def main():
args = sys.argv
if(len(args)<2):
print("error: the number of input parameters is not correct; input command must be")
print(">$ python post.py input_file_name[e.g. dist.ini]")
sys.exit(1)
Ini = inifile.IniFile(args[1])
Ini.Dump()
section = "POSTPROCESS"
postroot = Ini.ReadString(section,"postroot")
Ini.Dump(postroot+"_dist.ini")
chains = Ini.ReadString(section,"chains").split(",")
chainlabels = Ini.ReadString(section,"chainlabels").split(",")
names = Ini.ReadString(section,"paramnames").split(",")
labels = Ini.ReadString(section,"paramlabels").split(",")
dnames = Ini.ReadString(section,"dparamnames").split(",")
dlabels = Ini.ReadString(section,"dparamlabels").split(",")
PP = PostProcess(chains,chainlabels,names,labels,dnames,dlabels)
PP.ReadChains()
# triangle plots
g = plots.getSubplotPlotter()
g.triangle_plot(PP.getdist_samples,filled=True)
g.export(postroot+"_triangle.pdf")
def read_file():
game_state = {}
data_text, time = encrypt.decrypt()
data_file = os.path.join(
os.getenv('TMP'), '.save.ini') if os.name == 'nt' else '/tmp/.save.cfg'
with open(data_file, 'w') as open_file:
open_file.write(data_text)
ini_file = inifile.IniFile(data_file)
#print(ini_file.to_dict())
keys = ini_file.keys()
values = ini_file.values()
for key, value in zip(keys, values):
if '.' in key:
# value is in a category
klass, prop = key.split('.')
#print(klass, prop)
try:
game_state[klass]
except KeyError:
game_state[klass] = {}
game_state[klass][prop] = value
else:
game_state[key] = value
return game_state
def main(paramfile,verbose):
Ini = inifile.IniFile(paramfile)
if(verbose>0):
Ini.Dump()
section = "OUTPUT"
root = Ini.ReadString(section,"root")
# fiducial cosmology
BG = background.Background(verbose=verbose)
section = "COSMOLOGY"
paramsfid = np.array([Ini.ReadFloat(section,"ob"),Ini.ReadFloat(section,"odm"),Ini.ReadFloat(section,"ode"),
Ini.ReadFloat(section,"nnu"),Ini.ReadFloat(section,"mnu")])
BG.SetParams(paramsfid)
# clumpiness
section = "NBODY"
path = Ini.ReadString(section,"clumpiness")
if(verbose>0):
print("\n# cluminess (ignored when intype==2)")
# clumz is ln(B) as function of ln(1+z) constructed from a lookup table of "z B(z)".
try:
tab = np.loadtxt(path)
clumz = interpolate.make_interp_spline(np.log(1+tab[:,0]),np.log(1+tab[:,1]))
if(verbose>0):
print(" cluminess factor is read from ",path)
except:
if(verbose>0):
print(" table file for clumpiness factor is missing; cluminess is ignored:")
clumz = lambda x:0
# energy deposition
DE = deposition.Deposit(verbose=verbose)
section = "DEPOSITION"
DE.SetParams(Ini.ReadString(section,"epspath"),Ini.ReadInt("INJECTION","intype"))
DE.Calcfc(BG.dtauda,clumz)
# injection spectrum phythia
INJ = injection.Injection(verbose=verbose)
section = "INJECTION"
mspec = 5
INJ.SetParams(DE.intype,Ini.ReadFloat(section,"mass"),Ini.ReadInt(section,"mult"),Ini.ReadInt(section,"mode"),
Ini.ReadBoolean(section,"use_prec"),DE.nerg*mspec,DE.erg[0]*const.eV/const.GeV,DE.erg[DE.nerg-1]*const.eV/const.GeV)
INJ.SetRate(Ini.ReadFloat(section,"sigmav") if INJ.intype==1 else Ini.ReadFloat(section,"gamma"))
INJ.GetBinnedNumber()
# IGM evolution
TH = therm.Therm(verbose=verbose)
TH.SetParams(root)
TH.IntegEnergy(DE,INJ)
TH.ThermInput(BG,DE,INJ)
TH.EvolveTspin(BG,DE,INJ)
#for EDGES???
