本文整理汇总了Python中cc.tools.io.DataIO类的典型用法代码示例。如果您正苦于以下问题：Python DataIO类的具体用法？Python DataIO怎么用？Python DataIO使用的例子？那么恭喜您, 这里精选的类代码示例或许可以为您提供帮助。
在下文中一共展示了DataIO类的15个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: readTelescopeProperties
def readTelescopeProperties(telescope):
"""
Read the telescope properties from Telescope.dat.
This currently includes the telescope size in m, and the default
absolute flux calibration uncertainty.
@param telescope: The telescope requested
@type telescope: str
@return: The telescope size and absolute flux calibration uncertainty
@rtype: (float,float)
"""
all_telescopes = DataIO.getInputData(keyword="TELESCOPE", start_index=5, filename="Telescope.dat")
if "PACS" in telescope:
telescope = "PACS"
else:
telescope = telescope
try:
tel_index = all_telescopes.index(telescope)
except ValueError:
raise ValueError("%s not found in Telescope.dat." % telescope)
size = DataIO.getInputData(keyword="SIZE", start_index=5, filename="Telescope.dat", rindex=tel_index)
abs_err = DataIO.getInputData(keyword="ABS_ERR", start_index=5, filename="Telescope.dat", rindex=tel_index)
return (size, abs_err)示例2: parseProfile
def parseProfile(self):
'''
Parse the sphinx file 2, which includes all line profile info.
The output is stored in dict self['sph2'].
'''
self['sph2'] = dict()
self['sph2']['nobeam'] = dict()
self['sph2']['beam'] = dict()
self['sph2']['nobeam_cont'] = dict()
self['sph2']['beam_cont'] = dict()
data = self.getFile(wildcard='2',delimiter=' ')
data_col_1 = [d[0] for d in data]
data_i = 6
data_j = DataIO.findString(data_i,data_col_1)
self['sph2']['nobeam']['velocity'] = array([float(line[0])
for line in data[data_i:data_j]])
#-- Reverse this flux grid. Sphinx output files give the mirrored
# flux grid for the associated velocity grid.
self['sph2']['nobeam']['flux'] = array([DataIO.convertFloat(line[-1],\
nans=1)
for line in data[data_i:data_j]])
self['sph2']['nobeam']['flux'] = self['sph2']['nobeam']['flux'][::-1]
data_k = data_j + 4
data_l = DataIO.findString(data_k,data_col_1)
self['sph2']['beam']['velocity'] = array([float(line[0])
for line in data[data_k:data_l]])
self['sph2']['beam']['flux'] = array([float(line[-1])
for line in data[data_k:data_l]])
self['sph2']['beam']['norm_flux'] = array([float(line[1])
for line in data[data_k:data_l]])
self['sph2']['beam']['tmb'] = array([float(line[2])
for line in data[data_k:data_l]])
#-- Set the continuum value for the different profiles
self.setContinuum('nobeam','flux')
for lp in ['flux','norm_flux','tmb']:
self.setContinuum('beam',lp)
#-- Check if the velocity is correctly monotonously increasing
if self['sph2']['beam']['velocity'][0] > self['sph2']['beam']['velocity'][-1]:
self['sph2']['beam']['velocity'] = self['sph2']['beam']['velocity'][::-1]
self['sph2']['beam']['flux'] = self['sph2']['beam']['flux'][::-1]
self['sph2']['beam']['norm_flux'] = self['sph2']['beam']['norm_flux'][::-1]
self['sph2']['beam']['tmb'] = self['sph2']['beam']['tmb'][::-1]
if self['sph2']['nobeam']['velocity'][0] > self['sph2']['nobeam']['velocity'][-1]:
self['sph2']['nobeam']['velocity'] = self['sph2']['nobeam']['velocity'][::-1]
self['sph2']['nobeam']['flux'] = self['sph2']['nobeam']['flux'][::-1]
#-- Check for NaNs in the profile.
if True in list(isnan(self['sph2']['nobeam']['flux'])):
self.nans_present = True
print "WARNING! There are NaN's in the intrinsic line profile " + \
"with model id %s:"\
%(os.path.split(os.path.split(self.fn)[0])[1])
print os.path.split(self.fn.replace('sph*','sph2'))[1]示例3: getKey
def getKey(k,data=None,fn=None):
'''
Retrieve data from an ALI inputfile.
Returns the line following the line that contains the given key.
@param k: The unique input key word for which the ALI inputfile is
searched.
@type k: str
@keyword data: The data, ie a file read by readFile with delimiter set to ''
A filename must be given if data is None.
