=0.5, label='Actual')
weights = np.ones_like(flat_sim_matrix)/float(len(flat_sim_matrix))
plt.hist(flat_sim_matrix, 100, weights=weights, alpha=0.5, label='Randomized')
plt.xlabel('Similarity')
plt.ylabel(value_keys[i])
plt.title('Histogram of ' + value_key + " | " + r'$\mu=' + str(round(means[i], 2)) + r',\ \sigma=' + str(round(stds[i], 2)) + r'$')
plt.legend(loc='upper right')
f.show()
f = plt.figure(fig_num)
fig_num +=1
x = [r.age for r in data]
y = [r._asdict()[value_key] for r in data]
fit = np.polyfit(x,y,1)
fit_fn = np.poly1d(fit)
plt.plot(x,y, 'k.', [min(x), max(x)], fit_fn([min(x), max(x)]), '--g')
plt.xlabel('Age')
plt.ylabel("Similarity Index")
plt.title("Ancestors of " + value_key + " vs Age" + " | " + r'$\mu=' + str(round(means[i], 2)) + r',\ \sigma=' + str(round(stds[i], 2)) + r'$' )
f.show()
f = plt.figure(fig_num)
fig_num +=1
x = flat_age_matrix
y = flat_sim_matrix
fit = np.polyfit(x,y,1)
fit_fn = np.poly1d(fit)
plt.plot(x,y, 'k.', [min(x), max(x)], fit_fn([min(x), max(x)]), '--g', alpha=alpha)
plt.xlabel('Age')
plt.ylabel("Similarity Index")
plt.title("Randomized Ancestors of " + value_key + " vs Age" + " | " + r'$\mu=' + str(round(means[i], 2)) + r',\ \sigma=' + str(round(stds[i], 2)) + r'$' )
f.show()
f = plt.figure(fig_num)
fig_num +=1
d = [x[4] for x in data]
weights = np.ones_like(d)/float(len(d))
plt.hist(d, 100, weights=weights, alpha=0.5, label='Actual')
weights = np.ones_like(flat_age_matrix)/float(len(flat_age_matrix))
plt.hist(flat_age_matrix, 100, weights=weights, alpha=0.5, label='Randomized')
plt.xlabel('Age')
f.show()
raw_input()
plt.close("all")
def test_median_with_nans(self):
"""tests the mean() function"""
array = np.array([2.0, 3.0, np.nan, 1.0])
result = util.median(array)
self.assertAlmostEqual(2.0, result)
def test_bench_disk_paxos(metasync, opts):
"test disk paxos"
"bencmark latency of paxos with backends"
from disk_paxos import DiskPaxosWorker
repeat = 5
client_num = [1, 2, 3, 4, 5]
backend_list = [["google"], ["dropbox"], ["onedrive"], ["box"],
["google", "dropbox", "onedrive"]]
results = [['Clients'] + [','.join(x) for x in backend_list]]
# start to test
for num in client_num:
for num_prop in range(1, num + 1):
for _ in range(repeat):
row = ['%d/%d clients' % (num_prop, num)]
for backend in backend_list:
srvs = map(services.factory, backend)
dbg.info('Test paxos for %d/%d clients and %s' %
(num_prop, num, ','.join(backend)))
# initialize all disk blocks
blockList = []
for i in range(num):
path = '/diskpaxos/client%d' % i
for srv in srvs:
if not srv.exists(path):
srv.put(path, '')
else:
srv.update(path, '')
blockList.append(path)
clients = []
for i in range(num_prop):
storages = map(services.factory, backend)
worker = DiskPaxosWorker(storages, blockList[i],
blockList)
clients.append(worker)
#dbg.dbg('client %d %s' % (i, worker.clientid))
for worker in clients:
worker.start()
latency = []
master_latency = None
for worker in clients:
worker.join()
latency.append(worker.latency)
if (worker.master):
assert master_latency is None
master_latency = worker.latency
for worker in clients:
worker.join()
summary = ",".join(
map(str, [
min(latency),
max(latency),
util.median(latency), master_latency
]))
dbg.info("Result: %s" % summary)
row.append(summary)
results.append(row)
# tabularize
print "Item Format: min,max,median,master"
for row in results:
for e in row:
print "%s \t" % e,
print
def calcAverage(self):
levels = []
for each in self.players:
levels.append(int(each.getLevel()))
self.averageLevel = util.median(levels)
def combine(result_matrices, score_scalings, membership, iteration,
config_params):
"""This is the combining function, taking n result matrices and scalings"""
quantile_normalize = config_params['quantile_normalize']
for i, m in enumerate(result_matrices):
m.fix_extreme_values()