if(verbose>0):
print(INJ.mass,INJ.sigmav/const.cm**3,TH.dT21cm_edges)
return TH.dT21cm_edges
async def create_site(self, lektor, name, owner, site_id):
site_workdir = self.workdir / site_id
if site_workdir.exists():
raise ValueError('workdir for such site-id already exists')
site_repo = await self.create_site_repo(site_id)
run_local = functools.partial(run, cwd=site_workdir)
theme_repo = os.environ.get('LEKTORIUM_LEKTOR_THEME', None)
if theme_repo is not None:
with tempfile.TemporaryDirectory() as theme_dir:
await async_run(run, f'git clone {theme_repo} {theme_dir}')
example_site = pathlib.Path(theme_dir) / 'example-site'
if example_site.exists():
shutil.rmtree(example_site / 'themes', ignore_errors=True)
shutil.copytree(example_site, site_workdir)
site_config = list(site_workdir.glob('*.lektorproject'))
if site_config:
site_config = one(site_config)
config_data = inifile.IniFile(site_config)
config_data['project.name'] = name
del config_data['project.url']
config_data.save()
site_config.rename(site_workdir / f'{name}.lektorproject')
else:
shutil.rmtree(site_workdir, ignore_errors=True)
if theme_repo is None or not site_workdir.exists():
lektor.quickstart(name, owner, site_workdir)
if theme_repo is not None:
shutil.rmtree(site_workdir / 'templates')
shutil.rmtree(site_workdir / 'models')
await async_run(run_local, 'git init')
await async_run(run_local, 'git lfs install')
await async_run(run_local, f'git remote add origin {site_repo}')
await async_run(run_local, 'git fetch')
await async_run(run_local, 'git reset origin/master')
(site_workdir / '.gitattributes').write_text(
os.linesep.join(f'{m} filter=lfs diff=lfs merge=lfs -text'
for m in LFS_MASKS), )
theme_repo = os.environ.get('LEKTORIUM_LEKTOR_THEME', None)
if theme_repo is not None:
site_repo_host, _, site_repo_path = str(site_repo).partition(':')
theme_repo_host, _, theme_repo_path = str(theme_repo).partition(
':')
if site_repo_host == theme_repo_host:
site_repo_path = len(pathlib.Path(site_repo_path).parts)
site_repo_path = ('..', ) * site_repo_path
theme_repo = pathlib.Path(*site_repo_path) / theme_repo_path
await async_run(
run_local,
f'git submodule add {theme_repo} themes/iarcrp-lektor-theme',
)
await async_run(run_local, 'git add .')