(default: None)
@type data: list[str]
@keyword fn: The ALI input filename. Only used if data is None.
(default: None)
@type fn: str
@return: The line following the line that contains given key
@rtype: str
'''
if data is None:
data = DataIO.readFile(filename=fn,delimiter=None,replace_spaces=0)
i = DataIO.findKey(0,data,k)
return data[i+1].replace('\n','')示例4: setStarPars
def setStarPars(self):
"""
Set some standard stellar parameters such as Ak and galactic position.
"""
self.star_index = DataIO.getInputData().index(self.star_name)
ll = DataIO.getInputData(keyword='LONG',rindex=self.star_index)
bb = DataIO.getInputData(keyword='LAT',rindex=self.star_index)
if self.distance <> None:
self.ak = em.findext_marshall(ll=ll,bb=bb,distance=self.distance,\
norm='Ak')
if self.ak is None:
self.ak = em.findext_drimmel(lng=ll,lat=bb,norm='Ak',\
distance=self.distance)
if self.ak is None:
self.ak = DataIO.getInputData(keyword='A_K',rindex=self.star_index)
snp = DataIO.getInputData(keyword='STAR_NAME_PLOTS',\
remove_underscore=1,rindex=self.star_index)
self.star_name_plots = snp
if (abs(ll) < 5.0 or ll > 355.0) and abs(bb) < 5.0:
self.gal_position = 'GC'
else:
self.gal_position = 'ISM' 示例5: waterFraction1StepProfiler
def waterFraction1StepProfiler(model_id,path_gastronoom,fraction,rfrac):
'''
Create a 1-step fractional profile for water.
The original water abundance profile is taken from the output of the
original model without fractional abundances.
These fraction profiles can be used for CHANGE_ABUNDANCE_FRACTION in mline
@param model_id: The model id of the original cooling model
@type model_id: string
@param path_gastronoom: The model subfolder in ~/GASTRoNOoM/
@type path_gastronoom: string
@param fraction: the fraction used
@type fraction: float
@param rfrac: the radius at the step to the fractional abundance [cm]
@type rfrac: float
'''
rfrac = float(rfrac)
fraction = float(fraction)
filename = os.path.join(cc.path.gastronoom,path_gastronoom,'models',\
model_id,'coolfgr_all%s.dat'%model_id)
rad = Gastronoom.getGastronoomOutput(filename=filename,keyword='RADIUS',\
return_array=1)
fraction_profile = np.ones(len(rad))
step_index = np.argmin(abs(rad-rfrac))
fraction_profile[step_index:] = fraction
output_filename = os.path.join(cc.path.gastronoom,path_gastronoom,\
'profiles',\
'water_fractions_%s_%.2f_r%.3e.dat'\
%(model_id,fraction,rfrac))
DataIO.writeCols(output_filename,[rad,fraction_profile])示例6: setData
def setData(self,**kwargs):
'''
Select available data.
Based on the data file types in Sed.dat and the available data files.
Also calls the buildPhotometry method to create a photometry file from
the IvS Sed builder tool
Any keywords required for buildPhotometry can be passed here.
'''
data_types = DataIO.getInputData(keyword='DATA_TYPES',\
filename='Sed.dat')
abs_errs = DataIO.getInputData(keyword='ABS_ERR',filename='Sed.dat')
if 'Photometric_IvS' in data_types:
buildPhotometry(self.star_name,**kwargs)
self.data_types = []
self.data_filenames = []
self.abs_err = dict()
for dt,ierr in zip(data_types,abs_errs):
searchpath = os.path.join(cc.path.dsed,'%s_*%s*.dat'\
%(dt,self.star_name))
add_files = glob(searchpath)
for ff in add_files:
if ff not in self.data_filenames:
self.data_filenames.append(ff)
self.data_types.append(dt)
self.abs_err[dt] = ierr示例7: doMline
def doMline(self,star):
"""
Run mline.
First, database is checked for retrieval of old models.