m.subtract_with_quantile(0.99)
# debug mode: print scoring matrices before combining
if ('dump_scores' in config_params['debug']
and (iteration == 1 or
(iteration % config_params['debug_freq'] == 0))):
funs = config_params['pipeline']['row-scoring']['args'][
'functions']
m.write_tsv_file(os.path.join(
config_params['output_dir'],
'score-%s-%04d.tsv' % (funs[i]['id'], iteration)),
compressed=False)
if quantile_normalize:
if len(result_matrices) > 1:
start_time = util.current_millis()
result_matrices = dm.quantile_normalize_scores(
result_matrices, score_scalings)
elapsed = util.current_millis() - start_time
logging.debug("quantile normalize in %f s.", elapsed / 1000.0)
in_matrices = [m.values for m in result_matrices]
else:
in_matrices = []
num_clusters = membership.num_clusters()
mat = result_matrices[0]
index_map = {name: index for index, name in enumerate(mat.row_names)}
# we assume matrix 0 is always the gene expression score
# we also assume that the matrices are already extreme value
# fixed
rsm = []
for cluster in range(1, num_clusters + 1):
row_members = sorted(membership.rows_for_cluster(cluster))
rsm.extend([
mat.values[index_map[row], cluster - 1] for row in row_members
])
scale = util.mad(rsm)
if scale == 0: # avoid that we are dividing by 0
scale = util.r_stddev(rsm)
if scale != 0:
median_rsm = util.median(rsm)
rsvalues = (mat.values - median_rsm) / scale
num_rows, num_cols = rsvalues.shape
rscores = dm.DataMatrix(num_rows,
num_cols,
mat.row_names,
mat.column_names,
values=rsvalues)
rscores.fix_extreme_values()
else:
logging.warn("combiner scaling -> scale == 0 !!!")
rscores = mat
in_matrices.append(rscores.values)
if len(result_matrices) > 1:
rs_quant = util.quantile(rscores.values, 0.01)
logging.debug("RS_QUANT = %f", rs_quant)
for i in range(1, len(result_matrices)):
values = result_matrices[i].values
qqq = abs(util.quantile(values, 0.01))
if qqq == 0:
logging.debug(
'SPARSE SCORES - %d attempt 1: pick from sorted values',
i)
qqq = sorted(values.ravel())[9]
if qqq == 0:
logging.debug(
'SPARSE SCORES - %d attempt 2: pick minimum value', i)
qqq = abs(values.min())
if qqq != 0:
values = values / qqq * abs(rs_quant)
else:
logging.debug('SPARSE SCORES - %d not normalizing!', i)
in_matrices.append(values)
if len(result_matrices) > 0:
start_time = util.current_millis()
# assuming same format of all matrices
combined_score = np.zeros(in_matrices[0].shape)
for i in xrange(len(in_matrices)):
combined_score += in_matrices[i] * score_scalings[i]
elapsed = util.current_millis() - start_time
logging.debug("combined score in %f s.", elapsed / 1000.0)
matrix0 = result_matrices[0] # as reference for names
return dm.DataMatrix(matrix0.num_rows,
matrix0.num_columns,
matrix0.row_names,
matrix0.column_names,
values=combined_score)
else:
return None
def main ():
parser = argparse.ArgumentParser()
parser.add_argument('--host', default = 'mongodb://localhost:27017/')
parser.add_argument('action', choices = ['insert'])
parser.add_argument('-it', type = int, default = 2, help = "inserting thread num")
parser.add_argument('-rt', type = int, default = 1, help = "reading thread num")
parser.add_argument('-rmt', type = int, default = 1, help = "removing thread num")
parser.add_argument('-db_name', default = "test_database")
parser.add_argument('-collection_name', default = "test_collection")
parser.add_argument('-noi', '--no-index', action = 'store_true', help = "dont create index")
args = parser.parse_args()
collection = connect(args)
print collection.count(), "records in collection %s.%s" % (args.db_name, args.collection_name)
print "dropping old indexes..."
try:
collection.drop_index([("timestamp", pymongo.ASCENDING)])
except pymongo.errors.OperationFailure as e:
if "index not found" not in str(e):
raise
if not args.no_index:
print "trying to create indexes..."