await async_run(run_local, 'git commit -m quickstart')
await async_run(run_local, 'git push --set-upstream origin master')
return site_workdir, dict(repo=str(site_repo))
def test_load_config_file(self):
with TemporaryDirectory() as tempdir:
os.mkdir(os.path.join(tempdir, "configs"))
config = inifile.IniFile(os.path.join(tempdir, "configs",
"s3.ini"))
config["section.key"] = "value"
config.save()
publisher = lektor_s3.S3Publisher(MockEnvironment(tempdir), "")
have = publisher.get_config().section_as_dict("section")
self.assertEqual(have["key"], "value")
def get_line(filename, pathedname):
ini = inifile.IniFile(pathedname)
name = filename
desc = ini.get('vars.desc', '<NULL>')
filesize = ini.get('vars.filesize', '<NULL>')
log_lines = get_git_log(pathedname)
created = get_created_commit(log_lines).split(',')[1]
last_modified = get_last_modified_commit(log_lines).split(',')[1]
commits = len(log_lines)
return [ name, desc, filesize, created, last_modified, commits]
def ReadChains(self):
for i in range(len(self.chains)):
fname = self.chains[i]
label = self.chainlabels[i]
print("\n#chain name:",fname)
Ini = inifile.IniFile(self.chains[i].replace(".h5","_params.ini"))
section = "MCMC"
paramranges = {}
for key in self.paramnames:
paramranges[key] = Ini.ReadFloatArray(section,key)[0:2]
reader = emcee.backends.HDFBackend(fname,read_only=True)
#tau = reader.get_autocorr_time()
samples = reader.get_chain(flat=True)
log_prob_samples = reader.get_log_prob(flat=True)
weights = np.array(log_prob_samples>-np.inf).astype(np.float)
blob_samples = reader.get_blobs(flat=True)
blobnames = blob_samples.dtype.names
flag = False
for dname in self.dparamnames:
if not dname in blobnames:
print("error: not found a derived parameter ",dname)
flag = True
if(flag): sys.exit(1)
derived_samples = np.array([blob_samples[dname] for dname in self.dparamnames]).transpose()
a = np.nanmin(derived_samples,axis=0)
b = np.nanmax(derived_samples,axis=0)
dparamranges = np.concatenate([a[:,None],b[:,None]],axis=1)
print(" flat chain shape: {0}".format(samples.shape))
print(" flat log prob shape: {0}".format(log_prob_samples.shape))
print(" flat blob shape: {0}".format(blob_samples.shape))
print(" flat derived chain shape: {0}".format(derived_samples.shape))
paramnames = []
paramnames.extend(self.paramnames)
paramnames.extend(self.dparamnames)
paramlabels = []
paramlabels.extend(self.paramlabels)
paramlabels.extend(self.dparamlabels)
for j in range(len(self.dparamnames)):
paramranges[self.dparamnames[j]] = dparamranges[j,:]
self.getdist_samples.append(
MCSamples(samples=np.concatenate([samples,derived_samples],axis=1),
names=paramnames,loglikes=-log_prob_samples,
labels=paramlabels,label=label,ranges=paramranges,weights=weights)
)
# derived parameters?
#print("###")
#pidx = {}
#for p in ("ob", "odm", "ol", "decay_rate", "mratio", "nnu", "mnu"):
# try:
# pidx[p] = self.paramnames.index(p)
# except ValueError:
# pass
#if("odm" in self.paramnames and "ol" in self.paramnames):
# self.getdist_samples[i].addDerived(np.sqrt(samples[:,pidx["odm"]]+samples[:,pidx["ol"]])*100,name="h_nodecay",label="H_\emptyset",range=None)
#if("decay_rate" in self.paramnames):
# self.getdist_samples[i].addDerived(samples[:,pidx["decay_rate"]],name="lt",label="\\tau {\\rm [Gyr]}",range=None)
#self.getdist_samples[i].getConvergeTests()
tau = np.array([self.getdist_samples[i].getCorrelationLength(j) for j in range(len(self.paramnames))])
burnin = int(2.*np.max(tau))
thin = max(int(0.5*np.min(tau)),1)
print(" correlation lengthes: {}".format(tau))