@param star: The parameter set for this session
@type star: Star()
"""
model_bools = self.checkMlineDatabase()
del self.command_list['R_OUTER']
del self.command_list['OUTER_R_MODE']
for molec,model_bool in zip(self.molec_list,model_bools):
if not model_bool:
self.updateModel(molec.getModelId())
commandfile = ['%s=%s'%(k,v)
for k,v in sorted(self.command_list.items())
if k != 'R_POINTS_MASS_LOSS'] +\
['####'] + \
['%s=%s'%(k,v)
for k,v in sorted(molec.makeDict().items())] +\
['####']
if self.command_list.has_key('R_POINTS_MASS_LOSS'):
commandfile.extend(['%s=%s'%('R_POINTS_MASS_LOSS',v)
for v in self.command_list\
['R_POINTS_MASS_LOSS']] +\
['####'])
filename = os.path.join(cc.path.gout,'models',\
'gastronoom_%s.inp'%molec.getModelId())
DataIO.writeFile(filename,commandfile)
self.execGastronoom(subcode='mline',filename=filename)
self.mline_done=True
if len([f for f in glob(os.path.join(cc.path.gout,'models',\
molec.getModelId(),'ml*%s_%s.dat'\
%(molec.getModelId(),molec.molecule)))])\
== 3:
self.ml_db[self.model_id][molec.getModelId()]\
[molec.molecule] = molec.makeDict()
self.ml_db.addChangedKey(self.model_id)
self.ml_db.sync()
else:
print 'Mline model calculation failed for'\
'%s. No entry is added to the database.'\
%(molec.molecule)
molec.setModelId('')
if set([molec.getModelId() for molec in self.molec_list]) == set(['']):
#- no mline models calculated: stop GASTRoNOoM here
self.model_id = ''
print 'Mline model calculation failed for all requested ' + \
'molecules. Stopping GASTRoNOoM here!'
else:
#- at least one molecule was successfully calculated, so start
#- Sphinx, hence if vic is requested, the cooling model_id can now
#- be added to the models list
if self.vic <> None and self.sphinx:
#- add the command list to the vic models list
self.vic.addModel(self.model_id,self.command_list)示例8: makeMCMaxStars
def makeMCMaxStars(self,models):
'''
Set parameters for star_list taken from the MCMax database.
Based on the model id of MCMax.
@param models: model_ids for the MCMax db
@type models: list(string)
@return: The model instances
@rtype: list(Star())
'''
star_grid = Star.makeStars(models=models,code='MCMax',id_type='MCMax',\
path=self.path)
for star,model in zip(star_grid,models):
filepath = os.path.join(cc.path.mout,'models',\
star['LAST_MCMAX_MODEL'])
denstemp = os.path.join(filepath,'denstemp.dat')
logfile = os.path.join(filepath,'log.dat')
grid_shape = DataIO.getMCMaxOutput(filename=denstemp,incr=1,\
keyword='NGRAINS',single=0)[0]
star.update({'NTHETA':int(grid_shape[1]),\
'NRAD':int(grid_shape[0]),\
'T_STAR':float(DataIO.getMCMaxOutput(filename=logfile,\
incr=0,\
keyword='STELLAR TEMPERATURE',\
single=0)[0][2]),\
'R_STAR':float(DataIO.getMCMaxOutput(filename=logfile,\
incr=0,\
keyword='STELLAR RADIUS',\
single=0)[0][2])})
return star_grid 示例9: readTelescopeProperties
def readTelescopeProperties(self):
"""
Read the telescope properties from Telescope.dat.
This currently includes the telescope size in m, and the default
absolute flux calibration uncertainty.
"""
all_telescopes = DataIO.getInputData(keyword='TELESCOPE',start_index=5,\
filename='Telescope.dat')
try:
tel_index = all_telescopes.index(self.instrument.upper())
except ValueError:
raise ValueError('%s not found in Telescope.dat.'\
%self.instrument.upper())
self.telescope_size = DataIO.getInputData(keyword='SIZE',start_index=5,\
filename='Telescope.dat',\
rindex=tel_index)
self.absflux_err = DataIO.getInputData(keyword='ABS_ERR',start_index=5,\
filename='Telescope.dat',\
rindex=tel_index)示例10: __init__
def __init__(self,path_chemistry='runTest',replace_db_entry=0,db=None,\
single_session=0):
"""
Initializing an instance of ModelingSession.
@keyword db: the Chemistry database
(default: None)
@type db: Database()
@keyword replace_db_entry: replace an entry in the Chemistry database with
a newly calculated model with a new model id
(for instance if some general data not
included in the inputfiles is changed)
(default: 0)
@type replace_db_entry: bool
@keyword path_chemistry: modeling folder in Chemistry home
(default: 'runTest')
@type path_chemistry: string
@keyword new_entries: The new model_ids when replace_db_entry is 1
of other models in the grid. These are not
replaced!
(default: [])
@type new_entries: list[str]
@keyword single_session: If this is the only CC session. Speeds up db
check.