#pymongo.ASCENDING == 1
collection.create_index([("ev_id", pymongo.ASCENDING)])
collection.create_index([("ev_id", pymongo.ASCENDING), ("timestamp", pymongo.ASCENDING)])
collection.create_index([("ev_id", pymongo.ASCENDING), ("s_id", pymongo.ASCENDING), ("timestamp", pymongo.ASCENDING)])
collection.create_index([("s_id", pymongo.ASCENDING)])
# insert()
if args.action == 'insert':
accum = collections.deque()
r_accum = collections.deque()
rm_accum = collections.deque()
print "starting threads:", args.it, "inserting", args.rt, "reading", args.rmt, "removing"
for _ in range(args.it):
collection = connect(args)
t = threading.Thread(target = insert_loop, args = [accum, collection])
t.daemon = True
t.start()
for _ in range(args.rt):
collection = connect(args)
t = threading.Thread(target = read_loop, args = [r_accum, collection])
t.daemon = True
t.start()
for _ in range(args.rmt):
collection = connect(args)
t = threading.Thread(target = remove_loop, args = [rm_accum, collection])
t.daemon = True
t.start()
print "starting stat loop..."
t_start = time.time()
total_inserts = 0
total_reads = 0
try:
sleepd = 0
while True:
time.sleep(1 - sleepd)
t1 = time.time()
v = list(accum)
accum.clear()
ra = list(r_accum)
r_accum.clear()
v.sort()
ra.sort()
num = len(v)
rnum = len(ra)
total_inserts += num
total_reads += rnum
m = util.median(v)
m = ("%0.5f" % m) if m is not None else None
p95 = util.p95(v)
p95 = ("%0.5f" % p95) if p95 is not None else None
mx = max(v) if v else None
mx = ("%0.5f" % mx) if mx is not None else None
print args.it, "ithreads", num, "insert/s, med", m, "p95", p95, "max", mx, "total num", total_inserts
m = util.median(ra)
m = (("%0.5f" % m) if m is not None else None) or '-'
p95 = util.p95(ra)
p95 = (("%0.5f" % p95) if p95 is not None else None) or '-'
mx = max(ra) if ra else None
mx = (("%0.5f" % mx) if mx is not None else None) or '-'
print " ", args.rt, "rthreads", rnum, "reads/s, med", m, "p95", p95, "max", mx, "total num", total_reads
sleepd = time.time() - t1
finally:
print "total inserts", total_inserts, "total reads", total_reads, "took", (time.time() - t_start), "s"
def test_bench_paxos(metasync, opts):
"bencmark latency of paxos with backends"
def new_index(srv, folder, prefix):
if services.slug(srv) == 'onedrive':
folder = '/Public' + folder
if not srv.exists(folder):
return 0
files = srv.listdir(folder)
cnt = 0
for fn in files:
if fn.startswith(prefix):
cnt += 1
return cnt
from paxos import PPaxosWorker
repeat = 5
client_num = [1, 2, 3, 4, 5]
backend_list = [["google"], ["dropbox"], ["onedrive"], ["box"],
["google", "dropbox", "onedrive"]]
results = [['Clients'] + [','.join(x) for x in backend_list]]
# start to test
for num in client_num:
for _ in range(repeat):
row = ['%d clients' % (num)]
for backend in backend_list:
dbg.info('Test paxos for %d clients and %s' %
(num, ','.join(backend)))
srvs = map(services.factory, backend)
# init log file
prefix = 'test-%d-%d' % (num, len(backend))
index = new_index(srvs[0], '/ppaxos', prefix)
path = '/ppaxos/%s.%d' % (prefix, index)
dbg.info(path)
for srv in srvs:
srv.init_log(path)
clients = []
for i in range(num):
storages = map(services.factory, backend)
worker = PPaxosWorker(storages, path)
clients.append(worker)
for worker in clients:
worker.start()
latency = []
master_latency = None
for worker in clients:
worker.join()
latency.append(worker.latency)
if (worker.master):
assert master_latency is None
master_latency = worker.latency
for worker in clients:
worker.join()
summary = ",".join(
map(str, [
min(latency),
max(latency),
util.median(latency), master_latency
]))
dbg.info("Result: %s" % summary)
row.append(summary)
results.append(row)
# tabularize
print "Item Format: min,max,median,master"
for row in results:
for e in row:
print "%s \t" % e,
print
def calcAverage(self):
levels = []
for each in self.players:
levels.append(int(each.getLevel()))
self.averageLevel = util.median(levels)