print(" burn-in: {0}".format(burnin))
print(" thin: {0}".format(thin))
print(" burn-in fraction: {0}".format(burnin/len(log_prob_samples)))
print(" thinned sample number: {0}".format(int(len(log_prob_samples)/thin)))
self.getdist_samples[i].deleteZeros()
self.getdist_samples[i].removeBurn(burnin)
self.getdist_samples[i].thin(thin)
# constraints
for j, mean in enumerate(self.getdist_samples[i].getMeans()):
#print(" %s = %f +/- %f +/- %f" % (self.getdist_samples[i].parLabel(j),\
# mean, mean - self.getdist_samples[i].confidence(j,upper=True,limfrac=0.16),\
# mean - self.getdist_samples[i].confidence(j,upper=False,limfrac=0.16)) )
print(" %s mean = %f" % (self.getdist_samples[i].parLabel(j),mean))
print(" two-tail error 68 C.L. = +%f, -%f" %(self.getdist_samples[i].confidence(j,upper=True,limfrac=0.16)-mean,\
mean-self.getdist_samples[i].confidence(j,upper=False,limfrac=0.16)))
upper = self.getdist_samples[i].confidence(j,upper=True,limfrac=0.05)
print(" one-tail upper limit 95 C.L. = %f" % upper)
lower = self.getdist_samples[i].confidence(j, upper=False, limfrac=0.05)
print(" one-tail lower limit 95 C.L. = %f" % lower)
def main():
args = sys.argv
if (len(args) < 2):
print(
"error: the number of input parameters is not correct; input command must be"
)
print(">$ python inifiles.py input_file_name")
sys.exit(1)
Ini = inifile.IniFile(args[1])
Ini.Dump()
section = "OUTPUT"
root = Ini.ReadString(section, "root")
Ini.Dump(root + "_params.ini")
# model calculation with fiducial parameters
section = "FIDUCIAL COSMOLOGY"
paramsfid = np.array([
Ini.ReadFloat(section, "ob"),
Ini.ReadFloat(section, "odm"),
Ini.ReadFloat(section, "ol"),
Ini.ReadFloat(section, "decay_rate"),
Ini.ReadFloat(section, "mratio"),
Ini.ReadFloat(section, "nnu"),
Ini.ReadFloat(section, "mnu")
])
section = "DDM SETUP"
BG = mddm.Background(Ini.ReadInt(section, "num_a"),
Ini.ReadInt(section, "max_it"),
Ini.ReadFloat(section, "tol"),
verbose=1)
BG.SetParams(paramsfid)
BG.Iterate()
# parameters for output scale factors
#BG.Output(np.logspace(np.log10(1e-3),np.log10(1),50,base=10))
# likelihood calculation
section = "LIKELIHOODS"
LF = likelihoods.Likelihood(Ini.ReadBoolean(section, "use_BAO"),
Ini.ReadBoolean(section, "use_H0"),
Ini.ReadBoolean(section, "use_CMB"),
verbose=1)
lnL = LF.LnLike(BG, 0)
print(" ln(L)=", lnL[:])
# MCMC
#inp = input("\nDoes everything seem going fine? Then let's run MCMC [Y/n]:")
#if(inp=='n'):
# sys.exit()
section = 'MCMC'
BG.verbose = LF.verbose = 0
paramrange = [
Ini.ReadFloatArray(section, "ob"),
Ini.ReadFloatArray(section, "odm"),
Ini.ReadFloatArray(section, "ol"),
Ini.ReadFloatArray(section, "decay_rate"),
Ini.ReadFloatArray(section, "mratio"),
Ini.ReadFloatArray(section, "nnu"),
Ini.ReadFloatArray(section, "mnu")
]
map_varied = np.array(
[i for i in range(len(paramrange)) if len(paramrange[i] > 0)])
range_varied = np.array([paramrange[i] for i in map_varied])
driver = mcmc.MCMC(paramsfid, Ini.ReadBoolean(section, "parallel"))
driver.SetParams(map_varied,
range_varied,
Ini.ReadInt(section, "nwalkers"),
verbose=1)
chain = root + ".h5"
driver.Run(chain, Ini.ReadInt(section, "nsteps"), BG, LF)
def site_config(self, site_id):
site_root = self._site_dir(site_id)
config = list(site_root.glob('*.lektorproject'))
if config:
return inifile.IniFile(one(config))
return collections.defaultdict(type(None))
def get_config(self):
return inifile.IniFile(self.config_filename)
def get_config(self,ini_file):
path = os.path.join(self.env.root_path,'configs',ini_file)
return inifile.IniFile(path)