(default: 0)
@type single_session: bool
"""
super(Chemistry, self).__init__(code='Chemistry',path=path_chemistry,\
replace_db_entry=replace_db_entry,\
single_session=single_session)
#-- Convenience path
cc.path.cout = os.path.join(cc.path.chemistry,self.path)
#DataIO.testFolderExistence(os.path.join(cc.path.mout,\
#'data_for_gastronoom'))
self.db = db
#-- If an chemistry model is in progress, the model manager will hold until
# the other cc session is finished.
self.in_progress = False
#- Read standard input file with all parameters that should be included
#- as well as some dust specific information
self.inputfilename = os.path.join(cc.path.aux,'inputChemistry.dat')
self.standard_inputfile = DataIO.readDict(self.inputfilename,\
convert_floats=1,\
convert_ints=1,\
comment_chars=['#','*'])
chemistry_keys = os.path.join(cc.path.aux,'Input_Keywords_Chemistry.dat')
self.chemistry_keywords = [line.strip()
for line in DataIO.readFile(chemistry_keys)
if line] 示例11: combineRedLaw
def combineRedLaw(ofn, chiar_curve="ism", power=-1.8):
"""
A method to combine the Fitzpatrick 2004 and Chiar & Tielens 2006 reddening
laws as well as to extrapolate Chiar and Tielens 2006 to longer wavelengths.
The result is saved in a file and used by the IvS repository as a valid
reddening law.
@param ofn: The output filename with path
@type ofn: str
@keyword chiar_curve: The curve type for Chiar & Tielens 2004. Either 'gc'
or 'ism'.
(default: 'ism')
@type chiar_curve: str
@keyword power: The power for the power law extrapolation. Default is taken
from Chiar and Tielens 2006, as a typical value for local
ISM between 2 and 5 micron. gc may require different value
but not very important.
(default: -1.8)
@type power: float
"""
chiar_curve = chiar_curve.lower()
# -- Extract the two relevant extinction laws.
xchiar, a_ak_chiar = red.get_law("chiar2006", norm="Ak", wave_units="micron", curve=chiar_curve)
xfitz, a_ak_fitz = red.get_law("fitzpatrick2004", norm="Ak", wave_units="micron")
# -- Define a power law for the extrapolation
def power_law(x, scale, power):
return scale * (x) ** power
# -- Determine the scaling factor from specific chiar/tielens law
scale = a_ak_chiar[-1] / (xchiar[-1] ** power)
# -- Create an x grid for longer wavelengths.
xlong = np.linspace(xchiar[-1] + 0.1, 1000, 1000)
a_ak_long = power_law(xlong, scale, power)
# -- Combine the three sections
xcom = hstack([xfitz[xfitz < xchiar[0]], xchiar, xlong])
a_ak_com = hstack([a_ak_fitz[xfitz < xchiar[0]], a_ak_chiar, a_ak_long])
# -- Write the result to a file
comments = "#-- wavelength (micron) A_lambda/A_k\n"
DataIO.writeCols(filename=ofn, cols=[[comments]])
DataIO.writeCols(filename=ofn, cols=[xcom, a_ak_com], mode="a")示例12: setStarPars
def setStarPars(self):
"""
Set some standard stellar parameters such as Ak and galactic position.
"""
self.star_index = DataIO.getInputData().index(self.star_name)
self.ll = DataIO.getInputData(keyword='LONG',rindex=self.star_index)
self.bb = DataIO.getInputData(keyword='LAT',rindex=self.star_index)
snp = DataIO.getInputData(keyword='STAR_NAME_PLOTS',\
remove_underscore=1,rindex=self.star_index)
self.star_name_plots = snp 示例13: mergeOpacity
def mergeOpacity(species,lowres='nom_res',highres='high_res'):
'''
Merge high-res opacities into a grid of low-res opacities.
The wavelength range of the inserted high res opacities is taken from the
given high res grid.
@param species: The dust species for which this is done. This is also the
name of the folder in ~/MCMax/DustOpacities/ that contains
the data files.
@type species: string
@keyword lowres: The subfolder in ~/MCMax/DustOpacities/species containing
the low resolution datafiles.
(default: low_res)
@type lowres: string
@keyword highres: The subfolder in ~/MCMax/DustOpacities/species containing
the high resolution datafiles.
(default: high_res)
@type highres: string
'''
path = os.path.join(cc.path.mopac,species)
lowres_files = [f
for f in glob(os.path.join(path,lowres,'*'))
if f[-5:] == '.opac']
highres_files = [f
for f in glob(os.path.join(path,highres,'*'))
if f[-5:] == '.opac']
files = set([os.path.split(f)[1] for f in lowres_files] + \
[os.path.split(f)[1] for f in highres_files])
for f in files:
hdfile = os.path.join(path,highres,f)
ldfile = os.path.join(path,lowres,f)
if os.path.isfile(ldfile) and os.path.isfile(hdfile):
hd = DataIO.readCols(hdfile)
ld = DataIO.readCols(ldfile)
hdw = hd[0]
ldw = ld[0]
wmin = hdw[0]
wmax = hdw[-1]
ld_low = [list(col[ldw<wmin]) for col in ld]
ld_high = [list(col[ldw>wmax]) for col in ld]
hd = [list(col) for col in hd]
merged = [ld_low[i] + hd[i] + ld_high[i]
for i in range(len(hd))]
DataIO.writeCols(filename=os.path.join(path,f),cols=merged)示例14: copyOutput
def copyOutput(self,entry,old_id,new_id):
'''
Copy modelling output based on model_id.
@param entry: the modeling object for which output is copied
@type entry: Molecule() or Transition()
@param old_id: The old model_id
@type old_id: string
@param new_id: the new_model_id
@type new_id: string
'''
folder_old = os.path.join(cc.path.gout,'models',old_id)
folder_new = os.path.join(cc.path.gout,'models',new_id)
lsprocess = subprocess.Popen('ls %s'%folder_old,shell=True,\
stdout=subprocess.PIPE)
lsfile = lsprocess.communicate()[0].split('\n')
lsfile = [os.path.split(line)[1]
for line in lsfile
if ((line[0:2] == 'ml' or line[0:4] == 'cool') \
and not entry.isMolecule()) \
or line[0:7] == 'coolfgr' \
or line[0:4] == 'para' \
or line[0:5] == 'input']
if not entry.isMolecule():
lsfile = [line
for line in lsfile
if not (line[0:2] == 'ml' \
and line.split('_')[-1].replace('.dat','') \
!= entry.molecule.molecule)]
lsfile = [line
for line in lsfile
if not (line[0:4] == 'cool' \
and (line.split('_')[-1].replace('.dat','') \
!= entry.molecule.molecule \
or line.split('_')[-1].replace('.dat','')=='sampling'\
or line[0:7] == 'coolfgr'))]
new_lsfile = [line.replace(old_id,new_id) for line in lsfile]
DataIO.testFolderExistence(folder_new)
lsprocess = subprocess.Popen('ls %s'%folder_new,shell=True,\
stdout=subprocess.PIPE)
already_done = lsprocess.communicate()[0].split('\n')
for ls,nls in zip(lsfile,new_lsfile):
if not nls in already_done:
subprocess.call(['ln -s %s %s'%(os.path.join(folder_old,ls),\
os.path.join(folder_new,nls))],\
shell=True)示例15: finalizeVic
def finalizeVic(self):
'''
Finalize a modeling procedure on VIC: successful and failed results
are printed to a file, including the transitions.
This log file can be used as input for ComboCode again by putting
LINE_LISTS=2.
'''
for trans in self.trans_in_progress:
filename = os.path.join(cc.path.gastronoom,\
self.path,'models',trans.getModelId(),\
trans.makeSphinxFilename(2))
if not os.path.isfile(filename):
trans.setModelId('')
if self.models.keys():
time_stamp = '%.4i-%.2i-%.2ih%.2i:%.2i:%.2i' \
%(gmtime()[0],gmtime()[1],gmtime()[2],\
gmtime()[3],gmtime()[4],gmtime()[5])
results = ['# Successfully calculated models:'] \
+ [self.models[current_model]
for current_model in self.models.keys()
if current_model not in self.failed.keys()] \
+ ['# Unsuccessfully calculated models (see 3 logfiles '+ \
'for these models):'] \
+ [self.models[current_model]
for current_model in self.models.keys()
if current_model in self.failed.keys()]
DataIO.writeFile(os.path.join(cc.path.gastronoom,self.path,\
'vic_results','log_' + time_stamp),\
results)
for current_model,model_id in self.models.items():
model_results = ['# Successfully calculated transitions:'] + \
['Sphinx %s: %s' %(trans.getModelId(),str(trans))
for trans in self.finished[current_model]] + \
['# Unsuccessfully calculated transitions (see 2 other ' + \
'logfiles for these transitions):'] + \
['Sphinx %s: %s' %(trans.getModelId(),str(trans))
for trans in self.failed[current_model]]
DataIO.writeFile(os.path.join(cc.path.gastronoom,self.path,\
'vic_results','log_results%s_%i'\
%(time_stamp,current_model)),\
model_results)
for this_id in self.sphinx_model_ids[current_model]:
sphinx_files = os.path.join(cc.path.gastronoom,self.path,\
'models',this_id,'sph*')
subprocess.call(['chmod a+r %s'%sphinx_files],shell=True)